diff --git a/face-generation/dlnd_face_generation.html b/face-generation/dlnd_face_generation.html new file mode 100644 index 0000000..b3dc1c4 --- /dev/null +++ b/face-generation/dlnd_face_generation.html @@ -0,0 +1,58623 @@ + + + +dlnd_face_generation + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
+
+

人脸生成（Face Generation）

在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。

+

获取数据

该项目将使用以下数据集：

+
    +
  • MNIST
    • +
  • CelebA
    • +
+

由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。

+

如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

+ +
+
+
+
+
+
In [1]:
+
+
+
#data_dir = './data'
+
+# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
+data_dir = '/input'
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import helper
+
+helper.download_extract('mnist', data_dir)
+helper.download_extract('celeba', data_dir)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Found mnist Data
+Found celeba Data
+
+
+
+ +
+
+ +
+
+
+
+
+

探索数据（Explore the Data）

MNIST

MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。

+ +
+
+
+
+
+
In [2]:
+
+
+
show_n_images = 25
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+%matplotlib inline
+import os
+from glob import glob
+from matplotlib import pyplot
+
+mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
+pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
+
+ +
+
+
+ +
+
+ + +
+ +
Out[2]:
+ + + + +
+
<matplotlib.image.AxesImage at 0x7f56d19e85f8>
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

CelebA

CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸，不会用不到相关说明。你可以更改 show_n_images 探索此数据集。

+ +
+
+
+
+
+
In [3]:
+
+
+
show_n_images = 25
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
+pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
+
+ +
+
+
+ +
+
+ + +
+ +
Out[3]:
+ + + + +
+
<matplotlib.image.AxesImage at 0x7f56d19266d8>
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

预处理数据（Preprocess the Data）

由于该项目的重点是建立 GANs 模型，我们将为你预处理数据。

+

经过数据预处理，MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分，然后调整到 28x28 维度。

+

MNIST 数据集中的图像是单通道的黑白图像，CelebA 数据集中的图像是 三通道的 RGB 彩色图像。

+

建立神经网络（Build the Neural Network）

你将通过部署以下函数来建立 GANs 的主要组成部分:

+
    +
  • model_inputs
    • +
  • discriminator
    • +
  • generator
    • +
  • model_loss
    • +
  • model_opt
    • +
  • train
    • +
+

检查 TensorFlow 版本并获取 GPU 型号

检查你是否使用正确的 TensorFlow 版本，并获取 GPU 型号

+ +
+
+
+
+
+
In [4]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+from distutils.version import LooseVersion
+import warnings
+import tensorflow as tf
+
+# Check TensorFlow Version
+assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
+print('TensorFlow Version: {}'.format(tf.__version__))
+
+# Check for a GPU
+if not tf.test.gpu_device_name():
+    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
+else:
+    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
TensorFlow Version: 1.0.1
+Default GPU Device: /gpu:0
+
+
+
+ +
+
+ +
+
+
+
+
+

输入（Input）

部署 model_inputs 函数以创建用于神经网络的 占位符 (TF Placeholders)。请创建以下占位符：

+
    +
  • 输入图像占位符: 使用 image_width，image_height 和 image_channels 设置为 rank 4。
    • +
  • 输入 Z 占位符: 设置为 rank 2，并命名为 z_dim。
    • +
  • 学习速率占位符: 设置为 rank 0。
    • +
+

返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。

+ +
+
+
+
+
+
In [5]:
+
+
+
import problem_unittests as tests
+
+def model_inputs(image_width, image_height, image_channels, z_dim):
+    """
+    Create the model inputs
+    :param image_width: The input image width
+    :param image_height: The input image height
+    :param image_channels: The number of image channels
+    :param z_dim: The dimension of Z
+    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
+    """
+    # TODO: Implement Function
+    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
+    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
+    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
+
+    return input_real, input_z, learning_rate
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_model_inputs(model_inputs)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

辨别器（Discriminator）

部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。

+

该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。

+ +
+
+
+
+
+
In [6]:
+
+
+
def discriminator(images, reuse=False):
+    """
+    Create the discriminator network
+    :param image: Tensor of input image(s)
+    :param reuse: Boolean if the weights should be reused
+    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
+    """
+    # TODO: Implement Function
+    with tf.variable_scope('discriminator', reuse=reuse):
+        # alpha is the param for leaky relu
+        alpha = 0.2
+        
+        # Input layer is 28x28x3
+        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
+        relu1 = tf.maximum(alpha * x1, x1)
+        # 14x14x64 now
+        
+        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
+        bn2 = tf.layers.batch_normalization(x2, training=True)
+        relu2 = tf.maximum(alpha * bn2, bn2)
+        # 7x7x128 now
+        
+        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
+        bn3 = tf.layers.batch_normalization(x3, training=True)
+        relu3 = tf.maximum(alpha * bn3, bn3)
+        # 4x4x256 now
+
+        # Flatten it
+        flat = tf.reshape(relu3, (-1, 4*4*256))
+        logits = tf.layers.dense(flat, 1)
+        out = tf.sigmoid(logits)
+        
+        return out, logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_discriminator(discriminator, tf)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

生成器（Generator）

部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。 +在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。

+

该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。

+ +
+
+
+
+
+
In [7]:
+
+
+
def generator(z, out_channel_dim, is_train=True):
+    """
+    Create the generator network
+    :param z: Input z
+    :param out_channel_dim: The number of channels in the output image
+    :param is_train: Boolean if generator is being used for training
+    :return: The tensor output of the generator
+    """
+    # TODO: Implement Function
+    with tf.variable_scope('generator', reuse=not is_train):
+        # alpha is the param for leaky relu
+        alpha = 0.2
+        
+        # First fully connected layer
+        x1 = tf.layers.dense(z, 7 * 7 * 512, activation=None)
+        x1 = tf.reshape(x1, (-1, 7, 7, 512)) # Reshape it to start the convolutional stack
+        x1 = tf.layers.batch_normalization(x1, training=is_train)
+        x1 = tf.maximum(alpha * x1, x1)
+        # 7x7x512 now
+        
+        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
+        x2 = tf.layers.batch_normalization(x2, training=is_train)
+        x2 = tf.maximum(alpha * x2, x2)
+        # 14x14x256 now
+        
+        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
+        x3 = tf.layers.batch_normalization(x3, training=is_train)
+        x3 = tf.maximum(alpha * x3, x3)
+        # 28x28x128 now
+        
+        # Output layer
+        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
+        out = tf.tanh(logits)
+        # 28x28x3 now
+        
+        return out
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_generator(generator, tf)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

损失函数（Loss）

部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。

+

使用你已实现的函数：

+
    +
  • discriminator(images, reuse=False)
    • +
  • generator(z, out_channel_dim, is_train=True)
    • +
+ +
+
+
+
+
+
In [8]:
+
+
+
def model_loss(input_real, input_z, out_channel_dim):
+    """
+    Get the loss for the discriminator and generator
+    :param input_real: Images from the real dataset
+    :param input_z: Z input
+    :param out_channel_dim: The number of channels in the output image
+    :return: A tuple of (discriminator loss, generator loss)
+    """
+    # TODO: Implement Function
+    # Generator network here
+    g_model = generator(input_z, out_channel_dim)
+
+    # Disriminator network here
+    d_model_real, d_logits_real = discriminator(input_real)
+    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
+    
+    # Calculate losses
+    smooth = 0.1
+    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth)))
+
+    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
+
+    d_loss = d_loss_real + d_loss_fake
+
+    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
+
+    return d_loss, g_loss
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_model_loss(model_loss)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

优化（Optimization）

部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminator 和 generator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。

+ +
+
+
+
+
+
In [9]:
+
+
+
def model_opt(d_loss, g_loss, learning_rate, beta1):
+    """
+    Get optimization operations
+    :param d_loss: Discriminator loss Tensor
+    :param g_loss: Generator loss Tensor
+    :param learning_rate: Learning Rate Placeholder
+    :param beta1: The exponential decay rate for the 1st moment in the optimizer
+    :return: A tuple of (discriminator training operation, generator training operation)
+    """
+    # TODO: Implement Function
+    # Get weights and bias to update
+    t_vars = tf.trainable_variables()
+    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
+    g_vars = [var for var in t_vars if var.name.startswith('generator')]
+
+    # Optimize
+    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
+        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
+        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
+
+    return d_train_opt, g_train_opt
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_model_opt(model_opt, tf)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

训练神经网络（Neural Network Training）

输出显示

使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。

+ +
+
+
+
+
+
In [10]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import numpy as np
+
+def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
+    """
+    Show example output for the generator
+    :param sess: TensorFlow session
+    :param n_images: Number of Images to display
+    :param input_z: Input Z Tensor
+    :param out_channel_dim: The number of channels in the output image
+    :param image_mode: The mode to use for images ("RGB" or "L")
+    """
+    cmap = None if image_mode == 'RGB' else 'gray'
+    z_dim = input_z.get_shape().as_list()[-1]
+    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
+
+    samples = sess.run(
+        generator(input_z, out_channel_dim, False),
+        feed_dict={input_z: example_z})
+
+    images_grid = helper.images_square_grid(samples, image_mode)
+    pyplot.imshow(images_grid, cmap=cmap)
+    pyplot.show()
+
+ +
+
+
+ +
+
+
+
+
+

训练

部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数：

+
    +
  • model_inputs(image_width, image_height, image_channels, z_dim)
    • +
  • model_loss(input_real, input_z, out_channel_dim)
    • +
  • model_opt(d_loss, g_loss, learning_rate, beta1)
    • +
+

使用 show_generator_output 函数显示 generator 在训练过程中的输出。

+

注意：在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。

+ +
+
+
+
+
+
In [11]:
+
+
+
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
+    """
+    Train the GAN
+    :param epoch_count: Number of epochs
+    :param batch_size: Batch Size
+    :param z_dim: Z dimension
+    :param learning_rate: Learning Rate
+    :param beta1: The exponential decay rate for the 1st moment in the optimizer
+    :param get_batches: Function to get batches
+    :param data_shape: Shape of the data
+    :param data_image_mode: The image mode to use for images ("RGB" or "L")
+    """
+    # TODO: Build Model
+    _, image_width, image_height, image_channels = data_shape
+    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
+    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
+    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
+    
+    steps = 0
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        for epoch_i in range(epoch_count):
+            for batch_images in get_batches(batch_size):
+                # TODO: Train Model
+                steps += 1
+                batch_images *= 2
+                
+                # Sample random noise for G
+                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
+
+                # Run optimizers
+                _ = sess.run(d_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
+                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
+
+                if steps % 10 == 0:
+                    # At the end of each epoch, get the losses and print them out
+                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
+                    train_loss_g = g_loss.eval({input_z: batch_z})
+
+                    print("Epoch {}/{}...".format(epoch_i + 1, epoch_count), 
+                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
+                          "Generator Loss: {:.4f}".format(train_loss_g))
+                
+                if steps % 100 == 0:
+                    gen_samples = sess.run(generator(input_z, image_channels, is_train=False), feed_dict={input_z: batch_z})
+                    _ = show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
+
+ +
+
+
+ +
+
+
+
+
+

MNIST

在 MNIST 上测试你的 GANs 模型。经过 2 次迭代，GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失，或接近 0。

+ +
+
+
+
+
+
In [12]:
+
+
+
batch_size = 64
+z_dim = 100
+learning_rate = 0.0002
+beta1 = 0.5
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+epochs = 2
+
+mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
+with tf.Graph().as_default():
+    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
+          mnist_dataset.shape, mnist_dataset.image_mode)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 0.4204... Generator Loss: 3.1223
+Epoch 1/2... Discriminator Loss: 0.4827... Generator Loss: 2.2592
+Epoch 1/2... Discriminator Loss: 0.4310... Generator Loss: 4.2485
+Epoch 1/2... Discriminator Loss: 1.2898... Generator Loss: 2.9953
+Epoch 1/2... Discriminator Loss: 1.3641... Generator Loss: 1.3200
+Epoch 1/2... Discriminator Loss: 1.3577... Generator Loss: 0.6340
+Epoch 1/2... Discriminator Loss: 1.5244... Generator Loss: 0.5115
+Epoch 1/2... Discriminator Loss: 1.2379... Generator Loss: 0.8513
+Epoch 1/2... Discriminator Loss: 1.3270... Generator Loss: 0.8379
+Epoch 1/2... Discriminator Loss: 1.6648... Generator Loss: 0.4169
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 1.2459... Generator Loss: 0.9999
+Epoch 1/2... Discriminator Loss: 1.4056... Generator Loss: 1.7060
+Epoch 1/2... Discriminator Loss: 1.3388... Generator Loss: 0.7322
+Epoch 1/2... Discriminator Loss: 1.1195... Generator Loss: 1.1036
+Epoch 1/2... Discriminator Loss: 1.1910... Generator Loss: 0.7477
+Epoch 1/2... Discriminator Loss: 1.2023... Generator Loss: 1.2572
+Epoch 1/2... Discriminator Loss: 1.2410... Generator Loss: 1.6392
+Epoch 1/2... Discriminator Loss: 1.2184... Generator Loss: 0.7372
+Epoch 1/2... Discriminator Loss: 1.2382... Generator Loss: 0.6883
+Epoch 1/2... Discriminator Loss: 1.3832... Generator Loss: 0.4813
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 1.0094... Generator Loss: 0.9240
+Epoch 1/2... Discriminator Loss: 1.1167... Generator Loss: 1.6473
+Epoch 1/2... Discriminator Loss: 1.4142... Generator Loss: 0.5206
+Epoch 1/2... Discriminator Loss: 0.9967... Generator Loss: 1.5393
+Epoch 1/2... Discriminator Loss: 1.3248... Generator Loss: 0.5312
+Epoch 1/2... Discriminator Loss: 1.0156... Generator Loss: 1.1135
+Epoch 1/2... Discriminator Loss: 0.9628... Generator Loss: 1.9009
+Epoch 1/2... Discriminator Loss: 1.0585... Generator Loss: 1.5717
+Epoch 1/2... Discriminator Loss: 1.0929... Generator Loss: 0.7814
+Epoch 1/2... Discriminator Loss: 1.0840... Generator Loss: 1.0624
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 1.0431... Generator Loss: 1.1230
+Epoch 1/2... Discriminator Loss: 1.0236... Generator Loss: 1.0033
+Epoch 1/2... Discriminator Loss: 1.1009... Generator Loss: 0.8963
+Epoch 1/2... Discriminator Loss: 1.0246... Generator Loss: 1.5232
+Epoch 1/2... Discriminator Loss: 1.2980... Generator Loss: 0.6295
+Epoch 1/2... Discriminator Loss: 1.0272... Generator Loss: 1.3369
+Epoch 1/2... Discriminator Loss: 1.1724... Generator Loss: 0.7514
+Epoch 1/2... Discriminator Loss: 1.0532... Generator Loss: 1.2344
+Epoch 1/2... Discriminator Loss: 1.1428... Generator Loss: 0.8638
+Epoch 1/2... Discriminator Loss: 1.1750... Generator Loss: 1.1432
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 1.2411... Generator Loss: 1.8333
+Epoch 1/2... Discriminator Loss: 1.0503... Generator Loss: 1.0242
+Epoch 1/2... Discriminator Loss: 1.1395... Generator Loss: 1.0762
+Epoch 1/2... Discriminator Loss: 1.3215... Generator Loss: 0.5722
+Epoch 1/2... Discriminator Loss: 1.1445... Generator Loss: 0.7874
+Epoch 1/2... Discriminator Loss: 1.2529... Generator Loss: 0.6156
+Epoch 1/2... Discriminator Loss: 1.0842... Generator Loss: 0.9782
+Epoch 1/2... Discriminator Loss: 1.1459... Generator Loss: 0.7799
+Epoch 1/2... Discriminator Loss: 1.1442... Generator Loss: 1.4652
+Epoch 1/2... Discriminator Loss: 1.0300... Generator Loss: 1.2469
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 1.2653... Generator Loss: 0.6031
+Epoch 1/2... Discriminator Loss: 1.0865... Generator Loss: 1.2273
+Epoch 1/2... Discriminator Loss: 1.2002... Generator Loss: 0.6592
+Epoch 1/2... Discriminator Loss: 1.2886... Generator Loss: 0.6356
+Epoch 1/2... Discriminator Loss: 1.4369... Generator Loss: 0.4941
+Epoch 1/2... Discriminator Loss: 1.1444... Generator Loss: 0.9019
+Epoch 1/2... Discriminator Loss: 1.1936... Generator Loss: 0.6823
+Epoch 1/2... Discriminator Loss: 1.1784... Generator Loss: 1.4153
+Epoch 1/2... Discriminator Loss: 1.5582... Generator Loss: 0.4257
+Epoch 1/2... Discriminator Loss: 1.0860... Generator Loss: 1.3010
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 1.0424... Generator Loss: 1.1105
+Epoch 1/2... Discriminator Loss: 1.0549... Generator Loss: 0.9376
+Epoch 1/2... Discriminator Loss: 1.1714... Generator Loss: 1.1036
+Epoch 1/2... Discriminator Loss: 1.1158... Generator Loss: 1.0531
+Epoch 1/2... Discriminator Loss: 1.0498... Generator Loss: 0.9878
+Epoch 1/2... Discriminator Loss: 1.2111... Generator Loss: 0.8901
+Epoch 1/2... Discriminator Loss: 1.4104... Generator Loss: 0.4942
+Epoch 1/2... Discriminator Loss: 1.0527... Generator Loss: 1.3645
+Epoch 1/2... Discriminator Loss: 1.0832... Generator Loss: 0.8888
+Epoch 1/2... Discriminator Loss: 1.1123... Generator Loss: 0.8392
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 0.9938... Generator Loss: 1.1336
+Epoch 1/2... Discriminator Loss: 1.2817... Generator Loss: 1.7325
+Epoch 1/2... Discriminator Loss: 1.0349... Generator Loss: 0.8968
+Epoch 1/2... Discriminator Loss: 1.0611... Generator Loss: 1.3221
+Epoch 1/2... Discriminator Loss: 1.0041... Generator Loss: 1.2203
+Epoch 1/2... Discriminator Loss: 1.0210... Generator Loss: 1.1737
+Epoch 1/2... Discriminator Loss: 1.0761... Generator Loss: 1.2211
+Epoch 1/2... Discriminator Loss: 0.9741... Generator Loss: 1.1375
+Epoch 1/2... Discriminator Loss: 1.0377... Generator Loss: 0.8915
+Epoch 1/2... Discriminator Loss: 1.0852... Generator Loss: 0.9029
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 1.3093... Generator Loss: 2.5877
+Epoch 1/2... Discriminator Loss: 0.9853... Generator Loss: 1.1730
+Epoch 1/2... Discriminator Loss: 1.0564... Generator Loss: 0.8760
+Epoch 1/2... Discriminator Loss: 1.0396... Generator Loss: 1.4255
+Epoch 1/2... Discriminator Loss: 1.0599... Generator Loss: 0.9275
+Epoch 1/2... Discriminator Loss: 0.9641... Generator Loss: 1.3584
+Epoch 1/2... Discriminator Loss: 1.1231... Generator Loss: 0.8175
+Epoch 1/2... Discriminator Loss: 1.0010... Generator Loss: 1.1018
+Epoch 1/2... Discriminator Loss: 1.3737... Generator Loss: 0.5330
+Epoch 1/2... Discriminator Loss: 1.0614... Generator Loss: 0.9055
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/2... Discriminator Loss: 1.0209... Generator Loss: 1.0810
+Epoch 1/2... Discriminator Loss: 0.9956... Generator Loss: 0.9358
+Epoch 1/2... Discriminator Loss: 1.2294... Generator Loss: 0.6367
+Epoch 2/2... Discriminator Loss: 1.0491... Generator Loss: 0.8156
+Epoch 2/2... Discriminator Loss: 1.9660... Generator Loss: 0.3475
+Epoch 2/2... Discriminator Loss: 1.0618... Generator Loss: 1.2255
+Epoch 2/2... Discriminator Loss: 0.9658... Generator Loss: 1.0057
+Epoch 2/2... Discriminator Loss: 0.8519... Generator Loss: 1.6786
+Epoch 2/2... Discriminator Loss: 1.0465... Generator Loss: 0.9361
+Epoch 2/2... Discriminator Loss: 1.0060... Generator Loss: 1.0238
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 1.2068... Generator Loss: 0.6641
+Epoch 2/2... Discriminator Loss: 0.8739... Generator Loss: 1.2788
+Epoch 2/2... Discriminator Loss: 1.2420... Generator Loss: 0.5994
+Epoch 2/2... Discriminator Loss: 1.0455... Generator Loss: 1.3188
+Epoch 2/2... Discriminator Loss: 1.5271... Generator Loss: 0.4801
+Epoch 2/2... Discriminator Loss: 0.9442... Generator Loss: 0.9758
+Epoch 2/2... Discriminator Loss: 1.1125... Generator Loss: 0.8582
+Epoch 2/2... Discriminator Loss: 0.9858... Generator Loss: 0.8610
+Epoch 2/2... Discriminator Loss: 1.7708... Generator Loss: 0.3445
+Epoch 2/2... Discriminator Loss: 1.0218... Generator Loss: 0.9825
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 0.9482... Generator Loss: 1.3030
+Epoch 2/2... Discriminator Loss: 1.3452... Generator Loss: 1.9974
+Epoch 2/2... Discriminator Loss: 1.0928... Generator Loss: 0.7811
+Epoch 2/2... Discriminator Loss: 0.8769... Generator Loss: 1.3478
+Epoch 2/2... Discriminator Loss: 1.2244... Generator Loss: 0.6451
+Epoch 2/2... Discriminator Loss: 1.0969... Generator Loss: 2.4311
+Epoch 2/2... Discriminator Loss: 1.0368... Generator Loss: 0.8528
+Epoch 2/2... Discriminator Loss: 0.9872... Generator Loss: 1.3598
+Epoch 2/2... Discriminator Loss: 1.1426... Generator Loss: 1.1906
+Epoch 2/2... Discriminator Loss: 0.9564... Generator Loss: 1.2773
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 0.9182... Generator Loss: 1.0495
+Epoch 2/2... Discriminator Loss: 1.1554... Generator Loss: 0.6664
+Epoch 2/2... Discriminator Loss: 1.0098... Generator Loss: 0.9703
+Epoch 2/2... Discriminator Loss: 1.0875... Generator Loss: 0.7549
+Epoch 2/2... Discriminator Loss: 0.8910... Generator Loss: 1.1223
+Epoch 2/2... Discriminator Loss: 0.9773... Generator Loss: 2.6783
+Epoch 2/2... Discriminator Loss: 1.0080... Generator Loss: 0.8345
+Epoch 2/2... Discriminator Loss: 0.9571... Generator Loss: 1.3360
+Epoch 2/2... Discriminator Loss: 0.9197... Generator Loss: 1.3654
+Epoch 2/2... Discriminator Loss: 0.8306... Generator Loss: 1.2653
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 0.9596... Generator Loss: 1.1294
+Epoch 2/2... Discriminator Loss: 0.9953... Generator Loss: 0.8804
+Epoch 2/2... Discriminator Loss: 0.9510... Generator Loss: 1.1515
+Epoch 2/2... Discriminator Loss: 1.3993... Generator Loss: 2.3715
+Epoch 2/2... Discriminator Loss: 0.9080... Generator Loss: 1.4359
+Epoch 2/2... Discriminator Loss: 0.8572... Generator Loss: 1.2590
+Epoch 2/2... Discriminator Loss: 0.8892... Generator Loss: 1.4629
+Epoch 2/2... Discriminator Loss: 1.5488... Generator Loss: 2.5772
+Epoch 2/2... Discriminator Loss: 0.9225... Generator Loss: 1.1753
+Epoch 2/2... Discriminator Loss: 1.0175... Generator Loss: 1.2177
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 1.1387... Generator Loss: 2.3912
+Epoch 2/2... Discriminator Loss: 1.1287... Generator Loss: 0.7842
+Epoch 2/2... Discriminator Loss: 1.0247... Generator Loss: 1.9585
+Epoch 2/2... Discriminator Loss: 0.9418... Generator Loss: 1.0972
+Epoch 2/2... Discriminator Loss: 0.8407... Generator Loss: 1.2381
+Epoch 2/2... Discriminator Loss: 0.8819... Generator Loss: 1.0197
+Epoch 2/2... Discriminator Loss: 1.0199... Generator Loss: 0.8055
+Epoch 2/2... Discriminator Loss: 0.9692... Generator Loss: 1.6409
+Epoch 2/2... Discriminator Loss: 1.2472... Generator Loss: 0.7956
+Epoch 2/2... Discriminator Loss: 0.9436... Generator Loss: 0.9713
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 0.9250... Generator Loss: 1.0925
+Epoch 2/2... Discriminator Loss: 0.9684... Generator Loss: 0.9028
+Epoch 2/2... Discriminator Loss: 0.9477... Generator Loss: 0.9569
+Epoch 2/2... Discriminator Loss: 1.2281... Generator Loss: 0.6713
+Epoch 2/2... Discriminator Loss: 0.8281... Generator Loss: 1.1973
+Epoch 2/2... Discriminator Loss: 1.0442... Generator Loss: 0.7927
+Epoch 2/2... Discriminator Loss: 0.9155... Generator Loss: 1.0041
+Epoch 2/2... Discriminator Loss: 0.8651... Generator Loss: 1.6100
+Epoch 2/2... Discriminator Loss: 1.1178... Generator Loss: 0.7747
+Epoch 2/2... Discriminator Loss: 1.6798... Generator Loss: 3.0678
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 0.8941... Generator Loss: 1.0698
+Epoch 2/2... Discriminator Loss: 1.0571... Generator Loss: 0.8698
+Epoch 2/2... Discriminator Loss: 0.8889... Generator Loss: 1.0698
+Epoch 2/2... Discriminator Loss: 1.2752... Generator Loss: 2.7747
+Epoch 2/2... Discriminator Loss: 1.1641... Generator Loss: 0.7411
+Epoch 2/2... Discriminator Loss: 0.8820... Generator Loss: 1.0839
+Epoch 2/2... Discriminator Loss: 0.8707... Generator Loss: 1.0716
+Epoch 2/2... Discriminator Loss: 0.8608... Generator Loss: 1.2287
+Epoch 2/2... Discriminator Loss: 0.9702... Generator Loss: 1.0217
+Epoch 2/2... Discriminator Loss: 1.1194... Generator Loss: 0.8550
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 1.1537... Generator Loss: 0.7651
+Epoch 2/2... Discriminator Loss: 1.0387... Generator Loss: 1.4510
+Epoch 2/2... Discriminator Loss: 0.8105... Generator Loss: 1.4478
+Epoch 2/2... Discriminator Loss: 0.8504... Generator Loss: 1.3711
+Epoch 2/2... Discriminator Loss: 0.8881... Generator Loss: 1.4892
+Epoch 2/2... Discriminator Loss: 1.0879... Generator Loss: 0.7779
+Epoch 2/2... Discriminator Loss: 0.8741... Generator Loss: 1.2485
+Epoch 2/2... Discriminator Loss: 0.9110... Generator Loss: 1.7616
+Epoch 2/2... Discriminator Loss: 0.8936... Generator Loss: 1.5694
+Epoch 2/2... Discriminator Loss: 0.8861... Generator Loss: 1.0268
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 2/2... Discriminator Loss: 0.9276... Generator Loss: 0.9687
+Epoch 2/2... Discriminator Loss: 0.9666... Generator Loss: 1.0320
+Epoch 2/2... Discriminator Loss: 0.9749... Generator Loss: 0.8926
+Epoch 2/2... Discriminator Loss: 0.7613... Generator Loss: 1.6372
+Epoch 2/2... Discriminator Loss: 1.5822... Generator Loss: 0.4344
+Epoch 2/2... Discriminator Loss: 1.1882... Generator Loss: 0.8438
+Epoch 2/2... Discriminator Loss: 0.9924... Generator Loss: 1.1633
+
+
+
+ +
+
+ +
+
+
+
+
+

CelebA

在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代，或者当 GANs 开始产生真实人脸图像时停止它。

+ +
+
+
+
+
+
In [13]:
+
+
+
batch_size = 64
+z_dim = 100
+learning_rate = 0.001
+beta1 = 0.5
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+epochs = 1
+
+celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
+with tf.Graph().as_default():
+    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
+          celeba_dataset.shape, celeba_dataset.image_mode)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Epoch 1/1... Discriminator Loss: 0.6635... Generator Loss: 2.6208
+Epoch 1/1... Discriminator Loss: 0.5171... Generator Loss: 5.9576
+Epoch 1/1... Discriminator Loss: 0.9434... Generator Loss: 4.5532
+Epoch 1/1... Discriminator Loss: 0.9883... Generator Loss: 1.3031
+Epoch 1/1... Discriminator Loss: 0.4266... Generator Loss: 3.6278
+Epoch 1/1... Discriminator Loss: 0.6535... Generator Loss: 1.7355
+Epoch 1/1... Discriminator Loss: 0.6394... Generator Loss: 3.2757
+Epoch 1/1... Discriminator Loss: 0.5272... Generator Loss: 2.5773
+Epoch 1/1... Discriminator Loss: 0.4938... Generator Loss: 2.2639
+Epoch 1/1... Discriminator Loss: 0.6107... Generator Loss: 2.0199
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/1... Discriminator Loss: 0.4906... Generator Loss: 4.7177
+Epoch 1/1... Discriminator Loss: 6.6824... Generator Loss: 12.1958
+Epoch 1/1... Discriminator Loss: 1.2869... Generator Loss: 1.1015
+Epoch 1/1... Discriminator Loss: 0.8712... Generator Loss: 1.2599
+Epoch 1/1... Discriminator Loss: 0.9868... Generator Loss: 2.0520
+Epoch 1/1... Discriminator Loss: 1.0734... Generator Loss: 1.9953
+Epoch 1/1... Discriminator Loss: 1.7425... Generator Loss: 0.3826
+Epoch 1/1... Discriminator Loss: 1.1829... Generator Loss: 1.2268
+Epoch 1/1... Discriminator Loss: 1.4086... Generator Loss: 0.8518
+Epoch 1/1... Discriminator Loss: 1.2163... Generator Loss: 0.7233
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/1... Discriminator Loss: 1.2762... Generator Loss: 0.8202
+Epoch 1/1... Discriminator Loss: 1.0826... Generator Loss: 1.4488
+Epoch 1/1... Discriminator Loss: 1.5480... Generator Loss: 1.4554
+Epoch 1/1... Discriminator Loss: 1.1184... Generator Loss: 0.7442
+Epoch 1/1... Discriminator Loss: 1.3038... Generator Loss: 0.8651
+Epoch 1/1... Discriminator Loss: 1.2226... Generator Loss: 1.0445
+Epoch 1/1... Discriminator Loss: 1.2978... Generator Loss: 0.7549
+Epoch 1/1... Discriminator Loss: 1.2956... Generator Loss: 0.8022
+Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.6897
+Epoch 1/1... Discriminator Loss: 1.0502... Generator Loss: 1.4712
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/1... Discriminator Loss: 0.9124... Generator Loss: 1.4509
+Epoch 1/1... Discriminator Loss: 1.0586... Generator Loss: 0.9160
+Epoch 1/1... Discriminator Loss: 1.4511... Generator Loss: 0.4657
+Epoch 1/1... Discriminator Loss: 1.2195... Generator Loss: 1.5226
+Epoch 1/1... Discriminator Loss: 1.1125... Generator Loss: 1.1325
+Epoch 1/1... Discriminator Loss: 1.2902... Generator Loss: 0.7191
+Epoch 1/1... Discriminator Loss: 1.1498... Generator Loss: 0.8416
+Epoch 1/1... Discriminator Loss: 1.2848... Generator Loss: 0.7776
+Epoch 1/1... Discriminator Loss: 1.9145... Generator Loss: 0.2802
+Epoch 1/1... Discriminator Loss: 1.1082... Generator Loss: 1.2230
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/1... Discriminator Loss: 1.4833... Generator Loss: 2.0466
+Epoch 1/1... Discriminator Loss: 0.8034... Generator Loss: 1.5415
+Epoch 1/1... Discriminator Loss: 1.1546... Generator Loss: 0.6480
+Epoch 1/1... Discriminator Loss: 1.1812... Generator Loss: 0.8394
+Epoch 1/1... Discriminator Loss: 0.9518... Generator Loss: 1.0477
+Epoch 1/1... Discriminator Loss: 1.2184... Generator Loss: 0.9924
+Epoch 1/1... Discriminator Loss: 1.2908... Generator Loss: 0.8527
+Epoch 1/1... Discriminator Loss: 1.2357... Generator Loss: 0.9712
+Epoch 1/1... Discriminator Loss: 1.3955... Generator Loss: 0.6073
+Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 1.1717
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/1... Discriminator Loss: 1.5523... Generator Loss: 0.5931
+Epoch 1/1... Discriminator Loss: 1.5956... Generator Loss: 0.4272
+Epoch 1/1... Discriminator Loss: 1.2407... Generator Loss: 0.6906
+Epoch 1/1... Discriminator Loss: 1.5402... Generator Loss: 0.6955
+Epoch 1/1... Discriminator Loss: 1.0282... Generator Loss: 1.7090
+Epoch 1/1... Discriminator Loss: 2.0579... Generator Loss: 0.2622
+Epoch 1/1... Discriminator Loss: 1.1844... Generator Loss: 1.0045
+Epoch 1/1... Discriminator Loss: 1.5009... Generator Loss: 0.7875
+Epoch 1/1... Discriminator Loss: 1.2890... Generator Loss: 0.6643
+Epoch 1/1... Discriminator Loss: 1.2547... Generator Loss: 1.2426
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/1... Discriminator Loss: 1.3409... Generator Loss: 0.6901
+Epoch 1/1... Discriminator Loss: 1.3804... Generator Loss: 0.5383
+Epoch 1/1... Discriminator Loss: 1.4042... Generator Loss: 0.7307
+Epoch 1/1... Discriminator Loss: 1.5044... Generator Loss: 1.7383
+Epoch 1/1... Discriminator Loss: 1.4054... Generator Loss: 0.5633
+Epoch 1/1... Discriminator Loss: 0.8011... Generator Loss: 2.2206
+Epoch 1/1... Discriminator Loss: 1.2483... Generator Loss: 0.5992
+Epoch 1/1... Discriminator Loss: 1.3313... Generator Loss: 0.5899
+Epoch 1/1... Discriminator Loss: 1.2300... Generator Loss: 0.9554
+Epoch 1/1... Discriminator Loss: 1.3818... Generator Loss: 1.0496
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
Epoch 1/1... Discriminator Loss: 1.3802... Generator Loss: 0.7881
+Epoch 1/1... Discriminator Loss: 1.3059... Generator Loss: 1.2800
+Epoch 1/1... Discriminator Loss: 1.2707... Generator Loss: 0.8262
+Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.7216
+Epoch 1/1... Discriminator Loss: 1.1423... Generator Loss: 1.5386
+Epoch 1/1... Discriminator Loss: 1.3705... Generator Loss: 1.4615
+Epoch 1/1... Discriminator Loss: 1.3879... Generator Loss: 0.5866
+Epoch 1/1... Discriminator Loss: 1.2942... Generator Loss: 1.0528
+Epoch 1/1... Discriminator Loss: 1.2455... Generator Loss: 0.7173
+Epoch 1/1... Discriminator Loss: 1.4368... Generator Loss: 0.5770
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Epoch 1/1... Discriminator Loss: 1.3614... Generator Loss: 1.5139
+Epoch 1/1... Discriminator Loss: 1.3375... Generator Loss: 1.1554
+Epoch 1/1... Discriminator Loss: 1.5048... Generator Loss: 0.8155
+Epoch 1/1... Discriminator Loss: 1.3531... Generator Loss: 0.7889
+Epoch 1/1... Discriminator Loss: 1.2752... Generator Loss: 0.8632
+Epoch 1/1... Discriminator Loss: 1.3345... Generator Loss: 0.6996
+Epoch 1/1... Discriminator Loss: 1.5458... Generator Loss: 1.1624
+Epoch 1/1... Discriminator Loss: 1.8949... Generator Loss: 2.2906
+Epoch 1/1... Discriminator Loss: 1.5799... Generator Loss: 0.3830
+Epoch 1/1... Discriminator Loss: 1.3848... Generator Loss: 0.7840
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Epoch 1/1... Discriminator Loss: 1.1885... Generator Loss: 0.7337
+Epoch 1/1... Discriminator Loss: 1.4297... Generator Loss: 0.9953
+Epoch 1/1... Discriminator Loss: 1.1458... Generator Loss: 0.9505
+Epoch 1/1... Discriminator Loss: 1.3446... Generator Loss: 0.7885
+Epoch 1/1... Discriminator Loss: 1.2624... Generator Loss: 0.8597
+Epoch 1/1... Discriminator Loss: 1.4662... Generator Loss: 0.5536
+Epoch 1/1... Discriminator Loss: 1.1388... Generator Loss: 0.7493
+Epoch 1/1... Discriminator Loss: 1.4432... Generator Loss: 0.4454
+Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 1.0688
+Epoch 1/1... Discriminator Loss: 1.4904... Generator Loss: 0.4468
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Epoch 1/1... Discriminator Loss: 1.3577... Generator Loss: 0.9579
+Epoch 1/1... Discriminator Loss: 1.4756... Generator Loss: 0.6143
+Epoch 1/1... Discriminator Loss: 1.2961... Generator Loss: 0.8042
+Epoch 1/1... Discriminator Loss: 0.9653... Generator Loss: 1.3168
+Epoch 1/1... Discriminator Loss: 1.4968... Generator Loss: 0.7849
+Epoch 1/1... Discriminator Loss: 1.3341... Generator Loss: 0.8891
+Epoch 1/1... Discriminator Loss: 1.3685... Generator Loss: 0.7253
+Epoch 1/1... Discriminator Loss: 1.3404... Generator Loss: 0.9025
+Epoch 1/1... Discriminator Loss: 1.2645... Generator Loss: 0.8479
+Epoch 1/1... Discriminator Loss: 1.6065... Generator Loss: 0.4065
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Epoch 1/1... Discriminator Loss: 1.6008... Generator Loss: 0.3544
+Epoch 1/1... Discriminator Loss: 1.2181... Generator Loss: 1.1289
+Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.9682
+Epoch 1/1... Discriminator Loss: 1.3160... Generator Loss: 1.1959
+Epoch 1/1... Discriminator Loss: 1.2201... Generator Loss: 0.7981
+Epoch 1/1... Discriminator Loss: 1.2491... Generator Loss: 1.1460
+Epoch 1/1... Discriminator Loss: 1.2200... Generator Loss: 0.7130
+Epoch 1/1... Discriminator Loss: 1.4682... Generator Loss: 0.8232
+Epoch 1/1... Discriminator Loss: 1.4792... Generator Loss: 0.9793
+Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.6182
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Epoch 1/1... Discriminator Loss: 1.2531... Generator Loss: 0.8170
+Epoch 1/1... Discriminator Loss: 1.4623... Generator Loss: 1.2401
+Epoch 1/1... Discriminator Loss: 1.2366... Generator Loss: 0.9501
+Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 1.3609
+Epoch 1/1... Discriminator Loss: 1.4270... Generator Loss: 0.9645
+Epoch 1/1... Discriminator Loss: 1.4111... Generator Loss: 0.8603
+Epoch 1/1... Discriminator Loss: 1.2284... Generator Loss: 0.7865
+Epoch 1/1... Discriminator Loss: 1.4061... Generator Loss: 0.6807
+Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 1.0759
+Epoch 1/1... Discriminator Loss: 1.4578... Generator Loss: 0.7154
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Epoch 1/1... Discriminator Loss: 1.3840... Generator Loss: 0.7229
+Epoch 1/1... Discriminator Loss: 1.3608... Generator Loss: 0.7311
+Epoch 1/1... Discriminator Loss: 1.4321... Generator Loss: 0.7518
+Epoch 1/1... Discriminator Loss: 1.2556... Generator Loss: 0.9692
+Epoch 1/1... Discriminator Loss: 1.2830... Generator Loss: 0.7561
+Epoch 1/1... Discriminator Loss: 1.3029... Generator Loss: 0.9397
+Epoch 1/1... Discriminator Loss: 1.5198... Generator Loss: 0.4745
+Epoch 1/1... Discriminator Loss: 1.2650... Generator Loss: 1.0075
+Epoch 1/1... Discriminator Loss: 1.3682... Generator Loss: 0.6288
+Epoch 1/1... Discriminator Loss: 1.3223... Generator Loss: 0.6662
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Epoch 1/1... Discriminator Loss: 1.3569... Generator Loss: 0.8252
+Epoch 1/1... Discriminator Loss: 1.3871... Generator Loss: 0.7830
+Epoch 1/1... Discriminator Loss: 1.8022... Generator Loss: 0.2896
+Epoch 1/1... Discriminator Loss: 1.3923... Generator Loss: 0.6645
+Epoch 1/1... Discriminator Loss: 1.4469... Generator Loss: 1.4170
+Epoch 1/1... Discriminator Loss: 1.3721... Generator Loss: 0.7974
+Epoch 1/1... Discriminator Loss: 1.1933... Generator Loss: 1.0238
+Epoch 1/1... Discriminator Loss: 1.2738... Generator Loss: 0.9327
+Epoch 1/1... Discriminator Loss: 1.4144... Generator Loss: 0.9993
+Epoch 1/1... Discriminator Loss: 1.3502... Generator Loss: 0.6667
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Epoch 1/1... Discriminator Loss: 1.4132... Generator Loss: 0.5582
+Epoch 1/1... Discriminator Loss: 1.3437... Generator Loss: 0.7727
+Epoch 1/1... Discriminator Loss: 1.3675... Generator Loss: 0.7321
+Epoch 1/1... Discriminator Loss: 1.3730... Generator Loss: 0.9555
+Epoch 1/1... Discriminator Loss: 1.4001... Generator Loss: 0.6890
+Epoch 1/1... Discriminator Loss: 1.4468... Generator Loss: 0.4802
+Epoch 1/1... Discriminator Loss: 1.4452... Generator Loss: 0.6017
+Epoch 1/1... Discriminator Loss: 1.2895... Generator Loss: 0.9879
+Epoch 1/1... Discriminator Loss: 1.3213... Generator Loss: 0.7545
+Epoch 1/1... Discriminator Loss: 1.4087... Generator Loss: 0.7632
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Epoch 1/1... Discriminator Loss: 1.4724... Generator Loss: 0.4925
+Epoch 1/1... Discriminator Loss: 1.3801... Generator Loss: 0.7973
+Epoch 1/1... Discriminator Loss: 1.5132... Generator Loss: 0.9511
+Epoch 1/1... Discriminator Loss: 1.3006... Generator Loss: 0.9248
+Epoch 1/1... Discriminator Loss: 1.4002... Generator Loss: 0.7541
+Epoch 1/1... Discriminator Loss: 1.3821... Generator Loss: 0.6088
+Epoch 1/1... Discriminator Loss: 1.6036... Generator Loss: 1.3762
+Epoch 1/1... Discriminator Loss: 1.2994... Generator Loss: 1.0744
+Epoch 1/1... Discriminator Loss: 1.4678... Generator Loss: 0.6439
+Epoch 1/1... Discriminator Loss: 1.5260... Generator Loss: 0.4740
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Epoch 1/1... Discriminator Loss: 1.3717... Generator Loss: 0.6639
+Epoch 1/1... Discriminator Loss: 1.3463... Generator Loss: 0.7220
+Epoch 1/1... Discriminator Loss: 1.3487... Generator Loss: 0.9054
+Epoch 1/1... Discriminator Loss: 1.3769... Generator Loss: 0.7837
+Epoch 1/1... Discriminator Loss: 1.5438... Generator Loss: 0.5937
+Epoch 1/1... Discriminator Loss: 1.1925... Generator Loss: 0.8585
+Epoch 1/1... Discriminator Loss: 1.5108... Generator Loss: 0.8107
+Epoch 1/1... Discriminator Loss: 1.3479... Generator Loss: 0.8114
+Epoch 1/1... Discriminator Loss: 1.2299... Generator Loss: 0.8803
+Epoch 1/1... Discriminator Loss: 1.2651... Generator Loss: 0.7570
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Epoch 1/1... Discriminator Loss: 1.4447... Generator Loss: 0.6179
+Epoch 1/1... Discriminator Loss: 1.3410... Generator Loss: 0.9721
+Epoch 1/1... Discriminator Loss: 1.2921... Generator Loss: 1.2052
+Epoch 1/1... Discriminator Loss: 1.3283... Generator Loss: 0.9427
+Epoch 1/1... Discriminator Loss: 1.3041... Generator Loss: 0.7806
+Epoch 1/1... Discriminator Loss: 1.4502... Generator Loss: 0.6852
+Epoch 1/1... Discriminator Loss: 1.3364... Generator Loss: 1.0475
+Epoch 1/1... Discriminator Loss: 1.3068... Generator Loss: 0.9476
+Epoch 1/1... Discriminator Loss: 1.2938... Generator Loss: 0.9685
+Epoch 1/1... Discriminator Loss: 1.4735... Generator Loss: 0.5542
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Epoch 1/1... Discriminator Loss: 1.3093... Generator Loss: 0.8099
+Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 0.6992
+Epoch 1/1... Discriminator Loss: 1.4837... Generator Loss: 0.5886
+Epoch 1/1... Discriminator Loss: 1.3110... Generator Loss: 0.9667
+Epoch 1/1... Discriminator Loss: 1.4147... Generator Loss: 0.9200
+Epoch 1/1... Discriminator Loss: 1.3674... Generator Loss: 0.6883
+Epoch 1/1... Discriminator Loss: 1.2852... Generator Loss: 1.0330
+Epoch 1/1... Discriminator Loss: 1.2894... Generator Loss: 0.9344
+Epoch 1/1... Discriminator Loss: 1.1895... Generator Loss: 0.8798
+Epoch 1/1... Discriminator Loss: 1.4130... Generator Loss: 0.7914
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Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.7511
+Epoch 1/1... Discriminator Loss: 1.4302... Generator Loss: 0.6394
+Epoch 1/1... Discriminator Loss: 1.3354... Generator Loss: 0.7412
+Epoch 1/1... Discriminator Loss: 1.5316... Generator Loss: 0.4699
+Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.7564
+Epoch 1/1... Discriminator Loss: 1.3748... Generator Loss: 0.9328
+Epoch 1/1... Discriminator Loss: 1.3011... Generator Loss: 0.7149
+Epoch 1/1... Discriminator Loss: 1.3411... Generator Loss: 0.7330
+Epoch 1/1... Discriminator Loss: 1.3431... Generator Loss: 0.7667
+Epoch 1/1... Discriminator Loss: 1.5367... Generator Loss: 0.6442
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Epoch 1/1... Discriminator Loss: 1.3587... Generator Loss: 0.7105
+Epoch 1/1... Discriminator Loss: 1.4629... Generator Loss: 0.6467
+Epoch 1/1... Discriminator Loss: 1.5333... Generator Loss: 0.8289
+Epoch 1/1... Discriminator Loss: 1.4835... Generator Loss: 0.4824
+Epoch 1/1... Discriminator Loss: 1.3330... Generator Loss: 0.9264
+Epoch 1/1... Discriminator Loss: 1.4501... Generator Loss: 0.7557
+Epoch 1/1... Discriminator Loss: 1.3579... Generator Loss: 0.8731
+Epoch 1/1... Discriminator Loss: 1.4515... Generator Loss: 0.9340
+Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.8987
+Epoch 1/1... Discriminator Loss: 1.3188... Generator Loss: 0.6393
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Epoch 1/1... Discriminator Loss: 1.4152... Generator Loss: 0.6163
+Epoch 1/1... Discriminator Loss: 1.4929... Generator Loss: 0.6064
+Epoch 1/1... Discriminator Loss: 1.5524... Generator Loss: 0.4964
+Epoch 1/1... Discriminator Loss: 1.4786... Generator Loss: 0.7433
+Epoch 1/1... Discriminator Loss: 1.4437... Generator Loss: 0.6628
+Epoch 1/1... Discriminator Loss: 1.4178... Generator Loss: 0.7201
+Epoch 1/1... Discriminator Loss: 1.2742... Generator Loss: 0.8131
+Epoch 1/1... Discriminator Loss: 1.3555... Generator Loss: 0.7472
+Epoch 1/1... Discriminator Loss: 1.2990... Generator Loss: 0.8521
+Epoch 1/1... Discriminator Loss: 0.8481... Generator Loss: 1.3975
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Epoch 1/1... Discriminator Loss: 1.3439... Generator Loss: 0.6907
+Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.7232
+Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 1.2362
+Epoch 1/1... Discriminator Loss: 1.4625... Generator Loss: 0.6389
+Epoch 1/1... Discriminator Loss: 1.2682... Generator Loss: 0.9903
+Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 0.6646
+Epoch 1/1... Discriminator Loss: 1.5688... Generator Loss: 0.5454
+Epoch 1/1... Discriminator Loss: 1.1573... Generator Loss: 1.0297
+Epoch 1/1... Discriminator Loss: 1.1199... Generator Loss: 1.0091
+Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 1.0411
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Epoch 1/1... Discriminator Loss: 1.9337... Generator Loss: 0.2622
+Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.8398
+Epoch 1/1... Discriminator Loss: 1.2232... Generator Loss: 0.7815
+Epoch 1/1... Discriminator Loss: 1.4801... Generator Loss: 0.6008
+Epoch 1/1... Discriminator Loss: 1.3070... Generator Loss: 0.6860
+Epoch 1/1... Discriminator Loss: 1.3470... Generator Loss: 0.7634
+Epoch 1/1... Discriminator Loss: 1.5236... Generator Loss: 0.6591
+Epoch 1/1... Discriminator Loss: 1.3827... Generator Loss: 0.9191
+Epoch 1/1... Discriminator Loss: 1.2797... Generator Loss: 0.8605
+Epoch 1/1... Discriminator Loss: 1.3069... Generator Loss: 1.0828
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Epoch 1/1... Discriminator Loss: 1.4493... Generator Loss: 0.9787
+Epoch 1/1... Discriminator Loss: 1.3226... Generator Loss: 0.8376
+Epoch 1/1... Discriminator Loss: 1.3657... Generator Loss: 0.7043
+Epoch 1/1... Discriminator Loss: 2.0819... Generator Loss: 0.2141
+Epoch 1/1... Discriminator Loss: 1.4158... Generator Loss: 0.5562
+Epoch 1/1... Discriminator Loss: 1.2893... Generator Loss: 0.7726
+Epoch 1/1... Discriminator Loss: 1.3175... Generator Loss: 0.7764
+Epoch 1/1... Discriminator Loss: 1.3040... Generator Loss: 0.9346
+Epoch 1/1... Discriminator Loss: 1.5212... Generator Loss: 0.6124
+Epoch 1/1... Discriminator Loss: 1.3328... Generator Loss: 1.0946
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Epoch 1/1... Discriminator Loss: 1.3074... Generator Loss: 0.8244
+Epoch 1/1... Discriminator Loss: 1.5227... Generator Loss: 0.6513
+Epoch 1/1... Discriminator Loss: 1.3824... Generator Loss: 0.5856
+Epoch 1/1... Discriminator Loss: 1.5399... Generator Loss: 0.5881
+Epoch 1/1... Discriminator Loss: 1.3308... Generator Loss: 0.8962
+Epoch 1/1... Discriminator Loss: 1.4280... Generator Loss: 0.7422
+Epoch 1/1... Discriminator Loss: 1.2792... Generator Loss: 0.9444
+Epoch 1/1... Discriminator Loss: 1.3914... Generator Loss: 0.9070
+Epoch 1/1... Discriminator Loss: 1.3808... Generator Loss: 0.7781
+Epoch 1/1... Discriminator Loss: 1.4229... Generator Loss: 0.7619
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Epoch 1/1... Discriminator Loss: 1.3483... Generator Loss: 1.0495
+Epoch 1/1... Discriminator Loss: 1.3219... Generator Loss: 0.8450
+Epoch 1/1... Discriminator Loss: 1.3759... Generator Loss: 0.8250
+Epoch 1/1... Discriminator Loss: 1.4419... Generator Loss: 0.6523
+Epoch 1/1... Discriminator Loss: 1.2803... Generator Loss: 0.8917
+Epoch 1/1... Discriminator Loss: 1.3013... Generator Loss: 0.7574
+Epoch 1/1... Discriminator Loss: 1.3968... Generator Loss: 0.7108
+Epoch 1/1... Discriminator Loss: 1.3129... Generator Loss: 0.9058
+Epoch 1/1... Discriminator Loss: 1.4153... Generator Loss: 0.8783
+Epoch 1/1... Discriminator Loss: 1.3831... Generator Loss: 0.7992
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Epoch 1/1... Discriminator Loss: 1.4230... Generator Loss: 0.5748
+Epoch 1/1... Discriminator Loss: 1.3313... Generator Loss: 0.9232
+Epoch 1/1... Discriminator Loss: 1.2939... Generator Loss: 0.8525
+Epoch 1/1... Discriminator Loss: 1.3392... Generator Loss: 1.0006
+Epoch 1/1... Discriminator Loss: 1.4827... Generator Loss: 0.5120
+Epoch 1/1... Discriminator Loss: 1.2983... Generator Loss: 0.8530
+Epoch 1/1... Discriminator Loss: 1.4825... Generator Loss: 0.6011
+Epoch 1/1... Discriminator Loss: 1.3184... Generator Loss: 0.8947
+Epoch 1/1... Discriminator Loss: 1.2802... Generator Loss: 0.8693
+Epoch 1/1... Discriminator Loss: 1.4412... Generator Loss: 0.7739
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Epoch 1/1... Discriminator Loss: 1.3356... Generator Loss: 0.7553
+Epoch 1/1... Discriminator Loss: 1.4345... Generator Loss: 0.7667
+Epoch 1/1... Discriminator Loss: 1.3151... Generator Loss: 0.8760
+Epoch 1/1... Discriminator Loss: 1.4366... Generator Loss: 0.7039
+Epoch 1/1... Discriminator Loss: 1.3346... Generator Loss: 0.9874
+Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.7690
+Epoch 1/1... Discriminator Loss: 1.3227... Generator Loss: 0.7796
+Epoch 1/1... Discriminator Loss: 1.4459... Generator Loss: 0.7080
+Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 0.6979
+Epoch 1/1... Discriminator Loss: 1.6802... Generator Loss: 0.5041
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Epoch 1/1... Discriminator Loss: 1.2873... Generator Loss: 0.8386
+Epoch 1/1... Discriminator Loss: 1.3842... Generator Loss: 0.7583
+Epoch 1/1... Discriminator Loss: 1.4189... Generator Loss: 0.7991
+Epoch 1/1... Discriminator Loss: 1.3378... Generator Loss: 0.8856
+Epoch 1/1... Discriminator Loss: 1.3755... Generator Loss: 0.8002
+Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 0.8532
+Epoch 1/1... Discriminator Loss: 1.3158... Generator Loss: 0.9599
+Epoch 1/1... Discriminator Loss: 1.4263... Generator Loss: 0.6772
+Epoch 1/1... Discriminator Loss: 1.3762... Generator Loss: 0.8854
+Epoch 1/1... Discriminator Loss: 1.4065... Generator Loss: 0.7769
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Epoch 1/1... Discriminator Loss: 1.4026... Generator Loss: 0.5969
+Epoch 1/1... Discriminator Loss: 1.2856... Generator Loss: 0.8660
+Epoch 1/1... Discriminator Loss: 1.3118... Generator Loss: 0.7520
+Epoch 1/1... Discriminator Loss: 1.2754... Generator Loss: 0.7607
+Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 1.0381
+Epoch 1/1... Discriminator Loss: 1.3217... Generator Loss: 0.8141
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提交项目

提交本项目前，确保运行所有 cells 后保存该文件。

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保存该文件为 "dlnd_face_generation.ipynb"， 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。

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+
+ + + + + + diff --git a/face-generation/dlnd_face_generation.ipynb b/face-generation/dlnd_face_generation.ipynb old mode 100755 new mode 100644 index 59bd77f..4028440 --- a/face-generation/dlnd_face_generation.ipynb +++ b/face-generation/dlnd_face_generation.ipynb @@ -2,37 +2,41 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "# Face Generation\n", - "In this project, you'll use generative adversarial networks to generate new images of faces.\n", - "### Get the Data\n", - "You'll be using two datasets in this project:\n", + "# 人脸生成（Face Generation）\n", + "在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。\n", + "### 获取数据\n", + "该项目将使用以下数据集：\n", "- MNIST\n", "- CelebA\n", "\n", - "Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.\n", + "由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。\n", "\n", - "If you're using [FloydHub](https://www.floydhub.com/), set `data_dir` to \"/input\" and use the [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) \"R5KrjnANiKVhLWAkpXhNBe\"." + "如果你在使用 [FloydHub](https://www.floydhub.com/), 请将 `data_dir` 设置为 \"/input\" 并使用 [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) \"R5KrjnANiKVhLWAkpXhNBe\"." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found mnist Data\n", + "Found celeba Data\n" + ] + } + ], "source": [ - "data_dir = './data'\n", + "#data_dir = './data'\n", "\n", "# FloydHub - Use with data ID \"R5KrjnANiKVhLWAkpXhNBe\"\n", - "#data_dir = '/input'\n", + "data_dir = '/input'\n", "\n", "\n", "\"\"\"\n", @@ -46,25 +50,41 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "## Explore the Data\n", + "## 探索数据（Explore the Data）\n", "### MNIST\n", - "As you're aware, the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset contains images of handwritten digits. You can view the first number of examples by changing `show_n_images`. " + "[MNIST](http://yann.lecun.com/exdb/mnist/) 是一个手写数字的图像数据集。你可以更改 `show_n_images` 探索此数据集。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WaVOTftixyY0RCgIlpfF6vZy8lxLxSGuPLDmfSqWYQDz66KOYP3++qX9WYmoH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\nlCkAnCzZHPH7vPPOw/e+9z0AEC5Jd9GjScEYswhABMAL3bh2DoA5PSl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OjjzE3zVfIRXb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\nFmBaZwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "show_n_images = 25\n", "\n", @@ -82,24 +102,40 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### CelebA\n", - "The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing `show_n_images`." + "[CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸，不会用不到相关说明。你可以更改 `show_n_images` 探索此数据集。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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8Bkf3iEfR1IAtPHuThqm8+SnxJWa80S2apPGswuY6RYhv5xWubXLJ9aMCmk21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\niQG32V5TNX7uRQrm7TJtFbRE3HiN2gR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xvwTg0Dn3XcbYVz7t+y9a0aeCOzOb4vNXdvHV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uCTn/gEAODFF17EBlWEk1AgYWV/wR28C2JISpvNLi58dRpBCBPb1VWJCMrZvPO9f+8//hsY9imG\nsjlCw4q6qgokznOhN8LhuX398MwWwIw2Xq4rkNJXuKUAYhYlaxY4OwMM2YM+2BnhYMfOy/7eFO8/\nYKX64QnK5glVaQBKAwtGSqUJEWmHmlxjKOp2DUd2jMtIBl6zAabz7Ly2Mw40hziJEPN7XEhqAG83\np4RBQRhzB4trsOetPULS2eM1jfKpzxe+8AV88YtfBAB874/9pMdeSMBHE65XIcSTBUrjcQVSA2/8\nhjWi+W/+zt/FzV2rZfCv//gPAQC2hoEP/YU2tskP6z/ROh2CpkFJUtExod1v3r+P19+zZbF7R0dY\nME1VEPjyf2+jnp//BYtX+K/+3hIFkbBRHPhr0nYGZ2cX/rwDCuZsDWxkk6UxHvD7isYg5BwN0hDM\nFLCdRxhmdg6dX8ij8wrzJY+9rHBlyz4vg9hgyEhvMCzwgz9m09d/+T/44LeNMZ/HR4znAuYcSoHN\nfoIbV67gGj33xqPMg3RUB8x54Y6WK9x5YC/SG79njTL0qsKElOWXbt3Ai9et9uFWP8eASkZ51kPI\n/HRIuC/iDBUXgjSOUdBhZz6fI2FdIurFWLBy3rJFKozCoGdDyq6pvLDIsN9DSkGSs0WNc1aAndNT\nHEe+HtC2T7YLjfM8QUSr96KoUBPmPFsW6BO/HokUeWovchxcYMmH0LU6szjCiJ8RdxJn5/YYgidg\nzEEg0SdceUgQz5WNITJ+x8VyhZqwcm0AyZt72M/Qd8a6zCnapvUgpFlV46yy87ZsNUoqtVRVhbZ1\nvcz1ObtU6/Hjx3jzTRsmf/7P/KSvfguBJ0BN7l/jodtiTYyGbjXu3bX3xfb2Nv6VH//TAICdLdrB\n6xKqc+1e5VM+I5WHMYedWEOzpRPmDRDACbSuQVhGCKwYxn/9d+y5FUrB0IQljmJoR2s2ARzxW2aR\nt7A/IbV+P5S4sWEXiEezCiUt0M7nBerKic9oL8ozYMc/DEIMqIlZyhjzBXUs+wFaap3WdYIv/7/n\n+DjjkiV5OS7H5XhqPBeRQj/v4Qc+92lMhkOv5Qch0HA3OitXuHPf7gK/9+abuPuBDZ9jypztbU7w\n8oFVzn31xgvYY2h/dZJ76exAdAgTx613PoOZdXGGnYiYu0M/TVCWdgcWpsKqsBV1xwwM0EFwh26a\nCr3EHvPVvW0cndGvcLFCx53Chbut6rzFuf2F/ScMIsSB01i0L+ZhjpYRSGACRImNDh6enqPh63Gc\nIefGfXZBb8Sy8orESZIhouSZEMbrSgZhhC1CbL/nwHo73tzd8mFrEiboiFJqm8YLpyRpjJgpVJ+R\n2c50w//d3QeH+NLX7I7/+sNjPOL510J40pg76SiK0FGu7fHjx/ilX/olAMC/+e9+0TP8DOD1ItZT\npr0OpHmCXdlUDU4I2PlTX/gM9rdZdOzYRVLKR56ma6Dc610D7eT1tYHgLhw53EgUeJ/HUAgvcGI6\ng7OH9u/uP2AXSRuMnAaGFJgRF5DFIYaRvbdWxqBxboAsqq9WNbYG9ueR1N6ar40MLiiW8uC8hO5o\nGMTiuRYS5ws6V4exx6o8PFthwBtjdlFj5cKUZxzPxaIQBgGmkwkCSDS8I0rT4WxhH7DZcomvfNW6\n8dx/cA8ROwM589rdQQ8HGzYluDbOsEXas1yeIHV4jQ6Q2iHemJaclz7H7UmJlLlapUJfIW61Ro/A\noTFTjSROfG4dJhGu7llQVFnVWDA3TNMYGQFAjqWmVOsfjlAGCOFuoAB90iA3Rva78jTy9uxN3aFc\n2bqE0sp/R9NpDEZ2sdiZ2jlZrEpUtQMbKc8A1FqtH24YTAmQ+cJLtjU3zTOkbD0O8x4SCr72ehk0\n/64FEDBnHvYpU44OioCkvUEfV8j3+N9//Sv4f96w/p5tXaPgdXWpVJKk65QCAp/61KfgxrqO8PvR\nTcLAn4cVZ7HvmZ3P0ZGh+cKNlyC44DifjaBtIcjF0KpFy5RQqxba3SNK+LQiYrGlF4cIeb+0deU7\nXxIGb/y2vT/nM6YlHZAnrO10Cu70ytUCWyOKvlQlBKnmPX5XqrWXDBj2IlxcOB+KEE4Ttio1Lvh0\nJ7wv9rYyjEm5f3Q8w5DpH8IQS3Yl0ijCo9Nn7EVyXKYPl+NyXI6nxnMRKQCAMUCtNEqGrY9Xc0x2\nbGj7u998C48eWkiogfSAnJg99qnscIvY8pFeoE8DENkJxM4zUCYQNHtxZipaSGt3DqBsNeYr+90S\ngXd6apRG6GzeuUvGUYKa/Pg4CrDiqnz46GgtDQ6FvW3bMVisyHorlVfWDQH0KScuAAyJWdjesNHM\ndCOH0k4nUQBkcxaFwrK0u/y7HzzCGTsbw7E9tvEoQ92wmKkkHp2xT28MAicjHzbYdt2Tvo1MJkmK\nmLtgHocYbZLJNxhBspLdAl5tO+IOLVXjuRERNG6yJ/5jr93EKY9tVVao6aDtip1VVfmfu07h9PT0\nqd+7IT7079ov275qGHkcPX6EIc8lS0NIsmq1U49WNTTTgLopoMiiNF2LzqHM9LqQGDHny6MIfSfU\nohqAYN1enOH3fpcYgQs7P2mooBjuizBBxvlcVjVK4lO2xz3s0cU8oMN4ZjqkxFDUSqNmhKkajXGP\njlx5gq51zFx7ON2qwCYFXupBjjn1PQe9AB2pAFWjIaVTL3i2POIyUrgcl+NyPDWei0jBGLsjz6sK\np1zBr7z0Atz+8O4H9+D6d7ITPscbsbe/N4gxCmiZhQYpEXFJkiIjnDdJBxAsYoZ0kUYYQGmHTDTo\nsXd/dHqGamW/Y36+QG/TRiyJEwoynWcLqjbC/ftWYSd8QpEpDDR2qez7kO3UURZjxAJQLA1yRhVK\nd+gTCdgnG3Cax8jYbpoXFc5mNtqQpkJA1aDt7SEeU9WpZeQiRYCIbNA4lj6Hl4FARNj0IA5xa89C\nbfvZmmWXUhk6SzNEbK3KfADJXDUOAoS5rd0ocvtlsYSrDFZoEDpLvskE3/PSDQDAB7MC5495nL6o\npz0uJAgE7ty5A8DiEJ6MFhyOYh0piPVrQnjF6McP72O6ZaObAMr7S0jtsAktwMgLuoNwrUXdeRCE\n0mtiWcC6RS+KPF4mlhJLyrhFQYT5zEYIZWvPLYngGZ5dozDgvTCZJCj4d+ViYS3eAOxt0OjFdD7i\nuViUGBhXEJb+/ARqDENX07JzbIxBcWHvvf3pxBY1ABTLyjuld6ZD+2w8KD+ei0WhVQqPzs7QZSlu\n/AkrUpH1B7jzpjVEbbtuDYmF8V2CEcPaXig8/jzolKckh4gQMT2ANF4sI6Z5SZz1sGyouddU6DtD\n13GOiCCiqm1RsYq+v2eLaHVbQ/CBXhXNurf9hPZhv5dAsPCVkvY86sXY3ybGQGgPFuqMRtnS85Id\nlaaqPDAnThKMCHTpVOcl14d5gDTd4nG6nvgaott1ytO60zjGkMzIV6/v4IAFwcBRwMMA5gnhmMDT\ns1MI4jqEEDChE4khyCoIAS42Ik4QsmAWBhc42LR/d3tnhPdObJX8zMOu1wxOrTW+9a1v8buBJ+mT\n7kcvBm3Euvxo1vJuFyenuHHdYlxMW6MjWzNgcRFawfBnadSaum3WACjzpLa1k1YPDLbItNwcDDGj\nzNliOcfKUNE6JPchSCFJ8Z+dnWOXKe3eMEbJRfZ8pdBwAV8t7fFMRqlf/fYHkceArDTQOPNaCA++\nIpIaurXpBgAUF3NssphpjPTds+EwRdF9vFXhMn24HJfjcjw1notIQUQhoukWrt+8AcFWWNtqXwTs\ntIHDtMlAePu2TRaWhmmCmKlBLCUi93MUeqSglB2Eh7k6oU7bOgSAtqs9AUk1rZfz6g+GCCK7SxfU\n9Ne6BRG8aLXwHgOtamFc2zKIsCQcO+NBTAY5djYt7LpezjEZuNQmxQnJKk3jxFo1igWlyKoOEedl\nMBx5W/eiqrFkGO92OyEDdA523GkE3GniIPK8+q1BD31GWd5GXcMj94xRazMUrT0Ks+s6BJyXyLXp\noD0pK5ChLYrChuFj7mi3d0b48h37GWcUBbGtUuN/XjG8lh8SWfhwoREG6wjDaJQUOTVViQFRmlAr\nGLYkHQZBqxYdozjTtj7lMZ3xxcVAGAjn1cD0Q+gam0z5rk6nOCQK8WR5gSULgimvbwvj8R0wQI9s\nrRS1F9EZZQnOeVmdZkeVdBiRCNfLBbLQfsZ5rXwRe9lKaO7hpfMr1evrW5fK29sN0hBdbb9vfrpA\nSnHeZx3PxaIQJgm2XngRnZSQzgkJwpuTNBQCAYBQAJsEd9zctezF6cYYIxpy9Ho9xLzhpTQQFL0I\nO+H1Gg1NbFu0CDhhQQDo2BUNlAfFKKUR5Y46bI8nSwLE1Isf5n0IR3ttGiQ8Zt0qX/vYGNswems8\nQsqLGCapd2HKwh6ub9tw3mkfLtvOs/BqpbCsiZ1vGyxWNADpAEhX+V4bqbbMPYuy9PG3NtoaogDQ\njYIm5daH0dr4B0V1Gg3Tscy0EKR1R2Hg82/JsDXo1gAjIYRP8sJA+vO/vr2BfXYz3mMODKw7DUII\nX1/4WKGrEJjPbYcjCICusQ9sVZ0hoOal4XmaroNxx2wEhKOiG+3vOd0pv3A4Lk2nWkhS4zdHKbYG\n9n6ZF0vMVvR/dB2nrsOci3QkBMYsQg0irOnsbYVYOIq6/Xe2UEhY28rCDv2YaaM0aDiHpwY44WMw\ncwzOnvRaodACgtD0MAK2R/aeK5rQLyzPOi7Th8txOS7HU+O5iBQMBJSIaIflqr/rQlTXdX7HS6MA\nG4wUpqzijnsZ+rndiYb9kdfsL8uSzsSAjEJIroEuAjFGI+BurVUN0zrpNmP9tgCkaQ9nK+fvyEJP\nmiPvOXKNwAXNPZTp0I/tTqLbGkNGHjd3bHFxkMXIuOvIXrK2LtMtEmInQqIKR4MQDXewi6JAwYKa\nSnNoV+1v4FFzGaFvUZqgMQ4RqJ+o5Auf2rSqRcmoqXDkGhMgpP6gUNqnWKZYeWkyHUpIdigcCaoz\nxtu1KaW8Y7LSGu0T+IUXKcTytQ8s53/RrUvHQkjEDP3dxufGhy0LhIUx8hg0Lpa2gNmpCqdH9wEA\nuSkQCQfTdrJq0v9dEEpP8jJCwPu5d8Jb2bkCXt0qKOIbslBjg3iSB2cRVudESzL0T4VBS8m3UGhQ\nFwjDOPIkrl4gIajj2PA+rJTAY7qfD3sCGY8nDSKYyM5nHmov4DJvGAm2QNpzxkCRDREAGBmgLIh7\naGqU1ceLFJ6LRQEQEFrACLOWGRdAQyCIahrvmpOnMTbYIgoIJFGthnBKMyZAyBu3aRWkZDstGXgQ\njmDVXxuNjhOtqgq1ExVVCobV91VRQbOiXPLhByRa5nVl3XijkziMbY4KoKlaXDmw6c2rV21qEMkA\nK+akYZJ6g5MoECCrGyOq/CCSaHhseRTgaG5v/sfnc8QMfS2YiIfE95bLpQeAyTD0xjBKad+y2pwM\nkJK70AbrfNjVDuIwRsAFVNfK5+dKCoQ5FwPegDKKIAUXXqUQuHpOnCLkuXSrChusn0wZfhez5bqf\nZDT6hE1/GNi8Fn/ne8WT7zE4PbHpyL1796AP7Xnf3hnBefOekrJ8Pl+goGR+KwxCsmfDIMCQUOHN\nwQARhX4b3k+tbuEMNGMJz8adjoagQr9v+alQgBkBemLt2hVL6VPeThlf2zki2GjRGO9OhtKgY41C\nhQKB2ywSjSFb0TF9VVsF1FyE53WFwvFAghCdpmJTqNFzosco8SzjMn24HJfjcjw1no9IwRho3UEI\n7SW9FQwe3LdsyK5p0GN43M9z1DRquXvKYpAOkU0IJW4U9qme3C46LEu7jJ9XpyDJDCHBH+PBAL0e\nWYRG+9C1qVpoTo3qFGbUTayYajRti5bQWGlCv8sLZSBZoNvbGOITL1jm5pQhZ97rQ1H0pVEtOuoN\nRNJ4Tb0+dRqyQc9HHY9Pz9ZEHKXwgIAl0wKCO4mku3IeSoQs/JVaIGS60mmBKd2jb2xNkBD6qqhL\n2cYhWoKUVJIhSUgeyweAY1rKtdiJxw0IAU0tB2lidEwZOhFAEB7cQfro5gqxC4hYsyQAACAASURB\nVI8uVn7HVzCe5KSfCBXMEzqOPtkQa61JtBoLCtksispb22/2EvRj+33v3LOqzQ+Pjr3yc9iLkQ9t\nSjcZbEAWjLKKU+RkmPac4IwIPNYjkiEGTO92Bjk0u2ArRo1dB18wzQODkNEU4j5SXh/EGqxboxdT\nI2S+8IVtoRSkdFgP4btnJg6QM7oZO4dxKbw/5KxQOGLh8+6yQ8X0LpYdQvwLmT64SrTxeZ9SCo8f\nWnlraO0txVdV7TXtj9nye/+khyNWfW9M+thlV6Kqljg6tTnsWw+PcHJh8fUxc/3rV3Y9P+G1m1d9\nzSEUERqmCqHsQ9X2swti1Y0xnhYdh9IbuupWIafIxvd98gauMY/uO/PYXh/aG7tKb9Ueo0PC84v4\nMOpwffNfTRNEBB4Ne30MhzZk/p137mLG8255I42DFBkZl6Y2EMy7gs4g5+uJjFCVNhQ9oefity7m\nSHgHjoYTjEa2RjPOM2yzjToYD9DjwtE5AM5qhVN6GD46PcNpZRfnk8UCZxd8YM9OIfiA7GzZzxqe\nLDFbkr0ngNh9Lp4ca2r0OtVY61Lq1qCgIOy7dx9CsHazvQGkFJrRXCBNGK3FaOMUGZGZaZah79Cb\nwnimZcUFJhbSd22EbH0Ld9jLcNGzf1ezM1S160UBYYRTphXH5zWaas1UlGxFZNyFNnoxMjjmpPAL\nYSUkzvgZs65D5ajffC0MEkxyewz9sEZApa6jpoaiUMtLwxCvvWRTt5//xQs8y7hMHy7H5bgcT43n\nIlIwWEcKrkKu6hozRgIBBBpq4w3SPq5csfp7it2Ck9kc1duWu5+/fAMB+/RX9rfwzV/+NQDA4cND\nKGcTv21VdqP+BpbcMb/51tu4vmc/VxqJJV2Z2yTEzraNPJrHa7xEQGBKFAsPDjk7PsZ4YrUVtGnw\n/mN6RUqq6QYz9Dnjm3mAQUqZsyDAkn31hloJlRY4ptT38Xzl+9xdvUSfxbqNjS28dWgVpheUst/d\nGGCbnIm66pAzwihKjTB0lusSD45ttPXNd+9xLpXHG3RB6rH4V/oxvuelAwDAi/v72Nm25+eYqifH\nJ/jGe+8BAL569x7endsdsQgiaIbVYVMh4GdndOLu9XIfKQRSona+mb9PM9RJrz0BY3JzoQ1WBACd\nzQv0GEG8euMGermdgxH/PZtXOJ3Z+bx/coityn7I9obGMLXfPRpk6LEj4EVm9FphPBBrHctRL8Nd\nXpSE91tZAiHfG4UJLmg1P182WDb29aUOvNv2mHHRjbzFK9v29+M0gOIcLAuN09K+ftZFOGcnpWC0\nmZgVNi7sOW31AoyHNiLYyhWcAdYVofD513jT/SKeaVxGCpfjclyOp8ZHRgpCiH8A4F8DcGSM+SRf\n2wDwjwDcAPA+gD9vjDkXFljwX8A6TxcA/pIx5nee6UgEAEg4G8qyKLG6sPkudOd9CvIowvzY1gmC\nyK3KIUKuruONMTIWmeIwwMGB9eU7nc3wgEKqr79hyTff+NY9/NCftIo/o1GC0zObG/fHY6Rc/c8X\n55hu2ghij/oOQgI588nxMEfLPj0kMGAh8aJR+IAkoA/ObW1Ea4FrYxs17AwFtjfsOV2fjDDMbT58\n/9Ce26OzJR4RifboooZhBW47B6bcEcfDMVpld/qTBWHOqLA5tHWSze0xrp/a6OY9NUPKCKOTBppb\nyd6OjYJkEOOcvg8P5gWmW/Z4XjrYxpDEnvPVDJuB3elDRiBnH8zRpyHLKzdfxOLuI37GCruUwLoy\n2Yei98Uh6wz9LETC9qUJJC7Ycn06UnB79HoYY1yHEK3WOGe00e9P8P1fsM4CL93aRaZs5DTK7e65\nOVE4YUf5YraEuHC+oWeQbLnuTwa4QeHgfuBcxwPfqg3DGFlMshkCnJ/bXXqc2wg0bzVi3gtX4gaD\nob1OhYjx/rGNaLSAbxkPByzmRg2Uk7AOAs9s7YyAoDK3UK1Xx05YRNxOAxxwjvNAIUupVj5KcIW2\neS9vtTi49c8f5vzfAfi7AP7hE6/9TQC/ZIz5aSHE3+R/fxHATwC4zf99L6wF/fd+9FcYuiutQ6um\nrtdahBHQoyx2rFtMN+2N7GTS1LJARmDRwc4UCavFQivcum5dpNqqxXRmb8iobx+aTgSoF3YhQH+K\niJV86A5XKIt2fDHH6YkVeIl4g40Ga9ek8XiEe/ft73tJiM2BfdA3JyPIwP48ok73qJ/hGn0sRVth\nUdgFIE4j5Oxj77DAtzvdw0FleAyV5wSoZoUxjT6CsMONfTsX7xzZh6CQCkvqUm5sDPBnv8cqev/P\nv/nrmO6weJjl2M7sDVkxxH00W2B79yYA4OU0x/bEnuvVnsCUTf/z83Pvf2kcakobbO/bOZ5mOXZf\nvA0AeHByhsWJPb/q/AgHZJj2CTg71RKLc7sQiCjDg1Obamm9lqKX0kMEnug++EYFWtVhwYV+nA3x\n2q0bAIC9zRCLI1uM7fHB25Qh9l1HSQjsbtvNIo0DFEubpvYDAQc0iNk5ydPUF4RV1XlKtTYCAfOY\nHgvXfdNBObp0rjFi1yk1KYbX7WZQS4mKC6Rh6hN0re80RWHqsRldW3isw34ucZMcjIjdM9F1GJAn\nkScxwA5PGgKvjOxCtf3CEG26TnufZXxk+mCM+WUAH7aW+UlYm3ngabv5nwTwD40dvwHrK7n3sY7o\nclyOy/HHOr7TQuOOMYb9QjwGsMOfrwC498T7nBX9I3xoPGlFP9ncgjACWhq4KtLh0X1UtCgbJAn2\n2WO/Nd3EiKgyZ5Um+z1M6HB8ML2OEDRyaQts0e8vvZ0gu2NdqjXxAaNxjuTA7o5luQI7doBW2Egc\nAq+HQyIBW6Y2qimQjuzvz87OoMmefHF7jOsMtfcHIfZ7Y54rPSTiCClVfUUyxHLlWqQRIorEZBmF\nQAJgk62wzSRB5PC+0QBhRGHWZoZXr9rI41esszyarvOouvl8iU++bKOiK5NN7G44ZKVAnzvaBu3Q\nr+xuIxjYz+pPNpGzZSeqFYpzu+tWVYMqpHiJtMfbNspDlLe2JrhKMd39foCLEZGXFxnc/tPSbGWY\nJEg54cum9QKsTwqsaL2GJOgn6oxGO0HbGopSdy/f2McVpjxdcehJTgOeRxB3kGQqDqMYaWwjiDyN\n0duzc9RPU0TEADhiWxwGaEobjZRdAaGdPkWAHeKYD6Z0175Y+MhmIw/RI7s2CgR6oY2wYgGwxonZ\nBaHmZYcUFLgJ+j4CyUUNE7MI2g+wMXKiv0yxu8QfTxBGqDnHkyTCFiO9s4sKp18+wscZ33X3wRhj\nhBAfLhk/y995K/prt16wj53RiIgzx2KGnA/jTr+Pl3fsw31re+JFKELYm6rTAlf3LVBob+saJJWH\ngAAh5ddH2318gtXZVWGZdV3XeGzCcJxDU9euLpaeZZfH0ncEHHBnnA+hmOvdv3+MEXEIiQww4kM/\nCCUiUpyd8rOBAlG0WLQKTeUYeUDDRcHhXZAFSMnb6MkYktyHRsErNHdFi8TxOfhQ1V3n7d4HWYKv\nvm0XwqjTyIRT7JFeK9L1ueM8R0yocS9LfD9+oQzaxknVB4DL8h3YRkhUpD3rqsKAgiRXBgNsOlnz\nOMTZzD5YTmZddi0SHsOsrRFG7pp9SKfR/fiEQ4zrROi6QcYuyBc+9Rpiip6Uq4UnhSROhSpaq39P\nkgTuQmRxiJw1gTRNEDozVrcRtA26xoGJDHjLQUPihRfIA2GHQKkOEWtfcRph2KMIkBSQTEGMMki0\no9IzvRwH6HG++8M1d2e77ZA3XFikRuBxLeTlSAO3k7Vh4kFUiHI8ZFp4tDiHPPqjEVk5dGkB/3VL\n0QMA155436UV/eW4HP+Cje80Uvg5WJv5n8bTdvM/B+BvCCF+BrbAOH8izfhDhwGghYCERsKwbzsI\nMWXhZKfXw8HUFtSubI8d9weC7tFniwIj9r+DqAfNgmFdrVCzNz0/OcfOvu0iuMq6ltoz5Gq1RLWw\nhbHIxJ5oIoRBS9Sg39kb4IxQ46YRWJF1dFF0KEqnWhxAcJfS7EcL3UFRm6AoWpQO0acjJJHdpefU\nG4g6BRGzYh0AIKtRCXgfyPmiwYxdh9bhO2AwI+lqM89x/+hdAMBnb96AdLblRkKT8GUISw5FgJQM\nT1NrFMRpCNUiZoQRhglCpx3AaxMGkde7qMsKGRGZQIwodnDcFWpnDOM1EzvcumHxDydvvOP1IKSU\nT2s0uh9cnVELLwZTzhe4ecUWDG/ub0JUhH83lddFAO+FOJJIeDy96dgXldMoRETbOGMUOsLUW0qY\naV3CEN4uTGc1He1v8G/9+7bA+vpv2ULzr/18gnfft78f3w6xndjPSNGDodu2ltrLwmVMUVQkkBD7\nHGaRlxMctyli6msEEPbiAwhddCgNBO89FQYoCdd+/6zCG0v72KW9GLF5Gif6UeNZWpL/E4AfAbAl\nhLgP6zL90wD+sRDirwK4C+DP8+3/FLYdeQe2JfmXn/1QDAS0zy13h31c50RdHfYwTB3rK0LOh+3w\nsc3JW9WiIlWt6loIrhrtqvCmHvfv3cfJka2XvvpZqwOZbuUQTpWmEuiYO6qmWHPzdAW9tIFQIOz3\nnq9W0A6TDoWisBdu1etjxbbRcnHhTUzz1D7woZHQTJIXsxnq0qkDaUTUZnQ019WywNh2lZD0BygI\n3iqKFbp23bY9XtIzkItCFMTQvHmqpsaIrdMf/+HP4/4jex41DFxA2TYOVy58BVzGkYczm67GRUF1\nI0TQ0nFFHK1dYsUHaRcSCY16GmOgKPohtfQpiAtxkyhBnzWO7ht3cGXXPtziQ8pL0mWmwqVg8IWG\npirwyRfsg5mUZ9DsIkDVENo9yO4hBwQfwiySiJkeouygnEmMadEZ1w50kOIWwpn+Ntob0FaqxfSq\nnaMfntpWdTVb4eFDu7GUbQ3T2dA/CCKn0A+0Ap1LTajuFMgQMZm9Ik4QadcBabCkv6nUHRJeE8Fr\nEEXSGwYJbdDwWgahwJjmSIlosDflzfoWnml85KJgjPkLf8ivfvQPeK8B8Nef7asvx+W4HM/jeC5g\nzjAW3mwEoBgabY5HuLVrU4JhnGDI7kKc9nDMHvSCodW8rPC1160NeXlxigll3Y/u3cXm2IKJrh3c\nxltvvGG/7iv2vS9/8gVkuwQkofXyZ52Gl35PpMDtPXYRGIHEUeAdoa25mbMc78DNEUVZeo9Fp/uQ\nBgEkC5+rZeGLg3mS+uJRxp12WbUwbCOYRqFjCqPKEjWLdbUATleOVORMbwQSgrrKYoWW0crRvPLM\nT5jOF02NK6wJYWl+AGJhEDDlOZ/PUVVu112nDUNCqaPDh77wOT8/w7hHeTsZQJTcpVWDJcP5hXJp\nUIjHjyzUOs1zfOoznwbw+9WcfUHTMRyNQeRMUVbniFt7L5ydH8FQDyONEkxY/C2Zap2dnXnBlqDf\ng+B5d01tNRth5fEd2UoZp1nQeuOfsixRMlUq6gqGDNSYUeyLrw6xf80WsZuqRcN7KA5LRMLJwilI\nkp+ceF0UZcgY0UXBWiIvTVNEJLSFWnltDDc/RoYWzAErBlMyzXlwtsSVHRt5DPsGN1/4MATs24/n\nY1EAfQAg0BCthWyI4dR2OuOqguSNfnhyhiMuCmM+8Ct0uChtmPVbb3wVr9x6EQDw8iuveAed/nAD\nGzSIffvN1/mdFa6RAZkPU9RsLTZKeQHVQAi8dN1yJQR1+6QAQodyEwIJgUB1o9A4CXOZehUfwYcq\nTDNoLiwnF0u0TpBFRGhIB1eaWotRD2ALzagOHQVnmrZBw/y7gkDBdCOUTqxWYDyyx5MKjdc/sACh\nf/Czv4Af+BOv2nnZHUE5K3nnE9kpxJWztW+heOzLRQWh3XG03j7eXQ+lgdUF06BGY8gFOY9jOHXb\nqq2xJGBHs3txXq7wxjtWKWn7+k3I0C2gTzeyvO28cO24Dk7V5u7dB5jdsx3wpF2gPLF5dNC1mJAa\nnVHwdnY+8/wLtT1Gn+AzqBaic0K+kUdUOn+KpmtRsyPWNI1XYWpVCy0dgIvt3T2NFz/BlvodiYIy\n80kILwgr0QGh876gI1c+RMLdJG4qKOWUs0L0eG8p3Xi+DQJXG+rQKfsZhQJKLjYGEZbn9l6ehi1y\ngu6edVxyHy7H5bgcT43nJlIQlFPouDsu4gGWrMhHqxUen9si0uHhMTYGNkIICZrZ3d/FFneuZVkD\nqV0Z+zt7GG7YSEEGBlsvWizDXWr5ff2d96G42u9f20YnHT5defx5FETIaR6T9W3xRsIg5vclcYjQ\noZ60huvjx1nPy4s3rPrXbQsE9r2NEfhgZouEv/nmHdwgByFmYfTalV2E3vFn7qv3bQB07P+3KwNX\nMczYFcjSdXcl6jQWH9jveP3+I2zSafr2zggtI4Wqc/gHhdipZiuATQk0dY3joznnEJizK/H+vQ8A\nAGfncxh6bFa6RUL/yJ3h0BcJK936OQh4fVdl4zUo4zT1xb7g6TrjWrORr3dCoeG8fOt4iaxnr2k/\nD3D3oT3O6vwxPjixx9knFD5PMgyJoTg+X2KV2HONxPohCLB2BXfFQKU1GqYXTdv40N6mu/yZqdt0\nP8SP/IQtnn7lSyWO3rKpS9YICLC7IOHP27BTc7gsIVlQvDrJYRgJPjq7QMNIMJGxd8R2IjoGGg3v\ni0oJSPIyXnohwJUxjX9uZfjc97tI4cPA5D94XEYKl+NyXI6nxvMTKfD/XO5YhhGW3IHEssKKCjsm\nChFM6GfIAk+W9zAiM/D2cIrEqf12wIq5eCAaSPajb3ziZQDA622NN+6RwRgLjAdczZMAhsXBWBgE\nDmXJVmiWD6Ccx9+qRMNaQxoCijURpQOPCmyYT5/WKwxYULr94gvQd60WwrJdoers7n5wxe58SRKj\nbu05L7sOFNJBF8i1H4SWPoKYkCQl4wrbdDUul4VXfpaQOCMhTGnhFZk6SnVVbW19HQC0QYjZkloA\nqwJhj7JqwkCEDtdBX4jJCA2t1FTX4uHJGd8rvPJxq/RamZvszDAyeOUlCzFPB3384J/6Efv6h4Ra\ntXT2boE/D6dEXQYxPv+9lvB1YzdHp+1xrB4+Qj2313VINaLxcICIUdrF6RlOSMYKpUbgCoaBXJOx\nfG3OeDUoLYTXtTBaInB9aXfMMbB9y0a3L60CHH3AuW0qJGxnF7rDWWt/vqCWx7xe+Pm8c77yhjrn\nFyViQlz7aYCd1H75DmsLWvawhI0wVipDTFTsZz/Tx60D+54XXhbIxh8PcPxcLAoCgHQhkXHFtwAB\n6Z81OkyGZPUp4O6xLRhGhKduFAm2FoTRXiwxINT0ZLWEJGClEQrG2PeHIeHDvREaQ9afEL74ZhQQ\nMESXwmCxsDf6aGRD/DCKnpICdwVDYYAFF4BGV4j5kNWuV94AKfUMd8fb2KYsfZYl3ia9Y8FpKVsv\nprHqNCRDykR0qBmCny4qLwXn/DWDQPjCWdsq1Fyw+knsFZOLqgFYnXfSX6VpETk79EWBGRfhCLG3\nYg/iGIIMPSd6EyiN2tj3Hs5OsVD2+6q2REm8RNE0a+/N1qYXqlMIyOE4enwfP/uzFv/2F/7tv4In\nh2OHrrMK6aHpO3s7uHHbFoH3hxFu37Bg2pNWoYntZ/dYlMuzxDssqUGCsrY/F0XjAUlx0CGOHVDL\nMTWFr/BDrqXgbAfg6VzHCHhXs5uvJvjmgZ23428YGALRThuFxwveD0wphBHg7YaqqfyGlPVSVEy7\nHtcFllwsnLrlXi9G2bFb1bXYspANvPaaxGBApuzOGF3w8RKCy/ThclyOy/HUeC4iBcBGCE+h2aTA\naMOG1ItegoMbdhmcLRq899Du3G981UK0vvfTr2DKIpoahYi27c9b/StwNKYHJ6f4tX9m9V7ufWBD\nyx/4wp/EdNe2PYeTPgS9+Ky3ANdLoZGP6BTNnbaua5wy/Cwa7Vf2clnj3SO7Ex5s99DLKNRBCa+6\n7XByYn8fqRliRhtFGCAhfqFwbbNQoAKLYbHEmGmHVsDx0kYmb7x/jIhIuDnbrdPNFOeHxHEo7V2l\nX7hxBRMqKb99/z6mY6t7kFFottIKESHIVbPCikjJ4niJnMSs8XSKlGxVVwAsyhr3L+z1uHvvXdyi\nqM04iyH42SvVQBFNeDK34W5re5kAgFsHL+CN92wq9WErekc79PbzUviQ+sXbL2JAe7Q47rzOwN33\n70LNLQZiEFOQZpAjIoaiMq33AAmN8Z5u0lTQwomo8OuxZmVqs7bIc//+vsFUJExb3P6cPbYHdzpI\nuIJu4LhW6DO93Epz5K4TH0kvMFsrgZW0UcWJ0igZORZMY1daYk417nl9jE/uUCBmFGI4tfeFigSE\n+HiEqOdmURBPOEIB1jVpOrHpw70khQtqtra3kEX2Id0QdtKNAd58aPHn4ugE5u07AIBBmnj2WbWo\nIBlq/cSPWjDmzetbaJc27YijDppVZiEARQn389ML5BMLcAoTezxFUWJBJmKlgH5mP7dc1Xj9jpUU\n3+xHSF+0D4iDsEaZ9KzGTAsMEvuADeRaJt6xKC/KC6/KnGQRBMP8R3WI33jL9uYfLzvPich4k+9P\ntyEdq9MA167aY9jf2URBcM9stcD0of3u/Lrlg+RJ4OPGwXiI3S37+gNzFxV73pGGl2p34B/ddsjJ\nBT7Y3Ma1AxvOh3noFYYqo3FMluQFMRud1uAzjLZYYTj49r30NXNaIOYiuzvdheaEdVIiop/o1sFN\nYGnvkfOHdrF5/+gUtdNdNDUcZmt7a4KU0ulohVeIMk5MBcZ3JLQRHv6s/kA+gQEEOzgmx/VX7FEP\nr1aYfcCFBRrgQ9p3wkFlAa2I+5DwGJG6MwiY/o76AUzzdG1gJQY45P2rRYDTu/b77mxfwfe/8Al7\nSvJ9hN39P3Re/6BxmT5cjstxOZ4az02k4MY6WhDoyGRTcYrjhQ3Xr46GyPrcFa/YjsP54ak329i7\nto+YFefRYIRE2l16dbzAxtTuJFcO7M6/LE9RExFm6g5ZYHcr1bVYSFsw+9U37uJI2te/MLGdgSAI\n0TkrcyE9KtcYjfnS7gJfvfMI0y1qQFCOTbRLKO60hQmhWcxrICCdLbvzaUhjBIFzhm5suA3grQ+W\neON9SwS7aLXXENgkAWZ7awcPl64b0OH7fvCHAQBhcYxqZglRrdB4+75ltA+op3B7e4CMBcow7SHm\n7jm9uotVZOd+GKYY9uyc14xQ4kTgIiVBR2qvlVZ1GitGU6fLGt96wEjOX1+FmH36pqnx2U98hvP5\npMfDOqXsXP5gAkjqZ2Z5iIp28CoOMBzZ+d7YvYZRahmYV6/YyOXs5BAnDuvy+D7OTu3xjPopJpmN\nKurO+GspXQqjjS8utrpFw1RCwUATn6AlPUh1B8HukzY9RCO7W1//bIs3vvlP+Xmd12+YUG9hZGLE\nlZ3DXAXQhETPTQ3NAvtRtEJAsxfV2O+4v1SYU4hodxgjIyr2+K7AvbnVHj3Y+2G05Zc5o+7fbz+E\n+X2S2n/048XbO+bv/Jd/Eb/1q4/x3tv2JAUS638IYLkskJBpOBqNkLC74CCwSinU1HM8m81wdmbz\n6/l8gc5Zp0cxlPNYdJV6KT2sNgxjSNYGgiDE4AnsfMW2ptPte/Orv+zFOF65dQ2ffvUGAGBnewM5\nATK7uzt46WWbtx89tjdgIgKsGEbfv/vIi7wW9RyLmWXXTXKn1tODZAvt8PSxrwdUVYs5BW2v3zjA\n9Ru2rZew3nF8coqarL7lssCXvvS/AQC++eZ9zC7oSBVn65CY1yBOEwzY7ZmMB5CsL5yczXE6s4tX\n12rvWJRzIehnMUa5M7KJcU4B1tniAi3p2QhTxFzgd5gSJqlEyPZkEgsUrV1A/smv3INbFuwhMt/n\ndZICHsSTxBGG1LbczxN89roFDr203cP1bTuPg4HjzIQ+BUOrsWDK87V7C/yfX7Hp5rvHZ6jYiXC1\nmlgGngKexamHns+LAv/1P/khAEBKhqvWls4OAO+8fYrf+RWbSr7/tRIBxVLG45FXkSrIy6ibGrpx\n6UhnUwxY1zLDZyCQEsORnbsV+S6rqlpfR63X93UQ+Z+frNG8/s57v22M+Tw+YlymD5fjclyOp8Zz\nkj5IQMdoG4mKsM6u7bws1dl8gSQjU61pPEPPyb7PZjPPO1+WBSrqFIRx4nUBIKy0FmAZjIBlxWn2\nfovVCimjEUCgJCsxiEIfSkcs/AVhgJCN5clk4F2LsyjxXpD7+wde+t2ZsEgtUVFp+dbBTURUNn58\nXCELbei7OSCBqzfGiISv6I5ElHL3HEkfjaRphprQ5P7YdlwGgyGa2obJSZLi4SMbVSwK7WXZZSA9\nMctV1uumQY/z0xUFKhLMVFEi8UVQiZzy+VsjOjUPc2yNyd1PU6wYCTw4PMTjuY36LpoGNQtzKxZ7\n0URw3Dfd6bU+hXna99CH847hKNdaDkkQYMiI7cWdDdzatse0v51jtGExJRnZtUmSrpWh2xJpZI/t\nk9dS3D20P5+uCmjiOhyILs9zGOJMokAiJsGqUg3OWju3gXZaFyVWc3tsJycrSE3GqNAYEW5vhEbF\ne6sunMhO5y3kZABELKRmWbYugponFK2Vs7aTHkpvpe9dhKV9lA0AQfDxWJKXkcLluByX46nxXEQK\nQgBhGMKYzudLVWWgHAlIK7SkNZtAIFPs2bOglvb6MCQohUmKM22xAEVRYekkz0yHhn1et+Nr1UGR\nnNJ1BiFrDmVZoabL9Wg8RkJSjdu2QqExJXZhZzJBjzBgIwJs05JuMBxjNreFvRFVkldnC+yz8NUf\nDjCj9NrOdAcxlZsjab93urXrCTfD8QR9wphboxHFdmfL+yMY1h0qKhr1+wM/h11X4d4jW5TUJkLI\nmkmrFBrmsJJIw36aeAg2WoXMCdqO+ug7Q5WNTcScuykxJNd3t9BnBKJhMCey9Hh3E/doPPv2g0d4\n5CDWhPNWRmJJq7i8FyGKiAqFBJ6COlOpySE3swQNSVx5HOL2no0IXt3bDVoXtQAAIABJREFUxPUt\nKnBPJ+gPbZTVc5FCmPrds+sqGEZ6O0mHF7bteT8838Ab9yy+AdS16MUhJIvDT5bf8qyH86bkEdr7\ncLkyOHxgsRfNMsHqwvAYxn6ey2IBoez1ubVvo4ftzRGmGzbCGk+GGG/YY9/c2vSt5rIocX5q6zWv\nv251Qb789bdxcmE/qzXw/hRar+tmxhh/3z/reC4WBRgB6BBRlEIQJtrpxnPaW9WiYnpQFguUF3Zy\nIsJEgzDw1elKdShZ9W4a5VMQQPvioAtJgyCCuwG7Tnsn6SxLEYTOir71vokdjyeNQlynucnWZOB7\n2r1BH9s79vW6KiBZCA3InEyiBJub9vdN1yJjbz6NUv/ApsQu9PtDlHS5HozHGFJSvqgqBIa6g2m2\ntnt3NbRWoUeQTttodIzRoyjxUPKmVR6661KjNAoQdnbe9iZj7NO0JosDHBA+vDndxoBYgI0hUyZp\nIPmwFWXp07i9rTE26IbVSyN037R4gYYcgDBOUTF8LqsSAQuRtni8xquwwO8Xo04pX2jcHWZ49apd\nFG5d3cDmhNiL0QgZF7KERcAoShDzOpgug6KbUrdc4sYV+xmPVy0OuXjVlDzLs9gLoCxXJcLYKTjX\nOOd95oR1VssAp7Sabg4FLs7J89AZNFOzfgx87tO2M3D7Bdsh2Z5uYYdq5ZPNDaT8viAKIJlOtbVC\nQT7KztS+tzUBfuW3ftd+X9t5QRpjNLonVrCP20y4TB8ux+W4HE+N5yJSUErj9KRAsRBefLJu67XK\njxEQnesPC5Ts3wuyxZoWXh2n60oE3F6SMEDCoo0RZv15nnylfB88CCTSlAW8JPNFnaZpvJpSzRbS\nZDzE/g51GkINxRV6a3sKQ/Wbplwz3JxvwObGFAm9ILSqvalNL+2hXNnP7jHV0IFEw7ZZr9/zUU4c\nRAjoVp1kOWq3C5D0onWLkN8rhEAUsV3WAZrYgk5rvwVrtnKzQYzr2zYKeOXaHvr8jK2NCa7Tjm1j\nZwc9mqskJAzpcgVTEglqOr+Lp2kAQwxF3bY4PrM/v/OA6Uwce2i37koPD/7wWF8r+99d1/n+/svb\nY9xgyjAepejT2KaX95HxWjo/iSCKEYSOgAQI9vqjTmBny879jfMC93ftdT0qmY4NBt5LcjAIPamq\nKGs8fEi1KKZ8TSXRzO1cHH+wQD23v+9FGgHhyp96+Rpee9FGXgfXLSx5Z/8KhhP7vUmW+mspAE/W\nkrL1aczBDZuCfu7Tn8Ab37Jq3UUzWxdSjYFmWiyl9KnEs47nYlGo6xbvv/sY9+8tETn/xVHgK+uL\n2RxDXuQ8C5ESHiokQR6V9jh7gdTLWSkor+BrhPGdBqczGIShxy6EQQiXv2qt/F3YlIU/Dpeb7e70\n0aO/Yl0rZAzt82Efy5WtZyQysJhVwFvV570+WrcIJRkS9vqV0hiMKbLi0gHRQdFfN81zCKc4rAyS\nyPkc5l6mrea/Go0PvqMoQsUHVsoAgqAYbdYgHWfesjnqY5/grs1RHxnD+en2tpe962d9SNY+DBdh\nAUA5sZFQQPI6oO2QshYzHA6wsWnn6M4DC5pqW+npyQJAFK0r5E/C3QU/26WBQgjsMbW5tjnCBr0i\nszRFygUwC3t+jkIeg4xjBCEllU0Awy5KZgw2uNhf28rx0oGFd4tjskvHG17n8fHRsV8U0yzD29+w\n+JPA0HBGpmge23mZHy4RVnQqMyVGW3YO93e2sLNtw/89ytMPJxsIXZdBig+1XMjWDIHQeVf27Tnd\nvLqHG9fsAnFyUXrOhBQCNUFdUkqE4cd7zC/Th8txOS7HU+M7taL/zwD8WQANgHcA/GVjzIy/+ykA\nfxWWaPbvGWP+r4/8DliJs6YpUDkBy1ii4Y4fRhEONuxq/LlrfUzp4Vcy1XgwUzgsySxDiK51KYhC\nQZJT3RlIJ2bMSLXTEobphdadhxLXXYeOYbVNI5yyhj226WTkV9/WSAydMGbbQpFp2ctyCBYuR4Qg\nd8YA/L4kibwIaBwlSHv2Pa4hr9qlF2eJpUBDReEgiBA5TwatvQp05URgtUFAplGSpSi4Y2RZ5q3i\ngs4gpZSdQwRujDLsTm0FfGOj73hWSHoZUlbwbYfIPDkVUJ0VEAUACO2lByCNRwUOsgw5U4X+wO6I\nJ6sK0ilQBwEaRjrOd9NOhfbsSMXvHfdy7LE6vzEZIuLxyzj0xcooTBA5bAnnKkgz763RKeMt3hFn\n0JTb296aYINpzibNW7Z293B2anEfjw5P4PbRSABjwtd1a7+rq+B1KNpl56HrMmixz0hpc9zH5rYt\nNsfEt5gwQMditdDKM3SFkd5hW0oBQXKfi8Am0zFevX0LAHDn/YcoGQmHYQgRFPw7icx5mzzj+E6t\n6H8RwE8ZY5QQ4j8F8FMAviiEeA3AvwHgEwD2AfzfQoiXjPn2FjUyEMjzEJvTHg7PbRtvWSjUXCAm\nqcDnrtkJ/L5bOaZUXlqt7A1/PFc4Iy5cG+Nv2FYbzCgGsqwBwfZOxbB+URkrkw1LyZ3T3enBeYFD\ngox0p9Dyho24EORxYrUgYR19UlIci4u5h/y2ZY0+L7pTfl42NSKqRWltvDloL8s9N7gjvHgxnyFl\nq880DVq2EHvpwD9MnVK+1hK4ha7V/qqGYeAfJo11mypLYmww5QlY3R5kMTYJQsrSiLZUQBQGCF0K\nBrNeUXm3GkgI5gGRDCF4IEoH6BLX1gsw5sPbZ2fkeDHz7eBACLSto/dKD1+XwnitTBdSTwYJ9sZO\nvjxEzNpGFBiE5ETI0EBy8XVheRinCNhmhOhgKABjghgNnbF6+QDSmchIdi1cWA+7IDs9ztEgx/Vr\n9pjP5wTFzRIYPpi60aCmD7I0xi5bjqN+D3HiAHWuXtI90YQVHo5tu14UmQkCCKZujpeRj4e4fYse\nqpsT3D2b89iF37SEEIjiCB9nfEdW9MaYXzBr6NlvwHpGAtaK/meMMbUx5j1Yp6gvfKwjuhyX43L8\nsY5/HoXGvwLgH/HnK7CLhBvOiv7bDiEkkiRDnJRwofqyLP2qOxjFuEpb880sQD+zK+2QhbpJsnIO\nbRBSrnv2nYGQNgwsigIis1DgLrXhZ751BSayu+P5bIZHx7ZI+NVvvY9fpYXc0dkKbrUOWMlWGt4S\nrZ9qaO4uVWWwSc+FclFjb4uiLK2zLusQtGvJt42xDSNhFDTTjiXFTVTbIGUHoKgrZDH77nHid9VO\nKRgHvlKuEKkB/j4UkY8q4jDEqGfnaxDBKxtXhbPKaxGzEJeFOYQDCKgOT4JkXfrQ+cKfRBh4K2Zv\nDKP1mmwlA4NB3362t6PTZg2wwROe80J4fIcMAnT0RmD2gb2NDHubvP55gNRBguOe96MMJCAZavuc\nUQS+c6CNXv8M4V3Ks16OEVmjiwcWWFYrYEaSVwfzlFnMS8PX7Ht7tqNyGnU4FvR+NIBh1CeDAIpR\noQgShExzQuF0JwH5ZEDkrfIEtIMoy2CdukmXRuSYTm3X4uDqDh5QSVup1pvnqLZFU/8Rdh+EEP8R\nAAXgf/gO/vavAfhrADAe91GuDN65cw/aOTMlPW/ymiYxElKKhaqxnFsWpLM1N/UCsdMGTwboyDUY\n9HvrfD8IEA9tCwi5rfQn4w0goWJTucTRY8tqi1DgAZFiRd1iWZCey0Wq7Tr/cx/GPuGwobRzU5Iy\n9DfQjCg/BCEEeRcbW9uecdi0DRYre05VxZpEr4+uJmNPxh7vX5bVGkzVtv7hdAYpQhvUpU19pNEI\nGHKmcYycYWQedpgyfThqaMB7PsMRvTbHvZEPOS+KpV/UkijyHAQH9AqDAHAW9+j89YOQ8EFxoJFl\nTL2Y31qJdON/flLHSLoHwXS+Uzlg2L496iMnuKcTEUrYzxtFG+gI/JJpiogchSByaEvhu04isOhT\nwFLfA352FCXYZr1ifvKm/fdojpLIyzgf4owKV2ko8Lm9PwcAWLQWBXksHuOd0M7nSTeDEXbelssl\nvvFNy8TsZRk2tuxmkDGlCgLhxXUAASGdPmTovUe16jzCV/tcI/Ddoyt7W4i/bpXIqqpCzNStKRsU\n3dN8ko8a33H3QQjxl2ALkH/RrCFTz2xFb4z5b40xnzfGfL6ff7xCyOW4HJfj/7/xHUUKQoh/FcB/\nCOBfMoZSvnb8HID/UQjxn8MWGm/jGZQdqqrG22/fxYMHJ+gPrWZiEkdonO9g08DBkbU2yBmu9sjI\na+seWur9QRok3Eny/ggZq/rB+BqCnu0Pa+4eLRTgJNCjAKO+ff3l/QmGiTU7sbuWfU9B5mCjYggW\ntSIhsKT8WRyN16FmDfjp5XaX5X0PmTVt4wUyqmWBquFuxJ10OS/w8K6NXC7OL3x1PgoMxuTV9/O+\nTw889MdoXMxtGGkGud/ZEwmM3G4tgA1XEC1yzmGJt+68xw8R2Nmy0ZQxGkenVuth2Ha+a+G2q1Vd\n+11et43vjyttvN17G8ZerbhPZquU0vf/+3nkQ2MhJDq/FRovZ58wHYjDGPOl43a00Ec2CrvzYI6X\nX7L6FTevpohdwMI2ipCh5wYYrbyvZKUlSkqhLSp4GX/TOT2JpZdx65vQd4S2NzcgFdMxbffBMN1E\nht/k9BgvlZeEEpvEVhSLBb7+NQtNFsba+F0/uIYwWXeUKkZ6i8USJ6f2vi6LChXxMs5iT4YxUuYd\n/SxFj9fm5OICPUoEJqHEOdmqzzq+Uyv6nwKQAPhFAk1+wxjz7xhj3hBC/GMA34BNK/76R3UeLsfl\nuBzP1/hOrej//rd5/98G8Lc/zkHIQCLNM7Q6wIIMvygyqNhLrmsNV+7q4g3Ivt3xwwF9EzYDa9wJ\nWEMZIttkGHnT2Kiee38G11VTbYWW8mK9ySYi9qs3RikOBnZr+zXVIevbnTkobBTQaoE+t+B+EqFg\na3Q0BJaVjSaOD8+8/NfeVRv9LMqVX+0HsxOv+hQj9PDnx/et0vS3vvktNFTx2d6e4spVKyGXSImz\nU5vXzpo5xoTHllQuKoolyoXdXUJpILkmZ6HBtjOMqReY9LjzEsX48JHC+bndUX739W8g584VpqE3\neDm4cg1X921dpkeD1qPjI6yo9HR+do4VCVF5mmBCwpcJtXd2durRMBqtg6abEJErDKJ+oj0nva+F\ncNBfpbBFKPjtq3tIqT9xWissV/b63P1gBaPtfLljiJMcjbC756IocXhi5/D+4zPPMJW6hGEheHNi\n/+4bjy9QsX3ZnJ55qHwv7aElS7Ij83NVLNfnFEj8f+2daYxk13Xff/dt9Wqv6n2Zpac5Qw453EUp\nkqxYymJIMgQZCfxBhoE4iIF8CRAnCBBY0Kd8DLI6geMkyAYEgp1E1gYFiWBTsiAjsRZaIjkkh5qF\nPWtP77W+qrfefLjnvZ5RJGgYs2cayDtAY6qrpuu+e999557lf/7Hk9jI2vIiF84bhqy5drdIjY77\nxsoJhiNqVl65G9LfN5bZzvYWvV7OueDgCtq1JgF2ZXtMR8YqDMNpEWhNU2nwC9QtRf9gzLuRYwFz\ndl2X1ROLOPaVwxqGLCIW/sRR4nC9J9VnjFF7UtbbNf+eWWpwWmjdK9U6Yd8s6mQ8YWNbHqDUIsvM\npsk3aLOa4dcNLmI1vcBMR8pX5zo8t2YUz1e+e4O+cN91pRzXcZ2CCCPLdIHk0Wlc1FqcWl0pAqXT\noTRIGYUkEsD0l5fwZXPbtkUgbsfkwESyF9s+tpicjWYTL+8jWHGZWzCmfThJsCSwl4iyGQ/7pDJu\nFE4KoheAhTlRIPtR0Y8wf7hnum1SwSZU/AYTwWGMR1OsuyYSb2eKrrguFTFVR6Mx79w0bs7u3j6R\nAMDarSYTUciJTrEzszFtKZFOkriAmGdZipOb7T9G9Z8bmkK7Sce1aWLGDg8CXMso0xNLiyDNbKJx\nn4Mtc011Je6oBdNQqOL2ekTCpTlb85g/YwJ/Nc9mJK7StetGOb/82o0ic5AmSeHmjMdjIm2UqGPn\nFac2FT9vOGThSZYgilLeeNu4ZkvzAWdOm+pIWyDoOzsHrPiyFzwbT9y8mU6NrgDHgsQikkPNlqB6\nZ7ZObVnwD80Gr79pxnjjxu2izf3c7Aw3N3d5N1LCnEsppZT75FhYChXP4vRqkyfPtbj0jjFV45gC\nSedZmjkJAtYrDrfH5vR47ZapELt6JeT9a0ZjPnn+HNW2ORGD/T30yHxff3PEBAm+SI/DTme+6GBt\nJ2FRXek2T/D0C2Zpnr/Y408vbciVSnqvUsHNI2foArE4DcaMBR7t2T4Iw/TOjjlRak4dLSSg4Sig\n3TQnfrPbIRB8wowQfA6ylFu75mTb2Dug1zPzaFR91lZNYKtZrR62i8v7FCQxmQTJ4nBaoAbD6ZRM\nzNzu7CxxnjeXoOvKyTlqLXNazS+fZn/PmLaj3hYqlYDodFKkQHN052g0IozMeDPzSzS7xsLCUrjS\n3+CgNyCQQrG8Q7NtH+bdFQo757rgUJRSRRfqBUEEohTvbBur49VpD7dlXp9LK5xaNNZiu9YhneQp\nXuNWdWZmSGWN3SygJg1utFfHkpTkeBoyEpiykrbbaZoVgU/PcYqiuH5/wB+9+d/N+1rSn7HPQNri\n2bbCES6Lg/1eEYw+dfpJNoTIpVETmj5LsbBsiqNqnSaTwFxPHMTcumks2ev7I/ripu5tmb9fX1vl\n2acMzLni+cwK0tdzFKkERDutVoEifVA5FkqhWq3w/NNn0H814/P/9Y8BePtaH1vMsvmGw0pTzHU9\nKXjrxmMB3ng2g54xkQ4OutROG1BJuzlHbd+8PxNOaKw+DcBr14yJ+Nr1hJcEi2/bKaEgWivNkyzO\nGPjoxz7SZyqKZUs6M7meU2ATUFaxkdMwYjwSkg4nobpoHrInf/5DAFx+4wpv3DEm3jPVE7TFfHYr\nFbJRbhoKPFVXOHnWzKNz6hRf+OLvA3D9ravEgXnYnr9wjlrVbITMzrn8UgJZl0zH2G4Od4XbN01T\nkPXH1lh//DwAw6F5aC5feYe9q1fNtX/vFZoC8Dox02SmIW5FtV7kyAv0seXQbpuH0fbrXBdXYnNr\nmzl5kG0s2kLnnufmT59eY3zlqnzJYYm0URF5nt6mI+bzusRllA0/uCEsyTsDArmej1bq9ITf8+xs\ng7aYz0MpSW8GAX25N7Fd41s/MKQvb93YY17iNU07ozIRH94z1zvTbnB3XzJbuEX5cpom9CrmgVWx\nuAxpHVU3C7O8MEu4Z8ZuNdtFReTP/fxH+fY3vgHAjRtmL6yfWSkqfr2KhycKaxpq9g6M6/ns83+O\nK9dNdv/aZXMfb1y5Q12U6ZNPnuXUSbNGvucwkQyG61jUhRnsQaV0H0oppZT75FhYCpay8CtVPvDi\nE6RTo6f+yb/8KtuSx2/6c9g5WQgx51eMRvREpzVUSMMXshDXhYkx4Ry/Sa1t6uO9YURNaIlfeNz8\nvW0rTp45Kd+lcJU0bfFcXDnZnlw7wd1T5sSfpuZEuBPH1Ny8R4RLTkvsaEW3Zk7N3fGExZZxY1aW\nzCmxu9VDj00gsVlvMBHm4HgyLaLeTcmAEIDbMFaAoyxOLxm0+JJf5cySOcW7nQaO4H+TQGi7okNr\nRVkw2zHzOLk8x/KsmZ9f94gNkIKDnlmr6XREtyGReq/OirSNq3k2riX9IhxFEgtRi1SwVv0acXpI\n87Y6b4J2vluhJ926x71tmqsmazG7aNbi5Cjj5l1jBlsqxs7LK/VhPw+dZrhyEi61jSX0+Mo8a4+Z\ne7a9u08gFbHr51c4tWxKcNTggFhQqEivxfFgwETch37ocfmWab13a3eEVRX0JhH7d83pXxc371Mf\nfprvXDRoxI1be4UlN7cwT71h7nUgbpmONevnzTxfeOkZ/vBL3wTg/Nl1moILGWxt8PwzBk9hPW8y\nEp2ZBhUx8S0NvgR/V9dPg8r7TyScWzbjdT/+EXMNtTqrpxblO9qEYry2KlWGgqyN4xjPvdcp+9ly\nLJQCAErjVz3WheXn7Ppp7vzwdQDatRUW2lLJ5vm4tvEjTz5lNlgwGtCwBQhjaZRsXLtRY2XNAET8\nRodMzPWaRIgbjRq2mG1pqkmFE9GatrAlRXhq7SQvfcBw6uXw2i/9ySWakhaaDIOinbvteqwJMetp\nt4ojD3i8ayLZz55ZwTptFEUSjAjG0skpTonjvMGJAIyqFWKh8mY65MMvPgNAFI5peQLoUVkBq55O\n8wyIi2PnHI2w3BTSmorF0pzZVH7FBoFVr0nTlLX5p1EC/W3WO9TEfFZZhO2a+U3DgKbEUuqy0Waa\nNdycntxyySRWcWK2RX9qlFeWrGBLKnIopKXXr17C9w7rD1ThPugC+58T+gK0pFPSfNfndN538mQH\nLW6TXa/SkjYAUaoYSpoxzwxkcVKYxb4T80sffZ/5xW7QbYkLFveKNGEqYDK/ovjIBeO3f/e1q3zr\n1csAbAYD/vzaLwCwsWPgxeNgm/knzP1/oX2Om69cBODptRVOCMGsU3NwxSXyJE4W65RaVWIcWVZQ\nss/NztESGP5wNEBLduXsmnF36vUWVXENlOWwsmRc3m67w2hiXGSyDIt3pxRK96GUUkq5T46JpaDR\npGit2dw0QRTH87ClSrDqV2jIyexXdYERiKUJR6ProGKJ6sdxAYnVtoMvefV5v0oyMqa7JacVaUIo\nVYJUKtjSVVo5Lpkr2nz2BE+8ZMy1mpySJ2+P2d82VFyu6zIJpMuz8gr6+M78Aq5AlqtSnON4HmTm\n81u9flGspLUqAFWOnBje3DyhnMDadfBzevmsih0JzHcSFNRzeau4NMuYmzMnSTAdMyuBPU+popBq\naW6Rua45gXLroFGbIUnyVmNu0ULPcWuMp8bstp06VYGY+zLPar1WVIy2mk3SKC/a0cx2cvBSylAK\nr3Y3TIAvCQe06mZdBuMJlvAimCYsOaQ9oe4IXVwjb7gDTcEs+J5TZA4ylZKK9UMcYufUc2LaW3hU\nBTRUsTM6Rd1WjEqMCxVbGb5gVWzbrI/OQnzbfO/7H18uuk5/+X+/ytmO6RW5VDXrvbN/FTc0611J\ndUEbWPNtTp40boXyHTIl2YUccJfERQFamsQFTsPxHKpCztLsNIqskpXzMLgeTl4oqBVdwbU06w2U\nNpZCFqd49rt7zI+JUgCFRuuMjhB9bN7dRIoEGYwDqvW804+NlYoJKxs+jAOGgiR0ul1sMc+0gjQV\n31LZeMLzqAXrHmcaJea62+7gNI0/rN0apMbfH+xug5jo4cC812w22N8Sl8G2iPOW62FMJA+FZzsF\nY1GuxGxU0b0qmiTUxb1QWIWCyI23Wr2Nk3MfAq6Vp8gSQkF9xiEEkp4NJnl1pU9NypT90GVe+lWm\n0YQwzensD/12VbB9UtC343hFlL0/OmAoc9JRRLOdV53K5m+0Gcq6BOMJXene5ToOmSicxEsLv9vJ\n40AVl2leJanTe3KR2WF5dhZTy4ltZP6OBs86JGyxCoYln5z0cePmBokgPC0lTYOjmKyI29RoyHVG\nqWYi7mbTdUglPpIT94aJJsx9ckvTEtfz8eVFfOGB9IQdoDPXoSdxEt+q0JSHNLEBKfd3G3XIhJtz\nbPasTg4JhNMsLbp2ZWlKWpGMl+tjadnXee9b28aSfpxZPMURN29uto37Tk7kmxUcow8qpftQSiml\n3CfHxFIw7oOyXB5bM2bWh15a520hunAsG1tM6UZnHkcisjkRyOTgLvWuicI2ltZxWkZzR9GUbGi+\nI0kyPDHN7ZrR4K5jo3L+wUqNVIradRIXUOHe7jYdYc+qCU33dDREJ9I/cDItKgOv7fZYnDFR+8VJ\nSM0/5FUEY/ZNhR5OaWhIhsPFLoBTsXwXWVq4IkmUFe6FjkMyCSomkSYSyPZE4MCteoOOdFq26xXW\n540ZfH1zRJpDotOMWMyweqUpc1bFdzUbraJ2/+7mXbK8SY5fIczrRsS9sByfVI75vc27BD3jaswu\nzFKX2pQ0g8kkb4Zi7t18u82u5NLRuqha1EoXvRvREZG4iHnQ1dMenrhxnl/Fl2yP6/vsCcnIZBwx\nO2OsvoawZFtJymRkXMWDNMIXgFujO4crLpgVTckkwDieDmXcyWFXqywt6P5//sWn6cc35VYJdiFK\n0VIZmXg+J8+ZLEOwv0smmTSreUjhnogbmCXBYV9NpQvgURIlIJ27napbNPApaCrUYR1PFEYk0syn\nu1ArnpdJmhLnhJsPKKWlUEoppdwnx8JS0AjhqqbwZZ98fJ2TCyb9E2eKkfj1La3Ico6EPPBiWTj5\ne7MrZILyu3x1g1kBc9Ucm9HAnCSdeRMYsl2/6MWYJppEcvfKyhgH5vWkv09VKgqvD0xwsbd/UHQM\nDqchfeFy2N3e4eZtg7Y7vXiiSJEJixvaVkWgyqp4+MKaU2nUUJIODUWrj6cTPMnRO76LLRDCaJoW\nWIFJMCGQ68iJe2zbIpMTpdcbcXLFpG3HwQgnt0YSzVQCk9WcB8zKUMLZ0L9zk50dsbBGYzLx/a/f\nuU1f4g6qkqfTNBs3zYkZ9geEEhDsT8bMdY3PnFqaSOaXybXPzs+zL2ullDqMbSgKp1mTMRBrYl+Q\nl8lcq8A02JZGloj+wR6bd0zqV6cZjZpgOeaM1RiOhlTFt+4P97klOIWFTDG/YnAPaaQIBEGo82Kt\nSUwi8OI4mJJGxoKoVVocTAwvwiAQaHOyxKq3Jmsc0ZSg5ZU33qQvcRevnWJ7ebxCyIZRJLL2xEnB\nwzCJElx5HhzHOaTnkPuoE0ikUjOcBIWF0aj5BYv1aDIt0rIPKsdCKYAxg5JME4uZ2e8NCrqyK1t9\ntkdmoVamfWyJ1E6F2mx3Z48Q82CutFaxpfHsYPMGjgTd5h47V5SZZkkexbXI9GE3npwlN4um9IXW\n2yPl+nUTPPq9L3/FjHF2nUjcAN91CwKRWq3Cjdsme3LnxBnD0gxFX0rlOhzsm++dTMKiN+V4PGac\nB53ENqw1QuIsrzOwccRED8Npgecfj8ckYvLb4j4Mhn1+cNHgOxoTmolYAAAPoElEQVRzXdpCttFs\ntQp6tEmq6eVs1JYoQttjOjDfO+j3icKcS1JzbWPDvB9PefOKeX1jRyjxNNy8ZuDKpxcW6QgzcjAd\nsSfXXK1VmYjyGgvdnO25JBJQU/fwMmZZVvBOWpbNUGj8b0gdyPmlDl15gOLQMbhnIA5D6gJC2rrT\n40ei1Hel9DiKJlScPEKnmRUI9nQ0YLhjlH3Ft4tGxvlDGk3DojQ+CEJy231hcRZXKjs9x8zDt2wq\nqVnvMBjTnTWKaWu/z40bBhTVnJuj6sl9zXtUHuyxsykKLQqLOphWp42TdxTTSYEBKQz8JCUKzTMQ\nhRNiWVtH6YJPdBIlNAWU9qBSug+llFLKfXI8LAVpJ6CzQwQbtksq0acbe1M2dsxJ+uxqu7AQLm4Y\n7fonr99he99oyZXL+9ICDqLJqOhvEMeKE1KglJ/yNMApUoEaJSmr6XiEJad0tVLhzo45pV69bcbt\nrkxoSNDSsm18QaBlvs/2rskPv3X9HWqS9iLPwaOKMUY7B1wNDBLOca0CztuWFvduprDkxAx6B+i8\nA3ecFqduzkcAhxwRw8mYodDGNdUcb2wYiG4axlSEC3OCJpE1cKRpCI5d0KMNx2N8CUAORmMCSdMl\nVgUkDXzxisEbTIbDwj3ypwGZuFJexWWwb3Ah7bhBIjn5kZi4o+khEhRtCGfBIPpy98GyFBPpd3Fr\n29yDg/6ImZb0ZLAyrDBnytZF38XVlUW0nKqZYAUWluZJo9wKsAsocRwlBANjvSVTi1AstrGs8TCY\nMBarYTiNcKrmnl6/vccT7qfNXF1D3ecnVdxIMDRAR7AgtWab1y+ae7165gSWWAg1QVIG0wnbm6bY\nqVXxqUh/EmxVtBa0bItU3Aqdp5bjpLAU0igijnOEbFyQ32ZkzMyY63hQORZKQWMi4jYaR6Km8+0q\nWrIB09RlczdnmJlnJGbS118xG/7S7QAkArwX3kXJ5ohTjWObzXTr7ogLTxq46gsvmOrDuTgiqx/m\n6acS4e4NDugJCUcyHDHtGfdhTqC2tUaTpvTzmwY9skyi6FnMVKCoO/0+u7vmofDEh6xXa0XXpNXl\nVbwchFOvUXNz2u88DJ2g8vRykjLIQVaZVSgZp+qY7rqASnOfPKMlNPN1v8JlYfFxUs1yLYcu20VG\nYSCb/yAI2JI1TjU0G9KjMhgzSsyDkmTQlxLgnH8xdV1SYYi8Ox2zv2+up1Wt4sg1deKYhijnRGI4\nYRgVD79S3FNGnZGb6BXfxxLFcftASF9ixWRkrsf1XBQSa8gUI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "show_n_images = 25\n", "\n", @@ -112,17 +148,17 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "## Preprocess the Data\n", - "Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.\n", + "## 预处理数据（Preprocess the Data）\n", + "由于该项目的重点是建立 GANs 模型，我们将为你预处理数据。\n", "\n", - "The MNIST images are black and white images with a single [color channel](https://en.wikipedia.org/wiki/Channel_(digital_image%29) while the CelebA images have [3 color channels (RGB color channel)](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images).\n", - "## Build the Neural Network\n", - "You'll build the components necessary to build a GANs by implementing the following functions below:\n", + "经过数据预处理，MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分，然后调整到 28x28 维度。\n", + "\n", + "MNIST 数据集中的图像是单[通道](https://en.wikipedia.org/wiki/Channel_(digital_image%29)的黑白图像，CelebA 数据集中的图像是 [三通道的 RGB 彩色图像](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images)。\n", + "\n", + "## 建立神经网络（Build the Neural Network）\n", + "你将通过部署以下函数来建立 GANs 的主要组成部分:\n", "- `model_inputs`\n", "- `discriminator`\n", "- `generator`\n", @@ -130,19 +166,26 @@ "- `model_opt`\n", "- `train`\n", "\n", - "### Check the Version of TensorFlow and Access to GPU\n", - "This will check to make sure you have the correct version of TensorFlow and access to a GPU" + "### 检查 TensorFlow 版本并获取 GPU 型号\n", + "检查你是否使用正确的 TensorFlow 版本，并获取 GPU 型号" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TensorFlow Version: 1.0.1\n", + "Default GPU Device: /gpu:0\n" + ] + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", @@ -164,29 +207,32 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "### Input\n", - "Implement the `model_inputs` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", - "- Real input images placeholder with rank 4 using `image_width`, `image_height`, and `image_channels`.\n", - "- Z input placeholder with rank 2 using `z_dim`.\n", - "- Learning rate placeholder with rank 0.\n", + "### 输入（Input）\n", + "部署 `model_inputs` 函数以创建用于神经网络的 [占位符 (TF Placeholders)](https://www.tensorflow.org/versions/r0.11/api_docs/python/io_ops/placeholders)。请创建以下占位符：\n", + "- 输入图像占位符: 使用 `image_width`，`image_height` 和 `image_channels` 设置为 rank 4。\n", + "- 输入 Z 占位符: 设置为 rank 2，并命名为 `z_dim`。\n", + "- 学习速率占位符: 设置为 rank 0。\n", "\n", - "Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)" + "返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "import problem_unittests as tests\n", "\n", @@ -200,8 +246,11 @@ " :return: Tuple of (tensor of real input images, tensor of z data, learning rate)\n", " \"\"\"\n", " # TODO: Implement Function\n", + " input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')\n", + " input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n", + " learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n", "\n", - " return None, None, None\n", + " return input_real, input_z, learning_rate\n", "\n", "\n", "\"\"\"\n", @@ -212,24 +261,29 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "### Discriminator\n", - "Implement `discriminator` to create a discriminator neural network that discriminates on `images`. This function should be able to reuse the variabes in the neural network. Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"discriminator\" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator)." + "### 辨别器（Discriminator）\n", + "部署 `discriminator` 函数创建辨别器神经网络以辨别 `images`。该函数应能够重复使用神经网络中的各种变量。 在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 \"discriminator\" 的变量空间名来重复使用该函数中的变量。 \n", + "\n", + "该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def discriminator(images, reuse=False):\n", " \"\"\"\n", @@ -239,8 +293,31 @@ " :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)\n", " \"\"\"\n", " # TODO: Implement Function\n", + " with tf.variable_scope('discriminator', reuse=reuse):\n", + " # alpha is the param for leaky relu\n", + " alpha = 0.2\n", + " \n", + " # Input layer is 28x28x3\n", + " x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')\n", + " relu1 = tf.maximum(alpha * x1, x1)\n", + " # 14x14x64 now\n", + " \n", + " x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')\n", + " bn2 = tf.layers.batch_normalization(x2, training=True)\n", + " relu2 = tf.maximum(alpha * bn2, bn2)\n", + " # 7x7x128 now\n", + " \n", + " x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')\n", + " bn3 = tf.layers.batch_normalization(x3, training=True)\n", + " relu3 = tf.maximum(alpha * bn3, bn3)\n", + " # 4x4x256 now\n", "\n", - " return None, None\n", + " # Flatten it\n", + " flat = tf.reshape(relu3, (-1, 4*4*256))\n", + " logits = tf.layers.dense(flat, 1)\n", + " out = tf.sigmoid(logits)\n", + " \n", + " return out, logits\n", "\n", "\n", "\"\"\"\n", @@ -251,24 +328,30 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "### Generator\n", - "Implement `generator` to generate an image using `z`. This function should be able to reuse the variabes in the neural network. Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"generator\" to allow the variables to be reused. The function should return the generated 28 x 28 x `out_channel_dim` images." + "### 生成器（Generator）\n", + "部署 `generator` 函数以使用 `z` 生成图像。该函数应能够重复使用神经网络中的各种变量。\n", + "在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 \"generator\" 的变量空间名来重复使用该函数中的变量。 \n", + "\n", + "该函数应返回所生成的 28 x 28 x `out_channel_dim` 维度图像。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def generator(z, out_channel_dim, is_train=True):\n", " \"\"\"\n", @@ -279,8 +362,33 @@ " :return: The tensor output of the generator\n", " \"\"\"\n", " # TODO: Implement Function\n", - " \n", - " return None\n", + " with tf.variable_scope('generator', reuse=not is_train):\n", + " # alpha is the param for leaky relu\n", + " alpha = 0.2\n", + " \n", + " # First fully connected layer\n", + " x1 = tf.layers.dense(z, 7 * 7 * 512, activation=None)\n", + " x1 = tf.reshape(x1, (-1, 7, 7, 512)) # Reshape it to start the convolutional stack\n", + " x1 = tf.layers.batch_normalization(x1, training=is_train)\n", + " x1 = tf.maximum(alpha * x1, x1)\n", + " # 7x7x512 now\n", + " \n", + " x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')\n", + " x2 = tf.layers.batch_normalization(x2, training=is_train)\n", + " x2 = tf.maximum(alpha * x2, x2)\n", + " # 14x14x256 now\n", + " \n", + " x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')\n", + " x3 = tf.layers.batch_normalization(x3, training=is_train)\n", + " x3 = tf.maximum(alpha * x3, x3)\n", + " # 28x28x128 now\n", + " \n", + " # Output layer\n", + " logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')\n", + " out = tf.tanh(logits)\n", + " # 28x28x3 now\n", + " \n", + " return out\n", "\n", "\n", "\"\"\"\n", @@ -291,26 +399,31 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "### Loss\n", - "Implement `model_loss` to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:\n", + "### 损失函数（Loss）\n", + "部署 `model_loss` 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。\n", + "\n", + "使用你已实现的函数：\n", "- `discriminator(images, reuse=False)`\n", "- `generator(z, out_channel_dim, is_train=True)`" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def model_loss(input_real, input_z, out_channel_dim):\n", " \"\"\"\n", @@ -321,8 +434,24 @@ " :return: A tuple of (discriminator loss, generator loss)\n", " \"\"\"\n", " # TODO: Implement Function\n", + " # Generator network here\n", + " g_model = generator(input_z, out_channel_dim)\n", + "\n", + " # Disriminator network here\n", + " d_model_real, d_logits_real = discriminator(input_real)\n", + " d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)\n", " \n", - " return None, None\n", + " # Calculate losses\n", + " smooth = 0.1\n", + " d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth)))\n", + "\n", + " d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))\n", + "\n", + " d_loss = d_loss_real + d_loss_fake\n", + "\n", + " g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))\n", + "\n", + " return d_loss, g_loss\n", "\n", "\n", "\"\"\"\n", @@ -333,24 +462,27 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "### Optimization\n", - "Implement `model_opt` to create the optimization operations for the GANs. Use [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation)." + "### 优化（Optimization）\n", + "部署 `model_opt` 函数实现对 GANs 的优化。使用 [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) 获取可训练的所有变量。通过变量空间名 `discriminator` 和 `generator` 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def model_opt(d_loss, g_loss, learning_rate, beta1):\n", " \"\"\"\n", @@ -362,8 +494,17 @@ " :return: A tuple of (discriminator training operation, generator training operation)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " \n", - " return None, None\n", + " # Get weights and bias to update\n", + " t_vars = tf.trainable_variables()\n", + " d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n", + " g_vars = [var for var in t_vars if var.name.startswith('generator')]\n", + "\n", + " # Optimize\n", + " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", + " d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n", + " g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n", + "\n", + " return d_train_opt, g_train_opt\n", "\n", "\n", "\"\"\"\n", @@ -374,23 +515,18 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "## Neural Network Training\n", - "### Show Output\n", - "Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training." + "## 训练神经网络（Neural Network Training）\n", + "### 输出显示\n", + "使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -423,27 +559,24 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "### Train\n", - "Implement `train` to build and train the GANs. Use the following functions you implemented:\n", + "### 训练\n", + "部署 `train` 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数：\n", "- `model_inputs(image_width, image_height, image_channels, z_dim)`\n", "- `model_loss(input_real, input_z, out_channel_dim)`\n", "- `model_opt(d_loss, g_loss, learning_rate, beta1)`\n", "\n", - "Use the `show_generator_output` to show `generator` output while you train. Running `show_generator_output` for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the `generator` output every 100 batches." + "使用 `show_generator_output` 函数显示 `generator` 在训练过程中的输出。\n", + "\n", + "**注意**：在每个批次 (batch) 中运行 `show_generator_output` 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 `generator` 的输出。 " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -460,43 +593,544 @@ " :param data_image_mode: The image mode to use for images (\"RGB\" or \"L\")\n", " \"\"\"\n", " # TODO: Build Model\n", + " _, image_width, image_height, image_channels = data_shape\n", + " input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)\n", + " d_loss, g_loss = model_loss(input_real, input_z, image_channels)\n", + " d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)\n", " \n", - " \n", + " steps = 0\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for epoch_i in range(epoch_count):\n", " for batch_images in get_batches(batch_size):\n", " # TODO: Train Model\n", + " steps += 1\n", + " batch_images *= 2\n", " \n", - " " + " # Sample random noise for G\n", + " batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))\n", + "\n", + " # Run optimizers\n", + " _ = sess.run(d_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})\n", + " _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})\n", + "\n", + " if steps % 10 == 0:\n", + " # At the end of each epoch, get the losses and print them out\n", + " train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})\n", + " train_loss_g = g_loss.eval({input_z: batch_z})\n", + "\n", + " print(\"Epoch {}/{}...\".format(epoch_i + 1, epoch_count), \n", + " \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n", + " \"Generator Loss: {:.4f}\".format(train_loss_g))\n", + " \n", + " if steps % 100 == 0:\n", + " gen_samples = sess.run(generator(input_z, image_channels, is_train=False), feed_dict={input_z: batch_z})\n", + " _ = show_generator_output(sess, 25, input_z, image_channels, data_image_mode)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### MNIST\n", - "Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0." + "在 MNIST 上测试你的 GANs 模型。经过 2 次迭代，GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失，或接近 0。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 0.4204... Generator Loss: 3.1223\n", + "Epoch 1/2... Discriminator Loss: 0.4827... Generator Loss: 2.2592\n", + "Epoch 1/2... Discriminator Loss: 0.4310... Generator Loss: 4.2485\n", + "Epoch 1/2... Discriminator Loss: 1.2898... Generator Loss: 2.9953\n", + "Epoch 1/2... Discriminator Loss: 1.3641... Generator Loss: 1.3200\n", + "Epoch 1/2... Discriminator Loss: 1.3577... Generator Loss: 0.6340\n", + "Epoch 1/2... Discriminator Loss: 1.5244... Generator Loss: 0.5115\n", + "Epoch 1/2... Discriminator Loss: 1.2379... Generator Loss: 0.8513\n", + "Epoch 1/2... Discriminator Loss: 1.3270... Generator Loss: 0.8379\n", + "Epoch 1/2... Discriminator Loss: 1.6648... Generator Loss: 0.4169\n" + ] + }, + { + "data": { + "image/png": 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C2JVJQWt9D4B7tq+fAvCq3ag3ICDg/GNkIhopHZvLJelrU5Aw3YKm28e1sie5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\nnk8rNBKTAgAopX6oPY60CrQD7UB7bxHUh4CAgBjCpBAQEBD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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 1.2459... Generator Loss: 0.9999\n", + "Epoch 1/2... Discriminator Loss: 1.4056... Generator Loss: 1.7060\n", + "Epoch 1/2... Discriminator Loss: 1.3388... Generator Loss: 0.7322\n", + "Epoch 1/2... Discriminator Loss: 1.1195... Generator Loss: 1.1036\n", + "Epoch 1/2... Discriminator Loss: 1.1910... Generator Loss: 0.7477\n", + "Epoch 1/2... Discriminator Loss: 1.2023... Generator Loss: 1.2572\n", + "Epoch 1/2... Discriminator Loss: 1.2410... Generator Loss: 1.6392\n", + "Epoch 1/2... Discriminator Loss: 1.2184... Generator Loss: 0.7372\n", + "Epoch 1/2... Discriminator Loss: 1.2382... Generator Loss: 0.6883\n", + "Epoch 1/2... Discriminator Loss: 1.3832... Generator Loss: 0.4813\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 1.0094... Generator Loss: 0.9240\n", + "Epoch 1/2... Discriminator Loss: 1.1167... Generator Loss: 1.6473\n", + "Epoch 1/2... Discriminator Loss: 1.4142... Generator Loss: 0.5206\n", + "Epoch 1/2... Discriminator Loss: 0.9967... Generator Loss: 1.5393\n", + "Epoch 1/2... Discriminator Loss: 1.3248... Generator Loss: 0.5312\n", + "Epoch 1/2... Discriminator Loss: 1.0156... Generator Loss: 1.1135\n", + "Epoch 1/2... Discriminator Loss: 0.9628... Generator Loss: 1.9009\n", + "Epoch 1/2... Discriminator Loss: 1.0585... Generator Loss: 1.5717\n", + "Epoch 1/2... Discriminator Loss: 1.0929... Generator Loss: 0.7814\n", + "Epoch 1/2... Discriminator Loss: 1.0840... Generator Loss: 1.0624\n" + ] + }, + { + "data": { + "image/png": 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IqwON+YILLsDTTz8NoI6FPfXUU+Nu18p8tm/fHgCwYsUKdkzq2bMnJ0hRWdi0tDQ2/ZLc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\nC7gTeMV53wK0lJH+34AlQBcwPTw2HehKiN5MggW5GNgLCEEiS6rQfMR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 1.0431... Generator Loss: 1.1230\n", + "Epoch 1/2... Discriminator Loss: 1.0236... Generator Loss: 1.0033\n", + "Epoch 1/2... Discriminator Loss: 1.1009... Generator Loss: 0.8963\n", + "Epoch 1/2... Discriminator Loss: 1.0246... Generator Loss: 1.5232\n", + "Epoch 1/2... Discriminator Loss: 1.2980... Generator Loss: 0.6295\n", + "Epoch 1/2... Discriminator Loss: 1.0272... Generator Loss: 1.3369\n", + "Epoch 1/2... Discriminator Loss: 1.1724... Generator Loss: 0.7514\n", + "Epoch 1/2... Discriminator Loss: 1.0532... Generator Loss: 1.2344\n", + "Epoch 1/2... Discriminator Loss: 1.1428... Generator Loss: 0.8638\n", + "Epoch 1/2... Discriminator Loss: 1.1750... Generator Loss: 1.1432\n" + ] + }, + { + "data": { + "image/png": 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5+FmlVCFdx1Y2E/NpLE9VGk+Hw8HzQGNYW1vL9On6+noeo/r6ep5X6qMMagKi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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 1.2411... Generator Loss: 1.8333\n", + "Epoch 1/2... Discriminator Loss: 1.0503... Generator Loss: 1.0242\n", + "Epoch 1/2... Discriminator Loss: 1.1395... Generator Loss: 1.0762\n", + "Epoch 1/2... Discriminator Loss: 1.3215... Generator Loss: 0.5722\n", + "Epoch 1/2... Discriminator Loss: 1.1445... Generator Loss: 0.7874\n", + "Epoch 1/2... Discriminator Loss: 1.2529... Generator Loss: 0.6156\n", + "Epoch 1/2... Discriminator Loss: 1.0842... Generator Loss: 0.9782\n", + "Epoch 1/2... Discriminator Loss: 1.1459... Generator Loss: 0.7799\n", + "Epoch 1/2... Discriminator Loss: 1.1442... Generator Loss: 1.4652\n", + "Epoch 1/2... Discriminator Loss: 1.0300... Generator Loss: 1.2469\n" + ] + }, + { + "data": { + "image/png": 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G0n0nEgmmCd9yyy2sEk6ePBmHHHIIADNCc8yYMQDa4gs6AvVZJp7JZV5Q/ygku6qqir+3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\nU1Hm/sILL9hI2UzIFJLsujbr2Kpo79pZzp07l/WY19TUWGcxdyEzxti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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 1.2653... Generator Loss: 0.6031\n", + "Epoch 1/2... Discriminator Loss: 1.0865... Generator Loss: 1.2273\n", + "Epoch 1/2... Discriminator Loss: 1.2002... Generator Loss: 0.6592\n", + "Epoch 1/2... Discriminator Loss: 1.2886... Generator Loss: 0.6356\n", + "Epoch 1/2... Discriminator Loss: 1.4369... Generator Loss: 0.4941\n", + "Epoch 1/2... Discriminator Loss: 1.1444... Generator Loss: 0.9019\n", + "Epoch 1/2... Discriminator Loss: 1.1936... Generator Loss: 0.6823\n", + "Epoch 1/2... Discriminator Loss: 1.1784... Generator Loss: 1.4153\n", + "Epoch 1/2... Discriminator Loss: 1.5582... Generator Loss: 0.4257\n", + "Epoch 1/2... Discriminator Loss: 1.0860... Generator Loss: 1.3010\n" + ] + }, + { + "data": { + "image/png": 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wgHXnn376if3q6kOM1ydOD2b37t0sKtKks4JEEqSQqKlO6FgJSTweD2644QYA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\nMSsJ9PyTMeY+4GXg62nKDuWfAUZFpCM89CXgBDXQncBtuEVEPhVeA5V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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 1.0424... Generator Loss: 1.1105\n", + "Epoch 1/2... Discriminator Loss: 1.0549... Generator Loss: 0.9376\n", + "Epoch 1/2... Discriminator Loss: 1.1714... Generator Loss: 1.1036\n", + "Epoch 1/2... Discriminator Loss: 1.1158... Generator Loss: 1.0531\n", + "Epoch 1/2... Discriminator Loss: 1.0498... Generator Loss: 0.9878\n", + "Epoch 1/2... Discriminator Loss: 1.2111... Generator Loss: 0.8901\n", + "Epoch 1/2... Discriminator Loss: 1.4104... Generator Loss: 0.4942\n", + "Epoch 1/2... Discriminator Loss: 1.0527... Generator Loss: 1.3645\n", + "Epoch 1/2... Discriminator Loss: 1.0832... Generator Loss: 0.8888\n", + "Epoch 1/2... Discriminator Loss: 1.1123... Generator Loss: 0.8392\n" + ] + }, + { + "data": { + "image/png": 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S7NsDoJ7RiUKIO4QQK4UQK43+b8GChRODqCUFIcRlAA5IKX8XQnSN9HwZpBS9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\n0Rir+kCrZ9euXdGlSxcAQO/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Fa4/Hw2K1er6qRtCxeXl5LIoXFRXpokcpMQzRtXft2sXS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\nEkb60sXqBaDnWLt2bf6cm5vLmx7ZcgBtPkSq3hjASrJiwYK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 0.9938... Generator Loss: 1.1336\n", + "Epoch 1/2... Discriminator Loss: 1.2817... Generator Loss: 1.7325\n", + "Epoch 1/2... Discriminator Loss: 1.0349... Generator Loss: 0.8968\n", + "Epoch 1/2... Discriminator Loss: 1.0611... Generator Loss: 1.3221\n", + "Epoch 1/2... Discriminator Loss: 1.0041... Generator Loss: 1.2203\n", + "Epoch 1/2... Discriminator Loss: 1.0210... Generator Loss: 1.1737\n", + "Epoch 1/2... Discriminator Loss: 1.0761... Generator Loss: 1.2211\n", + "Epoch 1/2... Discriminator Loss: 0.9741... Generator Loss: 1.1375\n", + "Epoch 1/2... Discriminator Loss: 1.0377... Generator Loss: 0.8915\n", + "Epoch 1/2... Discriminator Loss: 1.0852... Generator Loss: 0.9029\n" + ] + }, + { + "data": { + "image/png": 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Rx2pTY4yBctq0aeLau9dee0nQlFvkrcm4nKi/ilJKcg/cfPPNsn9v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\ng5Fofv311yhPVbN6n3jiiYCTdbpnz57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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 1.3093... Generator Loss: 2.5877\n", + "Epoch 1/2... Discriminator Loss: 0.9853... Generator Loss: 1.1730\n", + "Epoch 1/2... Discriminator Loss: 1.0564... Generator Loss: 0.8760\n", + "Epoch 1/2... Discriminator Loss: 1.0396... Generator Loss: 1.4255\n", + "Epoch 1/2... Discriminator Loss: 1.0599... Generator Loss: 0.9275\n", + "Epoch 1/2... Discriminator Loss: 0.9641... Generator Loss: 1.3584\n", + "Epoch 1/2... Discriminator Loss: 1.1231... Generator Loss: 0.8175\n", + "Epoch 1/2... Discriminator Loss: 1.0010... Generator Loss: 1.1018\n", + "Epoch 1/2... Discriminator Loss: 1.3737... Generator Loss: 0.5330\n", + "Epoch 1/2... Discriminator Loss: 1.0614... Generator Loss: 0.9055\n" + ] + }, + { + "data": { + "image/png": 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cc88Blc+YaqREp2CMaQMcBEwHmlQQDIDVQJMqvhNWit6DBw+ZgaSJgjGmAHgTuMZauznC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\nvq+55hr1smzVqpXmX6hpnFApagSDQe0P4NtvvwU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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2... Discriminator Loss: 1.0209... Generator Loss: 1.0810\n", + "Epoch 1/2... Discriminator Loss: 0.9956... Generator Loss: 0.9358\n", + "Epoch 1/2... Discriminator Loss: 1.2294... Generator Loss: 0.6367\n", + "Epoch 2/2... Discriminator Loss: 1.0491... Generator Loss: 0.8156\n", + "Epoch 2/2... Discriminator Loss: 1.9660... Generator Loss: 0.3475\n", + "Epoch 2/2... Discriminator Loss: 1.0618... Generator Loss: 1.2255\n", + "Epoch 2/2... Discriminator Loss: 0.9658... Generator Loss: 1.0057\n", + "Epoch 2/2... Discriminator Loss: 0.8519... Generator Loss: 1.6786\n", + "Epoch 2/2... Discriminator Loss: 1.0465... Generator Loss: 0.9361\n", + "Epoch 2/2... Discriminator Loss: 1.0060... Generator Loss: 1.0238\n" + ] + }, + { + "data": { + "image/png": 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WZ89DDz0Ud4aYOM0kqScYDGqR07/++kvLctWoUUNj6DK2SPkIpSHe/hHREIQJWlBQoH6G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\no0MLCo1Go0MLCo1Go8M/s2DwvwtwYfMAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 1.2068... Generator Loss: 0.6641\n", + "Epoch 2/2... Discriminator Loss: 0.8739... Generator Loss: 1.2788\n", + "Epoch 2/2... Discriminator Loss: 1.2420... Generator Loss: 0.5994\n", + "Epoch 2/2... Discriminator Loss: 1.0455... Generator Loss: 1.3188\n", + "Epoch 2/2... Discriminator Loss: 1.5271... Generator Loss: 0.4801\n", + "Epoch 2/2... Discriminator Loss: 0.9442... Generator Loss: 0.9758\n", + "Epoch 2/2... Discriminator Loss: 1.1125... Generator Loss: 0.8582\n", + "Epoch 2/2... Discriminator Loss: 0.9858... Generator Loss: 0.8610\n", + "Epoch 2/2... Discriminator Loss: 1.7708... Generator Loss: 0.3445\n", + "Epoch 2/2... Discriminator Loss: 1.0218... Generator Loss: 0.9825\n" + ] + }, + { + "data": { + "image/png": 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ygLmCz0JBSpkFIKvifYEQIg1qC/rBAHpXfG0OgF8QJKHgCchB17hxYyQk1I7S8tlnnwFQ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\nUUhBD9nChQt5fBSdKCkpYfMxEEJMm6sSDs+QhzD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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 0.9482... Generator Loss: 1.3030\n", + "Epoch 2/2... Discriminator Loss: 1.3452... Generator Loss: 1.9974\n", + "Epoch 2/2... Discriminator Loss: 1.0928... Generator Loss: 0.7811\n", + "Epoch 2/2... Discriminator Loss: 0.8769... Generator Loss: 1.3478\n", + "Epoch 2/2... Discriminator Loss: 1.2244... Generator Loss: 0.6451\n", + "Epoch 2/2... Discriminator Loss: 1.0969... Generator Loss: 2.4311\n", + "Epoch 2/2... Discriminator Loss: 1.0368... Generator Loss: 0.8528\n", + "Epoch 2/2... Discriminator Loss: 0.9872... Generator Loss: 1.3598\n", + "Epoch 2/2... Discriminator Loss: 1.1426... Generator Loss: 1.1906\n", + "Epoch 2/2... Discriminator Loss: 0.9564... Generator Loss: 1.2773\n" + ] + }, + { + "data": { + "image/png": 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gdUbPzmw26wh0Bw4cCEBvCq5fv56fHwksX6MeNM8UZs3NzWXiYaA0W5SETqAwzAcDBgzo\ncElqCqQmffLJJwCAV155haVthQoVeLdt1KgRq1LVq1ePSMMXdyAz4s0332RexoSEBE7TnjVrFjp2\n7Aig1KHUvn175h6wWq1c+VlYWIjnnnsOgH9xfn/oxa644grelRYuXMj59x9//PFF+SAzZ87U7aY3\n3ngjAE2Lce16FW6Qw3P+/Pmc3kxrZMKECbyjOxwOZsdet24drwuz2exX9Ibmi9LRVSfhuXPn+Hpq\nlOjTTz/lyI6/oLVB/BTEFQFoc0zJWzSeQBGwpiCEqCWEWCGE2C6E+FMIMfTC56lCiGVCiKwL/1YK\naoQGDBiIKILRFOwAnpFSbhRCJAHYIIRYBuAhAD9KKScIIUYBGAVgZPBDvRjkaFR3InX33LRpE/bu\n3QtAs8WrVq0KIDR8CoFgypQp3Mvh3XffRf369XlslLlGti6FIAFNU6CdPicnx1eWHb9BGs25c+dY\nI5g4cSLPl7twWUpKCtvfiYmJ7FybPXs2ny+QMF8goJ174cKFHPqdOHEiAE3DItLc3r17s/PXbre7\nbSHnD9xpoA6Hg53HrVq1Ytv/gw8+4HwQd/OpFkdZrVbOcbnttts4H4eyNdX8lqKiIixbtoyvHQwC\nFgpSyqMAjl74O1cIsQNaC/peADpd+NmnAFYiTELBXXPR5ORk/rx58+b8d2JiInuWn3/+eQDgFzFS\nOHXqFMf5k5KS2GvdpEmTMglQiouLsXLlSgCal12NWXtDIDTke/fu5bh6ZmYmC1O1RkFNtVavQbyM\nH330Ef7xj3/4fM1QQCUtITq2Bx54AIDW/YmqVW02GycOBWPa0H2rad5qrgA936SkJM6niI2NZSGq\n9v+k5LTCwkI2f3NzczkKpAoLNXpCAmDVqlXsBAZKHdZqQ1tfERKfghCiLoBWANYCSL8gMADgGIB0\nD8cE3IregAED4UPQQkEIkQhgAYCnpZTnXdI7pacKyFC3or/uuusAaOEmql3fs2cPWrVqBUBTcx9+\n+GEApbvD4MGDdaqWpwrFUFWSOp1OViktFovuGupvAE0dpIrK22+/nduK+asaBjJ2m83GBTVz587l\nEJjVauWx0i63YsUKTm/u1q0bf1+hQgXOJo2URkb5GwkJCUhOTgYAJu5NTEzkHbN3796+9kkoE655\nD2azmXdos9mMzz//HICmsVIWIlCa46F2iSao2Yg0fwRXra+kpIRNs759++rStWlsARETB7PghRBW\nAIsALJVSTrrw2S4AnaSUR4UQ1QGslFI28nKeoN86IqZYv369Lu2U1LaKFSuy2k0qpUq2opYcSynD\n5jGnRbNx40aOiR88eBB33nkngNKkGbUUtrz7MtKi79KlC9u1qj+H7PcHHniAx6x2K6KchnBCTW+/\n+uqr+YWkPIWioiLccMMNALQy+lDMqWt1q9PpZJPB6XTyNSwWC3ee+uqrr1goEKSUuhfeXTMYp9PJ\nUR7Kc3juueewa9cuPs4HhJfNWWgj/xjADhIIF/ANgH4X/u4HIDiaIwMGDEQUAWsKQojrAawC8AcA\nErvPQfMrzANQG8ABAPdKKcssnwuFpkDSdfr06dxsAyhVt2NiYvDBBx8AAF566SUAmpe9W7duALQs\nMNrdvDlmAjUphBCsKj788MPo0qULAK3fQHlrA6GAyq1gsVg4EjFjxgyf6/oDnVur1crPrWnTppy9\nSJri9OnTmYk6VHPtT7t3Wp89e/bknBPi26hTpw5Hm1wrLWn8K1euZJOOIlgBRBn+N3tJqhVySUlJ\n2LdvHwDNrlXDNwSa9KSkJFZ9AyGm8AVCCPYyl5SU8Dh87Sx0qYFehCpVquDcuXMAwje3KiwWC0eY\niHeydevWPtHSE9yp8EDoG9e4wmKxsHBT61VC5NsymsEYMGDAf/ztNIVohsVi4dTUI0eORGTXjAaY\nzeaL+i4WFxcH3K3ZF7hyC6hUc//D+N80H6IZgfZ1NGAgRDDMBwMGDPiPS7JKsrzhr9OHnJlqLoQK\nQ2sIPfyJDBjQw9AUDBgwoMMlpykIITidlXjuCwoK2IHVtGlTduCdP3+ew32nTp1im14N+ag7t0qr\n5S4GHKhjzBuvgCvhJ6AVzhBrVE5OjtvxhDIF2/W8Kvztnel6HtcGJ4AWY3eX3u3vOOm5W61Wt4Vi\n0aYxuOv+Xdb8+lrQFkp/VdQJBXfxYdebpBdETWcmOrbi4mJunLJt2zZOBnE6nbyA1HRmup7ZbGY6\nq1AvIPWF9vbA6Huz2cwCzVOSSrjMDte0W1/hqRmMSm2vfh/s+KWUHklR3KUgRwv8GQttVDab7aLj\nzGazTrCEaj0Y5oMBAwZ0iNqQpCoFVQlpNpu5+Imq8/Ly8nSqK6UrR8O9GYgMVFPq7xL6tVgsXCh3\n8OBBbihDa71y5cr4888/AYS2ICpqzQeVsEJVBRMSEpgApHHjxgCAadOmsZp19OjRS3ohuEOo27Z7\ngyeBHI3wxdwkqOsomu+L7qlu3brMMRkbG8tp+IsXLwagMWmHA4b5YMCAAR2iRlMg9c+dI8pqtXIb\n+YEDB3KPRvLOjxs3jiW/SrU1efJk5uI7e/as330WyxNNmjTBhAkTAGjsvaQJCSG4CnTNmjVMYELz\nc+7cuYAJNmgnbdSoEcaPHw9Aa1FPFXzx8fHs4Sdn7ZEjR5iz4Pbbb2dG5UgxZxNVXKVKlZhLsri4\nGFdfrWnJ5IB+/vnnuU1bgwYNmMsilJ2vQwUa29VXX82RtqKiIl4DxDt64403cs9Mk8nEjulgNcmo\n8Sl4C6/Rgu3Vqxc3KiFa9AvncHscnVMl0qSmIRs2bCg3QSGE0HH43X777QC0PocAUK9ePY/3RC/9\n4sWLmU2KqhCDIYih6zVv3hzLly8HoIV9KWrjLnSqwuFwsJ+nR48eXL4crjVmNptZKNxyyy0sIMeO\nHcvEL0SEq5pgDoeDyWLuueceJsJVoyTlhbFjxzI9vcViwVdffQVAe77ffvstgFKfWVxcHNO5nzp1\nSieIicxHJXuRUhppzgYMGPAfUWM+eNtNSNotXLgQY8aMAVDatZeabrgD7WwWi4V5Fkj6rl69mhmH\nI90oJjY2FrNnzwagOUyJBZkkvKo5qTtwpUqVeDf7448/+PNQ7MZ0jj///JPV7pKSEp77tLQ03Hzz\nzQCAYcOG8WdEXy6lZFNj/PjxTKkerhb1agKUzWbj5jrp6emsKdBcFRUVMYdh1apV+Z5SU1N5zk0m\nE+eqRBrUA/Uf//gH8z7cfffdPGZ3z/fKK6/0SAwUjAYcNeaDH7/lLkuvv/46AM23oDY/9QbVs052\n+5gxYyIatahduzbbhjQWoLTE1+l0spk0f/58Vn179uzJvP8DBw5kwRgptZf8OPRv3759uduS+rJJ\nKXlM6enpPM5Qg4TC2LFjeb6Sk5P5c8puXb9+PXMcdu7cmQXVM888g40bNwLQwn7l0UVMCMFjE0Iw\nn+O6des8/h7QTE1avz6O2zAfDBgw4D+ixnzwNY/fZDJxv0K1K7O/16JzXXXVVQCAGjVqcNQiVGN1\nB9phv/rqK53ji3Y0Yhw+ePAge5PtdjuPefHixahXrx4AjWqMdhXq9zh58mT2wocjp4F2JNICMjIy\ndM5HSqyRUvI9PfHEE/j6668BlHZxLioqYs/6uXPnAlZ36f5uvvlmNgOOHz/O5yMNcufOndwR+/Tp\n09iyZQsArfkKzZe/WkIgqeDuUKVKFa7j2b9/v9fu6HTdN954IyyajaEpGDBgQIeo0RR8lbYOh4Pj\n9OScq1mzJr777jsAWt8E2km9QQjBTjLawUM5VldYLBY0bdoUAFC/fn0+z65du9gpt3379jKvWVBQ\ngPfffx+Axv9P56PxT5kyJay+EdqByUewatUq7neoXtfpdOL8+fMAgJYtW2Lp0qUAtAY99H0o/Qwd\nOnTg66u7J2mRsbGx3O+jT58+3GKtQYMGPDZ/Eap5fvPNN1nzGj58uFf/EPltwkXnFzVCIRBQd5z9\n+/dzz72WLVvixx9/BOA5KqFW7BFddiSaoLZu3RoPPfQQAD3hSufOnXHq1Cmfz0MJN3369OHY9bRp\n0wBoyUSRSOEltfzFF1/k5CUALAjWr1/PUaLNmzf71f8yEHh6QUiI2Ww27uPZvHlzFk4DBgwoF+ci\nUGoGZGRksKm4ZMkSn49X2Z5DiaDNByGEWQixSQix6ML/1xNCrBVC7BFCzBVC+B4WMGDAQLkjFJrC\nUAA7AFBDvNcBvCWlnCOE+ABAfwDvh+A6ZYKk/ZYtW3T9+NyBpGtxcTH3GvRnt/BXQpNjdP78+Xzc\nuXPnOAPPHy0BKN1hEhMT8euvvwIAc0iEG6SRkYOzVatW7Fy02+348ssvAQCff/45Z5BGA2u1yWTi\nrNGkpCTUr18fQHD5KYESuJCWRY2B4uLi2BTxxeFKJtHIkSNZK16zZo1fYyjz/MEcLISoCaAHgPEA\nhl9oJdcFwP0XfvIpgLGIgFBQxnQRwYYr6PuJEycGlPvuqy1J3vCZM2cC0KIl9NCnTp0aUJWbEILN\noptuuomby9CLabVag2ou6u3aL774IgCwGaTOdVZWFsaNGwcAOHbsWFgp3H0FNRseOXIk+1+EECHp\nb0n3TpuQrz4SSvCaNEnrtnj48GHO9ShrrkggU57OmDFj+Hm88847GDVqFIDgE/GCNR/eBjACpW3j\n0gCck1LSqA4ByHB3oBBigBDidyHE70GOwYABAyFEwJqCEOI2ACeklBuEEJ38PT7UregJZaU8kxT+\nv//7PwBaRmS4MgGFEPjoo48AQNcnkK49efJkLmYBSncdaj8+evRotGvXDoCWuUbx/WuvvRaPPvoo\nAG2Hotp6ikiEswVdxYoV8eCDDwIo5ccESgt03n33XY75l3emLM0nOTu7devGnxUXF3NeRzAgjcyf\n1Gir1coaYvv27QFoBXpkBpYFWgMTJ04EoOWsrF+/HoCmuVH6/tatW4OK7ARjPlwHoKcQ4lYAcdB8\nCv8FkCKEsFzQFmoC8C0jKET4+eefPX63aNEiAOA04XB66atVq4a7774bQOkCXbx4MVdonj59mlX+\nDh064LXXXgMAtGjRAoD2wtP4li1bpuspePDgQQCaYJkyZQoAhN27D2h5+WQHk1/j9OnTeOeddwCA\nG/hGA6h6kmo4EhMTdb1EV69eHbJr+SMAe/fujc6dOwMobRS7fPlyv1R+ut62bdt4PY0bN459DcFu\nDAGbD1LK0VLKmlLKugDuA7BcStkXwAoAvS/8zGhFb8DApQYiNgnmPwCdACy68PdlANYB2APgSwCx\nPhwvg/2vbt26sm7dutLpdLr9z+FwyLS0NJmWliYtFou0WCzSZDIFdC2z2ezxu5iYGBkTEyPnz58v\nS0pKdP/16dNHxsfHy/j4eJmQkCCPHTsmjx07xuNzOBzy+PHj8vjx4zI7O1tu3bpVbt26Vf7xxx/S\nZrNJm80mHQ6HPHDggDxw4ICsVq1a0PPmy38pKSkyJSVF2mw2nk8az5AhQ3g+IzGWsv67wMshq1ev\nLk+fPi1Pnz7N4ywpKZFFRUWyqKhInj17NiRjNpvNZa4Fd2MrLCzkcbRr1062a9cuqDHQGjl9+rSs\nU6eOrFOnTlm//92X9zkkyUtSypUAVl74+y8AbUNxXgMGDEQel3RGowoqcPGEgwcPckiHbDJy3PiL\nspyTxJzUvXt39hnQ7++//35O0W7YsCHbvUCp8/OOO+646JzJyclYuXIlAM3nQKy+5OALB8gPUqlS\nJbz33nsA9M1eyEn60UcflVtGoIrExET069cPgOZcJIcz+T6Kioo4FdxkMjG70f/93/8FzaEQGxsL\noGy/jtq/gQrB6DgaE+Cfn8tisXB+yrFjx0K2Hv4WQiEjI0PnDVdBCzYlJYV5B8mpFw7yj2+++QaA\nloJMMXJKTmrZsiW/WIcPH8aBAwcAaAKEKMHcQQihE2iUku3LIgg0waZhw4YAgGbNmqF79+4AtLkk\nJ+f992upKOGMdvgCetFfe+01JiqpWLEiV7wS7dqZM2dwyy23ANDug6pRt27dWubc+wJfHI2Un5KT\nk8NCgf4VQrAD9/z58z4/q759+/J9fvTRR34nwXmCUSVpwIABHaJGUwiEo4C4EAYNGuSR/58kdGJi\nIlfGkYZOO3LGAAAgAElEQVRgNptZQicnJ/MuCJTusP6qxlQL36hRI96NiB3n8OHD/PfmzZu9Zh7S\nPbVt25aLeQCwOu/LjkJhKpvN5vP8pqWloUePHgCATp06cWbmuXPn8M9//hMAeC7LE2azmefirrvu\n4uf666+/4vPPPwcAzlxMT09nbfLKK69kolvS1gKBu6pMT6Bndf78eaSnpwMoZeCWUvJ4fAGZqPfd\ndx/nYVCOQigQFUKBmI19jbUTF9+AAQMAXNw3kODCZIvrr78eADBr1iwAmiAgOu077rgD33//PQCt\nnJYq/yZNmhRQDoCUkhNSaPEcO3aMVX9vSVMWi4VLfUeMGMECQmWl9ge+dE2izls//fQTcwNu376d\naxfsdjtatWoFAJw0ozZ5DQUztj+bw9ixYzndWkqJRx55BADwww8/6JoIA1r9RfPmzQFohDo0t++8\n8w7/tlatWixY3HEfehqXP6bZ999/zwzT//nPfwAACxYs8Nh8h0yMatWqcZ0D+aKsVitTyYUShvlg\nwIABHaJCUwA0KWyxWLyqYomJiZxqS1LUE8xms84MoB2NGoWsWLGCd48hQ4Zg4MCBADTnFKlzHTp0\nYM8/SXFfU6NJ+n/22WcANPo0InU5c+ZMmTti/fr18eyzzwIAqlevzh7yGTNm+KW50Fjj4+P5/jt2\n7MhVdZSFWFxcjG7dugHQUsVJO7j88sv5eqmpqexgpB31wIEDnIW5ZcsW1oROnjwZtoxR0mhGjhyp\nY3Pet28fAI1vgqILNN6bb75ZR39H0YBBgwZhwYIFfK/E+E0p6seOHdNpHeo9BXJ/zzzzDKep16xZ\nEwB0psOwYcPYYdixY0c215KTk/l6ZPL06dNHZ/KGClEjFMgr7433LiUlhW1cb3A6nRw6O3jwIKcE\n//DDDwC0F+GLL74AoKVH04tXo0YNZGVlAdCEiaug8lXFpd+Qqn3HHXfgsssuA6AJHjpvWloa3z8x\nMA0aNIhTdAsKClhNVJmdfVmUanp0165dAWgq89q1awGAPfImk0nHdUkLNi0tTUeT36ZNGwDg6rzX\nXnuNIxBdunTBihUrAGjpz+r5fC2fllKWaepYLBYugVZf0qysLH65mjdvziZmtWrV+DwkIEtKSrgx\n644dO/hZ7969m01Id5GdUAg5p9PJAmfo0KEAoCv1nzp1Kv+tdkz766+/eG3Q2P3xE/kDw3wwYMCA\nDlGjKTgcDtjtdq+S7+abb+ZGHwTX3YV2roMHD+KFF14AoLEdEwWX67GA3gt94sSJkDD10rHU2i0t\nLY0dmMnJyWzG3HrrraxBdOjQAYCW2ELJVW+//TY+/fRTAFo7OdIq/Nm58vLykJmZCUBzJBK3AJkz\n8fHxnJsQGxvL9y+E4B3YbDbj5ZdfBgA+1+HDhzkJJy8vjzUCdd7KajXnDt7aB27evBmAlv9BfBJp\naWmch9KoUSO+LzJ99u3bh48//pjHTOzSrnkWkSCEGT58OIDSCs6aNWti1apVPE6KkgwbNow1tunT\np0esU3bUCAXXF9sVKi27qhLTv7SQhBAcppo2bRoOHToEwP9GomUJBX/Dp6Tup6en4+mnnwYA/Otf\n/+LkJvV8qqlCiTezZs1ilqaePXty9MFut190X57GpX5+6tQp/PbbbwC0xQZo5hOFUJ966ikOOS5a\ntIhfnF69ejExiOrXoAY2nuDvXJW1+EtKStgfsmzZMu7wlZCQgJ49e150DjJ39uzZw/cRDEV/KEHz\ntnPnThZu0QDDfDBgwIAOUdM2jjoae1OR1IgCkZekp6cz597KlStZAgfVT8+HuL6vIHX/66+/ZvMg\nMTFRp42QQ3TEiBEAgE8//ZSPU1Vck8mExo0bA9BSfMk5umvXLr6WqgJ7M4PIGRgN9Qv+Qs2RqF69\nOj/vM2fORIRfIprh4bkbbeMMGDDgP6JOU4gGss9oB+0CZrNZ18PCgAEv8ElTiCpHo7GwfYM/OfcG\nDPgLw3wwYMCADlEjFPyJZZvNZh3hRzhA5owBA/9riCrzwVeQQCDPs5SSVWlPZogvL7h6XChNGdUH\noHZT8lQZ5wqTyaSrBFUrP9X8jFCN3WQyBTQXnsbgLi/A9Xn4cw11LihxqmLFipyzUVJSwvNMiUC1\natVCdnY2AC0Fm3wxkfZjWSwWvo6ahKaydKm1NfTbmJgYtzU3dFxsbCxHX4qLi4O6l6jRFAwYMBAd\niBpNwR+QRFRjsZ7Sal2lMhB5Bx2NweFw8C7vT8qqygthsVh09x0OEyfQ3dOf8QS6k6mFTUBppmph\nYaHunKRBEI3d/v37ubJTnftIO7ftdruuWpPGo2qN7sbkrvhJzdOQUnJuRrD3FDUhyTCdlyetdevW\nXBkYDfdsILygtGESBOHqBHaJIfzJS0KIFCHEfCHETiHEDiFEeyFEqhBimRAi68K/lYK5hgEDBiKL\noDQFIcSnAFZJKacJIWIAxAN4DsAZKeUEIcQoAJWklCO9nCekWzepjklJSZg8eTIArfqQ2pu9/vrr\nAMLbNs5A+cFisTCJSiBdxf/G8ElTCFgoCCGSAWwGcJlUTiKE2AWgk5TyqBCiOoCVUspGXs4VMqHQ\nrFkzbrbaunVrNhUqVKjAZBrUynvz5s38vcr972q3Gogc/K1gVKMMHTt2BKCRkBD1eXlT0LuDSloD\naGuT2MRatGjBrFHJycnsf8jJyWFylcGDBwPQSq6//fZbfy4ddvOhHoCTAKYLITYJIaYJIRIApEsp\nj174zTEA6e4ONlrRGzAQnQgm+mABcBWAIVLKtUKI/wIYpf5ASik9aQEyxK3oiaq9X79+aNtW61pn\nNptZKufk5PBviPfOarWy9BVCcHVlqM0KNR79v+jk9Gf39/Y7q9WKRo00xfPyyy9nRu9OnTrp+Bpf\neuklAGD+h2jR/IQQvNOTxtqkSRO3jOTqvKWkpKBdu3YAwFwYAHDttdcC0JrahGrdBqMpHAJwSEq5\n9sL/z4cmJI5fMBtw4d8TwQ3RgAEDkUSwjsZVAB6VUu4SQowFQAyUpxVHY6qUcoSX8wS9fXbq1AkA\nMGfOHA5HORwOpu46c+YMhySXL18OQJO41JAjJycn5DX4JP2JIXjnzp0+O768MRBFC4QQumw8NcNS\n7Z8Yqntp2bIlU8mNHDmSuSXUPJTi4mJ2NhO3xLJly3DPPffw9+WF+vXrc6Ma6qFhsVi8pu2rVHc0\n/ipVqjCNHzGMeUFEqiSHAJh5IfLwF4CHoWkf84QQ/QEcAHBvkNfwCHLUJCQk4JNPPgEAXdPW4uJi\nfPfddwA0pmGiMaMmLQ6Hg5uehFq9NJlM3PyTOBo3btzIFOjPPvssU6Nv27YNS5cuBQB2MjVt2pSZ\npocNGxY1AoJo9emexo8fz+za69atYz7HrKwsXryvvvoq/02C0mw261LTCZ5MDXrWvXr1YpW5cePG\n/LnT6cSRI0cAaAlLZEqQcLjtttuYofnDDz/Ek08+GeRMBIaDBw8yIQ71waxduzabtjabjSneV65c\nyev3hRdeYI5J6hBlMpm8tjkIBEEJBSnlZgDuJE/XYM5rwICB8kPUZDRSoYgvOzappW+++SYArack\nMSNbrVbeVZctW8Zc+bVr18bx48cBgP8NYrweHWLUJ7Bx48YYMmQIgFLzITY2lntCuqYD05gplFaz\nZk2eiyuuuAJ79+4NasyhQGxsLOd69O/fH4C+mlQt8tqxYwdrAj/99BP69u0LQAuzAdpzIo2tX79+\nOHr0KJ/D3Rqg3ghr1qzhtmsq9dyCBQvYudi5c2c88cQTALS+kTROgpQSEydOBAC89NJLHI72910I\nlPH7X//6FwBwH46kpCT+rmXLlrjjjjsAaM2K6NxZWVk8B0RDWFRUxKayj6HXS49kxReoTU2oo1HV\nqlV11YfkR/jnP//J6qw/DTx9GaunikASOBkZGbjpppsAlKqwZTWxoe5GKpMS3RPl75cXaNzbt2/n\nBjUEm82ma+xKguD555/nPo+PPfaYzqwDtPsjYdmnTx9u1HP+/Hm3YyCh+f3337Oq7XA4mMJ/3rx5\nXBOzZ88ebrjSrFkzAJoJc9tttwHQ1tCgQYMAaLY45bVEKkJBPSQpMmK1WrmaMzY2ljcGGiugmcKu\njM+nT58OSf9OVxhVkgYMGNAhaswHk8kEIYRXaZ2SkoLFixcD0OK7gL55SWFhoY7rPxDExcWxWurO\nwedr3J16KpDqLKVkk2LJkiW4++67AQCrV6/m1nJkXuzbt493gcqVK5drui61guvQoQNrAvfddx+A\nslugU8u2a665BidOaJFp+jcxMRF//fUXACA3N1d3HD1Lp9PJjkSVk4J21ZKSEnYeelo3dFzlypXZ\nyRsXF8caSadOnbBlyxYvM+AeoWgYRFAjOASTyYQZM2YAKJ1vAGxqdezYke/JR1w65gOFteLi4tg2\n8qQWDRkyBK1btwZQ6sk+duwYjh07BkCzrejYmJgY7iaVlJTEv6GFbTab2T9ht9vZZuvUqRMvvJkz\nZwbs+afEGvJ3VK9enX0Dhw4d4roMu93O1yBhlJeXx2NPSEiIuFCguR0zZgw3iQFKbfSdO3eWeXx8\nfDx3k3r22WeZwt4bYmJikJaWBkAT9hS1IGFis9l8sp/phaXn26RJExYgcXFxbNKlpKSU2ZvTarXy\nOQoLC3W/CaVQUIUeYfv27dxMFyg1rWhe/RQIPsMwHwwYMKBDVJgPJpNJxsTE4N5779X1+KMdPzEx\nkZNUli1bxvFdwtGjR7Fo0SIAmopLO2xGRgY7ydwlh9hsNt6Zly9fjrp16wIAGjZsyL0l27Rp41a9\n9WfeqLvwm2++yTvNe++9x7vtokWLOMZOcedt27ahXr16ALQCGHKGRQo//vgjAKB9+/a8kw4cOJDV\nWU+gHo5NmjRhzeubb77x+bqVK1dmR/LOnTt5d/dkCpInftiwYZzjkZCQwFEgQklJCe/4MTExvDOX\nlJRwMlvVqlVZo6G1l5eXx+bFL7/8gp9//hmApua7U/l9AV37iiuu4H/J1JRSshZD4wU0LYZa+VHh\nVwBJWJeO+UAci0uXLuUXOj8/X5eAQiq4OlH04LKysrBu3ToAmh1LHmfXjlOugkEIgU2bNgEAbrjh\nBjYZ1JoJd6qqv4L0v//9LwDNE05Zdffffz/mzp0LAPjss8/4t/SgnU4nj4G8+5HCk08+ifbt2wPQ\n7pX6WM6cOdPt72mcvXv35hDwrFmzuAmtP8jPz+fknrp162LDhg26a7jOPSUhjRgx4iL/gwqbzcbC\nvWLFijoBccsttwDQ16aQwN63bx//9uTJk0GzNlksFn7uFA1R2bQ8weFwcJVvuJvgGuaDAQMGdIgK\n80EIIYnp2J0UNJvNrCauXr2a1WqqanzggQd4x+/atSt/vnTpUv5bCMFdnmvXrs3HUwSgc+fOnFOf\nl5fHEQ6qZAsVaNepV68ebr75ZgCaKeHq5Dpz5gwn+qSmpvrsqAsGFPlYtWoVm2gWi4WTjLZv387t\n3kmDUhOa6taty5/369cPZ8+eBeB/12mVH9KbM49ate/evbtMUzE/P5/XVlJSEmsVrqB5pm7WDocD\nt99+OwBg9OjROt4NfxyNZEotWbKEq3jd5a144rksLi7m/Buq8wmX+WBoCgYMGNAhKnwKAMrMUVAl\ncVFRkS6ODWi2HoVrvvnmG85edO1dQCEc+tdkMnFxTatWrdihdvTo0bA59mi32rVrF3bv3n3ROGkH\nS0xMdNvTwV+UFW5ToVY72mw2rFmzBoCm2ZDt26tXL4wePVp3nOr72L9/P2cKxsXFccbewoULOS2a\nQqtZWVnso6C4O3AxM7e3XZiKh55++mne3Tt06MD3QseXlJSwv0rVElx3ZvIrzZ8/n39L51q6dCkX\nKAG4iJXZFXTeGjVqsJOwSpUqujEBmlZIWtXSpUsxbdo0AMD777/PTseWLVuyhkHrZt68eZwyHUpE\nhflgMplkXFwckpOTOZfA5Xv2yq9du5a9tuQ4qlevHqt9vrSzJwgh8OmnnwLQHH+EZ599lp2DkZof\nWmCUuv3tt99yBWdGRkbA6ay+qrhCCDafevTogZUrVwLQTIJHH30UQGmuvor8/Hx+yebPn89e/5yc\nHBa4JSUlrObTi2Cz2fhZjx49Gj/88AMAzXTzZ84p2lGvXj1+7tdffz2uu+46AGDVPxQdvw4fPsxr\nr7CwUEeeA2j5JjQXaWlpGDduHADNlCLExMTwcyVn7r59+7yu2cmTJ+Pxxx8HUPosnU4nE6+QaeEF\nhvlgwIAB/xE1moLFYoHJZOKdxFObsaeffprZmCkz8dZbb+WdzR9UrFiRC5FSUlI4V6B27doRp00j\nNfH7778HoFV+UgrxQw89FHBWpT/OMG+t5xITEzlPgUKF06ZNw1VXXQUAaN68OTsrp06dioMHDwLQ\ntAPaQVUOBdIq8vPzdVR4gcy9p9wRyv4bMWKEW8ee2mSF1qArKFdi2LBhrFnabDY2R+ie69evz6nd\nTqcT7777LgAtu5PmduvWrZz27i0rVIXVamWzgsy55ORk1jqaN2/OWZ9l4NLLUyhrMdB306ZNwyuv\nvKL7zkfWmYswYcIELlvNzc3lFN5ICwQ1MkJef4fDgQkTJgAIjjPS3w5PNB53yMvLwwMPPACg1PNt\nNpuxfft2AMDPP/+s886rcNeVi4RwKODpPolty3UOKUqyd+9eToxTx07q+M8//4ypU6cCACe0Eahq\nkUyj48ePc85DYmIiP0vX7k2Uh+EPbDYbR8K+/PJLANpmSIKJNpVQwDAfDBgwoENUaAr+oKioiJ2O\ntKONGTOG6cEA995gi8XCLMBUu3/ttdeys2jnzp2swkYaSUlJrILT7mM2m3kX8DetOlD44ohz7dFY\npUoV9thTV+fyhtls5t2fHMZ2u11nPpC3v379+rooAhWpEQnL+fPnPc49rbMdO3YA0DSNV199FYDG\nNN27d28AmhOZ8hSuvPJK1iB8KXKjsTVu3Jid6ddffz3fp+rYDNX8X3JCISUlhf0OtED79u3LnvGK\nFSviww8/BKDZsjRRw4cP56QnNUxHocwxY8ZEvPEsoWfPnhg2bBiAUqr63NxcJtuIhEBQ7V4ppdsF\n2717d4wapbH4Uz3AqFGjQlrB6Y8AFEJw9KGoqIg5MYcOHcpmDm0gqq9ACMG2v5SSE98GDx7MPJ6+\nbBBkgpBpoNbInD17ln0qzZo10wncP/74AwC4Xmf69Okcifjxxx91Ido777wTgMbWRD4YMnmllGw6\n++BP8BmG+WDAgAEdoiL6IISQ/uwQFIP+6aef6Hivqq+aFEMOpe+//56TPw4dOqRzRkVSaxg7dizz\n8pHJcPbsWa6Gi0Trs4SEBN51gVL6upycHFZXp0+fznNE+RSBEtl4QqCawvz589kUvPLKK3mcaqIS\naQtqLsuCBQs4R8Vfhy6lrJMZ4Wq20rU/+eQTbkCkgu7T4XDw+lXHVlBQwJ9XqFCBx09r0+FwcDRo\n8ODBvszbpRN9APxTkcmjTBlzgwcP5geSkJCg86JT0o/FYmG+ferQU1BQUK4dm+gh5+fncyiSkmNW\nrlwZFv49T8jPz2cmpE8++US3wKnq9PTp0yyQwyWo/I2W0Bzt3LlTZ8PTC0kqfVFRkc5HQ5mZ5EMI\nBHRtT2Oml/fBBx/EiBFa65P9+/e7DY2qHKO0LhITE3WmLj0Tqlo9f/48076HEsG2oh8mhPhTCLFN\nCDFbCBEnhKgnhFgrhNgjhJh7oSeEAQMGLhEErCkIITIAPAWgiZSyUAgxD8B9AG4F8JaUco4Q4gMA\n/QGEtJCAJKbqKSaT4IcffmAHzsmTJzlPPC4ujhuuREtjFWIz7t69O+8UlChTq1atiI+TxtCrVy+O\nKOTm5rIT64knngi7KeNvpIWeu9Pp1FWSkqZAWqUQAsuWLQOg5R4Q/0Yw8GeclNL9+uuvc6SMnMqk\nwajjdj2/zWbjhkeUQFWnTh2/EqB8RbCORguACkIIC4B4AEcBdIHWVxIAPgVwR5DXMGDAQAQRbC/J\noQDGAygE8D2AoQDWSCkbXPi+FoDvpJTN3Bw7AMCAC//bOuBBXKIQQmDevHkANHotyu774IMPAGi8\nEVu3bo3omGiXOnLkCPNX5OTkMO8DVfpFIxo3bsxOxz///JO1Bsr7KCgoiDiDlSeQFka5C6+99hqu\nueYa/o40gr/++otZmlJTU3WhSoJaDUpOyTLo4XxyNAYsFIQQlQAsAPAPAOcAfAlNQxjri1BwOVf5\nh0AijIyMDFZna9WqpWtgA2gvprtei5GA1WrlcXz11VchbaQTLnir2zAAIAJVkjcC2CelPCmltAHI\nBHAdgJQL5gQA1ARw2NMJDBgwEH0IRlNoB+ATAG2gmQ8zAPwOoAOABYqjcauUcoqXc/3Pifa4uDjO\nYsvNzeWqw2jZ5ULZ08BA1CC85gMACCHGQTMf7AA2AXgUQAaAOQBSL3z2TyllmWRy/4tCwWQycQ48\nRRyiBZGqtQgFAqVZDwUuQcEZfqEQKhhCwRAKgcIQCn7h0spo9AbVkUQLgYpdgNLsMZUz0Gw267LO\nXFOh1ZRSIQSnrZaUlOjSnIMwsXTjdDqdunFSIZG3l1BN465QoYKuM7V6PkATNvR3oOOWUupSgt2d\nxx3jsrtxA/pafxqbOhfBvFTlIQwIgfZ9cB1zWedRC7kilbcSdUJBXWAqQas6ca59F9VejK4EpOrn\nrpBSchhOTY212+26HSjQHYF+r7646svkz3nU8dM43aVpB8pc5ApVoNE8V6xYkasHVXpxT9ejz9Xf\nhmthXyrajd1u92s9qenPkYJRJWnAgAEdok5T8IXe21VTcD3enVT1VrRit9sDaa7hE9TqtkB3MzJB\nevTowfTj4VQnScVV/R1SSk648WeuIqH2us4rzXkgmlm44c9Y/DGxQuXjiDqh8HeEN/vcl+OJQOTW\nW2/lJqfHjx+P6GIvKioKex/DUIEqEb1VMl7KUE1e1UcT9HlDchYDBgz8bWBoCmEEOYmIGuzMmTNY\nsWIFAK2mgHgEX3nlFWaSpqrOatWqYezYsQC0hiwdOnQAoFG3US3Cp59+yrwAkdgJY2NjOYxK1OK+\nQAjB5o+6c6teeF87WfmKWrVqASjlHsjNzY2a6lh/4O65khbUsWNHrF69GoBvfI++wtAUDBgwoMPf\nLnkpWkJTai8HYlWqU6cOE8mmpKRwV2m1SzLtnqdOnWLG4dTUVPYpuMat169fDwDo0qULAM3uD9f9\np6enc9OdN998EwCwceNGrkgsKiri8TudTiajHTp0qC70C2ia0ksvvQRAqwwNhZOXHG3Jycn497//\nDaDUQTtu3DjmgrDb7Tz3J0+ejCoNokGDBlyAdvbsWZ636tWrM+vV1Vdr+Uf79u3jjt8+4u+VvOQO\nNGEOh4O94gMHDmSm4VWrVvkUTw8HpJRc6kp06EBpKe/Zs2eRkpICQFukxPBLDMDXXXcdMw5bLBaO\nAiQlJfF92Gw2vlcqxw0nCcp9993H9zJ06FAAGk+hKrDoBTt+/DhT6gOlZgE9j5iYGG6c8t577wU9\nNtUZ27VrV9x3330ANA5GQBNC5IhLTEzk52C327mkujw3E1q//fr1w/LlywEA69at488HDBjAgv/u\nu+8GALel1KGAYT4YMGBAh0tSUyA1kdTz4uJidrhkZGTwjjB79mxuMZeWlsakJZ5am4UaFL4j86Ft\n27a8C6xatYpJS7Kysljtph1s79697NQ7efIktxr7/fffua/F0aNHdVmdQHh2O5rvXr16IS0tDUCp\nw1A1GZYtW8b9E1u0aIEhQ4YA0JqlEGs2aQx33nkn80mEQn1v06YNa1bNmjXjzMuFCxfqxgto9Gf/\n+c9/AACZmZlMZFKezkjS+NLS0rgtXH5+Pq/VKVOm4LXXXuNxhhNRIxT88QVQjcIbb7wBQOv0RC+T\nWs9w8OBBtsv79+/PjWlHjhwJoJS/L1yg+3nxxRcBaE1Xn3rqKQCek386d+6sOxYAPvvsM4wZMwaA\n+4StcIOEQoMGDdgnQC/gxo0bkZmZCUBjgSYuwjVr1nAPRiHERTH0t956K6Rj7Nu3L79YGRkZzGTl\nzrSpUKECR35eeOEF7N27F4DWiKU8UKFCBW5UlJ+fr2PcojG3atUKrVq1AgD2xYQLhvlgwIABHaJG\nU/CnAQix4FJsPzk5mXczm83GrbRmzZrF8equXbvy78lj26lTp4iYErQT3XbbbR41BIpAkDZTXFyM\nX3/9FYCmaZRnJiE5MdPS0vg5Ubu9OXPmcIv0goKCoCs0/QXN2xVXXIGqVasCAD7//HOOypCqrVbP\n1qtXj82gs2fP4vjx4xEZqye88cYbPDbSrlzRtm3bkOYilIWoEQq+wmw2c/NPosYuLCxkr3tOTg7z\nC+7evZsjFPXr1+cXi1TXcNU6eEJZD7VTp04AwP0C9+7di169egFARJvCuMM999wDQFPB6QWikN+c\nOXN4XsvDHiczpmXLlkx4+uOPP+pavxNovTz++OPc2eqFF14ImCY9VLUGd911F5+DfGCuaNiwIZtp\n4YZhPhgwYECHqNEUfHU0ms1m3HDDDQDANORms5k1BZPJhP379/M5W7RoAUBLFiLJfuuttwIAvvnm\nG95RwknWobaxU++RknsGDRrE90LjmDJlSrl1wVYRGxuLxx57DIB+bmnXslgsnCBUHqzPv/zyCwDN\nhCRNwF2uhhACw4cPB6BVmtI8Hzt2LGANR21hHwjI2ZmYmMhjcJ3DjIwMAMAtt9zCjvVwI2qEgtls\nhsPh8CoYatWqxWq42pOPFqbdbufeh+fOnePQk0pSQskfmZmZHLIKJ2icffr0QevWWouLu+66ix+4\nCrr/t956C4MGDQIAtG7dutx8Cq1atULz5s0BaHNIoT4ind20aRPb9cEIhUBUcSEE+xEsFgvWrFnj\n8bdt27blEGlcXBzXmJw6dSpg9T9QYUIJYOPGjQOgrY99+/YBKH0PCHRPdrudI2meoNaPBGPaGOaD\nAYKFAHgAAB77SURBVAMGdIgaTcEbSPKlp6dzemfDhg0BaLsEqYxbtmxBgwYNAGi7gGoe0I5GeQ4P\nP/wwq8PUjCUcIE1h0qRJvEu40sOpJgaNsWnTpgA0dZi86aNGjeI+iCaTKeyJLIcPH+Yx5eXlsabT\nt29fAFpiEuUmBINAdrTY2FgdmYq7DlYdO3YEoHVhonoHp9PJ5k92dnZQXJaBgBzHFNVxOBxsKvbs\n2ZMrO6+77jo2iQ4cOMAdsKg7uEo9qFLllZSUBOX8NDQFAwYM6OBVUxBCfALgNgAnqP2bECIVwFwA\ndQHsB3CvlPKs0LaU/0LrPF0A4CEp5UZfBuKNcJS+u+yyy9C2bVsApQ6e4uJi/rtNmzasKQwYMIDT\na9etW8e7CknqU6dORSTcR2MvLCxEUlISAE27od1h+PDh3EmY/B3vvvsu7yQAeIdesmQJfvjhBwBa\naC3cmkJ2djY7a5cvX8529OHDWuOvQ4cOlVtqcGxsrM6vRH+bTCbWBp999lkAWm4CaTxnz57FjBkz\nAATnYA7UbqexvfzyywC0Z06dqGfOnMmcG9OnT2cN9siRIxexY1esWJGLuXJyckL2HHwxH2YAeA/A\nZ8pnowD8KKWcIIQYdeH/RwLoDuDyC/+1g9aCvp0vA/FEE+6KxMRErglQHyi9QBUrVuTElPbt27NZ\noS4eMj9+/vlnrkILJyiFdf/+/TyeyZMn44svvgCgCScCCYcFCxaw8yk1NZXnx2q1cupupJJuiFBF\n9fA/+uijAICxY8eyEzRULNK+nsdqtbLKbLVaWcjGxsZylWTdunUBaOq32vJdnfNgxgr4d98Wi4Uj\nTdQVbO/evejWrRsAbdOjdO1z587xBlhUVMQmDyXvVapUie8jlMliXs0HKeXPAFzb9faC1mYe0Leb\n7wXgM6lhDbS+ktVDNVgDBgyEH4E6GtOllFTMfQxA+oW/MwBkK787dOGziwq/XVrRe1V9KDOxcuXK\n7Dwkya82hVH//7nnntOFyegaZDL89ttvnIIcDpATiHaUNWvWcGGTNzKUvLw8LtDp0aOH7h6pkCuc\neQw03/Hx8bzrJiQk8L3Qbrd7927cfvvtAKAr5AkU/ux4p06dYk3glltu4TCj1WplZxxxVtStW5fv\nad68eZymHQwCUdftdjvv7vT8Tp8+zZWR99xzD5sMffv2ZVNj9erVOHDgAIBSLYcckqFG0NEHKaUM\nhDlJSjkVwFTAN+YlMhVmzJjBKqNaLUYvjZSSF1ZmZiarZSUlJUxUQvbknj17Anqwvqq4ZJpQfcU7\n77zD5oMvx1PKs8Vi0TVWoehDuOoLKlSogIceegiAZvLUrFkTgGbyEMvSE088AQCoUaMGszE1a9aM\nqxMjBRL6c+bM4c9sNhubiGRqxsbG8ss0evTociVUcZfPQfk08+bN4zX57rvvsuCYN28eNm70yT0X\nNAKNPhwns+DCvycufH4YQC3ld0YregMGLjEEqil8A6AfgAkX/l2ofD5YCDEHmoMxRzEzyoQ3yU3f\nZ2dn4+233wYAJvRQ1WghBGcKXn755Uz5FRMTwxRiVNUXTq+51WrlNGZScZ9//nmfnVPt2rXDVVdd\nBUDzptM9njt3DkuXLg3TqDX069ePHVw7duzAlClTAGgecBo3ZeO9/fbbuO222wAAjRo1irim4A5O\np5O1SRpPUlISvvvuOwBgj320QErJGmRWVpbOKU7rhbTbSMCXkORsAJ0AVBZCHALwIjRhME8I0R/A\nAQD3Xvj5YmjhyD3QQpIPh2HMF/VodP2OvPIq4amUkr26wQoDk8nkNZRVoUIFPPfccwBKIx/du3dH\nmzZtAGiLlNTy3bt386K44w7NZ/vee+9xCLW4uJhVzn379vkVRvXHQ04+mkmTJjHB7IgRI9zarnT/\nq1ev5kSmHj164KeffgJQPhWTKuj6lPDjdDpZ/bZYLFHb1EZKyUlWFouFIz+RbKTrVShIKft4+Kqr\nm99KAE8GOygDBgyUHy6ZNGd/QLtEo0aNdLvqn3/+GZLzq12gXXdg+rx///7s7SZUrlwZWVlZADQz\ngCjk1PPQ8a55G3Tc7Nmz2XmWk5Pjs9nlC1599VUAWtSEHLee4vl0b0VFRfx3q1atWNsg9b28QNER\n8t5bLBbWeMqbn8IbqKAvLi4Os2bNivj1/9ZCIT4+nl+KEydOhJX+nEDX+/LLL/HCCy8AKE02Ubsi\nkVkDaALAXS0E/TY3NxfffPMNAC3hiu7DbDaHNCy5atUqAFqmJKnXrtfo0aMHAHBdxoABA/jFy87O\njsgc+wKqx1CzWCn7M1p6g3gC8XQC2iYQaRi1DwYMGNDhb6kp0C7Qvn17TiCaO3duyHZVNRfCEw4d\nOoTLL78cgJ4lmHarunXrokaNGgD0dHLEFZGQkMA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 0.9182... Generator Loss: 1.0495\n", + "Epoch 2/2... Discriminator Loss: 1.1554... Generator Loss: 0.6664\n", + "Epoch 2/2... Discriminator Loss: 1.0098... Generator Loss: 0.9703\n", + "Epoch 2/2... Discriminator Loss: 1.0875... Generator Loss: 0.7549\n", + "Epoch 2/2... Discriminator Loss: 0.8910... Generator Loss: 1.1223\n", + "Epoch 2/2... Discriminator Loss: 0.9773... Generator Loss: 2.6783\n", + "Epoch 2/2... Discriminator Loss: 1.0080... Generator Loss: 0.8345\n", + "Epoch 2/2... Discriminator Loss: 0.9571... Generator Loss: 1.3360\n", + "Epoch 2/2... Discriminator Loss: 0.9197... Generator Loss: 1.3654\n", + "Epoch 2/2... Discriminator Loss: 0.8306... Generator Loss: 1.2653\n" + ] + }, + { + "data": { + "image/png": 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RwQ5di8XCY0lOTsaaNWsAgHduf4Hmefz48brKTUCNQhBRTbt27bBjxw7+Xduu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\njWIaSikvqt8X8gF6pHJWqVKFOwGRXbt582YuR92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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 0.9596... Generator Loss: 1.1294\n", + "Epoch 2/2... Discriminator Loss: 0.9953... Generator Loss: 0.8804\n", + "Epoch 2/2... Discriminator Loss: 0.9510... Generator Loss: 1.1515\n", + "Epoch 2/2... Discriminator Loss: 1.3993... Generator Loss: 2.3715\n", + "Epoch 2/2... Discriminator Loss: 0.9080... Generator Loss: 1.4359\n", + "Epoch 2/2... Discriminator Loss: 0.8572... Generator Loss: 1.2590\n", + "Epoch 2/2... Discriminator Loss: 0.8892... Generator Loss: 1.4629\n", + "Epoch 2/2... Discriminator Loss: 1.5488... Generator Loss: 2.5772\n", + "Epoch 2/2... Discriminator Loss: 0.9225... Generator Loss: 1.1753\n", + "Epoch 2/2... Discriminator Loss: 1.0175... Generator Loss: 1.2177\n" + ] + }, + { + "data": { + "image/png": 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XXVUqDQGce5Ici6OPPlrNBsnWk2PEWSnZmD/++GOF9VsIBAIaqZg2bRqLFi0C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\n6tWrq9awZ88ezVh8/PHHmThxIkCloBFzw+fzKdn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BpEOSjkn6WdLWWN8q6YCkk/HvnIRt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\njuPkcKfgOE6OfwEnoesT8vs/JQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 1.1387... Generator Loss: 2.3912\n", + "Epoch 2/2... Discriminator Loss: 1.1287... Generator Loss: 0.7842\n", + "Epoch 2/2... Discriminator Loss: 1.0247... Generator Loss: 1.9585\n", + "Epoch 2/2... Discriminator Loss: 0.9418... Generator Loss: 1.0972\n", + "Epoch 2/2... Discriminator Loss: 0.8407... Generator Loss: 1.2381\n", + "Epoch 2/2... Discriminator Loss: 0.8819... Generator Loss: 1.0197\n", + "Epoch 2/2... Discriminator Loss: 1.0199... Generator Loss: 0.8055\n", + "Epoch 2/2... Discriminator Loss: 0.9692... Generator Loss: 1.6409\n", + "Epoch 2/2... Discriminator Loss: 1.2472... Generator Loss: 0.7956\n", + "Epoch 2/2... Discriminator Loss: 0.9436... Generator Loss: 0.9713\n" + ] + }, + { + "data": { + "image/png": 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kLi5O7A4lJSWSyCQWUErJdv67775bdoxqrWXLvNl3Ei4c9cHBwcGfaKsORn1w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\nDuywHWdK0Z+wJa2mpei9Xi+jRo3yey8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DDz4IWAbHcLhO6/SkYNiyZQvdu1sh3TfffLOE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\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 0.9250... Generator Loss: 1.0925\n", + "Epoch 2/2... Discriminator Loss: 0.9684... Generator Loss: 0.9028\n", + "Epoch 2/2... Discriminator Loss: 0.9477... Generator Loss: 0.9569\n", + "Epoch 2/2... Discriminator Loss: 1.2281... Generator Loss: 0.6713\n", + "Epoch 2/2... Discriminator Loss: 0.8281... Generator Loss: 1.1973\n", + "Epoch 2/2... Discriminator Loss: 1.0442... Generator Loss: 0.7927\n", + "Epoch 2/2... Discriminator Loss: 0.9155... Generator Loss: 1.0041\n", + "Epoch 2/2... Discriminator Loss: 0.8651... Generator Loss: 1.6100\n", + "Epoch 2/2... Discriminator Loss: 1.1178... Generator Loss: 0.7747\n", + "Epoch 2/2... Discriminator Loss: 1.6798... Generator Loss: 3.0678\n" + ] + }, + { + "data": { + "image/png": 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krklFgUBAq1BNmjRJw4DdocSCtZYHHnggbmOLFndbPcmGbNGihT6HlStXcu+9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\naf/88087ZsyYsHlW0fyLdA4sX77c5uX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rr5qLEsvGMH6Ys4+PTxgJoym4Vb6ytGvXTreY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\nNgVJGZuCpIxNQVLmAYKx84FNOBtDAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 0.8941... Generator Loss: 1.0698\n", + "Epoch 2/2... Discriminator Loss: 1.0571... Generator Loss: 0.8698\n", + "Epoch 2/2... Discriminator Loss: 0.8889... Generator Loss: 1.0698\n", + "Epoch 2/2... Discriminator Loss: 1.2752... Generator Loss: 2.7747\n", + "Epoch 2/2... Discriminator Loss: 1.1641... Generator Loss: 0.7411\n", + "Epoch 2/2... Discriminator Loss: 0.8820... Generator Loss: 1.0839\n", + "Epoch 2/2... Discriminator Loss: 0.8707... Generator Loss: 1.0716\n", + "Epoch 2/2... Discriminator Loss: 0.8608... Generator Loss: 1.2287\n", + "Epoch 2/2... Discriminator Loss: 0.9702... Generator Loss: 1.0217\n", + "Epoch 2/2... Discriminator Loss: 1.1194... Generator Loss: 0.8550\n" + ] + }, + { + "data": { + "image/png": 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DVGj2snXr1jA/gZhp5eXlaqZJX87HHntMKeY5OTn69z59+iRVmPl8Po0i9O3b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\nyrKDwhe4s1ahr1eKLhutjEV53/ctfJ+ZL810beBo3T+q1Bo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 1.1537... Generator Loss: 0.7651\n", + "Epoch 2/2... Discriminator Loss: 1.0387... Generator Loss: 1.4510\n", + "Epoch 2/2... Discriminator Loss: 0.8105... Generator Loss: 1.4478\n", + "Epoch 2/2... Discriminator Loss: 0.8504... Generator Loss: 1.3711\n", + "Epoch 2/2... Discriminator Loss: 0.8881... Generator Loss: 1.4892\n", + "Epoch 2/2... Discriminator Loss: 1.0879... Generator Loss: 0.7779\n", + "Epoch 2/2... Discriminator Loss: 0.8741... Generator Loss: 1.2485\n", + "Epoch 2/2... Discriminator Loss: 0.9110... Generator Loss: 1.7616\n", + "Epoch 2/2... Discriminator Loss: 0.8936... Generator Loss: 1.5694\n", + "Epoch 2/2... Discriminator Loss: 0.8861... Generator Loss: 1.0268\n" + ] + }, + { + "data": { + "image/png": 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IRCLKIkSHa9WqVYN1Qw4EAro7GGN0Z4tHlaX6IhQKqR3BnRAVyxxlZ2drnEUiXb/PP/88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\nB4CjgolA8vl8UV4eof2nnXYawG40Vew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hHHHEEYBz/UK1N2zYoAIuNTU1qmOTNH0VoZEs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\nl9SXUPJP4Ckz6wP6gcei3h5gwsx6gYnYTsVuYCbTfhZ40cx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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2... Discriminator Loss: 0.9276... Generator Loss: 0.9687\n", + "Epoch 2/2... Discriminator Loss: 0.9666... Generator Loss: 1.0320\n", + "Epoch 2/2... Discriminator Loss: 0.9749... Generator Loss: 0.8926\n", + "Epoch 2/2... Discriminator Loss: 0.7613... Generator Loss: 1.6372\n", + "Epoch 2/2... Discriminator Loss: 1.5822... Generator Loss: 0.4344\n", + "Epoch 2/2... Discriminator Loss: 1.1882... Generator Loss: 0.8438\n", + "Epoch 2/2... Discriminator Loss: 0.9924... Generator Loss: 1.1633\n" + ] + } + ], "source": [ - "batch_size = None\n", - "z_dim = None\n", - "learning_rate = None\n", - "beta1 = None\n", + "batch_size = 64\n", + "z_dim = 100\n", + "learning_rate = 0.0002\n", + "beta1 = 0.5\n", "\n", "\n", "\"\"\"\n", @@ -512,30 +1146,844 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### CelebA\n", - "Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces." + "在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代，或者当 GANs 开始产生真实人脸图像时停止它。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 0.6635... Generator Loss: 2.6208\n", + "Epoch 1/1... Discriminator Loss: 0.5171... Generator Loss: 5.9576\n", + "Epoch 1/1... Discriminator Loss: 0.9434... Generator Loss: 4.5532\n", + "Epoch 1/1... Discriminator Loss: 0.9883... Generator Loss: 1.3031\n", + "Epoch 1/1... Discriminator Loss: 0.4266... Generator Loss: 3.6278\n", + "Epoch 1/1... Discriminator Loss: 0.6535... Generator Loss: 1.7355\n", + "Epoch 1/1... Discriminator Loss: 0.6394... Generator Loss: 3.2757\n", + "Epoch 1/1... Discriminator Loss: 0.5272... Generator Loss: 2.5773\n", + "Epoch 1/1... Discriminator Loss: 0.4938... Generator Loss: 2.2639\n", + "Epoch 1/1... Discriminator Loss: 0.6107... Generator Loss: 2.0199\n" + ] + }, + { + "data": { + "image/png": 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WK80+Lggu15i1rWNhMQZtLRyQujSXx1pc6HgWLlR8RE3fvoBb3h6W97iPKisB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\nP5J3VRCxuOg5KoryIrMz+joYr8iP8T/jGEbvyU29k0iz75WZxPZ+WV/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 0.4906... Generator Loss: 4.7177\n", + "Epoch 1/1... Discriminator Loss: 6.6824... Generator Loss: 12.1958\n", + "Epoch 1/1... Discriminator Loss: 1.2869... Generator Loss: 1.1015\n", + "Epoch 1/1... Discriminator Loss: 0.8712... Generator Loss: 1.2599\n", + "Epoch 1/1... Discriminator Loss: 0.9868... Generator Loss: 2.0520\n", + "Epoch 1/1... Discriminator Loss: 1.0734... Generator Loss: 1.9953\n", + "Epoch 1/1... Discriminator Loss: 1.7425... Generator Loss: 0.3826\n", + "Epoch 1/1... Discriminator Loss: 1.1829... Generator Loss: 1.2268\n", + "Epoch 1/1... Discriminator Loss: 1.4086... Generator Loss: 0.8518\n", + "Epoch 1/1... Discriminator Loss: 1.2163... Generator Loss: 0.7233\n" + ] + }, + { + "data": { + "image/png": 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ExHuexPLd9H7gm3N5/8/LjmeKFHxTK3pj2emZJ056ikvFDdzLtV3Sk99KX1WzipmXZ/uz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\nA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Hi6xpP8dvtAAp2THcHzPXCjY0jl7tot/pUb7LYpscrs7+vu3npCJ7ERbwlWMFl/gDYV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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.2762... Generator Loss: 0.8202\n", + "Epoch 1/1... Discriminator Loss: 1.0826... Generator Loss: 1.4488\n", + "Epoch 1/1... Discriminator Loss: 1.5480... Generator Loss: 1.4554\n", + "Epoch 1/1... Discriminator Loss: 1.1184... Generator Loss: 0.7442\n", + "Epoch 1/1... Discriminator Loss: 1.3038... Generator Loss: 0.8651\n", + "Epoch 1/1... Discriminator Loss: 1.2226... Generator Loss: 1.0445\n", + "Epoch 1/1... Discriminator Loss: 1.2978... Generator Loss: 0.7549\n", + "Epoch 1/1... Discriminator Loss: 1.2956... Generator Loss: 0.8022\n", + "Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.6897\n", + "Epoch 1/1... Discriminator Loss: 1.0502... Generator Loss: 1.4712\n" + ] + }, + { + "data": { + "image/png": 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sb58xvyMSwVvPDNj7TVFX3P2UF26JhLBdidh68UpMvSdi5qg01KGIdf1iwOiy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\n18hMKcxkJjO5Rv4fYYSrPFJzp2sAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 0.9124... Generator Loss: 1.4509\n", + "Epoch 1/1... Discriminator Loss: 1.0586... Generator Loss: 0.9160\n", + "Epoch 1/1... Discriminator Loss: 1.4511... Generator Loss: 0.4657\n", + "Epoch 1/1... Discriminator Loss: 1.2195... Generator Loss: 1.5226\n", + "Epoch 1/1... Discriminator Loss: 1.1125... Generator Loss: 1.1325\n", + "Epoch 1/1... Discriminator Loss: 1.2902... Generator Loss: 0.7191\n", + "Epoch 1/1... Discriminator Loss: 1.1498... Generator Loss: 0.8416\n", + "Epoch 1/1... Discriminator Loss: 1.2848... Generator Loss: 0.7776\n", + "Epoch 1/1... Discriminator Loss: 1.9145... Generator Loss: 0.2802\n", + "Epoch 1/1... Discriminator Loss: 1.1082... Generator Loss: 1.2230\n" + ] + }, + { + "data": { + "image/png": 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m3Hy/n9EsJa5+oeWSaEqz07VLCwU7lXfdnc9oKO7DbMMHBQQNhiX3tuV3/d1r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\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.4833... Generator Loss: 2.0466\n", + "Epoch 1/1... Discriminator Loss: 0.8034... Generator Loss: 1.5415\n", + "Epoch 1/1... Discriminator Loss: 1.1546... Generator Loss: 0.6480\n", + "Epoch 1/1... Discriminator Loss: 1.1812... Generator Loss: 0.8394\n", + "Epoch 1/1... Discriminator Loss: 0.9518... Generator Loss: 1.0477\n", + "Epoch 1/1... Discriminator Loss: 1.2184... Generator Loss: 0.9924\n", + "Epoch 1/1... Discriminator Loss: 1.2908... Generator Loss: 0.8527\n", + "Epoch 1/1... Discriminator Loss: 1.2357... Generator Loss: 0.9712\n", + "Epoch 1/1... Discriminator Loss: 1.3955... Generator Loss: 0.6073\n", + "Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 1.1717\n" + ] + }, + { + "data": { + "image/png": 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VBL1/TKFVqWmlxPKB0IhdGHzpVPbK1ByMzE140dMchFqHuifmyiyM8TUrdNLI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+ueX8fckkk+r3y09L+vxxH0SzJJXfqsTj+DYzDLEFrIKrpMuoG/ksg6wj7KjKXmPmMLOkJF+vbp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\nDA0TbwGaQT3ZN1ypKTbe9Tg+FSFZeNixZskbCoMex6CValE0xjyi1X7HM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.5523... Generator Loss: 0.5931\n", + "Epoch 1/1... Discriminator Loss: 1.5956... Generator Loss: 0.4272\n", + "Epoch 1/1... Discriminator Loss: 1.2407... Generator Loss: 0.6906\n", + "Epoch 1/1... Discriminator Loss: 1.5402... Generator Loss: 0.6955\n", + "Epoch 1/1... Discriminator Loss: 1.0282... Generator Loss: 1.7090\n", + "Epoch 1/1... Discriminator Loss: 2.0579... Generator Loss: 0.2622\n", + "Epoch 1/1... Discriminator Loss: 1.1844... Generator Loss: 1.0045\n", + "Epoch 1/1... Discriminator Loss: 1.5009... Generator Loss: 0.7875\n", + "Epoch 1/1... Discriminator Loss: 1.2890... Generator Loss: 0.6643\n", + "Epoch 1/1... Discriminator Loss: 1.2547... Generator Loss: 1.2426\n" + ] + }, + { + "data": { + "image/png": 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T+faT9BG+oS7VnV1euiZqk3Fyh5O54OjeR/gns6WDeSrr3vRMFr4cyGcO5RDHL6S8kgiu\nlls15SO5LtpLTLUZ+YZJ51jGkVsy3qBymLkyD/O+x2wu62ecJZSRBiIUDtVS1qxS+0PizCnvqcqw\nVXN9IgRk2ayZ/Tnz6ACfsYUIf/V+gPusHOiDvEnsiqfhet5TJF7g1LKnF8mSgSXfKK2S9obMezJa\nEDhiH9npCE62ypKt8q7g5fkX+Mev/kDGuVw+VQFKDB5MfzI/0Ep9WMEKVvAh+FRICrUDyaaBtQ51\nJOKS/96IwhBu5oZzlkPhlIZXEM2FcrciMRzNm92nQU+e08OohEJHVYWlxsii2SMy1bKsgU5pdMqy\nlm/YxZgE9YlbawTVmYyj61D3hXtk4pDADsC7Itw/uYgIVNRMnSWtTaH+3dJmXa3EUU/G2cgqUl/D\ndQlIA/m7tayo28J6DTUW5ZaDqZbwqmVRlhr9SAPv0intLdkwRSQ2NMql1ZhycUNEZ/vUJUZ9865P\nrM85tg3qUbiyI5JZw1tjXUN8jWaJpUbXqm7AXAN9rA5GoZZ449JAW0BLxtlNR8xqkRoGvcf0rovk\nsX1yDfeOxAuksYznQ5FsH4SgYOOKqEf3J4f0moKvyUCe+Pn2K9ihBnXlGUVfOHAVzckqGdNiOafQ\n57y5iOpLq8JQQ3FUX3CqRtdlw8CuhfPWiyWGBmdlvopCeExq2Vv2oEF+oF6g8x8t8lyGEDiWwebz\nYqz9Hn2+qOY/b3aTzrpINycLuXuzn1HEsg6uG5OppBAECVks+6lnWdQNkSZqR43nW2165ecBsIoT\nfuUFwfHDV2Mi3UcmNY6GrZTJjxzyn4FPBVGw65r1rMCpMu6qvtSoUkYq5rtHKbtrMtTAahCsHQBw\ndUPDgNsenVCu6yKkqWG3I6um5WsIblbTdHWjGxoG3PO4cyGYsoqcNBOxLjXmRC1ZgOZhm+1KNnoR\nyuHomDn+XJ7b77k8KITw7Pb32CzkOXdg0XFEdO3aIkYPBiXlROZRrOV0kA1LY0F2GZnWlE1nRil1\nRzZmUpgEOg+rtLH0UARFQKcj36g80S3LCWz7Quie33/A6xoAZSYZiWMpDku8VDZs3RHbge12cdZE\nLXHLAFMDekq7xrKEiDpZht2Q61wDbLzQwTBEDeqUY3x1wwZlj4MNIeTurSWPS1mf+xr9aZT1j3Sh\n1eOci1MRzI22T9KW641ztR+9EEElBNluT8mngs/cKok1rNotbObxpXFA5lGZGfZY1ncxd6liwXOd\ngGcIoVpaDnYi65pqjkNcLzGWgu/yvZr2TOYRtiSyGMDTiUSArYznxW6X3URCsLe+4nGwJfhq9bbY\nVluS15SxN/rrmA0NyKscZsrU3MAkdNWmYgZEapCxVNsJ5ga9Uuw2xQaUqRCIrcY3cBcy/3drA9O4\nzPr4eLBSH1awghV8CD4VkkJlQOJCli54MRQZ/Rtdn+kDIYmTqqJbCJfur9/CvCEk027J/7tOTKBT\nicMSQw1qHSeiqjTT0J2Sl0Ktu6759L1GSzjX0bGB6Wn2XdQh0pgGWgbmpkgC+UOloUEBTZEewlnG\npjBPyn0IXOHSgZkyT8TybV2XORnVLsG6cAnb8sg0UcVJGjjq97Y0qKayc6q5qB2h51JoIFDR9jHj\ny6zNjDIVzpV1JaDHNSxsWzhi8b5NZF3iyEHDN+gYId7L8u2DQMVsp8CZCMcvnzmnjESKsZyUqpZ7\nUyfB0WCvhhpzy2kHsyNrY9W79C1RXeKqzXouUsxp+Aw3NRCteSb//1/OYyY/QlRwrxi0Q8Hb0cNT\n9izBR/G8zG93MSDcUwNs7VH1Za6zo4xCA6uzaso81cQzVSlMI2OhUlHZOKY3kOsmS/JU5tpaxtSa\ngzCZqFejgLly8e2DTWZqHE5nEYFKnpfa3KCseXZNxnvw7C5OX9SnlwdTurGqec9dYGrSmBuKVBGa\nLRJfvU9xQLejokDUZKmZmKYdEbRk/w018jlKS8aZqB3O2iH76u26vdfmW3diXZMKx15JCitYwQp+\nCvhUSAqWYdCxXWqzzamhhirrgsQVw0nkRAxy0YHPM/hsoTUHVJftxTa1RgoOrAzXUC5WBSxK4bbd\nyqTS0OXcF+NNmcwp1O3pJRXRSJ477OR0WyJtuFWDa9WBjKMjMbztNGSrI1yp297kjZ5w22fKW1Sa\nreKeBtgDMYK1z4XzOQNoxcI9ylZJR/XvqhVT5RLfUIWaGFM2SBGuWjh9vJbo1laVQ0MovzcbsOzL\nPWuX6Ue1QT0Tn/jR9ru0tfZA16zxTXXfDfp8efysvEPtEwOngdHS92YutkoYZuGAJ+NsREuMQO6v\n1YVWbiZYZzLPRWtOcSK4WDhzeKQRmb0F6T3B+euJuALtEFzNTvxgGPRLxg3Ykve18s8w/KxId1/v\n/uuCl22fRiFrPc1naKY61SwjjgVH8ySjUYlkYarebhQW06W8t7+IOVqIFGMmJhcakdp3vKe1Dtpq\nPE6qmmtTmfN2XPKvzWV/5vsptRob12sRFfc8B29NJMWXDjwiS9ZyLXwO76aMec/xiDI1MJ6LrWm5\nHdMzBVd14IDaX7IwYl3TpaO+jxGLXaVoiRE8fzwBdZOHwzWySKS0zk6LLx/L/N7P66fh9lPmfBz4\nVBCFyjCIXQdv6VKpUaS2MmINwmhUHksNANrfDGg3FWmxHPh30pwdDUV1tttUWgxlFsegWWanVkr+\nSO6fbsiGSccly1zuPZ8cM7QFaTvTHlkuG2HhDBm3BcFojHzRC2mqsRNzyu2hbNyqPcYsRSwt9mzC\nUqzocVfG7scOsRoS7bxNtaai+8QnUzHeV9Vm7kD6huZc7A9hLt/oNVuYsdwz6s5wh0JMihtifMM/\nIVrekffOQZ0EWE6DgS//2Oh0md2Q+XmaDbrEwNKU7LKTUup15c9wIxmb5XexMy1aoji24opEc00W\n7484sQVXD0YRmSO/E9tUz8n4/vqhENaNt2P+yY8wNVqfb9Iq5bBY9mP2myLa566sjWM3mbly+KPz\nEqOS359Mz4kjOVhm6dJtyVzXDN0rZkEzlLWh5ZCdCz6T/py5hm5bZUqigVF5R/aKubQ5u4xPabvc\n2paDXmYO7X0hEFe3JS5kdrLFCwcSN+Lc6uDdkwC4aDglk4hm6sOSI8QAOTmSLOB2OaD2hGg23ZTY\nVQ9VELAstLDFPZjuSEzCo0z3W+5wnqg647nsbosqcWW0xbkjYfFRviCzoj+D5z8PVurDClawgg/B\np0JSMMoaZxZjlQmzTKi9l/OUYs7iEemJlEjqTw3+5ESo+MGOcIGzxRD/poht1WSXzZ5WYapy0qVQ\nzEfDCef63Ewr9Jye58xS4caN1OQwFbHspNPg5oEkZvWCgF0NlS13hAuYSclyKtwjCCz6feF4WbXJ\nZKJiYDYl0px9L9Hw6b11zjWyr789w1gK93R7NWi2W1rLey+eLHjzkXCG8+89YaZxA1e3r7A7EE4z\nSg4JtoSTXj1TN2XDwCxEzGw0HxO4wlWc0iTVsNtFtOTR2yLGc0tjKOI3KBLBd2nX7Dc1oShIGLii\nurXXW/QbyrlLMS6m6YKzU8HLnffucjQSHEbunGUm8+s4Bg9yWYcfnMr8vtM0cNRgehkRAHCj2GPp\nqRTz0hdxfVkr2xYOPl9E1FoIhWTG8YU83TRdhpngu+tckESq3nTUyJvU1HPhutESopFIGGmeE9fy\nXN9p0ssEH+NEY12ShPNSIzq/OeGBxgrMb1n0C40n0BBua/uCJ5bGOY5Peef7opr62VvwO/KOb5Ey\nqeU5Q13Eg4vr/PyzLwJwsLdNcS7S1KiEXFXIi7fHFK/K+N+MRTpYJjGZGjYrw6DwRY1ZuDUD9ZdW\nC5PyJ2T9nwqigG1SDppwHGOo3/VtO+bu+6IDDkczOip2P/Db7KPlhFQbLZoejy8EUeZmjbuQjRvY\nUx4ngqiz4zFvXMjG9Baykc4ikxRBamwmmKp7nc1t1lJZjEa3hdlR/32mqdp1TG1cVh7ysNTvvExm\nLNQU7fomdSkiYbImm2AauGQLVV3uBxQ7cl0uuoR9mcs4kjHcfXSHbx1JBaXxYoydyqFIXDhVUdqd\nZYyH6on4mmzmbbrQk0CfYtTE78u3rxk9IleJU2hQ2XKoHw9ls26WAcfHWiwm9DkbaMmw44zjplxv\nFj2qNVmHhi9rcz4xeXAoB+9dOybRIiVlcQO/L7i1Og7XJzLmqx3B4fjBjO/wZ6H38j7PTITYPz55\nzIWmkqeJENvHo5hQD+lJnpCjaeTTmHksBGk58mjvyO/hSLNSGzlHKhjPCou0I3guK4OOBi817Cam\nqh2HkerqccbM1hgC2+F1X36PpiZLXfdDzUrdd21iLXXVfev7PB7K3ztFzDSQd/SHAWVP94uqyouz\nh9zRakyBn9BvCCE8mVQcfVfW8o4xxp4JwXkd2QvupMvAVxvI7QGxlgRo9W5z9LoQlsRfkJUflXHy\nYVipDytYwQo+BJ8KScGuC9byIQ/qkOxMjHOP3voT7KfVhS2qWMQ2Kw1pCSHlXKnkQRUy3ddEltSn\nDtX7sHQoK61gHOcEY6GB5yOhnMN8yK4jxhmjETNQCrzsxBwstOaj79CvZEzmQCh1dZxj18IdA7dJ\nsCmcJlx2KdMHABw+mnGmsQeBMFXK2xnriXxj2BnTeqIZnOszzGPhUHEmGUyNkU0rFk4yPwk50+jI\naycLKlPm18i6eLeEOxzoe5uhQToWw9coOGVHDViOEbBuy/cW2002E1Ex0LiQo8dzDjNR0TqjDZZa\n2LTd6JFqVJ0zjjDUh56osXM+PSHUOo9rixbvPBRO+mr6Oi/mwkEP9wyuaH7kvan8f7oD2yJd86T6\nIXe6FW4TNeW5W94VGh2Rlk4eyXNH90e880CyMt8/SjBr2Qyma7GmXhKj2WNwJhJC1dcM10lMciFz\nbZQZG6Xsp8PjYx6MBbdxJ8JTo9yGVomOLYt1jXVZSy36aoD1dkrKh7K+k1hUsRcO9pnlskdanoOr\ncQNnCSy11N+wyuiMZZxf25IEpxMzZn9D1rrRyCGW31m+QaWqZ7ps8yQSqe9Uv3c7S3gg6OFG2yCd\nyV6/+WzC91VF+RJdvnFZspyPF+f8qSAKpWUzb61DecjyLREBZ+E9hlobcRCadLqSLfby/hV2t0Tf\nb/UFkbERsK7Ze+3uGm3dxLGf4pxrjca+y+DS6hvKhr/ibPGcp3Hv5BSR6JZDb0muRS+mayVhrLkS\nnmq/vgm+jKeyTbzL0Ni1AjPRjdkrOWiJqDzYEpvE7toWVaUqz/yciWZXtpZL5qFW7U21lP2uz059\nAMC8OsJzZJNvDwIItb5iy6GtwT11Ww9BK6dM5XCXxRy3Jwes39rAUlfEIM1ZO9DS6F0hIPMs4XYm\nKpHpdznoKSFs+kQzwW3etik0d2GpatJi5sC62A6CTkGQi+i/P7XxLSFCrWBOyxeidfPzgtf02xNG\nlqpMZE+rQtkbzzDIZR1mR4f01I0cJzJnzzOpAjk0vfVH1JFcB40avyU4cGrwAtkbaSSHfFhkLPSg\nh0EH41kZj12nNCz53TJSKvV2dANRx0q/SaSuzkPbYk/dge9O2vS0xUC3I+96/+3bZK9IjdFx1OT5\nrhyvh1MbWwOIBrnJ2r4GNX1F1Ydonaa6hjtxRtXRys6nAVNHiJ6zvo41l+/taCySs3C4qhW0dsJN\n6hvy3OLNIRdXhSjsXGQYWiuUj+mEWKkPK1jBCj4EnwpJYba44F98+9e5+08O+cM3hAOlwZReICJu\nc61i60DCRLcPutzS4iq9PaHqtRsQNEVGd2IfxxED3sWFRWtfuMdW8zbPrIkh7dmWcMG+dU7PlXd5\n0ZTjsYjuD94658JQCeOtCLst3K8zOJDxTn5AFAg3tvshblM4Xqvoc72lPRKeu47fFmPeda2D2HIb\nTFUs3ygM0kKNXXVEoZwy0KrNgdHgi5fW9ME6ppYP2/auY6rretEpCFRVMizhwNbcglo4V8922VsX\nHHWCDsNYxtZcn+JqyOxuV77R7OziFCp5xet0NKJo7s9paIGbomiTaZ2J3FEvkVHS0GCw9UZKtys4\n+kUjJLWVo4/OOVQvUHym2YAv7nL+jrz3yXJCrPXPoskhtY5zPG7jaF0HSyWN5/cDWm15r1NVPHgs\n3w7yc2igmQjoAAAgAElEQVSIpNP1JpiVGpt1HsPDJ0RnMub97S7rfcH95LkvMduTtUyNDONYJJOR\njuH09BRDa2m605TvagLWqJewVG9GtRBJsRvamKWoVy/evkbhidfmuSaYpjx30zNIpiI5NtfUc5I0\n6bkSQxEbc8xMxrazsY6rxu+DxZKJL0bMs5lIrl4dcj4XQ+Tr8THTf/QAgOP2KeVbMqa7Vpv+ukhs\nhxcfr2vjp4IoGBHYrxq8f/QIcyY1B23ToXBEhBvUXQ625PrG3ufpt+UADLqqL68BC9koRW9ErsFE\nnbWKi0rFRCdiLRGRP9AowM3qFkFP4/qPungdQfbFZMA8kwM9WeaYW0IUZrVs4jyo8D0hNq7hY6j4\nWXCB35RF3HZzWoaI5g21Ngd1gzKTjXQ2MplY8g07LTBUlPbsy2KvS7x12QTXzSc4udQ+9Jo5SXjZ\nOSrF0r4W6HerZYqtB30+Smhq1aAtu4u/lA1bVG1KrV1oB6JS7Ycm1lxVieY5xVzm3LaWJNoMpjAT\nlpoHUcY/7MA0qrRk+WCbW03BrVPcJtVS9JHh0uhokNT0Mv33Ku6aHMC1H9TEqgMH7gbJUvBimn/E\nbK7ViyrZ2E2rzw2tWJQuP4PrC7EsZj089QaEyT6Zegwm54LXlCWRVot6NzZ4xpJx7Ny22ZnK3kln\nDzn2tDGM2kk2xm2GqprWicWJLfNfRBW2RpGWifw2Su8TakGeN48f8nPrsvee+0KT7c4vyzrd+w5l\noDkY1zQSNIqpFjLOoB5idGVskV9RD2U8YT8g6Mr+29sVRlYXp0weCfE7ab2KrW7iw8c5iWaHPr+e\n8bY2Gvqx6ep/ClbqwwpWsIIPwadCUpiVY35n+n9hP/a5UEOVM4u5opSx7nyRPUOoeZi36XdFTGy0\nVFJodTCEuJINc3JPA0/mNkFDOKEXP8ZU8XLDEEOP3THxNOvP612QnQpV3nZq3KlQ4HvxiJta2/B4\nT7junZOcMhAOZXrbaCc1+ssu6Le7lkvmCWceNHUMhkkjEpHS7R/T0jJZeVxQq6W6Unm3iYXr6nWy\ng9kWzuW4bZaZvNfO1zHWRX0YZJq30EiIR1qYIzhjkYiqFF5x8DRmI+rYbDjye0ML2bTNEHWy0Ix2\nqW2RaOKow1SzIBtzIL9snqNdnOyULW1o6ZLTrTTMe82hmKh/35syv6tzVZG7vd3GOZNvv3Jwhcca\nOJYZQ3xLxp8tPs+kISpduxJuXdZ9GiraO25AoyHqCI0EV8vOJ2sWS42dcLQ0vp345JHgKPQimAje\n9s3u06Y7p7YLZ3K/f6FZlmnMTlM3V7gkVWNfKzQ5PNfSeoW863rL5bXXRU36yivXKbRMfqfaxde4\nEO+XPw/3NGy8KwiP7O+RFzK/5fsmYSV1MEeDt+l1RdoqJh6NWO4vtZao63YxA+H/2bzLqydy/X4Q\nE6un4nHc5crBXwbgB+9KdfSPgr+wpGAYxr5hGL9jGMZbhmG8aRjG39bf+4Zh/JZhGO/p/3t/0W+s\nYAUr+JcPP42kUAD/ZV3XrxmG0QL+2DCM3wL+Y+C367r+u4Zh/Brwa8B//ee9yIwqvO/FPFyWmNoQ\ndNMJSLVfYW/+PlEhbq/CPcVYqI/d09JgpYOlXKDKSqKZRkJacypNlLLtDSz1yWQa7tvMoVIOy9xi\ncZlU1dvEr4Xq3his424IJ2lc9vBrQtAQw2fS8Alj4R5xw6RxIpwiD3s0m6JTWrnaNVybJBTpJ70o\nSAINUY2WJForK7BlzsvUpMpkHmbYJ6i0+k9cYidCy5fGlDV11RnKravMI1I9ez7J8Pa0n0S9xawj\n76uTEVFH7rfVRVoZHXzNqCw9m6pWo6MVYaotgq6FrU14E22MY4UOuSXzM/KKoim2liCNSVWEMiZz\nRto0Nepo9+XSw97TMdsXFEOtNpR2iZcqNc0esxUsdXlkzf1szqItc2pkObaKiEXtEGnl42Bhkvua\nHanVs/OwhbOn9qOoYKq1LJJ+n3IhhkbjpGSuVZiObFkne9DA127dZW+PoCGh5ydzD/V8c6qZld5i\nwdpU5j+cbvNKJe3vqvU2llbBNo8s6oFKHm0NmzcOKFQyiY0InO8KulufhVgTm4wxuRqHW46sR70I\n8AKRrK0Lk22tiuWepvS03frLPYftn5fx/fPf52PBX5go1HV9DBzr9dwwjLeRFvR/Bfi63vYPgN/l\nI4hCVJR8/2KKk9jE2gP8pEi5NtR04CsNRpFs3vnpkoe2hELvaIppWriEtVqT4ymcyb3nxZDwXBC4\nNA021GBYmhor4LqU2vyDqiCoRajZNGf012UTmkc1Y21ke7wu0TanFzMe9CWte9sPmGtz2MayT6Vl\nzIaTMU0t+9bsia/ZokN0qhbraoGteRDLPOZkKpvQvFAvQjigp+O1kzHrrm6kHCw9sFadUzfkfctU\nxFN3ueTsWA8gMwpNMy52U0otINJ2Eig1z8GU32Ijw9GQ4iqKMEytcl3E+C0N8y66OOrHT7RjthtN\nmWmmaa/lk2jWpRtYlFo3rOGFNDVduHEuhMld79LVzM+TeAfXENy+/da3CRfCAB6c1ew25bCkGhfR\n6kJ8We/Qz5hqdy6nnlPNNNitSEFjCPpdjTtYjKg1vP3eNMPf0MYwd4/pbqjBMI9pxbofTjTYKI1o\nDjTfI14QqVpZ2TkjTfL0NcjusVEQar/Oh4sz3n9B63UOAzQFhWbvGONUS+gdy9ino5j4/AEA528V\ntLS1/fKtNznVM9C66uFMtdxcQ6uAZwlKw2ga2+xuyD9e8R2+GH5Vxvb83+TWrwjR5u/8PT4O/Exs\nCoZhHAAvA38EbCrBADgBjRj6s888bUVv/WSFYVawghV8gvBTEwXDMJrA/w38F3VdzwzjaUtc6rqu\njcuqFX8KPtiK3ret2k1czqsMV3PmunWN1dcEniiltiWe83TmM0nEiLTUiruN9V3uqQtqeq9kESs3\nNmLqRCi726mxlAv76qMeTg3slrabK0oKDZl1sib1rlD8IMg5NP8EgIciHJD4GWjLeNfZxNHKwFE1\no5pqX0Vvgamk8UJrDLTbM8qFcMrppOBkKSpDkaXM1b9dq5rgd1v0VJIwtzrMtBBnbXl4U434CyIa\nWsAl1ai1cTrjKBLj3JsXFs99RdywadbEVLHzIu1S1SIGd9SV2/QLPDWyRf4Me6Gif5FSaIm4uZlj\nXgpWmrg2HQ+YtQTf/jBmoCrfLPep8lTHBHND1iwzxFjm2CmjXa11Mb3LQ+1PUVdN3jsXVhnU32au\nzXVGqooZRYhXyt9nU5fjpYj+ZurT7er4K49Ysx2HC5Xx/RGxFputnZSzQ/n72s0FsfZywKwYlTLB\nQy2xlxchrqpa0a7P9J6swzyynhZsvdzgARWBVldNF8c0/0Dbw/3KQ8yxcPzZa0+w17SlvCf3Phka\nTP5QVL4/GU/pRtpjtH+FmSdS09rjhNY17Ro+lueNRcI8lrHP/SGBlpgbOA2imxoB/Fcfsbbxi/wk\n8FMRBcMwHIQg/G91Xf9j/fnUMIztuq6PDcPYBs4++kU1OAX7lslI/dUTw+HnlqojbnkszuT6UfUm\nG9ogxF6TDXY4vWCmIlx58QS0SrJrVlRdWeSBAa6Kz7MTLZphzwnP1JLrLMi0fuBFfMH+c1qd6cxh\n91X53jKRU744hAtbevz1nIxUxbmNRUiqwUnDsyVJLSL9nmbkWeEuvqY19xoP6amvvDBiCv39oieH\np1M2cXWDjh7HDPVQ9RIX2pqJeegT7Uo2nK3x9OnojKWcd6pgzvANIXTJjsHmsRKkVpdd7XCUaFFy\np5wzbWqdwHFKoVl9RAamI+92l2Pm2jxnqYTpSfaYnUw9Ec0m+VjjAtop9oXMP6umlKIa80ADxK5d\nHZC8I/Pfu+/zrang9o2jQ/ZVrTi9PyC15Ns327KN6nCdWOtVzjITL9YOWHaGqwFARmhTRTLXfKFF\nUcYe0+IStwWRpqov7g05U8+ON81ZqMrnj7SQTT7HtuSAbRl7/Fwhc/3d4BghR6CdRkkxsLTy1ou5\nR31L7n38ako/kHwNL8oJ1T5yGfrdsGsa12VOjbN1sr7Ep8zXa57RvTwuYtKxitThmb6rZhYLgawr\nh5EGn4UvNeieaFzL9Gs8evdj1nZX+Gm8DwbwPwFv13X9333gT/8P8Lf0+m8B//Qv+o0VrGAF//Lh\np5EUvgb8TeAHhmH8if723wB/F/g/DcP4T4GHwF//qBeZmLQMn7FbU2jRkytmxdlAS1RlNZpkhluZ\nmA2h3JbWHogCi2ktlHazu4+fagORps1A69I5psX5VETGx6WIu2GR01ALeauTMFPu0Q1CkpNLY86U\nN2NJcnnnVL7Rt2xiU8TaC3ODgY5j2rDgfS2QEefYWucxr4Xal6VDrZ2k63mPYKCW+FGP+ZZQ862J\nljBzSu5OtI28mRBrM5jc9Bho2bFhkLBbaUs7W0TRWWZwpFWU3y9ygoZw3XTWYXQuc3qQHWNuSPTi\nrha1mbKL4wp+srCBO9b6ikGT5VLEVtO18R2J8ZgF8t0N5wmmLxw6KCoWtfAZezYn0+rZcVI+LSxC\nR8Zgz6+wvS3fe/P7J0zU61TNcx7o+p37UwKtIXDefU5xbzFbyLfN4pi6UoNnMadU74OVGNTaR6Oq\ntR5D02ZdozHz4TFnmvz1wM7Z1BDkkppKK5Lk2nBlahgcai3J4nmD9D3t51hnXDajvywS0wKeUwNZ\nfG2L8p4m2A1KMg3X3p9tEpuyH8xa1IsiXNI40PkFDmdaPXqrCZnGUEzHFYlm5nqG7MN4bjxt+jIt\nfVyNWUnnX2P5oozjPDnnov+TtY37abwPf8APG+L8afg3fqKXmVAGsBbBUIuXnJU2+49l09zfDfDH\nsjKTRotdLYASv6TLssgJtKZi4Lvc3BOFcB6n2FrFp4gtSrVBBCr6ut4YUxvFJgmUXZnOo+MpDLSM\n+Hce8f13BfEz3VSNsmQyF3F3bd7GvSlBKn7mcbEhC23ePeRIRe0XctWn64BzVZ5nSUF/rkE4ZcDo\nvuiUdzRk9sAPqLSSTn6+JN+WQ+XaDdI1EbvDOGaiLeptDfoaRyW16pmdIOK73/wNAG6vfw7DPpB3\n32jjIEQt25G5BXaFoxmjdlQzy7VK1WJEoGnPRu0Qahl8P9ew7GaPutRCL65BnsnG9Ns1tdaUb6YN\nOhsy5kqJ5l57wc2J4Ps7kxB7KWLw+Mn4ad/M2mjgDWT9MvWW2E0IfBlbqwo5Hwq+jkYRTiR605rV\nwba0MIz2XWSWUqrX5q0JeKmoFd7SZ6n9Rm82Gzx8rMRQvbDzLKFW9/Rfblr8wVALsvQXLFTO3ta8\nFQubVDM1X4o6nO/KOqydLlhoKLxXpaQaZr88lfE8zn32tD6oGzTwtKvZee2xqWN23RhTVWhTmwQZ\npctUvTKROWeqhXjK+j4vWF8CoL91Ddf/iAaefwpWYc4rWMEKPgSfijBnwzAJnJCFkxCgzS+8krGv\nYb7DGcZVtbi2S0pNGgpMobiH4SHpUqjk1iLhzNa6hOYJp8OBfuWMxNBuvrWGDM+3sfa1L6Hn49XC\ndfdHmyzvyvU5EWlP1I1EM0ouWiaf1bb2Xu2Tai/FKjp+yqW9LZNGJd7YI+1gPBs94UkpHGp8lrN7\nqEbJfkl6LJzy7VR+ey8xaR5oYRls3KVIDcbVirY2B6zdkroQNSAuhaMkRQ0HWup8CIfK8rqjnKYj\n1YXL+Cqd5x4A0KhFbA0GS6xIa//ZIx6riJpVF3S0oYrr2NgqHq9p7EJSNYltEanHqU1Dk9Wqqk3t\naEh036LrirT08rn6+edjThIxRF7sPaD4gXDBpF1hqIrVHJ1jWBq01lRXjnGDPRHMWAxtUlvjApYz\nAo25KBopa6nElpRaA6PlzXhX8TYzFox0D4Qdj/11ke5qy6G3o8FlI41/qCouDE1iOz3iyTPqaTpK\n6GjfyVJxf92ueF4d8MPuGc1IXRjPLelq45eT+xVdTVZ64kpsxqTZo3lffpsFHhueSEJx8wrnJ2JU\nPIwN1gfaF1Sl6SqMCR0ZQ2cWE0/Uq9bdYLGUee/sjllsi7r9ceFTQRRs22RtLeBKbjHW7k6TKmNT\nm32P6pLTc5mw9/aUB196DYDr1fMAPHv932VdrbCNyiDUgqmRv8vmkdqI521ydZclWvgUu8Kba3BT\nEREt5EDeyx6x1LTn5bHNZCRoCi8DcBIYjkXcv7rfZnkoG3O9sUFXrcj+ewbLWtyoa6b0WMA1eUbT\njJdrZ/R8cSfeMCJOdMP2NLBl4IcsNUCotbXHoiXfuOG0MZRYLv0OlmZzaoAhgZewcy4EcmxE/NGZ\nbOi7ye+ztSHfe/TqKV9IvyAoeEUOhJ928EPBhRfF3Nbs0cjYxtQgpL5t4oQaydiTCD2rzKk1HyAv\nXFjXuoxGSTIVApjHcyLdpHcvBG/B9oy9b2hXr4sKN1IvSJ1QTzTnBYfzc80MVMO72xvjqJuyE5xw\nTWsmhtv9p/0NvGpCX7YRzmUk6FqH53ONlu33ONKqVldvb9DZ1H2xMPn+64LPqQY6LeY5P9DKRQ8v\nUhpaw7GZQrsp73gl1uI17Q6Wfu8Fw2AS6j5cuFj3tD7oesBl79pbz34FAGP0gMZnxDVcFzHzNc3B\nGaXke6LetpIFsSvzzpU7FVnJ46G65/Meh7Fc7z075dkvq2u0ex0r+3j9Hi5hpT6sYAUr+BB8KiQF\nqzZoFT5RJ2Q5FooaWDYP1Oc9cw2SkXCdLDPo/KGQ2vtdEXefb+zDuoYun/ssN4Wrzs49HPMyJHgP\nY1vrURXCHS1zSKFdkxbREbNS3mfZLWbfke+d5QmRVpKutC5h6NtUrnocqprtQOswLH0MU8bfafnY\nM+GgtYrieRnQsrWYSrdFrKXNhm5FlWi8v+Yw5KFHqeG1RjDAb8jvS9PCNoQDFXOoTBnzYioSRmok\nTLV6NPMKL9KgpqoiTkQUfbZq8Z5WMB4kMl7X7hCopdt1GjStH/IL09SuUGkLtPxXoWXn7GJGZYv3\nwfRT6vKyF2ZKnmgV68XbnB6qaK+hyPf+1wXfKAQvZ0ZOogYzr4oxtYzbxCjwtc7EQjMn+4XJItEK\nxrVLQ7uDbzYKxhrs5bubFE+0TmdDJcFhRar9SGvfIdzRish2iDp2OHv9Dm8NRbIaqieqriHW3JbE\nzGlqt+qsMnjGF9xP2pqt65Xc3hRc3Y8bOIgaMB9FnKs68sxhxPgzgrvraDxzOyE5kjVNezHVExlb\nas+oCy1t38jJU9nLQ+2MZmRjZpHWHS0SOiq5bGUNmqbk5pjxGKPzk/H+laSwghWs4ENg1PVP5q74\nJGDQb9X/5i99HmNi4S40WywoQQ04PbfkZCxUNzALzpTD5rHok1legXK52oJCYw96nkGofmrbLsnU\ngxoXqocS4DQ086y/Q6k9Gm996QW+el16F+6+sos3F85VaH0Ec32HlqdRd+/X/PFbvwnAa3/8Btup\n2DuOhiUbPdEBl9qzwG9WzLWbccNIGU7Vr24VT5OVHDU0zuKYmZb5KqscR5uzbPR6DK5KtJrf2eL2\n1w8AePnZvwrA5l4Hx1C/e1Xw3/+3f0ee6w45uSP6+aB3woOh4CIPJXb7JEq4d0ddp8OCV+8Jh3Zq\ng1j3iGNbmCqFXL8qNoVn3AGDTCQlvz0j0a7bswfH/MHoAQDHxyPqS/bzwc4vPwKSpMBSA2WaQzqX\ntfr2GyLRnDz4h/y/74ldYvh7r/HGIxn/5HCOoX0Xi9LAVCOgqdGvvZ09Niwp+Nt3MnbVbe03A662\nZcz99hY7t4TrF662YHuuyV7vswC0d8OnpdJKYr76ZbEDjI/VRVjUTDWicWc95MqeSKGbZoCnsSVH\n8ZJUQ+HNy/B4y2ajp31Rd9bxNYpzIwhIbfndb1g81PvnD8RO9npywQPt71DaoH1jJFBAj7VleJSG\nxilU+R/Xdf2FP38FPiXqg0GNU2dMjA12e2Jt3nEWvO9q04xoSa2++dloQUerOJ+cy8I6YUSih60T\nOiy0ycag28bUDe07DY40J6KO5LeHWcWexgI8mY0YaJGO+E+mvPKSGKVGyyts9jQwaK4eDsukQsTE\ndmvGbiSL9aoDb74pYuC4bbI4FSKyeSBEap5uYGktyeGFR6ht6Z9cJHRsbVqjfv6W5/BIG94GhUGk\nomjQ22CmQVhNZ0b9+7ITbt0S9aFZNOlq2rCNhXVdy5I96pK5YsH/1psp/vPybeOReGc69R2m7woO\nsyjlsn+IWf9QnAwrn69UWjF5XULMX84tplvaaNW6Sabeh+m4ZlMNsFFtMFOi/ZFhNLaBqcTbtSum\nGkMQtYVgfeu7MSdDifU4/ME9sljwWZZgaMCSWddYOs6GpoP71ha7qko2r13FMbTgjBlxmMvv7S2L\nhxM9sB1Ny7/XZ/0XdB8WLZqhdrVKPMwLDfnWwjNN0+S6Kdd+c4Nf2RaD9hPPwNBy8EbjCQ+yS49X\nqXgt2FuTe6/s7ZFqubZOaVG3ZWymY7OWiYHx0RNN1T8bM9Fvj9P6h7j9AJ+v6xQNF+HjtoRZqQ8r\nWMEKPgSfCkkhzjPeOHtM5+iCb1dCGZ+71qY3E1Fu0TdJNE89MFIsdVu2WsIF24nHmWay7bhNso5M\na9fb5LKu6WbVwFRpYvhAMyqrGelIo8uSjOREDD/V6ZR//k255+e+0GTthhhtHFfG9lryiN4dES9/\n884bbEQPZB5/+B3OtV9EfS9iqmGn4SNRI4K9Ib5G8S1bKTPta5EvpyRaCNbQLtkZFV6lqlRekFw2\nFjl+wPJCONegeI+T9+T+e5+Te//a+X/Clz6npdksg+hVrbNgvcVr78o4TyYRHe0vkag00qpjTrUP\n5jmgDb9J+SHnqOqMQMvXvVKIVNV5aY22usWcIqHdlgdfizs4tnK0qv5oCUEhrWsqzcUapSWJKXO9\n93tS0Pf88X2WkagPy2jBvPwhW6xUKjQwoJS52LaI8M1RSeNr0vp5K29hbco6lYvl0/4bj548IddW\nf9biTQBOh79EfVsLlmy1CEyRFguz4l6ihs3LitEGeFoF/MUZlA359tUY0nWRUouJzzPaItBfyl44\n7azxuU1pGxf2e7Qt7RW647AbX/ZFjai0fd9FJGpgHJYsqj9fAvvJApwFPhVEwSwM/DOXTr7NIpRN\nHJs5yb6G0uYelaEpq5XHplrou6EE7oT9iDVtthFi09HajUbTxteuQUE1Y5KJyDtU78TOtEc112y6\nMmektoqiPOUb3xZRzev/Eb1SA4dua3DPb4c0juUwrh8/JDK+B8BF44i5ZgPO6pwN7Uc4cJQoLK2n\nh9sxZ+S64Q2nSaLVfZrqB/cXS1quzCNl9LSSThpHZGq1vv+DJlb4bQDu/X1JZb75hZc46H9R8LPn\nchrKnJJ7BneONSgmzjjRbE63JXOr3p+yrmnv86J+2kvIAkIV59u+zVc0Lj+6quK1s8TRUPLYdJiq\nV6Lx3Dn7Gqh0zxySPe329efDcFoy0Hsn4ynLsaTVpOo5OXUzpve0m5JrMY6Kp89W+vbQtKi0LPvn\netof9EbAYEvmsU5OFsq8HzkeHXVKpcMZk0Rw9PCOqH5r67/Pe4HEE6x/fYOGvrfwoVGoumJcqig1\nVwLtMfpsiKnXUdCgmsjYdpxzDG3Qk26KOtf3dwg2ZDyd5gBD16RX5bga9+HlOeOxEItnvig2hfm3\nN7lnCoF8XP1Fjv+PhpX6sIIVrOBD8KmQFIzawKlNFuRY2mtw1DJYG6vKsG5ieVp1ucrobErY5i1f\nrfutDi2VJAh9GsoFy2aTRqWiFiF7S+Hc46Vw4Fl0l2Ol8iQpWSrcuqpzlk8eAPDWG11e0KYmwZpw\n4Mmj90iHMobj104Y9kS6WRymT1vUl6FFpFmHibZy72QOhrYIt8oayxJLfitfElkaKajRc7kf0G1p\nKbJ5j1kpnDJeuBQLea/hpAQap2BF8vxjHvLekeTrb9ptHrwmefzD8X1GE/XWFAU3bOFAfa33uHtl\nk7cSkUC2shhbozdLTEKtu/hLa5tEm4LzaxrmvRwFZFqJuJ+6pL72n7BusnVL1Iqvjecs1av028rQ\nfpzEcPhoRqFr8s6bY2LzjwC4eyjJTvbjY5o92QvZsEO7Elx0SofEEsmk3Q1Y034W21dEZbj52V22\nWxr6bFe01OwWFh3SuVQ5Hp8ZxBrXsbS1vsHoPta5eB9GSckyl+c80yZUdbJdXBZpMUm3JAJxP1ln\nbyDX03mNv6Zq5fazrOtaLbXZkdWuWOuIFNsMHVINY/b9BYEaT8OWg6sVtLuheH7mZcq/c1fW7Huz\nJd/6GQkLnwqiUJsGhe+QBQuKWA63nXfJDkSMSrwmXqEhs/M1NFYGsyX6Xb9lYZoikpnmlPqyTLyf\ncVlwJTQKFpm6mTTN+Am3cCxRE1IzJTrVwp6FjdvWpiVFytiRcZgjDX0m5qIrjdRbzz3hgRbdnHcN\nSrVk13WJWwgxqHrq/hps4Wus+mzk47gaTFOZ2Br7WmoR1LZlYGrmXKtTcmeqodnunFIVfjPK8NRl\nlQQyhrPlGYkG6aRJiNcQMd8y1vDO5Ru+VcANzdEoheAlfsVf0jTsd8eP8dX1tp6P6L4kuDU6XZoD\nUdnyPSWmiwnZuYznzE5paEMSZ71Bpk16y5e+hP39PwTg39ImqP+MHw2TbEE6FyKzsT3mtQs5FAu1\n1BvrfezLxilXnqGYyvo90zjAVo/BwQu/SK7Nfdc6Mj+7ZdCxNYvQSXC1ruStbMqpK+HyVmNOVcsh\nKx7LfhvYVyl21a2dJVh6YgzTxAkFR722vDfPa3otbVj81TXSrny7354QPxJ8W+szSk2lD7XJjuM4\nVK3LuqEDPLUrFdMO6Z4ygHwNX1se4AsBsXYfYP3ivwfA1d/+39lRF/c/+jG4/biwUh9WsIIVfAg+\nFZICVY2ZlBTxklkqFH4UR/QtoXydKy4dzXBcrlns5EKNGxpj0IlNlk3NaluGTwtkuLFJacn7XMPD\nQ7KfTJYAACAASURBVIyKO45S6lswHEkg0Dz+HjNT6zCQYGuSj708hXti2Dm1hFsFzMmHwlHee/SQ\nx09ETCZbPg1CCk2HuBaR8Zq2L3fXTNoakGUHBrH2BJySYOdqzFSD4iBoMFGjnm32MTrCuc5PW8wm\nErAT0eFc/fS9TMu8LR7jaCXi+8kRM+Wq1ZMjzOKycjPsnqiIKjlSfD3pEd8Wlehz1TMkarhdjzze\n35F5XM8L7plqVJuJKDubTjAWYl2dJyaBBgLN/n/23jNWt+w8D3vW7u3r3+nnnnNum84ZFhVSokhV\nJ5YsKQkkI0gAJ4ZS/tgw4MAljhEEQQI4yY9ESP0RI5ADQ1JcIUuRJUeiFNCySA41JIdT79xy7j39\nfL3svvfKj/f5Locq5KVGUq6As4DBnHvK/tZee+31tud9niDFXs0QxS2wfv0GAOCX3hWMgZ/kj+vm\nTN4DANwqgZ/L2r7+8F2cHsq1109IW69cNE3xBHVvC997TcKD3jMvoE9sQr7ZhlERyUMQU6181GxW\nCpISoNCO7XcQ7YsndDIZYsyQtcmmubPFEW5Q57KNCjZJTR7GCTrsxjVTWcvSrLE05Xl4Rznam+QE\nvTSALmHcyw2Y9CwCwrLjoEBzQTmAZgar5JxDDW8mn1FWl7Ag+0FRYfx5+zqiltACjm+8gL/3umhF\nrkBRwJ/i6oM2amRuCmU6KDRJLkoTpcl4ud8G+EJGAMwewTnsDCw9HxZf+KLpwSKYPa0dWOTvz7QD\nA3QfCbbZrXvwIAt53Oohj8h3mBhokFq8v34dCwJycoYitZshZlw7tEyUPLwKAFbA0AUOooh9AKG8\nKG5dIlXycwMzYKWsFPgwAnn4Hg+02nDh+jLfes3CTchL1XYe4B0lB8DYHMBiW3YUyWZOtlqII3n5\n3YYNcD399QhpTE1EZWFIqfXrWwT52G001nhQqAhtzse+1DggA1Y+Abo2F51kK+sOMKcQrlYGSpYs\nI3uGUYt08KaJKfUJ/kwoB+jsCyN8tfi9mYVYp6j4uyMzwWJK8VcCj/xtB/Yl+zZ8A1tdia+71x04\nSl7krpogzcl2xW7JqXbgs0vS8iIEgazbxAqwV64AbilmtqzzkSFtzdadAB71I+cesICsZ+A6SHiN\nDjtHC0vDXso8G1aAiqFdGVbICMJCS6OnZP6FuRLpDQGClELdQO7L2prpELkre8CrHCREepql7Onp\n7josm30X1SZ+HNKVe/LlMYas4KQADAKqyvrJ0MtX4cPVuBpX4+vGU+Ep1LVGtsxQxgss2Z1mOhYM\nYvhDBHCb5P5za0SmJFp8R04+o9YIqLZk1C5sJm3mhQlnRUhR2UBLLFBNxl7oCTxaF686h03X3jAz\nKAJTouUY2aEkz9KG/L03vkA9ogjLYoyYwKpmEMJkTT/o9rG1J9aqSz7E3TWNObPXUd3FNLcez/mU\nFZM14vdNZSBlgnNR21BUTM4yF0YmFt9Ic4DiM6OH4nJ/8toQzYn8brmzDZ9WcH6ePE5y5qrCM025\n732qLVnNdaAjVqmnNmE0BPcwr7rwmLTztkwsqVpV1HKtrG7DdGQ+7dyEomeizRYKWijfnsNw5f6O\nzwXGvrGRYfNUPLBfqTRWDq87GsGqxJsw4glsJpiDtnhb7VkI/wbXtbWGnVCSeTcaHibsefGMNcQJ\nMQvcIw8rAz12xKa1iY4n83FtE61c9sv5EMjHdP+H4v3YvSEGD6Ty07z9PFICiOpIoyLb+IQYisLz\nMCNP5ERNsDekAlY5R01CGb94Dpr6kIocluFahYBdoK4qURD0FVgaFZtFDK1RFzInn2FpI2miqchy\n/SJg2t8DAPiL8VfxHZz/307nSD1WcwZTPMm48hSuxtW4Gl83ngpPQUMjUTkM24LD0Ms2HYRUeW61\nNsFwF8uFgWCLdGrUaFR2CZdNQKWh4TMR5YcLIKYSsbNEzXg3YBdaVtqIqXHYPT5EdCkIs2xiQ/fJ\nADXSCHbFCp+NJEZ0TI0hCTVRpVDWStuxwn5LLPDORoDWmkBXe4KSRqP0ESpqW6YZioLJ0VkIp8GG\np1Isd2Xa8OZiadsBsKCKsN+30FrxTJQx8pEsmM0S47vTKb69LdcolMIyZPluo4mADVpNG3Busac/\nkKTt5qaJKdfTacwQL9jY01wAnqyRziyY5QpiLvPNRoBmvf7UjXGN35/7DlrMwTyKazQNsWjdlngS\n4dDBL3hkiU70SuUNmZ9gSOGbLW3gjDHzspS/azobCDx5/s0ygrVLHokqRIOK3RoOTD5rh/mlvV4K\nsERqGAsoaiRsROqxwI3bdZGzyc7VZH8atTHdlOf7228d4Xt2BHuQmwoZuSU6EfUcVetxV257fx++\nQdo8v4FswbxT4xShIwldg7mfyGqgbq5EiZpoFCsItoMslPWslAePjOULepu6lSKDzCcw3oPfloRp\n+OkXMaDw0U/mD3HWk8bIt3/mZ/Ek46k4FIxaIcws2KjgGbJZ066H25a43Vv9AC2Sd2zdqOCBtWAm\nfcLMRuHLrvLSJZTFVl97AwhkIVXqYVFJBttkok7752jO2QOw3cUzX5YN+AVzhH1upts7LnZJ526y\nQ848O4Ii63KYOXjIvuAX/QDtTTmEnt96Ac3bktrf4kZxdYmCiUFtedg0VkCnClByTxZp10LDwcim\nMInahGrJC91RFaJ9upEPryHlPXW4yW+YNp7x5GW8oxS22aPgOkvAkZf+og8clFJ1WTCkUqM+Kn52\nlTUeZ9md2gU8JrisCVyHHYyQje33zuEYsm7BWQk3EOo5yxtAXcr9XdYTHFMBKjuS7zWfTfEXpvIC\n/pMqxVf5Yka5xXQwcFQlCDxu9ExeDtMrsaFZp1/zsZFIWBF4JgxiD2rLQ2DScLBCUCY95D6FgvMC\nNkmeddmEHcpLtns2g+7JehRjwS48apxhgyQzfcvHGj/j7rRGwL6EkIKOQaNGusWW7YHCbIfAuYcJ\n4kgOxXJ8gCqUvdOpKDwctBEy6eoFxWPMTbGbIkrlgNfuABXJgYK2PNPiPEVCDkpvfIDRuuyXD338\nJrYpgFzhJn6bTOH/4xMeCh84fFBKmUqp15RSv8h/X1dKfU4p9Z5S6ueVYlB/Na7G1fhTMf4oPIW/\nAuAtrKCDwH8D4L/XWv+cUup/A/BTAP7Xb3gFQ0N7NZpBG0vyFLR9B+U6EzVZBjTlBJ+kQCuUE7hc\nUuijymAzsWQFAeg0oFQRAsJgF36O4i05mc9uSRLNTq/BtykF51zD8PrrAID+sQ8jkM9OXQcXfUkS\nzahF6KUF5qwlnw9KKMq5T8wSz/vkG+g1UGSkLmMX3jwpsKSqcUNnqJtyXgYGkBOu3KKXE1cG6jOx\nysPNczjEULheCdOT5FqxcQ+3ibkYMGm5WHcxIuS7GzZRbcs8I2cHbUvuI4gcLJv0PHLyH7QaKFlC\nm1c19Mqj8QoYcxLRtHsw2Zxfu2Ktmobx2PupTBuzSBKDZdlCQuTpfJpg5ksCz2C49uw7Dcw2uBZZ\nCsVux9SFiIAASIoGWkykDQyWVtFFsko6pinSjqxhNk8wof5C3xmj0ZCEJtHcSCsNO5VE26IoEJOu\nLe1kaFKTwTZNWCTWne3IPG9dWjgK5CInPRsnTPatrUXgcmFrW27Ubm+gog3M7RkWbILK/RJpJvNR\nez1ESlz+uiHeXTlTMEn6YlUadVOepV8EyMjlYRx5WHaJaGQYpHe7UJfiEY16czQM6mE0vwf2dfE2\nqrRA1OciPOH4oFqSuwB+BMB/DeCvUkru+wH8O/yVnwHwX+CbHAoKCq6yUKcaPmu08ExMRhIvvr1I\n0KPAbFXVsB3mDNp0F5WNrb64VnXpw6YKka7HWGSy8Q4nD/Hm2w8AAKPX5FqjzMUza7J4g2mMnOQV\ni6LGLJMNNkgKJO9RDKYnG3R+cR/VPbLl5ksYWr424WPEl7p5MUKX2feTM3khen0L46m8mG63gkHX\n0AgymHwUubVik9IYV1RHemeIMXsRPOVjxnzGQivogGpJfNk6hY17EzmM3M0YB03ZgFaiEFwnzsIp\n4QdysMSaPQyXJTJHXrw8c+E1Zd1OL4dYZ/u5nvWxuc4Mvys/Nw0PMQ+Krz68j/JNgR2fmAo5Oxjn\noyneZFt6ygPE2PHxk8RvnDQq3GHG3Z7mMAln6mwGaBL2PZ0RSrw0cTrgod4K8NYdmc+tNYUTHoZB\nUEOVq0OWfQaFBU0BmHuTKZJjORQn8zF2DckDpTp6LIjjOXItdV0jWcra7k1zHCfyHLqLDDbb/Adz\nOSg2/AXiSAzHYJFhQI5KdzpBwvChpz1ELzLXMJf16beBPBdIfNQMRFsV0tuyGMs856enOD4R0NqS\n+piB9Sxc5hneS87ga1mj3YNjmI193oeDG+a39pp/0PDhfwDw1/E14FQPwERrvaowHQH4fUnnlVL/\nkVLqVaXUq0X1h8FdXY2rcTX+OMYf2lNQSv05ABda6y8qpb73W/3790vRh4Gt87DGtu1DEwVotqPH\nnZGLKkNUyOk6Mky4ZNHtMsE3L6bILuV3t9c1jAWtRKvEiO76/TeP8Llz6bQbE1dQ1wGWZGGxOwp+\ng0misQtjg2Qac41LWzyWR9Qw3FpWmBNPMU5mjxFjcRJjSrXj2HWQnMjvRCsqucRAYkti6OjSR6tN\nQe6qg36bOgup/O7F5AJ3Sbs2KAvM5LJwHAt9up1rVY2SmgOLQFzj06/O8ex3PpDfjTswurJuu0kP\nqMTVvAhteCG5I8bijeSNLnJb7jkOU0xisX6T+hST+/L923sFnLG4wdd25Fpp1sTsbKVdOYFDzcSm\nuYOI83zdewRNWbicHJQZ1jCSplPAsWCQCi51lqj5zLpuhPO2fJ5bincwGQI2valHEwMBiWzerBVi\n6kw8Outh63nycE7Yfdpa4Ig0fCdnI7w9pOc5AQYOkbORwjUqgTc4tcX5BpQja/ulL3v4s8+LRff9\nTRTELOxvSuKz2d3DlMlaIwpgMhFu6TYCWuvsto+M3k+SMPF94cNqUUl6uAGPCcN4aeBiLJ7XG/EE\n4wGl5cgZ2rpuIKCO5+LiKzg8FnzHi5/41xCQ3Ttoh0gZjj3p+KACsz+mlPphAB4kp/DTANpKKYve\nwi6A4286CW1gIwth2xobSl68eM3FJiXO708G+NK5uE6DKkZkSqgQrckDfGF9G9UaX+hhgXyNuoQn\nNc4u5ePnZyVMKuhkFE9N9ATXqalYqhzPegcAAHtzgk9GshmzPMIGexT+QS4PsRydYjmiMG2q8Ygl\nu/3KwWgp1747OkVqSL6ifZ9KT5sBXmhIuGJ2K7RrdkT6gElxkiqXh9lYAD5FWc2pC81uzp20h6Uv\nIUhbtWBQnWq9IS5w0LIwjmWjOCdj3LblnpxGBWe2yl5r2Ims830KiJyfnuLUYUVlaWHRZQw/ylFF\nFBw530LTJaR5IfcEc46Q9YL9qo/DI5n/l+u7uNWTzy7SMfYSWcPXCNH1o1MkrwuR6g+4e/g1V9Sr\n7MSAZo+Gmk4Q8WVao/Duop7h7B25RhpMsXdHvh7sBWgRAHa+kWAj/hoJDACUJwmmS3mpirtnKBZy\nH2fjBU582S/fFt3G/X35/R1fXrxe4MFlmdkPbXiKIdisQJtCuK2FrI8TdLFFspQoAeYL+bzDd+7g\nmF2wn8gq2B9mCMJnnrRrZPfEANS7JirCuHPMYJ8ShDXMcXhH/u60LwfaD56to9qUz7g+u42T3oDX\nHcGzPyZzMnqPw6YnHX/o8EFr/Z9qrXe11gcA/m0Av661/ncBfAbAT/DXrqTor8bV+FM2/jhwCn8D\nwM8ppf4rAK8B+Lvf9C9MAA0NvxfCt+SEtq0SDumsOpaLy4Wc0Nf9LsK2pClu9gVvEHVclCv4reui\nzsVKzGAhYaeeud5Gg25+wxZ3MTIL9NviKFo9H9tsbOlbU2xDLOHrawaCL4uX4l0jwcbrCtNgxa9Y\no0kKbccxcIPEKSdWgIDgltAXC73T3YDryf011RylwzN5mSL2Cb4iXVsRuWjSAl/awBYp5b3Mgsds\neTmz4HXFaxjO2HMYfBTdWFxtvROg2hKPp5HZUCBFvVsgp3cTuLKuOlDYINmgm0fYXvEk1jFySFPS\nWBnYYLViUYibnBcKoNK2W9Zw78t632is40UmhNeqjyC1X5VrvCPPJjt3cP6DYq13v3IHqkNOCqXh\nUiY+yR3AJDy4v9IPdWAoeTZr5QLdfUmYdiIfl0tZz36dI+M+qmqx3ENXYXksLrq5tYVuSpVuU6G7\nuu/gAIHLJB8rBG5YwKU3NSpuYGvOCs5GBzqk1F2PMnabNkAt0fWNHHOCuqyhi2uWeGadZ7ZxsCZN\nV1Uu3sHw8hTuptzfdlohbXLxswjWLrEVpYlgR577M12Z2+Z6Fw1C77P+OT4+kHuG9QqsHvd9XqJL\ngZonHX8kh4LW+jcA/Aa/vgfgO/4orns1rsbV+JMfTwWi0dIabV3CziuMGAPu2SZusBb2vPLx7L7E\nSP1wAlORuspYEWMCDstfWWkjJELNLGs4TTm5I38bfVrmu7RsTXOCrbac2m5hYdmiLsThAK85QtFV\n/vIE91w5gTfJ4/9bRQqbjECxNsDyOFynh52X5cTfy17EBkVq/VrKQ52oAeZLEdYpkpSSdmaCBdmg\nLE/uadNto898QXPLg002HizXYZgStz9s5NALmZtDgtaod4H5Qj6kvrjElkGL0ciREy3qqAkc5jNC\nJnarbh9+JNfFeANVKW3kZieAS4i1pT24DiXkCrH4VtlC35e1/+RmB7c25NmE5gydNfFSFl96D585\nZUv5QvIs52tL3PiMxMa/kvoguA/2ooBtUeynCViZeCRZJR6N708QkaF51xvBSyRHExkxsi2ZZ7+h\n4ZI7waAXkM8KRMbqurtoBDKP3q0d3CTq1Y730Gby8GEln+ssM2Qsl66b72GSiWfSGyg0iVXJStk3\nxtKH2Ze/6wXPok825xNjH0NHvK39dvQYLzEiojW0TpAnsm6ZZ4HVZdRIpK0awHXXgP0h2X/9lnjF\nu+vPwL6UffGgrrDPXJO/PYPLsiYiF2H9NX6FJxlPxaEAS8HoOTgwPJyn4qol6AKmuFcb3R66wYqK\n/CZKZoOREIdvTTAm+KX2E9RUMokaPmLWvI1ZDG9TDpl9JYmltnmA9Q5rxqmBlLwIc7gYzeTv7mYp\nrl8Tt7ucycZcmxk4ZDdjaBSPD6fW1hS36xdlHls1dk3pfYAn92HrAEZTNuZioLEk3VoeZ6h4GGhW\nVBZGjGbEsMMdQceSlLM25picUfXInuCMh+Fic0X4YWKNX8/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tsLEu1uxR5QBMlE4bJUwm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nJU77xNVinduTckQzaZUDc8Oy8x23K9aTLD1opDOEspCwnNmlULe1e+l0xl7svlOROlhvd2UkLl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\nvyYzShUWt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WG8FJuCmRvURibjwTPMpsJy4+8RT+VlOnnnMU/PZZXOsogt5MWatLRLbZWT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\nV/td+OREjPICTsEEKW3iNJTxrd6XwzL7WANPO4T4X2uiQcAc13RQQMaCV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3409... Generator Loss: 0.6901\n", + "Epoch 1/1... Discriminator Loss: 1.3804... Generator Loss: 0.5383\n", + "Epoch 1/1... Discriminator Loss: 1.4042... Generator Loss: 0.7307\n", + "Epoch 1/1... Discriminator Loss: 1.5044... Generator Loss: 1.7383\n", + "Epoch 1/1... Discriminator Loss: 1.4054... Generator Loss: 0.5633\n", + "Epoch 1/1... Discriminator Loss: 0.8011... Generator Loss: 2.2206\n", + "Epoch 1/1... Discriminator Loss: 1.2483... Generator Loss: 0.5992\n", + "Epoch 1/1... Discriminator Loss: 1.3313... Generator Loss: 0.5899\n", + "Epoch 1/1... Discriminator Loss: 1.2300... Generator Loss: 0.9554\n", + "Epoch 1/1... Discriminator Loss: 1.3818... Generator Loss: 1.0496\n" + ] + }, + { + "data": { + "image/png": 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V+NQINBuyZ5h8RuoL3rzxAkZrFdoFlPyzKMU1vNFd+d6SF/ecDukdLRYSx5RT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\nUZgJQUu8GO/BgL26sFlXW4eEmo266atI0myTVqyFVlHJ7EgzO0cWfU8zL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SLj3F6VeROu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\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3802... Generator Loss: 0.7881\n", + "Epoch 1/1... Discriminator Loss: 1.3059... Generator Loss: 1.2800\n", + "Epoch 1/1... Discriminator Loss: 1.2707... Generator Loss: 0.8262\n", + "Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.7216\n", + "Epoch 1/1... Discriminator Loss: 1.1423... Generator Loss: 1.5386\n", + "Epoch 1/1... Discriminator Loss: 1.3705... Generator Loss: 1.4615\n", + "Epoch 1/1... Discriminator Loss: 1.3879... Generator Loss: 0.5866\n", + "Epoch 1/1... Discriminator Loss: 1.2942... Generator Loss: 1.0528\n", + "Epoch 1/1... Discriminator Loss: 1.2455... Generator Loss: 0.7173\n", + "Epoch 1/1... Discriminator Loss: 1.4368... Generator Loss: 0.5770\n" + ] + }, + { + "data": { + "image/png": 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4qAbRB8cLLiKJIdieNthtynxudYZ4deFcDQ0N3ogNQV2kg3FrSHQuMRRWvaTn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\nAul6PKOGvEIE3jzBRQb8PnVJi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9pqYafNeM2q9C0uMjQJZHJrCVYsGRfPLCFPSYqbG8hJ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\nWmJpF80CBTe7jcsVpCylNxKgGFOd6wHb0n1YtmVbtne0R8JSUKYBp+aj4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3614... Generator Loss: 1.5139\n", + "Epoch 1/1... Discriminator Loss: 1.3375... Generator Loss: 1.1554\n", + "Epoch 1/1... Discriminator Loss: 1.5048... Generator Loss: 0.8155\n", + "Epoch 1/1... Discriminator Loss: 1.3531... Generator Loss: 0.7889\n", + "Epoch 1/1... Discriminator Loss: 1.2752... Generator Loss: 0.8632\n", + "Epoch 1/1... Discriminator Loss: 1.3345... Generator Loss: 0.6996\n", + "Epoch 1/1... Discriminator Loss: 1.5458... Generator Loss: 1.1624\n", + "Epoch 1/1... Discriminator Loss: 1.8949... Generator Loss: 2.2906\n", + "Epoch 1/1... Discriminator Loss: 1.5799... Generator Loss: 0.3830\n", + "Epoch 1/1... Discriminator Loss: 1.3848... Generator Loss: 0.7840\n" + ] + }, + { + "data": { + "image/png": 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SZ3JM4K1Rq45Ldw6K9P62bGBYdnDVGh7lNbWKzGV/ykL96qkZYmsRo7KxBp0kGQGCmAWG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\nPV02Wky158JYG8h4NmOkkpJ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HMoeztiHUMPUL9ynekRzi+8cnFNrcd8t5HifQmpaOqJWz\nSU2RaYBb4VA4KouTU+rBi1qrvKth0+VDIXq1/zaHSiCvzWp2FhqEtRvy4b4Qg8OxXLtfvs6TvhhE\np5VP/47sf5ZmPHBlvNWojat1HlNXjNKD4gl3NXP3vD+nUtxzpgWJ5pWENqPQNPjJpTYbnuakV1nF\nYUiknpGqtcpKJHtii4TMLOMUlrCEJXwX8KmQFCbAL1N+5LtZBW9fynef3Yb1PeG2r9fXSW5o38Sh\n9i5onzM/F4Pb+k6GfyEiZ7A3I7+rxTt2zrjzDaGwj1piqHr7w4K7yhlmQ4u24kMZ2O8Ldhrw9BdF\n9Pvnt7/CyUAoccNJ6anfP274ROrWvKl9Ag9bK4zUDdd/mJPGV9WoDZ0V4bBdVWd6Bw7NhUgNsdtn\ndSSi+FF0gf9U7iucOVl1VTxCJazzmssV4Vy7w4zqqbzzk0VJ2tQErHKdAy22Oj8r9feMWI19nZUx\n9SPN6a9SOmMRtQ8/u4+nKtGsq81nspgdNQK23DbJlWW3uU8UqFswqxitaWZnLvEkeXEH+1C5meOR\nV5qsdDZloEbce9EZsZHiKi+vaMfo2ZjclTm3pmNOQnEXPj3KmHgaFdmp2V4THNhQ4/IiHTIbyhiX\n9QBfi7U+ri5Ya8oY5V5M+FD24dFQMjhD95BDjRZ1bk9YdLStXNakdVcMusfaSu6Xy/uMEuH+r49P\n6GhW5n2bs6mVn7/veErrsUiLX3kkUsWHHxScF1owaGSpNQvW4D1re9hcJAyMqNah1fbzTko2kd9H\n+QzHEVzYby4YDWWMti1ZaELex4VPBVHAgimuFAeB0Bj8LXmx//wn/k0aQ0GEszznYEsLkVSCoHve\nLiNt/Hn9C5uMNHx2299k/LockO7eJq/9CRFLzQP57rY95Vd/9RsA/Ob9R3xwLBszMSXZH2BqaXkX\nvOPJARt5DdxSnus2AuJYEGE/7pBo5uYb2xLXXq81OVS9/WzvghdL0f1niWXPqk+7o01RAstYzent\nPKa9KhNqpz4VQtTqic+56s51Ry3absmp1j48zEqMelpSWzPZkrG3pimDphyQJ6q/Ps1qdrXxyvn5\nJmMrIq6tPLoa97DiBpTaQ/OgKym79bRi7UXxt+fjGZHaM+qypNZck1nxkLClHadaMkaz8qiuMgfN\nPmsq4l6sVRjt89j74nU6KsZPd2X/3/vdSzIrz+oOCjo9iVn4gRcdTjTW/c/sf455Qz6PfVmrVulT\nqVpSjjr0tfnrWrjL5bnYCW5lX2RU/o7Ms6GequNLxlL6kLt1jxde/WG57/KUaFOI2lmhmaoHHeZv\nyxhfbhouSp37sGSm1aXb04CHZ+JdefdIvqsWBUa7O3lJTaB2Dc91aGg17iKqqbUydXWldlkfo8R9\nkXmY0VU9xw6uke/Pwy0OplfGqY8HS/VhCUtYwkfgUyEpGMB3oa5BCSY/em2Tv/QXfhKAw1deYqy9\nHRN/yvZMCwRuKPepK9ZviYcg8raJt7VmIG3WI7nGZYUN7bE4jcV49dLlIY0fkd8/G/we/yIQkfF3\nnx5zP9dqxuVHJRiA8WCdV1PhiO/Gt6EplP2llsfriUTCBa8kvKL9ExulRiluHVJrZeDP+FsUWt6t\nrmqMxgsEjnCDQVnQuNSiIfmcpnIjm8Q8p3MbNUt2Ko0QnKlxtZuw0VIjaQjDc7mvGRncRq7rHRFr\nIlivkDkep3M0IpqwqvmChnQv6jafeUl4xxrXiK+LtBEEItGEL4JvteTZSkGt4d/Wc0nHmvBVBYxT\nTVZSicAYB08DBNL1jBUt3mLyJre08U1nI6aTaYftY9mba1WLe3PhiM+1ApLWN6NQzYHMac/doUi0\nb6YnXLLv9tiIxDp/5/gCV+tJnGdNDrqqMtw+YX8ouHOqksLGbknnqYZK7yWYQoyZ/o5HfiT3rWk7\nt9khrGmRllb/EY4neFr5Y55TtbHlVjxXqrFSe4y2Yh9Hu26v1CVRW/bkIFnhPSvruZhMSbUVfSNT\nqapO0TITVH6GoxmjTBfUbVGPYm/MuPyoav7t4FNBFCxQGwfPh0gP0k/86C4vaX/Fab5g0141YF0n\niGUxk5aGpIYL6lCCVeoyJ1Dx0u8sMEYs7tYb4GuJ7PaahkcnoB4k+p+fsKsW6TmWwalmNk5SLpUq\nBFc2h/wrfGjkUO1cZDhqkX9lf5M3vl+IQtcPSBJJ963HotfHu20a6mZN7XWCSDcrCIhVZFyov8Of\nDAk1hLcenlNrwRlvZvAa8n5rpxkzDXPtqbtqalfojQRxG9ev46sLNPFavHEpSBPtWZ6qvlvrO8dF\nStLUPp4Hm2wfCFKttgJa3ddk3TZjXE8IQKT1B914E6P2IDOLQD0YtY0wobx32O4Qn2kOSiii8ypz\nNtranOb+QzgUGX27m7O1InaHzw6v4a3K9b+thXkntxekpRD1rPcaz90ST0tzr0WiNpHAXcFRHX3U\nllUES8MAACAASURBVD3fDWKSrhz4va2EDy7lvYPTAVdJKPudivc1f6Kl9Tq3tgN2VrTEf9ylcypr\nG8Uv02xojU1NrT/4rYz8muDC+vVNXv9QA9yOa3ZU7Xpa14x7ssc/NpW5vRnXfE7dmm7D50/uimpW\nrXT58UzG/s1JH/dEVJPRVLuT9Rv8P+y9WYxtWXom9K09T2eOc2K+EXfKvDfHqkxXld0eyrjpboSh\n4cFqIRA0qAXipYUEUnfDE0KN1Dwg8BM8NKAWAjWmkZBo3GBjyu7G5Skrs6qyMvNm5p1jjjhx5rPn\nvRcP/3fClabsynIa+7YUS0rdyIhz9l57rbXX+ofv/76ndFH0XKPic1S5CZPAuLpy4FDT8/O2a/fh\nul236/aZ9kJYCgYAR2s4sNGyZUe9u30TanUaJSeoqQzTbm0iJFUWWdFhuw3UhOWa6QK1L5kBVfag\nItlJjWUMzc8YxCM4gY3NgVgjvnEfxYmccr0S2B7Ijn/y8Bn+5yFNYmIeRqaD6gkl15EjJJjEUkBf\nU9RF23A3Ka3WJJzXt+BSrqwReiCRNAqlYZIO3azls56Zw2mJuZ+pCkVCWno3Q8mIej7IMB8y1z0X\nEE8SJMgSsY6S+QRZykh2OsN31uTkfnnWR/KUBC6kC1+zbdyn3Pubt7uI9qXa0TEchBQyUUaFwiOb\nsZZ7GGYORbxIZaUgAxmMRg2XZm4d1kjb8izzU5m7fPwQQUKJ+14Hs4C056mFfCb3OHeewFvKJB9+\nU/qu7QI3vX0AwN79PvZvCq68PVhHMZPreVGFfEleikBOzCoOEZRy7xIpLM7vb41OMIzJO5m8j6or\n43lKsh83bcEk16azniFovy39rKZXkGafHI7DOyb2pmJhrT+2cKMp4xndBrZIKPPlssYxCW7s16gY\nfdFG4Urf87UAWy+L1RuZt1GlYpmoiyUuCbM/ovDPeHp4JZiUocI8IYVgvsQ6jQNb+ait70fb/PD2\nQmwKGvLCKVthQLWe46mJHZKHjkcG/AU5Az0Am1TsybgCSwsmORwNpwNVkp+vkcPQKx4YA7oiOShl\n1pVdwegQmLNsoH9LTP+jIENPiYvhbXpo/xMpne715GW8PCowjUjOUgPdkqXFL+2j7lDnzw+QH8tC\ncBuswksKVAE3qVzBpJCoMc9RWSQAoftgNRsIGL1HZaCmFoKathCSH3JhdzHjC/SAqStvbGKbQKCb\nnoZiqs8KLHRYcj4txzjhalowe7HjODADlnWjgfETeQnbWy5qluw6tg87k8+XK9O5KmEQpVinGVSb\ncRIzhaJmIiYnUETV1Z5kbU49F6AL3KpDWKxBGWuFgASy3a0mjt8ThqTnGUl3UwNf36ZOYhwjY4n0\n4YOP0bspcZ50UVzVnShS39vtAUaX4nbUykVoSD/13j7S5/LSfzhb4P5I5mH3rsxdWQ3xYCX3PjKx\nwU2mTmywNAe1w8wQUtQLZrZ2m9hl+f3dzLtyKxoFsN4W908fUjls10ajT2Sqs42gsy/XrWuUJsd+\nGiJsyAEW7kp8LZ9GADkzvdxATDcurS0YrEEJygai1en5Odu1+3Ddrtt1+0x7ISwFQwGBA6wpBQzE\n7rGCHAe/IepA52GJrZuyS7a7CjphkIz07CVMKPIW1rYFRcWiclGgtlmJmCmogp8xVnLqJlQhO7TV\nMpGTm6B50Ieu5CSpevfwb3z5XQDAG1tyQj3MctyYipl5gDOkDBJ1kjkUpdGzsI3ck4i5r8XqMD0L\nyGQfLq1LgNgLFVioCEKpyQZd6QJVKj9n2fQKUGXaJhqkl79QGQzIKZacUOimGWJJARFUIfoUeHnV\nDzAPxHxuLGwcksYLFCZpf7mHm6+TK2AHcAu5Rm43ELKi0lF9wJKfyyEtBSuBTYpzOBolFZbg4Uo6\n3WkH0BdyvdXJHhkWcprty06JPs2Gcz/AKz2xKjxrD46z4k98T8ZKp1gE1I/cyjE2hOatbtQoPpAx\n2Nm1kGYD9ol8GidHWJZi5scxENWslC1mSCZihcxOzhEz+Hl+InP9Yze6sJlFWXZcTGbydyMswOJZ\nbDHqf5jZaBNj8ZpKYGyKq1iGPtq0Fr3wFgJiCApmmuyoxCE5MbudOdJYsk6m1cecNR8IahRjGc8N\nVknutVOMzmQszs0MNtF3RVUiT+XVnjZSRHQxPm+7thSu23W7bp9pL4iloOBYJho9B9v0BR+88xwP\nG3ICB+cVNsliZHY6qEKm8licYrgxVC3pNMMoAaYegQRIybATz1AnJCv15ZQ0KgcWVZJLH2iGgmOw\nt8dYUIhlNrHw2lclRdS5J2mz4//tf8H7lgQz9TD//Xx0OUDJgKE+/AQ5tXUD+vK+soG2/N11O1DM\noSNzYNCi0cx71pdLaMIqbS9AoXg6jhVyErcGeQeTWOjKplTSdi8LTFb3WK8Rkri06FiIKFTyeHqC\n81zG8zaVqF9pW7jzys/JdScNmB6r/cIaDrEelu2hnlN/g3BmXdWAwZxt6kMHtCAKBQNyOhoqQMNl\nBZ/5VMaiyBGvinme5PC/zFSnbSMNZTyjmYnhSE7xMzIzDZSFZCJj38RXMWdRWXkZo2iKPz9PXkNN\nqb9pSpq6skTNKspFHGPBE72xtPA0ofJ2nOJbM4FN23OxKjrVGl6+eR8A4CzHOHkkf/fv3QUIYx8T\nrh6dZzhnHGEabeA+NRs22haM3k8DAJLCRGQSX/Mai8cqEwZTripXCJmKrhoFyrn0f3ppoGC8aUk0\nZlPZMCMZe3dmIzNlPA2oq6JA5AbsJllsP2d7ITYF29DYdjXutjzUVCMKLAcnjyUo9bgM8QYlvsM1\nDxbp1masevTOIlh7jN4nJoqANNxnGobPyH+SI++Q9ptkIvVGG4osym5TYUCo8dJooM8NonU/QsxK\n1qAhJbZ2+EvwZqwyLBPUxNw3ggS1I99TOoMd0iVgcHHhO2guVzRmNkxiE6oIsBlItYljsJoK2YoZ\n+mwOI5BNr65iGIRPd+MMfkmQ0eIpACBWBaxUzEs3UYjaZKiGjZRYj74RYFjIGEwpJrPeXcOA68ju\nT3HMuoX2RynKXUby6xAF4cPW8Yhj2ERGvEXqhKhJemJFLVgrtWDbg6K57toUTqmXaHDs7TUf9UoH\n0qiQaZKFqBx7pL6/x2Df1C0xIOfgJ/Pfg2nIc1yMAZcVk5PdJTYIvgJBVj5KdFtUumq3MOH9yvkc\n2JYNSVcZnlMg9mwhh8JHlyUuIfdYC3bQJBO453swPR5UZIm+RInmSJ6zc6hhbpPSrX+BtrsqI1co\nKFjsXsrmZ1+WiKiclW+kcJWMUTFP0WO597LpIClk8/IpsjNo53hzIcHVPDvAR8cyRnlRwCHnZVEm\nQP5Dinn+QPvC7oNSylRKvaeU+of8/5tKqd9RSj1USv1PSqkfDXh93a7bdfszbX8SlsK/B+AjrIgC\ngP8MwH+htf77Sqn/GsBfA/Bf/VEXUDBgmy4M5WElRflodI4HF2KK7vULPB7LqXNnVCCn2Tk9Jnvv\ndoE+q9A8u4X4WI72slnAol5EVgDjQzFFY6oo++fPUQVyGtvP1hDsyM97RY7qnpj+3tpNRKmcMEe5\nnPKLmYdJKCdpnSu4Mc1nYwfjjHRkyxydkpRn1JCwGjOku3KqDiofBk33cpzAYiWeSZh3rdqoKU+u\nBvdhVGJt2J6HlAFKr5kjZKFNvyXfv/Bt7O+JteKsDeCactJqDwhppHzveIxTYi7uR2IdLYoBMuoS\n5lWOxlhM7WTTRKtFerAqQBUzWEd+gMX8DPWhnGZnk2M4tCSayyGMlgRYndkcNtOvvktBnq0tuOSb\n6HgDaHIvPLIeo6wEpWi3HSw9mVczoLr2ugc/kOe74dzGu59IuriTLfCUrNI3TyOcMg23wX/XLAd2\nUwYgsH2gJiN0+03sU2Fa2b+JrVgsnfdJwbbeb6OiCMvleIya5vpyWSKr5FlmTVqsiUJKAp/KbyBr\ny/o8+8DEdCCFVrOD9Cq4nXdkrBpw4bGIzU03Yd4mN0gaYy7TjsfDxZXrNp1IHxdWgClT8aEbQFvE\nZGRAQgo9u2jAofX9edsX1ZLcAfDzAP5TAP8+peR+DsC/yo/8PQD/MX7IpmBYGsFaCQc5HrM6bzcp\n4fPlH6UGUppAT56dYDKSkbowZIHaZz28ThOwM7gL3xbTr1qWmLPy8eHxAmenMphPyb+XDJfodWUy\n/pk764D/GgCg297Hmi3MuMpWUInkt/da+3IPBewSojo2JqioO+lElwjImfgIFo7H9H1rWZiBMUf0\nSMy9aruNjZq55AZQaelTzHLx6WyJ0YwbWmmhSdGTpj1Ac2Wu97Zw/1VZpB9+csp7GGhvSt86nbuI\n+rLA9hMf7+WMNZQeHJv+NRexF0aoSCkPp0ZCt8JOIpDaEYZvQrXow5MzU5/kGJIUZFFOkM3kRYm8\nAQy6eY6RoWQOveLO1A3WYHrMBvk5TFPG7fnpED99V+C8bvUldEmseHNb/u61A7x1T+bp1RtbeOmO\nuAejyQn2HklH17suKsrZO7yH260RkUk71cVV1qV2p7ixLZtX1N3Cb38k43xgyXprKg+WL79bzjOM\nF/KyXZ5eoGPIGFkUzz23auwR9/LqPQ2TMaPn7iUefUsyad96WmCwRRbrD+Uc/fGXtnHTEbDYa18K\n4AasUdE7eMI4Sf30Yzw+FNemcMgY7XrYYmzkedNFaMrGO1OTK4BeYpZolJzAz9m+qPvwXwL4G/h9\ngqIegInWelWBcQhg+wd9USn17yil3lFKvVNUf/aUcNftul03aX9sS0Ep9S8AONdaf0sp9bM/6ve/\nX4q+6VlaZz6MhgmHiLmJVihd2e3vt31sm2JyjatneJ7Ijv/hA7EInPAQBk+BjdkFfEtOj83tCOMj\nsQ4ePHmGT0+pukzevu5aG7pBaTa7gTVGb12rRmUItVU+2YTFgiZlscqyynAayOnRGmmAAcM8bmNG\nzsT18zPMGOQMaY2oQsNllsG0J8iogWAVXZiB/LyYygl8dP4U44l8Ni6W2F6XQGvQNJAHcnr6VQPq\nSAqGhlN5tpFh4vJc+l6bC2BVkdfJYTAQdZEsME/pdpAG7MmHD1BAIutq3MOTXCyC3RZwYyABs2bL\nh1Ziiq7RWTxPpkhIFffx6SXahVzveDHDFqXl4sYeHBKElMcylun4EfyenI5+aUBTsu40TvERi3nc\ny9/Fs+dipX2sZVy+bHQROBRD6UTwa3m+nupg61WepG6EKmaQ05FxawUu4lisqYPDI/z6XNyO4nSO\nFrUqLqoai3P5XsKiu6rKkZNLclElSEfMZngh4mqFORHrYVcrTA1Zs8tZF3s3JGDYcoDwq5Jx2Hrb\nQcbK1f5bLvuWwyWDdXaxjeaO9LkqCkR0Cbob6+i2qDd6Y1++FwVYkufT+/ZTTA/+EQDgZKqwZGB3\nMtU4nf5olsIXFZj9y0qpfx4CVWkC+EUAbaWURWthB8DRD+2EWaPXzhCETcxYervn27jL4oBmEODh\nkJLpIxsfnwooKOODbxtdZDLfeHZ2gNd/SjYIjTa61G685TpwCIp5v5CJa3ZC3HGkkrHVsNEO3gIA\ntPc2YXCxpd5v4fzZPwYAfJDJC//yy1uYfEfcnIf2EXaasqDX+oBPH/hXPznBdyf0NRtSl7Db3cAb\nN4WQxDirgXXyHPY18nPCh7mYfWWgcuUlHk0iPDyR3y/GI+wREr27uYHXviym9OZv/gYAYL7IkV3K\ntFbjEfY2WcqrtzE0xSzfcJqYavl5uZTxfufwHEczMdH99gJzUOR0tIEO2an8wMLamoxdRV5DUy9g\nsJzYuVjgm89lnoKDKV7pyM5x784QHVLXO5a8bE6soFryovtGHykj6+ZoCn0gk3k46eF1pqiDlDqe\n1RInT0mGo7+LNlYCPzFaifQt7JswGzKeLjNUykxgcG4q4wzGx2KWf3eYwGYJc1FniFcajA15Ydfa\nfSQbVIg69TBiZuTx+Aw3WBNSUanMsWv85S1Zb6+++SqcHfn7w1/+FO9/R17MoTtGvy/uyktvSqqz\no2uUpfTBeuUcupRDzaiqq813OWkjoUvkErzmVhZsplnvbFv46V253uPhb2PKFGecl3jEFO7nbX9s\n90Fr/R9qrXe01vsA/hUA/7fW+l8D8A0Av8CPXUvRX7fr9k9Z+/8Dp/A3Afx9pdTfBvAegP/mh36j\nNqBjH/OwgSpZSVx52NmXbXJzvQc3o+hJXuINyqFPGOh5pRlhxCzD2WSMwZmcLnf6XSTkBtxPFzCZ\nDbh9U647rE28+rpcN6tCtMmlWBkxkElgaBb34Bk/BQB4c0uCmSf2LrzbIhU3eBCgRzDKev8NpKGc\nQLfu3YJD+fh11vQbTRtbNylSUudYkULWh0v4V4VL8vT9cAe6lv7e6poYxvKz37aQE3ClzAgLtcIC\nyDN1WgV6oYxLYgANsjyXVg+9QE70D6oYgUsZeFbybfZa2N2iibs5wJJ091u6B5804q7bhyYFXoNZ\nG6e/jbVum30/RJ/ZlcvUwGsvy3iZ1gwd0sxlc8KLO2tXpC7Ki+AQW3I5LvFL3xOXoec+xWRTvnfj\ntrgwN1s+mjfEjev5LVg22aXT51BkqK5qBbBIqyKk3UwaaPN+r269jvM35H76WY1xLq6XOengXVtg\n0yGVmmPDRGlR3zRMoHiiu8qD4ZEWsCFWwMY0AFySz3T3rtilF24DAcflrdY+3E2Rkh+8LM+0TJ7j\njDqfwaFGm+zZ2srh9WS99KwpPIOwaSXP5jnAUSZux3q0C/+OFI+5HzfhW/JMdqqQJ9TV/JztT2RT\n0Fr/OoBf58+PAXz1T+K61+26Xbc//fZCIBorXWOWLxA9P8SUQSS/leO+loDaztZrWKN/ahg25oS+\npqzjLwZz7Czls6dPDXTpFdUW4K0REuzcw+BCTqlgXXbim00fW5BTxw2XsGomTcwQGLLs1/hNONZT\nAEBky173yr230aWP+D/2vwl7l3wKy0Ps7Ukqs/XTXXhEzRkrBqW4QhlIMEznCtWIcmxdE5ontz3m\nceYPsOWKX5j1ZrhvSn9KI4dLNeesOsF8Rj+ZKcT0osaEGhjx4RhgmfXL0wTv0sK60/DAeBm6hDD/\nzN3beP1VQWyaVhsLkrjGSYoGU2sBXPgrNWaWiwcdBzZpzpxGF/2WnFBhO4RH6jnfjlESpl2wOGxd\nl8hZSKUwRrWQU16HBVoLCUa+mwM9QrZDQ07g3d0vY2tTmJn8qAmsBIEKCwaDv4bjID9a5VE5B2EF\nk2jK0BzgZ3ga322eIyZq8vnZUzTekf797kKuNZxmyEtRK18kgRS1AVizAmwQer0S/XnsVhhUEmv6\nytkThG+KJXDv7du4f0mNks012OQcVEw3NoINGFPGksIcBDHCsAG32JfxcuZQSta1HclY5MsJdgIZ\nq8fDQ1RjCWhvbX+MRw+ZfjWX8FdajJ+zvRibQgksxgrvqBLnDgEYcOB8VRaK51roRPLyVsqAZaw4\nEuTvi3qKkS0BIKMTodcU86wyNDxDNot2Z4I+FaFBiuxMObBpfjvNfegGWXJhIDEkPjp9v4NgXQKF\nrTYrGO0LHK7LZDSepJgwupsqC5jIi95xNDRkkixTJkW1I5AiArPqAgtDXjyVR7AV6/RZq6Dc0ZW5\n2LR6sJwO+zZDRf695fgc46On8j2+YFGjxvhSsg/DfIaC1Yzv6iEeXcpns2V6JTLSbMoYvrSzh+76\nDY63h8ZYXpQJjpFQRt3sGvCUuFsmo+LKCGCvyUO1Wzto+LJhl1ULrsfgsGrCnMnPHrMaS9OGT9KQ\n2fAci1z6MZ8aeM4U9Xld4pJQafMVugaxgo7lxYsTjTCQOXNmTdgM/NWVgWpDxjxNCEKrEhgEHlmd\nHK1cnjXo9LGkCvl25KBkoPHwfQlmn43OUbMqd5hM0KHP462FmJ/IOkxIkGLkFR6S5m2spvAeyxj1\nurdQrsmGpIpLWKyJKVl9Op+OEZfyva7xCupU8DemiqCUrDMTPZir2oYVHL1IYfVlTXvDU7T0x/L3\nqoHakqxbuSxxoX+00OF1leR1u27X7TPthbAUCqVxojKM8xwLouN+zY9x81QCTndu37+iLgs7JXxF\n5BZTkul7BxjTtOpZQOVIvr0ubqA2xPS1kwya0E+TO3SjXcEzyc1gXsK8YJlG7xMAclK8v/Xf46iQ\nz7c/EqvjKxs/i41cTodCfw82r9GKx3AZdDJzHzqS08pkQFQbGaoznp7LFD5PYLuYwHTEKnAqVhaO\nZ1D2SgsggEnMg6o1siXlz47HQCYW1OuhyNid+ya6hZzy8eNTmAMZi2w0wEEipr2LABH7H7Gox6p8\ntAiVth0XBpmZ5r8+Q92l7F3Yht1gMdlc+ma0TTgLl8+Xw6LL4zqAYlWqUS+gVwxBpoyh6RwgtmXO\nRgczPJhT5Xu9wIVMOwoN/J9ERQ7mUhz39uBtWKWcuk6g4RSEh0cJFPUpSseAnjKl6qxUxXswCBu3\ncg81ZfEMJ4RFnMkkuMQaBTwDwrir0kfJdbbV8rHeFFdpsN9Gj9Wx2RPpz6GeYF3L2HfPXYSvyc8G\nTHgr19RwUBEtqUhmq+IUm3SVXWsC0xKYt2GaMGj613UGrJi4AumvoRQ0C7iUlaHpi1t1b3+CXWpE\n/MbHH2C0uvfnbC/EplBXQDw3YJgKoFDsa4UH0yIFuPaQ0wRylhYqjy4GiUkuI4V6yUXgbcJrUhWq\ncAFyFFauj5r5facp5pvtKYBqRGq4QO6Jabw4nmBMDsPB8C/iEcuS/9z9nwAAxEsTJ2uySIu6RpSw\nDkBto8UyXBU2YUxJwzYgG3CeQ7Wlb2UxRiOUBaa0BbdLEBXpvLCcQ7Mk12rfhpHyWqpEFsv9kjLB\naSLkI8f0+7tOipT3PT6eIL6QF/YwrjA/Y7ymjJE1ZSFvdTkW/RZQsOS8paBmLDlve1AEJ9WOjdIV\nbIEqV6W5MapKNjSUFQyHifUyhSLtXT0ZoXYobpsKPmIOYEqKtcexwngh17UnEe425UX+OK/xV3fE\nBXspkrJ1U/VgMGNiOAZg0gHPEhRNCgiXM+Qk2jFoci/SBTx79YIZUMSqmIGBopB+LOMl0oruDWSe\nJtUhUkVxliDCy1vimjbW3kJ68B0AwMOW4ACSWQ4rIYQ+T1FeiAnv7N+DoltZ+wU0y7lzn4fafAaf\na9JVHpRLN8hIURGHkc8nsNvUUyWJTq1T5FrGVasQ9tpD6Xuxhe6mrIH+QYSzFXx9VbX6Q9q1+3Dd\nrtt1+0x7ISwFKKB2auhcgRQCOPcLGKzESRZL5A0xmYuwAdB0rWMSUxQ1DDCYVzRRjHkK+AoFsxlh\n2YFLdJwuZYfWtQOTlGe64yNnwLCeXcK8EnV5in+5JadDy5Sdug48vKnfBAB8I3gPE55sY+Mhtmtq\nJY7PYJKTwJqTCyHqIb+QIGARjWCcye+bb4fInoll4jfkpHp0kKHXk+mJj4+giJEwCucqqHiRXmAZ\nM1vRl5Ph8GGBj45Eo9D5xMLNu9KH8ZMhstVJYVWIMprMM7mvjhcwWD2qYhtkeUNRlrDIB4GFBast\nJ1pekCbNM2Dx1DW8CiWFWkzXh17yenaEjBmFtJa+p5XG9FROubNygtOZjOHX3goxpshKbeZQXWJA\nyIhd6hMkQ0qyr3WhGXXLiwwpqfCWlyMMGfAzPXl+07KRHxILYhvY3KbbMeujIDozmy1Qsjp0TAo2\nO3ex4HVzZeNiS37+klPjgMVkdydyrV+rMngkDZ4WR9ggHL2efQCwcKle1Ki5yGMGX0s3h22R5q3b\nhSaBR+lMME0pC2gWcCbiKtgFkZSFg9qgtWFcoFL7AID7/Rl++9uyXg7SfwyHgVuSEP7Q9mJsChow\nShOBBZQUNblveBgz5fPRx0fwlxS62N6GS98wm8gEeNrEfCKm77JXwGEprKnmsOnj1n4MxDTjbQF8\npHEIk2o81qIH5dM0LjZgs/Lxfv9fR3RLNgM48gKef/sh3vfFVKuNHAvCip3EwSKRvvnmCFYlC7qo\nSU8e26hdMZ/b+X2YL1NLsd6Gvy9mcpELWKV58imqWlyUKluDZqVbpRykGTe1UYZ8KddLjmVRHZ9c\n4NGpvJgVHIQcq8dZjIpsSxUUHrkk3ngspvOb6++gzdhBs9VAdjjlZw8RX0iEu9o6hxFLebUdsMrQ\nKFFTr7G6rABWeyr0UREerVMbjiMbR7KU2IG+vICmrqaVVvA9eVFc+6u48zV5MTeTGrsGmaPIOn3y\naQmLsHHbMWE58vsscWCwPiIZ18jIFRlPZR7tfAjy4mCjsYE5Kyrt9QTVnK9LPgbDQ2iQ//PcjFEo\nucc0maCyJaaSdR2krAN82hb3qp8ppNwg02mMnGlIb+ABuTxfkT6F5qa+qlQNk12YfZ6GSYSSGZW6\niKAzuV8xWsBoybuhUrpl+RJ5ulIqasIpJQ72zuEE734k63OUlCh/xHrDa/fhul236/aZ9kJYCqYB\ntP0ahTLhcYeerFl4lRp/lZ/jlDvmmjmASTInl3JY3mSOVlt2ZV3OYBosiEoBtyMnsFPXMLijqzP5\nvtE1YLCqTXdquJTvXtpr6NNsa9zagclIrkFiErfdRvNCCqnK7B9Bs3gmcxcwm7L7GzpERbNNrfQd\njBmCjHoDURvRFouKmndhKAKZpmJStu6MMWLkWS1r1OSlNOYpLJ6IyzJGlogLMifAyjDNK73GuVFj\nGrJ6cgGkrGg/VSZ6zMTMa7nfh5cLfIkVjmZvAEVz1v+tHeS1jJsezpF3xIJQK37FyIeRERMQxFAU\nzqnMBUihAK1i1KXczyHpixUVCJlZuNleRzyQMbzTaUFpsUzWTo6gIpmrDUv6YNtT6IQaElkK0yal\nmaPgMFC8FdQYTIgXp1bC8uQcmkrbyqyhG8w+zEsUDFw6toW7fbn21pFYeePzElUuay/w2liQgTpP\nZ1jboiL0iZjqHyQnWA9YxNdzUa3Tgorzq/GylIPCFzfAiuV7rqFgch5yI4A5ZBC3AJyKFbrrrUtP\nggAAIABJREFUHkwK44DBU5XWAN3mubHAs1KsuNHBr6HPStOjWv9/tFB/WHsxNgUoNGsb5sCGS7r0\nt9Y3kcZMvRQxjlki2h/E6A9kYU05WWlXAyQlbbe2YXPyvU4T7kriPahRkgfPalM4xouuiCCwyJAN\nKJDy/LswKHcfFvdghaQ1Jw15OU1x0BUT1o8MBNQtnJfbmCTyJnTdCKaiOzKWYV40DczekT4/e/jL\n6Oby9852HwYp6kPqT06jGv4q1fdja2jR/K6cJeYs5W2vrcG7KWg74zuyGB/WC5Q1NyPHhslFvNGo\nUbNU+55v4HIVjyGaKktynFFcx0ocDFmV+mR2gLig8E15Az6pgIJ1WdCR4cEip6C2c5hNZiJUhiqV\n8czKBRxmfsqmjLjjA+MVonO9j1caMk+z8xwJ4wA3dzrQtfxcVdywF0Pkjrxs9VhBN8S101EL9gr4\nZY5hd2him0SxdtpIbdZdZAukE3kmw96BWTOuoh08N2SuCmp3XtQLLGlQF1YCj0jHi+MG+mReetqR\ntO9yrHDJeNXpJeCfyAtdej2EHRLzjkrkkO9NL6UPE3WI9qlsaFXjCbJa+tDwGihypkhbARRrTFay\nBYV1hpzvyMU0xEfxNwAAv/veUySJ9OOPQ1Vy7T5ct+t23T7TXgxLwQRabWCjsHG8J6djmmookqGc\nVTO4tpw0L7+8g9wguQVl2LNFDcXf6aSGXVAyzCxQuHIKYGhdacrXXdIz2+FVhLwwl1g+FrP1XE2w\nXQi3gll8gHop5rheSGbBBPB1ioX8yhJIadpfXo6xty6ZijI0UK8i7YX0Yf5gig8LyQw8zk6QM1A6\ncI7w4VPZ2X/yDfls49DHjS8JV0J5/ACa+pCu7cCiaZhZFQKfNGUNBhePOrCackLdaTewThDSxsKG\nvUkq8rwBVUnAL6dM+afzDCMG89qTIySEEh8ZJTrOyj2aIuBpvGRlIZRCSHZhyzJRrYlVVWflVZWn\nijNUfVpntVgE9WGBZEGehn6OG7aM2//z4UfoElz2uLGNAaHLoSH38JXG5Yg1DuYc9Vye1UuWSF2x\nQrJDA1ZLfu7SKtSNDE3yFMymNspExjlTLtKurJ2WlaNP6424K+w0QpiUzeu1+ljMZFyG3/sneGlX\nvvcT5+LC/LcYYkKw1GE6x/qlnOzurQWSTH42vBjJJYOmFkltoDCPTjkWLsoTeb5Z6gApMQa2Qm1R\n8dohACzwoR4LFmJeHuDBQ3JPVDni5Pf1I1dFAZ/XaLi2FK7bdbtun2kvhKWgYMBQPsL9ECGDh3aq\n8d2VvNtwhkUh+9ydpw/R2JRdt7yQXXQSD2HNmHdunsGm9sJ8eQKnIddzMEXAAJYinNdMxigZwEoW\nHp49egcAsBiYUFuCtS2f/CQOvvt3AQCDviAazZ2Xcf4l6VrneR8hZb5Cx4VHKTDXWoNDXgNFhGEc\nNHHPFpXkW2//OHYj2fEb4T6Wj8SCqBkPOHnvQ+RkWzo35tjoEZuRFfBd8eGbXgO9WO4RduTvUXOG\nHsdio7eOmEjI1i3gLqs19eUUxwmnPpO/n4yO8ITK1TtqC8VcTvT+RhPBXO7X3nChCDHWF3JqJapA\n6RIXYWzC4HkUuGtwPOqVmX2ACEr9RNJm5+kHeHJOpORBjZ96W+ZmaI+gbbJFVQlaPnVA1uX75plG\nlsnpqB0XDgQ3kS0SnE/Earh4eoaWJ5ZM3RIRlpl5AHcoUPDD0TOELbG85hsLbPgyLvG8QhHI9Xo9\nGdcbpYHclbPz48kpnn8s6eyzcYy/8IZYjkdfkvjLa59olNTtiB0P1YLaE4t12G1WsdohljERiUux\nfqrmOgZbEhDtBPuISrJiZQukM6GNm5wPUYzk87ublAWMQnj3JYYTvzvHBVmY5osauv59u4CQE3xe\nsPMLsSlYpsZas4YeWwjI2mw1PTRP5KU/GeV4ynzsb33rMbqMON/YlTJXPw3Q7slkRnEBq0NsQraL\nHnEPUdBGdYO54CUVncwE85GYWcf5EaqpXOOtVwFrUzaF991/F//JN4R448Nv/O8AgN/4z38FOx0p\nkb4VvYu5L3379GiCtU0xL+2Ff1Wq6wZSkdfbPcfoU0b1HyyRBEL7/XT0bXz4nd+RflIDcNyssdWU\nF6X/8j04DHJWoULWYk3FpQWTzPpZLM+2kbrICY8uzCFeJlOxW0SYZTQkI4XugbgbJ9x3D2ONb/+W\nEMu8Gt6EuUF+wdMOfIrPqMsKaSYLNmXefXvLQRTKC6b2+6iGVJYyK2jWV2DNAVLKwAcS1f/2dxI8\npineqBr4aFM2i9AwkR2RX7CcodkgNTyrMms/REmqPFN50GSothoevGNx6bZ9E7Mn0o9HnlDpPX6S\nYs2QcdORA91jZmfRQX0igLLKGuFTZnbcjvT91vptXBKO/P63vofnqfT503KB5u2fBwB8iQze8B/i\nzBS3JH04x2MyEZquB5eHhR+EyAd8qb9F+PTpGMdn4j6MgxEKMnq3XQcWK4GdtgltklyFDNcaBsZK\nNrrT7x0gfS7PoXX9GVfhR6t8uHYfrtt1u25/oL0QloJRm/DjJmy/xIK52PNyhnRGFl3DgKYFcTgu\n8fRjMV1jws/6aEGzUAcAlnPm1e0KGZl6PXcLjlppHTAYVvsYXciJOV7mSMg4vDG9C/09MQk3Gl/H\n5COxLP7GW7JDzx9dYml+IH1vn6NLc93tmzhleitKWrBYrGMYlEFDAX1Bd8UsYaZiMhrWA9ThPgBg\n5yWZkt55jf4G8Q1VhMITI3AxyZEzkTo/miJfyom2ulf/VhtnR1SMTgfwl5Sj82qUJYOqqUKbIikj\nagwU8wTPR3IaP3jwGM1aTrZg4qNBERVlOHBbpIUrySuw0YftiYVlWhMsif7LK6AmY7S1rLFciMk/\ny2QspssYC1YyLkwT5om4Gt6wwOVSTP+pthEdMD3ny9gj1ViSpKQFC/M506iVBYsupo5KWEznHlcy\np5OljbIlvwvLCD6DtfF8jFlO6rZMYz2UlHBMLos7+wOc/J6c+G39FGkpFmRoulCX4q4cUB9zpsdY\nkh3bMRXOqFAdnih41AxpVD5qzoNNCkGr1UDoCpI1VhewSDZsehkcBnYNZ446F1cqp2p1vhjjg+9I\nsPL40QkSjothWGi4Mg/zTMMk/0hVf75Qo9L6z15z4daNG/pv/wd/E2p4gu+eiKn+eDaCcSamaqdt\noheyXLbwAUKXHcqiLy9TzFyZWB8+uhQ/9eHAajH/fTLDmKYhElmAuWmhpLKU3bDgkH/QdFo4O5XB\nPtU1HE40GrL4L5+e4MSTzeYv/MRLuPPnhEX3p9e+jt1bsslMn9lo0W8/eCqugdpaIj2RRep7PcTU\nOQz8cxx+T16aERfrw7MzOHymVqeHe7ek1uLOj/2lK5M6tXzUfLkfjZmP325ggxmFRquF3/0VcVHq\nfIoPzoTD7zSfAUdCyLHmyhh++nSB+QErNdUYDvEibp3AoIT9+k4fN9ryoufnzJVXBh6dSywiLjS0\n4ovQ6OPH7/0YAGDv1quo+gSa0e1aliYGL4nZfVOZCDlP//Y/9xO4JAfhZkuhCGVOmpy62pzj6Jw+\nz1IjJeu0ClyYK9ET10GDY9SNZK2MTxIsaEcfz5fQ5QryrVHWLEvfdgFBByMOZGNaDoGMdR5VAawM\ncx/AWzdl4xiReOW1nQDWujzH5tSB35UD4uIMCEmnFM/mSBvcnFf1FYEDn3EU2y4wZEl17ZsoCFMP\nrBLzKbNqFgWLXRs3O3JgLUoN05efTa+L1//ZHwcAfG33NpobrwAAtl55/Vtaa5mUP6Jduw/X7bpd\nt8+0F8J9AErAOMO5cYijZ2KSHR4+RMTddas/wBsNVhpurkEThwBCdS/86VVi2YMJi9BlyzTgVPIZ\n37VQrH6fyU479BTCWn4uAx9tosdiY4Lal92/l8eISxmmlEQuz8IK7URcmHvzV1EM5Pdh8QTlWIJu\nXf8BaktO2A5dAtO9DZOciPBvwOEplxi7MLYFmZg8ewoAaJlzmKyxD9zyigvCyp5g2pQKTldnmB8w\nUj2T78X1LdzpC0kHbAUVyXhO0jPkH8qJ/tHR72IzJ8Jwl9H2OL+iFWuZGq1NsbZ6YQ+WJWN/t92E\nSe0My5WA2ydPnsEs5bOZFSMei+U1zIbYPZBj95Negr2ZQHANJRZYZdkoHUnhvLp9HysZ4mM7R5CS\n53GxhpNKLKjJiC6MlaNMxFK4LDVcnpq9UGMrkL45nTZuNEjlRyZtfxDjkwM58du2f8VWbc1NOKSR\n6MHF6J7c5/feFXflPHsX+gfAAhMAxyv8AhGtg2oNKbk+iqWNjCzKvpHggi7r0KxgM2Pkcj2FDQ8d\nnzyfXhM9FqvNTQ+sy4PdjdBlgeCjuazpbp1jTBfTPo9xeSyZkUsM4bwr3/vJjW247VX+4fO1F2JT\nqHSNabXEk987wScPJQUz0iZur8mL+WY4wEv3xUSP2luwHfGpFhNZ2BfeY0ynnIAww5T4e1UtIUp2\nQKcxRZxRLGSdIA8dImKUOS0tOCF1B9Nd1JH4kQfHGqFNoRb69eFoiRnXyTcnF/h55+cAAHvBTYB+\nHaIWNK3ZZsFosdmGcfOe9M0KoFzpRzWq0N6TZ4ooLf+lW0eoKVVudUoYffE5w4aLguXOqgVEtkTc\n7zmS6lx8MsVyXRZPrzBgM6UX/18P8NsPvylju3Dx5r4syNsEb/m7fby+kIW5t9+Ay5qBxu4WPFc2\nN98HYlYfTlyZJ2/ewEZDNrTTZ0scNeWFPitbcCigGp750AOJkve8r8l10x6ydyTiftzcxF7K+Mls\ngRHt/Mw7h0rFRH+pzzTeRYCYrEle5eIX7ks/txvb8Pe4BuwNbO7L70vGC+rkAG+TsDfPLjA5pvhv\nUGI2lnk4tz7FYiTPNd9ghefjP9y9NqcyFkMSAH0yX2Krks3vS7s50rG4vEfpITCVz9zAEsVc+qlX\nVZt5hJ/8mhwmVdyFpVgzkW1j97Zs2gotqEj6PBvRzW0cYPY96fvx/Wf4Frkk68szWEesRxlOYM5/\ntPzDF3IflFJtpdQ/UEo9UEp9pJT6CaVUVyn1q0qpT/lv54vc47pdt+v2p9u+qKXwiwD+D631Lyil\nHAABgP8IwK9prf+OUupvAfhbEIGYP7RVZYH56QW+M36Ic2qx96oS+3tySgxubiMcyEnpZSGMhuyO\nBjkHQ38N9kRMQzvqYDaTYF/pRnAYzCkbJvJyRW8mJ992VsP0ZTc/SXK4PTnRvVmFJdWRq2oGPZMT\nu5lK3/oDhYRWzFf/yk/h/onYvuXXbaSP5NREUsE7oBTYV8TcNwoLZsScdqGBgFmQ+RwOtSLX6FKY\n4dtIKYxjdTZRU07OtmwsZpKPbu+9dgUWqljMdbmrEfRk3DLUaJI9+WjtCAbH8HbxDJt3BUIdkWps\nq9OHT3Ga3a/uQa94J7t3UK+8NScDTo45zmLxOPZTbC7lWtr/BO2pBBRvJBluOmLFfFqfY7MjJ6gf\n00p74wyXBQu+tjauiEwMo0IREGfRaiOiZZIwX39cLqAJO+/kKbb2xMJo3ungZl+sJTVTCO7KOKec\nf8v5Ghos8irMNk4fiStVqTWcsFAumkf46Lflem/PhMviGxj/ftHcH2iKdPeGI5/wNxuImA2qdjs4\nPhNX6cE4RzAh/qZlYdSU51sn52JrqdGIZI0lThMdT+bPToHWbXE3q8yH05Uxb+4Rb5Ftw7SFEs6a\n38PkhgSPy3YDOpBxnvUGyIz4D3mCH9y+iMBsC8DPAPg3AUBrnQPIlVL/EoCf5cf+HkQk5o/eFPIa\n4+MF1ibAkDUOnY6Dr2yKSfXyxm04IdmUnDE0ETcuq9Sy0oK3SbaaqokBNRAuqhwFtQBQhohslld7\n5AssKtRUZmprE4Epk+w4LuaUj1+z+zhtUEGIsYzOsInXtuRlGmx00d6hEtLwPdiUeNK5gnGPZn7M\nku1WBK35OxPQZBuCa8IgsanTk0WgtI+oIZtJmSwBIuKQFWg2GKOIbTQHsqnVhbzwe/kYdcGKS9+B\nMmSz+Tn3ZaQ7Mm69mwZeY3R+qyMLTbc9tNeZwfB7MF3ZFGEVsByyHk1r2JQ7VxYrPLvbMCzZhL+8\nLPGYNRideI6U6d5Nt0AjlUV/+035N8A2tCfuQxbPYQZkP5prdMjSm3ccRLZsHHsc+4Ht4RnL67+y\n56BpiSG60wmvVnN414fi5mRTpwFVAZeRej0x0OnJNY4WR3BZxaqqPv5iKC/cgy2Z848nYxz+IdZ3\nHXO9EFSUr3totWQzWrPn8MjHuTzx0F2X+eu6/tXYK5b4v/q6By9nDUsvghOxZiJKoHKme30DNWUA\nTHJfamWi6chmm7We4/VXZVNcO5xj0ZX7tYohdLL2gx/gD2lfxH24CeACwH+nlHpPKfV3lVIhgHWt\n9Qk/cwpg/Qd9+ful6GMyCV2363bd/uzbF3EfLABvAfjrWuvfUUr9IsRVuGpaa62U+oGRmu+Xot/o\nhjovDjB2EuyR6fZOaw9btwVIEqw3Uc1kn8nqFjKICWtVrB3ozGA2he23hoa7kJ2xVcyQkRm3iBMo\ncurlqZxyRSPBiLUBURjBZTCo0SuxRoTuxFEozsgIHIpFsLWh8VpXTOYvD0107lCopf0yHPIpFPYv\nw7L/RQC4IgoxzCa0KaejzrdQawlmqkWIasWSDIEla3cEzUBVlTwBIJaA4S5hsWbCjgCTFYwW4dzt\ndIAqloyEcnaBSjIAWcfE2zMZF8e7he27rCmIxJUykzO4gQRzLd+GMqlqVY5QrrIv2QUwp8lPfIMd\nNOBCnqksIvTOxDpYOBHytnxm3I3QIJdgg4zLjaaHV2diCdXnH6HckvkrkcNlzv7eRQZvnyzVkfSn\nrSJ8dSHu02tv/yVs3d2XZwq7MCDulva3UZiSHTFiOYGVewHTluc3jedwIRZUlI7g9GWNnB5PcNKT\niR9W8pw3Hns4JU7lD9KarTImHa6xr49MrL1BXMXgNloT6U+2eYJOV9S3NvcDrPdlnAOqYKvBSzDI\na6GKEHaH1q1xA9qW9WQuI+iKlalgHYl5CYs0bq1jB5nFDM1OjYRZLnP8FMaNl/CjtC9iKRwCONRa\n/w7//x9ANokzpdQmAPDf8y9wj+t23a7bn3L7Y1sKWutTpdSBUuplrfXHAP48gA/5318F8HfwOaXo\nNYC6rhHEQE4qsXrLgLUqYFEuMuYAy84YdkboZ0d2ZSfYhGIgqqwW0NFKZKUFh4ZKnieoqZ6sCYgr\n7QjujMw+jSXgyL2TsgGHBVgtDSy75BAgpZajbIzPJcVW1WOYmcQJ6uMURUNOTad6BQZZnhUZh7Vr\nAbRuYGXQKyJV5wB1Lr83bFYA5lNU6qn0c/oMiihO5Bo1eXmtogtzFQYjfLhIj6E9wSmU5RJOxIDp\nxQkMCpyoNYWKZLM5A5vNIILtER3oGqhKwTegUqipiakyE2WLYjCZfD/LZ8iouVFbQOQSVddwMKUG\n41IHOJoTnTqmXuX8HKeOYD0a/ltXDMZ5WcMlj8ZJL8KXfUkpD2Ii+3oKd2+vEHoRooFYaRVyGBRU\n0aqESdRq1ZL7GqYHXbMIarQEDDmB7dLAkniBZtTAG5rxoXOZ//2XL1AfyWc/OCsx/z5roSJuIGMw\n85GX4p4hcYTGhwuUPfn7XtLF1l05uQe9Puq+/OyyYA6+B70kNN1ZAjlP+UZ9pfdR6yVA2L/aWM2/\ndaU7GfU7yBj4DI+eQXOurfAlpIRjf972RbMPfx3A/8DMw2MA/xbE+vglpdRfA/AMwF/5oVepgSpT\nqFs1FIVWd+wWrDUJNCbTSyzJS4eJC8sRk7fImE0wDdQeJccNDyZpwKyGB1TyGbPVQkbKeJ+akqpO\nkGhWGVYVwKBUWSW4bNA29DK0j0mVRUh4OrHR7srkp3spkqVsENZkiWoqMOai7cKlRqF5ICaejr6L\nypC+I0hRH5C+2xqiIoLGaVPP8jRCZbHizrqNkCZjhQDOUvppDELoFWNyveIcNJETVJMbMRYXZDWu\njjELZZFuL2PErCq1V+pVrRCa1OG6qq4EUrQRIqtWDM0hSi7SypbnyO0JNKsW83yImCXS5TzHvJIx\n6l4WUG/LteepbARGlMEeMmB2R2NpkvItN1D48ny7bQ8hsf/hG/LZjt3DYF+g0lbUhyLAC7MjKAb8\narOCEUnmQ1GqXTue8EYCqAwfJUVpVNBGw6aAS2ijtOV+3buyOZyEe/jautyj8e4T/OopuTIVUJO+\nxOEhtL5uIpvLczyOpsgfy5w0swWmibyYkbkJsCqz7pASrlwDWO1poAQarOGAhjJJSlOPYazo2Dhn\nhgpQM7uUh03oWNzRieECLKOeLcdoupIF+bztC20KWutvA/hBWOo//0Wue92u23X7s2svBKJRaQ07\nrbCTRJh0mf7aVPBo55uwkNU8ue1zNGwxHx3SI0DbMK7imdaVZkOWV1hRTOjMBlj4UrHm3aptNEw5\n2ex8jpgMzsrOofVKOKaJaYu4B0rZRw0T25sMCI4bWJqUCHdPUdEa0cY+lgsGRFllqM/PUUCuZYcZ\napraXuZDG3JylZQ1N4MQZcYgWTRBsSJvMT0YaxQWSQoYxFNokoEWbRdxyXsUDVyeiztjpyYSLZbC\nYWihy+czK34/91BRJVrnPkqeXEVcAgxi1kYLhrsS4pG/u/ka6lBcCrfsAGQfzpBD5StWYoXzI+nz\n7W3S5qUZTqix0C6mVylLz6rxFaaMXXcXr+3L73cLmWxrbw32mpzi1kYAMHWsghB6Thi7A9RM261S\nslW6RJnT0lPAKlhX+edXWg1FXqAsGWEOZe667j7iGWHH/gi+LZZgWxno0aV7iZD4hjmAT0t38u0h\nDsi9EG12YbVlTvpeCYeRvIJJN8+cAuSZMEqNeiL9rDsFDGIZVGahyhmApDVj5D5MWrTFYoqa81ub\nJVK6jb59gfKYUn6fs70Qm4IGUKgKs1aODb7bu2kblUFzqThFNhYwie3vQg1YkkxRDRUYMAyZZEPF\nQL7KsedXbLZpNoeqCAxa+WlVBSKXoToWNHntKpUioA+LWsGkIpHtkxHIKlCQXdkYL5EFNFun6/DJ\nG6mXpzAvCaZpsSpTB1cbmYcACit/0Ye2SLhRR3y2FKYhH64dA3W6eiYbOhHTXbVToCRWlpqC5sJF\nsNpM2w6W1Ka8SI4Rs8rO3+7CoTiq7fM5jBhqxRSEDLqiixUp2GTEVkaOPCMdOrtjmBbsgrEar4Yi\nJebxwQjlUO73JD5BMpIXpEPhmJ21H0dNynnkMziE8ya1gWcUnt2bHyAu35Zr70m8YOBtwGtSKLZc\nBxSl6qttoEsAFNpXwjArFujaqGBRsNcv58hB122ZIF+pV8U1VETG5xk35M0IE6pvlZ96iLgpRr6N\ngIdBqyVjeCOvcDGUTeP51IA9ITO0P8Igk6rFOr0BvTrAXHn+Gg1YMQ8T1UJNcJNdOACxCUoZqBlD\nswyufwcAKzQDq4GM68kJelgpMvvjM4S7nKzP2a6rJK/bdbtun2kvhKVgGcBaaKKeBgApw7S7gFnK\nqTJxbCwmLFbqVphpCVY5wT4AIJgVUC2asyVQE9GoLwool6q8Vola8xSmajNaLSCRgAxKA3ZGN6E0\nkK00EVWAnRZr8kkgEpkVDEb7vbiBViF/N0sf2Q4Dm08XKNqkEBtzt49aMJdyehZujIxBSavjw7uk\nSMr3Qb0SxYzD4wxYp0WAKQru5XbsA5QY0yU5CZ0KqHnKVRq2zWDWxEBIvMHWzRlckqhUAf+daFBF\nHXWtUdEMtuYGKsKO1SRH3SdMmZBvw61AqxWW04HzTIJd+90WRtS9OD7u4Hwsz/rxJ2IxnJ18A9HL\ngqaM9BYUx8VAjoRQ6ELZ6JEpu0P+g7KVoaYFogcpVnwgpZvBHtHNacRQOVWemVEyRhVAOLKR5yi7\nDIh+vMC0S7TsPINDmbquSX1Q10HrTMbiZ1oB/uG5XK/QJtoB9UWIhM0QYpNBwOk4wfeoY/ozHQdB\nk4Qz1iESXwa6ccGA4rpzJRxUNVKY5wz4tg3knrgMWaMB81Tm0toUy61Cjpp8C7bbQrNDt3GaYUG9\nSldZ0OPPqyIp7YXYFGCaMKIGrH6MrKJ9nXaRFVzci0uMlUxWa1njkulHxVRg2DbRKVYrM0I95HUD\nB1VOJqC5hTOCPyz60fX5CBmJTLLLFCmJNepFhjnJS3xXo2lL/CAkAAdxAW8grk34qgEHBPToCtlc\n+hFbDZSkH5+d06XAOVxW+7lxhtkFqwgDD5ub8rPJF7deFBitTMoih1qS6ANbcEhx7tyNoFkrApaF\n10UGi8IqpQmYmSw201vCjlZ06T6UI8+UUaVp6ZVIL4Wjcf74FBOm29wtAw4FYAxzDe25uBVbO6Sc\nVwbqlUBrMgUgUf80OcOCVXtVPoTWFFG5kJqDod7G62OyN325BR2toOf2lfy6rUsYiYxL2l2RoOYY\nUajFuHiE2SNhQprHOVpbMiftpgPLlH6aLBE3+gY03ad5qTE6lk1q0etCpfJzspHjYkgXoy0+fs8Y\nY8RK2stNH86TlUy8AZ8Q7Lwp4+33KnxyzuyD0ugSznxZdVA3hUx4UQUwuLGMO/KczdSGIk1+epxC\nUR/TSmOUp/L8h8OPYTa5QWbyzIHZhJ+TfOhWB4rvCNpdODUPIs9HTs3Kz9uu3Yfrdt2u22faC2Ep\n2IaJjaCF/mGKhyz8qeIpbBanwATsPTkJo5YLl+Cl1KHJNTeRkgLdKwIEXUp85zWykvTqixrxgoQk\nLK7Z6jlQjLJX9hxjRn2rRYEFg5GW6yPZkOtlc2YfUgP7AcE0fg+mIadYUZxfuTxJUKFcSJ/cPVoB\n8ymWq5NvXuFiKCfQRmcJ62VKzZMluSpS1LFEwCuvh7UdOWncvAtFOXRd4irQVGWr51jCIiWaZzQQ\najnZ3mwNcE6W5ySIsGRQyqDi8jTNMVvK818mCziOnJ4nh010CgbEshEcVjNmhAar3Lg/hLotAAAg\nAElEQVQKYA4Pj3A4XlGMFbBrMldbCsOEoB5qZuqHI4zX5fmQFvAYRW+FFW4RTOQEFuarytZa+qOs\nCDmFaKzagQ7FJSwbZzg8Y24+jtFaFxN95QaUsymyA7EsD2fA+VjWQlyPoEeEYAebCFssJIrkWt3y\nNcARXMG7wwNUJGdpKhvKF2tqn/yYMUwEtNwcv8CYXIxr2sXDj9l/dw6DlsDiUJ5zszmHT7nATjeG\nQZzJbGTg2Zm4yqPjE4wZ+94kL0TDHOJuX55fLbZhEppthRswCvneLHiA4vE/hSQrhqEQBRaONk1s\n0vQ/DSocHROY4VQYPRaTyQ4vELHabzEkOKZroC4lep2mU6iSwp1tIOdLYTVnaDoyqk0SiwS9HtZc\n2WyGF5dQHMgxZjg7oWR6p4VqhZknb6HyNGpvX/qjmygX0unzk2NcnlHQNXkEi3UJdYOp0FEKPRFT\nfJlofEhT8810B/cYkM5JcJo5GZ6OxP0I/CPUj0RjoDuoYJ9Jmq3eeAZTi7JSFR9yMLdg0Xc2TQtO\nxHRb6KFPvz1OMsz5klWMwsfJDDFVo1TbRiJWPvzlCZamXG8Wp/BMcRvWjuVFWSwLTJh6O8gS0JNC\n4QVYuyu1DZcPvoVwJNmjZMJNw1JYTOX5k/EQNdOMtdGEvSbjvFcpTOfSz6cX8pK3qgzKWFWMzrEg\n4OzRJ0M02c+q1UPAmoFhwDTlcIEH77wPAPj28+d4TlCbndTYWpO109yt0R2J+9NglWhsTnGRy7Oe\nzGKURLourRppIi/bE6JC95+NcLgkI9UM2CcIqTxeYBLKvDvo48kTbvbMVC3HGWxPyF22xjdx+54c\nAL7SWONGPm+auME0Y9SV9bgWeigZO7BGCcoJD8BwguWEsY+zJWyHYrufs127D9ftul23z7QXwlLw\nPB8v3X8NG+4ujq0PAQCNbgOOLybcdHiK//X/Ze9NYyVL0uuwE3dfcl/e/qpebV3d1d0z08MZDvdV\n1HBsmZRNm5bsH5JNmIZsQ4B+WZYNUIANgT8EGwYMS4Ytg5ANi5RpEqAtypJIcyQus3B6erbqpaqr\nupb36m25Z959Cf/4TtZMDyRNNRumSsYLoFHZ+TLzxo2IG/Et5zvnN+X9o9RD70BYiV/dlNPz+25e\nh58zIt+04PaIxYcN2xBzr9HUSEgBfv9UTuPDwwk8cur1nQANRtmbYQMp+R+dvIZLSfgGA3V9BzBy\nOcXr0wg2adxa5wkeFCJq8tU3HiFnrX/REIvnktOBVa/r3D30CJIY7gYoCtJ6UxPz/MFXcHwokfzR\nmxZ6V+QUvLp/CHUg1sH16Ca8XVbOEQWuGhNoBvugNba3JeCWPqlQ1WJNNAMbNU+0hS+n1ddfP8IX\nnlCP0hqjTSCXZXm4QVepvemhz1x5zAClEU0RxwKQWs5L3H0kVtiqXsINxTqozjL0qKRcEusxWRVY\nRDKGTx7MMOzJtT++66AfyElp2wH2Xfl815f5SMoZvva78r23JxFyX248T0o4uYznzcEKfldO1YO+\n1IGMyhVqmtdxWmOvIa+PYOGI1vXiSQxFHc7egVgmGy2N1qkESV/pznHvhNmFEvguxvV8Utk/mafY\nIGhq6SzBqcSbh1McUj/S3xghbJL/UxGHs5pheS7BU+daAZuYG68RQxPmvFrWuPNA5jodyXgfGBrt\njvzWzmAPNLYQncXQHdYCHWlMqz9GOraLdtEu2v//2nNhKZi2hebWBnKV4Ir1SQBArU30EtlJExe4\nuSs7/oFhYeOyWAgb2xJk6V3bR0JG3SLVMKgxado2FOXWQv8SEtb397eZ/ooS0OWElWn0uvI/s/IJ\n1Jo2zameButqBpHM2kDGoiR1Q0MRKedevYSdYzkRXvvEEHVIJCAL8ZVZw7wvJ97MjXGFvmxv4wpc\nfy1lJyetZ2xClSyOuupgRXLQUdJEdV9O4GGrBT1mIVG5RnSG8MjeW+oStkNEX3uKNSdDpWsE1K0o\nKRryyos+VCC+7mTRx7wQX35XeetYJjrDLZQ8ucNMrhc5GTwtcQa9PEWLrEjZPEV8ItbNSTlFp1hD\ncMmY7fuwCumD6xUYrcSa6jm72NmWC74wGKLREUunaRBhiSV2D2Ts3W6KilDxpTlDvJ53ow3blrVT\nCKYZu52rmLcl0Pja5RyzgYzRx8IdjGMGkH0Dc5LGNpT8bmzVOF+P1e4lDO6I1eDbFnxiGpYMpGwr\nhRWDslvbPdjEJjiqQOVRZq/dRbAja+uSJ3NzNrExWpJ6L+uj3ZZ50sjRpYxgap0h6AiI5YSxjGu7\nNqqFxIHauwNUtqzvSewiBdnCohDJBmGmz9iej03BCtEefjf6yQ7ebr0OAJikKTorMZOu71zDn/sp\nUnb7DgpSnJc0Yc1mJWVrAPTRCs4au+x40FRs8txtbNM9aJDy7ZUyfFp9qScLJJT6zusNbHTE1F4l\nNmaOLFhjLAt7Z+M6eg3WVJQOapO0594CO/uiGxl2e6gz2Uwyak0mJzNEm4IF2G0NEDBI1O534Vi0\n/cjc0dzy8N2OBJRWMxcBay3q+AQZg6MFbGSsIlxS1r4MFXxyVzpOG0Ff6kS61SHOF+J2jTMPrVrc\nh82dA+lDr4Ub16T6cBydY/qA96xPYXWZOchaaFJox6kprZ4E8Ljgf7C9i9UO4cVxD/dzcfnuf0nj\nHVK29Ww+ELsWNg/koTh/UsCppJLv1WttvBzK+4d2GzFLtPuEM/f2BwgoS39TByjb8vDOziPMBlKq\n7btdbFuyibRI86bMCFduybU7T9pINd3K7RAvNWTsC7vC4kjcB5OU+stjBxVLDiYjF20yVHcSEx1P\nNoDrtLfvmxo9QrdbKxcbl2Wz3LheoEucAgIfBaUEckfWd7fTx+Cc5Cwqg7Z4GFgFbLqCw7KBRiD3\nfY3z4XoK5ljuKXFGGD+Sjszyxzg/lrXqtE+weveDCT5duA8X7aJdtPe158JSAHKY1UMsggjFUk72\nk8MzdA5IZqlLbA9l1zVbu7DIC5BpauQlEVY0tct2DsNZE2NGUDyNjSCBBTndvJ64InWhEGRiJsdb\nJfSIsubFbaQjag3qDO8SQRi68v0rWxUW5Aow5yewbFahOQXMki5NK0RMS8FZn9wND75DAphGG25A\nnYLGHpyQcGxD/m24AeyBUL5tVRYqrAlaB5jx6CrLMaqRpMtyFjDlJxo5A7TGIIBJeGfWCKBJKjt5\nPIJmNao/knFzmzZKQ/rWq4HmgZxsltlGXssJZSOHuRZcYVFPoWOonNWgzgC7bVLT1R66cxF7qW9W\nOLtDGDOl/nabH4OeUGF7/i7GsVhCrzUbSDoyZ944xooEL8uWzHkvDdEfiHkN1QRIaRbYLXgnxFBY\nNUyetoqkNsrWcHxZT/bwEN0edTTMLuAxYugYCIl2Xa1kXSzMc0wJhV+MV0/TkJ7jIGcF4ykJgcs0\nwlFEbYaWQovEQDtXPgGHlbIoViDfK+pI5vR8lUAxbR1ubEGviXMKExZRjA13Gz7TqFVJLEQ0g+qJ\nJWGd57DSrwEAJo9WmJIKbhEv0d+gFfqM7bnYFAzTg9N/CY3JBGYh7oO7sYsBhTxarRq+Kw+e0dAw\n+GAZBBPF8wVsLlK7AFRIH2rpow6J21/FIHkuDNLVWIEFxYG2EgW1kIE06jYaLUbZZzXOJ7KgT1ne\n/EPqALOpJPKd0TbaL8uEBysXekP6qecaVovDS8BO4sYoprwPp4GAUGqjEcGo+NpZq1jlsNvERZQe\n1Do2EGsUBO+sHi1QKHl/tSCRxo0molp0J73ShDsQpqPqOIYFcR8GWxvYIoQ6IFhMFQEsbrKG6cCj\n4IrWPqwGawZiwKJeo+uKa2MHCjUBTaWvYGnm/4MOasiDteHY6CSykd2pZfO6EWu88BFxAz//1Ufw\nIe7Mlcs9WIYAeZLpu+jTFRxYLHFvFLBtVkm6CjXnvfY1FnMKvLgKJnkT64GY6EYGGAU3kGWBgJuM\nY5qwvLVgTIKU4KSa2Atn1cUuiWH+1E0Pv/5lmcv7SYWf3KSoL7U24yKHpeT+qvMltq5L36wiQatD\nrkVvA2okv3f89x4AAKaehZcuy2ZqBTFAtvJ6aMIc0021IlgEQ1kN2dwtM0RdyGf1dAZHE+atCkTM\ncEyOp3grXuP+n61duA8X7aJdtPe158JS0KigscA4exeRQSq12V2cNYT1tlmsYJN1y1lo1FtizlUl\nIbNhBb1a8wsGABmhcy+DqiXAladN2NToc+gGKF1AEf1XLVKk68QyJnB9uWDHnSCeiDl7SAIOw3Cf\nBsDKoUZtkbcw6KMiHZfyQuicpy35FqpJ9ZTSra4mqELBGxhZjSpcU3AR/2CPgYimuB9AF2SBLlJY\nU3EZsiLDYSbvH8VyYgzPhrAvkcHa0rAVqcaqYxiksWsad1Eqccdqir643SbMmZzmtduBLhgwzH2g\nlPur6hU0RU3AzE9ljRDRXbFSG24oJ2UWvYOIltzCmOMx5xXEJkzsWxhF0rdrQY2xK2Zw5ZxhPpZ7\nmuan0LTSPHIXDPrb8GjFmTBheGuJwAR1Uyw2Fxp5yLGj62aqFmolQd6i00BN3IQKHCiq3WhPo1rK\nnE1j+a2T+Da+TtnAa7vXYH5dMj9eXCNPxCyPCQtZPEyxPJP7i/MSV2ZiEWxffhmtlfSnbAFJLuPy\nyJXxrjIHzvZNAIDfbKD2WCW5nEMx41UbDdgtImvVWn3aRkXrtjQ05onc35lho6IYjLYa0LSGn7U9\nJ5uChazsoWu1cYdMSaMSCKM1QMhGXgrgyDzowiA7Uc0qtXhSIq9IVBm3YW6yHiBViCuCQhCjw0VT\nLkjm6VmoIjHXqzxCspTFn+U1fPr4eboJw2e6iGxDhTHAkGXRqtlCRtYgO9FwuuK6ZOcLKOL5I8KZ\n09kKCclDA7eLkhqGVjdCTT+xpPFWnQXQnnA/6rMxDAqm1mmJJR/eJ6sYxVzu7/xY+jMNHuH6SPrr\nmruoDImfhE6GSf0AAHC2NNDOBRjVWjJNqb4JnCqnESJmdmojRs0H2pn7cIas1mRpcu62sDpj1aJ/\njOkTwq3T6mmNSfxEwyEZTLclcxO6NhbMGD05P8JVpoDjrES/lvv77GIJl/TqNjkHg3AM31y7f0NU\nxXrcLBSGPISlDlDX8ts2qx2RKdSbZDc6qlGHco1iOYemuGtRVchYVZqTo3I6dmEzhvO1B2McL2Qu\ns5WG2aXpfkTxmrpAStBXGWvMxrLpzQ6/iuGWPPTmov9UYDZbceNVEZaPZKMPLAAJSVyLc7Q6snk3\nNkpUJMwxuiSrjZbQlczTcn6EyWhdwWnj5FDu473yDGP271nbhftw0S7aRXtfez4shTJDPr2Po/k9\njMgcnE5MqCtiZs3KAo1zgQ/b3gsweKJVqZw6Zj2HjgnrtAqYGaXb6hUyWq3aSJE/oWTblgTnzDxD\nnsr1ltEMOYEnq3iBZCKvT3SGkzGVqUmZVhczzGhe2t4IlU23xEuBGc3g5RPUuZiXy5FYOcl5CdUm\nzXoWIKEkRjEB7IFYKTHJUvK6hvEOKx/7FsJITnzD08gpOFPNJ3hwh/wEBFMNjR5OD+Q+m8M+3CUZ\nfpePcB7LSXrycAVnVz4fM+Box3MYpYxbPH2ElK/NTcBXrOAc2rBtyUqoptxzNU0QU8hmcThDXNGV\nWKQ4sWSeotX8qbZhGtC9uDvB3Udyz6a28YJL/gYYGLvEdYQ1xiygshIZw87DCgE5MKpgiVIz2Jw0\noGg1GDARlaKxaD5k0LV1goLBXLsaI6O1qaopNHkeC6WgKHN/asp75/oB3iDBz6MHJ1iWMvZJbaKc\nECNBDsFNvYXFiVh3h3WEL74rgdb9UmFrKNkVb2MT8bmsgXYqv/W1UxuuIUHgB+8dw7ZY5brh4wY5\nOM1JBw4rONWCJ//qDBn7U9cG4ieyFp6MZ3h3KnPyxoMIioCxZ23PxaZQGxqZV2F6DsTHMmCbWxZ6\nDQKSgjkSshs5ZQSf0dc1Gk9NPNA1hlmmMAj6MRYddKg25DsRnC1G+Gl+a9/FU1iHkaOgrxbXHh4Z\nYs4VIxMWdRtKagUkSFFSg7I3HaIzFHOu4VeoSYgK00WRUFSUEXm3s3oaqygSjZjReVgZ8q/KwlPU\nvfCtczgvSUzFPwvR3CPhTGABLembfqSRkq+xTOS9Mpwipz5Alemn/mk+7iA7k/sYbpnosApQ9yjx\nvnCQ8v4XhoLJmpCOvfuUHNSABljnUSyY9o1rDKmJ2bx5Dct7stFluxmcQzHB480BVo+YVWHKtvNy\nhsO5fLa1pzDblf50AgPOXLIP0I+x58nn+3QfVs4c8Uqu7Xg+TKZZTb9EayEb8un0GJrgnUXvDbnu\nxEBzU2JJfnsJtyebkzFSAKtnzbKEt0EVrbGsw3cX+yjf+SIA4GO5id9aMi1YVGjtkCPUlT6cLiKE\nzMrcDDIEHM+75QqvpbJRt8IIDsVkm8xa/NC1NgbXJLsSbN5CmcrBEIQO2h2Zdz0oYTDFWRRkDqtK\nZIwpzdMZVmTTWoUn8FZrQh0DZ9Ufo/uglPpLSqnbSqlvKKX+jlLKU0pdUUp9QSn1rlLqV6gJcdEu\n2kX7l6R9GNXpXQB/EcAtrXWilPq7AP4MgH8FwH+jtf5lpdTfBPBzAP7GP++36qJAdPoEJ4t3kfiy\nT3UtG1YoNQ6RcQwzYb1CmaBTMjBESGlU50/1IZvBBtSa6rpjomLgR88KJKBpy6CWASDlrjxPKyxK\n2V2jZIwplZvHVgyT7Ln9FuXN6wIxsx27t2yAuImyCJAtHgAAkuUc5brsf8UMx24Peim7eeqdIRrL\nDj42M4CArBXr4LuZAfWHYorutXqocjGFfN3E7ImcDrEZ4wl1HC3yBNZpC10qDqfKQLFgPUNyF6XH\ngKGRIrUkwFjO5QSzNVDkVLqqMpRUaE6MCvlSIu6GNpBT4AaJjMnZWYbWmVg8ZVIgpbk/3ahRMup9\n+ugRmtRSPJ3K9/vGFj713Z8CALz4wk2019D1L//XOJ3KSRgm86ckKw5rHFQcoUMOCIUnKAg9n1oe\nKgY2H2GEaiKn8HwiZnlYbWG7EPO6YV5Hl8IwHecSPNLjm67GUssa+SLduC+OPo/TiWBAysJERpo6\nBUU4GXCf4KddtDDfI6Xdez5cQqnTWYHjU7GavEs2DrW8nlisytVTtNpy/+jMEdhiedhlG1aT+JRi\nBfRl/dUMUBZliTkh2HntYEE1rKM7Bb5Bla0o09D+B3vMP2yg0QLgK6UsAAGAYwA/BtGVBESK/k9/\nyGtctIt20f4Y24fRkjxSSv11AI8AJAD+IYDXAcy0XhP64xDr0rxva0qpnwfw8wCw1e8iGye4Oiqx\noJ7hkWNjl3RXVlThjLJo/VrD0pRTk7gZHoxHKEmptb1bYY++fxmaSCLGA+Ip6lS2VasvwTfX2UdG\ndqQ8miNh0Y0OAxgrOXVc3UZ/W763O5RT7sk7Ywy6hLhGLyBknjooD2FahEfXC8xPJGiVlLJr2w93\nYOwTEdjqYcMWP3trN0B8KP1Y2sLAU7x1jIzsSL9ynOBHAjmte842qlhO6dUqxGBTWJgcslBZXQuL\nY5nWjTDDigHT9izFEVNr81rDiuR1z2LqFQlSnuwLtYJ1KOP5IFxAsUq0YXoYtOX947GcYMXjc4zP\nH8hvPKjxO5WcVtfPO2jTojMcG1+8LSfo9e854Nw1cZU++eXmBlpMsz2upui+K9bPF0+PEM4M9o+n\n7k4L3QHTz8kAdcnXyxiLllher3pXoG5Jn5vkZliVgKlIFDupsJoy3rHzBEYkUGiroVAdySm8NZJi\nLuvhFAOHuIJJ/VRHBNA4ozjQC8QdvKFNNFkclwcRzicy/4+SMa4u5J685BA1xWXOadkcGyXsr78D\nAAimLixWZW5t96HGYkE4PRfpSCydXBGnUSUwiOJMVIRRInPi5QmMfJ0CPsarLKD6Hx7jmdqHcR+6\nAH4awBUAMwD/O4CffNbvf6sU/UtXd3RtLLC4ZCIk1XkzbcLwGXxJtjCr5AFphi7CUPaZ6Fgi0tGs\nQkTQSeMsRszyYz2uEPGhiM/uIqQWnyIUdXc7R80HwWsC1Vy+l6YJQlZUupmN0BOzbactT/+d/Bxt\nn+QXiyNYGaPznWswCaXtdE5hrWQDeO9c5OATq0ZAN8iyTBhNBiXHIXwtn40SyTLYwX18+b4sns89\nPMLVrlyjN6xB1XKE2xu4NWIW5Jp870vLR3jwtkDF+73X0KnloUk6NuyINQVLH25DxsI2yTRd+XAY\n9S+yHDNyVMazDAYDumHLQXQkgbT0VAKbiycVVseSwflaMccbxB4YfoQuszJvnVb4MmHD51+VjfVP\nf+qTaG1Jn4POAF6X2JKoj+WQtRvnPVjMw89ZJ7JtJDg/lQe3OwzgbcrcLE8yKJaXH89j9FkSH+SS\nfYgXr6O4T+i6egO1LdWsvldig3UH7iJFxID2w7ckW4D+I5y/yYPl27To3yB8+CuWjPFJ7eAGg46T\nyzXqhYzh2ajAOyO5p8F0Aj0jbyQD2G1vF+aWzE3DvgGrSVh500OSUDnsQQuRIePc6u5yjAOEG7Kh\ntfIudhp/AAB4vB2jIubGmldYfEDw0odxH/4EgPe01udaNNZ+DcD3A+jQnQCAPQBHH+IaF+2iXbQ/\n5vZhUpKPAHyPUiqAuA8/DuBLAH4HwL8J4JfxjFL0hmEhbPShUCKh5Ftjq4IiceuxSnB+RnKLIIF/\nTdyHYEdO/O1lDW2xkrH0ATL45mcGuuv3N4bIL7HwhQGgedLAJJO/jzHFOYOSRyMT87mc3JdsAwPu\nnU4tu7ZjFpi9K33I5xso9wn9DRzUm1SPRhv+ZTkVd4wD+axeYHqPcvetOVJqEqjNGt5CjrYNR04X\ndTVCWbPyMwIen8hY3LjswrxO6bZ3e2jdkJPizSMSxk5iZCdiVbkHb+NHX2F6dlJAn8q4tJoF6hXJ\nb8mP4FcOFNON7aAJn7pw80c22n0GOash0n35TDAnqct2hDmLp+aHJQxyR8xXFR6SdPRL8xSLMbEV\nEJ/vs/e/gRf/dQkkV7WBMqOk38cyHN6Ta+e3lyhJljsmXdntysQmg4+9RwUuCVIc7kYDeEfePzyc\n4MFXZa5mqaQk705q3GSwst3XuPGauCvmqg94dCETjcPF5wAAsSHBxfbZCPcbtBC+TVPlIYOHyT3p\n4xIWuhRqMdMMMTPO41WN229Jf14YPEK/LWZ+vpbxKyIUtayxyfYJ+ivpp3lSwO1S83K4hBsTOeus\nVacbKHdJynPSgBeKdXP8ztsoqY49zzIk7gcriPowMYUvKKV+FcCXAZQA3oC4A38PwC8rpf4rvve3\nvuOPVRWq+QRLfQKtKHJatFCzQm41mmJKeGlv3sH5ISvYKNveckpYuyQCKQIUtSwgr9VF7ctvWMt9\nOIY8yKOYJq5jIB7LxMwXLuZHYtRUukSbRCxmXz+F1U6pirSRaMyarK8Il5gSw9+IujAZWc4zFzal\n0deiIa6/hVUgLo+GhiJgR09OkMXygBS8j3t3hviUkr+/Xjh4eUPKqI+WbeyScTjXHqJ4/SBIHxbT\nCI8TRu/PusgIQc7KM1SlrNI8D2GzMrAoGcmGi2bFTbMATIrLuG6Ccu3DehpGLJuoawpsVw9zPPia\nbDw3bAdHNI132jv4ytfFifVjA2PWhOScg9vvfhVf/R0hgOn/iRvo75L16ssb6FEg18j3sE+3au6w\nLFpbWMZ0RZIemjTL3cSFwyrYW9d2UY2JWSiFeKU/2sEWZQLaCNBpk436bIbHJbkizzK8/aZcZ/So\nwXlqoZ5Lf5SqoL/Fg4jlOcc0kzFc1Rq3M7J7KQ9TfljnNR4RIv/Wm8cw9+Uz7jFBUe0nqFlfkybH\nWFJs1r26C9gUe3E0jLVAMrMveVQiJ7v06vEZspmMbZEqmB1KCRwr5JGND9I+rBT9LwD4hW97+z6A\n7/4wv3vRLtpF+xfXlNb6O3/q/+PWCTz9Qy9exlHtYEs8AqTaQk0W5ayIQM0S2GUDu5dlJ90byr/L\nYoz5mZxQR+eHWHL3rJGjJMuz4Wh4xBsELHaylAuLuAgzUiibfF1YaG5LwDN70ISxksDda80DAMDn\nrbcw+ap06K06x1rlEQBeXCsRN3xURFDOEnI0FhnmGS0Xq0JNrUhDKYDYyozv1aqGVXC3LwoM1pVz\npgVF8RmEGeqBoP82WHDzys/8LP78R68CAA76A/y1v/C98r3XC3xpLriHm0rjjFbMdSLmZr6DR1RB\nzucJ1gmkOAG0L31q2BZsi7wVlHUvbCBOWOBT1YioA1mb1lM4stcI0KCmY8hgbRFPcV7Lb/hJAuWL\nSf2ZP/mD+Pjn5L5/+e6v4hUWXv0DgvJe9vbhvSi0cfHOLkpiRG7NDTxaEQnoLPHeOenmCvI3XGtj\n84cFIXqAy+iYkrVJ7PtobEs/Zu95MBiktm5I1qLeaiNbyTVWYYr8SCxBO2jgt37pf5LvrXExKkHG\nAPXmdIndHY7LxEcjEpdoYSxhNGidsmDqbJri/lT6XpQZopiQb9NBSai47wEB1++gLUHnK9dv4doV\nCdaWfgOtpqyFr7z1FrLLMq+7J3egNmTMf/4//Euva60/ge/QnguYc25oPPJKBPM5miOZrPPwFOac\nwp+ehS6p0a2Oh72+RM6v3yDEd9bHnULMxPHCh1nLA1sVFjKa6FA2+tQrHFBVyOkGcBOW0PYtBLls\nMsN+ihk1Bf+XR19CkoudeDyVTeql+RK/+y39/1alvje4QH5gr4v+JTH9rk3lGqNZgcJkdZ5hQNV0\nHyyNYk38UolLtIprLGg6VyiwYF2GpU3M61Penw/rTDas8V1xffKpgf/or/5nMm6XLGz8I9kI/soy\nR03lqK8ZwDUK2R6SoehjBwfoFQJ0miYultGabLaCwY2u4TtoN2VcOmSIyuoCZ1O5j0kZQTNnt6w0\n1ujaRZnCYH1BXUpqtVQhOozt5CsDxGbh2n//a/jMbE3Rv0KXD+n1LRmfv2/P8OosTCIAACAASURB\nVG+89BEAwI3tAu6dAwDAb75zB28a/xgAsDfrwe6LSR+xivS98OP4k4wNXW9n+L9+TWojfq3/C/i3\nZj8h9/dqiUnyAgDge16SAWpWOXr+WpHLxhaFVZ44BiYtMderkayP4ImHu764h0ERoiAv456XwBzI\nmm14FpptVuvOJCDy9mQM76F8b5IasDICjxwDlZa1026F2OTTGnVkU9jpmzhqyf3diD0cncv9p8YU\n5kO5Rr8Kcbx7wdF40S7aRfsQ7bmwFFDW0KMFTuYFxg65C3QPH6MrEZYemntyUuz1L+NjLx7I+0Mx\nix6aRyiXYjoZcY5sRFZbA1gx79wMLXQJwrm0QZ3BzUtwLQKkTAXFKrpB+g38TVtO4HlegEkA3Kb8\n/O1/zq3QIsY3ggn+6ksfBwB0CS/GqYMwFL6B+dkNwJfXTtKC05d+JE/ks2f+XXzxq2IdfWW6QhrL\nybwwU9gZVa7tFQzSmxXEAdw7/Tp++1Csg3/n5S7+C2ZfamDtoWBeAV8+F6vh5Q2KlHQydFL5LXfj\nDA5Pmq6bokEquI9c7cB3OXaGjEVRhaj3xfR960EHI09AOvemFhSp3iZzEwbpx00lZm23DUwnMt5B\nqJBTnOXfNcZPxxAApsQG/CGDcpduRU85HT566Sb+/kCSW1/7wu/g8SPpx93SgKL6d1NTPfxLX8OU\nLND/ZeMcj39L+lbPgH9k/RIAwPgnFn7s039WxoB4jM+0P4NOl6Cg+TmSvrzegoVsIYHbh18jlHwV\nwdqVNfnylRA3AhbK7Q7hs/gptBswLBmDalP6uD9JYF2l9uMxoEknOB810N1hxWQUQG0ys1PKPHU2\nSsRHMi7Z7jl6DyguU1VYvSOWx+O9AG1yjz5rez42BWgAFSq/hEVQkK9qtF8QkIafFNhlOulgYxO9\nAxn4KGWKrRWiH5Iv79Y+RqyD8AoHE3mJYQcYEUl2eV3G2mrAYyXmMtFokmP/0L6FW3fk92Z7X8cX\n7ssk+Zb8PaGf+09rf+6ymHafvvUJDK79MACgz3LifKtEq5SIu7FdoaIbM4oUBqa8PtmQDu8v2+j6\nstjc33+ARxTTXSwLnJFHsAMfY/L7a/qbYZRiaksfJrEC/wyl8DRy7hsKGMo4/5Uf+BkZQyvDGw/k\nga6sGGlfNpNNZeIKsfjXN0Kcs3y8R/KWbBXDDuSHp0aG/pG4f40wRpNwld+2VrBqia7XlEiPXBtx\nn7UY6gCb3LH09P3juV6guiXj87f+g7+GrZcEkHQy8dF7KH2+ttPEyaFsEAduD4eZHC77NPfHRxaS\n3xNzffQkgbOS+0vrAnbOkvhlG+dfp6Drx2QeJtUhmp+WA0erNjqkn4+WS/SUbCz3BxTrbfbwAueh\n9/Gb0AtWlHohwpRZhK0QxVIedNeXTarnZ/APDgAAzc0VbEPWZ9Qv0dyjvsZkhnAg677HuhzLbuBR\nW46oVmMToHZl1qoxItvX9sEPwiyfEcrIduE+XLSLdtHe154LS6GugSyuYVcGShJWbIQOLpFAI7C2\n4fRkx2zul1BT2SlDRsiTzMB2jxBeawPXKGWeBys4WszgOi9RkPk2ZoTfNQJUyfq0MlBQPdhRNl7e\nF1yAPZnjwewBAKBLLP/bDDx+e+vvmfgLf/1fkz7X+7BpEtuHpHFrBfBiiqwkJlJi5pthgdKW3x4G\nBKhYNdqKVHFXU5xRuej3Hy5QR/IbC8vCAVmOF8wGxFWBRw/l79nlJS61ZSzmS2DGzMH3bw3wZ/5t\nyUpcvSlm8vHRObaZGWneddDoyeu9rsI18i56nottl1T05DnwbBcW6e6vh0usrsjYXpq38YTq0J94\nmD8FMtl0g2ZJDaOWvqu9Er5LJfAAoNgXEgU0yXXw7/+kZLl3PrID7UrfWov3kH9ZXm/G97BJYRh3\nnKDDAOWQvJraq+EtH0g/KyBnVP++MjEgBeA0j6FP5eQ9+XWxDqxfuAbrWCo866EJxTXXbfpIF9K3\nS6WsvdNLLWxuyVhs5Cu0HTnxc+VCd2myzQs4VK/SdI3sroZFshTf6aEkd2PTBxRrftxWFyV1UZ2Q\nZ3k+xQZBZmmxeApk0+c7WC3FXSm6I1xvErL9jO3CUrhoF+2iva89F5YCtEZdFdC1geGRHBO9T+yi\nuy3+914jhFKkGHM3kZPkNCNbcp2dwW3xNAuAkEq9lmrApExZkS+QMsVnnBN/atvIS9mVbbNAwdST\n75h4SIzAzLUwpJii02VB1eybloIG8FFKif2NH3sRQ/UDAIA2bCQMdhWQNFwjvwaDrM1pssKiFiRd\nEXfhdZieg5wYR7MjRGfyepomGLPCsdYxspDSZJEHu8N4Rym+fFFpqC/8bwCA25/6izB4en5kC1he\nk/v4qes38CILgmbMj3t3ZsgWJGA1M7y2K9V5OwMLwUBOnSSq4TDIaXlEShoW5kwtGovu0xM62ynR\nJwmq6V9G55CUbaHk5s2ogKb1458vER5If1RpgkLKKEKNf/VAxvwnXhDL7fj2DKonFt+Xf+MN/Oq7\ncq8nIyDPiJrUJVaEQr9RyRx4hY05Lci4KpBmtFygMWd1bKUU3mP15L3z3wYAzMtPwCA/RWCaMFnE\npE0XgSvzl1NJ+6OJxiud1wAAw919OIw5ROM34UKwI7A82NT2qAlph7WPilaxrXwoh0VqzgA6JvFw\n7CBPJX5ShzLXdlCj4cvvhlEEk5Bw1UvRMCWulL/zJuzXfgofpD0Xm4JWGpnSqJICEw5IURSoKAHu\n9LtwljS57AJWRFIPBt+a3SFMn7DcqgJY9ebnBooGq7hnBSjuA5OU3XNdoCBJBdoFohHxDVYIj/1w\n/A48j/yJhJd6BlCBICMD+JlrMkk7n/kpBOsVfTKH05XhNc9lYwo2QoDX0505/HtUiwoTOFjrA7Ki\nrauwjGTzGQcTLAmg0Q2FNqsBI1XBqeSBtao1GUmC2zPZID/yZIVPQwJjP3rz+/COL5/9rkaCkgzV\npydyb661RLsnn7WVh40htRi3bAQezfzkGEmbtOS5bDaWrcB4KFQ/hZoQx2AHaFmywTcaBvR6Xh+S\nZGb0BEkiG0SpA+Rk2H6lpTAgLPwfrCqkS7o3LHveC3OcfVZwGr93fBuLY3H/zGyFiGpSJYCaFZ8V\ncRNxncFjBWdpAgUf7qIGFNMdpsZTOr0vEjJ8+wtHuNWScnb/2h5qk7Uk+ltwGNFaQcxH0JQ1lB8e\nQbPy0cqaMCj6a+QpigbnXct8wKuBmALJjQIqWi/UGaqUPJ6VDzWXMTRCUrxnJeDSHW3UyBPC6I7P\nMI+lz+72dUQZOQaesV24Dxftol2097Xnw1KoFcrUhOnUaDDg1CkNVJfktEq1gtHnTju24TR5mpI+\nzWsqgGar6w5RkryibDswcvlsYQcwWawC1rwHWYkJWYar2EZAa2NirhDl8plqCLiPSP/GFJuhDNgO\nKxx9ExvfLyZcu7+BjPJoVTTBbE7mZkNOgfj8HjKmRZMowfRUcsmp5SLYl5NQESacHjUwXcp1J/dt\nWLbc67io0WOgzeo6yEiuYFAW3VgBr3CsFm4Cn4VbT4ZLBJa4MZ897uCFrpyOhw9Yd1+Y2GuSmm1v\nF3kgr2O9A4tmcN1UyEnDZvAwq21As5IvTZbQRGbqwIAilNipYrQ0A4x9mYPZyEahZCwGgY8Oi31m\nhUJBPUqrHuGukrF7++gBAODRYYL/+76M8e0vfg5R/k0S0zW8QRkKFbPGHq2qUgOa1l1R1LD5flHX\ncMgOXiigXRL+Tdm5cXUPhyz+2pmEaOzJmqzrGslSPttkADce1sBKxnsRtFCfSipQuQodisRUdQM1\nC7pUwGKuwoMiGzfMCQoGV4OyBVQkbs0WyOmmKt5cXfioyNqMqy7qTCyo88xGQAuyDgPUBd3lZ2zP\nxaZgKCB0a9i5g4J01Iav0Y/INOzYSCm5bm8sYFNtx2EFZFH7sGieVlkFizj7JM5QFbLwijIHUlaZ\n0bdUtQXPIK15NnlKL7+YmYgp1X48cpGzVmLIaPFjz4DNUtfh9T5effGHeI0+VC6Tf7J8B8mEgis1\n+Q47ClVJwZJ0hdSV3wjsEMaYmPqQtPXNHL2IrL+7PmqWcu9oDYeArNL1cWVHfHGLZd9ff6kHc1tg\nwD/iN/C/srpw443X8Y+vyfc+qQPcfktev7eUe+tfslBmspB2swVmZKS6ogu4uZjapmnBJEzboOis\nk5uwaO5XVYpqrddZ1TD5mBq2B4/gpR4Ffy83TZyRHSj2M3TIWHXaq7HHzbJqa9SpjOe7d6SU2VxG\neBgJyDzNI9SMGWgNrFk8Cq1ZT4KnJr4RAGBNjOfop5uGsr/pYjgl0OkRps1NuvfxAG3ZS7H8xAou\nAWxG5cBkNa6viMEwm3hCYIiRJChjrr2wi8SUTbYNCyYVzLJEXOLQncHhQeXmNkriZWJ7DKxl61ON\nOGUpvZYxUdqGyWzQcrSCIuOY07MRJ8RnlCt4BWtlnrFduA8X7aJdtPe158JS0AAqbcF2a7QzBqS0\nhYo7sFuW8EvqMEy3Ua9rzOk+ON0KJqGjpuWgoMsQVCkSakXqVYV0LSGXy6m1XMVYMbgYOwtMlixA\nMmNMzuWISaIIDRKS1F3J9zqehcsNOVV/+No2GkRFlnqJxd0HAIDFl9/GOQuJolCsALNsIluIBRIU\nAbJ1xLlRw2vJjp/wVF5kOSbUDTAHDs6oVREXwHlBerAKmGmJSCtf+Bi8uwna5j8EAIwbPweXQiZ/\noA38Hgll3p2tnhblFClh4FkPw4H8vWW7SFmMVqoVFoydGvEc0ZLmOGncGtpB3iJj9rREzgCfXVqw\nyJKsHYVuKNFwc1O+1z9tYEkXJX0So7wup6Y3U3iXoi7ZJEdKSrc37wml3Wr+JuYRtURr/dRlMPFN\nxGbHNJ9Wm16y5LdmysZWmxybaYYhg47vIkeb1sQUCiYL5FLybCw+O8f5J4VfcviWgfwFZrlaHZia\n5CzMKLUnBRS5OyelBZOBT60PUWtBU+ZuDSrawSzXZn0PGnR5YcIkaY1ZtWCwQA5OgXpNEEnotkpC\naMrfOYUFg8IxReI91SVp3JtCvcAJfMb2XGwKhgn4DQ0sTazZ5ByUaFEE1G42UM7FLAsbJXRNMome\ngDXc2IE1lMlwCg2TlXw6B1ymJKvGFIkvm0h6KIuq3thCnIjfl9UNmBTZOB6biEi22oMLvUWAEB/c\nvfsKbrBmI2qhUTFqb89x1hPwy3gwggH5zCyUxXFiLOEQxJIeNUFlcWybCdxCNj27lGvFaokH3CBP\njqeIIsKxiwI5XZc4mSM4k8+HvlSJzpoV7sUyLv333kJzRxbVb57leJtiMPfKCi1CxG+25PstPwDI\n3HNynmBGkZHRmxUafJCdtEDdkgU5J3mL2/Cg1qnTsolWJPfX3Blio0U4rt+EZ8iCblF56aBfYzqm\n22FFyFJqVLZKPKYCVp0Ad9nPhkUiG0cji9elygLfBqTmhF4hCltji5mfqyTHzVzgQMm9fnyY4ehc\n+jbLcvhzmVfXU8hZPj9IZM4Wh5/DaFvu6fja72Nr+oMAgPbyNYSMO2Q0903Lh2baGwvgnMkA6xzQ\n3Nys7BhBKGsyJF08vBI+SVhUy0Y4YSZpX6GmOxLbLZiMSxgdakm25qjJY4pVAb1+eKw54pgZlVYX\nOlun2J6tXbgPF+2iXbT3tefCUoA2oAsPZjtDSAXghg7g92RHVCiQEEyUzUs4zBL4hzwxbpjoM4Js\ndEIw2A/ncgsmATRVx5WIJgDToyk3fYwVxOo4fGcFh9BeP8lRM4BjbAbYc6TGfmpJfw7Vl7BHS6Gz\n/1EUgfR5GQHFE7EajudbePGyRN9TCrV8ahDg8YnknXdfCHFMBuqg0cTZQnbzLBWT8uQEyJmpSMs2\nbF9O0riwUTLgZGobiUnqtR6JVYY+9m5K8NHrX0c+k7H6fkvhGyxG2istLBiJN6nEXcNBTlh11FZ4\n+BbZsx2gOZbv5Y6H6UOxaB6f0sQ1CmRryvnyGAGzQDdP5ujsieWx07Fx5YpYL20W6gx2DnCJKtj6\nPQ2LOIRoYaNPP+BMleiQCm+N47jUMAFSuwW2h6grc/aDjQLvQTAE//knbyHa7bD/cgK/e3uMrYGc\nqn2/Db0j1/j8Fx5ioOXk/j/vPkJzKBbJ9JBQau8lTMYHAICNew2854sbdOugQKNBHEJLxqe/OcSw\nkH5GfgoTnDPbRjUVF9SCgUks1+gM5WjfiG1oksXkoxlyjkU1NkD0MxIdI1/D8+dr4FgNcyTXdi77\n8Fiw53Y30GowiO0PkT61v5+tPRebgqk0Wk4NI7ZhhtKlztDGcINqPMrBsS0TXlpLrM5l0OY9Mf17\nj/uwL8lD3E0CBH1GvZWCJknFcqRwnslkrBbUXUwXsFayIDa8OR6sZCEo24WixdVwdzHckYV5fSB9\n+N0GMKCtpozVU/ScfbZARFTg0inx+Xfkeu2BTNys2ETNWotxXMAmlbcuF2gzZrI6loVWRSsErNVA\nUkExc9JDgimrNGc50KB/2SIJS+NgE59+Reoabu738H+8SNak2zX6VGWPTzS6pO3bYrbn46928f2v\nCtlIHBzh+GXZWJJ3zuAsJCbyMKvwFsuFHaIA0/MaM9YDWEaNyJbf8y0PNqPvdXuEOWnw+x0+3EEb\nmoC0UfsUBe9psK1gPZD5mzQM2NwUfmBf5umG4+PJFfnsj5Yb0J+W+Q3nL6JLdqqDratPazNmJFL0\nnBr1uWzYgQuUM1Lct0aI78hk77tPkM75QHKzybMUl67I/NU7Na5Q/3NyZK2z4LhqyO8mVonCkr4Z\nRQpzxXXRCdBsMIc79VBwcZlMN3oNH0bFcauXKGzZTMazU2Tk4FwsaqwYrxj6VAVLAvRajCMsdwFu\nBBpDdNrMYvkOmvZay+rZ2oX7cNEu2kV7X3suLAUNhVyZCMIKZGFH7JhYUjnXCwoUY+onVimWBO+Y\nh7JzhttHKLijrool7EJq/d2tCnksJ9royVfxZEZzbiF58M3hEGiTcdkI0c4ZwDEeIuLJu8iPUVHm\n3vLkFC/hwOzI6bBTVihpeUTTGBWZeC8lDmaM0J+xxv6dByPs0DQMNwuUI0qu2xlatkS1l7b81lm6\nwrus7aiSAkWy5nDUGDE3n+oMY1ZH7pOubj/Zx3Wazl4/hKblsdevsENcQD8r8Jg5+4QR8jt/cB/2\n6n+U+YhtfOForSC1gJ3TekGMe7REN305iReNBGuu4Gld4QVGwO0uUDE3MJlk6Blfl88vhROg4dcw\nPelPz7Bx5lCFqmpjZyAXiUPgu10ZrySTuX4nzWFRSeThoMLp/yOWx357gc1NKozbBhpKTvSztZvX\nu4XZQrAObzy6jz+kK3V8Z4qcXKDnVfU0YwC6BOU0Q3Uq19i58iq8hlhQegC8zqrSfIPKUwuNhMIw\nqyyDyayMGZ8/dY/MQRNuIfe6Ya8t2jkyRp3LDGgR8hzVFoj5QjgEWgbZti0Z46ATAgT7mdlIBh2A\nGaUoLXGRA9cG/A+WfbiwFC7aRbto72vf0VJQSv3PAP4UgDOt9St8rwfgVwAcAHgA4Ge11lOllALw\n30KUp2MAf15r/eXv2AkDGAYGjMRCwVN1V9UIW/J6no/w+38g2n6vn5So7S8BAG4MBX76E/pVuEQH\nqnCJkpWDTt6BJsLQczM0Hcp7URTl8WwCIxDcQN/w0G3LSROoy1gmAmPTFlAp2ZkzniL7TYWagc00\nNREyJzwcmBi6ElxcNDqIGRxtP5DfauoKLfrIg6xARm2JLc/GLCJLD/UW5qszFBPef2HhKtGbyqlh\n8ZQ/T0r4tbxv0ydfXT9CwkBdMy9x60VSeDldeO8ImjKfuXDKNRxb7um2U2P2nlggUWog1fL3w3sF\nWqzjn7sKr7VoTVFz44VmgCOyEr81UniLv3uiLfRciYmkRYF4ItbLj9+SUzfc72CnJmPyeYYVg2E/\n5Fh4jyxFxuMxFqzKbLOwbcuqEQ9knpYvDnCg1yelhhnJNaxoCXtPrnOZKd66b8OK5JSfzN9Gj1Jw\ndxITNy0Jgl4NNLQpp3S64HiuHiOtJYhbmQ+BRCw6ZQaw2nJ/0Zh4DPMcrQ3pW9NQWC1kXF5/8xiP\nKRdouAle3pJT/HtfkP5sqc2nqUzXd7E6pgXhKYQMQGaVwuFjWdePeZZvGY/RaMp8HHR78C7TjNMx\nLE+Csaa9CeMDErY/i/vwSwD+OwB/+1ve+8sAfltr/YtKqb/M//9PAXwGwA3+9ymIBP2nvtMFlDJg\nmz6MYYa9ngRfhp1d9CieWpyVGG4KNdsn2hUUockbVIjavLmHuSmDapYWqiOCgvo5FOnDWs19RJ6Y\n9i/S3l3Gc5SWLLbJosBGIBN3oo5hpLLABq1NHFyREm5Nrj5HNzAgLdelzT6sgFyDAwdmQtPOMbFg\n7r3vXQYAWPkJ+hSCLd0MISG8K6eBgJuCG8hive71UDJA2Z3XqLjpvdrycVat1bAMrAh6uU9cxeVR\nD8GmRP1Lx0dQSoAv2LuCj+bU6bRSvH1fFuz+ZUb37QCGlgXdb7tIYnGx6oaDmKZt27ZhhdL/Vwey\nGJMK2OjK707DY1jnFPBBhZCRzTiboOImmpNyP0MT83LKa4S4zvue5NfwyS15WNKN+wgWMj9NCs0O\nX7bxUkfu72DzB1AfyBqZ3v8tFI9l7ZQNEzZ5JZdKnoggMeGQj/LW5GM4uSVszje+aGLGbM4LZhMP\nN6iuRfWmZdzEO0cyVs5hG9vbjPb7NuxUXKhMyXub7S30XOI+tvuYZWTHTm0E5MRMqgKtjqytktSD\ni0jD5trzMhu+v84IWWgzMLt0NdqKkOYxJex7PahENiZ7v4+KcemFU8OkmlR7J4TBrNuztu/oPmit\n/wmAb6+o+GmIzDzwfrn5nwbwt7W0z0N0JT8Y7ctFu2gX7V9o+6MGGje11sd8fQKAKgLYBfCtLJFr\nKfpjfFv7Vin60DbR0RX8OIR5RXZ7q2fCYpXh5cE+jJ+Q3bNaxShDOYHsiFoQTQveGl12OEbQ4c7o\nuUAiJ7DdvoJ9wkfLvphhZr6LJQkVFidznBYSwSoTF22eND1/iE1yJHgkLOkEJj7RZUouDOAYcjqY\ncyDoc0jTBA1bhmVCufdL5zacy1Ln3mpex3wp/QwfFzBaFDWpZA+99dExvm8iwdXXT05grFNPZY2X\nYunbF+wFAtKKBZn81iLIYZcktDVMnFHbcedwCh6qcE5DbDFH7vtiwrdxgmu70s/jVOHJtlg3qnuO\nLSJH47qB1YLS70w9Xr0xRJFL3z81K/F6W8bW8wNcIcXcK04b2l9reFDzYFI8rVZthhV8isScFhNc\nnsmRd+naJXTv0uobietjPukjJL32zscrnEYSPPS9K/D3KCN/eQiDjN4BOSlKbcEL5BrdTYUfmsvr\nJ+EJamInxuUJtpRYVuOhWDEbDRPdTOZ6aVbYYYrTVAEcallcIkv4zAhgNOW9jtnFkBWVg/4OsJL5\nmcUJ0mQtW0iXwalh00ozAhsFi9HMIoKC/EaggO5gbZHQcnNMuAzmRo5GRgt4PrPRGcoY6TqFFa4f\nz2drHzr7oLXWSqkPLDP1rVL03cDVEYBWw8amxSozQ6EM5eYdeLg6lMWbbnZgIGfvSTySVlhqMvq0\n5zAD+pZeBYA52nwOs6YghyNPhw49+BXBP66L9KFsEMfVHHrNsNMLEazdhg35nmo6WHnMuycPEVWy\nwNxAIYjltR02UXKiQ4e5/WIXhbM25RQcVgwmfvaUFapmVafTcNHtiBLSC64Pg+xPZ4sE455kKNpl\niRj8HjMd1nttOIzLaF8hkY9itaPRywkLr2c42l1Xo66pxQvcz6niZPowHnJRmT7sPfnelSubGN8X\nN8WL5OG3qgE627JZXv3eAtntdwAAJ/MabcLKs6iJwF6b3TIW0TLH+DGzR70aAYFj+d0EC1fmaU8b\nWGZrMRdCf5dTdNoyNy+ZDjb60jcjWaDVI4ehcwneSxSmfY9MUfE5Kh4QxkCh33kVAPBTbQujVD4z\nTeZ4+EjWke7IWvCaHkwyLxl3QuS3WD4fRFhRSOcJq0TV6hHilcw/+l1YkPndDjYRM3NgliOUAat7\n2Z8MJQgXgWPZTxmbKreHIpBruKWPPmScC4KYbKuE9mTzS85PkLOCM52mmLSooraRQ9kfLJ/wR80+\nnK7dAv675ic7ArD/LZ+7kKK/aBftX7L2R7UUfgMiM/+LeL/c/G8A+E+UUr8MCTDOv8XN+Gc20wBa\nnglDWXDncgKHRgfNiCbnfgmnlNPa9xyhfwZQMQCW5UtYfM8o2zCJc7aqPWiDVWbFCiqgfUX1aCNQ\n0BkRaKWCS2xCHQ3RZWngR65dQdMVE86ihsAVM0fG76VpA82ApC1FinpI6qt6B05Phteay8mXNRNE\nIzlha2MIYy59bhgTVBn1LgbSt8BTmEGO+S5WsFcMEnZSmBQ1CZIMd6YUhin5/e8aYUECkWZlwCEn\n4sjx8bAU8ZmrN/exeyrGnUWV78NFBpc0d529HVhKTujZeAlNKDjmLTQ6YtoXhGi3Ag+dDbHioBWy\nI3F5ek0HDSpwF54DZ63eZ8lYFHGCyJFAaxhtPj2dXhpEiElddr81xUtbcjqGSsb1fHaMJSnm5tUJ\nNuiitNsreAykhgMXitoKDWedAXHQISLQNw8wq+Veh5tNDMcMULYVDli4dO9tVkuWNew5181Lj5FY\n5K/IamzQ5VueUGtyWmFrl/J/owwembKt2IMmTB1uhQXdkZQVs0kYwGHWSdUuKgadlVvBII+G8mzY\nhDzbDblnyyqRrYuqTlc4pbp0ns3g12KxZdmL8BI+A8/YniUl+XcA/AiAgVLqEKIy/YsA/q5S6ucA\nPATws/z4b0LSke9CUpL/3jP1QivoysKRm+FlxgOcqkbiyeD4p4DekQk38gJgpHbNQGP0DZgzAkU6\nLkwtD4jRsqHT9SIOoSsZVbshD7mJFJWzLnvOsPTIWIR7OL4qQ/O9nTa6Q9WboAAAIABJREFUL0gK\nacpNKo49kBIRtV9jRY4+7ZQwzuQPpZMj8Mj5SKHVYmlgxToBnZyhJn1RVgdr6kbkfBgT3YVBMFUj\ncmGtzetlF1aD9xSmUJSSjwfSt5udj2Frh0pBlo0xsx2NRoi4lteep/CEupin1N38yukKH6W5G7cB\nxQfvcJ7iUirjPbcsJCzhXo1lofX2U/gOSUZywPflAcvyERxF5ix7ifKcNQFU55l4BR7FpJTfamKv\nK67ZncUjbHRkk7lebUA7hEozUxFuWBifkSnp0EG2Ie6hXWuAZdK6yFE3KC5D0Fe5VCj5VJVtC9aC\nrl3aQ+LKb5c5sPLXlYuME+kMo1D6cFB30e4QK+43ALIbzQhRr6wSBTf9xeUOzMey6Tm7WzCn8iBr\n34HibGfcVEwAPtPhVVrBJDFvvExR1iyfz3M02xJv8su1qpmHeCVnbhZUwEQ2nqgP9DIeZK0WSrom\nz9q+46agtf6z/4w//fg/5bMawH/8wbpw0S7aRXue2nMBcy51jVmSolW5OExlF9yrY+Qr2XUbnRBV\nTLBNoGBMebqzVj5Z1rCYfXDzEP6QphgskMIQqSqwjk+iZO2+GYNaGygKE/OYpqYFvLxghsO0EX3l\nDflaLViJ7iUHNwc0HQP/KQTXOqtgDGj6LacwO3RHCLDJshmSMYle0ggrcoUt9AILRSpykpg0Wx4c\nhzBnO0eLBTOhkaFFYhSv8HBFDnRYufRtf+BDpWvREAu3Z2IR7L75ELOX5J7evN9CNJHTb7YirLcq\n8am+WDljNcPjOU0hM4G1Pv2qJubFnGPPIK9tAgWhzXaOhAIwQe3CIwQ7X1RQDAq3N8WKic8UNppi\nwu/sd9DcFPGVt8rfhnEmeLcnl/vYS+Q0fsr9eK7QpDiN07JQEroOc4aCdIV2tEJKncqHE3E7ktrB\nLkWEDLMHs5CA9sJboEU9UT/yENM6m5QyPp29BlxXMjGe04a1Vv9uekgb0qdtZoDecSzMU1mns6MJ\nQrklYAIoZoTySYHRmo6NCuXNXo0WBWLcvsZqNuM9zbE44cssh0EeEWPAbFhswa1JzhP5MHfFXWuX\nB2gOuTCyBUx/Dx+kPRebQlUD86xCbaforyTFNFp1EXU4WYkDz5PJtcpt5Jn4xiV56Aw/gqqINPMz\n6JQAofQUFR/0cnYMlcnijUhm6loOFGtTdRwhMVn5qA28zszq27/268j0ZwEA9lJATFv2BlYNiaf2\n3AAZy55X1QQBE7I98x6MkqkgJhyy8xUsohjzykWxRpusfChXHt7VegOZzaCoRdjrOkhYWZeNMxRk\nZ7KH1jcBK6T9zh83Ub7CNCx8jPl3Zxii54n5OU4O8aWVLKDRiVy3WVT43D15UC6pq4iW8sAa2sf9\nEUuVzVOsDilR3xSzdXY2RH8gQKCzbxwiWQpgJ1mY6KzjOZYFj6nRMicJS+jBMASROtjoIewz1jC1\nMGcpc1P34boyP7cbrPeYxbhRCwvT9GgDoU0VppEF3RAzPr7bwZgm+oPPUdC3v0DiyRq5dmWFdEQC\nXTOGoruJKkM5kfHvMaMSbHfRasuD1zMuQZFNyyxSxHShTnJ5WJ/M5jBNuUbTMNGeyYPZiHMoZgxO\nl6dYrTUmmZEJ3Q0UDVnf6qwF1AQ6TYGUmpiGqlEyBW8w8+X6BQp7nXEZIy/E5fFrD9lS7qnIMmiT\nLtEztovah4t20S7a+9pzYSloVaNwUpRpAw1G4Q1TQxFYVAY29Ep2bm3OYeh1HpfMuksHhSMnV/n/\ntvfmsZZl13nfb5/53PnN79Vc3V09sZvspiialAhbsKSYUmzZiQNDjuAhNqDYcGA7cOCYEGA4CfyH\nh9hWAg9xrMRIIMmTrISQbJqyRFsyaXHoZpM9d3V3zfXmO99zz7zzx1r3dZXULXbTrO5CfBdQqFe3\n7jvn7HP22XsN3/q+yqFSmvRqMMdqIrEuLKalYYWKbVRrYDVcqUKHjioeH2U99v/tZwHY9+c8/4bW\n5H1xA37yj3w/KG2XHRvWtsTFbaQZk0o1A+chNQq4UjGOYNvBWahbHbSoGrLKRxs9vFpTjUeKdScn\nWZGdyKmGmELG725VNNrynY/sd3lZu+EuqsfZPh0xGS6UniI2VAnp4V7MTBOsD3cK6tsyli8rq/GE\nmsTIDpW1jnkkEDf61iBlrCHG4ewqc6VKi3THPJjs01UxmOJiBc/I56sbDrXuiCbI8ZVqua7O6nMa\nk/dkF6zsDkbBV6bc5QXt87CzCTsN8SY+rNyJ13yHWhO+XbuJq+rYUTdirmFMdpjj6O59+jEJq86v\nn6Wt9H44wLaqgR0nlErfF0znuOty71qazAzmIWFPkoB5fIi7oIwvPFjRTsrb4q34aUZDhYiaYYCn\nKtDGTym0M9LaOa6Cr7xKPyszpnMFZzkF2a4kKEssLVeJdtpdvKlSrEVK3RZ7WJ1Pnb7hWEFtzvoR\nZUfeo5pHIFt6Cktb2tL+A+y+8BSc2iFMQpz1ksYZWT19GyhWD5zDIZ4mD6syRRdacl3TKuORKONy\nMUqoXYmHvaCgUFbirmlQuwutSK3tm+CEULOwKa+ekt34+q/9Cl/Yk11sNqup8kVNX7kZjgsSX9l6\nPnSKSPvVvZWzmN3L8nu2ZF4LpmuBXCwdF1c5IibxMU6l4jNHk5MuyWwocfFeadlQFirCVba0YzQ5\nTtnVjshrrSEHmWDDnlcy0HOTFM+Ra5/PungqVDJ0ujzmC9rucCfl8DXZVRMVWzmew1eHWv7bqygQ\nt8nPMhq645XzNUyoHpnCgSe15dUjHb8Zky/gvGXCVMc6o09TxWxa2n06X22yNpJdbmYyOirgc/WW\nZaMhXsoDZx7nwuYFAFJlhj60I/qvyU752Tde4Hu39R5NAiJltp72YTyRsRhXjmuTkFuaJJ3tj8kV\nxbgRDtk5p/G+bTGy8p3cyHzq5Q1S5Ho2BlOGiFfgreQnrF2ThrhKG16XqNZGrLBDQyHhVSMC9dic\nLGfWUS9TcSo3964T7S3K3TnzVJ5JM97hzII0yWSUirOJNelc5B4zPcdh3GAylusYFYZLx5LldM+n\nTK0mlt6l3ReLgjGWyC/pVS1SV7OmaxFWM72u5zJXuKvxDVZBAvMbSqzSrE+EW7EuPe0KGyU9ylQF\nXo7GJ/Tb+Y5myDmFVbc8u3mb9KpMqmuDKzQ0M5wUFSo1SK4/fP3Fm/wn2k58ZW2HJ85JQtEEU0ql\nFB5nCXasvJENeZGKowa5JhKrvRzX1wnoFuy9KQuZVYWiq9MJhyqs0lxLOZzLfdlolNTH8nvXjwNq\nZWP+A9/zCQAeWd+l/+JXATj8yiZ9BVn9AGNGSgce2oCm4nFKJWzJ65qJCsq8MrvN+Q2Z/DMT8d3m\nAgD7H06o39TFWas90ajByqoCstIRL8SqH3mYMZrKwjKxDhfOyMJYNCTUOuV0qM/JhJ4Pj8msuM/7\nScqHKpncpxtP0W4LfqGtLeCP/bsOP5dKovGNWymtn5Nn9uDHt7i6K5O/exByRQVwG6kyYntdfP25\nf/A6D+3IXPj8Efy2lkKJ/ZBSQ8sFfHi67rMxk9/bzYa4c4W/X/FJFVp/TtmXv0jOJzZl4/HKmlTZ\nvJ20YqZVibFjyBQ4lkTybDZOdZlruBKGlsO+VraqfUKthvgmwldFqbbyciapzzSVcd5KfbIz2ucy\nOUPD17hwmhG339trvgwflra0pd1l94Wn4LoO7U6L7dNtNlsK180NRFr/p4VZEIL6PRLVPFygFT1b\nUarbtn22QaW6AGvHCaPjhX7gkMHeDT2GJoM296gX5aTZFZ79kuxAe4eWbCarbn0HEe6il+l1k9DV\nDsez6U2eagr0Neiusz5UBmd/TqLdcMVQk2h+SEOFY8xOD5QgpU0Dc152/KtH2qmZW+YLJuqhZX1F\nrnPfuIyR3zsqU1q1agYqxuCL/7Li//qpfwiAbyOOVSvzK7MpsWobxtbypn+3JmRu4XghKT9JyRTy\nfGq9wfVFmfiVjP1dTY7Gcqxpp+KsclasRtuUI3kmt7IUT2nDWk6JMpexXcru2tzY4uCKXPu18DbN\nBwRDMawdhrV4Xtsba+yck+9M1VNqbL3ElfFXABgkQxz1GqfjnPPfLed78BMXafZld5/o81stcvZC\n2cVH8Rm+eahydKM+65ooPbexReDKK2Ha8jzOJeBoc9T+sObqs+KFuWGH5rZ4U+NNRa4ez2k6ElL4\nGyt0lQoudCLCSHb5uXsTX5vetpriPXVWzrG9Ih7BaDDjtBK3TmY1ua9oS6Y4fVX6Vmm7tMooFcKc\nzQ9pqNf74FMP4W8LMUxQtfHD9wZpvG8WhZVOyMYspjOTRWEtcAmVf26yYvAVy+4Gc+JIatpmW1zZ\n4mpFs7UQgAmhLQ/DT3qsfFiqAXuvTPCRG+VVEg8XhctAY7J/c/AaN4fi5j8UZcw+Ijf+2VcABcUo\nPIBOPuH117UeP2tTXVKJ8LDA3ZHJ1D7aIGzLsct9Ze3dDChVV7LdbaCNhgzKGaVCsM8p1HjFLUma\n8gL2iyY99QbTsWUvUUKZWxEPKg9gXEj+4ucf/Cd8oZIXOp86fCoWZ/DZb+yD0oz/9tYpzk/k5IcL\nvUcLubrGB9enBB1dFJsR5y7KmFb2WqyF8lJfU93JB8I2v/PSxwAYn8t49l9LTsQOXQYzOd525NNz\nZOFs6wKy9+KUg5aGcfsBBy1ZDB/aqOjri/DS9cs8ffZp+Y6+CNkjc84bedbJYMIVzUWMgys88qYs\nCqvrOzSV1aitWBZ7mNAeybU1/Smf0/r/7izlypUFPDrkoTMCVFor5IVvn4o5HCujcnaF44FC4Vem\nfHJNjrd7Tdmoxnsnz6Z5MME/L8+yVdWESsqz7Z2mqTyct1/RPNh0SKCTzMYxsfJSbjVLEu14zcoV\nklqrK5nC5tMxrxWy2ORHIZuX5JpT3yOO5TqcaI1aCXjerS3Dh6UtbWl32X3hKQTWcKoISTcL8rY2\nzCQRlSIM44MjEqW1DQKPSllw0SqCqWaYVFZr2k2iQsVL3BZ+pHXeuSFa02MrYqw+OstXrsqO8fKX\nLWNFJhZRl0xJOv7T7ZJfeEOOt96VVbvse1Tq4h04t3lJBV6eDDZJK9nlZnmFq3oIhWIQ0kGBOZbf\n6/v7ZLdkp7hRFUxV1MPVGGWjY4iaskOfGtccqD6mV6b0J7q7d2CuBC8vPaseVutxyGWcG02LkhZT\n1jWD6zKm51eOOBgqqYdSiVFXFAtIeFZzWyXVG9eH/LpCmsNJyVg9i6Zm0w+qmmNNZhaHGY1dX++x\nR9fX+v/W5klt/hj5u9+fMDiSc+RRjz19vs1inSdVcKUqn2SkPARGuQfi/imGiXgpAQVXND6oqLhy\nQ77TPPo6V7wXAHiiJaFIvmuZqVf15tERnqNJ51FGptc5HhWkZ1QGflUrRt0VisM3Adg7LsgdbX4r\nHPa/riJAmfz/JJ+QHUr4OItLjifyHArPpVsuuiArvEA83SoTj67sDwkU0dk0KUEs2Iq1tRyOxNtq\ndkpyRedOlO8xnYE7UWlC5lxSNXIvL/GV3TyjotaE5rs1YxeqnB+gffhDj9hf/Jm/h/e7/imPHvxd\nAHwLC6fnvOOwru2i5UaDWnsGVpXbJali1jaVjSZcY3tdvrsSddl5UEONwmeUyAIwOdKH1XGIh+LO\nb330UTbOXwLg1Nmnuf6MQHefX02InvlVAI40RPn9n/6LRKo7+fWb3+Sbt0WA9OD5X2Z1VVtnCYn3\nZaGa+gph9WqKY5Wqn0KpJc62F7Gtx3bacr0NmzGYysScHV3hIVVYmh+41OvyUlCVFD15gW6/KlqS\nr6UZvlKEO3GH7/uYTKCbf/s1/kGtrElwohm0qHiFnkOlGfeksDgqZppbB1fzARjDYj0+aca1llIT\nL6Y2VKhsPZwIqVIbrD6rd5pui1P83h84T/mrEqJ8Ph/R088zXbAuOD5zLc313YrRXEdSWEo9tuuY\nk47JoJbvFk4BqsGZlxnaakJNfbIzWgORhm+OdkuGpsUkV+KVwlIgC/1ZYOejjwHQUMBdGMF3qfKU\nmTU50rs8LubUpS6AlSFUwRiUc9FzHTIFd7k2p1q83M2QOFT+z3bEqnKBuh0Fy7khTeW2nM0Lylqe\n5t74WMqgQK+e01uV6/xz/+NfesZa+7G3fwJv2TJ8WNrSlnaX3Rfhg/fKFda/98dozN6ej+U1W9Pc\nUHblsx1WtZMQVdbdWqnxtf+92XBptGT3P729QrsnnxcjOFQ9wtpIku316yMcFQ35tZtH/AFN8Hje\nEfMVCQMuZBXRVD6/trWQR9tnP5Qd/+Zzt/jqF/6JnCPJ2HxIvIPvjloYhZp6Klled1KGU/ksmKW4\nuovlYc1INQgbShYz38/50g1p5rk9OmZH2ZdXWy7FSMb0yOZpnLmqXKscXatIyRQOOw8tH/9fnwHg\nL95xP/M7flZ0NN3QI4y18ceWTBW/YEJY1c+pINAdLVc3YJwnJFr/z+5wAyre2nEqLHwLh3ThuTz0\ny9f5q3ccR5sET2jKX6DkwRXxjlarOYWyGSe2wFkwN3suVmHYSpFBWkfMas0Y1zV3An/LxQ8WEvVP\nfaWWn9ojbP2bL/46sKEe56Feg92c8vTqEwCsrZ1hXclpwtBnpjcjH1UEygfSVbDIcVXiaEg4rHOK\nmdLMm5JOLAnK9kaLTQXB9ZVvYbt0GSgVXGtWcTtVObk0JVXoX5h7ZGffW/iw9BSWtrSl3WX3hafw\ndVvQqN+Ztc1auKKouR95+ElayqATafNQc+OArC+NNs2zCf5ckj3b6yGRkZ/LMyO4LCvtfqzx224b\nfMkzNF/2edX9twB4fD+nlTG6//JlRlp7bhTy+4Rr1J8TVuZnf/3nuan8DhfjigeHsgdd+PgOO44w\nCDXOynY1Gc4Z6jiPqhGjhrImjS1T7ZWfvCSfPdvq88qurPBJarmlDDze1LCWqKTdfMal9gUA2h39\n7JaLFyiZp2/49Dve1btt5OZ8ROPX4FRFqhRj/naB39dy2vkZmxP9zobsZs98LeeZRe4geet4Fqi+\njXTVX30Hl2KxWde15SAW/+bpcpXWaTnptT3IAoVjZzXhisKKFwhfr6Y6sCfX9k62+L+8+tYUZruO\nlFEHqmVRH1o+eyRJ5z/3xGkeP/1dADSb4BrJCfnlFM9XvEFDE9HFGnUpu/zkaMa4khzUcBTjaet4\nUNeESoQ7iVTBO5qzMpbnkK3kuEN5Byp3xmFfPN2ws4Jvo285ljvtvlgUsMBvAc/+wxfX+M+fEqKn\n6GPfT3MqN3C8JRiEuChY3dR69JrHROvtDWNOXD+3bGJ6EiqYUroFH1/rQyYv7ovVEZNC6bX6Q3hE\nwoPjpksnEDXmh177N3KsdI8vFV8GYLfZIs4F3NPaWmPzMVlAHnrgKRqJTJbmObkGvzVgY03q7hfi\na4w08feaf8BgICFB2pXznt6LmK3K5Hj9dkrDU2KZwkPxSFRJyEjZr0vV18w7JY7iMDZP/OJ3tsd0\ngfyec9s89H0/DMCZYkJhlGSkKk4msdOpGStG4MKeisKYLzN/5mW5V1XJTY1NWg4o/gvfCjjqXdm3\n+N4fuHSa33lJOiev97Z5oFAYexu8PblfTf80+1ae9VGmGJI3Ev5FIsnYlybpbzzsiS3Wt3dzub4K\nrpxXLk67vc1/d/ZHAHj6sYuYSMMZp8ZfUOF1G2QKZou1Zyb0C6yKyBRVjVerzIFT4Sl8fzACb0c+\n39JKVNXwyEayKJjVVTYjmfd1HJIfK2lPdIlG9P6wOS9taUv7/6ndH54CYMxvLle112XN+pM/+QeJ\nKgkD3EaF95okanoqJRcGBaFq7jkmoN1Rlp5iiqc7W1HAtrIxhxckZAiaq+zNZadpXK8Yt+W4m+fH\nsK/0WNE69ZZ8/1RbvIrxOOaTE1nZnzMFqH7gD+5s8zu2PwqAv9mltWh4Uu3KdtAiV32GqBdRKdru\nsbbhliNew6ornYztcMzmNW2+WevjJLK7HLslnu5+pi7IFdEXaNLVryLqWNwu90Rj4jebr5oaP/m/\n/x4AutkaUUe0EIrrE7yWNvPMBqBMw3k2p6uaBG4kruzFtQf4s6p/cOvqIT81Enf+QzZCiYDYPEz5\nnHYtHqv38k4d/m83DwB2Tsn5/uv/5gdxkNDuYuixfiDaGM2VDkUhXlZcrTBOtRlrKM9gvlmy+U3x\nMH/mzUPeULq5iX3LOwCIlCFp4WQZLMWi1+43XFNb51xHE9iPbjzJhSdk3nQ6LUwoY66SnEyZrb2J\nj1XIfq7yf3VegrJj10VNUikPhVdSKNFvo+lTqwr5yb2burQWKMfSod1VLESjidHy5Dx26USN33xD\nfwu7fxYF7r7pOyH89McFqrpaPkWjLwvEcFRiFD/f7QquIO6AUQEYYzMC5UzsmDGZwmrzYkyoWPSN\nVFmLwxGB9ifMTm8TqAbluu/it5Qya7RCrgIn9iF54evZF7kaSuz/dOxRrwt8+qMff4LORy7Id3Z3\nqTxx42tXwC3GrNHUyke5sU1jrMI3vTM8rBnnA+3S255MuX1WFohL18a8MpEcxt5hyZ6yWO/UHs2G\nuMKrqq85Kl2KqYwjPvX2in1bTcNP/eCjAFzo/ajck8xnVinjcPgK1Yq8eDb2mStpTW1zfOUj9D0F\nE8UP0dISxsM7m/zRgUz0i6tr9PdkoftG8xo7N7QLVCnMjt7BP3d4ax7UBp7syfn+0nddBKDtPsG6\nhkqTIKS5Ji963YvoZnK/S+thxjIHSqXDj5pTLjoSgn7P/As85sgC8rlJyRlXzvhmAYWGm7VehY/B\n3DEz77zsRKtHXXXP14KrlLUsUm6ZgYLX6mKE60lommcpkc7J0iq/ZNwmyRaLgqUoVTin1SXXKlBU\nethMocuBhC2pmeE1FpW4CW0FXAW+S7Qq70P/9jXS3sW3v9nvYMvwYWlLW9pd9u1K0f814PcgJe83\ngP/KWmkuN8Z8BvjjSOn5T1tr/9W3vAgDq6FhVLzFWfDkepPg9z0FgLW3mSrE03Y7BLGslItEjksL\nVym8nBycQFxDO/GplM7ZSTJqXz93NPkUrOHW4taZvKBSNNq43yF6UzLLE56h8/CHAait6hrGHTrK\niHqbBOcBSVxW3oSxah5WwzHOhrL1xqoH2GwTREKf5tiY1NeOSFtRduXcq6nuIuUhD1QSHk1WbvLI\nm+IOPu+9xpdVO2FsSsKp7IhThd+WuDhau54VE0LllgiMS6J++R/aPs1Hft9/qWPRBC0TmkqOWmQx\nqEdQ5B61egWum2JVgMdRn3o1Dmmckyaior1DfH2kY13j9Cl5JpdWO8SZeFtlJM1Anx/ljCeqNM1b\n1vVhrhiIDMvmiux+vU9JcrHVO8RawYKsthziSEI644dY/b3SSXEUAhnoZ9a7wOlLsjOfiT9Feij4\njvOvH3PmQObDzwcZX78pHttAx1dg8QMZR1hVjO/omo2VKs0qWU4xaYFyHqSzY2xXSV6nKc6WPD+n\nnDNXKj9v7p9cb6nkLukaOH3x2Oa2wmqIYbwAo9SBmSceg82nzJV42DgZUw0lw+kRI4Xy21aLqRnz\nXuzblaL/JeAz1trSGPNXgM8A/70x5nHgR4EPAaeAf22MedhaW/FbmOs4tBtNfDdnWMpXP/J9W+y4\nwp58NPSpFB7cNDHlhmRhFy9/GtXE6mZZfGrNLpd+iaOceqa5glWabddVEFOVUWRS3onrXbINhRUP\njnlZX4RNIprK4eerdHw2HzPxxG1Nd0IeiPXBtHonDM12dY1YOQGjdQkDbJXhhcrbmI8xm+qq9ksC\nFSYtVVjGMxFG+RBNeZpEBUBa5SpbGvG2xxVTZfO9ZuUaeyGcacpLE4dvyZDXgUegHIyDJ9boPST5\ng2SqvQVpj8VjMlsR+Vhe3sxaPBXlMcbDzORFyHtyvX7tUS+EaooxRmHC9DK8hlZUsh5nTslimJyR\n5/HJ4YC9l+RF+MLUnrissevT0nzHwIUf+Ji4vuc35e/MbBArF6NTNQhOywZRThIWCj1+M8RRmG+1\niNWrFSptc42n55kaFeG9tUv022RhfXxynUyTHv++WFQoDHprCb0IM5fPfcfgazwfNeQ5ts5GsqAC\n6ZolP1SuzDo7YWEyORQLLnpX4cw2JMvlGuqioFRBYi8IqbW93tYGp+frr+lcdzrYBQS73aRSIqI9\nz8PRCkXtNgjqd1GGusO+LSl6a+3nrbWLM/06ohkJIkX/j6y1mbX2CqIU9fH3dEVLW9rSPlD7TiQa\n/xjwj/Xn08gisbCFFP1vaWEccOlDp+m/MaApuUNObTzFkUqN5WbKIF4ItRyyU8pOWFTqhjnuCdjE\nL8E01I2cOdS5glDKGtUpIddMv7ElbqGdmO4mI21a8WKfjURWa9vYYKSK0N1AJdFe3uN8JtcwCWfs\naojywHBCuOBrXF2hVlfS81VWbTOkHix4ISzVVI5RR8dkR8ol6C7Yp6EqlBKu7GMXZA51ia80b1On\nwKikW0ev0Q865EovFlOTa2rMzXJczU7/3o99lHKosmiaqBrPruBpIotrhiTS67A5eaHeT2ZB73kx\nF5c0xaHQWvm8SBkG8nMx7RFO5vr8Jtg12VceGksY0GvCT7YVLDabo9wzrKwEqHfMzsMrPPmEhJBO\n9rCMs+NSaAUnaFQYrezQMeAslLstlRKOeD15ZkX/gFQbjdLGPokCjsaPOtx+Q+bD4HZGrjDuRYeW\nU0OkGu9JbNCGSnZMQKDq110VqnG9BlY7EmcHBaW6/vMgoFkq5Llu42gH40yrF6QjaleeTZhZMp0D\nDVw8bVmrmS9oHmlr5acsfNoaCt+qB0y1KpXVFaW2kgWrOf7sTmD7t7b/oEXBGPMTSPXmp7+N3/1x\n4McBWo2Ac1urnFvbYuWCyqEHDsdKjT6Y36ChLEtVcQ6nKROvVnWccrKLi2R3vYbF5ppzyI8w6uJR\njEDLkwtBljo3+LUSZnozokUlYpYRa+cbYUB0W2M1LTfdrvYYBpJBmR38AAAgAElEQVQPcLKKhna7\njWuHcKGQNHwDkIlcBupGVl1qJU6x8zVMKd2VXhZROUqyoe53mdwkd5R+Ph3RV6RcMgHb0lh14uE0\n5L74pRJzuAF+oiQzO80T8FZhDejLnb55heoTcp1JXxiUysEVjg81692oqHItSTIn0pxIxQhXw5VQ\nF8LCz5iqqEmdljQDbaNO9xhqNcPEDp11uRfuWTnWcfeI6BvaDm/mGA1zOnHEudPyLC98qEM3kvth\nOxpzj6Y09ZGa0UXqtrJv5S2sdg9ShrjuTO+zPEfj7dBUcdw8eIBTKk7z2Oo6L197HoDXXPcEcGUW\nXYuuYYouPImLUnDidGsinTvNFc0p1AOKfSU9afuUmW5I2ZgyFVRoEFXkOv9IF4pVAaGGdsbzaSiP\nZ1yG1P4idJNuWgDXU1Rpo2AUysKzctBgMJVeGac0OI5caHY4wzyoAsDv0r7t6oMx5o8iCcgfs2/1\nX79rKXpr7d+31n7MWvuxSElAl7a0pX3w9m15CsaYTwN/Hvgd1to7EO98FvgZY8zfQBKNl4CvfKvj\n9fwWP3L2E8yaq+weSpa6Mi5Xj6S+fzwY8WAsu3TrsXNkSlFV6UrsHPt457SrL3OwKqHmzeKThJP1\nJ9RKieWOJGFYr2+Su+KNxPUqkQKA8nTGYCxrWZUUdM/KLhdNxM1s1Dm5CpL0Dwb4F+TzXj5nqnRr\n5aFDprXw3NfOO9vDV0BS7eYEuYYxnR6+wpi9huzgYepBIOfgaJ9HLjwEwObaLhszcSlfvrbHjcNF\n2CE7TeVDQ13KTEk+ACw1i0jqrx/f4FHzNbk2ZUZIrl4haslx7dTgrMk97kRbuJ56MfPyhH0gUPm3\n1GngDMVTmIQ+8z3t8owdwrF6MVQobozemuz8n8ot7im5zv95DGPdVx5pO6BJV3fk4hil49fQYOZO\naNeaSN3xcZQxuYx8vJFWGqI5rnaKOpoEDAqD48vvhblLek7mxcZzx3RW5N7GjsO/qKQq4d1U0Zfa\n4miSt3aqE7XxK/2Cpx9T6v+GDK7nNE529nw8ZKTViaif4F4QL2aSjsEoocpcE9ibHrW6+EnHgV3t\nZ1g1uIpJSQOPtiqFOyqDYEyNr/TzTI6ZzuXe3rp6nVCZu1dWtjHJohf23dm3K0X/GSAEfkk18n7d\nWvsnrLUvGmP+CfASElb8qW9VeVja0pZ2f9m3K0X/U7/F9/8y8Jffy0WYyMN7eItmGnE2FpaY5978\nMuNdbWqZp6Taafh9SUbRlZV5PFZ+gwe6NBYZoKhNqZ1jtnOHGIjpkZWLMo2s2s5kyFRZiuaJh7Mm\nu0OctJiva1IxCojOqKqypyUv3zLVnvaiG7CpsV7kbZBpwu+4tNTaqdZ/UzsYy1doaNloPfZZPyuI\nzbUpNJVxKVA15CwPqWKlYFs/TaIl2cxtkSvc1To1blOZdwpF+a0bTje3dMxvQYZdx0AsO/CfvPBp\nivSC3Nt92Rlv7NfEY2nsyqMGW6oBUe1M6SoqkMilVA2EWUcTsf0ZM63jl/OAcF1yBsxGTHcU5nur\noNBdsTuSc7gPb9N9SopW8WuXUSoEZu4K55vyfJsrj2AC9TwWOY66S98qvDixlHPxturxiMqT+dJo\nO1Qa7xeK42DdwUvk3tdBzVzppU3vEW6eew6A598IiFV3xNU8grXFCczZGIcFyNjBnDBp7yhNX3tn\ni4Ynv58VFaWSuB57htZIxWm8AncgO7rZ0uc7DrCaUxgeQqCJRqZzcqWxOzhyaClT17qK/BarLvlI\nQ++2S1pJ3mUY5LhKQLG93iS0760keV/AnF0vYHX1HPHNI17RpFw9DbndF7fHTCvaF+UBdBsdvFxe\nwlrVdEfH4G2pEnNSYVS9qSgrZn0Fo+wlHA0FW3CoxIXBLCFX5aHVFR9ceUl7bWjqwrEeRBhlWl5k\niKuRw4cdebCv73gEXXkR1s/06CkN2F5zxGqsdXolQBnXBRyqWErDobEmL29YjhkXknQ0N2VRODQV\n06syCW4cDbhxWyZ8VmVEWlG4WllWrIQrm6omdXrtHGd35NqyfoG3qIUbh8a6Jru21kgTaeG+MZIQ\n7YXRdWaXNUvdKLhwSsd/6PPImraOb3YJNDTx57LgpccOI53Qk/mMVkup+MsOW5mMf+Dd4OpU2K/L\nTJSLVho9XpvK/dxsQKbVgDhyUCJiOm2PgULWw0SuraoHzLQqMxrWFBO9jrWE+UGlx25gFzyHHRWF\noaYYy/iPC3CMwMZvHNzk8ldF1cs7vEWtOAVfoc9hDZUmQWd1/VajhLG4StkW6PXGWOYKIErmE6Za\ncZj7zRO+UfbmZJpmu56qDqTvYPrKAt6e0jinHZVVj5Fez/TgKmPlxes+oJR/1Vm8joRoh3lML5Fj\nnAlnlKkCwNwZriah360tYc5LW9rS7rL7wlMQRPSYdDti57as8L86+iK3+kps6pbsaa/89Sv/hjyW\nBqSbtxVS2xrxxFgafOrWGrEru2fUqbGKNxhMr7I/1oSLumF+r0EjXshyeYSleBhB1iDQ3cjx2zRy\nWXVdXUMzJ2eoeoDbdUytjMpT18doSTIYDRgmmtDUWrkziTlzVsk2mhuESqAxGxli1XG8noqnsH80\nZl+TgHuDMYmmZrx2k1SvZ90paSrybiGlN+kPSRSxWbkhVtVemo2QR7ekYai5kVHtyuduItf2Xa0Z\nYyWPZc2QKkuyUyVUtepI9FNyxTWYUu7rraM3efFAnsPYBHRflym1024z2VHS1Lxxoj0wVvq7w/7z\nXHtDxhpWDuWCNNZ1CNbEg3ggqk6g5bO54i2CBkEuxzpMb7CnVHeHX+3j1+I1nFkLiH1N8q5KiHK2\neYbpWMqvv/LVr/KNoZz71msHzGYy1r6pcOvFc5dr8JyaiXoj1R2tnYGBMBSPrLEh19AtG4wymWMH\nh1OssnjPzIDGXBuifI9+X+XyDmVMx9WUiSJLz2yfY+tsT+99Cp58d1YVtLRsnWui1fEL0sU83Z8L\nGzBQjkfEPfFe/d0Uu7rCe7H7YlHw/ZCd7XNUCVweCzX3udWzXL8lru0oKfjSdRn87eyQ3qrc+EAz\nzw8W2+Q9edHH7gD3jNZxTYQTyiTshDWVxqpvKsnTqDZEKgK72o54sCnV1G7Pp1CodO3OiCvpGDRK\nF7wW+RRj7b6cT9m5KBN6veczU5DRc9+8zS9flYfkhTKpzrXW+dTjHwHg4jkHT0OiZnOT3YEQlXAs\nj6TVssRN6XJM0hUSFf2IwzmNWFzUrhOdMP9ePtBzpSVHh1oFbsW0tKrRjjtsquu+fRiz/YjG7foy\nZvGDzNQNvnnlZQpVMcqrklKPEa6EdBW6PO2rMK9pUR3LPa68isupHG9QZZzJZZE5vzanvS33sOOq\nGMyo4OOn5Zn94sAynGunYVlTaLg23PN5YFUXX+XoLJw2g5nU4/NjGOocGY5GNJT7sJiXVKfkZeoG\ncp2lW1ErTiPb3yPX3pV+CI91ZWF80PjS3QjMU2nV7zjP8gu3lOhkzEmbZFrDqabS0ntyT5KioNJz\n5NNDrg7lvuwdJLzS0TxPK+WcLy9sEGhoVEaME7neuFWeYE6isMTd1xxTNuXmVVlkDnKBoD+2mWCV\nGj8bDig0zBnkFYXyZjpxgVu8VYV6N7YMH5a2tKXdZfeFp2CwGLcmCDo8flrCh/HOG/Qqoai/7h6x\n2ZJd88yDD9PckFX8eCSy5itBh4VufYZHMtKkVcPHKJXY+s6DVJuyYj7UUF6/ZMqGKlGXmSXeVDe/\nNNQqcJBXOeGiS9Bb6Cm0KVTWvBGu0wpkFwjic+SF7GLR6Q4PqGS8ZyTc6W55FC1tmPEjGqW615OE\nIJCdtLkl13Zq43GOlef/k2HB15ty3K6bM9Z++1ONBtd3xe05sAsegJx2LjuXX4VYbcoytWF1Q0Kw\nWTMm1HvYsTKmwIsZTTQJGj3Mm6l4UO3ExVbyeVmWDPqy+zfUS+vFEU+flwTt9ZblCVdc1bJ2eHhD\nrn887tMINCvvy3H//WRG45Q0ij10Y07QVuhu7DOslffi1DoNhfQ2t+S75QzydTlHYwZnCkHrxb2Q\nVYWhNxoZVaQck+GmXucGZlt246fPP4h7QXbxpyanaCr7dyst+NJI9EQfPJLrfXb2KB5C4xb7c+aL\npqooot2SawtiSXyfO3OGuXJYdgloKMP4mpsSrcp1NFrOiYewoYzTb+zvMwnEm6Ru0lDcg+M6bG3L\nvd0xhsBfhDZyf3K3wVyxIHHYxWiI6fa2aS0KRkSYE1WNd2f3xaKA8TDOKi07Z6wlnVO906xMtadg\n5TFOPSGLxeqZHXLtKJutyXfn5RSrsWB5nBIvsKiNJoGyD5lgix0tEc1imZgP2AaZQom9aUKkgrZ1\n6lDNZAHJ6gKvlMkUZcqdZ1qc1Rj5hlsyVTGU1TyhuymL10cfeYwnd1RFKpfJY0uPzqpCWLddfJ38\nXteSn9JSnio3NcwmbdVaHJ8e8kOnheClLIeku3LcW7NrbIZyDx5al8lRFzG5An2S/hxcWdA2vBnX\nlRo/nt/AHIg73u1JzG38KfGKvByTY496IJPb6R7Q9FV7M0uoi8VLIf9/xq5BTz57rFUwVAn0OGji\nankvOmgzzqXys38oL9CpvYznFGD0sFewUmqn5XTO+UhekL0s55RWcALt8QhXmlRzyRdcXG1wW1u8\nu/N1Wmf12uIVKs3RrCptf2BqViIJDy9eLFhLj/Te9pkqXD4rKj6kC/hzY3n+q+0WP1BLQ86RvcVL\nR3KfP7XxPYTIPTivYUsjDmjEsri365ANJVYZXKhotuR+5/gUI3k+aVNbucM1Uq2uBA2XsCuLXqvV\nJGjLvLhUtdiMtX9CpQNix1AoW+6N/JBM15XQSRhMFAseWRqz34IA9W1sGT4sbWlLu8vuC0/B1iV1\ndkjqN0hyqWcPpn3sGdlpzq579DQzHtiIWDv4Il/c9mzqMFnwKXQdmrpaNyIX48hKa4KCOJadvpOo\nam/h0daKQ9kIqRZucj4gmalidOUyryTZ42oys7IT9pRyvX/7Jr4SvJw6fQZH6dRW2l3yppCPNBXm\nTLVCFGlWP2yc1PyraUirt0iILcRWhnR9cR23ttdQlXjm85o93QXL3YJdrXB4iYx/4E+Zal7JD5uU\nWli/mTtkiSQzZ/7TOOoVhNoB5MQu+VAFVLo5W1a8m3mWUGvDEzYHq6CuSpK9k9mM1JUTnvIfp6Wk\nH6FZQRnyiG2XaizHiEdSAXijMefoddnBtuO3gFXHBRyoJ9g4fo4q+e1yjzSci4xHZ+2CjK/hEs3f\n0GfSw1WXOXUyCl+7ZnW3DtsN3GOlPV9LOL0h1arN6ZR5Kd8Z+TMqI+CqjTV5jtHxBi9uy878/eOP\n8of+ilzbQx/67Xzpf/pxuQdWQoPVLMHRbt0kymloV+qpOqJUr6k5S5krg3akzXpeNGT9onp0vbPE\n6gkEfggaKm6ur9NSUZ7KU2kDt8GkkPDRvVqTTmSeFmlFoCFvPpnjb8q5363dF4uC41iCqMaNmkz3\nZOCtlRVqLTk6sYunbcbhlnOCH1lkesvjBGexUKQxblduTj3s4K1oKWsyodZSnavkJW7Ho5pqW3Bd\ng4q/GhPgOcolmA/paDnJDGVSrW+tsK+6CMP+LmeVq88bl5hNWWScfA0UeecqF2PpDiGVz7ytEFdR\niHUnJbilHXD69rv9CV5HuzNrF0cnhBt4eArSYdAnGym6b0WuoRe3uPK6vHjXqyN8jTOzYsbBQM6x\nP3wOX7PrVt1a5hHhqvYc9Atc7cEoj7MTslU3KwjWVCdDCWa9VZdyrgxEzhBX8f5x7OHMtEclKDCq\ngFQHslA+fWPA9z0pb/FnvzjjxVqew/deXKM/kwVre7xJqLmGUEFhtOfooyHwDFbBWzVgB3LvypZP\ncyLPLLqU6Xc7+Bfkfq5/PSBXmGI8m9M7K2HF+jwgULao+LoyKH3oE3z8efnsU3/rUfa15LrZ7/Bz\nR5KXcIzMq3V6NM7JC7iS1zgtmbPmCIKmxvst96S0PenLmOdzn2ZfyYa3j4RNFvBO2ZOffb/C0RK9\nXff0vqbkGh4nk0MOlFCoGKSsKOq3rGvmucYV79KW4cPSlra0u+y+8BTqsmR+dEA9iRiNBKdwsDek\ntym7QNH1ybUXPhjVmFiZlvuasGlkeIX2o/c2cLUPwmn5mGLRJ9CiUnfda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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.1885... Generator Loss: 0.7337\n", + "Epoch 1/1... Discriminator Loss: 1.4297... Generator Loss: 0.9953\n", + "Epoch 1/1... Discriminator Loss: 1.1458... Generator Loss: 0.9505\n", + "Epoch 1/1... Discriminator Loss: 1.3446... Generator Loss: 0.7885\n", + "Epoch 1/1... Discriminator Loss: 1.2624... Generator Loss: 0.8597\n", + "Epoch 1/1... Discriminator Loss: 1.4662... Generator Loss: 0.5536\n", + "Epoch 1/1... Discriminator Loss: 1.1388... Generator Loss: 0.7493\n", + "Epoch 1/1... Discriminator Loss: 1.4432... Generator Loss: 0.4454\n", + "Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 1.0688\n", + "Epoch 1/1... Discriminator Loss: 1.4904... Generator Loss: 0.4468\n" + ] + }, + { + "data": { + "image/png": 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bR0232RnDWPnpHh3xovLzzMMCxa9+c4uPz2lTmxmPug60PSpxxcj8HJVlbONu\nytFQ/l4OcnxN+U7yiPZIvn8269OKhV/nW/Iek0FIFgvfMmeOTKMP3b09gpGsof18yO6OzOtX9wQ1\nWUmXxtwaAKeeXCY4FtulNRmx0pT3my9HBCj6fof0vhAKlutQXGpS9jz8mpQ993Y8Eu1fPezZlGva\nOMTLmK3JhD2sZbF+ocKML4xuFR3OzcrmLRWWKLliSpQejSi8JPe4q/0Vj3oR2wV9Rjdl+6S/pZNy\n2FDW3AZ7UduEe7KIh1WXxV1ZPI/0LcrnZWMVTz9O8KbY1O3mAWFFazcuiNC4/Ogs2zWBzHO9lFpZ\nxhb4I0o1ecb58xLys0/VOL0uQm/v/AHrGyKkrOEmifoz5usBrZFs2Ds3ZPB37nncndEOU/Yu6c+J\nPRw/v46vacULZzzOrXwSADeW3x0f9PmARkAqF5e4vCafC+UmgdZrJE5CPND+kD15rpdUqFxUM8f2\nKV6UBTg4KhJuyubuxMcsNOS92g+LYO4/+wbfNNp0tTyAY21YMrdCIRO+uJWUovZHLD71CAC1V4ZE\nNfUH9BJyXzsTFRx62k2pVPT4wBUxj548I9A5evhxLk7kXut7HRLlW8XzKBbkfoNikar2RHQUzV+p\nJHBuTcbZ3SbyZMOei4dEmtR16k0NIZaGWNqVO7UDZn0Zz8j4D2z/1mKZVW3KY6eiFIKwTH+kNRXj\nI9ya3LdeaxDOyhq6nBVY0LTouW3h4Wh4zJov83T68gfZcMXvNjz9LAWNuiwW63zYenchyan5MKUp\nTelt9P5ACo5FsVWhEBaItALwwodO8yvrAlXN3IQ7WgxS64ZkM+LgKatHPnR86lU96KNqkfS1Gs4Z\nkhYFthZ6iyzVRdK+lAkMmyuOGQ7kWr9mMdJeBnXXwdYKzUdNykvaZKSqVXa9/oAt9Ze/nuzz6VSK\nZM5eWiC4LM7P5tUmuR4eU1sUjViKtshm1BlWgfnLUnRUSlbxtDGK2xRIesrUqKYyznKSc2FV7tEf\nPUHUF5gfJTcZabtv35XxVCmz84o0BXFw+JkDQRjjwOGaFtdkOxazj4jmOh9IBWSpUSeIBGEUzz5K\nKxEve0SXmtHKRqeGc1J9Z0sVoe10KC0JOqiFJSoI8lj75Yt888aJg7JHqo67Jc2QeixY4XRd+Pry\n4C1N1qoHLJbk2qP9PkMtXDp9WRBPfRzx5btfA6BQqOLVhcfFgkdPO17XvBLLHxZkMtt6BoDO7BGP\n/tSfAmCGmRcAAAAgAElEQVSYhrz00qsAvHY8IIg1WlUts/yYRJ16VVljhC3GE0EbmekwnhUeXfjI\nGrWNewC8pGvzcDB+0LTmiTM+q478rlvOWa5KUpfjVchtWePDHYEj6ThmsHRSzWqRlAQJEDg0F8Tx\n7IxyAq0EXpqTcxyyQgv1i2K5VexQHNf7Lw/Z0p6PH/o49KvvrsHN+0IokOVYg5A8z3By2dy1yZjl\nBZkgRjGrS9LFxusdMnFlsQWaxXiUTqgW1beelihoxtvkZo/iOQn7LNtN/BlJFho9LQv75tUtCpo1\nuXnvFhPNz8/8hMlNmbBXjMucNupYfUI27h/sjuC2PCO5G9P4tPxuoVckduWaD11cITvS9vHqR5h0\nU2KFjMY9pDjShh21GK0oxtcszdbYpVQVgVYuFslTDa0dpHQCze7bHDLSKMAru7KI1197g4OqwlNG\n7D0jZxIM/q9XaGhD2w99zOHMVe1apVWPTz+1ijURyF1u1bAnMg+TtEuqkR3TC0gDee+Bhhuz8gRz\n+6S/5AXKayLoTGrx06n4SQbHEUeeRIxuacn5emi495ys6NxyQRuIdOMBh6EIxqI15IwnQi0YyvO2\n1p7mkvZiDBvblDSUVy8YBvuymeZmHc5oY5hgUdZKwS5iFuV51uMfINPejS/cep6xI7w9BtxdGd+J\nibo536Py6xJxaJ+LOX9ehMzZtTJfvyo+k+RVLXHvgzcv42x2C5wU3S7NLWNrY5/ZWsCgLbzd1Vbu\nUXmI1ZdxDkspWUcre62IwqG22rcrOA3thpWdVLOG2IiwMVnCkdZG3Ei60nkIaI+qTCrvrnR6aj5M\naUpTehu9T5BCShb1wKlga/pspexxsSkatrPWpak9AydejSjTo9mKojHWGkskWn033OgwVJjca/k0\njxSuBxmzHxfN/alt6RJ9vrrE7z37PAALZ2YoOSLNj+8fsmXk3l4hZGVFpPUjfdE+v3d3i2Qiz3jN\nG/DRb4qGWr5w+0EUoWgVSRfFgXWSXmuVPda0FmN/PcMsCwR3vCXsvl47Fi+0VZjH09+3KJF1ZWwH\n7iGlVK5pNC2Oe9rDL5MIh7UYkXdF687kLi99VkwJ3wQMq6KBNp9N2XlccjL8gkDRmaSBVVJTyk2x\nK/p5HNLf1BOJ2Mb2BD6X9kSzXb2+jalqf8g1l3KqMDlqYPSoNHs5Ju/LUivEoofCQsATZwXlXP1G\nSn5S/3Bzm9tV5VfDZzTQLsi+3Kuy3OXJdUGQu3mF/X2J1iRNn2daMifhUUTYE/SSoi3+szK2EaRQ\nKgWc+mUxeQrfOua1l8V5etwZM3tK3u/Jz0j/hmTriBe+LoigVRlx5oPy99Xm44Tf/kcADDU/4k4W\n0tzXFOxHetRrgt6Wyh7pSbVnEjKr5aNBV+Zscz8k00OLGDqEh5raHKYsnNG6mfmz1G1Z95NYmOVY\nPkXNWcmKTdJjWRe1fvQA6URbbV79g39PjkZjzKox5kvGmDeNMW8YY/66ft80xnzBGHNT/238UZ8x\npSlN6d8//ShIIQH+mzzPXzTGVIAXjDFfAP4j4PfzPP97xphfBX4V+Js/6Ea5bZFXA+zIwa+KNBzs\nhRQa4g8Y70SY5RM/QhFTl2HPaP8DF4vuSR//2QLxoRaijHyiGXXAnSrh9rVtWkM0xqDW5IMfFT/D\n5jVDLxIN23YTQo1JHU4MxhXtt/CkOH3ar9sQy99r7Zjg4xq/SmpgyTjsQgFSPdehqVqg3cPRVlye\nY7C1T/8wzQjUeerGJ+dQpLh6toQz0yRK5BkzBYujW5rdZzfpH4m2LWr14e12CVvTa/OSYecxcbhN\nfvtNzmt4a+bCAmVH8hB8deq55QyvqmclVN0HDWSNXcXWRrFBoUCeihZ3ijKewswc3VD4nUcJblML\ne4ZgNFt0fGtI1ZbQ2f37XwKg397m65E4D/F3MXru5rrnULmvvTPKyYNzaOxzgsAak5CZNe2XEdkM\n9UyK3vYmqWaQznmGLBNfRG9PtPJMo4al5zD4Z3xO33T1PWocFdXxV1ugORQ/TnH+aeHbwQGTK1rZ\nGddYXvoUAAutJZ7TKlZL8xyaGZzSJM18UsBWdFPwCtiJPG9iYpyCOoe1TskfpDgNPa8yi4li8eH0\nMgtfOzf788fUZoQHlqZ+O9U6lmZQ+l4K6icZVsqcRG2jY1hMtNLyHdIfWSjkeb4D4mrO87xvjLmK\nHEH/S8Cn9LJ/BnyZHyYU8pw4jkkGPeyyTKxbyylrZdnpWpW8oM1LnBIlR8BHpPkG/WiEF8qrtKIG\nk4kwIRjvEB4pd85UcDQWPHYkiWWmUaeQ64lMl85ydVOcYStRlWheoPapnsUHPyCLd3FOBMh88ll6\nyrktN2d4VTbF5E8PcSYy/sQbYGlrubwjEDY8uE9/KF798VEHuyEbyN0v0hvI+eu5RgDmFmcJfIWc\npkNBhWW+nVHWKMgg2mP1tCzIT6kZ8eFZl1sjMXNCE9G6L1V9t1sZ+7Hw4lP3Yqq/KoLRPdZVbPu4\nemS5VZgjVyEUD9ZxHXGMpamP0Q7Uo1B4WM/LmDUZm2/mtd8kmHKFdFfbvddctnsCwSurInjOlnzW\nKgLbf3PjpAc0PDw/S2rL93t7W3gIj85rhKNxFLPekbZrjYYFb8j4794dExiB46lXoaHXr9yRDb+9\ntI0byXdR6nF4U+oPrt2586Bk/Mmay/zD4mz1NGlosVoi/4hEaMrjmNVlTdeu1fhMJuvwhh4ke33T\nsKEC4olKBpHMSWz1KVoSoXKDiEgLJLrHsqZzF+Yq8ruiVeZY2+RP+oZI90C8F9PREnbPk+c6RUg1\n9X5/e5t0JML7rO8wsUWwPr+/wQvXeVf0Y3E0GmPWgCeBbwHzKjAAdoH57/Obv2aMed4Y8/xx792V\ndk5pSlN67+hHdjQaY8rAvwb+qzzPe8a8dcJtnue5MeZ7Bkm/8yj6y2uz+ah3iD2JcLZECsbpJiMN\nz+WRjdHUZIsqvvYIcPRR4zxkMnD1vil5RTTzME7ZcbUL8uvXMGWBsKknGiUMU5I5QQSze0XuN4Ud\n7TsTjkeiuz5aPsXjn/mEXKPukWGziDUUaX9+YpE/ImObvDLgxINSjnhwLPnwSJDL8DAmdkSzhV6f\n8U3RYslMwEDzCZqq+RvrQ5IL8gw7CjGaXmvcHr4l71cq1rEq2quhrJl7w5xz5+U967bHsx8Qvqz/\n+ktUNLRY+I+LlDdEH9jabMR1fDJftXw+gvTk7xaxnt2Zbh6RaEpvtyMafzjfp3QoSMKcy+FE52cR\ntp6kbaUlPE3j7WvYN56Bo5GgKhPYWFrN41cDrIlo4+TGkEDTxtH2anbZp6btz9Z797i9Lz0b3HHG\nRDMBRy2wjmROOhc123DfZTCQClV7d4/tI9Gke4MDxkY0/RPVhMeXxVQ8fVbMiOx0CeuWZk1eeIxa\nQ/jsWA69c8KL0jXh8UU/YFjWOb+fsKdrdmF1zMCR93cmEWNdW4n2v8usAbmGHpMgJKiK87dS75Ae\nyyLvV2KiUO5RUCe3bSWEoSDL0Dli3xbeDms+sYZ+94Zj0ENi3in9SELBGOMiAuFf5Hn+Wf16zxiz\nmOf5jjFmkQfndH9/SqOYw/tbRMMY6yFJ/kmGUB0Lgw+LMZbC/Cjuku9rTYTawEXPYn8sm2pj/YBh\nTRb3TDqh/5qgkGBmQmlVIaitlXOWReckj7xYpNyRCMZO3Odxjekvf7TOqqb/Gq1ELHQNxpXxHPgp\nX/qWPKPYeo1WrNWV6QwjLdUe3ReoOllaYFb7+gXFJTqJQPBROaOlJ/oUQ7lXFm8RH+rnURX6CjWj\nCaYi93AnkKlvA1f+LdUtmmXZNLmxOfqX0p04iTM2NOHqc7++x6dXnhUenJG8AkKbTPma+9uYmpgg\n5GOyWHgbHQ3RRkZUa9ovsO9TntUelL0d4rZW5EUDEj3ncGJCRkOZywtz8p6PVR0aSwKHv34/UfEJ\nK16NeL6p81TgZi48GI+0I1XJIs81Oa07ZrAvm3viGn7xCYHo3XaRYy2P39QqUmccsTnQYwDaHRJV\nLJPOkPZY3nvHRGw9KYLj6aJcu5d5XDLCz8o8+LlumTRn5UA0wKbmsdzfP+RYj4k/v5ZgtIf9KIyp\n6jOiYIilpm6hq0lPvZCdTHhRwKaFrJtKsYil6f2TLMJoUl4nlIiLa0qUNZV+dDih3RezqzBIuHRO\nxnz8+pit5N0ZBD9K9MEA/xi4muf53/+OP/028Ff0818B/s0f9RlTmtKU/v3Tj4IUfgL4S8Brxhg9\nbI+/Bfw94DeMMf8JsA782R92o8w3ROd8si8dkWtV0qASMdJ05N4hFDWN1zYzJHo+ZLQnWsJquOR6\nIIu1UmBhKK81O7+CbYnETx2XMQKvXPXUOwsuPCSOv+1vHfK516WRx3C3Tfu0aLTLpxM81W69trRo\ni2sFCuq0NE7ORFt/7e4OKZySe1fiWfJYT26uyXjqWURlUbRgPihT0mYik4MMb04PkzzWdmz+iGNt\n/hEczuHEgm6YGWIfifPNdnKSXdGgaShIoVytYQLtzRCm3FgTjd7+WsyJJRdaOU5JIGphQbRV5i6R\nGuFVkgzwOneEn0GFKNbWa2cn2McKRW1NLy6Vcary7EneZbyrRyIXXNKyaOvRLUNpJKZLWpb5fd7Z\nYrAu4xyEb/WPHC/UmYuEF/VTZfJDjXaUtLHKwTGVpkBjP83oVjSiNID6oozzgudyUw/u2bohkHut\nWGFOG7zcm/cYaeOYxBgKWq16zw64tPcmADv9T8nY+33mH5Go03xt6cFBNVE/5fhxQWz1l7VHY9Oj\ndqzrcGjR0ahNujfgWA8psSyItcL2zlBNnMRh6QQhZutUM2lJmHs1/JpWWo5DhpYir6E6zL0SZijv\n6QV9hrua95COaaSaLTvj0zzUngy8M/pRog9f4zuOJPwu+hPv5l7JcEL7G2+yc3RM47QsFHOvjN/Q\n3PlOh1FXW3nX2gS2HimvSUrHu22StmzG87MNnIZMlt2NcRS2eStzdLWMOF2QCZgbl+BlWcRf/cMb\nPHtdFmxnAucvKyy/ZXNn/V8AED4rz/vlssdrTW2iOQ7p3NcknYcOcNpqi7YOCOryOS1J3ULW6xIf\nydTYvSEFT8+PDELiW1rhdl/t3qVVulvaBHZlk6oeAFOfOUtaVMi8/yo7x7J5S9rvcDG0qOltB7bD\nx78pC+lzBobayOReO2E3l4NaZo40ZLkypKTJMal9mfFdgah9s83kW3Lwy7G9RKDRkw1LNls0OqKu\nXvh2P+PhD0sPxoWV86DhvSRa5zCXe2zfkN+vvJTypbam8KZvuZ0uNZpUtavNaNfhlCvCcGgJvM69\nkNQXc6zS8imEmtRmDRltyzO6t1YonpJ3ORvI/IZJiOmLUvjkpUUO7ouf4Hfbbdo9Fah3jvla8BIA\ns49KG/nHrU/SvCLzV/OLZFq5e2SP+Mi+nje5qI1nnh/Tlj3KU3MpkR58M44jypo/FI3GRJrAhUYL\nZp0SNe16leQukSbiuRSJu7IHOumEQ/VjnZw7WpqLsG1ZL+t3b3JfIy0z4w5z2ky4eLTPvXSa5jyl\nKU3pR6D3RZrzxKRc9YeE2xPOHOtBID/XYbwvTrByUuVQq/La3ZhIU4KLRRHL472YuzuiPXqvbDE3\nI5I0GY4IPHnFam/MwmNSkz83Iw6bMDnk7/+maNov39wjVq+ua+DZl+Tz/3jnGms3JH/Bz0WC21dm\neeK6SN/RjstuTZDHay92MJfkd+Xza1ia6JON1XtvMrracdiOYsJN0TTWWpOjOwL9dnqSsHV09Zja\nGdHAa3aBkq3FMz1D2hL4uP6HXbb1OD17T3hy7pl5woJoNjM54oWL4vicPL/HSRwoKuRc/xcSvF77\nr7XfZWiRaTfgbLzH4LoWCW09y25XxlQY9Whf0nTsPdHgB4dH3D4U7d9phZyzJafDLTdJ1Izzrpzn\n+m8KMvm958S5+vn22xHCCfXSjKp2K55tpfT1OL37X5Tf9U9lnM+1NX7tiK42FhllEW9++STx62W6\n/58gucG8dtw5jDDqaH1pZ0TzopgEeeTSU4fgQS9i56ogtie/LU67j/yXbVztkZDUHdBTzId7PTaX\n5PNATspjJi/SWZbvjnsWkZq8xxnEHZnryDX0u7JeOrGsw6pdJ9Kj3awsJwll/sZHQzpDQWy90JCf\n1nZs2u05ipocBzIPdw4H9HI1mXoWD+s4+nkFKz/6d/j8g+h9IRQ67YR/84/3yHLDy+fuAfBXrz7D\noxdkYQ7mLAo35OVv7kccTWSTnl/VpJmLdaodPd8hC5nRLMbkco1WX0I2j6+tUP2QJvUci836G195\njS+9LsIkztIHJyPYluFYIdeNwQ7fekEm71fG8rzFn6xhzWnnoskO1zsyAfuTA9KyLujOASuunpYU\ny4Y3xRrlZUmOGe/2sZtiHswslSg21gDIX5VnnCkkVM7L58zNSfTUpN2jO0Tq+/j2i7e5tSML+sys\n/OuXPfJMFkFvK2bjd8SetIxFpmXI4xF86Z4kNS38vvD1qV+pMpdJlmPBdeBxtY2XVih9Xlun2xat\nonw/0Tb73q7L3b7WYrQsPC1htwYpmbadP37tGl95/jUAvq7ZmHn6PU6XBdxwyKHa7VlY5PqWPO/u\nm/LO7caIhfYTAGy+OqE/0jMxXYvjhvhrlvsBjkagntLMy91ynYn6Rq7MPsK5n5fPnz2ocOfwWwCk\nWUw6kHFtDbR8+82cxad1Q4/adLdlyzx/dY9bn5f56x4I4N51HNpaXbpcyXFtgf5x0aOnkL/essgr\nWhGp1aW7nTGeLYLXjpoM9JyJ0UFMoSLzXl9oEWjZpWtkPTpeyKAvQuzGq9vc1db4lhfgWXJtMD7g\nQCNljE9iPD+YpubDlKY0pbfR+wIpRAY2bUNxBJ/Ss/Hc5SP6S6IRCuE6nbJo92TcZ1fbcC9oGnCw\nXGHhjPbGa80wo7AtnAQUIj2h+snToI0+trSe/bO/vUWoDUTyzGDZmlKKTRSLZL/b72G7onW+YYlm\n+In6wzQ1OeRgzlDYEa3ZtXOGmZ4CfG9C71Fxjo0LopVmqgarITkP+VGfg4YmYe3uE1oixe0T59tP\nNCmtyjjDvQK9rmjr24UJ7Rui/e9N4gcHgDS1v2S+PCTW8xAjr8fEVa9jZj04aToGvtWX333wWLT5\no6/7RJfFKZmfeQivL+Mpe+cYL2kcv9RjZiy/G/dkDPsh5Je0HfpKHasvzsVho0tb0darv/sGL23L\nnAx6J9rqezf+uN4fMasdo0e1hNFd4e29BeFh9GV4/oq839duewwLmjgxzrmrGds7uyHesmjHtVB+\nZzdaRJuST/D8U2Xsvjhut2avEb+m28DEJHoy1vqxIMz1SY6vXaDnhjF72hjFeXWDb2pzlqI203Hd\nlFlFB2GYUjilkZ8+BKrxI8ejpDUT2bE8ayGoUFVU6OceBe0fmc/PE2pFpD9rg+bXTDTnY+don6v3\nZc6uR11idWDG3oSvFLVSuOpQGckaOImG/DCaIoUpTWlKbyNzEnf946RWo5r/7KeewWxuc9jTVM3I\nJU3EiTYOYwoFdRj6VYKKHvWmQ8/DGFcbt1IK8GJtGNqbYHt6HmPFoazXJ7FoMBufPBCk4GUFIl9+\nl1tVzj8mCOMnnv4M9RWxtevNNQD+0z//U+TqcJpzXRw9cCXMInw98XrBTvjoJ6WAavVjoq2s4Rxf\n/ezvAOBmhuf1fMXTVhWjacAL2hn5KI4wkdiOk1LCJ5+RTE//1KNUfcncGz7+FK3yBQCM9kIoZQ5H\nXXXWuiXeXJfioXoQc7wumubclUdJWhri1T4Gh+GQqladvvrKNwj7Yu9WKikLZclpmAQehUTGvH8s\nDsA8Srm+dU8+j1OKdRn/mcbD9F1tMTbscPO2+DBC7fXQ7m5QzESb7w0OyUN1DI62cSI9N3NsyBLt\nqREJSjucRDiaIzFJHXJtxBBmUNYj7SzXeUvb6vo2ccZEuzSlWUyq4dk8zkjUl1TyDEZTj2f1rNDE\ndTmruQIf+/AjXPmlPwdAs36B4MoHhUe63m7dXaejyHO498aDLFTCmKYvyGN74w7JRBBkNpaxFwMX\nZ1bO5yjFhpFWSRarVfKWIJpgkjHQ/IayFkQdZRbpWLtLz1osuOI8DasxjwTiuOz0Q8p6NsSlJ9Ze\nyPNczsD7AfS+MB+MZfAqLsc46Nkf9IMhk54myAQFZlqysZbW5lgJBAYW9ezD2POwA4FIi1aZgbbT\nPs4ijCeQqTj0iRU+D7XSr211KPVkozTmGvQjrXZMDpnckQV0p3KVRxfXAMjKMllZ1cVR+N2oOQy1\nzNoKDUe2fp447GjDDSf9GQBe+adf5F9pqe/MnQ7pSXutUY83dDG6GzK2uOWwcCQCMlmF0p44n57c\nqTHzF4QXtSinpIlRqZ53ORmnlBVS9/MRq7NSj9be7jCn0ZWu36esx6CPPXnnkm+RpvfkvsUipqPl\nyVYLtyTvWvUC9PxbakXhWyftMGPLgs8ZEBRFmHrzLstZRZ9RJl7WQ3LqYgalWzZE4oWPopBoLO9q\nrdQ4elH4fN7NWNfWc4cTXapFQzUQATjTcEDPEPWCjIIta6Bc86hoe/XxWObcuIaBJmeZowmH2vnZ\npCl9S55Xji18FWpWoqZWOuDaoTwjf/4u9c+IuVVutmhp+D/SOa9HEXsdiZKNb0TY6gR1lzJiI0K0\nUCyTR9pNXAVIhk090YpS1+JkW3qWRUnv4ZTKOLa2B9CToCrdIkYd3q2SobIo8zA+COnoCVGm4HDg\nvrvah6n5MKUpTelt9L5ACpmBsQMbyYQg1qPFSfD0CLygVuHsRYHPj556iGZV4FOtplqpMKYaCHTy\nvTK5q8U6WUw4EGfQeBCTTkQLp3qkd3fS5UCLZAISqnOiga7eeI2uZim+fu8+i2dFu51kUtpMaOvR\nXgvVAl6omZJWREEPn6HmcLQgsfV/8NnfBaC69yxbeyLte+GITlePKs8yDhVBzOuZDhsDi+pJZWQX\nOtdEY2yuPsfCjsTQf/qvt2nUfh6AVu3TAOyOJpS1sW2ejLCqAr+3d4vMPyJaN8gjBtoApHiS8YnP\nWIvD3EFCrM61+docfvFEG7uE2hTXjcWh6GQ9nHnhWzbv0qiLtirXAmwjiK5Q6GLrITLsCO/vpT6Z\n5iCUhrMcd0WblU2Pe9rZOvcK+NrswCqHynuf4ilBKQuVGYIzcr8Fz6e2JJ/Lbp1c5yFT1FQJPNKJ\nvPNR+5iBQnfbgWws5spWb4yrKcSmKb/bvnqb9VS07rd3DrH+kZTyeE/3WfzbvyDv1BaEEqUBxx0x\nA0fpJrk6TOfCZWLNJ2kPJ5Q0pXm2rB3InQRXbWErNMzonNlOiNeSdzKOReDLHhhlMt5ePWM80azI\nc/OMYm1cW1/g5W1Z95XUZ6KFV++U3hdCIQW6WUprGJEn2iXYsWkUZXGfX5jhnJ55WC1Uma3KYqvr\noZ2F2iJOKnA4qAU4Ch3zaspoWxifOG3ikWy4SCMZxX0Xr6q5/1FORQ8eqVdmGdkC1w+yIRsdmYT5\nG5JGG+cwryCreJixafTcPjtHzxKhmTq88GX5fjiSw1V3t/p4rlwwcrwHkHFgoKD366st7EU5/ROf\nSQwvqA3c2Uh4piGTv/7aPHVNN04vyoavFQakuTaWuXWXyUXxOVw+A0ZPNBrvGCqr8k7dI3n/YBxz\ncEfTa+2cosa2TSEn1YhD7nRAjzV31a4vDGFGS6ujxKbUkGc4Q4Ot7eydgcuMpqyPeloOX8vZUL+M\nW+xR0me0jzKWNYJDt09b/QR2SX01vs/D6rcJAodmWXsmlgrU1ByzTItSSeY9cNQsmysT9bV+ZH6X\nVLtDW9UinX3h3ZnePhNNPXa0pHyw2aViZJx7Scg33tQU66Mv8OTf+R/kWu/kTNAetIUvfmxIOmoS\n+QeMBzK/eTgmSUSIeFrD45sGQSbfZUGOp1WuUX+CG2nNRzQk10NrrJG8h+/ZjHWuDQ2KmYzdNgkr\n6iw66GQMXpLU7XdKU/NhSlOa0tvofYEU7CSjfDhk3RlzSp0oTcvl/JU1AC7NPsyFK6IRgmgJp6KO\nFlc0fqFscMciXS2vBidIId0n0C7QY6uJZcQecY2mGvs2aagmQZCgh/mySoWwJA66ZivgjELJYz24\n0O4l7CmkXA2geqB9/32o1WVsq3bKGdX0H9COxN9asHBiudbzHS65IpOvjXI2QtFMnjq4Zo3NS+oM\nG04yeooqjki59prc76cvfZk7iXj1ZyuioZONJnNX9BToxQm28qpqCngjeUZWd9jZEFQwOZD3cIJD\nomPxig+bDnX1yJtkTLwv94ujCGeiekTbfZHEZAXRXIWkRKCxcM8qE2sxU32xiFpHnF2VAqxBajit\ngODe7hHjsoyzvz2gpxGOS/MBLUW+ka0FPsUil9akEUqjtUpNi9+azRmqejq2HzWw1QFr6ZHydjPF\nDGU8UTRHqnzOCh1aNe2zsT3DRiaZhfm28PjM0gRnW3IBbo8NI0WyX2nv8be1yMnKhCfHN/ZIgxPn\n6ZhCLBEA25vQieR+jl9kZkbzVua1x2jfwakKEnZyGyZazWuVsIuy7p2JT6ZH3VnqlCYoUSnKIIph\nxGAk39/1I8aR8KvaSMgvyD3eKb0vhEJsMg6ckCx2HvSkY9ZiMROToFo3kMhQx/6AmjabSAsyAXnZ\nISvI5FtxShZpDn+YkNgnpawpuZY4p7qeszQhkD8zDnzQa/Nig1yr65KiT6z55X4oCyltGkrbAtsK\nUZODhtjis4lPLZHv40rAjkqZA0cmqB7llHQRPz5jYZ20UT/uUTuQDevoIR4F38fR8N2rnTGeQuo0\nN+zoBrt1p0+wIu9k//5XAQhXH2Im0/Ta7K2qvnSc4CqEPww7pB0xQXZvSO9E/+KYsZ41OXuc4cp6\nZphOSHWVFHbHjDzZvLamOyfFGHcgF5iKCyce9RoEWs5t+RW0hwyWNilZLZW5o3a906qSnbTwL2Wk\nXfsBRV4AACAASURBVDUDBlXeKMk8XNQDZitVm6ymN6tZNDTaUU4sHKO9JIMYe6w+BS2hNp4hT/QI\n9yTHCk9SqVNC9dTb9ghvKHNyWJQQaGwcnIviPykdHLF5X/iWj0IiNRstNTlMEZxNESDZTkJUENPu\nsOcSqclT69sUVcn4mljmtAIKyHNzH3Lt14hbwELezyk5JGURLEZ5URpnGF/2SLHhYJ3kcbX3GWjP\nTv+My76r/HqHNDUfpjSlKb2N3hdIwWQZZjQmdDP8lsbxi3Wqmg7arC7jKkQjjsgt0R7GFylqEZD1\n5bu0MCZXzZxODMmOSOs8jTGBaE3L0Z4NkwNyPVK9wJigIt7do6NrtE66nHk+lla1HSskKw4zUk16\n6i+Mqe6KJK7XHdbWBKo1/n/23izWliw9E/rWijliz8OZ773n5h2yMrOysmaPbdldNpbAwiC5GboR\n0OqGtxYSD9DwAhIgDDx0Wzx0C4HAQEvGMjy0jdztdpdHVVbaNWRW5Xzne8Z9ztnzEHMsHv5vn6yU\ninams2xfS2dJqbtzn9gRK1asWOsfvv/7pkBky07iEF1qBxrXduXavfYGag6Vhk2IsJBd87UhsQtJ\nAp+CHj/X8/CHR9L3ySxHwLH45skInX8kfbvzs1Jw5LdzJCfi+iyTLrbII1jkBslILI84TlEOJGBW\neuIyhE80LCo4Ww5QEYehnQwpaepSpZASnuzGhNSmUwQ+3RVlwSUICdYFXFK8IzKwbFLYe7Lbdd0Q\nVVsGee/+Fl7n5tgKLBgG+VbNFTaWYgGqulgB/aCNFs32bmhDMbKrfR+eR5cgLaHWFM3cPU3hopyK\niWgpB5pRe7vmo3BkDHXuoeZL8Vq4I6bSYJkgeCDPtFvL4FvS/6PSIKArmBL/cjKa4GAilsKOO4FL\nfoq2jpC7BB7VNFoEqAUBra7Mhcexsmr+JcVetUzgrYVx6i2UnNcmXvMqKKAj74hjRSgL+X7H89Ag\n1+dsbCNJP95r/kwsCnkFDNICtaVGv8vyUMeBs02SzGCB8LL2wQZqaypypqs8DwYywEXoQRmaWU4N\npUdz3lWw1zJEJct/SxsJX+4ILkATPbJCDLZlgH3Hg2aUeHcuxw5hcJP1BZ+3QpT78n2n18VtVgk2\nP53Df18WmX1fJmCj4aH9gixkVtKDblDJKTtH+4k88O67MiHmywWarETs3rBx510xcf+3wxg+SUnN\nxKC+IQvLfSo3/Vi9A4vprc2yxEks97RllbDX7tFihsxm9SQJUxv7HdQYA8iWBQpLCGdUWMHO5f5L\n14LkigCdMrNQOEjalID3k0tkZZVqZG2ZXgFcWEScWvTrrY0cNfrnfgO4lst4/sHSoEX3brNuw7GI\nLGTas9aM0GYVbOhYcElx7rgltM0FqbIAl3GlYj0X3EsmrCIqJYUEAFkBw/oCYytYC1ngPboRe+4C\ns01qZbY7WD2WmINXlpiSscle10ZEObZcMec95aNtybxoX7eBAcu9NwOEPcbCIpZLzwGHtR22awO5\n/L30E1ib4rpYnoJLHyyl7kPhunAJ2rM9F86anCaoXaJ9szLGZyjE/FHblftw1a7aVftQeyYsBV1U\nCIcJJmqJvJTVrqV8dFi1aM0LZIxq26UPl5TCaspqyGAGi7x+hU6hqKyUezFSKuikyGGRzy5xxcTL\n5jlymuKFZ8MmcKgTbaICGZzrC5RrM5FS4Nd9D5FgpeB1I9wiWGjz9gb8lpiM+vAB7rwkNQNBn/Tl\n4eeRK9lp7GoTRWutLGTDN0JjtkdadOMrBDdkdzlbePjihQChlqsDfLcSKO3DkYFNHcMgFXdgcFDi\n+i2xUEqnhGeYcVilyEhHZqUZypqYpW5FF0eV0BkZh70K2YiAC2sFzZx+VWoYQpPnlG7yltllVaae\nFCgTcTXMwIZPTkT7ooTySV/nyA4c1Lpwc3lOkavgbsrza59XGGmCjJIVnisJPorJyt1eIUioxp3V\nYRlaPzFQ1tdBZQ85QXDkHQH8OVIqgKmqhFnI+cpII2WgcF5pKFbexjT3w0YTNw5lRy+iIZyF3PdS\nGXjUBTX8fRqP0GrId63GBiLOT69cwe2whLPRhp/J+VREdaeoB9uWGgetS5SkandqPhTnFrIMRUG3\necrx7trAnBykOr10V0wjgF1Q1cqxYG6tpXY+WruyFK7aVbtqH2rPhKVQaoO5W0IlgFnKKpi+VKAk\nzHfuxpdScCrz0CokaFURUusXDijTACvRKOkDGzcHSF3lJCUyT3ajhKmwVZDApSBJ6uSwy3WBVQkn\nIy9CVsBZi/6t9RRaDupMm91u7qB5XXYKL/exmlKT4mSCkHoB7S1BFeq2gaIuRGXlCByJmRiVobtH\noZoac6QTICWVXLssYN+QYz/VifH2uxSOSVKkW3J89Ib0cdQpsDFnIDVcAKQam49mKDIJuj56+h6q\nx+In5y2xGKZphmUl/r5ZOXBdBu0yINf0/eMCZp3282ULXlkpwiWLh0ILizVlcH2J9mPx5/0tB3bO\n+BCl6cKswCJjMVq/j6An9z/z1Ae+/1zh6QYVv7njZ4slxjO5SBo9QWso41koBYTrwh8bFuX78ozW\nZlTBYizKrkpkbWplxhMYoiWtuQ9m++C5tAhSjdUGYddxD6kvVhoWGbK1VTSR/qjMgQ3pQzlYIeu1\n+f0HAd3a0oNpy/ElA9eZP4bDNDIqH5oQdCgHSNbxjhKKIkDWHmMERqNkUaDj5tCM2/h5iaJkStLN\nMQ/+EgYaUVQoxyuUtkZ7R27cSQMMz4iNzwAnlkk8skrcoondCUllvrBQMeij8wqmI2aUW9gwjPCb\neQI05HhnRlBQOkTGoE3k12CNqf3nFDBLmqtbIZJUZsp8It/ZZxmI18HCPkd4IAGg6s4CoFBqVcvh\naKparclUMgflhNo4egrdlUVNRZuw7tBEfSyTZz78LlBKOa33pTb0Q3n4jUkDPxvIC/TPpsf42oFM\n2O6mLJqdewYXfSmXdpzPYHNT/h4nCodvC9/fG4NDdDMZ2+7aBD4MMaGgju1FANWWao6HkguLWeUA\nq/IiBrKWABYX8rsis2DzvTzTEzg26cfDPaghK1656Fl+/VIT8/lHm/gnLGePkgIJJ3HaBtSCtHcu\nI/JnM4x9Ca5pu8CCJriKMrinxGx4PmxFTAK1Sa3EgaOIlsoT2MS4ZK6PfCZjofMV0rW/kQlPYuIe\no+Aiu9Wr4BLrkmgFi2X+OedHXhkkVNZy7AUMCVdsZZBQmxJVAsMApB3KfVpVBMM0ieq2oJXcn1nO\nUaYCtKtaEXRJgFopEHwz9FFRGsAkgMWaCEe3YIck15namF38OUnRr5tSylJKfVsp9Rv8/5tKqdeU\nUveVUv+XUkSlXLWrdtX+UrQfhKXwHwF4B0CD///fAfh7xphfUUr9QwB/C8A/+BedoIJCXClsVi42\nSAjRv+5B0YQ7HY7h2bLaLZ9M8ajLIqAZBVScD27FURk0LQEnakHbLDTZm0KxKMXpy+8aRRcLMiNn\nWYVovZMsCsRNrpfLFCuKVgaUuB86FW6Tgqtf9BHeYjVjrY1qLAHBiXGRP2RV4o6YnHb+HpYDbqUD\njcYPi1thV1PoF+UzWFyk2z1MLygDdvt5NBzmvH+shuA3hD641/t19AfSj4uJ9He61FgsxWzve6eY\nrUV02kCDRTL9kwx2c61cLd919AQx8/ilNYO2hWDWC+ZwWVVq1evQDLraxASkiY9zJdZNOYrR4I7X\na2+gqov5vPIq1LWkYkuSh6qmDTDIaTkDPL+U/v+G61xS3b3iBTB1uV5OqTyrLDCianO+MAiNPLNt\n7cBtyzlcS8OiNaHKNR+BhyymWb7hXOqEJOUcCxLvpiaGdS6WiUvLxJlrTEPS1K002DU0DDCv8ZhQ\nnn+308RgzZWQHmF2KMHf/mYf3lSOmfYmuGAgvM30Z7+rUO6KVVEVKTRd4XSxQKbkmSweDeDZtCxp\npcUdwJoQxh4GKMlsrbdrsOgHuV6BXknX5CO2T6oluQfgXwHw3wD4jykl91cB/HUe8ssA/kv8CYuC\nZQxaRYEiAgKaQ/1kDw8XYtadxRNgwIrC4xmGc2oznsrDOrnhoUaGnk6jDt2W7/NGHY5m7n2V4YRx\nCZ3JJD49u0CzLrj3NLZQETegtA13KC9NrlYwVJkak6txa1lgRcKLOBijraQs2Bk7mM/FZXjnn5/i\nmBVuP87ctp/YeOtY3IdazcMPz+X++l/5cfgkg9EdwfXHv3mA3/wDYRn+zj98C7NC+vZTGw1s35LU\nx+PvznGwWLsPMpYX4xVul7JoFkGMtiXj4vkVFpXcUxhtodGUhWPFst9BOoBhHGVlKeRUG6qUjb4r\nL3Rzvw9ty0szOhW1LHu0QosS9+PMwG/KS7HTDqF2xQQvHhxg4QmcOljypas14fBcyjLo3aVE/Rsa\nQ/rUx1mOFsFQeSX9fcdUqLFCc3pRIGQmaWAdYm9f4hb92jaaHda5tOTfcuVjvtaSfHeKx4/FBF/M\nJniSLPi7LtoBcSSMZUQbIW68IUxXZ+Y+mmMZoxMNeCsCubT8PrZz1AgDPz2o4DPbkzYKuKU8h+qk\ng8P7wkx93JNr3Oluw6ZL6PlNFOQCTRcV5k/k8/HiGG6bFZqWPOx68zOwOYZ5VsLO5HNp2XAsxhQi\nBxuvfDxj/ZO6D38fwH8CYM3X3QUwMcasJWkOAex+vx9+rxT9Olhy1a7aVfuLb39qS0Ep9XMAzowx\n31RK/eTH/f33StFHoWv8fojtvI6mJXyID1sLPPy2rKTnT31kTUb10wIvMCKr78oKeHoQQ0N2ktNR\njD1aBypLsGDc6GI8wTEDPKPvyM6dujPsUH69ea0N+4zwaFfDzJj/9eqomgxg0RVZ1i3sMnDWzvuw\nnHXI2sfsiexM03oAh/wFbiR59ePoHE8HYsrtmBxnb8s9RTtvwf8hUdrTdFUK7eOdSszFIxTYJhN1\nPezhsJSd4r3oEAnhvO/JhomVv4L/UMbtxfIQjXUhVVXBqYmFYQfnWB5TQTuSHWzl7CD35STBeQTP\nE0TjcBAh2JD77xXlpZYBtIzFUX6KMS8+ckc4o2wenBFapVgbWbhCSZbqMhSXov7gAaotcVGsXKHX\nlx16sRmidih71aap46wuD1Az2OdZGjMW+Lj9GhQtKKViTMkEnpSnqFhJ2NRiHRXWHJMLse7uHx5h\nyMKs1SyEVZe+5XMXi4AoWRa/5XNgSWj2ybCBMpTxCtICpUV6vnStc9pC5dFqgI2CO7qatPBgJePp\nzs8RN/jcY5k3ozxF75QB8W4Ip00yoNLBgHrcQ2PgzEnm0qGLeb5A1JM565gJFCkLcb5C2ROryQos\nKFYNf9T2SQVm/1Wl1L8MEbdrAPglAC2llE1rYQ/A0Z/YCa2wEflwwxDVNdYRZD4WZ6wyCxYYHjCO\nMC1wRnz5o5U8zGvtCKuZPIztoLyEe1b7fRzel9/dezzDoisP43AqD/P0fI4+sey7owQ//HnGDnSE\nGqPyVVfBo1bihCnS/irHmGQoj6szvFIQkHOzj+d+Wq49+b9P8Yjkn68OOSHKEi/syLHxYo5FjaKi\n5QJlTDEQLYZV3lniyz8tC+RXNvfQHYk5GHYU3G15QarJI/z335H+V4WMhXta4hFVjqxxB7f+NVb7\nrQzmK5qldR/bpSwQfo+1AycTHB3LeS9WI7gLmYz1qEA4lkX2urWE1ZfYR5iKST18801885Bgm3yG\nNhcQL3PQdga8XhO7FLpN+YJFTQPF2g4dAzmfyaZdg9qhf92NUM1YHbuSsUqrJSyKEFuTFgpDAtqG\njU0ybjUXJRqFHBP15Pe228DwRPozfjrFBdOMZ9MVjh5JnzrOEB0KBdWpFboTdbFF9ibv9hJv35Pv\n345tqJHEj/IZyXZRYsXy9J7ZwoUnf//9b30D52dUk1oV2LolL+y/9OI+AGCaKozPxb2y23fhKgEy\nJcUxEqaOT7MVAtaK5GttyMMci1dE9ez2ThfFShZhz1LQJJdB1YApPp4l/qd2H4wx/5kxZs8Ysw/g\n3wLwVWPM3wDwOwB+gYddSdFftav2l6z9WeAU/lMAv6KU+q8BfBvA//In/UBpC7ZXxwvhJq5tSr7+\n7OkI9k1KsB3soE6uurRpoMl9d/0mySNsoLbWv44chA2u7PY11JtyjmwjQ4fAocUezWQTICRJxZ3d\nFmptsQhcaGTcHSLLRhHRQqAk3zSy8TJZQ7aXDojtgWp20aC/svliB3hE2rjPrmXrp2is6aqNBfvx\nY+m+saHctcsj97nxU5/DT74nlkb9S7uoxhQLaWbIqDD94l9/gjvENRyxSOq8MjhYyGD8+PwYB3S1\nOmmCWk/2gDvTPQQ7VJhmsFZtWnAg99+uz+HQLLeSFBuhuAR+7xZcBgGdFhmVay+hfVvwDziP0Oox\nwNXZQrIp5wiTClbU5qMi63Tbh8fnWAYxuhDrYKEtPJfTMtnwLwlMDlnYtZ0ZBOQh8DsNaHJEtO0C\noCSAmZc4IjS7S0Baq9ZHfYvs2KrELZKv4PoEAYOxKzVHNyBTdlfmYWlszFx58MtVgYRz4aZrY2bJ\n61PP5Rk07ACZL8/c/fwO9Fvy9zs3NfZblNO76eEWyVXadZr7qYIVUYav6cKqE+C13IG6KRawdzLB\nRleCkVZEN3argTYBeVZzAyUxHUWtBZtFUFopWNnH2/t/IIuCMeZ3AfwuPz8E8OUfxHmv2lW7an/+\n7ZlANIa+i8+/sI9mvY7CyGre7gR4qUZmoi+6OKskvTUerGCTXTgkxdV0OrsspFJBDfNULAI/Aqqm\nfN7KDHxXjrdK2QUq30J0Q1bfnvwYAFAWE2SMKdRyB6FPgQ/K1X25FqIsmd4KJ9AVuQKCAM4NEQi5\ncT7E/ouywxrSxiGZwxqLv+9t9xGzBNgc1YCK4irML+u0i5rL0tzxHFax5hV4HkFL7sn81mtQ7h8A\nAJqh9CevNNqkhBve2cbNcl1UNkdC5KHj76K2xTFiCm5aeGg1ZQfq+RY0YzRID7HVkAIrp7sHix5n\nrgV7UbvTwZevy3NaPh4AJZ9fu42YcGWnFsPYHAPyMKzgwJpf8pmh4lg8bwErUpqdX1RYE3FtkeG4\nsCzcvCHj2o9uwmtJn51ihPF9GbuhWUIRvTpnYZe7Z8Ml7dpuaUE5ModuWhb0nlicudtEg4zP7abM\nEZPOUb0p15hhhjuEaR/XKzAzinjNFLVh0CZxbeS3ceNHfgwAcNe8jCiR3y3DJvw17JOEsFHuoCAb\nk5/YUJxvwU4bm+Zl6X91CkVo9o4l70IabGPrNi0TRHAY/K1sDV2uWaAV0Fib0R+tPROLguXYaG40\n0U9cKPIdLqwzuHwJfdPDTl26upFqFFQeMgymjGYx3ID8iXMFbLBarHQQ1mRy90Y5jC+Te11eroM5\nNjsEhCQFEvItFMiRD8hd2PXgcbEAI/2tT9XRvE/TcdpEKe8V7JkLUN2nef0mTEXevWQd3Tao7rwi\np7IvUN6TibeqL+G9JvFYr0dosLkBRbNd+QpgRaGzmKMg3LV+x8Zze2KWf+sdBi39CsuuVGf2ru+j\nqkhxP0sQZ+S5rMcIyA1hk66tHjagyEHZWClUjkxis3ELLjkpvFzBkAwGKV0718JWIAuMc2sLq4ZM\n3GwRI1gywJUPUc1lgctbZOA+niIhxXmpbfgErVVNG+4hWWliF5XDxY4w6LpxgdmaUv0CETM8qtCY\nVjLOhQ1YY0Klu/L3ZbzEqpC54NqAG5I/0VEobQlAtrV7yXEQkFviYrTCcClBy6PlAisCubY2gHhE\n8SDCw1sXbdR2qfq0AmwC0Tq7W7BB/E3lwSzJ1kwIfoUV3JWct3JTKAa0HRTYiij2cnMfZot4AyXf\ntbwQPrMrjq9h6JouZgUUceiBVpdgp4/arqokr9pVu2ofas+EpeBqG9ejHlQRIaEEVzoBvJx6Ch2D\nDe5Gwyy6FNlAU0ynO/kUDwaM9tkV7FB2Td9sYb5kKsx20ejJ+ZqKuet5GynVlQPbICQG6/Q8RjyU\nczR7PgoWSs2IHtxP2ghZ1Xe6OUFrIihEvJBD2bILBGYTFTULMJRdx+3ZQI3FM4MJfDDwNbkA9ugS\nPJW0ktpewQol7Wc5e0BOpqRGD25DxuhG+SmckL1oRebnXVvhU601a/NtONSVnCyB2VRQiA1vC9d2\n5P48m9WLeQ15yt3cnsJtynmDqAlFZKYxMSpF1eVzPrx0jCaDi/6dDrpkeV36BU5jUr45CkGd+Aam\nFpXOoDneZh4hYaC0CQtj0psl2QJDBgxRyj15lYdug1wBqQND2LQJLIRa+jlYJHB71FYgBNmKLRim\nC7O8QMACurAoMV+QNNbKYUfSj1JTO6PIUc3EetuzMhzQZ1guSpw9kUGoTeS7U32B3UfyHOvbU1ic\ns77twrIZYFYOsC7kq4iFyDJUlB5UeSmy4ABUvkJApWlnP4RN5iVDC1pnLlyyYGvHRkZiWq+q4BCT\nUxkFVX68lOQzsSgox4G1fQ21KoFN/sV5liAmqYk3t+BwUDt2idyTQYmJ8ffsBCFfjr1+Bx3Kj/th\nHVmL0vZ6jIA08I2ePCw1W6GgaGe6Gl2axlVS4dTI5NiaJXBflAnWJmw1/0IPGyc0B502bFb7wQ6h\n1kQYnWtQY1aq1cU8xcoCCE7CsQIo7gFvBxap5qtNLnieAnxW7zmbMGR+VoGHkpWY+pqLF8p9AMA0\nEtO42wbu7kqc93O9Ph7RpAwaPmqlXKMRdODWBaeg6dfaqYUyJlWaTqAJlbbVFtYaxJWeIZuva6NJ\n17Z0GJABnGUCqy6LTGmA7ia1JOcxHEbU10WIeWijojiPKeeIOCyregORS81EHcNQK7Ik03Tg+GgV\n0s/cT1DQF3TSKUrWUmzYLp7fFVcp8hlT0AYFAU1GZ6DQFZxOBIel9EpVsJM1aQ05DtMMqSefkwKY\nkbRnY2pwxgrMYCB9aHnlJbjJNBpQJGrRjgtFk1+FGmCcx1A63swS6BorI/U5LFLTmSq79HXttITT\n4utqGF/q1KDpSivLAtL1fDOXPoA2AMqPF1O4ch+u2lW7ah9qz4Sl4Lo+9q7dhWWd4OmbUhk4jBMU\nNBnrsY2lL7lgTzUQsBDFUN9hFMdoN2Qlvr7fws46cmyNUZFodA8hcu7+5kz+dfIKNiu7iypDmq8F\nWVJomngLM8PsSJBpo6diKfyHP/tT8LZlN8vvP4BlmG82DpQraDSsRtCuRImrFTn9dY6ChUSFymCd\ny33kWYRkKvdtM4tSbhl4DEgV5RnsSIquVOnCIvZi+nsLLG1BvO0zem1lLopQ0JGzPRt9I9u4CY5h\nj+TcoduGoqyazWrPrBhAExKdDCsUNInz7bfhpWTNTkIoEoNUSiLojh3CZdYCqYWSRC5BfQNqTCET\nt0IeTznOco00teBT2xN1D8aXsdhv9DGuyzkGx3NMStmZqe2CUmUYxfL3WtWEayhjZ2eokVCnETWw\n0ZMx75DObTU7h08kYEf7cEhTZ08zVMRk+E4dKpJjilwsL1U46JENOWnlOD8Xa+S78xj/bk+ery7F\nEnS0BlHJQAGA1ZDlcgFN3RLYLYBszdmSwjO5BZwS3n5ukIWS2bF0B3pDLDqrsgByfFgOn4ftQblr\n3AtgW7QwNEADGMpVuEyTfMT2TCwK0Ap25CLRJbKJvMST/BzVKZmGuxaC1roa7AIqlZe6oplVOS0E\nlBPPj1McTCl3bsUYkD/PSlJEjEHEMQsFtEbPEFJbpRhpecGO0jHm9MvNYQMTpsue3hNzPmhG0Mm6\nJPkVpG1xY8xpDBNSq71WwShScTM2ArOCIbchwibsrkCGIyuGoSKVukkFqayNgnVl2stRrYVHkKJc\nyAu99Eps7sgidMaX49aNFhq3fhgAYC/sS1x/lUVwG5KSc7oKWMisKWL69W6FFYVT4skx5iu5dm2R\nokYocXBmoaSOZ1qRFKYTI1/j9qM5bE5Ay5pDa7o5cFBRGCZevzWHKyi+uNoNsOYbDq/XMKdrZqo5\nKtLkk1IRmVtiWVET1MwQ8j58V8GjIrF2K2AsPxhwoU+y7PJzzXGgXFnIFvEMcSr33c5K1AOq4DjS\n31WRoqyRWMXpYExlsGVeQrFMWifrdJaFuCXzLVyml5uME+XAehHOJzDMJBmLgkNxAsVpU9gGmlD3\nop9fVmKWrRKKbjNcCtykPhQzcMoFtLMm86kuuSaVUpfu30dtV+7DVbtqV+1D7ZmwFCzloO5tQgcl\npqFAZk8P7qEVsEoyn6CZyMo+z3JYuazGQSir73ONGs4uZDU/HkxROhIMWy4zzCnDVvMsqEs8svw7\nPVgi6MkSndUjOGOu7LUSd5fMle8D7kz68f5DMfcQB7CYV4+dN2AIdNK7KUC9P5O6QEohmhnP1S+h\nSU7ihRrVy2IGGrcBRcIOjMRNQj2/JDTRXgDYMi5VooEai5KGc9yfyLl/llZAv7mJdiYWyuODc9ym\nOI3yQzgkKim1Bay4S3F3KYYGZSYmcxrPYVrrHbiHnOOC5QQVeSMLMmPH8QgGk8trNKlT4BZnAIlT\nEACGlG4xVa4xWsKJKNeWhsgmMm77nVdgtcU6eMc7w2ws21yD7sUkizElo7SbaSgG2izbQcT8flzY\neHxB05xck4UyKI3snjO4KGmFVQoYDaRPdn0ONyLexSL2ItCoEVbd2y/wnbfkOT1NCjgVsQV8i9J0\njnjJatD8GKAATjEpUS0kMFvBR0UtSUXtR0un0A3qN7R6AJnLy2WFbCVuZa7qqBFDoYg9KZceVPgB\nX+Oaj9PWGupyu1eXY/9R2zOxKMBWqLoerIMEW4G8SGFRwqwrPnMDQxPWgUFCclTPojJRr41NR9J3\neX+B82PxX+v1FppKbrGzqVGQM3F0SNBJtsCRYfRa57h3X9KBUaPEq32ZeF86KHFQE798GBLlt2VD\nM03l3+wgfcyXJm0AljxEHbZQlTSPtxgDWWXQlrw0hZ/CXrsoc4VyXcnGakGzMpfoxspZoMpJj89/\nCAAAIABJREFUbBXNkF+IezRsPkGfpB7Jyy8AAPbuKsShHOv/7lOMXhSw1HYUokGx2WymUJHRqCQp\njN+IkJBctejP4ZFEJmq2LlOVib+6NC098kumC4XZWCZpHp8jXLF+REco+utrJNBrEZmMfu9WA27I\nLBByaKZvay9tof2ePMsX2veRrORZVnzhM5Uina7TqQaLNU1RFkDRxfT9XbT6BKKxZsJrbmHJrEXg\nVCiYBhnHCzgk0RnFLuoEXHVJ/jvyJjjdlue0CQtvUSszKErkXdbYEDRl5znSifSh6nculaegW8gL\nihV1bNhc1NYbS5YAVo2gNSuDZh1EujRYpOLSVnqJoJB5qKmTmXRKeHTzXMdFRQSp5StAreMIBurj\nUTReuQ9X7apdtQ+3Z8NSqABrYZCkYxy+J2bycjbDtW1WgC1cFClBKI4BVdIxpxBGOBmhR7M2uLaB\njS1aDeUCEwJWmvUmTmjynk5kpz1frXA3lmvMLyo4XMG/caQxfSKr/68uC7wbfwsAsD8jx6PxL6Go\neHoPli2sy6qmoBYEMnkxtEOpMHITVAZIj78BACjeX8GtE5MQ1IET4V3MyV1g7D4yl9LqvbeBptTN\ne+1Pw6E1VfzONzA+Flqxv/HvSM1FJ/sZ/PGbvw0AeJKf4OcpKxZYDjxWA0bdBkom6i1SaxrMUSPY\nxi19ZCtxY+JcI2B2Jas14bBmYD6WHcxPPOSWuEHjCwuTNnkCXRsqJ9hG15BzJ4y25diqcKAITkhd\nF4mW4O+u00fwaTGTv/1ajipe4wbk2ZwWwDVaB6XnruN3sDPg9o48n+52gLBGCPmUAeoghU+WZNcO\nMSc3ZXwwwpyeW23TQUwcSU4rNKg81N4Ua+Voa4XtlViL7yqDcO02ksvCjArUWK8TBAFc4kJK24Ui\nHLmEhZxzxxRigS0Pl9AEJtlYAQS1mUONc4d1GbUI4boomJgV2wthEYdiFKAdUvsrBayDiwowH081\n7spSuGpX7ap9uD0TlkKhDAZOgsOLpxieUMyzs0I+4U4ZlsgjyrvNM5wz120nLCh54mEU0Je/f4GS\nTMyLaQJNotD68yOkhv4uA5SNrQDTU9mJ4s4K/+8Ri2CmS5xpshutssugzftYV/XlqEgCay4iVF1+\nXiogJ4xXhQCZoQzoZ67mKCuSa3oFTCXBQcvvoLwtRUyrJ/8UAJD88Rj2Fvvw/BZC+plZ1kDG3P1b\neQ/XupImPfm6bAfujy6BRAJc6cV7WKOEQ8uGR+xFqVPYc9nlYm4poe8i4f0FeYrs0g91sQzo1z/O\nMWJB0zr9lZkKo6VYHUWjREErLg9yuKy0jC3m8AH4RPxVyka+Rt3ZDkjJjWPHYEyrbzO08Bbl+aYP\neV0rx2P+sBOkWCzl2CmWsN+TzzuzI8CjNgSDgablI3DFKgrqGVYcmNhTqFMUtmFChDWJ+ThNQtPP\np/gmWZKXsxTnTGE73hQFhYwLBhHVPEFKvgkTb6NUROR2K1SGWJXhEtlC7sXbkUH2brThnJL3oxnA\nU2LlzP1jFO/K2A5CC7cbxJywWlLPcsSU46spSyoiAUlBcmwV8LELop6JRSGdJXj42+/ija+f4A1W\nlUaZg9YOsfhRDyyyQ7wCbF8mliYEdAkXc1Kcb/QbiFry8vvNFIY08JvRBvwtGbS1kMnvvZXi0UQu\nqMYJUmLnY69ANV9zz+Iyz+uzorA4nkFfyLHz+UPYC7Hr9N43AJ9U7fkCoDS4qgiU6cfwHZmAy+88\nweSctQhbHhY29S1fl4nk9DI0t9diMSUUhWgm51/D4FQWwN994w085eS++0MyMZ/vD1BGpC7TN+BV\nJAhZ5PC0LBamWsEnHr7wWQK+DFCmcl43KtFhjUaj2UNMujLTKeCzbD0Zyb/D0QWCJWtKdupo1qh6\nNQOKpVzbdQwsax01pgsGA7silHjqYTpkxuRbR5g+lcDlU7ODswmp7FgT48JBSVz/ycTGTkfOW8DF\nKRe4auFjb0sWhUZXXLtaawvxlG9KtoDPhe7WTgc5edsbrRKRz0rLWNycB2eneH8i13u+aV0Cj4og\nQjaURS8/k/PO4tnly7jVex9W83npzyxHsXpb+h90obYJec5Z9n1+ACoYwG33kBEDo4/PsEWG8Z1X\nnkNIWLyOGfDWGWwCpIyrsI4oKsuBKYlvgIU0vdKSvGpX7ap9gvZMWArjYoVfu/g20ncHsMhHUGtu\nYsZa8sC3kTAVVHgpMpKW9C1ZDeOyRAQWQe03UKemmxlaSC0xu5cNg5IBoYTagZiXiDU1GEfmMmW1\nWqQfBGqAy8/xWh+mfgaXOgTeMaDW6T3tQPlvAAB0+hwMzcvKXQeAYugaT7aZQoUssPJm8CqmqZ6T\nR+I9vwnnGnP65U3MkvcAAN88i/HbfyS7+1MnhiGt2OOmWAG3FLD3ggQd28PvYrDWP4kSlBwvqzBI\n6vJZkaqrsgrkK7GEZl6FHg0lKyhh99aK3jZcQu8cBsay5DrUnuxc7Y6GR72BvFwidamLYBfQ1I1U\n6yrYoAKYbkyyChUr/P7p03fQ/aN1Gu4cbaZ1Z0auoYMA05zWlKuROHK9a1sBSlLZOXUDe0esLJc0\ncJaqLtWzl9YYLgvsbNuBZrZXeQYV1sQ9ct333pnioKIat9VFui392DnWmBLToHZlzjoPCjhqTcjT\nRGVJlaif76Bgn1cbFmoOUbaZuCLLTCPdlefQM0t4DVbMtnuYvSL97Deb0CEDtwn1JDzAcaS/JTbW\n8BuImgzT3ZkNa7HGXn+0pszHxUD+GbRP3b1j/qf/8e9hI5zh6Xck6r258wqeDL4KAKjOzhBRrHVj\n9yU8ef3rAACnSSWodA63QY6/SQUrYF59UQn2G8DR0RkKtSYIkX+8oH4pRV+WKc5owp5nGSpO2Jsb\nLfQalD5v3QYAvOcniChU8/TBAqEWqPHT9CEm5/Iid/oRdhovAgDcGzLBrrk15KX0re7UcGhkUjnJ\nGIfHMoEK5smPlgfIKV6SxwvEa8pxnQIVTWMfiJg9UJ5MzFbUxj32PZj7+B/+wX8BAOhl38H7v/77\nAICxXyB/KPj6YCETdDIsMWLwJE8LnLFv7qxCk8pLNc9HPSLWYST9XIQllhzPk6LAaMgJ6Ctca8ix\n9aiOu3vrDIZMeF1WaD/H2oGNO2hRbLf1c38bXcLYf/N3X8fxhbwUf/z6L8uzef8QISnOe/rHYO3L\n9frlJjKIC2YnTdw7+47c66mMRVacIV3I7wo1g8lls+i0XNQjie1k9gKTc4oMLx8CALp+G+NMXJjR\n3McRCVW8ysWr/4/07d/8t2Ws3k9S1BgbWdoaP92XeRO3O7jelLnlBgbPkbQGjB3cvrGF5mc/K/0p\nXdz/xrfl2bS6uJ/L53e+ofDaI/m8V8rz/64dwyM1wHg2RofxnKfVh/e072nfNMZ88fv/6YN25T5c\ntat21T7Ungn3wXVdXL92DaP7j7GTiA10XLyDJ+8TajqN8akvCPYg2htgt5JIvZcIAjHJtqAiWcHd\noIliRVTkVh2VJZaHwnWMJ/J9ztx93gU2mEvOHAWbIh1ecY7ZIRPgVYG0xoDflvx9s3Ed939HzlVP\nczxqSMBwcGyjEUmfd7e38MoXxYLY7oroSUPZyC25duICN5gZmQ8SdPoCy82V7HbX7m9eStCdZzNc\nsPqwHKwQtcnfUH2gefhkJPexeFjD+W2SeIwG2OrLDr2676DHKsJ7B48xIXchxZdRuhEQkY9gHuAl\nZnvOewHuSqwOntmGRx2Fs5XcR5yfITmXXb6nh/iuQ3xDscCMaswbvQIZ3bjckp17MnCxGIq11flr\n+3BLudebSmNIhGBnucTvDL4m9/c6uQnMBnq7Mm57r3TRoeBKrwfk2b70uZqh+zVBeJo2cS+xi7BB\nXMDAQkArs9PvY/vFu9K3gykmu8Qh/L78/fjYwcOGYE/K+WsAsy4pUnzhP/gZeQyx4EKggAVJaHZ9\nGz//BXG7vnNe4Yxu2otRE9f7oudR/5SMyY3eTfSaUgVbBAaP74nVN/jmCg/2fgIA8PY7X8MJ2cbt\nIWHOtouEeIu5UZj/gKz+Z2JRUFrDrUW48G4guStmtFWNUNTlpW9lA3gdMfHU3EONIisuo7GdrR5S\n+gQmqmPGCrloo49iKqZdzVMoKOASkvjUr1WoSFyqVgk2X5GX99GjtxCQ6vtodA6fCjseXfzsmoW3\n18AibwZNcg+9maBdFx7IFz5zDZ9+XgBHva48cDe0YdEsLx2NMqXrUqWIU3nzsoUsDuM7CbJEFrrp\nIsfRRMbirQcHcFmpV/oW5kfSqUEhgCx/+wDjE6pXLTJ4hGjnZg9H1ExMnQQWmX4mBIUhSnHNpn/+\n0iY+sy2L8LKzQHnBOoHGNpyI921zYZpuoyAJTbxYoul+FwDw++9leMqK0OxRgi41Jh2StVY6w6Ej\n/Vm9amBVknvM/5qH08eyIH9rUuK9o8dyjC8graYb4e7tnwQAfPnOHaTeuqoxxi5Tme3Cx+CEZYd7\nsmjcia9d3vOjRw9R1OTYWzs93HpJLOpyJ8GTCwGRvXVNFrfz2grloWg/Qs2wLnK1DFBccDFYG+sG\ncNcfM41vaD6Trdu4UedC3d3CzX3JStR3Zd5sejfh09UoiwS3Py1u1YPWY3zju+T8bD3GDtO6ASHx\n80mKnKCuH2T7RO6DUqqllPo1pdS7Sql3lFI/opTqKKX+mVLqHv9t/6A6e9Wu2lX7s2+f1FL4JQD/\nxBjzC0opF0AI4D8H8M+NMb+olPq7AP4uRCDm/7cZCH3Xrf0UiwMxI9//vQy727IiNlQLnYms3Lnv\nwiLphclIG+5NYS3lc+nM4ZI4BekKTr6G+U7QISS0YrDPX9hIQ+7yykWNOok3689h3BFiFa+8ZACA\nplzdfHIbL7qyCzxdDkDZSey+1MZL1Gu8s7kFQ9otxYo8O6ugyKlXnhfwI9bCKxdeV7aYLCGQpjHF\ninXznXYfbkZegOtjLM7Eirm2EeLrA9kptmk6DhYubGZZUlSwG3LP9Z1zbFM38423LRQ0O++wgi4w\nNva5++81XkTjulhmtdstZI9ZrON0YChnn/fEIrKPQmh+l81mqG2Qlt/ZwJtvS24+iUqEpB+3Peoo\nTioccludP52gqYjDKAzcmgT2HPUt2A8Z7aeYyouNl/ATn/mC9Hm7cVmiuJjHqNaQ4AsH+9dlDsQn\nYoFtvdRAzOKh/k4P05nMp9rWHXjMGHQ+00Lyh+IqfOUzcq5f+a0/BEBX0oRY68SXSL5vNG89V/LS\nwp3ycwCA9yoHzaU8h2vtJrr2Wilc/u7GLXhrsp94gL0dsW7mDzX+Zl3G65vmDvoMsL7OotvO7AKn\n5oPCpx9U+yQCs00APwHg3wcAY0wGIFNK/TyAn+RhvwwRifkXLwrKoHBK1KomHIq4fupuB6cDMZ99\nv4vwurws1UGBgkAfRf9OeQDOadf5BayC6LfVGIY8gEVZICQIXLvk/ctLeIzqeyXg0JeFCTG2xSwN\nOzY211VrC3nJ0zjFnP3cbwbQJCvd7F7DZzYl47CzdwNuIkaSwxhGlWg4rMVwlQsVkHcxB9yMPI4k\nKVlELahCXv4wuo/NazJJZ4gxsOVFycocjZb08xokrrHRUnjjifz9pEjgkhsQjWtoLeT+bt6ysXVK\nEo5cFtNbX/bR+fwPSd+e/yJ0V/QGoKfwN0jC2PCB1VrolfyRbR+GNRWOdrH5UF6aWusWrjEd+A7u\no84YxoqVg3cjg22yBg2XIxwmMoav5BnMgmnZ+xY2Nhk/eF7u86+0fgifenlfnt9M4ykrZlcVsKXI\nllS34foSU9Bdsi21HRiK1J6rHcxGMt52x0OYEf1oN7D7ZZlnd0firu3d3sar/7tkPp5YBe5zkUpy\nC4pgobVouoZQawLAtc4Gvnoiz+RH956D71Ph6u4PodMQdzJYk9E269A9xguCLvRYfve5n9nBC0fy\nzH7mR34Bj++/CQD41gn1LP/oCb62ELfq5GSJlGCwTxpa+CTuw00A5wD+V6XUt5VS/7NSKgKwaYw5\n4TGnADa/34+/V4p+NBx/v0Ou2lW7an8B7ZO4DzaAzwP4O8aY15RSvwRxFS6bMcYo9f2R198rRf/y\nKy8ZqAzFaImyTjBOPkWb5l4WP8VixeizrRDGtBCI9a4yg3RNShcrFCQnMYGNjKa7yQ0MlXfyjLTo\nlYF9TtEQFSNiMMyoCmFAzMJCo0adx4qCNFMzwnQo57DsLmYtcUE+o3uwWnLtXBUA8RLenGQchYeK\nZCpK16DXVFuVDVMn4IoekTuYQdNqyLEBi7LndVhIfLEaSnuMtfBScp28lDULKTEE1mIJZbOiMlsi\na0v/HzyJ4SeyS1/fk3+juy9D70u2RDt3oKZcqBsKhjTpykQAGbYt0plBWQBdEbVYwmXZormw0Pth\nwXXcfWJjlpMsZCVj8qQq4ddkDJ9OKlgTGe9BPsboAfkitgsUQ7nOi5mY9b07ISri/UfLBPGFAJ1U\nqZCwJiByA5DNH0mLOI4ghSYr87W4jgU1H2f22Rq5DD1coduRwLN3V/5tVl3gK4Ih2Hi8wL2LV+V8\nwxVUwDoOEvJopfHZGms86jFeiCTQrMIK13ckkBilGrktQVClCEaKCmF7AQDlwm5TRt54sAkGCy+2\n0OzI/rozFCh9ktxD9v5rAICvjt8CEpkjiTFSkvunbJ/EUjgEcGiMeY3//2uQRWKglNoGAP579gmu\ncdWu2lX7c25/akvBGHOqlDpQSj1vjHkPwFcAvM3//j0Av4iPKEVvQaFuLKQKWF6QaXlhUOaye9j5\nCj4DkPZ2CS+QlRRrxl3loWDRUWkAizBSUwVATD1GGyhZm2+YmrSqJUqW9eUmhj+XlT8uYoRLWaE3\ngwXA+vc1wnBj00OyySKo4RF2PfG5g3YLtYjw2mGBqi67bclVW9Wa0OQYQL6EWevXBRYMqcvUuezs\njqegaWSZ3KCaEYdhDFxX1lnH9UDh48u4hu8YhKwSHZwa6Ep2SjuI8HhAqwEKdbJB9Ynss3opbI8x\njnYOxbiMqVmXfAFQK4AFUUavMd8pFJ1Y7UzghSzycTzYI0m9edkRpg1aN99mqhAxolh+10xT3BvL\ns9kqEiyohWnG40vRnVtfkt/fvHYH9lz6NpidA4Q/hzUbGznpz1QM21+zEMnV6sZCzAIty2rAIgN1\nlLTw5rFAyMskh92VmMc+JC3Yd4bYJKu2uZ3g9fepATE0aKyhy5xPfUdDWTIuW26EeV/69tm9XYQ0\n6Twzhdu8xvHk853MAE5pFTZh5WvzL4Ud0oL4tA0vk6Cp/1S+++ntx7B47Q1/A689lTjPk7MYI6a+\nv9dg+Ki2wyfNPvwdAP+ImYeHAP4mxPr4VaXU3wLwBMC/8SedJC0LPJiPsBlfIMolwLNpLbBigE6r\nHE5HTKPA24W6rAvly+9acKhLCK3gEQBlXAdOQ74vXMAdySKTrNV4CgtzEqtgpZBW5Aw0IpUOAM2F\ng0UpD7dDFt5/fDLHHiHR9brG9Sbly/e3EdXIqNu0gbUoLP/ueYBTI217rAGfM7ZcQVO4NGdQs/Iy\nFDRLq7hCGUs/YxMD56QBa9fgsLpuRLN+u3MT8zo5B41BQnSSW51jnyQcF8cVos9JYCvqUNFqpwcd\nsE7ZswCf1aPW9TXnB7DpAznNddaamCoH6oy5z0uolDUOyyEcchE6x5vw1kQzrN5z8wJnfCleSww2\nyHz81fNzNCcCMdbhAp9ti6m8uS+0cmFXo1pDsA88hD0x812rQL0h18szA1AhqkrXQi8eLEb4q4aB\nxeigQgqXrMwTy2DzjIph2ywN1zmKTfl71ruLpab7YMwl1b66kL97noW5TxezZeNfJ6FMs9OFQ3q3\n1bYBCJJTIQl54MI0uSBbGZRD4hudAAE3i9LAJhu5x8rJvR+/hRe/Ka7WRdXA6ZgbnDrC7EzGNq0q\n4GPSsX2iRcEY8zqA74el/sonOe9Vu2pX7S+uPROIRpOmKB7ex8n5GSISVthDDd1gBdg0hSlI47Us\ngLWWwbr7tgfDXLMxChURiFWWQZM3oMoNUi0rbMqCoXSeoWCwskiBMiMPgw5hU0KuxBTlgtqGKzHh\n90obc1bT7WUau5BVvh/4cGKa3V4B15adyXbkX5VpFOtMdm4j565Z5UCpZHdPqAm5mGbIFtLPVXmI\n4zMxfee6gsvU6qo0aEbki7Cl71tqBW8gLlNpgIDuSjZN8Nb7cr6ZFeNGV8bTprK1LmKo1Zq3KwNW\nTBo1E4A7F7IK4BiBaWG4BiAtnslXMIQ5G3OE4pBuUzS6lKGr9WUsum8XKEgOu3sR45S27ecPLnDy\nUCyFzqjA5jXZjdublLnLIozHtCTSQygt91df4JLsxlr6WDH1C2pZLNwpslP5Ll/F8HntwSzG4T1J\n6+ktjdqZHH/YJjNy5SHIxdzfPzpCdUrrFAo/uSnWyAPqWea2DaXl+b98rQ+PBWbBaoo+kbMt+zrU\nobhCxQ32t9y7VBVH7KAiQbCCBayRo5UCArrQfRLQHrTxXCjuVXnrFH+1JYHiz/1eipTn+MUHM+Qf\nE8PwTCwKZZVjvDhB/s5TNGy58biawT/iyx0aTEcCf160EgSxTF6LjLXKVMjXzpOyAOLsbS+FMRTh\nKHLkLGFeMfQ5tAsUK4JGrBKrEVmT2ho2JcdTNFGEhASzQi4zORKSrFz0fRxEYl9fn2WX5cL5cg6L\nHIwt5sctvw5Q2NS4NvRcJmBRaKguhUTpn3unCRaeTLb5sQZY7u2PissIf6ptaMVoPyfjPN1EXicL\nFRQQETaun2BAMd0/WpX4yrEsBp7gZ5Af1KE+fU/G6uwOlGLNiAJQybEq7QIlbVGLsYFoA2AcQWkF\nnMrgGlMCZJXWeYbgmuT/203pw+nQQUFVpCVsbBE7UmUjTHmOMw3YLClO+UIvzDkeHUtsOx+OsAmJ\n8Id3WigoDlRaMfRCxjkt1jTsJbImYyOTFIeswbg4P4ITrkVzMwT7VGTivMljDZeVn99aGFTWuj7Z\noH1H+tZ8Kv9OTYU2qeibcYUwIHO3t8B5JnOnNpwi26NA7JLjFl7AI9GLcvxL9i7j1aEoD6AiDyC8\n2SKrVPP2NlxPdEOtyRYGU9lQ3xzG6D1kibs7R0nSlo9K9H5VJXnVrtpV+1B7JiwFFCn0xQOk8RLj\nTFa4XKWoWGWHJMP5SPLwzvIMddKx+eRadEoDQ60+R6+gWeFnaw8FZDeu5gYZsweDqZhWw3SBinx/\neVHB3CIycWLQCuXaTtODP5QdbbJWe1ZjTBmo2pjOsTyhad89hxdRPfhgAkXtiJAQZmf3BpyVmLtl\nsYKiwrHdtABWHSYzFkQ9HWCwkJ17fJ5imMjO1qxsFFp2tjwrES/WkX8ZqudurLB9X3blc8eGtQ4y\n2T4eMIswm5RYOLL7bRyLyZn0Y3j3xeKpumfQzC4Y40Mp6ZNKE0E1AgB5KBSWMLM1+OwIJfUUyvE5\nDCHIFQbQDJiVh3If48MVTCp9ODY5LD7rzxUjOK6Y163ZAAHxIO/eewAAiGoFJl+Xc5yO7iPYI0p1\n4wbquTyTVb6EO6U7SWyGu9KYEaZ+9OAxpm15NvqiwhMWXb3UiDCdiHXjSfwVy7mHwVzmpLUzQEBz\nfmGt8P5r8v0BOUN7oYVoLuMzXK0wXIpLuOEUGMQy9tHq4Qe6oCTIqeYJtC3HWrvXoWkBm2IIRX0R\nc30DOiFeJhEIvjmfoaKYUR36MoNVq4fY+hHSuB0oPCIM/6N6Ec/EopBXFQZxhrBK4VbywEPtoqT/\nuTjXqFySWwwdlDQD64xCBzCwCPJQng+HMuJ2s46Egq1zdYrFlEw/jHr3wgZmxI7bqxI6F/+1cGKM\nmPZsZRammsQpLJF7da7QIFNQgQIB3RJ7t4Yko3swGyGbyEOsNSnikYw+GPEMl5D6YlVe2myTE3mC\n7128j0f3z/j3BQKqO838FA3CrW2/i9lU3IM1mesi30S8R1z/NxRKW9JUpTXCNVr8pg7svSixYPc5\ngdQuTQOgyV01HsIiyamV7sHiymIaFkCRVhQkIs0yVKlEwLO330PGsdfJBXKSrOAUyEkNnz6gLqed\n4P6FfB4WGr/Al3Dh+ejRVzf+NdieLBAVy6XfffsEq0wWqfNkgQPS9ZfTBuYLWdQcODAEnJVzKl11\n2ihJTnLuzPHkDXGxDo+HaDErc5Ivcbsm1aF1ygscJkOYrmwigzQAQhLjTBROAun/iJH+PUdh6wsy\nn7784m2Av8t7dSjCow/tIey5bAyWJfdmag7s5npzOoFTyqJeJCvAknvKnpwgDCXFq7khJa1jJKes\nS1n4WIzl84PZMb55jyXungVlPl764cp9uGpX7ap9qD0TloLOCrgHFxiNz9EmCMmvAuSFWAeF46O1\nDmYZoMaFrzqUHTzbcNEgTXdgtaB9YgGmj+FxY7PjAtlcjo+oRF3ba6BF9eSTZIjQXsOOY5QsOhkH\nK6yxO4YFWDvTBCcETtULDw1KvOs0hqGK82RmYRavo8vy+4vX76NxW/pmLxoItqi7qDzMjNzrg0PB\naRwOhpjOxLKxGin6u9LPtt9CQmbnlTNF6MvJb7FC/VZdo/tA7vNIKSRPhAvgO//tV/FfDcUK+duW\nwrQl1F5OJbtW8f4hElesoygOoS7EXLduHsBrS/TddgGVU+AmENemmjooSPG+fDTEMJbrBa5GQdl2\nL9yCYuS8+VckkPf83x/g9Vj62XOBfE929s91biKm+IyZ1tDrCVQ6ZaYm6+R4nYCri6cGSklw7b1/\nfISQiZF2vYubN+R37aZUc66mY6RTcfPuv/oY7z4V8pW8nOOcgWmrtoVwLM9h/vgtAEBD1fGj+2I9\nwAxxkMgcOLc0luTqoKGIgQZ+7p7M04vPu9hiluv0qxPkM97r8y5mM6HCy3bE4tmw+lC3BY+hlItF\nJdZfebZESps/SQp0P0+MCEF4trqO5SO5xtT7KqqhHPtj7Zfh9eU+fvWNB5fiSX+p3IdEV3wrAAAg\nAElEQVTKArK6QnbhI1cyMY/1BbJzKvDUVsimdA9qMbaJQnQi+mkXdai2vKROowN/fWITY8mS1aKo\no9oVRNjqCRV4kiVsvrFxrUBA/y1r2ADRb3nZh67xjDWm3q67UEOCorCB+x05R++kgB3IMZXWcAla\nWsVEPzYGKOkjB84I6pzItUAjyenaTOQ+/GWF7g3x96t6AIdlz/m4Qk5dzdW8QvwBpBEAMExbyJmm\nctVj/B/vybG/+OunyPhivd9S2GadWlyTF36yuIdpKH1vzgCX1ef2gzFq1J+oVX1YrPOoSL2eZW3k\npE63dr+AfleqRLUaoTwiw9WqgCLt/vK78jK+Wo9xQVr+T1+L8KVbcsEwcuEUsljC6mNJV2/KDWD4\npAA9FHjXa1gu+Lv6GGOWFId2jOVQxqNOqnpbZRiOmNU4vUBB4tbcql9ySZZxE2dTpheb8jz8qESn\nlDE6rXVh+1xApgpLan0WjGXcLm10viSfm4MFRrHcx7tPXseU9SHXqzr2X2T17Nsyx+JNwDhybHt7\nA3ks1z6ZVjg8ZoytM0b023Jtv6Duw/wNhNvyu+WpjxHBd28+fIrrC5k7jchFsq4U/ojlk1fuw1W7\nalftQ+2ZsBQsKIRKQYcuXOZgs2WEg3sE9BRTFAvqPxYGAcVArndkhf/yjRb2S1aQ7cVwSLe2epAi\nOZF1L4tcKFJ8D85l9X31j0+QGDGDe16EL/6IrKit5SYCmvx2lKIsJMiVJNQqdAMsCP6ZeQUaBCfZ\nOkSpZUiHh4/wiNBdBxLU2ulH6DVll1iVBQKqFld9G3odBEzF7OtHNcTcle4/vcCrjL5byQoRuRZ3\nn2+ia+0DABTh053NDC2CpnIb+D9/S+jRNrTGkHbkzamFJyNmA+ZSo//d+w+wpLCIb9WwR3zAxp4P\n94D+U+cQsMT8N9yVzeMjlCO5T3XLRbWQfsy/foCziZjHSaixzyBZn2rQL8KF317rfGbwZ7Lj1bWL\nzGaQsJmANAtw+RzDl2u4UYpL8M58iTQjt0Rho90h+U5uwXdlQDUh2DqqIbak0/1OByYkR8YwQ7WW\nqI9nmBMjoHfEXesMAHuXmqatCbbqEoAdjC9QELTkc2u1GxrmG3KuJy/P8K4WS+CsylHNpG+lSRA3\n5RpdWkHj0wk6kYxVuLmBypH7Oz76Ov4/9t4sVrIkPQ/74ux5Tu55b96t7q2lq6p7umd6eobDmeE6\nY1K2ZIIw9WDL9oMByxL0YsGAAQOmHwy96IGADRuGvAKWF0G2RqQo2DQpWKQILsPhrL1M79VdXevd\nb+6ZZz8nwg//l8VpQuR0s2GpCNwAGnU6b+ZZIuJE/Mv3f9+dt8UF1QMbcGXeT+/Id9P5HMMN6e+d\n2yHysdznQrnY/zGx5K5NDUYXH89/uLQULttlu2wfak+FpWAAlEqh3/AQdmSXX2UKhyEDbRMHFt3v\nG04HzPDh1hVZAVtuCLcjXzChAjLm1bUPm/5e5+YuGudy7tNSdqLhUuM4lfTdntNCMxI/rGV7yJio\njnIgtlhsxLTnu7qBgzZTb36FISHGjdsHCChhXA6OUTFtp5vUJmgBbZKLmqSGbnAF10t4lazPvRaL\nkq624ROlqBs9LLkLjNM7aBIjcGVjH2EoMZjcZxCgChG3xCJqWg5GLnUQS4NtFuuYZwfwLWEmUqlQ\nprnRJp4Zyrm2nh0iJMWabQE+LSXTaUARI2G3ZTezwhnshviy1qyGek6KfKrnNfqJ9KF9lqNDAtU5\nuR7yG29i+pY8/5bvYysnwhA+Wi35jutEqEgaa10Vy8w8stC8wb7qXEFKQR23TKAoqIO8RJe8CKHH\nQqyqQNMSa2T3szEOWAVb7udYUqpwZebIiWXZ7khw0docoNyVZ7ZUD0umJFtwsfTkOCN+5XoZYP5v\nyHUjdQv7jySgOIdBf1f68JnPDbG9JfNsdSyWRNtvwt+VcfcGBgXh5sP2bay+IPPT0wb+FbFSLmyx\njuLlO9C0fg52DnDOeaYfvI+3PpD7ONlpw7n/8dgLnopFwTIKYeWhQA4QI9/ttPCpvrzE6W6IPjn6\nBv0b8Mlxp5cMyuklfJc1B14LdckKt+JNrPj5ZrwN0N24QgEVN7qF7LFEfR13jDaBAyZdYMUsQjPy\n0WvIS59ZMgDP+9cxG0mU3V4ViIacpJ5C95p8VwUvYotw7IQsydb0ESyChgKlUQUkAMltZDbz7VQj\n6g77CGsZ2KEp8KNfEc7Ek9Mh+pSR7wybCCGLwclMXC2rrtHnonHXNvg7lcz4X227eI3ByJd8jeDZ\ndU2EXONZpTDck8nqBVtP5O716oMnFaiq0wdY1ms8gm0GDgJPFgiVWYAr13OeOYBXC0gHz6SwWMPQ\nosrRF8s38GgozzwxFf5wKYvsF7sDBBSoKecLBHSFUmYqupsRakrYR36IMmTJ/HIGQyhx17VQE2Jd\nUyl3Xp4irgn0iRqgUBVyK0M0oACw3ofalnH3mJGozQrZibwmkTfBQUDw0kaEFtXMagazZwMfV09k\nIbR+6hbam4Ro33cQZNJv7e3PYosVrejKItZXTTgbXITiLkBlqe7VPXx2KHTwzjRDRsq+na/I4vZg\nugeXQeegU+KZVMbMaii0Qplz1dfeQPkx0w+X7sNlu2yX7UPtqbAUYBlYXg47t+FCVtRlcY6anKNR\nEaJNaTZ7vkCTUNuaFZDQHiqLK/uZh8oSC2I60UgoB588mEK1JHjU6slu5XhNlM+w6m3aREFob2Iv\nEZB7YaEUWn3qLbrc7fbaSB/JfV5MDVybeImzGNQ0wbDdRkmKuJqpQBO1ULIgqnBnsJnfzyyD2ND0\npdakPcoRtCmtfuUA/VR2pX0/QUUCFLtZY5nI71bkW5gkHvKe7NztqI3uZ8Qsjay38WNKdo/P//wm\nmsR9VKfSb91rDXiblHSLNqBiSqw5O9AMcNXvO0CXBU+ktquzGqDeo213oajcbesFImpnVPYeHFaY\n5iRIefMgxOyRPF8804CS332uKuBW1GgMLGhiMlwG6izbgs/0pt3T8Axdm8hF0afl5Sxgz4mGreX3\no2UKFwwixiWitmh85N0MuZFxaCWdJ2k7QxblZKUxOxI34LhpoaTuxeZmH/NMzpedy3y7Ufh48eeY\nfi7bmJSyyw8/3YBP7c3u1qdgFRI8LGm5+YGCH5PZu1fDVix46mvYLPb0eruoBtvsc4oSRS4WGede\n/BArLRbIw/ES7telPyu3AWBNbPPR2lOxKCgN+LmC6xu4XXIfThtwanlIr5Fho7HmZWyg7a0nG0ku\nsgkUI8Hp8ggBJ6O9qJEcEozywkNsMmtxJZC880RVOKYLYkU5NGXIp/drNJlF8CMXDv3ggi/ujZee\nh7ctE/Px4jdw8ZjXfvYMtSOmXVi2YLho2TRblW+hSez89GSBwhagkh1soUHilItT+cy5FqExlAka\n6gAx4yBJW6PWhB0HHuKFnHvMiTnsAV0K48RVDu+7YnL+eDfCN4j6Ut/LUV2VrIQdyQRr3NiHpdbs\n0jFUyJcw1qjGXOnUORRBWyj43fkIVpOxn+0Eql6jREqAZDdWtAK4IK9l66+PI0zY3//gJMWxrHn4\nt6oKWfpHjFPLXBbncM3B2W0gIjTdsh0EzNA4HQ9rlNlyUWNMU3tBkzlMbDwmQbCrbLRvSL/tezuY\nL8kFiiVAxqYWgRqT1TFOHstC6GyU2Gowi9Bp4eweCWzIflTv2bBeJpfoFw2aWxSnMfvwsFaxXcGQ\nSOeGYZ3E4xSFL3EwvdmGQ/xGFi/gkB7av9ZEM2QZdSqxnxQJ3EL6Zw5Ak2RlURzC7ZBl6ngB9VHL\nI9ku3YfLdtku24faU2EpwLFg+gGaqY2AdexpkmNur4NINvw9WQU7rS6cvqyU9Up2Ve14AGvenSsR\nygWrFt0SM5ru0ShG0mB+mLx3rTbQ5I5gkhJVyVr4IoU7vAYA2LYjlGtOxxU5Fq5excY92T33H3pw\naYFkZQs1rQnHeIDL7ENLTE6dJrBYlZmXKZDIKp+WBVIGGCvutIs8RndF/sFhF2Euu7wdRchYtWeU\ni4zYi1Eqv+8vtpBFsu0OPQcPf44w3396jC9fp7V1awOuRZKVQKwKp2hAcTczzRGUs9ZuHMB5TBj3\nrRbAoJpqENGpCpiK0vEl4PRI0OCsAGZa1KqEBWI9RlKg9b7K8Ktn8tnj+RK3WbQzNTUiaiSETo04\nkbF2N2kdaQ2PHBJOw8BrcPyQQNlyb3muUVBhvCZUvC4yOLQg4zLDJuXrggMfihWa43YBNxdro+mL\nK4V6jAl5NPoXCnVb+uj5qIW3unL/9Uyu9SW3j+orkn1phREaFasrlQsdiKVkN+coF0TUBvIcnY4L\njwVj5TKDR8yJU7YBKn7bxoNDpOraIgo3NPJHFBq6KJCfyfvw3mtneOdIxm9kgGpNqP7nCeasNOBl\ngAmnKEv6QlrDmRBWbJdY5QLG6GdN+DmrzxhVTdICHsup3TCEo+Xl7Y/exaFaqyUVqEYyQdKxLBpe\nZwP9Ccuo0zmWZ+KfLRdLdHfkuNUNUVAcdEYil+c6B5hck1RR/ls+ZqyJqIITYC4+ZT7MYdNPLMlP\nWFcaRSn+aTxaPRErDWsXOpHzaaYmrbAJvSK02Z8CzDKgsGB4vIwnQCXnJuIbyB/DmclEyeoGfoxM\nSYvnm/jtGTkfj2awfoou1lpH3j2E5TIdWjRhGqxwLP4A1jbBO94AhufThbAf2a6CyVnJOPZRa8nK\nWI4PaIkDwQlQkXuziJluvHuKg4A0+srgEdW0bKuCS21OVAbdviysLHeAO01QefJ3NfOQE65sVjVy\n8kemiwTxlJmfOV0D8wAOeRL72w20XJbiQ8NhOreoGlhSEmA0Fr+/mi+hKQkQmxz7pH733Q6236Eu\nKKtdH24AX3yNhC3/9h6cEcvP9TkcqpKZZANmKudLtUC+6zyEZrrUyUJoMPOT9+B6XAhKH6YkIIsk\nsGbiQdHliwcxjo5lLox0jYKxqyyr4Jq1VMBl9uGyXbbL9mdoT4WlAKWhnRzLcY22ZvBF38d7qayM\n14MIYCAxDiew1Fqhl6tyYKGgmIY6WiDjTloOdtFoy46fzguMKahiTWUXyMYJZuRMXCwLjBi0M46L\nIYNdaaJhCL1epevIuwumnWHXBdKhnC997zZiVqd5fYNyzRHD3xWlh4r8irm1QnVKiO7A4II19jOH\nwauHOVrPkUsyVbBr0p5nQE1LqKozjEhUMs/lOXW+h5wgJd8BGs/J7j/3K/yoGCno/AcGFpMd5oJB\nxF4FrQWODT8EDAkOEgeKAiemdIGQ0ttL2Wm1WqEmzyWcDBizYKphAaWY4Krdgl4JTHtxJjn4B26J\nkCQ53bZCTYulzlKUlLfzOyE0LSGXeGetHFjEi+hOCUWwUVqtUK7JsXUMfSLPkpKX82gewevLd915\nAGtbdvlCp6i5NzomQ4Nq4qbBQKU9x4USK+cFu4/NF8QdK+4uELzATNmxjJP51hHMfyE7uz8rURPr\nYqk5KkfAW2U8RpzL/a9qums6Rn5H5kiy14fFjFntz+COZV54rQw2xV4UrSMrWMIhnffy0MN3vvEA\nAPD9I42CwVN46gmV/J8r98FSNhp2G0WwxBH9sGyusUsARl1nyFgZFicJMJKJFbDmwLZt1OTGG40z\nuD3pvKSoUfClqaFRX4i5ahEXb8wUi2TtyxfQkbykdupiHehtNgyWI/l8wii8W4fYYLpp4R7g9Xcl\ncnyj8y6aE3kJw4aPlO5GOpUBr7dD+JzcqWqioBah61pQdBsyVlFOhxm8O+J/d7ZrmBWFYH0LBU37\nGAsEpLnvUOr8U9sxFsJDivFiAeub8hJ3ogiv3mDM5DcstP/it+RZ6cs62S4UOLmnC6gB6xmcGmbE\nFcQ5BVr07Uk4Y2UOLFKkV4tDlDT31dsJ0Gd/PnMF5K9BaklfPNOwcZUkJWWt8HVmzVS2wHTBtOwK\niPvy3Bs509CRDaQENMULLGnmp6FChz5GoQqkRvqj4oK26ftP0tOh50MzVVlOgEVGktYyge5zcyEa\nNUsUXJrleGmBNmtlfuN3JvDGctMJUbO/b+V47r8TDYkv//X7UJ6cw826iBMyap0/Qj2UxbLDuMx0\nCixYlevEOZr+OquWI6/kd95FDpBox14yJlEtn3CCzpL7uM+Ua13nsGwibtWTLDcdih/ePqkU/X+s\nlHpLKfWmUuofKKUCpdR1pdS3lVJ3lVL/kJoQl+2yXbY/J+2TqE7vAfiPADxvjEmVUr8M4N8B8HMA\n/mtjzNeUUv8jgL8G4H/4U88VOgi+MIB/8hj2lGy/aYaE1oFXAJoA9drynsiPrxWW1PUAoSVmm9tT\nUFPZoQbXrwKxwINNNcbxsQR2zhZiGtqlxiGlxa30jzQTW40acShmYteqkNBcNaQyjyMLGXfMvWv3\n8Xgpu0u6CLFizh7Khk2KtAzyr5tqKLoijdYAPjURfbeJ6BoT9VuMUusCmU+cAyq4Fs1B30JFog8H\nNqZUwJpRPDFqXsOoK7unm6dY/AQpv/6vAT73k7IDNfoOnOZ1uc38FTlvbWAyVuS5BUD4M1QTmgwV\ndruESta1BMyo1ClMRDNg2YfOKYDTzGGY88dxAbNBerAjsRTuejGuteXv718Y7FSynR1XOULujqmK\nUExkXPUt6RdracNpynjMFz4o0wmdLoFQrDTX6sG/JhaSu5LsheOlWNpiiiu3QKoIUc4BWxP41tcw\nZEy2ihG75RgPGdjczxu4d09Mfm/6Bia35bknVPk+Pp5g8RnCjtu7qCuZb3mRYsEbnZkSbVpWjYFA\nm+19A3+L2ZwiR9SUYK6varibYm2k9hxmTt5QurZqb46UNPrp4xqHBHsoU/+RNqVlIeEc/6iiMJ80\n0OgAaCilHAAhgBMAPwPRlQREiv4vf8JrXLbLdtn+BbZPoiV5pJT6LwE8ApAC+E0ALwOYGWPW5PiH\nAPb+eb9XSv0NAH8DALYHIdSjMS6sC+ilrMrLiwK7HVnBE0vDz2XlaxQZbIfpq4goscUSzbbsJP5w\nGyUr+OxsDI87aXK+iTuppDVz5rCDrEBA3zNFicaU6cu8gXJbEH/LbAN+JT6cHYrPtq0tLPqyS0zf\nA1bUb7D8FNaF7Mx4zoJhdeFyIruVOS0R9gnbdRRc5q5dq4l5Igy9PqszPR0ipuTZtBgjYFFVhBZK\namwm1hgNsiW9dCK71cWdVzD+AyFjfbTKMfTEIih/JsViJs8UjStUtxknOOQ9fDmBarKwaTFGXUhg\nzER3YLvCWAXThabatLbekX+LLgzxBlnqQ1vSx/bcgkvOCR0C5UOxSE7vyN9vHqVPitWuzxJ8h7vY\n7P0HOD+SHdYPPbg3fkyOyX4V2A1kluyqYWpjeiGBXZ2dA7UEU+xThYYtFkZJGrjV/BxYybQMghCn\nJDwN+ymULxZEWDUBIgQXqQQXZ6MSz/gyR5bHOf7+vd8BAOSPNQ6op1AQGv0wr/Cd35Tff+U/eQ2D\nlSBnrR0HvT7TiIctVBljEcSpREsN5ck4FH6OkpB1la8AQtOzSYZ6i3Okoir1SQNJIfG1sfsGrlwh\n23hu4Yz3NK9q1B8T0fhJ3IcegF8AcB3ADMCvAPhLH/X3PyhFf/Nm3zzqVnj3JMXGAzI0fyZHPZKB\njeoCYy4EZVygwXJhl0EmdexhFHEivXe21syAsTXynNHk4RwqIDyawbXEZEhTOa5yBwuCbfqNAvEF\nKcLtJbItyXasQIyBq5EwSLjr1Pjepgzu+Ztz7H6a5CV+AzZhvjVp5vNSwRBk1LAs2IUM+LK1QE2w\njE3SEFTqCaDJtjUaDTEjgzpA3aa5Oz4ESYvxSkFzfvIAb50xL13XqHIKkjjfxfWaeIovNmGPWYo8\nkGCmLjdBSAe06aFmTYXyNmFYt67at54oLlnVT8jvkwvkilyaZQblEy9yZQh0hJrNRBpFKtdpthmo\nezvFrU3p48d2A7Ul572wE1RTWTgen0fobsris3csi1vV9eG7shAunQIOS9UnyxR+Is+3yk4BuRyK\nHQYlH08Qj+W8mWkg68uCdDXqg1g4mMrAvS5z7uTXpaR8dpTiG4/X5e4x6jXcuqxwl1L0OfdAXQP1\nDQaPv7aA/QtrV6pGwczP6uwQhvgaN13jH1Yoz8SljXdcpCN5+VVZAErGNbiyiWfWlZ9MDBUXY2R9\nef53P/AQj1kdqz0UOakF8UcUjR+1fRL34S8AuG+MuTDGlAD+MYCfANClOwEAVwAcfYJrXLbLdtn+\nBbdPkpJ8BODLSqkQ4j78LIDvAfgdAP8mgK/hI0rRj49j/P3//NtYXSxwyMqxF99u4y9/jmzAVR9V\nJbvmKPXQyphacmUlbvRdFDRh3W6IBnX54jaQPWbKbW5jm6rEK092moskhBVJKqjvlCgpxzaZVFgQ\ninq1GyKeyypesYBldpri+L7c2/fia3jvoaShep17OFt+Sa49nyMiU/K1LTHFF1iiIslpvVqiJhFs\nEHUQxnLtUSaWwmqWoSb0t9Xy4FiUlFclVC4WUjyK8S7lx197LObillUiS4gENQrnJKlt9Yew918E\nAFivfQvx0T+V/iJa0d1vAkr4D+xo9URRWWsN/a4ET8vRP4FyWF3JPtT3QtSO7P6+68O9Iu6TO9+A\nacuOh8rg/I704W/ekXH8fg78CAPGqzRDBdm5z7/+CsaH8p3D5hQvnQps+Pnn5TlXdhPeXPoqPn0T\nHY8Bww0bzYjQbb2CvSeWVYN6EUunh/vUAMFJAqcl+2cQtKALGSdLG1w8EMvk6D2531emMao1I3jL\nwMSEvBsbyQcfTvJVAL75tvz9gx/5f/A8Gba9aginlHnWa1uwSCYbdOV+83sG6ItFs9FSyKjvmR3G\nVCkF3G0PTiFzIEuIwn18jpM3hE4vm01wTLKXPDPwCc3PtQFJyj+y+PQniSl8Wyn1jwC8AumPVyHu\nwG8A+JpS6m/zs7/7w84VG41vlynceYW9UEZgu+1iyU5o7mosR9KRoVui7spE9tiRruWjTX7vxkEE\nm/6UfjRDQp7EQht4JF50yMPnRhoJv6tazSeRZ7cJTGjGN7ZitDj2z9CM/juTI/ivvgsAOK5ew7CU\n+xzrGsaX3+XLDHaTIBSSxXRUEykrPxd5EzlVqCxdI6slWl4tCWF2pvBJMKIcFxbZnxK/QkZFqvOx\nj+++Li/9HUui5e/MNGoOvwLwNlmYPvNGF61/VeTc8/EUOQVcnA6Zft97HxiSseqZISyXxCqZD/Qp\nJtt2n2RgbALB1JUKwWBdLqxha3lWEx6jolldjh7hHl2998/l9w/cCv87Ycer0EKfFM3voIDH+w+1\nhZJ1JY9sWYyG6RiGJrUTNVERhLStm3BJ4FKvdjBjRajPegjoFcpawlurnfvYigjX9jzEFLiZWxoP\nXpa+fb1gxeGkhOFGVcUOYK/ZsgqYtaFtmLEAcM436h+/qnD13zvmONwEBgRc9bpoEk/gNKSviuMl\n4h0KHKFCMJOTFF4L5bMyt8ItoCYj03IsxvdDPcedV2Tc31waHI/J+mXpJ2A+A/Nxleg/sRT93wLw\nt/7Yx/cAfPGTnPeyXbbL9i+vKfMRueD//2zP3rph/vv/6m9j6Jzj8Luyug4OXkBiS/CwuVwhPmfQ\nrdnGg1dEdXhGnvu2qtDdZPCxuY+gxR02d+DwOI5rVMwiVNwF+tsb2Lj6IwCAyFZYEiHpBy5GjCwP\nQoXxmNWKgbgBv/if/XVUDBhu+wF8V3YxF4BDVeJmM8LBrnx/e1vMxI1O+ETXoXVlF+6m5Kl7gz4M\nc9PdnuTaH9y9h/NjKS567eWvY0ZCluJ4jEYkO0z/R2+imd8GAFieFCjd2ryKs/ckoBZXPfz6q78i\nx4sUTWYwdkMHszl5GBIGDksDl7X7O54NRaScN9ewSDJzrd3C9q5YEG3yKTgDjbO7sru+M1/h0bH0\ny6Kh8Beuy859+/oz6P+M9DNyCRh+7+XfxPmU43FawNTS99mz22iTkOX0KENJPPlhIa5Iuarht2T3\n3+p9AXpTnmN+7GA6lezDMGyh4LN4dCWz6gKKGo1Ox4FPKzToWdjdEeXqaz/+InoJcS270t87B5/H\nzWcoIx80YNEsVwr4P7/2P8s4nIql8ObpCngsIju///o38ehCrL8kTVCsMwCuxoI4E8W0wIYfICXp\ngQsHKWneHMuCR2SiawXoBPLcUYsYkhsNrN6T48NHv4ef7ooF9c1+jPxIxuc8X8HX4kLlyF82xnwB\nP6Q9FTBn13GwNxxi/MYFBoSwjpZvADMZoLzWAE1t2z+BaslLcZ3mW1IH6A/Ju2iHsF3i5C0XFRl9\nmraPwsh3FuQnL6oCbkFTbjhAZw3hdz04tLnKSYWwJxHp3JXf1aEDi2Z+p++h7NJdWTXgMDrd3+hj\n56Zc75kN4YF0PQv5gACUvgU3JflpohHuiAnucMJvDw8Qk9jT7t7A3fF35dorF7f25Dv7W7vY2ZNr\npIeyOJw/OsKRkXt7sHoPiyfiJcD1bemXWzctvPo6adRp+oYtg15PPrsRRU/IPfK0QEhQV09todOT\na3d6kqaMgwv0MxKoRO/i6FT6+ywv0brPGoYrRzhwpKTaCWXRP2i3cXKPwCtH4YLanJ/e3sDRd5iq\n0xXegiwGpxOWjgfAwBDQNAhgd2Sx0SffRMqy5p2BQalZlsxU5uNzQK9rX4yNRlfmjmc7yHqsPhz5\naH9azl2TqKbZMiD1IZpbgE9vRAEYVfLcq57c7777PjKfoKm36icxoVzbUKx5KWoLhhWTIGHLra6H\nGV/cplXhEfFvTUejJN/mZjeCz0rgjSFjVKsWMJB7P33g4pcnTDNPFABmsQDkyPFx2mWV5GW7bJft\nQ+2psBSgFIxr4QQDuHtittlmiYSstlG5QEE6Mi9LYRO2SRZyXP/UHgxl1YzloSA5SdDaQJ3Jl/JQ\nw6HZ5rXE1bBUgdojX97CRniVUd/kBM2WmJRja/pEArxiJV+lUixIHX6l3cGARAuGGWoAACAASURB\nVB9VQ6HfEjfgYK+N4T65EnuMQnttkAYS2l/AkNwjrzM4/IM1JwQ2jOBty651O34GhycS2Hxn8xAP\nT+Q5bm8eoaIC9QtDidJ/42SFlrWWsz7BmPDwjaqE64mpuTw2GLQprtIRk7PZdvBiS6yVzkYLW5G4\nNiv9GIZqxkYFMOSWsFqys/WSBryr0i+9VYQtCqC8NzP4biCW1fSf1dj/8ZcBAP1MNB63wzbud2RX\n3amHaFky1qmb4w7N6q52EZfkQ3DX+AgHui0WiBdl+M7JAwDAVW+JYSH3lKQV2iRAGZNQpzfoorbk\nWUfLJaYEwz1nSjAejJPsEIszsSy+elsyMVYyx901K3dQ4daAPJAAvvmOZJ2uH0uA9jnP4O49Cfyd\nzxeYm3W1o0HDZcFeodEitaC7BmSpCBE5L3NPIWRWJnAV6D0gbzjoUt6uM5Rxeue1Cm+cfF++gDWh\nxidvT8WiYGCgdYkbeytUxyTeeBBgk5MNJwq9UlJS82mJHdZYRUSB+VqhsuTY6BJcB1AHJaxMOjIM\nfSxoojoROQB1GzGj6ZU7ggfSbOdtgEjBwC1Q8nr2BzL4ZaFwhapC/bQGuUPR6wTYJP/eRtdFSElx\nn6QYQcOBvSkTsx5FAGsfXDRhsTLOjmXy236J3Uj8wnyjgW2mr+7de4TVGnz19j6GG5JFMBTD+Pz1\nBHd+V2Ix8ZtvwKbMepAYRARkjQKFgJoS1zZlkm81ffTpEgyjJqJIJmy3/TzWOS1Tp0hYVap4vUjt\noR1JXxV5E9+8J33UTSqsmCI7MQUef136YPisLMLG38NfvC598e6dCZwL+Xxxvo8dkqVcTGNMqO+p\n+CIN/RC3Bj2Og4MrbYq8zhrYviF9mDa2sdEjyS7fqv7VEBlrZTYPD3G0kGuEvQBLh9mMjsFGLOPn\nRRIPeataYXQhsYq3VI0rHYmhBzkQn0vM54Nvihitf6uFb59IPOd4kSMgWCyMfChXNiKvBYREVr7w\n/DUAwETXGLJsf6or9JoyTpU2cAvSA4Qhnr0pG9VRLa/t8PkZ9AOwWQA+JnTxT2iX7sNlu2yX7UPt\n6bAUTIWinCKKevCpHt3zfSSZmGKmv4maRCXd/BQB6w4CUmE7WxHUmNBfr4bPnLZdpVjb68qx0ePO\nXBO7sIznyEMJ+gRJGxhJ1NvMDRYMKqIIEXo0V68xoFZqjMkf+fktGx1NAFCjib2W7KBbrU10KNvu\n0X1wrAFsgrAcr406kt85qQ+L6teKu3KmLbjMarTbOXbaYjVEpgkrlx1BjV/H198Rc91viyXVam5g\n2JLd9YPtAJ9msPJhlAOsDGz7AQ52xfK4uS+Uadu7HYQtCo8EHly6WNXsGM4NHvsFmgvyEnbX6l0K\npiN/3+u08NXf/R4A4NQqccyIelIZrC6kHuNVUGZ+C1Cx7NAvHQS4W8vn3354hMlcrMWBU2LAfnEa\n8t3dzhZ+9AVxQTZbNzFNZNdd3uwh3JXp7Di76PGeDM1Gz66gaDXONw+wtZJsjTUrUFYyB/IzG52X\n5LikiMxu0kC9kHlx70xhdEAyGwPs3hcL4TG361ffrnE/FmtU2xouGcFfvB7hc7vSt3MDKBL0tPdZ\nu6M7WE7EFVlMKsFLAygzg6a/pvqLcLArbsP1iEzb99pY/Gufl377f/8QxccFJPwJ7dJSuGyX7bJ9\nqD0VloKChucsYR/ZsDevAQBG9TnClSyp0/kjJIX4XL5jY4PgTy9iMX0eoFgLr5QuYNEH9jLodW46\ntVAxRaRtQo0jwF7Ibjx3KzRWYplYvg17KrtD4g7RbMsulStCf1s22hM59rI+TgayG90qbGgGxrIk\nh0914ZDkocbKgDZzWt0IDgOYdeDBCtcRSLkffx5D8zg6uI3m+/L8w+gDnBDld6wtnNM/HUykgOdR\n7yqKoVTnnbkODJWYd059GI/CKf0SPQ69R2EZ1+qgJm+EV4fQpGlzBy0omxwCdgStifmllJxezYGZ\nWA3uSRv7Py0+9436exg9JjS5rnH3nMFhaj1cOT/G6IAcCc4Olm15vmWRI07IGA0b59R3u0WIcm/H\nhTsU66YOA/SJUvUzFw2moqPIB7OoqCyxGPxeCTdjLKpYIXAkMHu2+hYeEdJuTwu4iQQYbzXEEgyh\n8N5SqN3ODzfw7kjmSH+VIfsq093/h5z3SK9g1pxnmUKjK+N38/ZNhLdkl99bRZgsmC5k4NOkczis\nhnStNqaBWHehCxy0iGK0B6iJQtUBNU9vt/GFpcQZXvFeRUnsjPmovGt/QnsqFgUHBj1jYNpATXhx\nz/ORj8VlKBcTdPn+uz2DxqaYmnpJmiyloWgmlnmOam3u2yHUlNWO/hKKgaaANQw2EtQlyVuMjWJO\n90FnaDvyIjRaNTQnypIs0MPAQUFsgnMF2EtZyeZ48Bmg8p0QYUNedDckvZhfw9JrUZAEymMkvwEg\noQpVTOEYt4bXkefr6wY+dVVeoOnVHXQXFB0djRCfy4vn84UYtucwRzQ/Yxv2XCbuwtFoXpMXup+G\niEg7394gqUvXgw9qXjYKOANZeK2mD0V+SAUDTfp1zQpPY+UwfI72zRqfe0Gg1AMnQ/t3ZKF649EF\nHrKE/cr78vv2DQ8t1nkEyRRORVWkRoKMwUMrq9Bm9WeH6k8b3S1skmbeivIn7qFuamxapGyriifR\nfs1x8nIXyVxM/xox7AYxJAfP4DSWsu6TV95H+TyfOxFsia4u8Pn3pMTxt7dLXBzJ+ZKTBp4/l6Dq\nN5ssz7/QyBK5917gYaPHa9y4ib4jQeeymqLFys1Nbl4PkxxgmXyjW2ObcO137o4wp+pT0bOxx3LO\nU7oom0GEg6/Kuf7q27fxP92XTESZf9iP+HhKkpfuw2W7bJftj7WnwlKoDbCoDPquRkQO/tWiwNQj\nm/HJDL1bYq5uhPuw6T7ULglRaw9gUMduAyS4he8FMBuyPtaWD59cADZVi1G2kWiyBRcFVEN2oHpV\nYRJSJThqoKacOzdulK6FLRKo7DlNFJHsfm6vgSbz963tEDbZXw0Vs1XoPeF0MMslVJeF/FbyxJS0\niLDUpoZNOHbY9jDoC4qtd3UP8/epR2mlaDI9+S6JX/+VykE5lA74qdzH7zFeejV3cD2VZ6ocF94G\n+RlYqekPtmCRMszuR7CJnlNhD6RLgBoGUCvpc2tAsZQqholJXlDO4VHv8NqnP4sfobu2/L+XOCHz\ndkpLY6QNbtMyGXcMbiYyfr8xB+yZPPe1wIO9Qbn6q4SB920E27TAlI2alG6echH4tF60DdujdVMy\nZen5KLy1jkiIgBZZ0I5glTIOo90UW6x8zG+zmKnlY/Jp6YvWO0sEC6YWT8aIr5AFWhPlGdtIfBm/\n650r+AyLmbZubMFOiUnxTtCqGJhmodXA8zHj3DOWQoN6IL2WgzyT+6l1hYT8Cy1iQVo7W/AnMp/2\nn22jQ8Xrcb74kFXwcZ2Jp2JRUAB8DZi8huYMbGgXnpbJtsQMA5pExfEI6jZfXqopKaeCzbqFBjxo\nluGa3MBljZhl26jYqZqColZqwSLoI53GKBvy8jupRhXJguTPKuhKBinO5bNdx4GmRofxa/Ro4kae\njZBwbMd48ElF76wFZhcZtE3ykkLD5Gs9dBumwUpEvjza9qEJQdblBC4Xy43eFk5bAvQ5ujfBaLT2\n8eUab88u8FxHXtjTosZV5srToMKclZb7joMha0KCgJmRZQ3blWPLt2GRflm5Copl1MYJYLWuybEW\nF8btD1GTWEVnE9RrVqzBDm6QZOaf+QYZ2ajf42dVJ4ZLBu7njIuEboA7LjAl//zSCvCcLVmSg5Ys\nYk3YqC4od68MbMrIN2Ibekh6/LoHxfhPqOS8RVXCyskh3HDgEU4+Ti5wwPjQpHSxmorLc3QqEPsd\n+zZuHYjf7rcm+DYFXQfWFL/7q7KwPLgnm82iMLhB4RXv8wl2m2S2vvMuGo7EeVL4CBKK2WzJ74Kl\nQrsti80gr5HS1drMPVhcvF0Vwjfs51OZp2V9B5/eEaHcV67E+PnPiMvz9stv4B3iG5bafFyG90v3\n4bJdtsv24fZUWAqWpRAEFqw4AVgzfjZ9B3ffkqjvxTSBdhjV39HonDGCT74Bq/CQMknbdBTWxE+2\ns0RORRaTWCi5AznMBsQKyHLWrvsuAloSJtQIaXZXDReGkmYJ2YkLx8KA5rqjfBQdWdnbaEOThKSs\nMyy0FPM0l2JS26ENixoQgII7ZWGM14Fa01pS21LNUlQuC1yWKRSj7xFSaGoNzh3hIgAAl2I5ptvE\nySLk883Q68jfb8ws7KyDnP0K65qckpoFdrlAzQpAP9lFHVBSPosBm6IuUDDEHhAiAqX6WNN31EEb\ndkxfw1XwPytBzN6bLTycUICH1ZeZnaBFfoq61cbFYxnrM0+hzvisnguLsHe9ks8mzhR5KjusFwXY\nXpGrYm//CUrTRCkazKroNRq1pVEQz+ycFJg4ssvnyxiLDnUrVjEUlb7Pp/L35pULVEeyi5dngBPI\n+C3KJV6bcl4smAGxgGhLPvvpqYfkmlggb9w/R6nFuhsUFtyhnK9F7YlV4MJlcNjuBjB0fdyNEh7n\ntTVsItfk3LDEUohPQ/gdwVu0q1sIQ+nDu7aPlBwRyhKLCgDKjwh4fDoWBQChZcNt7WIxk7TLhVY4\nPhLf6aSYo78rL9b0sIBdrKnIpfO8qAGXmnpVbmDTNKzLCjoVkzlbJJjHEu1WjD8URkEZ8u9FTST8\nHJlBK+QEimqUC07Ic2Yqljmihpi13m4EXHCiOwFcXm+aa8QjeZYm/cLWdgMOI/l2EKNNE965cgtW\nIm+ZiflsyyU0iU2rCqhZTtzq+7hNM/L8scbrhGmHTBV2ihzVWNyu02WNPYJ/ehs+ms/LrIhGA7h8\nWTSBUFVhQdWEMOdHcFjDYSYOrL68CPZiA2pbUmvKFdcIjoaaUkjWZKhoPsMkaBH7/4VrIe6fibtx\n54S8lfEKIWscWo8q3H2D4jLagWHKuBh6mI1Z4tyVF2KVVYhyZjsGAYgLg762g6ZmWroqEdC3rymS\no0ofxsjLP6uOYHcJZw62EI1el2tPM5wwxtI2AhWHm2L7XBbh5KUKV84ECn46ayAhQ5SiYV5DIb2Q\n57+/tcLokDGAmXlCHJN7PnbJD2lGsuAleYKIsS1Tt5/A2z+YGMQjmQ92z0VQy3cOL2R8s6nGIYV7\nD6YeFiTMaVgWSOSFRmTj5kqe9dWMm8wPaZfuw2W7bJftQ+2psBSU68HeOYCVeQht2VXjexc48WWl\nzZcOPBZ79COgXJPOSeAVoTuE61LV1/VRJWL61aXGeCK7+3y5wtGR5JUrimlEgYWqJ+bnRuagQ3iw\nzmqs+hJJHNpNFDTXWpQX8zwHPneHHSdCel1W/nhksNRy7YW7g+SRHB92KNX+2EI7lvvphSH2bkqQ\nqB1P4PnUx6TYSlGvMDolPVrqIX4oVsexfQFrJGZ52xi0mY+/iOXfvAS8odzPzbwGOnI8hI+DSHa5\n8sBAsxKvqgmkaWyjmhLC66ZQqRQBORngH68zFNefKH47NyQbopQH45Ky/NH7KB7KOHi1jZEt1kFv\n2YPP80Xst6PE4FMnZGW+4aO1Ideen7YwyGSvum478OUyKG3K7c1y2ISmu7XCMpQx65kEOc1uS1fQ\nFl1Luhpuy0NKmjN/K0BGApTWzj6q6KvSFy/9FnrHYrElsbh+975/geLFTwEAzu+UsGwxTfpLF0yu\ngOqA8AyQdOmCmU1sGdmZj3UGn2I3vYENt5D7X/EE6SoGWd3RT23YzEpt9wLEhHfnFxN4hJvvEf/w\nqDvFdM5AeZhj+i4xLmGJmorXtYpgSFGPj2gpPBWLApQLZQ9hdI7RKQVhjxIUS3mIXBtMTqX67OjM\noLFLEtMNps22l+iT/jqsuqg4cZenJ4gZ7bZHCWZnYoKnBIrEDR87BN54fgB3RUacLICdswPLEIYR\nbB3JC3ujGWGny3SjcRFdiK020zEaRFYqf4WIFZGgvkP27iM89uRFieMQ4TazE/djpEMxVw1FSUfz\nFI++L37oSXwPU6LgoroB5YvZejFJ18kKdGn0nccpdhjpT1wLVxZyPG+XuLDlHM3DLdRNxi46LCkd\nFUgergV2LWSHkpI7O72Pflsm9PZzM3Sf+4rcx7Pi2in7OvKFLKznvz/GREv0XrUbiJSMSblvYfk9\nZmDoXrhG4zuM7bz3xkN8KpYXes8UyBtyz6VroZfIuXOWTtdmjsW5vLgXqzHyIzGvD1+7jxZ1Qvpe\nF7c+K5H40BW30xk8wpLxo/R4gcd07VYXR7h4h3U1sykek2p+ckJVr7pCV8vxZz77BXibMmb5vg3v\n79JdWXtMloKfyjishkucsMIzP68wKchtmeeID6XvliumP4saXdY4qKub+ExfFp7tah9H92VjeDc9\nRPItAVG9eyq/H2QV7L70xZ04x6cZj/u85+F7JNQ59iNc2ZCV9bVvM97zQ9ql+3DZLttl+1B7KiwF\nY4CiNDDlHNVaUddLcc5qx4PIxqsXYmodXZyjNZKVe++WmOLT4ymu7soKvt+vYWyxIAqtnjD/1lsu\nujbNS+pVrtQc2UJW6HmzQEV15bJd4GAm1479AcqQtOZGdvnWRhMDkGk62EVqi3VjV11khF6n0xjp\nTM63FYgVoPdqDBbEu9stgGZyHeZw8rXcu+yoQe2g/cyn5T71FTgTkXJP3y+REr8w8rpAg5YHZcqt\nLMcZsw8tVSIhx1yYAL24z+ezULk8HhH/URc4pyZmYzXFnHx0pzODvCLpx32N9vOMagcU/jIKTpNq\nUi/+JOJYiGWC5dt4cEcsk6MHc7xPw+uCrtsCgEV3LJlopOSKxLUuNqg63UcTq1DmQ3okYz5bVnB9\nunl+A2Nu06dpgvyxjEM7GqHgpnhAbcewiHC6kh32zjsjTJj5SHSJjYDAsb0IN8k/8fqx9Pdpcoz8\nfbFA9vYfY/gFgXG7dQQTMWA4k/ttQeHKAbNSR0DOeoZFlcEKpA9Du4Ep4e+HE2Z4igqnc3mOTmsF\nu5Zgbt06xeGhWIvfnKdQE3mWzYGc9zEsbHFs3i01VoqBy0JhSJ+mc63Gl56V7/+6UJv+0HZpKVy2\ny3bZPtR+qKWglPpfAPw8gHNjzKf5WR/APwRwDcADAH/FGDNVQpf730CUpxMA/74x5pUfdo26LBBf\nPIZ3UuHRVFbt+3dDPGJKZ9JZwuMKfr5SGFHlOD3jytjTqO6Kn73xQoL2PhmL6haWFwxmzTKMEkqM\nEdH49pHGoS9byk4B3Hqe9fgzB5UjOxTsGBVJQ2Py6neUDZcMnpVaQq/kfI2shdO5WAWvvvIAb55w\n5+Jz7t7awucI120GDlqW7KSevYmaRU4J0Xq1AsCiJfdBil//huSjHz46Qi+Svuh0fIAYCkNNTN9R\nWCzEz17lAX6claSdQYjqpuxAjWWAhHGHRS0+6/n9OQ7JenU+miPckd9d3+8hovXS3avhrivTCsYI\n0gz53e8AAObmGO6pmATf+tYKv3tfOBQaaQxNhF2+lk0AnlSzxosSHVpNXQ3kTKeN3ArTMS0TBsnm\nfoFdppFPKwc1LQ8ndBCnEhM5X5R4cVNiBmlNctVRhYdrduXlEtOWjHtkOrh/KOPgqhRXflKssy+3\nJAj8tW8cwZ/Lbj21I7ywYqWi/QJcwt6ZQYXvWTg7YgHe9gx5Q+bp9R0XExISw7Wx15dxtc/kfr+/\nKp5Un87iHElP5tvi2MM3H0i8Y+JW6DIOdD6VubdrVagZO3j2ROG3zmmZLiocUkXmL/kRdloSKAW+\ng4/SPor78L8B+G8B/L0f+OwXAfy2MeaXlFK/yP//TwH86wBu8b8vQSTov/TDLqCNxrJM0TitsDeU\nDvvO1jYq7zUAwPiowsZVMXevbijsXpco7M421X/ONEKbnGiNAIErA1OE29gaUNFnewzvXEbRLxml\ndSJUHIxb2z30rovp649TFAz26MqgUPKSRZX8vnIbaDJ6H7khKub886CCOiG8dtjB54zc8941Odfm\n7R48woC3vA78HSoT+SuYKUlWOMPszhY2KgannvHwwgdC0jGZLRE15He39hrwQllEXrtHjkNjofCp\nFpWXsHoyOwbw0W9LtWfhVPApHNk6JabjYIhWS+joXvxJCw2Cl4KOQsgXr9n24axLv9e1d9qC9ynR\njLz+ygDVV+S7VtnDyUr67fUzDeQM+DJfb2pAr8ubSxsXrANoGBebjH02VYmVRbeBmI2+FcILpe+v\nBAMYwso7To3HJJHpLhvo7kiAscXxrxMDn0Fld+Bh25N+abe78FiXUDU89CjOmXcfAAB6rsJRIfd+\n57sz/OiXxJXsTU9Rr2tlUtZXGAWPtRiF9rBL98KyffSYdTH9EB5dOr0v5+pXJVjygl3bQjuQcSij\nOQa7cp/L0xhDriu9gPfQclBtyvGj1RzdC+mXcVghLuUa9/wWnrvzDj5O+6HugzHm9wFM/tjHvwCR\nmQc+LDf/CwD+npH2LYiu5M7HuqPLdtku27/U9mcNNG4ZY054fAqA2WTsAXj8A99bS9Gf4I+1H5Si\n39vZQrO5B2vjBHdPxKxra41eJBbBSVJA57JLd9sbsLs0g9cErM4UC6aSsm6EqpKgo+olCIZyvJtt\n4jyXlM6Egcqbe320mWLa6bcQcScpuwUKUqzlqwIVg1mlJ1tbBzZsCgza1gSdnpyjqz1YP/uTcu7T\nY1Rr7UKLAiNqAl0IGq0furAo8W6VGlktq7nJ5Dm9Kw00IbudZT3Ez/yU7MbdvV1YOVNZ0QKZkme6\nNl7zQmhMF+JKPciBdoOwazSRGrm2l7bQCqgz8IJYD60yh32dPGHeJhyb4gMXHyDclHXduRLBzIXg\nA9RGhAlhsfqyuT9AlorFcu1WhB+biiZDd3QH/+gPRPOw4bAvYWBIgFPrGhZlzq5ELnJiVWYV0GJh\nVhXJmLW1QpsaGT1zAD1g1Wk6xuNC7tnySmQ7YnEWhHYv1ASFkjFrdNpPqlxrx0W4Qe2M5h58uiYn\ndygnuBLxHADYbfk4Hck45fZLaBI3XPGZlo6FAbke+rs2FK2qRuHCdeV6/bCJmnwI8blYGN3AhyLe\n5DjxcXxHaN5q5SE7pWr2fIwZY7G1kt/bqoMp9U/fOh5hRQXuL4c2wq480zxwcJc8Ex+1feLsgzHG\nKLWuw/pYv3siRf+Z526bYpJAWTW2KPaZDG0EbXmY5jxC3ZLP2wHQZpTVJsff2AKaNVmRThzEWyyR\nVTYUTTsECr225K7rFuXZ5zG6SgauzG2M1nZTlaHJMus4bKJeVw9ycriODX/ty64iFJDJ6Kl9OCTZ\n2NreQ0lpdE2mZo0I9VKO/Q0Lei0IqlNkdLaLNm3qd87hbsvze1ub6EXyor80DHBKyvFZbeGRI3iC\nBYljnLRERuirgxgu60A8O4F9SFdqcwY/E+YhU8tECvotGObx/W4TdSyfV4PrWNvz+qKNuiX+t6b/\nqpwmFJd8KxzAp4qRaoV48UdEoCb+w3PUJGXJSnnhM60Awpwt1wLfRXjDEAGJTOyVhZz16jZLRsJ2\nG7t0g9yGhw5rHEboor0li0U3NOhVspCVzACsVg0YWxbF+L0Sdl9MdGfTgk04yU5VYlFQjIg1M6l2\nMJ3L/TQelwgO5e/P9D4ACFM3rHrchIVPHTAutVQI11mNsAmf+IwtBHiYsm9Z+dqwHTSJMUgchUcv\nM5PUX8JQyMX12yjIQpW4/DdxMH9PjldJhZyb2usNhb95U679/rUQBVW7Pmr7s2YfztZuAf9dV/kc\nAdj/ge9dStFftsv256z9WS2FX4PIzP8SPiw3/2sA/qZS6muQAOP8B9yMP7VpDQRuE3ZLds/puUJC\nVl/fmuLGQMykqGzAtyT4oim7tVX4UIzCWyGe6O+txhVsW4JdTtRBtEd4cCir6PTBDKsZg48mhlkx\nq5EXiEiG4hggpxZDzgKfRryE5zMQ6cRwqnXQbQrbp9aD14RLuKFacyQELsDAULkqUGQCpXW6ITyP\nZu67soaWwRwehUyaagOaZrTV7zwpXKoeHiMeM4/PgOlux4J9Jn2YaPVHAjhhE4VLDoRJE1ZIvQjm\nvD23C4uCJJaq4WzJcwSphXop1ki9egOqSRcDEvjEYgRFpKe7tYCrxL3wsYWylr3imS2F/YaMySFN\nYGP0E5owaI2I8mgd4+KM1Y6ZkyFbrCsmWaC24WOP8na234fPoPJGp42IAj3j5AIJEZARd2UUNdKp\nXLG7YaGzJX3YVS2creR4nI9gR9J3++TkeMuqkTBIurAWSGPxjlf1S09wFh0GQbc3A2Aun01bGhUt\n2tu7Llq+BJ3DWuHFG3K8eU/u52IxxSEts3YKrLoSCPemATqETdeeQZjLHHjArNz34iVAmPem8nDB\n622nBfx9+e5uVOLXzj6eIf9RUpL/AMBXAWwopQ4hKtO/BOCXlVJ/DcBDAH+FX/8nkHTkXUhK8q9+\nlJvQlkISuVBnGt62vIwX3/GfyLaXhY0hJ9NWz0MRyMCtU2VeYKNZyu/6Wy0YRqzdQqEwMoGCyoZr\nSIdOYQ7jt2BvSUfmyxgoWSFXWPC35KX3jQWLsYuAen/uRhMRF5DG7gYSpiqzUqNklaMbNOGytNsM\nWcqdVlDE59fTJUAiVbXM4IUkgt0Ws3Y5P4a/kPttb+zBkN2p6dlYncjzuxqouHDa7ItVMASa/G6c\nI9iVBTAsO2i2ttifGo6S36kF7217G6ombLxfPynlNgGAdWZElahZ5YjNteS8gm4QHj2uYLN0WLtz\nOD1JAc6OWk9IVBS9OWV+kPTDQrrm4A1bGJAMps5X0OyvjKxKHW3QYel4YLsANwulS7TJK2nnDoK2\n3J9HgpuhrjCvmFlYraBryt33cnSY4hv70TrTinyHC8gHTZxT6Wo1VkgyOa/bTGE4ZhkrVbcDHw4h\n4YgthFxYmnYLBz1xZ9xNB0G2JhyVPqle9/GQqlfFEtggaU86rGHaBE7NeH0FWgAAIABJREFULFhk\njhqxniUuNM4Yt+r3arzUJIDPJJjacs+P9QauULTmo7YfuigYY/7dP+FPP/vP+a4B8B9+rDu4bJft\nsj1V7amAOSsoNJQLuznD0T1Z1ZJ+iRZX+eM0x72JZCW2gjY6pC1vOlxdEw2Hud+g6SIgACoJR4hY\nzBSEGygTCQiuZnKuMDBQK1bFGRfKZ4BSp7AZqW6GPlzu6CWxENVZipo7X4UpbCPX8yoXNunX6zx7\nwllgZYRaZymykkEfKwBIdGI7BQrNHR8E63ge8rlcI56NYLOWPj31ENOdOTp7H2MWf/Udufcte4oT\nmqKJ3cAOzX3XzmAqcU2suPdECs4h5sHkJ7BtyZKoZRuaLli9vIAmX0JVduAQNl7l78vNz12UFNfR\nZ0vYSp5Pj3Lkd+U7iT7CxYoEN4xiuT9o0VpAROGb/Y0eCoKQinkDppJdtUUyGceyoEsZP1/3QY8O\nVZyjIMFL6gDDTCw9paWvzpYJFAFeXtBERWKU8aMMBcVXbFvBJvdFdUbXwDe41pF72+6EqGdi/Rw1\nN9BnlaPflLk3MiH2mJHYGPgISTenyyVKTQbm1lUEBM9NM3J57PdxeynnPUGBb5PZe0tV0CwqW5kV\nlgxGG8697bb1RM06HwG3yfHh77r4/h2ZD//ry99FkH680OFTsSgAGlWdIC0UGraYuGH+NpZ86S14\n0LZ0yNQeI2Sk1ngUH40rpLW8VKu3j7AkAMVtmCcS9k61RMr6gBX1HeyiCcWILlwNKyEazXJgz+Vl\nq9wGPFJra/I5JvMZAoLrvXAHRUfMfFNdAzIqGmVj1IzEG6a/SmRQFmFwbgmbqdEy8gGmX/NArpue\nGuQDTuIygZeQPSdPsEjkO6PFEvaag5aL38iUiEgW0w1cWGvdi6IAJrJIFY3HcEuJryhFGfbCQ+WQ\nlLWuYdRasBeo2Id4v0Z5nQhKlkibLqCJOqxsHzZrBorcwrzPm6tzeOTQVOwfrSyANRwN18KnKHHv\nRkCWrR8qQMr+qC+IeFQNjFgxWoUP0Dlmys42sBmX6EYNBAFJS0jK2g5qeH2ZN9WjFTJe+yKLUTM2\n0KgUTIPVry2qMA1CmJLIIsvFlGxJvXyMgGTCzqska7UMNvblHo4ubGhmV9zlChX5P73RCjVp210y\na23pHpwvC+rwYG7DJ0eju29D3Zdnfc8qn9AApMxkHLQ89F1ZyBd7LqoteXd+5eXfw+EbMpaZrqA/\nJnXrZe3DZbtsl+1D7emwFLSClSigAVQ7FCcZXUPI4Nm5lyGntPMisbDjiYlqkUij7dpYERp7dHGB\nTp/BIM9FVMgunlwsYWgSWzSZRydTIJHkiNt2YQ/F1DZLwKV966gJzJKBH5JjLI+WcEsG1IZjhJqQ\n4FaNEtyhco1sKcQiti/1BcZrwCH2QqOEacguYIURVE6hGrJW+24JxUCj6vvIZhLAzBdLpAvuGIsE\nvs+oNcFIKs+Ru2tosA814067YaGqCF7KAbdBRmFDEzdWQHbBe1sCHQbtBhEsUtQXgxiVEhfEJNTB\nTB7BYubHabioaWHo5WPM35TsyvnFEUK6P2v1IscyUAQs7UYWrtI1K1c1zklI4ukU6ZzBZm5fkzTB\n+aFkTvKWDdWQe7M2AnS0HMd2jupMfrDkdSuzREZ+Dss6h01V7aZl4YhWYW4phGu1auJerNpGk3yc\nuSmxk8p3J6MlDmj1zIgtSX0Xj96XsWn3DWKa++OVxpAKWEU6h+/LtUNiS4JuCkIdkFQ1wn3pWyuO\ngV353f6wD4fEod+nWpi2c2xdkbm+vWziN1mjc/LBAgWtEcDAMcRHY/3Zn94uLYXLdtku24faU2Ep\naMdCthmhsepjh0GbB4sD2Hvc8VcpbBYH9YoFauaxc6YQ7VAh4k7badpw6KuF0T5qi5oFrgO9IlUa\nSTLL2GBGv78PByF3hNANYLaEjs3WBhmDVaBfj2cd2K9S23I1gg4pjOI40CX5IFBBU7TG0CW3nRLW\n/9fem8dYduX3fZ9zl3fv29+rvbqqeiebTXKGM5yN1GgZSaNlBpLsREEsQUlkS4BgQIGV2IjjgYIE\nRuA/DAfyEihSBFkxkMiSHS3ReGBJs0ijmdGs5Ihrk002e+/a377e9eSP3+8V2fQsJIdNduL3Axpd\ndd+re+5Z7jm/9futiibkZzl2Q+PtqcVodtvYEbswHA+IrDrt2kNyhZhreS321ReRmhGOkibmVflu\n//qYoSOa0H1AeF60nEo/J9BQ1qQ3xdU4vqPeTlMugZ6OlGKMsmOb0goEShzzvireTdEmrKPjE+yA\n5ilM9vbJLmqKstPh8qpoYXtfi5loRaSvmknKEX8PplEgdaQfbT+nqGnaxkSE6hyclWFlxlLQ+UsL\nI0Y1zd7ME0xZ5r3bjzlMRbPav6ZUgJOMoKhOXDsmKGqWasXBU4TqadFSzGdaj8yjH03oFeQEXi4n\ncFyxKl48oHRO+SIUBi3Mu4THpY2mV6ejzzDsT9htyLNtNOwRfV13KhrPxXZKWWHjui+NSXfED3LO\nnTCqqIblGLZ7+h1Fglr0CrjqYHePx+z9jjh2J8mYTHNnCpUi9+g4P9Z5bZrCXbEpAGAdqsvgF2QR\nP3TmXTxZlhfoSrzN9FAWypV+xKZOUllz1gNyavryNxsNMnWuWTPCqrfYLjbpqmfY82Z4jhGuBqZH\nWUZNX1J/EpLaGZuQi+/L9UJNJ/zpMXsKpVXav4K7J5No3lsn1BfSej6eok1n6hhKW0UiZKGk0xxf\ngU7i2CNVPMNUnUx5z9Aaah5+9iIhszz7jKF6qsP+mGgoC6TSk8VzfZQTLisGYG2ZUk8WY5xEhB3N\nmW8V8TyNlbsS23ZthqN1EpRDZtCNWWsPVx1cZtXHU+5JxlJzYXe65OqdH13aoT+V5J7BpREnr0l7\nn9yZcNCTOdOKbZouNDR9aWEw5ZbmDZy5ts+24lwu+pZaoBWxS5q8RpmJRiT8QURalpc3pEJvKmNb\nGZXIFY4/U5Owe30HoynRVbdCd6TOvjRhoglOZS8g6YvJN7ku87HX6rPmyHjen9fpXhPz6a9aZfoX\npb0bM6Ii2tz7kozFse8ZYQ5kvSQHKY263K8x2iZRB+RzL0ppea1dZHlB1uFSDbJDGVuPkKVY+v/i\nYJ9EoQoHmnC31gwJlfD2wldeYKrjsukaVtbl71pbDcodSTen8ye8FpmbD3OZy1xuk7tCU5gmlgs7\nU+xSytllORFK5QHrNU1L3ixw/Ybs7AdmSuOy7GVnXeU66K/RTWQX3W2lGD2B4pJPRWvPs25EqCrv\nSDEbDrIuE1XFF1YWj1KJw6qPr5rHaJpQUEDQVJ03w2OG5JISyjzp0PyooiD3mrgzKrh7i2SHqq4r\nJ+SwtYttaXZgrUaQiANyXE8pKciInZF/NOrEqZwC0dij0lANZJyyelb6dLXTJ1M4tk9c1vBsmrOq\nBVg/eHxKVlGyl8Oc5EklXznbZtqWsQ01WzOLfKxWmmJWyLSwNX6xTbatmsLFEmZLTKX88CUd1wG9\nQ/ncnlvFD6X0JVm5yq9eVL6B8ZjWq4hIDjIoqEmR9mCrKOO2W8iYzABcEg9/XbkqPDn5pnlKqmZV\n0k3QaCIjP8NbloKoXmGb4RU1O1TLi4rLDHoyVjuHOxSbcr/KmSqZgt0cjA1G8R5e1NN4mo55saXr\nKe4y6ol2miXbPDGbV+XOKKWWD36PPPuW2eSFipgHT3S38RTLY2M7oaRO87JmObaifW6+JPc4s7iG\nrcjcXMiucevr4mw+vbpAsqKAxb6st68fhpzTDNLPXSvTGst6OpxYhpp7cX/jB3n3w6J5/MULvCa5\nKzaFdDqgffFzfHK3RufsDwFQH5WoNyR2e/rMk1y5KS/k1U7KH6n6/+N1WeTveVdG90BTPBs5dWbV\nk306uzLY66c8Fs7Ki+Un8gLtT31yTYkuGodGTT3ZSU42FVs0jQ3ZoQxTpOXSf/Abbdb05f3s0iE/\n9HnZbB78ka9g6u+Te4x7eKkyASn5h+uHTLT6rlwq4DfFfGg2apipTPhIc+snF69RTeXZl483qbhS\nUzCudohbmghEwLPXZLH19e3wDByojv7EfpUf/Jom01jLbiA1DM2LdRob0o5V1T+bjPEjLYu2z+Ck\nYnMXH76HcSYqtd0fEY60ylEJZuOrDoWJqNGlzpR2Ij6HF54b8+eXNZ9gmpO+KlRuDGihKd08p6l5\nzuuTlL6nG3LTw2/IJnNsU+ZuGBZxbqgp2WsximQjWzt2jJWCjOfXruwz2t7TdhQH0XM4GL4M7X++\nLnO94BSYamXucJTQH8wOF8WXHFsUs4ZOzWE4UHAd5zjdSxJdmUVwrjuGzz8un//kT4+oOjJG93T6\n3NS2v3x9j3eeFW7K1XV5of/g8T1ae9Lutf0+Qy0vuT40NJTm4F1Fh7ImnL0wki8c+GPGV8Sc2e/2\naeuazHOHRImHkzgge+rHdNQ/yWuRufkwl7nM5Ta5KzSFUZryxVabzeshE3WSVW1Gdq+og2tX7uUr\nvpxAk8QeQai1Yjkl2wmcPKmRg1qZugJvsJ1SUpOgcqyEv6jZhIqdWApTfM1cq60GOBorz50xEz3a\nbDnGJKJ2eq6cLk/XPbZvyAn8/XFOuqTP0y6w/JBgKWbDJfwFaS/bOAdAOI1wd6W9pB0Rr2omXZSQ\nqIZw8LTyAXYvUM6VaTkpEFVkXPZaEV0lmXmyO+FGVzkSdCxTC44+e68/4mBdNBD/psvSrBirHBE1\n5BRzy5pDMW2SFeTky8NV/FBj+pWQwgkFLGkasql8341lvKe9fcarWkjV2uOFoVz/ra+36Kpmld1W\n/KRioaVmnpMadpTeruFAVbWQugfH1NQzy6KNle2UdlNp+qKAQS4myjIx8THRJh84e5yeVnkulBQL\nwXNoT6S9aeeQ1U2Zm27P0tO08RvJmJY6B2fOw9HE0imIdjdueYQVNW+nMZpmgVFHspfDtppjl55w\nuPcRdShWiziqzrcJ6AeynjgrffqRi1t8XW9WJqGg/Vvcg0ArgZfOWh47VNg/xaS4medE1zQD1qQk\nySwL1WOkWCPbnWVOmMuvHv1vKcba142P8qbLya11+z/8vZ/Dvdlmuy/e5IozoT+Qzjsmoh7KC3Js\ndY1cE3VmaaKH2z08rZwMGguEiU5cfYGa2uLd3oSOkruOhspyVHPwZ+pgwSHeUKSjvYzd5/8CgPq6\nR+8peaauAqj85C/d4O/+1zKJf9rbp6k2YtfNeE9NUXyKBWqa0qycsZjcYazkqY1ayOqW2I7FSYX9\nbUHb8UvKI2gOaSlbUXvSpqnU6gd+gj/Veo1pH41IcfhNprF9Re73Ew8YvjCWfjiYo9TXcMakFAac\nUmDQzc0qD2vK7FOXp6y8QxCiPvhjH+Bs43ul7ec/B8AD7/8e/nL0WQBe+M02v/74vwJgawSXtDDh\nB+o13JNy7we2ZIy/92d/gvvWvkvmoXySYkXG7Rf/xveTa4KQE0HgiBkTeTqWuYOtaeJUUqc9kU00\nyR0qyhvZrKyzcFLT5WMZ771+5wjcduIN8QYzAtoxvi9rJHOm+FbGq7GivpNpgWsHz0t7EUw1UlEK\nl2mWRXWvR+JTWchcVtWkLRZyGp4mOl2fMC7Myi8Tnu2q/2RHNqNq1Wd1Xf4uicrsqulzdq3KgZL1\nDPabPK/RlQc1mez6yRreDRnXK48/xyN6wP1qL0E5kF69GT9urX0v30bm5sNc5jKX2+SuMB8wLnh1\npmFMLZHTfG0twb6gXJJjQ1UTb6hDTeHRxur1nY4OcBRxNxv0yRR7oBw0cPSEqVjvqJBkf6AFPA5U\nFa6t2myyVhTHYPkhS3Xh/dL283sM1mTvvLIt6NLv/h//NlH7V+R5PEtfMQVXApefPCen+Bf3XbpK\nPnOqqWQypUU6mty01WiwsiwncGojvrotJ1vvJfE2t5dqRJpjkQQOnlYiGuNhZ8lXGA6/jaa38OB/\nJz+M/+HRtRx7BMb843q6Pp5CHMrznt08DosCdd7f+QKOwnk9/WwVf0GclZ96STznpV9/hi//l3L4\nXPt3v0J/WSsAxxELqvp+5IGASwpD5ylpYv7JKww+rPiY79rAUYdaYanBwdNygp4sWHYUhi6P5DRP\nKi5N1Sqi0gj3upzojjfGc+WUL25UWK3J82kGNgvFGhPlBC22QvKqFrfZCrFyiJq9Io015YpcEdM1\n8mC5J9f2Rm2muobGZsTpJZlX87zcd2MJRka0Bs8rsa2RqIGB4pr0b62zSL8g37k1mkG7FaiuCVSg\nGbX4Qkf+7vB6RPe8aJPOQYdrmp+zPpYxrFwtUNqU+/52UuQLLY0efYcy1xTmMpe53CZ3haYQ5xnX\nBl1W9iYUq0pWur+H683KV1OKS+I4KeLQ11LWaVvJVNKQoVWSDifASWf18TkFzaTLAgdPkW8rVU2p\n7e4yWlbosl7GxvuFAGTQ2uf05rsAeDy7zsJUTqANI6d49IV/BmjKXwqL2o9jYYGXFuV6GC5zRmHf\ngmXRAu5buZebSv+2OKnT0QKt1vYe7ancexTI3/dbOa6yFhMdca8wzXIydVq9Jhn9T9/w8gxq90kt\n8jJrNZorcgp+dZSyNH0KgBuHe9xSwpjn99t8UqHgDlJNQZ+02f0tOcVd8yzmQJm585ypojf94V/t\ncuZe0cI6bfn8eO1Jhs9K24unHyBU1CeCAbsV5XJYqLKoteH7ZS06SnJCLakf9xOyUNrwc49lLTar\nV4oEddF6EnXobBSKRJosPSjnZKpZrucuE6vs4FmLbiqn/tqSaBru1LK4JuXJN4opvjr7pnaMm8pc\n9aqa61ENWLTSp36/S1+ZwPu7Q3Yi+bm0OuU9DVkPa/fIWJaLHs9q2vmw3aY7keuDfMqFF2Sul9p9\nIs3reGpfxqQfxNx4WvqR21clgnwHcldsCsZaApvij3vUGjLot8YlPMUMbEz6BLMXPQ7pTUSt7rdk\n8LxKyolAUjmbjQWyQP6uUm/izjj1KlVGWtXmBvL3zeoKo4FENTompL8tL3E1KALynfOnm9z6unyn\nMVS4ydveSQfNbSGMDPVtQRoeFkJKmodwb00WVR3L6r2PAtC9lbKkpQbj610WF2VDGsd97ec2s+kp\nOIaJ5rtj82/gyn/9MsNHNEVRxU26zKICeqS+R18Zq4rFBZJInikdDzkcygaR5PJCdKMxs7HKMnu0\nkY+xhBoFuRY7GE04O35GXsz93hgzSynuDij0rwIw7WXco5ZiM0rYV/h0V2tinFGC0Y3FnQ7JlNRl\n0S9SVnTlqqmwUJJNZoaxEJ6ukI10I0ug1ROnHWGZjvJOdgctZmTjZqKp7XGLal3ucTpvsK1rb3qz\nBRvyHPfqW1QduDyrqn93MqLQVej3KGW1I/c4Uamw+qDkXmw+KmbXeG/M4rOylj/jdzhXkT5dNhH1\niZhjPTsmVVCXA90AwnFKdJQAYnhTFgZz82Euc5nLq+Su0BRKjuE9gUv/tEva1zwFJkd8CUtZmWVF\nzbGjlEomKtPCinJBNIqsBJKN555YxTnUk6SRo5omk3JIrSX3U6xP2uMRE6PVl0mPrHsRgMODhOcb\nsnNXCotsnJNTZ5hIJtr9f3aRPQ0trrgh3aZSz5WaPJPKsfP+s8eoaPHkmRPvACColrDDqwDc9/AW\nTkFOjNNn3kH1s/8OgOx96px6bpWXJvLdm7fatDVMZ6YZmgpAllmyb3c4zFQCe/uloiun6oFWgH5g\n1RCHohE083W8FRmkSXuPBVXLH1xe4YUD+XmnJ/1MEo+SnsadyCfXLMxebJhF4Q6GEctWTtgbL2nY\nMAhYekHZqn/kOQauaFXt7gRH09fv3aqyqGQcF3YUbWo4pqYOzCyFNUWnOra4wpkzEu6seyuUmpID\nUFUG8kI1wakqWndeJCgp0tX4FoQyFjs0KRa18nYkz9s6GDHRa6VywFZD+j+JKiTbokEeavHc+WNw\nXDWClR70NVv0XUnOmbPS12N/60N4J/6GPD9XASjfv0z9fhnPD58rU/r0xwF4ICuxFov29qmDlC+/\nKN/Z1XCjl1u0yJccO0P3O6Lje6NyV2wK+JCvuYQdcxSPNn7KosaSS2GIoyrT2LeE6YzWWzaFpZVF\nGhVZEMXyBrHi4VGKyWfU7lOD78tbGq2cBCDx9kiuy32HNuZQ6dAn0wjbugDA1cI5luqSbn3YUMLX\nj66x8nGJUQfLBc7r5pUEHltleY5wscKZTYkueKFCvEeWxXXZvJZWj1MsyndXOj7+DyticF8mfm8l\n4YErstieOvZFLl75GgDPXx5QUjVyaHNcXQFHFs2rNglHcw/yQXy0P5QccLWKcK14CgD3+DInNefr\noD0miZWFqZFw75os6OWVVWobMicXXhTGJzvq0lbewvDAZ+qoSWfGDHWDyD3Y1nlYV9zCl3bHNDYV\nY7N9QFFzia2f40y0WtM7xZMV8cFMd2RdjKYJNxUFeblR52RBxnB9dZn6umyyjhNSSjUyoAlpXs3B\nVZMgiCyhpkRP6gExMubH9x36mkIfl2TcvJJLYSB+hqy5wkTrErKDPo4+s38o9536ixw0hGC4f1OY\nxACW3ncfC9/1fQAUTv41TFnadnzZuGzq4S/Ihry2UeU9kgqC2b15lApf/9oY6wpB7KW2JCM9tx0R\naPRpZEDrXokcIH/jpsTcfJjLXOZym7xRKvp/Avw4EAMvAX/LWtEPjTEfA34eObz+jrX2T79dG25u\nqcYTWo4h1dh+EkU09WSuNlYpqbrrJxOCppKk6G7erASUFOKqEKR4M8jg1CVXpmhTjHCPK2XbUDSG\n4mRMXpIdejSZEGvGY7YfEaizZ6s8JlfClaQju+9fb/f442V1YGUpVzuizz24WCRcVoCTe09RsXIS\nhKqBLJ9dpbEsJ1tQrBEUFYKt4XB/Qbzd7bGcDAvFFqVH5fMPDDf5o9+V9g62P8cgldNsI/QY60kR\njV7WHmaSA2uaEr4HOKoqxBYaim82UTCRraUi3WuiMpuDHpvvkrZPNitkeqKddBKmZ8VpupBJ5d3z\nV0KqA8VQOOaQKSBLfcdwVePmBouvK213fzZWE7bUpEi6z+NF75YxqgWMZpWPtKl2lX59oIjRbsaq\n4j4sLtVYOS/a2EJzkcV8hrqd43sK1KKnudcrYDQyUAwqVFTjyWigfkSKoxGHVrMpdQxfGnXJJ+rY\nW4lp5MpQbYqUNK24vqp4GYWEJS2C8mptzqzKWth4/ybl94i2ZVaWMbPXTpG/TRSDapOlk3BGiWOG\n5QWSijhEHzjd4PjkhwH4xB8+BsC4/TVuTUWbbPiGTG3JKBPNYdaPmYn5Wq2KN0pF/yngY9ba1Bjz\nj4GPAf+9MeZ+4KeAB4BjwKeNMfdaa79lDC13LHFoCbMpjnrZTXWRck3V63oBp6FkIrsugSd2VlUJ\nSSrNJXydRq9ZwctkMdrMww5mJIUVPLXLC2qIxdkirqZV+9Mp5LLYBsEhlxW2/bsxDItiKpwpin36\n1CrkF/UlHEGxKD8vrCzwfQ/Lsy2dOUl2VfoSnZC/K60tUdQog2ccvJpS0UcRtRPC7Zhfl9Bk43Qd\nT5Gl/JN1Ru+WBfHMlSd4pq1EJbHBVzOg78jmFo1copkxkUJ9Wfq6O8qPFoU1AiEP8L7z8gzVrUX6\nz0o/DxpTtlQZrZVXObYiC3pxeYFI1dLkjIRsC26P0VjU51vDDmsL0v+b6SGtsmwWlb5loNycJU3+\n2fahlMuLWywuUHRkg9gbOGxqlWcxyfEVAWpaks2kmhTZOi3PfO/mFhsbYo7VmgVKGknJJjEFJYzx\nCoqDGJQwI2WyWgzwdel71foRiE6cW9iTgyGx8rzFwGNXqeo3bRl/S35+qFViohiMD8by91uNgPZV\nxdVccVg+Leuz8aFH8MqSDGfKTVAfxAye3Q5iHPVL2bxAoIS+JptgtDLSxh5LxyXB6T/9oGx+rf51\n/vApiYhNRylKi4r17BE3aeraIxj81ypviIreWvtJazUIDV9GOCNBqOh/11obWWuvIExR7399jzSX\nuczl7ZQ3w9H4c8C/0Z83kE1iJjMq+m8tWY4djOgethhkouJt1ZqsvlM0hZJbI+0IQkSlWsBfFM9x\nMFV8BNfFz5U6OPEglZM5t50jVS039gi3bpJLDN6mBrVAGBZc0okmgsQ+ue55l5I2p9py70FbHJif\nf3xMq6WFKo7hVCI7/ul3NtkqyokfXu9QKUsab7EqO3wzX8BRfkg/K2EKitrrhVhXNYQNUR3TxOIX\n5eGSwohzP/oIAI9+6QF6rSd0cMcEmiDzkJHT6h1bDr9ydXA0tFP93DcvJ0A5FjJNK67flO92PvkU\nRT2VT/tNHtTx9FYLLDpKNb+yzpqyMl9THIrTToFxKvNwpp3zZCgRnEbnFD+8K2P0fHzI9PDlkwtg\nxSlwRXkXl6ILbPrCTbkauEwbCkTjxCzqKXdMcTndmuWMVkCeqqwTlqXtIg4KYk0xreCpU9nzZxiP\nGZ5qD05QltAFYL0xYVWul0eLLCgW5r7C29UKNZYWpc/HFh06SuwzKrucUP7PQPk4vUaZB46J5lL0\nBgTvlDwEN08xiYaiHBdSXasa+TKVFIumawcOeU1MhkIxBFb070K0zo+VD8sr9eGnP88TL4hm87VR\nwoywfMN1WBUrjj8ZZa87e+E72hSMMb+MYHD+9hv4218AfgFgaaFCN8s5aHdwEgU9ubeBo0CpcXNE\n1JKMxclyg/KO2Lj+ikxgEpXIi7KgC7GPyZR4pOhiUU/2eEDiaM2DEo3m2RRfSUrcQoXJVFTYoVOl\nNiND8ZYZWFl4LV/y/p8aOXQ1/tMMLe/V2oitWwGT41rWO24xUvj1MwNZKM7W8lG0gDDFqL+DzMNT\n02TGMOXsDjCBfF40DiXdLH7s7/4A4b+VF+S3/vgxYkVLuhBpee+tAKM8kdbCnqKjRkeKHRQc2Iql\nnVi96cNhn1RDgWejEeVNzSr0l6kd08rOgYenGYTHF8SkGHkDprtSyzK4AAAgAElEQVSKA3nM432H\nmiB2esp+IArkjhlzSxmZTEEGbmeSUb4l99q84BCuXpW/Ww1wbinBbN/nqi/zmkXKgLW0SOrI29Eq\nDNkYaG1EpXAEL5+GBWpa2m01IcsseVjFpbTDlLSuRENZEa+snI9NQz6W8RipCVaOi6zeUNxMU8QU\nNKGsVqCkwDCuhg0db4Vx8UkAksRnWcu2470K/r0SlfCmddmh4eWNiSmMlTksiY5MOxNWsB0xeW2j\nj3tTSWI06/fB/+Ih/vNtSSa78uXrJMpqtmMdUgV18V1QaNJvH75WecPRB2PM30QckD9jX66/fs1U\n9Nba37DWvtda+95aJXyjjzGXuczlTZY3pCkYY34U+PvA91k7S9AHhIr+XxtjfgVxNN4DfPXb3S8a\nTrj8xWfobfcpb2j04VZMujVDNnbpaP1BcnWMqyQqXkFi6e5kh8BTfWk5hZpoEk7mkWkeeTpoH50k\npiqbkENGsKPxaHdK4iuPYH+KO5WTJKgV6Shl2e6+YvntRwS6nTYXXZYyNS+OWS71JKGlGaaEmWg3\nw6kmQm1EGEUGtvkA4ynfYal+RGwzUykzN8bOgM3DIkZPvlNL7+CepYv6bBE3rqmHX71MrdUER3Pj\ns8gyPngZ1nuWpxDmQrMGcB1RPxdWypTGco8kDykqpF25MSaP9HRs7BEuiklXVQCYAS79qVYfri5g\n3ylaVe+ZAx7XZJuwFFAIFdZczZmDZESlLj9/5fA6zkXJl6iXJ0TqXBtO2gxaMzi1GU5+gDGiVY0H\nMd1Uza68LjznAKUQa2T+3Jo8uz9dIs+Va7LVJdXq2EKwTlHtDtvPcbQqM9TaiHw6xNc5WawZcq3t\neGq7y/1q6oYnVa/P9igpK7VTScgUu3IYP4d7XTAZau8p4SrnY66p+9ngWVx1xFLfxDjKPdq7SfJX\nki8zqbZwF05Kez0ZK+9WkWMn5dkbT1muiOJM5GQo/Aa1gmHQl/F8rTWUb5SK/mNAAHzKCJz6l621\nf9ta+6wx5t8CFxCz4he/XeRhLnOZy90lb5SK/l9+i+//I+AfvZ6HSN2cw8qUUW/MGQ3H+Jt1EiOP\nN+ruc3mo+9zhmNEpsc8qY9klw4llWfEWguImziy3OSsSadx51D/ksCuniufpTp0bDlPZXr2OS2BU\ngzAjRuoEPBH59Etyj6rWs+PmZJrF9wG/SvGDYjGV3SX6ynxsyhuEXTlhOmc1yy8fUOjLcRYGLpUF\nORHcyQRXHWbWagqvM2D7RelnPT9J1cjJPK19luENcUTl3YRUswZTDTheP/TItYiI6BsbkVMPllSz\nOqZsz+NiE1/heir3VJlqKG+ahVRC0cj8UhlNF6Gg2aSFSUq9phpG/yb1YwK861danH5UwmXpYxsc\nKur0WCHROoCviMnpk1PONhSAtFklVO1g0YOGErgMHM1/WGjiVZQoNo7oIqdxGk0oZ3K94i+TBvLM\nVsPTXjBluC3z3+1f58LTAj23WTnF8oxVvDbGn4hjLzP6bO2ITMfei0o4Ws24MsrorCjlXk/Ru06t\nkr9P+pF0yxyqbyc1RewzMper4ZdxC/Ic6basq3y3TeW8FPQViwnecXEkToeW/ZuS0fn0V5/jTF1C\nvJsPy5hMHkqY/l/i2/HzHYy6FLPU0neVFd2a100we1ekOReinM3rQ646MQeuDNS5wwmxEmRErktZ\nX3qntk7VFWdQ/3GpsrtR7ZIua1x5dZMQiU4kcYdJX9T5mzeG3LoiCyHW1Nnqeh1jFMnXMWSqwlkn\noanYjsMwo6Dw63lBFspZ3yNTTMG1lRIbuSYphRVKFY3jxy6FRJ2HY2mj/+mL9GvirCzFy5z+XuUU\nLDsoa/3RBO5dzvjcp6V8OTv3RY4dqlqapVxvK7NQa3KU515RL/v3blg+8dLLaSrubKG8YrxLuaGg\n8fblGbuyDSmfkec5ubBGRc2O3qRDUS3EpP4wJYWCS4yMZZYmJLmMt/WW6U4el/utLfPwRLApzTmP\nnW1Z0J1INrTTUUJbfZ+j3ZhPaa1BIU25Hskm2h47BOpAPaYYnGuLPse09sHzKiSaQzC6fo1+IM+8\nUG4RaHpzrS7PNkpuMZ3I55efavPcTTGbbqxvs+nLS7+6tUWgeTI9K5t7d/8WpXWZ33r9JGNlycnq\nMU3d4LKaXEvCPsVY6mO83h7jVbnXlT986shMrZz0SJVv8s+/IpB2yXTKhzyZs2OrdfwFYXqyvUUO\nu+LEvDLwmSzJhtO5Ihb5RjsnvEfxQb+aoVYXZz2DstXz/CjnZRfza5N5mvNc5jKX2+Su0BSygqG3\nHlK6OeVspinIm5UjskF/ukJFi47iQplcVV9Ps/GWJyG+0oplY4dcsf6tGYHmEBTqdUonBetgqg6e\ncdzGqHMqL/lMtP59mgcErqL9DnNcR07HvoJ2BsshKz2N04eLFDUEPXFLuHp9wJCikQ9GLeUzLFoy\n5WeIS0NGL8j14LyPq7hhuZIhTK/foqpkIdZrUDwhJ9t0+n466szEfYxcAVdGaj48cTW+TVm0M4/o\nJD86AVbKcM9Y+rRUE9NnAmRTVYdjSxzI2HoYIkUXzttTMsQMyLRyMM1g0tIs1GUfu6+0cnGb2BE1\n+Zi/wun7JB352rMy4Nsj8McyxttJxKo69sb3NRg+J33xM1g4I09d9uS0LgQh1zVU3W+1WNA5GTpD\nHB37abKDe000BH/rYQCKVQdP08qzYukIlXlsQ0augq/suJhT0l5XeUZ6lQhPyXAm5zxiNUscr0w2\nIx3aUQKfdo1EHbetasjBEzIWh3ZC1JQ1ee5Wh7Gi7Q50rTvEpC8ogO4JB5C+pm5GpEzapa3TdDXk\n6KlZMth3uaxjlbm+IMsC13Ooz+bS41X4H99e7opNwUY56bUxmfXpa/Qh77p4SjM+YZdnvyLe2wud\nMfFIJmlL0Zc/+OhJ0HTmfHiDTMuocycnUQr3MCuQqp11sC8L+9Klm4yGsnALZYets6KqFdMCComI\nm0b0FJ1opCQsq9YQaRmuOVukEKg/w80ZZPIck2ttnlJV+foTYuZUHlziXasScXDHQ1yk8jEdfQB3\nLIsiviZx50q8x9pmX5+3z+c/JWjP/377XxN1pUquYOwRyYqWOLDnZLeXSWt1ogO4M1fDyHI1kwUU\ntxSJ2LNcG2pC1u4B6/fKZvHAag1XwWkinicpa1Wl+iKm21eJ9YWODgwDRbbee/oFRj1ZvNOTTc6f\nEj/O3r68VDemO4SujNWNJOKCJjItP9fFKFlPqxxg97TOZVnBd3Z6TJ8RX8W1bsxYY/3VxZAPNuWw\nGKZjljwlsLlfkskKlTPEsaj5K26RW1qpmHW7vHBL5vXZzousXJXrSyd0rdxMqRZlMYSLhvAlGYvd\n/R7v14pepyjPPnLalEqyJpfTRfLTsnl/9flbvHSg/bCPEzV8HU9ZeztT+IhYWhTWlnE1dyQ52GWq\na72wUmFJ63suPybP++UXLtFSMyfNEgaZtGE8y1DT0T1ehv9/rTI3H+Yyl7ncJneFppC5Dv1Kkeb+\nmHtD8f7WN8/i1UWNSkY9qk05xc+bJZx3iQPvlHzMQm2TYMbsbMekYzlJ8qSM74gaWTqxyIk9Vdf0\nXolZptURp2MxSllRLEUT9mlrTnAlSemPxbEXJhpFWK5xXrEeTlYajDRTzviGYqTAMMeXOZmJxuLf\nL97kpaWcRia7vY2nWC2kStnB3VOSmD05GbITqxSvyb1ObFZ4cVnadm+8yLaaRFlujpIPZg7H+NWl\ncJo9l6eWWYrZIIQFrXx87ynRXG4OHO7RE295uczxFTl1a836Ec9EbDpMtCIyQE5ENy/iNDQFeTyh\ntqB8CWfeRWUkzsh6rcCaMnof9kQb2Rt0OdQ560QJvuaFdHLIFW374SDk1GnFx6xJdqTJplyuSv/X\njAvL8vO51QY1jQxYG+BpJWXoyBj7xZBaWaI9q4/4vOemmGbDbMTBdXFybnduUNYclsa6/L95CElJ\nnt2mGdOKPH99lBCdl7YbmkFbDytYzUhN60t4it537pHTvPMljWZ97wfor4mJ8ayyuD26uMrmO6RE\nKFgogEZzgkKTcx/+HgCOX+3Q/C4xwRZLoq0Nx0M+uSfPEwYJFWYAN/lRFmNiXj9I212xKRRdh3fU\nyhwWYlqZTNDyqE/lpFZDNt6NW9YKuDyBolQaFhMNMYYJJdWNM4rkmlIbFw+PzIBSY5NAVV7blRds\n4f0bXFd1seC0yRwZyf4oJh+IidHyIkqaODTW6soPlRo4RSVH9VPcqajEjdUSa2dOyvVSTjmQfP6s\nqeGxUYcsFnWvUV0mCZXgZBqQ+3J9GsuL5CyeZuOM9DMpjvnrH5GQVVwacWFPXpQ//dJXSXpqO+qC\nMK9iY/I1t9UCWvqBHxmiE/ISriwoM9NSnXKuoDVrx2nEM8LaEcVYwVACD9ORJROXJYqSmQLVddlM\ni9MFIi3FboYBnZF44stliCrab4Wjep8XcEu5FJ/fH9DWh35gmKBNsF/IuW8kv6zeI39XC3yavdlc\nu5RLomrX6WI1MtBMChR1s3MG8rnN2nhaklyvbxFq3UK3c50tBd1ZG5+nURTVfO9QXtxmIyXf1LL9\nvImJZe106oaTDU0+6+rmnh/ghTLnlfU6xWMflPa2n6LyIXmhx8Exioq+Ze6Xe506c57i/bIpuGkJ\nm8pBFRzzqfuyGVZZBMWHPPleudcj00fp3hAz7y8+9znsVJ69NLF01DcVJfYoae21ytx8mMtc5nKb\n3BWagvFdCus11rYHVPSYMOEIdyB7lpMNWNAs5jhdwFOVuBDqd4OAzFcwlQNDNJnBqvVxXKXv7icU\ntPhpaUlOj7hvKK/LPfpJmWGkRCxemzxVQo5RjK80dY4VNXLzhEdtV06HcLLMaEOjHfvmCBKrdv4s\nBS2kmqmyGYtkjZmp4WI1eScvDIkPJQadIo5G8zSE5+SkrawsUQmlHz8+LrP5mJwCf5E+Tz9v3TaW\nr1YV00Dh2OIIRQjHL8NDL4naWXxISV3KBUqKZ7ga+ARaoTkxLklFTuD4UkDckGeOFLU4y4f415St\ne6FOcUn+LnQc6sqrGOUVcs1BLmm68mWvyFhxGGKvi9XPk6U6nuI/nko9jp/XYqU9ZdVu5GwtaZFT\nHFAtaeFTFNJ1NELFlEIqWkFUkef0BwWCmj7D1KOoKeale3xS1baK/ZjJYEZJqP1r7tHQitj28ohW\nJm3ntkL5jKyByr6aFwcbGMX29KZlPE3jLn3f9+EkmjYeu4x2Fb5OyWQqC1VcZSjPvRZWtTTHHxNG\nykdaXcI0ZX78oTig73v4PNOK9P9Ln6sy6Er/9nOHLHk5O+H/k+aDk0LQynEbx4hPyyPZTgG7LCqc\nCX1C9eo63Sm+Jji5GqZ0AodYs+BSt02mmwV9iDrihWbRx6mKqlUtyATlkYufKg5ksU9Ly4KvXB/Q\nUvUrsBHujINwIJvNVvNeSqvyPNRS7K68mDZIGFflOeo2p6DRE1e9xt56AavchulkyGRXahjMioMz\n1WrGC/JCeMUBpiyLIHDOU9DtZrXgs6QRmnIlg9G3htXJ05kX+uWElnACj6npYrqyCT26ci8FfamM\nExEuyFi5kwP2lJA38wZYJdkNZxwKBxPyBZmn3PMopoo7OLIYTTJySxOiWWl7rD6OSpnvvl+ufWl/\nh+0ZGu0gZ60oJ4BZdLlyVX0C7xBfS5MqS2q3Rx74CufvNY/TjDQc2O/jxDe0PTU7d6skPZnf0rEi\nRqcvcGuk6ncw6RjXkxeupkb5jVFEGirhbbmM/6yYTZ1Bm9Kulpc3ZYOZjm4R7CtO5OkxTl0rRr0S\nRov+8taYYEUrMH3xOZk8I+s+Jw9kDrAzk7B3lVxJep2tHn5DTBMU5Jaxx8aGjPHZJXi+r2bV8Dur\nLJibD3OZy1xuk7tCU7CBR3zPAhsvxGwsi2e1WFrBKMaAjYc4RoEsTBlT093TKA6fV4OJVtMVLZnC\nXRXcnLgqKvykM6bky0kSrElQOAstoToP05ujowhGeJiQjaSNRrFEZOVUXdBEoMrDZ1k5VObjY2Ou\nayHiNA3wtKLOcQLcsiak1OR/mxqsr2nHO/ugtfnR3iGOFhWYdTklR1lGZahx8K3mUf/Kpz3aX5Fo\nSBAsQt6djeI3HFujLNb5pEOgII2jkqEay/UTCmHm5SUqeuIFCyF+WROSvDrlKzLOpXVD2dPU7VD6\nGRlL2hMNKotbOAXVFIoOiotC2h0S+Mr1eSix++k45jkEJTuuXyTYEwflpFKmojD/mw0PT8EdM60j\nCWounib3FBo1QoXpC9wER2sY3JYHykI+9mR8fMfDnaFLM8U3sxyDBt5Yni0oR/R6ooX6Gs6pJiFB\nQ1+TeIqna2/F9Yjul7Go9WXcisfK5EFJx20FM4MWDIugpqepJPh91chm9S79Llkm2ub0uev4ymlq\nii5pUQYxnPg4ivlolaPSX2vj3lT8kaBMPgO3TF+vwXC7zDWFucxlLrfJXaEpFKzl9DTHlMaQqx8h\na+BomrPne6SKTOPkPkYdVLlVe9HZAUWBdqM6RT39J9PnKWrlXBZ62Ex26KyviL2FELM7C9i4DPoS\nDhzkHQqa8efWXJoakhxM5bvNsAJaoBX3fCqK7hOUAtxEbcckgpHuuVU9JUyG+i8x/gKZ4iyY0Zhp\nWfrd3VPUodWUqhKhlKMOjmaoRXtDVtYlVFk2f0ZQkGeKZve1YI05+nnZlX6MXUOiZ0CYOqw/JKdt\nrDHbLB1S6GhqcH35aIzT6DL+qvTPd8v4seZTINpKIQtINGU4H1eJduXE9wo+eaqhWDegp8Q2VOXa\ng84B9bH4M4LDoSZPwyO+R0cddHt7CW5FOR/PyjwWk4DckT6VpzlBpLklNqLXlTbiToTviTPZHSjA\nbnCN2Eq+iFnPoSfaiJfuYxU7Ih0b0LnuT8SHYR2L11jWsTCMBuKM7ofQCCRb0jmuXvDOLfyuzJ85\nU8Ggtn8+5qjiLStiFeErV7q9vDdk9FXRtvrXBrAlIUkzcvHWFTMkXMBZVye04nfkB2MKCmLcmRxS\nKSmHyQAydStYviEf0LeUu2JTsORMvRHFYR/nmkxy+u4+zow+cZBgAlVRw0WsApVgFOE5K5IqwJPt\nWFxFFE7tCvGqIgNTO8IldFwlD4wbpEojPxmmTBXaK3N8SoGmRycnCGpatqw5BMnYkHUE4jzuufQU\nJr6YnMaqumfzhLygACgKyU7uYrSSL88mmH15jkGhTLwrC2iwqeW210KiFfWAj1vkmvQ0LQwpKRxZ\n4JVxZpi6uhFgLUZLp4McCu+UZLDuZ67hKMjMxppLrSv3CzQRqDgZMFFIu2rUISko8Ijv4aKYj5MC\nqSZc2VjnqXJIrolcUZhj0CSr2MdXtOIkC8kVzl0LFRnVEqirI7bUIOhoEs6pMssXpL1CXsCXfYpI\nU5G764Z6LCp6Ukxxtf95kjNRB9s0jrG74mhMV2QDzdNFnFWZ/+KtPnZR2yuGGHUqZqMekdYMpLqB\nFmplAq3gbLtV+joPJglwFSbZiWSenewE2abCqvUD0GgHaRMcJSQeTo/Uf6tkwvlwCvvyPJOSD7c0\nx+L+mFJP1nK8PqHQk77aknzXLhiKSi/Qi1x6Y+1HbrCv2AJe76YwNx/mMpe53CZ3h6YQW+IrMbbn\nsVeRHX6lC2ZJlEpTdjBDjZv3euSxOKv8FTlGXEbEfXWy9KfkixJCKixW8NQM8HwX2xeVMMk0ffrw\nMqNbcsrHdIl9+bkS5tQaorYtbhQYDuWkv6L/Dw/3cG6pSZGYo6KV2kJKrqBX+WBEVpDrM8o7W1o6\nUiOdskukVW+5v0c6Kzq6pTwUTsBwT8Kpw0qBwkT6NB1cp5s8DUA27DCLRc5wEzBQnPELppbKtj6z\na9AoKy9sJ9y/KZpVfSon6dlCHSfV0GvnKm54XNuYkKiJ4YwzjJpKjuJJhGkRW1ZNaBqRaUFUfNAh\nUyKTtFBmrE7h9XWtrixssnFGTsw/9odcV4Cu+xeXubimBWjDhESLtE4syalrk4BEqyGzpE3aE7PL\nWT9BJZT1kC575Ieihc3AUrwkJd6W+e0XIwqq2mfTVZjIPYYHE8ZT+Y4zK4IKINcwqpOPoCPzO4j6\npMp6HmzM8lBuguY0OOe7R5VINiqC8m3a8QACNXU9ze9YNhTeLc/eTBKsanFuvk+woZWR9MhRM1Sd\nrnZ0gNVM2FFnwCTTdfZKLcFwxOk55bXJXbEp5KEhOhdSe7pFpjZrtB1jz4m31dgOFBV0bvQyOu3M\nI+8vr4KmuPrLU3xdmKbWJFUbkcKY6EDVOasq3jSjV5W/61938BZVpfY9DpW4tGS7XFLNr6Yx49GS\nh3lWsPMid0Js1OYsBriKwJy64GpVpqOz4sQjTFlMEadkqTwiURD/ZgFHcSc3NNlmkg/oTOTFLX+1\nSOW0Lv61J+ldkH4cRDGZ+hrsK3JZp5qQYEiJ3intjZ/bEycDEHiG875sBhsnNVffMUymmutRGOBo\nOq9JQ3pqBpQqHdyxmlVlxSX0yjhaepxlHews3dqfoi4YRoc5YVmuHx5Txqf15xi3ZawupbkCnEP4\nvVucUdj5rtuiq1RHTqLp1W6Xof7sJ0WmCnxTHXcprGnOyXAFqwxfk0z74XgYjSgMnBRvqKZiOsWM\npN/77iETrXkpai5LwYlJFqWNYX9CT30ppWFKtqILUfNX3JMNss5sJyhgAjkUhKVFzY6mwdXqUQpK\n3hkXCO8Rf4d7KSTZ0I0pybAFWRfT7iH5tpodq7q+T1/HfkH6sTNJsDP+yFfaCa+sfbjTaM5zmctc\n/v8pd4WmUMThAVOkneaYHTEZIreEk8rPnlkiKavzDBjd1Pp9xfOaZodUxtqV8hZoLsB07zKpYvu5\nsUf3luL+L2rVo1PEy0WNNPU2PjMSkinuJWn7YjrBLypElxYXFa9f5XJLTu6SyTBaV1869xC0lIGY\nCWFTU5or4pRjb0SsrNLxzh6RFtJksUOWyAmc9ZT+bTvnyReljf3TO9x7oNwROzkvPHZJrg/HKF4M\ngZ7KBhgrqQ0W7n1e7tsyMNAyyd4EdmtXATjbFrXVK60TLCjkXbLMSIaI2LuFqzkNZlplqt78gTIf\nO9UVQsUwHI9rJEqv7hsP4yvKdTomVbNqNRJN4d2fG/EvLija9fbkKLS+0RrxlI5362aHA+3L+ZMy\nboVOQFaUe5UrlsKqnLB+32W0I99x+hOsVjbGkTxv//AWYazcnVtlUvU1mzNtUgVq6XZTplPNll2R\nAa06AcmhFsplLUY6f22vS3GWn6EOXO/C0zhjybMx92QwVh6kYhfjn9QpKR3lp2QtcVbnTx+QGcmU\npODivigwbdNnhuTKr/HiC1dZ2BIYtoKC7yzfqnLzs6Kx7vejoypYj5dP+9QeKYivWe6KTcGELv65\nCrVbFVqXFOjj2CHjy6J+lcoG976TAHjtGKPq6rAjC97vpuSKNVg2U7KGQlpPl5i0pX513B7QVS/x\nopoJ1UINWxDF1fcL5BNN/U0criuI6WA6JNfwVi2UBTOsRfT78sJ2XvBY/wF55qTvit0JRPGUaCx2\nuT9jnorGjC4KhuH2c1cZX1GV8oEQ2hLZOLwp/1887HJNcSfPFtaPIjGffH7MH+i4jBKOFsIRzv6r\nFsATCuoysJ2j2od61VISzBomW/LylANL2hW1NrYuZqS2fymmGmr9RFjHKaltPxEuycTuEHdlAx30\nhrjLCv7aXMCxCsvfbEBNwV8Vpuqf3vw8X7isNSoZhPrgF0OXiz2ZV/cgovIeTfbalmcYHUuPXmIz\n6dAYin9oGkV0t+Xv+ocDsoGC8C4qFH2e4U0kpNe7ucPxeyRxasstM/EV3PewT7Qk/V7T6kvPBCRj\n6d/eeEo702S3cU6ioLCFoWwUyTNlnB9Q9rFhSU4wwLhljggdrY/VHTDdl01h9PRT2Cuazv3+Lbq7\nMmcHNy7x5GeFGa1+ZgVXE8NWC7K5ffkxh997Vtob5S9nuue8HHEwvOKXufkwl7nM5Y3IXaEp5BPD\n5IKPiRe5WRPn2vQ5j836ZwHwTn+UwkB2edcpsrCgVZIz2vqsAHuyW+fOLfxYVGLHpsTbopYloxa1\nRYlvLzTFwVNJXSaKb1DydjlU9/zFWy0uadz8vF+ipaQfnbG09/nfvMTeZfn8SdPlo1+XHfzUypfI\nm0Kpno1vUL2pjqS6QsUVyxTf8U4Alm/tMVlWh1mtR+LJSWr2RbOJhxFr4uinVpqy3ZXvfuIzL9E6\nUCox/kMnkuH2A+HgL9RphQNGQTgG8KVlGc/0mpykH6h2CQIZt/pCCIp5GR04pAfyTJNggFvTKsFU\ntJXOjT64clo5BY9V5XmseUvkjqrzUYGuFpX95Zf/PQBffqaPBl/IcojVOfr7/+tFCook3YkNxb/S\nIVRH65pdoTlSp/JBDSdQx9/SMYonBXNiZ/dpfNXYqoHqUCZnotBuGzWf5bMKDDNyiA9VUxjuExbU\nw684Du1Jj85AI0aTGIaqpWGYXJAOGC2o6h2+RO0vRd0v1H8f0xCAFBu4oKRE+BbjayXm2kNyLfo6\ng95VAJrXDslVozE7cGKWW1KJaGsC34WnZA7+5yevc9jWXAduFw1AkQNl9UJPX6OqcFdsCmkh53Ar\nomQ9Ng7kxRubkFaqzEQ8jtPRAfRzklDUq1Dx/8NpQKobBAtFHJ2k0OtSXpaNINwCr6wZfbNMyOqU\nSFMM40sDWgcy4buHUw5dZfdZXKKkSUjNkiz4L04yhgpxVCFlqqGgm8UGKw3lDDxYIGqIOj5WTsHQ\ntPAdSSYK7rmPib6YWdaCtvT14EA3oAdDVlU97fSX+fRXBfb7OTumPwPds6/eAr5B6bTaDMYaPNUL\nM8fhspYiv+9BaW9vYgjKak+vLVDVrFAih6HiX8bV5AjQ1inIRuCGU3L1xdRWqwQFmZu8Gh9Ffuzo\nMrtX5fpv/L6EnLv5+ChMBxmO6rjPpWNcxcIsFUbkqoNfUUU+vAAAAAU8SURBVLLaWnNCrJWalCcM\n1Nd0pjSgeEw2543+SSYaJq5qKHPNFOhomXWwuUx9USstxymDGfrMYpGgoWaKOmsm4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3577... Generator Loss: 0.9579\n", + "Epoch 1/1... Discriminator Loss: 1.4756... Generator Loss: 0.6143\n", + "Epoch 1/1... Discriminator Loss: 1.2961... Generator Loss: 0.8042\n", + "Epoch 1/1... Discriminator Loss: 0.9653... Generator Loss: 1.3168\n", + "Epoch 1/1... Discriminator Loss: 1.4968... Generator Loss: 0.7849\n", + "Epoch 1/1... Discriminator Loss: 1.3341... Generator Loss: 0.8891\n", + "Epoch 1/1... Discriminator Loss: 1.3685... Generator Loss: 0.7253\n", + "Epoch 1/1... Discriminator Loss: 1.3404... Generator Loss: 0.9025\n", + "Epoch 1/1... Discriminator Loss: 1.2645... Generator Loss: 0.8479\n", + "Epoch 1/1... Discriminator Loss: 1.6065... Generator Loss: 0.4065\n" + ] + }, + { + "data": { + "image/png": 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W3YrWSsYZmBg/lIOFPbUsxxuUAyH4QbRBeym+8sBZcfeOHADh0zPijoiUizfl5x+eu7Qc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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.6008... Generator Loss: 0.3544\n", + "Epoch 1/1... Discriminator Loss: 1.2181... Generator Loss: 1.1289\n", + "Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.9682\n", + "Epoch 1/1... Discriminator Loss: 1.3160... Generator Loss: 1.1959\n", + "Epoch 1/1... Discriminator Loss: 1.2201... Generator Loss: 0.7981\n", + "Epoch 1/1... Discriminator Loss: 1.2491... Generator Loss: 1.1460\n", + "Epoch 1/1... Discriminator Loss: 1.2200... Generator Loss: 0.7130\n", + "Epoch 1/1... Discriminator Loss: 1.4682... Generator Loss: 0.8232\n", + "Epoch 1/1... Discriminator Loss: 1.4792... Generator Loss: 0.9793\n", + "Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.6182\n" + ] + }, + { + "data": { + "image/png": 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iktfGMq9OJhZDt3ZwFIL+/mVOY8QtKbMYV9fOLF9yc6hBYcUHVHQ4PVN8wOUS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B1HQhZ3w0Obi6a1E6Dw5QHs9PvwyaHf1iFcvqRWc9IRw2EJydMu4pjsTmdL3CQL0cnxCN+Ysitv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\nWrWbUKjuh08fTxW91+0eiULzd++MOb0pXvR7tcdiUSjrksHkmMY8xVe69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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.2531... Generator Loss: 0.8170\n", + "Epoch 1/1... Discriminator Loss: 1.4623... Generator Loss: 1.2401\n", + "Epoch 1/1... Discriminator Loss: 1.2366... Generator Loss: 0.9501\n", + "Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 1.3609\n", + "Epoch 1/1... Discriminator Loss: 1.4270... Generator Loss: 0.9645\n", + "Epoch 1/1... Discriminator Loss: 1.4111... Generator Loss: 0.8603\n", + "Epoch 1/1... Discriminator Loss: 1.2284... Generator Loss: 0.7865\n", + "Epoch 1/1... Discriminator Loss: 1.4061... Generator Loss: 0.6807\n", + "Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 1.0759\n", + "Epoch 1/1... Discriminator Loss: 1.4578... Generator Loss: 0.7154\n" + ] + }, + { + "data": { + "image/png": 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LCaViM/Bc3KE8o2qLq7i4jc4JR+K0unZDJEr/0qfUhBq/XoE6zzKbU5+KNPJe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WHMYLO04JOxpmURg38QwNvSoScluktZHNmyxrMPZNGfpMWV6VqQFCMwClbAyGyjYOhnNwqwXIJ0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\nAp/Z+iTBRXVS3jwk0k7a3RDacnTCG4eujFVMDOd67jvqIWfeT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cy4vte/Bbxs\nrf25t/3pc8CP6ucfxeUa3lOz1n7WWnveWnsZd53/1Fr7w8CvA//h4zy2jv8AuG2MeU4v/WHg67wP\n144LG77DGNPUPVgd+325dtk3us7PAX9aVYjvAMZvCzPeEzPGfAYXNv6AtfbtjcyfA37IGBMbY67g\nkp1ffC+P/S3ZB53UAP4ILiN7DfiZx3ysfwvnNr6EY6f6so6/gUsAvg78KjB8zOfx7wC/oJ+v4ibC\nG8A/BOLHeNyPA7+t6/9fgcH7de3AfwG8AnwV+DtA/LiuHfi7uNxFgfOQfuwbXScu2ftXNf++gquQ\nvNfHfgOXO1jNub/+tvf/jI79KvD9j3PevdN/a0Tj2ta2tkfsgw4f1ra2tT1htl4U1ra2tT1i60Vh\nbWtb2yO2XhTWtra1PWLrRWFta1vbI7ZeFNa2trU9YutFYW1rW9sjtl4U1ra2tT1i/y+ueZdNY7uB\nSwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3840... Generator Loss: 0.7229\n", + "Epoch 1/1... Discriminator Loss: 1.3608... Generator Loss: 0.7311\n", + "Epoch 1/1... Discriminator Loss: 1.4321... Generator Loss: 0.7518\n", + "Epoch 1/1... Discriminator Loss: 1.2556... Generator Loss: 0.9692\n", + "Epoch 1/1... Discriminator Loss: 1.2830... Generator Loss: 0.7561\n", + "Epoch 1/1... Discriminator Loss: 1.3029... Generator Loss: 0.9397\n", + "Epoch 1/1... Discriminator Loss: 1.5198... Generator Loss: 0.4745\n", + "Epoch 1/1... Discriminator Loss: 1.2650... Generator Loss: 1.0075\n", + "Epoch 1/1... Discriminator Loss: 1.3682... Generator Loss: 0.6288\n", + "Epoch 1/1... Discriminator Loss: 1.3223... Generator Loss: 0.6662\n" + ] + }, + { + "data": { + "image/png": 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6kkia3WCDlZqoi25QEvmaMemGbK6Jw+hV0yQX4Bm/+Ouifk+++8sMEpEYu+ur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\n1UKmmrLyHvUo1H8b9oZENXmOqUqRlW2NXAtVkpN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fe96etcyVAGVQFDT0L41G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\ncAWj5rUXr4CS2KZTS6muSanI1Cf2V7jTlDXe/uw9jnclTnTu2lVKVfZh7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3569... Generator Loss: 0.8252\n", + "Epoch 1/1... Discriminator Loss: 1.3871... Generator Loss: 0.7830\n", + "Epoch 1/1... Discriminator Loss: 1.8022... Generator Loss: 0.2896\n", + "Epoch 1/1... Discriminator Loss: 1.3923... Generator Loss: 0.6645\n", + "Epoch 1/1... Discriminator Loss: 1.4469... Generator Loss: 1.4170\n", + "Epoch 1/1... Discriminator Loss: 1.3721... Generator Loss: 0.7974\n", + "Epoch 1/1... Discriminator Loss: 1.1933... Generator Loss: 1.0238\n", + "Epoch 1/1... Discriminator Loss: 1.2738... Generator Loss: 0.9327\n", + "Epoch 1/1... Discriminator Loss: 1.4144... Generator Loss: 0.9993\n", + "Epoch 1/1... Discriminator Loss: 1.3502... Generator Loss: 0.6667\n" + ] + }, + { + "data": { + "image/png": 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SHo1Esp2kC/7C85J0dKcYMGuK5vGtmUjuncPXuFiXaPObRIrhHbSqawn6oc05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ga94Xl0K/FJg8inWIOrUp3w3KW9rWXdg5hetu5ziHU0zbah7yUuhT7QdZVQlNLn6vQYlHAkaBpS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\ne3b5gZ5UBL7/tSqZdoPNFapUFP5rqVTAFOT1SKHG7OEuQ0drD0YeIyOv5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.4132... Generator Loss: 0.5582\n", + "Epoch 1/1... Discriminator Loss: 1.3437... Generator Loss: 0.7727\n", + "Epoch 1/1... Discriminator Loss: 1.3675... Generator Loss: 0.7321\n", + "Epoch 1/1... Discriminator Loss: 1.3730... Generator Loss: 0.9555\n", + "Epoch 1/1... Discriminator Loss: 1.4001... Generator Loss: 0.6890\n", + "Epoch 1/1... Discriminator Loss: 1.4468... Generator Loss: 0.4802\n", + "Epoch 1/1... Discriminator Loss: 1.4452... Generator Loss: 0.6017\n", + "Epoch 1/1... Discriminator Loss: 1.2895... Generator Loss: 0.9879\n", + "Epoch 1/1... Discriminator Loss: 1.3213... Generator Loss: 0.7545\n", + "Epoch 1/1... Discriminator Loss: 1.4087... Generator Loss: 0.7632\n" + ] + }, + { + "data": { + "image/png": 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kJM9lcweDzefJUCojv79/7zFXtkUl3r62S+N53EWd2hMZ78ssoq/Jg3Y+rYyr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\ncVeZiUpVGsrAHS7ViXuKEdnL6Uy0z0R3QkuDZH0N5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eu793kUvGyqwSyO5qDrJqCrlWWJ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\neN1dVhTCXDQLIuXSLLIRNfWJaq0SVV3/QrEZhGu0zmhPz5WP4bXEmrJFB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.4724... Generator Loss: 0.4925\n", + "Epoch 1/1... Discriminator Loss: 1.3801... Generator Loss: 0.7973\n", + "Epoch 1/1... Discriminator Loss: 1.5132... Generator Loss: 0.9511\n", + "Epoch 1/1... Discriminator Loss: 1.3006... Generator Loss: 0.9248\n", + "Epoch 1/1... Discriminator Loss: 1.4002... Generator Loss: 0.7541\n", + "Epoch 1/1... Discriminator Loss: 1.3821... Generator Loss: 0.6088\n", + "Epoch 1/1... Discriminator Loss: 1.6036... Generator Loss: 1.3762\n", + "Epoch 1/1... Discriminator Loss: 1.2994... Generator Loss: 1.0744\n", + "Epoch 1/1... Discriminator Loss: 1.4678... Generator Loss: 0.6439\n", + "Epoch 1/1... Discriminator Loss: 1.5260... Generator Loss: 0.4740\n" + ] + }, + { + "data": { + "image/png": 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LebmkpxDj6SYz9L1r3FD+/mFV46hj8JvDAa4rGlu6rhm+pElqqlW8+WzGtRbH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5xH6hLNzuSFOJ3MGDxQbc+NOW1d/L7JeNgXc/2N4wFT3Sxc3RSM5Zw/sgI8LeUepc55lWTY9pBe\n8B0dyJrPrCZz6k6OoZRN+NRvU+vKPfJxQF9FXL+mNPNxMMPvacVo5jDVitmahUT5GMtZidV05yLa\n4QUlmfY5qzmkA9nUgzLn9btS2/LwQDr3yrXLNOpi+j853SU1MkZJVeJpjCJNcgrt/0Ff5vrs8QSj\nZdgjd8SxkugEaUnjRA+nVxo8mOr3Hcr93t3bo7Os2TXjkCvV/mg6plzUa9iKZEFU+pTt40rR/yfG\nmLeNMW8ZY37OGBMZY64ZY75qjLlrjPlbqglx0S7aRfvXpH0c1ekt4M8AL1prE2PM3wb+KPCTwH9l\nrf15Y8z/CPxx4K9+r+9zjKHjB4SLXLrn4i7o0bYKPKPmXhwxKfREULWltLRMZnJyD8dntN7VINL1\n6yiilNnMx1cgQlFTDgXrM1UYcJn2qbTq0jMVrZ5Elk0/wV8ELpXDcH+W4GiUOXyUgHIt7g1P6SgP\nZFb0mUzlmoW70ndywkw59ZpXqN2QZ13PCpIFm6+eBo+XdknVato9cFlS+rf9Wc4TtSqGs5KrbTmZ\nNlbFCjixER1fPovaLhOFEqejOSeaRWjGJW5bVbo1K5AXQ0ZqVE1nBb6Kk5hsmaghgKyGaXNFKdOt\n4gqq4YR7q2Iy16xh2JdnLYoDTu6LlTIdpBhVkXIWFAoJWOVIMKXFan1FXvfIS1mWbVOnpRwImYKC\n0vZ3ODbHZsTwUMZr3KgTW7Eabl17gfyajF0Zyzy2/Qaxnk9Or8LXeOIsm1NoFmEWlud1JZVZZIly\nZjUN4BWT81oMUtg9lWvHqh7+rT2HH1ySvg1Sw6nKFcwZE6h4TjIa4WoWzGr5pRe4DBRjYEqHlmpT\nBjWHPQXcOfsz7k3E8hoqFmJ/mnF1Sdyk5dyFRF3FOYSKcQldl9j9aOfyxw00ekBsjPGAGrAP/Aii\nKwkiRf+HPuY9LtpFu2i/g+3jaEnuGmP+c+ARkAD/CPgtYGDtgpKTJ8A/M8phjPkTwJ8A2N7exhhL\n5Bg8rSAz1lCq1JZrAtZeFV82f+Rx96GcQGelpF2iTptQCVNXems4mmZLg4Iz1VishgWOnni56iAW\n/imu4gNsbgibcnxkM4/KkR26zGKKnnyHq5V3v/XOe9QTpeLabLCdaMFQ4jFRfobZcrpgb8NoAK9F\nA1+1H9tRC6u8/2WZ4TTl9G8qK3W938aqpoO/5DA+lmvfO9jjUP3aeZBzSenkesrGVFteYqzWQRD3\nmKi245OzU0xNrJix8Vn1xMqKmprSKzOMwo6LuE5NA6ZZq4av6cnSbVJX9etCsQmms8rqHenb2fgx\nuWpxHPc97g/Frz8sJ8zVIvEcHQt/wpnm7nELaCzQfxmhlTHwLy1xQzUSqplYQreuZoR6Ih7cjnii\naVs/m4JK4RXTwXmgsKXxlXrDYPxFpWVFqfdOozlFqXwXnsHXSktXMQ3TYclYg5mV02OusZ2qmPJw\nV07pkbKCbV9tc6aWUB4YJot5ci1zLaBL5y5hpAJECnfe8Cyhkvw6hcNMx6jbcNjSuEtqAuKaVE/e\nH8j6L8r8HAuSTQMOx2JBPJmMmanOZdAMqJofrSDq47gPS8BPAdcQoO8vAD/xtH//26XojQXPd3E1\nyFRVU+aVLCrXbRIEkqOttzK2rkpV2tkdyYOf5iGrbdl70ukpI80cTN45ZHtZ8ubOrSXsQN0O5c6r\nFQ0aqpRkmiskU/k5jkrCofIgFmOmq/IyTRrSt+Ttk/P6iV57jbOWfG9UW8FX3H7m1KkvS59i5Rk8\nzEYcPBDCi2+evoerUehxOsMovLutVHHlukdLX8Ct7jbHvrJLP/mAsKZ1AM2YW7c+DcD69nPyXcld\n7EhM7rC5hlVjsPJCXGWS9r0lJqqVWFNT3HEsrprMTjRnrNWqh187ZDCQeYi8GjWtK5mqOxPNUoaF\nXBs1PF7a+SwA/apg+kCeO6g7LJfSp3c3tLryiYedaqalcimeyMvWCUNGqse5fetTXL6kNOipRtmz\nM3LNRNTbm2ilCfdGp9xXBuOy8GkrtHx9SeY6OnOJF2pgay1KFemdnQ2oa3BwubtEpPXcRVPl4GdH\nRFMFy8UltiOb1O7wgDiUzXCmr1GWWU41gFmYdZxYNo0yHxMqgChuVIw0uD17JM8/M/Y8QJ2NQxTq\ngHsUEi4rfH08YqBis7nO/0rNZ+2S1k+0G4z2lO05qGh15ZniXg3HXeKjtI/jPvwocN9ae2xF0uf/\nAD4PdNSdALgE7H6Me1y0i3bRfofbx0lJPgK+3xhTQ9yH3wN8A/g14N8Cfp6nlaI3gGOwNsAofZi1\nMd+8I8GlF9Z2yI90/3p+zubhywBMBmIuvnHax6qJ5DW6rKladb1lqZTopOiPcEdyeqxe0iq7KsZq\n8C11C1wN6jQ6DSo9mdN9hyWFUGfK0zAuc+xUduvOaw2WleugZafEihcoZ/3zVF6oFGbxrM32DeX/\nj9LzasD+3oBaR7EAI+nPYTbC2xRaORtFmImYog92c+bKPfDZWy9w81WxEOptuXay63C5p4QsQUyp\negpJOiXuCjzYX90k0nRZqnwFy1F8bq3YtCTqCcR650W4+7aSkwQ++VSu76n7UGZjiKTvvWufZK4n\n4rQc8vo7YmHM03PvgJVKsCcn5SM6a3KP6X6Gs6Zp5JdeJm2IVfEHP3uVUJdodbQoKusSp5KvL9Zb\nrDRkjI/TiDRUy6PVZkUDvkuV/F3tUgPTX0Q5K9yFpbPVxKvLPYIA8kqtJXWAqzCmUKm4erBJqVbf\nZB7hKeWZp9WunlOeE+tcWzKMM7FSHxzmNGtKAGMNoWILNm+JC8coIkTW73g8ZUtJhEI/xlXZuLZz\nhfLogVwzU6ZxLI7KFHa9GKPu5tHejLUVZSmfeYwV9v607ePEFL5qjPk7wDeBAvgW4g78EvDzxpi/\nqJ/9z9/72wzGGjxjWGSIM2P52b/2/wDgrOxy45GYXJ2lOc+/IoMdXpLB7Z4MsSfi9613e3i+kq+U\nGf0jZReeTmBZF30iJlfmzUhVCMS6UyKjbkLuUAzk5Zwwx5+ratCGvNzbXo9JTQEtxuBdkYmxRxlz\nzUQEWU6mtQ0dZdiJy5xEaweaNUMxkZemUS/wlfFn6osp7hchlRWz9WhsePOxRPh9U7C0LAvs+o0r\npOJeEkUK/rElNY1VTAqP0Flgfx1yra4c2TPqCk121A9NWy6eJrfL0qWRaT+6DbaviC+bjwakdRln\nM1YQz2qNENmEHX/EnUPp0G9+8w7zqc6lZxh48tIfK/x2Vs5BMQ8EKaXGCT6zsk7ck81rNYoZ6Nj1\n78gmNM0OiYYLfciKtr6YW2s9yomM/WxWkioRzZFuUu1HKyjim6XAB42lxM01Si2dtkZqHQBmVjMc\ntiI7ljGcrc5JFG8Rt1zWVNDVKBamTsWkko1pHC3R0lRLtxN+RyvUNzR0x1lf1ThQo4UdK9y8HVLX\nw8mPfUp9HwJnRpktSH70frUl4prc435/yBN186p5ztlUXWU7pT7+aDDnjytF/xeAv/DbPr4HfPbj\nfO9Fu2gX7V9dM9ba733Vv+RWb9btC6++SHNli2uenDTjAYQ1sQjaQUSzK9v8xuVXuXZVzM4ral7H\nrRBX97exN2GiIeC3dvfgUEy0o6slPUdOoK/cE8j06bfPOLYiKFMrSg4eS/hj0j+iqMTC6F15geVV\nodi6qUfN//Q//CUWCZYqm5/zB2ZVn2KkiDcqCkd26F//xn8NwNe/fEI9FYtn2B/T0ADlB8Uar8y/\nBcCvDuXEf3F6yM+lYmr/qU/f4xdjsVZW/+6M22q5dF0Ho6b2ViSn7qPBGr21BzIW15f54l9/V/qJ\nT3dLrJvBIVxe8P25Mm7GN1RKYbZSj7n06VcB+JEtj+c+LUHe2imEPQlaZROxMHrdNVIt1UsNjIYS\nSF3ffpXtnasAtNo7+CpQU82VE6A6xq+JKAo1S+jKyfyf/rk/w7FSvT24c0JDcQEjtfLKwzmDx2IF\nTKo5hWaSmrU2jkbqsQWZKleHik0osGSaRfHDgPV1se6eu7bD2qpYQmFVYiZy0lczhYFXDuNCTuBk\nlvP2+/J8aVqy84XfA4CrIbTa+ITDQwn2JcmUQaKW2ThhrpWyjTYcV/JMHa18PUzBmytTuOPRrMva\nmvqGZdUDGdVjfu9rck3vp0SN/flvlNxT8ppvfP0RTzTrtNyN+ZM//YcBuP4TP8aNtljGN25+7res\ntZ/he7RnAubsFBW1fsIkeURfcfsNE3HrOVnw661lttT/7PQ22b4kUX2/rew5jRWsAj46iU+iUdqN\nlVcxal5macZwpJ8vSUyif2PCbl82ikma0t99CYD7j+/w8PVfAWBw531ClWWf3ZLKO/ISo/fz/ZgF\n02hQ+lhNPZXZGaWCcPa/KS/me1+Z88or0vfW6hr+rmLjk9s8MvLivf34Tflsv8WTA/Gd/7Ov+5xp\nFV3vkkN5qvDuT7bY0mj4SiT/fnn8RSbCLM6DLx9xrrdCztmj74z5E2WqqmmVaBS5lJqRWGsE7Ggl\n3g/+5B9kBTXnfyQCJRmJNmXDprRYLVUffPAOjoq/ZnlIV0vNo1qAG2lMYFGjMW8xmsombFllpq7W\n2f4xRw/lxWws13AyJUBRt+Rob8BcXzbHCVnsA1le0VA/OsZgPGXJUrYiz5bMUTr/KmWu7uHp6T6e\nVphebRoKBbNZdVuaeOi7xt4p1DU4UkYVhcLXzwZyyLRcD18Jbb21nCCXOWkcJ9yZi/s3PsxIFf4+\nKGVd5en0PONgKVBPAovleNGfecIXvy339r8sB9boP7pE92taIn41gbfkHoPTEX/3//4KAH9wc52V\nTy3YqZ6uXVRJXrSLdtE+1J4JS6EEBqVlqVFgFIL84pV11nw5jbrbDW5ty0na6a7iK9ttEWqIOCvI\nUjHbbeUzV1O03aoTaJVkUm8yV/q2LQ32baUx63MJFh3OS3yNhDT/SczMlYBa8eY9ihPZ2RMlfcGD\nXHkTwrbBKD1cmfTxFrDc/Iy9f/zfAvArvySMvOHWGi+uiStSzyZ8U1mCP//8TZ48kef+cS1w+odP\ndvnkjjz/vaOSz9xUkErUptET07YYxHR7coImyidx6/pNbv9TMQk2t2cMb2uXAyg0P+4Z6Dny3F5D\nTvkb7S4v3pLnW33td/FDL4tr9upzr+Aq8YjXbFAqPX7UbJxPXlVobv/yNkFdOSTmMD19ID83dmhp\nwNNRs97ahAKZRzsuWN5QJXE/pL/Q5iQjUCq0vScC8Z3NxjjqijRCQ+oJUsExlrZmIjZjl7lmiuJI\nrBwb5SxpgO5kPOdI+Q2GR0OCueAbZp7PzmVxJawKvRwwBhX7KetT6r5iQPIJh3vKWRCJG7ifuVwK\n1bKZLtEPNYibp7Qz6U/fFPQ6yvI9kc/CKiVTDIljhLEUwHPAURe0Fjm0AhnzH/+jKifIF0ieF8ty\n9XiVo6ZYWPuHCW+pG3Pti+/zE9d/gI/SnolNocKSmJSu3WBdK8jKmqGrfIctv0FdiUnxC3JHTT8t\nafWtYXomf1dzhpSBkpr4CZOx+vD+iMdaRhxqlD2Z1zhTKve402ND2Xjuhz2u92TBntYqJloafJaI\neWaswVPWHEqD1SizmfcpPHkhj999mz/7X/wyAN8+kZf4h2tdhmOZrOPDBE9jFPMqZasmL/dXHfm3\nMjm7SuW9UkuZqNm9RZORxgF2uiWzPXmp500xKY/6I/K5+MAj7zuGYFEYGgulp7rlSq7ErD1daFe6\nbLwslZYvX41YXdMK1SiHSPUvvRTf1cpNs2C0MlSqJOvNTsld+d7779/hW7vi/nzhp7ZoqJiuszDh\n7WPmx/8IgN3RMm7j9wHwnNegX5P5LWY1HhxobYP6+p4PDUWWxnEHV4lubZGe16b4S1tsb8oYdZdk\nU1hurODN5fmTKOXuXYkl7T8ZMrGy6XuOw7GKsXYUmRlPuzStzNmgyOmrlkPbyXgw09JpXXtNt2Ci\nRLiGIaNjJWeZppyNdPM2FYUqn7m+bLY2KzELDLALtcW0xdBWly7twCVNW37jnj6gVPMAACAASURB\nVBwyz3tfYqogqytFm8eaBdplzORENtHf+I0v8vt/8iU+SrtwHy7aRbtoH2rPhKVgrMUrKqIgpHvl\nJgBXt7v0VmSX762G5FpfkM+OGc3E9D3TGof93BCqeenUQrbWFfBiA2al7OZ3jnPGCiDxdDefTFNc\nX07KtZphVWXSX7jeo3KlH9ODjAcaGCpmWidR5ZQLlWRvjFmYz8WUKhGTcFA9IlVQy04o9/jR13Z4\n7Qe+D4Dy0SPqChk2TkG0JCfCi18Tc/BTvTu8rbTnyckIjZvh11b49LaYuOu2YFd1Fw/lIGUDj6lC\ne8enE9RTInIDLmlgtmUyri7JGHzfD0hg8IXaOtdfkM9W1jfxU60B8DNINbDnN6Fa4DN08ooJqHQ6\n7hljDQJ+49d+lV/8denUrvuz/Nn/UCLmdUfxEeF1vOoL8hX1b9E/k7lsLcX0tuTeT3ZnFPNF1E3G\nsuW12VwTNyf2G7ha4ZgEGddekCD1rd4yz21qQLcjY7W6FpP2FQsQZby4KoHro+kBr78pmZ/5IKeh\n9SEd5dg7HJ/y8EjVpaM6va5aOv2S+a5YFQt8RxjXSNWCqhyHclFTYUpq6lbU3ZhuLPO+ti6DeHY0\nPJeLj52Qz3RkvbxwMyaq5Fl/y5kw3VXsTCD3OHh4QPCcUtK/+BxX57JOHw1OGSTyrKPQ8k/fV7rx\np2wXlsJFu2gX7UPt2bAUDAR+xdKKyzXVjFx3oKGCJH5S4Gjly2Q25dGepPjeOtVips4VokxTSGlK\noRDetbWQ+QIVOPVJPDl5l+ey24+nM8bqGte8iq1Q7t3AYamQn6/dcKgfy895T4JvJcW5FoBn6sLq\nBGQ2ptB4R/qk4oVPiS7AH3jxFgCv/djPEDXEP686dQKt9SezEMrPL/5u+f2lT1+j/bb4jo/vl+R6\ncm30U9Z3ngfA7SzjvyWpp3kuz+y/+FnSdcn/H37lNzkeK4YgKhhpinStV+NFLe7qaS5sfb1BOJRT\nx9sK8BtqEVQuRk82XAdrxQ9mqvwW8ykoZJjCY5bIta7Z4XEm/Z//na/y737y1+S5PnldH3kF/4pS\nt32wQtZQNOVmC+dITveoukeeq06GBoyvvHyNFxSu3R9bgoYWORFxU9OFm0t11hbaCa2F9oRHpFD4\nWm+FtYach5ecJr2eLK7TR/s8nIl1k/nSh+HtESfKmhSYio7Syrl+C6P4jFwLo9wowK/EpDtLEjpK\n7Va4c5ZD6c/SVsjOZbHO1uqaDgacSObP8R0+vSpjtH3jJnFP1t7V23e594GMxYMziXF8a3qb4ycS\nU5rEA4JCg6D1iJpaLN4wY/COztlTtmdjU7AGLw+J4xpXXpXc7mpU0Vgo6LQsjir6VFXB/TtK7bUt\ni6N1GuJ3NX8+9UG1CG0yRPcBtsKY41WZhPqCB/J4Qv+xmFy36zU6GqhK/AmXr6kWobNJpIy6pimf\n5UVKrnZ56FbnmHPfpKAS7+2VTf7NbdkULu+I+EdcM0SRbgReF0fxDzgORokwfCXbaAaX+cMrkg45\nuLXL0R0JYFbXK9avyqKpH1v+6gdqzm7Js/3Mj77A46PXAPiHpwfcfiR/t9QKeXlHNrXNKuAzn5Dg\nU/e6PPNm+wrxjmxIfi3EbSzp3DiCSgKsU4COHbkm1jOHUmHVbm2NNvKiPB86xKmMywdHR3zpr/xN\nAD77Uz8KQLCREr0idRvD3g1uxQt1o9Y5V+SEikhrM9A5eGXnKtuXxU0Y3L7DMFfMRjeiqW5Fzw8x\niRKjhMrHGXWIFWTm4RGvLupflmhqsPJ+UMJASVIe6+bQDTnsyxjvun2mc/mONRMShrL+FlTuve0d\nqkeCvVgipbctY3ilFbBk5Dkuda/z/PeLK7W1JeMdJhVBQ37vL18m0M3N9bqgdRlfePH38nkt0R68\nL3P6F//Kfwe59LPMZ+cZjHpZkCq+wa91uPmSjNfTtgv34aJdtIv2ofZMWAqu57G0usKNtcu0fBXp\ncEMmSnpSN0PmCg19dNJnb6xpIz3ZO2uGfKJkprYi+UDJWdZG9EulUGsVeLuKTFSJtndPhrz7SNJm\nq+Uqbixpqm0PwmU5dcxsyIqi7dxYLBfmFm9BQJr7oErSvrcKiezi9cqhdVV26LCppvF4iGWRj3Yx\nSiFnoi4oXFdrYYTwY0VOrSv1NVqashwMBywtaS68FtBzJVeeaRDt+q0VOvp34eeu8Atf0jGuHGoD\nLSrbWKVSKbGmypfTrs41D4pyipfrebHZxCpfgh0XOMqgvUBxWkZU6SI9O8A71V/XHeJ1TUOeJfzv\nj78NwNlbggnYaH6ez44FIZr3/y/M5r8t4xavsxyKXOCxzfGVVXppWQKA6ytdQjXns9CjoVW1m70W\n1zckeBgmJ7hGxmtBqhu4GShxjmcLHOVkcL0Io2zba42ASvUotxYM3qMG7x+JVXE4HJIUMs5BJ6Ct\n0nO9ulga61HA6YoqRj+ZcEWh6d9/ZZUVDXxeXquxfF0syIb219gM4ynuo97AWSwCN2QB2XRdB7Rv\ngZLL/hv/zu/nG3dkXOu7I15/7y0AZpWlo5RvvZphY/WjBRqfiU0h9F1urDa5ueTiKVFg5cwhl4eZ\nlXUmbc1d3xvjPhQfqa4vm9lPqPmyGqdHM5KG+GfvHzbP4xLz+bsMtsScGx/K4M6yErsrENX+bML7\nSjhSW2ny0pIs9KnX4rgnC88pVJwlcJkp40+Mj6OT5UQzqhPppxOPQDkmkxOth8ifEASyUThpiKvZ\nADM8xt1Q2PBE/fN4jFHmqaDZpFWXDak82qUaarT/8jVeWZHF9sbpBwAMPzikqxDs7tIOdTW/P/n5\nda6o79/JcwrNGBzs6QbTPMGbqSDuZsS8Ly+VV29j5sp56fo4mT6fAoXKwwHpUK6dJ3tMtWag17vK\nz+zIJvvXnDlv3pd7H+3LPP1J+2V+VqsMu0c/z+3L8oLcvHaJr38g4+bOYaOnPJarsshXah7BQDak\nuhsz8RRY5VuabWWtqq1QKm/iXGMScyI8VaEKW02M+v5hs4ZB5qQzOsXJZM0dBfL317Y2ODyV5zsc\nnZBqbGMYVNQUfBWonm8nrEEim/RSraKpmJuaW7DeUxGdyzFRR+MSTfl740bnsgS4HmgFK6XDedqp\ncsGXuXSNrJXv+8Qmj5/8pvS3C20VFu6Ohgz1IOv2fLyebEJP2y7ch4t20S7ah9ozYSn4nsPGSp3Q\nt1RqzrrlGZnmyifTCff21D14vEujKbvnkhatZGZIpgUu9dqcYqymfTbi8VwsgZEXcvaGIhNVeOTk\ncJ9MzcFJv0HZlj2y0dphe6ZVeUUfTzkDjVHqMtcQqqyYLWNssDCfffDl2vFxzhnyeT1WOq/aClmi\nJmCQnVsYNsixIzmhjKfigEWA21SILiW+/tzo3cRbuyq3M7DywwJhvXxbZdzyQwi29echvo7R2d2U\nRvuS9jNhuix9Wk0kyj5PHCqFIldBDA0d23SOo5V6btvHxPJzlWvVo1fg1hX/MalTqGUynKXsfEHc\ng09XMf/gkVgNH1yWfv7c1wLeeP+vAfD8Spdf+JuSnfgv/9J/QD2UbMC816KnQczLW2JVXI4vcTQQ\nF63Zcqkp7mElXCHSzFWjHpIvuBrGMo/pLCPPlENhOCHoaDorMbiK/KzFDWxduSMyFXhpOVzeUJn4\nD445mYllMogiKg0C2kxdqTKh1HXR7DW5uiTf1b1+md6KjEW8sYmnWSyjQWnCEFSBG+vIfyB4dEUx\n4lSgFc1Wrd9aVOOzz0s1673X3yJ4WQKYR2bGB/tCUHTmpIyU9u1p2zOxKVRlxWyYMJ6cYZWINLWG\n+UzVhOKAZiw+/vIty9LqNQCMctXVRnMOx1r2PJwSeZKyqVyHZbPQoHRIQnlJZ1pl6E6OGfUVFNV4\nTDmRRVXUcxbkf45bsawVfpND2Xiscc7JUaNGgVFp8GI+YnYg93776BFn98VEf3hffr+1Zbl+WXH7\nzSZRpYCr1EPnGaOsOvn6CXWNyDtmiUIp5bPWBlY1A70ipteRhX5zTSDK5cxn/7ZsrMeze/QUsPXJ\nq212Z2Kudp0j7p8ofHZbbvxJf4tTjeSboxPiDRnjpTDGxMobWSbnfq0JVKXJ71BaidGUG1sU9yRm\ncDRKOErFXTua3WV1Efk/kr6/OX6Cd0MW/ze/OeQP/yEpSa41NsjqMvjGndNry88bPcm41Pw2uaYQ\ni4MjDpWWPymPuXRDrqmCLnNX+tRXvcZsmFFTTsjJ2YTVHd1MGqs4GptymxGhpg47jScAtOojJrGk\nlFf27/HoWwqJniakClOfJuIe3nMceroJu7QoNGMS+Ts4ms2hcQlCTfGGWr1YWFB9TKoCFjTylcUm\nskYq16U80hTo6T0Avvw3fpm9UPpZFG1+eEPekep9h7sqs3V8N2UwlGuetl24Dxftol20D7VnwlJw\nDbRdCE8cbCQnfnNpFbsoovFi6Golom3hKQlFzReTOqvG+HU5MVbx6czk2rE7J9coul+fc1lFXTwN\nAD06S3CUcMAfZLg9MdWOjwN2B3LiX84TpotRUjizY11qC4GNwsOqQrXJJ5wd/VO59+4+J6obuZBf\nv+tYJgqg2UmfI5otTpUPCDwJdh2cPQDg6DcPcT/xdRkLu8VIzeHRm8c0t+WUv765RaTcjVZBPMOB\ny8ngHQBmJwOSSPp8egbPueJKzfyEyUBp2dUyu/vkHoFyB57OA7ZSOV0+ceMGcayU8amlGkhRmKeR\ndRPNQV2GxEx4cCz3+/X33+D+kYqhHFlMrEFOhR3cJ6E4UDdvbvnKN74BQI2AVzfE7E5aL7LckTnr\nrUig7rFzj7fuSlD18d17HGkgsb06odkVS+d33/w8Z2OljTsTS8F6TQ6nEjA83T/k26dimbw4ecya\nBuJavodnZJyDStZWXowIh/Jd+eGctFpU5uZ4K+puJnJCN6f7+Lpmq+KUvYG4aCe/9GW8S5IG2oqu\n8dqPXgXgxmvfD4DjdDCqmWncMcZR3o5iQn4kllcS3eHsTQlM3z+WNfa37r3PYSqWS9cG3FC4dfz8\nGsFjmb9pmvLmewKdf9r2TGwKjgtR1+KXYyrVBnQyh0ZLVtCdd/Z4/4PfAOD+6SndQCLu139QQEHP\nNw2BSpY7tRKbqhqRa87LZdtJxaECOmLVN1jyfXBlIRUbIemRIhPLQwYP1PdvNenFSoSoIKXSVkyG\nYoqGniVQ8syinDO6r65JmHCscufDfXkBgwPLm5qyO9455HImvuVSFNPclqj93v4DAP7J63eZvCdR\n5s1rhxzvK8cfPtcrcQNqdkxDo9ZBW9yE0cMRJ6eyoT06GXBdy8W7rqVQ/7tXFUwi+buZMve8ftQk\n+ECeaenlF3l/LPPQnD5h1dMXr9nEy8ScjVWX0RAxuCt/9843H/OGkXs/fDDGtqT/zVdnvHYoY3Q8\nV7PcnfHrp1onYCvKoYJt2jHjUrkrVy0/9NnPyfWx9H3vwR7Hx+IvH54e8+RM+jY9zXh45SoAz3Uf\nsBzIvctYNpjaTo+ZCuwefaXi9relz2/c/zor+VcBuPnSJ3lZS9d7z8sLPxt5HGqFY5/5eSlzWmVE\nM6Vz1+rTZlyjcqTvnSKmOJJrt28FzJEX2t1Y5+F7shF316QPQf42cSgbsksdd0sJaMs+udZPVIPv\nZ+WHpG/JG7L+f+wnv8RXXpeUejSa4WgK/NKbE2YKOHMKSzn9nVWIumgX7aL9/6w9E5aC6xg6oYdT\nVTRmYrY5SyWh1jPc7LoELaGWqw32sFM5Hd5Uk3O84rKzqsEgp89Y5cCPzzIWOOfcK8mVKsuo+EdU\ntVhfklPHlG2OawoZNRkHB2LOxVlITTFLdaVsd12HSGv6yyKnUunwwiYcIn3bu11yoFwHY2ULTpMx\nh7cXHIAttp5X1t7mDVxPvntZTfhPPXeNA82rry9dZ0mVjjqbJQ3lL0isR6l8hjXNYRerKdVUTuUr\nWzW+clfGYl4aCDS/H3WJZ/Ldp2dy8h86T9jZEuhzbdXjdF/+7mAwJ1ZwD7PH+ModEW1ckbHwPSpf\nKce3AhgKrj+4WuPVVTnlWvPP86QjFsSdIwEm3d+FRPH5rjUcqYXluSW3Lkn/L5sf46XXxBqcz8St\nPLt/wHUlUDGNNajJ2B/1p/z9/1Ny9vaHZvz0j4hIziUFCEVbO+SRfG/6qQ6Ho28CMEpT5qom9UGy\ny0ZH53UoVt40OWakJDuttEVbRSjjwMHMVUo+lwDgJLdEymBdXprx6ZqM0Y3P/Ai9ZQ3WXvLIT3W8\nCs0+zGNMXawR4wYYT7MaQKWkLtPikNG3xMr64qMFdf4+v+v67wLgky9ZHBVBYg4335P18K1DSJY+\nmkLUhaVw0S7aRftQ+56WgjHmfwH+AHBkrX1ZP+sCfwu4CjwAftpa2zfGGOC/QZSnZ8C/Z6395ve6\nh4NL7DbxmjUaV2X3rIVdZofK/NtbwntHIbrzmG9r9eB+XXboh+9WlFqgY5sxYy2eqrV85hJn4kl/\nwrGCxgKVDCvnOanK1Hn1ikZHTox5knHkiI/fsh6eBuUWcnPWllQqD+b5ISbXarhJnUir2t45G4Ky\nELUjSaH5UUhTWaGMB6UrVkHruR2W9bQtHkrfDzyLo+nEX/nKV9H4FxvvNKlty/2uBjHrjtxj8Lqy\nSl33qan4TOiEeJWcGLljGWtMoU7C2qr4pRM95R698QbvvP8AgMN/MD+vHPxjn/4cGxoc7d/eZfmz\ncnKvdcTaKg6nJGMZ2Ec24It/X4KjB6dD3lP9DaK77ChMvVSqtXGSES2r3z6wrC8pq7TjcjSQMfr8\nJ1fpronl0X9fc+1lzlx+zc16k4ZVRKc/ZXwi1oSp2viKQ/C2xMzznIp0QTB7eMxIOQnuP3yMUasp\neAeOX5Y+v3BHrIr66ID3x7KIhvWMhjJemyRjcCKfNxTxeuz5XFNWbTsqGayLdfTgq3+P37gnP//q\n4xO6a0It+KOvCsbkC6+G+ArHdq63QLEz9AuKhxJo3J/X+Ou//osAJN+WIOmm3yB8QYLHy/XfRy2S\nAGyVvU+hhLWD0Rnu6Jy996na07gP/yvw3wN/47s++/PAP7bW/mVjzJ/X//9zwO8Dbup/n0Mk6D/3\nPTvhOSx1a2ykEbGW97rVjCwQM+ngcMb9D8R0OshL3osfyN9F8lKt5nAy0Amqb5ENBB8QtNvMGxpw\nObY0VP1npMIrZfqEU60yM8cVM8WZh3nBLJNNpjOtyFUNaVOhvRQQ1uRlM46DdRRqG/usKIPxp25d\n5u1HYnZGNRWTaXS4sqzS8LOK2NdKy/kEo7qRSgeIs7KOO5BFvNmKKRQgtBn3aBm5aGclxnfEzH9U\nifmZVTVwZfMKN2MchcnGucErNaga1fB6svCutiUQ9+3mgIZRsNRSxVJb7hHWRhwsGKoDl+bxG3KN\nRtxLPyXSF/dzOz/I/L5UPj6cfomxAo/e+K3XeTcTN+1EXb95aXEm1fl4nmr03XcCXruikObOOsqB\nQ9zRuoXGGb2WfDg59cg7Cm7qtsla8nJvXunSrsnO4asCGH5wHrRLyjqOksT4USFjBiQND6vQ8npd\ny+QLg6dKZCadM9f1WbdzxoH2WQOOS/78PNPkzh0KXW/TNMFbkbX6cn2V6LqM840XNYjoz7HKxm2T\nwXdgznFJ2NCaj1rIn/7s7wcgu6HuzM1tQg2et1otykTel3BpDtuyJssHGcmWrJGnbd/TfbDWfgn4\n7ZCon0Jk5uHDcvM/BfwNK+0riK7kxkfq0UW7aBftX2n7Fw00rllr9/XnA87xf2wBj7/ruoUU/T6/\nrX23FP1at0mj1aDjtAlXtMiJiKWuph7vlVz9Q7KTDh8f85mxmr6xpJ2q6S7oDj88SJkoRdeqrbAq\nf+ZGhjU9mG5dkd9nyQZ3xDpj3+2TDfTENzGeVgEm+QwnFathTwN8BeNzCXCn5uIomWkYrbK6Kub1\ny6+NqdT8rzQl5NmE9ZbW0HdarHbktPWKAhSxWW9LjvqGNyCYq0TZqkOjI1O1vLpJtyk/r3Y3OZW4\nHQ0d9q+/43Ll0gMABuNLpBp8qq+5RL6akVVB25M+by7LKf8f/8wmZ6fynLMgo6anf82mdLQfjw+m\neJmyVWuVYZWcnQfMNvwpf+SPiDWV7P84wxPJj/9SUONrX5br+5oObnoViR7XZVBRTLUwC8i0WMmP\nczxlMK4pscpz2z/OUfGrAOwe3ub0LcVvAC+syzO9cHWJQK1I4y7IYjI8xCK4tN0mdwSx2ew2sBMl\nUE0DtpRte+cFWW8n9xzaJwv1c0OumIVjmzDVdG7Tk3WYrtQItWgubMLOkszptU9cwuracbpNQqV8\nqy/pWgjGGBTrUq5hNSBsspBA9TVWRrDyqr6uc5kD4/lYR9d33cOcSN9rk1/h6E11t1sVneuLc/np\nLIaPnX2w1lpjzEeWmfpuKfoXttdtWLl4tYB8oqWi9VNcR0zb3kqbQiGcYa/L8qEM4EQhzKn9BFMF\nq4TeNyn2ZCFUWYBdSK67Y5pacrqmlYVOFbEUSte/9r7BUb3PRuUyUbbjWWExWr5r2zJcnhPitBbK\nSjFY5eKLDdGaAGEu9T9BWcqLGigDTzSc4amobBW7xIp8NV5M5SwYhhQ73+pyTV+EzmB+LnqyVncx\nCqV1jEfRVtWnB3JtsP0ErES93/mVCZUag4kf0lTwlu9ZQgVcbW7oAn3hExirwB2vAYpHyMNl8qG8\nQEXys0wSrYicKRHIPKS3pSw/lzfAk3s762CWJWNUj7bZe+t/A+BbqrZUnXhk04VQKlSFfkcYsbGt\nm2XUBK34dBuysbZvXuKVyVV55isjWJJ5d+MW3/ecbMhXt66ea2saLZc2WIKujNul2jItxVAsHY44\nTTR+FHjEjoxBXUlYqvlVrinV8u35IfvDmX4+w1P274GKEa+0OiyX8ncv7sBznxLoebe9Q6Dupttd\nw2gGg4YKCM9H2Lmem2UfcnnpcTzcuuIX6iVWwUlWgWxOPcBmKh47GpOOBG9xUoD3ivbzTsijL/3O\nZB8OF26B/nukn+8Cl7/rugsp+ot20f41a/+ilsLfQ2Tm/zIflpv/e8CfNsb8PBJgHH6Xm/HPbVVV\nMpkNGTjQmGjkPPVw2rITO02IpmL6hdUAu6QIskCwCaPijEipqkx9g+mynEatakbdqvDGKTzUU6Cr\nCLX1oMGwEPNg5hxQV4ETJ5vi6imWhAFFsthpZbfP8hmO0qYZL8CocIhxPdATr5wZ1nM9pWpK0XZt\nh0r5HAfzJ3ienAhuZ4vqUPpsjcJkZ3PaXZXQizOsBuJC36dUcpZympGfyXidTeV7X975JA8fKu/i\n1pRKMzhFlZFoFLPKpsyPVC/hU1pUZvNzcg9/2ccmV+XzqGCiit7VbkLnNcn/d67IuJWHDZJ7MlZx\n94zahgQanatNKj1ho3qJW5c+dZXo5mE1Pq9OLFKLr2zHGId6XWnO4hCDclUYrcSMY5rrYo38wMkZ\nX/e0enLrFq99WuQAGzWDuyjcUhOryhK8uSJLg5JGJf25fmOd6zonjhugbHJkcyWcmU4ptIApW9on\neVNO5jKDrCnj6fqLwqf6OUI2eTyjVOq6YK2Hp9kOp9MEIxawUW6GKi+olKbPlpcxqi1h/AirEoi4\nBqOs4I63KMxzMbkyl1c55lvi5v36L7/F/p5WAh9n/MirYvX+E56uPU1K8ueAHwaWjTFPEJXpvwz8\nbWPMHwceAj+tl/99JB15F0lJ/vtP0wlrDXkakqQpXlNfjqyLrzrrpioIawtyjzpWJ9QMxMStZyOm\nU1m4vuuwvSaDGjlL51x7jc05lzTTE6qU+bg5x2j565Wax1BFQGdzj6mmOMv+lEIXd95R7scUvJrC\nfK0BNSMd08H4UndQ9Z+Q12QzKZXwwosM5ZH4dY+fHFO7JvdbbV2iHssiHCle3lYJjkbv/dzBLIhc\nbE46EOMrK6Nz0Y+TuTxncmAZpfJ8Xp6cvxRx2MZR0M98mnKsRK/pqVxbdfYIlfbcrSz4svir0mM0\nEUPw24d9fsLVBTlVDkf3FKsAsOzUp7YkrgbNOdWib+/+Jg9OZC6Hqbx1mYUy1SCPhUIp810c6pEu\nftcDR8dWYyBB7ZBY4wRRu8XyWO6xvBERBrpGxhWVzolKZeIVUFi51k19YqXzrxUORjkaC2spci3d\n16h+nz1Gu/J8Bw8PONUIf91C3JJ1VtMYiK3n5FrZunc0YDLRMZweYmI1oCsw+lKj6WKckkrrddzW\nHFytojQWs8hEFCUo2xfqzjIrsJlk5crHpxw/kVKAf/jBjMeJcneW8M7oo2UfvuemYK39Y/+cX/2e\nf8a1FvhTH6kHF+2iXbRnqj0TMGeckqo+oirqVIGciOkgoVIQkm8nOMpEbMdjKiWkWEiL58UZ5VhO\nvvm4oqZsuHEnY7QQ6hjluCoRV3bku1ZbNWaHCkhKxmR6CmZ41CMZmv40P4fjovHUwiQ4WuHoxxVG\n5c5N7FGcqIx4dMLkTHb/tWVxE2YP4TjR4qG79ygeyen5wjWHdktOBzuR4OTx7oyNDTmVakFE3NBg\nZc0nG2ql3nqG05Z7rGZq4o4MVzQbMHUsfT0lTscT2sqZOM8L6oG6Lo5Gvf0u5Ym6SaYQ+xjIa0dM\n3hPrp9ifkb8nY5B9QiwMjgxFqs/3+ps09Dnc+RrDN4WK/5dvPxYCGqChvAp9k1NphapFLDwA61gW\nhYhB4AjKCzCLzIlpguIR0nzMhi8Wiz0dUW2LK5HnGa6yJ5dKuOP1mriZmPM2DM5FZtxoTKHzVPmG\naixrxF1XjgWW2B3IcxwePKEeytrqug5HU7nWaGboNK1YidWSImHU1wpN8xJ2pu5tWFKNdZw7CnSa\nT3AW1Gz5MsRqCZR8hwtztAdKg8+x6nW6huJMrJgqcnjnrgzcILcUC68jsJx8l9r407RnY1OoDO7M\nx80MhZqOZf0UV5l0rB+QK7+gE9bJRzIQeaIug9dlZUNrBzYyqkArABMXU7UvzwAAIABJREFUt676\nj70jVhJZFO2WLJR20D6vS3B9BxKNE6QVxpfFnZdzlK6Pelt+77s+rqsTa53zicMN8DXddLY7A32R\nKy1PTo72ePu2vOjvH8zpz4Qs453dXfxKnnVJ4yhXmg1KVU1qtlqsLGImRUgxU1/8xBIrAUq7IQu0\nXybkCoQyjTojTaeemZj1hcBsEdMv5cWa6KY5H59g1UQ3h48xrox3ehoyWNCaX+vQX1Y34NuCnvOG\nKU5D3Jn1m69QRbpwJ/sUo7cBiI5rGNXfSJU9qKiq8zC3seCGClpzDK5WHWK8c+LShcnsOIZMZenT\nuqWpdQJuZMmGikwMwah4b9BclHg3cdTttJ6Dr9Wz5bw6v0cx6mMz1dhUAeHSznCV77EsLb6mSFdX\nuxyrRujpoTxb0MoZOcr3uOSQazp48vgB3jUlac0SUBSqVU7MaujhLLRSI4tVblJbGIweSNYfQKJj\nq++CTSzZQ52Pd4/5rcfSnztJRbVICBaGvfcWQpVP1y5qHy7aRbtoH2rPhKWQFhkfHN/nlCbNqZii\nm+v+uSR3GcUkE83jmxnoKeCua6I/M0R6Wk+dHqXurkU2IHQFElqeZqBKPpGaVtadMlHNvcM0Zz5f\nMElDridzVRU0FYS0qrTZw8EZYU0iyGHTgFnQnnsEPa3ArPUwY6WC0xPlyEbELTkdbq3t4IdieYwj\nw2qg0WfF5/fimGBD7pfNK5QSkf58RuXJ/4SlQ6mCK2GlNGIrDWbqlkyPEiYaRd8/e8LlrlgxK1GD\nSoOq47EGXc8SSn+hGZlSKsY/rVyctproT1bYVUhvWzEixdkZyw/Fqoo+9QKVgrrYn/L4ngQVX08n\ntJUMZV2X3D7fyZ0bFza25X5lWZ7zPHoYviNaqWCxMORQFagn3/oGZz2pI4hX67inSldW9CkaCu9u\niasYTc0503LulVQaxM3nFk+thvk8IddgXnYonw1Pzngw0gBmEOKrb5ONJ5SK+yg1GDjOG7TaSvE+\nb5MpXVsaeyTH4oL5ewF2SesxlCNifniX8h1B0Xmv/jDBSCtezT5mKM8R3FqHkax7o1DrZLjL4aFY\nwrP7T5go58Y0t+eUj1VlCSK1+hKeql1YChftol20D7VnwlKorMMsaxIXGUFNUiwm22HekBM2LgEr\nP8+yAhNofKFUGLH1BCMAZMMZc2WEno5yhnoiHc5zIuVWaH2nRobMkxNzLfbZWwi1nFomY7mf53qs\nqb/XU71HrA8q0UVZgb+AZnu4kZwe169e48mj2wAcjCV4OG7W2OyoPFr3MuGGEn8Wc7qLnP2ZnAZJ\nM6MY6M6fTChV08CvapRWLIHCROQKibWe5tr9goHGO5xpilUf3skDxmPp24prKZQPYjgSS6mztEup\nxK1O5eAqAo+kQaOjQcnLI9yanITtpqZs3QaHV+R7b0z7oGIx49G7nPUl1nB1taLbvgrAWV9iEc77\nopsI4FhLFahFYF08R2M0fDdQdpG+y1hL5PPD1OP2Azn9ezWIlPy0PoH5AlqeSx+W4za21CKhU4dK\nEY9eaZio1ZQ7EVbVbDIE8Xg6GlM81qAqGVYrP1s2JGgr89Kp0rX1DzCVoGWHsynjXOZsdPimmJ+A\nZ3tUM+EB6fRE3s8N24yUKcqZ7dOpKSw58chWZS698RCrJMOlFr9NjgrGmopP4wmDtsxlfmjPs522\ngiT//75K8l96K2zB/9vem8bamuVnfb/1zu+7p7P3Ge65U92hqqu6q9o9YYiNITYQC5sYR4kixRZS\nICARJCRIFInQ8ad84AMiIiESIUEhiRSZIcGQWFYSxzZOJPCE243dQ1V3jbfudO6Z9/zOKx/W8+7u\nIjauavetuoj9l67uPvvss9c7rHet//D8n+ekfMIku0b/ukvktGZOLNoxU1iq1E2a6ekpl4J5lqrd\nT17YJX4opuZFQ6kFpA4KIvndk8rDF2vvpYAwSR7SCOLamrwjKmYVeYSx+yH0KkaSbW/2JXs+fxd6\ntwGwptl0OBJ4BBJt6d0d0H7ZTdJT4T2Tw5g8lss5fYv7T9xxlD7MEue65wpbzqeGs9Ld/DsvjNmX\n+G0DeGr3rsyc9ZmbCEbkJ4N6SMqJzrOlVXKt9AylEXGICbiiZNWqEI/k6YxKlYhVDqHvQpv2OY/y\nDfd3i7fOMDN3/NWuZO3DkPYNN8aR/4TnXnRtMF4c0Uq1KrqachA7/MLwH7uwYz9a8VjJwyCtMNfd\n9W6aJY3o9OIwxig0s0oGetEeg9/lrv3O17+AfezGCJYp8Y4DPfnRMasn6htZqOLwQkDYPZhBTNt3\nx18XLa36GZpwiVFn41q4kS++/YQvzU51DAFWzvXRsKZcujHCVt2XdcrZfXc8r1xvuXboNoBemRAE\nrt3ZG40w6sGoM1Wc7GNWb7rrshveptwV5DspaU+ELQlqgtB9phLv5OJiyVwdumb/Ll84cziFflBw\nXnd0eRZpIb9v24YPW9va1t5jz4SnEDSG3WnIzijGVs79WgcX9NU5GHjQDiTEciOAJ84lXKsZZLYa\nc3Db7Si7QYOVcvD88WtYiahkn7vO/bVb2RdCpbXhgB258AcZzC/ckvrgwRGFRD1uP/c81a50HQSB\nHewcbNxraz2Q6Aueh6fuPH/yCkfZr7jjv+J2j7iXsjhzx7y8XFF4KodGAWXpPIH1Y5dEe9Bcslg4\nr+HgYJedO84DSVLDVF17l82SVo1ZE3VzefikIh/NG7NBBLaBwZdWxdrEHFl3MtcOxSUQxQxF/eWZ\nOcvYeQfHr055cOpKp23UY1du/MMTtxOdnaUMbrjjWTZnsPeCvmOPO/+m+75+/GmSmdNkeOfIkbDk\nTyI6sQvjhzz8aRHeBj1a4UVaDF4XQshTMEFKfP33ufd+1xvcEhZg4l/lQm7yYJ3SH3adlkr81QFZ\n6uDRZs+SC+NSN2uMOtNSHyrVn0tdzy9NL1mLk2H/uU+wUiKyuHxMJKh70UncD3c30OeDz34nN+64\nBq12+hgjmrZwco1WYkS/+hM/C8AvvPqrHL/r7kd/8hP4a+dt7acpdxxqnDvfcZPb3/Ovu2s77DgU\nSqqFG+/412oeH4irY+ZjbdcRC7b4YP2Kz8Si0NiGRT3j8UXJJ66p7lwe0sYdQGBnI8baCyYEuy5e\nHMjR6e3vMr7j+AUjEtraPViD+VXalQtHLi/vcfFVl6/oDxRvhj3mK02CYI+1BGWaOqYU3HVoQ8oz\nd9PfPtFiEvkgyLDn+9CFD6bAep2Kj2UkcpXB2E3G/mTEvHEx68NpTax4pceAoVifTnJX577/oOTO\nbRdb/u7PfpKxBEqrxQov08N0OmMgMhFPuQyWNXc6DsqiodFCUF2c0w5crdzeuM3lucMQHJ+6hfLq\nYEwj8ZJBL2Vv6I756l4FX3bXa7GsmEh+fTQWS1UzYiVmomxwm0a19KWtefV1te++MCDUQn2lcMfw\ntfKET0j16eFjn2jiXPDK1CzVS9LDgO0wC7quWKzeyybX2Ou5p2Yv3SVT34zZ2SFK1JWoluUkM5uK\nQ+td0HY5ozKkEtS9CJdcVloMvuKqBe8eTZlLvPiluEdPWq0Pipa9664CVetvdm/d4vrLjjMxMzXp\noTvX3uEui/ud4EyLX7lr+OIfcMf28B3LJ/+gqiGLkIEQ0atZyVVVjA6ef4lQgLPVoXuvvTgjloqa\nZ19nN3eb6JQn7A3d3DuflqDQsm3e3+KwDR+2trWtvceeCU/BsyFJe0h7dsqjI6c5OLwypp25w9tf\nlgSiI2vsnJ5Wdl8y5PZkzpPTX9V3+RRWHY6nZ0zP3U54vjqlVAvcoOdW3Hm74uSJW61XqwXnl+6z\ny3JOlbvd6I3jM8qZ8zyspNr7Zp9Q7MvQYjsYdFXSSrOgPjmmUjdOphq9X0X099yOcaMZYdQQVIQx\nvmrlOz3ngXzqygF74pDYGe9QtS6htJgVrHM3htdGeIINe6Uy4G0Ogmjv3RzQ1fnnFysehWpcqhaM\nVWk5U8Z+MZkSNW739HspsXgC7eCCq5LnWy+nGAnxzN+S1xTfI9UOdHbyZXxpad67POb4vrsuR/mb\n3L9wSbCvfOVVHWfDvamwIF5F5+0Gjc/A76Zls8GqoKaj1labDsbLJwHrCxfaHO0tGFXOK6zfukdv\nqMSl8CvZcIc6d/ffCxraQAri/ohCFajlWcP90lWKfvGffBGA84tjWiFBH9RrLpcOT5Cfz/hD3/eD\nAEQdBPvGNaKJm1vV6h2Kt1zCNxg8ZC3+yNiLQQnkQEnC7/m+5xjedcde2R1CVU6asiQShDqcBLRl\n14zmEp/NecPFucMw/9w7v87lue6JsSyFSDVhywbr8T49BdOVrD5Ki6LQHl4Z009HGAF6wlVJJlrs\nUdAwWwp2WrYcXHXu8TjVgxkEeKW67OKaUerirAqoL9zf5ckKXwpPy0Jlv5XHwW3x5Y13yfRg1vMd\nml2VJ1vLz/2jfwbAauYemv/wP/szHD9ylEc////+U3KV9dajmutqxd4b9qnE89hEUnFqLGcCwqyX\nOf7E3fxr2QAj8FIo8M/KX1GqZfvkfL4h9/AGMd9xxbmtbW6p1AdxpoTHsp7ydUFfy8ay+7JzNe3B\nH+BHX3Yd7r/0xZDBOy40mUtEZ06J1glu3L7JrQ6EFYYM1DGY7UekEmldl8L9L0v8K6oilENyLU6X\nU0shgtWxTWhKFzZNL+TWBhckCgN+5VdfZ6FO0n/v3/kcF1rIv/T1KS+IT3P4wy7O/v7rL1HMXM7k\n8YOHrBNpRs5zzgReOj2+oEjdPc7UOzFd5RiRmabjcAOlfvTgYlNd7h0MmfTcnDOt+/1qVbHuBFmK\nmGXo7l+d9vm1X3ALXWPc8bzwmSn3XRWaO7nHNHTjVS3UnjuOfm/MtasuP1Qv3XzKUrMJjw73r5Bo\nUQxtw7E2gNXKcn7uruFl6Y6nyAt2++4iR6YhSVQ5sTmJWK2Oj5YY3ML45vHFF6y1jvnmX2Db8GFr\nW9vae+yZCB8MDsBSlZbRvvrchzGJ3OA0SIi0q4QHfQ7HbjUfqhlouDOkFYtuWlfkIp6IBgnNnnu/\nWM5ZC6K7MxDIw8bsahesLqC96sY+uOsxO3JJyS/+RsGtlwQm0c53/vAhr/6Gc9uqsCFXpWKS+3gS\njPECn4M957E0SkpSZRzoq+qmZigZuqWF3kjJU5FtLKcXPDlXh2eyTxO4ZF5arikbd9571wPGN9xn\nHjx26/ubjyL2B8JH5CXea87DePDmT/C3/6kbPJwP+OQPueN8SYrZ67lHPHA73kvPDdhTt+Oot0+i\nJqedLGApAJQv+nrfb8i0tzw+aSgzJcNMQDZ1u9w7D2q8Xbdb7V1142XvHPMF8SvevjthJhq6rDbc\nf9Pt+Leeu8Pt593nXxw4nEM0T2nkfo9vJnjy0qK2x/7EAYCqa3tcrAXZLrouS59+h2MIcy5ERXxt\nMOd06ca7vtOnEPgqEDVdXrdY4RveuJgRn0pWYHaB3cCGnXf0xi9A56q/M/E4FB7By1pSeRM37wz5\n9B1H2ffcnkvmBqHHoTs0iircJKjnxXoTarz6cMlS5EBfP3Yew8WTd1guFKIehOwMXbg5P3/C0ZEI\nbmKfSp3HG36038a2nsLWtra199gz4SlYoMLHsKZaqn05C6gkCLoIDf3Y7brX93vcfc6ttCgBtL+T\nbRBv7cqjWOu1aWkUW8ZVD09w1jJQz7+f4yuhGBQLVqdut7rXPqBvXW4gqZegxpa+1JdPy0v+2Vdc\n0s6mcw4GbjdbrX0Oh27H3x8eMBy5eDfzXDzc+CmxmJgzCip5NIu1wRdRbCIvZhiPNrFskDXMdJwn\nTxrG3fvlhPPAjXFwy+0S63bBqXX5gOz8nCPFrZ/41ITL6W0Afv/HPMZrJcR23Jb58ZtjbqiEtnt1\nhJHWwdW9DCMqsSDKqCQbnemaeMxBlGl+WrAs3K65ai4p5m7HuzJYMRWEet64hFsvHdP3xYGRpFxJ\nBUH/9DUu/7Hbgvs7l9y87o4pftuNuxxfUCmhWEVPNsrd4TjFKie09D0OxZkRCNEa9n0GKmXj38Dr\nu2TdbpjxvFCdtk426NT12nlYi8Up5dqd32E/4tVjNy92+ks6MHYycNciX3j01Kx2y96hHrlzfWF8\nyPMvu9Lpp37vH2Bf4r6fe9mdW1i1+JIFXCxyQiWVV4sVK1wp+trNipWYyPZfc4mLXy5X1HWXPG1Z\nK7k4HOxyPHf3ta5bkuZfQpizsRA00AaWpnUPRb60RKLdik1EkrnXu70RYz1YPSXyeklCoIRSleTM\npm5SVbahbtzDkg7WJJrcZde7XydYsSGfPLkk6yTV5xn7pqPqXtMP3ASbiFn38btnLNeuO60p4Ubf\nLTLxxNLrq++/lxNa9/mxkqdJnDLYcTFI7PmU4i1cPV5RalErRJYy7hVUQ9HD7R1gSvVGmDdYClab\nmil9UXeNlCXs+7CnRcpfZjw0btF4517L9x46qC0Hhn1du0AiJB8fT+gpSRo3CzJhOXr7Q4ySn2Hc\nYDsBm7E6J/M9TKwkaHlKLgbuRd5nrTCmeXtNVjjX/tHaLTZpek5vIG5Hk3GQuLE/drzDa2O3WAyq\n50gfueOY3XW4imCW4A3dGEEeUOs7pkWJ6SoKRcRwLAo8bSbsNCxOhHVI3yHruB/TlH7orteCHDN1\n1zzUfWyXCbm6DJumIlOLrQnDTVK/WAsA1/OoJfZze3nGY7GH/9E7L3H1Ew54dOemYaj5sNuTOlnp\nQ7DUtbAg5fXM+DQCTl2J+9jQ3as9dehOvF3+4RdcsrMXZ9TCLGT2hEjgrLk1RHywRWEbPmxta1t7\njz0TngIA1hCnIZXq7mW9xqiZp9frMRbzbc/PGQhVnHpqbPLXSOwZLwroe4KfRgW1Gox8PyTtKTQR\nvqGaLVkqBNnxEnIhKBfHS057Snhev0IUa6fAuW+PpsfU6moMMwgTd0CpGTIQy9IkSwlVF+5r556k\nuwz2u5qxh2fUYLUTUIgtqph39fOYXup2klVWMlIZzk4PWFW6ANZSiKVnOZf3Ew94cd+N+46/4jUR\n7HvLmtJ3u3VoLP6uhFFuuh0x6ln8vtut4iYgUtOVqWo2bHRegi/uAE+UYbZu8MRanPR2CQfuRoTH\na6JGSde9MRfStYjeUgh2XrJ7w4UwFkujZO09c8Gnv9NBopdNwWokurELUeyZAYHnxj6enrASK1Q6\nSSk66rK6oF65Y+rtKpTEJ1fysMpDMu3Wo2FGIK8prQLqvhiu5OL7pcEXKe5sGbMnVeon05JIDV2Z\nGKtu7O1xEKjh6+qEF77D7fhpr8etF91573nDTZNeKFyIsQmeuD5MDkY0IUFj8EQwa9oEM3DnN0hc\nSJFVK0rrvKqvHt3n+NwlV5/cW+Ml7tqm3iV2/S8hzBlj8COfKM7IBBNelTW1cgCF9TqkJrby6Viv\nA9GqRaYHyh1YQE2QBATM5AslUboRoW3lTrWhT9elGyYJRqGLrWqmS924qAGFMfO+m4CLYk3ccfUl\nIbUEZ9Y+BFKAykqzubi7ciPH+z3ioQspKGZ4qnxE6Q4dsfHKdKI2Ma1i3UVT0h64Y3jR7DPXorCY\nLnjwUL0Uokg3Zk2oCsb+uEej3oEyaDiZue++Nb7CoBNXWekaRg32VJWF3Rh/ouvZGIwWWT9JMDpm\nu+yuZUkQi8o87NHUoicfhcShC10i/wqBbtrjyL3n+7tUYole2RW5Fs6siNmP3AK49EvO846ZWlWN\n6ztkkoBfVi15p1S1SKhx969t1pSFsBVSo23rhlKLAm1CopChR0It192rINBC7Il3s4guKbTgtmbF\naN8dx2VzSatrse65a3VeTtkVZV8vMZTnXW/HGesvuge2/tcmxApBmoXa2v0+plSFKjPYS012ryLo\nCcZOCMpXJDtuvOdeus7vDdR1+6UBZ6cOO1OGPlRuPviVwWrzeb+2DR+2trWtvce+VSn6vwL8UaAE\n3gT+A2vtpX73eeBP4Vr//5y19qffxxh4YYjnW2rP7cYtDW3pVsTKFiRD5zKNEkOQyB2SV+Fbb7M7\nNh7YTGvdCvYkzlGVBmJJiQk3MAhrAgmBnNuK9bnbHdIkpBKZxuV0Suy51XokFNyyqSm1QtteQKKk\nVdiEDDReNBiyK8VnP1GmPooJOxGZnkdjO6VlA8suHJFke9sgti+CRctetxuPQ84q50G8u7LsDdx3\n1HKHV6scW7r3spXdaGR4xrB319X6Dw9Tbt50hfF4R7oQUUpoFJbEFpOoQpPu4Ie63kGKER+EVf3f\nDz2s3HYvTQjanj4a044E7BjOaY7c+V0due+9aAoK7YhfvXfBZO6Oef6ZS+ZvqrFpN2Q4Uffnsbtn\ns1VLEaihzTT4cvVMaoiFubBmhS+5uAs1tiU2Zij6s7oNKSTas2w8RgrdqtrSdJIK6lAdGEuujGKS\nZVxKKXtvYIlUqdgdO+/h7s27DKS5sf/cARy6MfYn15jcUfJ7Maf2xLztd4zRS0p5xV4c40lV3A/W\nGPFamCRzJMFAJLi+TSfc1bHPpgVHD1wi+bxZ0qxEvnJUEn+wPOO3LEX/M8DnrbW1MeYvA58H/lNj\nzMvAjwCvANeAnzXGvGg3fZy/uRljCD0f33rfINMIUkwq1uUwIROWPU17BJFIOEREF/RDjB7uAJ9Q\nF7vNPOq2c+2XUChfIdRJbQpylbEi2+InXWwZUoiL72K1Zr+riKjFtjQ7eJ4IS9Y1pSpdpC19VTCG\niUdPQJhE+RDPyzdKT21TQy22pCAQHyG0HWuxKWk7evq2pFH8GfqGiVzDPAvx5K5XejDni5LlhYtr\nI5pN/iTwYrylG7vXGxIpzNnBAWGiaEDUugnW4KH1jDiNMRLD8X2LVQ+CJ2CZtXZDuIxt8cIurm/x\ndC1C25IKkn44dA/r6Tzl56cuR7M6mWJy5TBWu7wu2forszNGu65r9IH4I/tPKuyuG3sVwFylzKCe\nEUnh6sAfUvW1uKp8S9tsJAPqfkjbumuRFzl1qdxUCKHWxVow4UGQ0KiScTnzuLxwx5w0Bi913xck\nYsFerwkUBq2Nz57KzOneLrUIfiqzxJu6Y0p6DryUVys8LUJeG5GowxGTgXItNCs8KVVZsUuHTUSm\nJ/jjLz7Hg3uu0nZSTXlNYjBFOyf8gFKv35IUvbX2/7bWdrzRv4TTjAQnRf93rbWFtfZtnFLU7/lA\nR7S1rW3tI7VvR6LxTwJ/T6+v4xaJzjop+n+hWdvStiXgb5iRA9uQCiba2xuzK0hz4ifEoXrlA+kL\nBuFGbMPPog0k2uwGrE7dqlrQ4ml38LW15as11bTjOGxJxCdQeR6RmqeiJOU4d57FIHe7dT9MeGTd\n9y5b6LbVOzczYnVwxn6Mr+yo6bLi05JGDTXVbAWifDPLAq/b2eZKTkUtrN2OEiY+oWTPvbaiU2JO\nBoYd1f1ntXNbB7ElENR6afMNF3IYW0Zikj4YDUiVmF3p7yNrsGoUC/tDaBUepAm+ztWEyabTzojO\nrL2oQNwFdl3DpHtd0CiM8eqWMHV7SOg5VzsIH5KqgWdpW5ZrJfsOcsbCCqxsSi5wUk9e3nzXsjhx\nx/P4ZMqpwo4bBzEjQaWfGPDU/NRK4n3pVaSeMvl1S6vdlrHPQMIx4WAfv1KoMHS/94oWFWJYlIa5\naPPOZjmB1fwcu+uaBnv4Cmnvzy+4VCzy3OWM0cTtm0HQslb37MqKCIaayuv4HwyIerBdrp23ANiy\nhbTbh5U4NDmBNCTiwZLnxw6/8MbZEZcDqXGP+lTdub5P+x0tCsaYH8P11v34t/C3fxr40wBB4NNg\nqGxC1tW//IA4U0myMkS6oQkeoSiuA4GYQpN0oDriJCJQXsJ6fOPB80dIro/pQheptGQ9ueJF4pSR\ngNxbsJLrZ9oVnm7eUtnibDikf+Ym9/JyjlTZGfs7XBu4NTCiJFEoEHVxbxRQlCqLVTmxXMO2MTRd\nq57G8kxA1Hcuft1WeJGw/KVHuBFdDfH6YvSZumsx8kKyXedmNrnB1zEkyYB9kZX2GrMhixqoJGut\nT6C25yTMiNUC7AU+vtxrLwtAmWzbuaRxhqdJjFdtOvKMb/Ek1NJGKajPwyrsIggZiXiXMGahfIZ/\nEZKe6z5kAY+tWE1899nTRxVnEtM9mV5C3lHxN1ijvAo5g1aLjNeJz1gqoSrjyJJqIfBsDxtJzt0s\nKDR3Es+Vb8vh2wT6deLV7F9zQLY6uiRIRRmvVud2rySXA91vCu4EDsmaZYcc3nA5HC8JsWqiSRL3\nf9D6mxKoZxJ8lc9Mz8eIat9LPFAey2jRdwuYwq7LJYhY+BNXr1NJXChqjzmd6ppzn/dj3/KiYIz5\nE7gE5B+y3+i/ft9S9Nbavwn8TYAkjj/6/u2tbW1rwLe4KBhjfgD4C8D3WqsUurOfBP62Meav4hKN\nHwN+5bf7PoulxWJsvtkRQrOkzN0uvvZrSiUVc1OQCiziKZvshZBkbmUPonojGeabjHCotMniCUb8\niTZUvbppKLWzFT0fc9mx8zak+u7HM8Px2n1fR8N+7e6AL7/WIXp8dndcMixoY6ra7RS9UYDfeQKC\nYON5eIIrh+loI0uPB6263UxXdy4LWiWy2iLahFWtHzDTblQvS3wlK1OJkKyrBcuVqg8NREp29gYe\nveuuZ8TrB1TyyGZSZ96pI6ywF3bUw/bkzoaTDZ2XtQtoO8CIXFjf0ojz0kQRRvwOddnQdv0o8Rhy\nh0nokrmT/Rv0b4hF+a236SlJWKUFU2ErXn9wzrXG3ev+wHkuT8qKxzO38wVVRaYd/3xRYaUVmtbw\nZO48Bb/LmoUNciyZ5gZ6+l7jKloA0/mU4a67jiHCWLQxiUK7q0FKICBQO5+RStSlkwqcJkP6hQhw\nvOvY590xP/exV/Bwr8NrLb60PjPhV7zyOWphOkyRY6zCHBvTVtrlwx08VXasvErrNZv+izDscf05\nlwhv9jLmvuvtOJ6tWXff8T7tW5Wi/zwusPkZSZ3/krX2z1hrv2I/fnzDAAAgAElEQVSM+V+Ar+LC\nij/721Uetra1rT1b9q1K0f+tf8Hn/xLwlz7YYRiMNQQmwAqWXFQWzyh56EVcXqgJ6MBSrVRuWbhd\nsumv8BTBRH4MagbxUm8Dx43KwSa5VPldt2RCVUg8dJ1vNsHAjwlXbuWuiopGO69yQfTqPYaRmlmi\ngrsHbheIRxlL7YhRukukelHb7aqtxYqHIPVSyLt4d7ZJVjba+euqpMhVsisjqlIUc9OKuWrvjXU8\nAQCxqOJCsybWcQ7DHknkdpc7o5tcm7jx8lVJrrJtps66RQS5ErHVw4Z07t4/vN6SCdoc5hGk7piM\nr3xHkVOvXNDd+AWedva6rWjyrvR7gsnFqBW53WxSFGQiUk2DmN2+ciLxAfe6PEJxTql8xqPKjduW\nBb7uTepH7O64Xbc/3qVRQjQMCzpQoKnFEGUMnhJ0RVHSKrGZN3MqibbEaUhfeaPgmhiYqggjKHnP\nzrmQuO84GdLbFzGvFKMzf0q5che/2rP4u3cBuPAC2sYV8KavrjZQ8Jtjl5/YHd7CLJRzqRYgSr58\nXbCeur+rdh4yGjqmbKOEeDOtqCpHR5efGiLPHfNuW3BFUod7ewlDq6aw92nPBszZttRlTl17eALh\neNaQCQ8+iKAVbdjDh2tuX3fu+nTa1WsbjBV8drhLLEVlW8Nq7W7ASX7GvXvuAp8o0TgrFhA41y/w\n1jRTN7lPZktqZdlDP+wwI8wEGrr7/BX+ny+734/rHW7fdMfTiywruc/BumR64nDpraCz/qokGSvb\nHzRkvksG1cUSP3CTd7YSMcn5JY/EtFxisMoMRsGE/cxdl9gz2LILhdT+a6NN+/V6XnJr353f7edH\n7MYuOz3uL6F0D8WZiFz85oj1sbQY05jeSKwfxOyIHXu4ext/2VV51NV5+ZiFFqniuMXfcw/I4vIR\n/o6rm5tFSyMIshXV2qKesxYJy0EWcnBNrvRhwMFt55a/tX68SaqmgbtW88BuKMtnocfs0j2Enz6A\nm5nL8PvBBfW5qg5nUtW2K3JVQy7zgkKVin4d0FuqVftgwnjfPah+5M6jKI+oVlLMntlNR2hscixa\nOFSdSHu3aP17AEwOdjncceFaL5rw6jsOgjx9+A7T3J3rD/6AC3n74QlxKH7JyzWtL63Mo9f52j1H\n9nN+lrH7SYHhpup27e8ywc2FKIVcc71pDWvNncM05eKwzwexLcx5a1vb2nvsmfAULAZrfLxwwNWB\n6vUm3KC5gsZs9BprP+VMBBLD1q2AQdBDhLzspAWVylFVFTO1zp2/vH9ErSRmp0Ts0bJeuc8mRUg9\nFAFpkVPOBB+er+kFHXLSrcp72YCXrrhd0JuVeJ1idFIyO3XJs4sTaESAUZ26UlCxrsmO3DHfuDHg\nUA1diR9uBEmCQrvS8SlTaQe2QUA61s7lW5pWZdbQ0qjkGFYdR0ROoqRdmxkODkQQsneXHfFIRDMo\nlMzL1NRDNMaXV1GlOZ5KfbOzGYGufZKdEnXUXuqAbFY5SwmynE5LVkoCzk1DefwOAAfBgNFQEnmx\nkqt5yk0xdJurVzj4mNtVk/OQsfQ/bx9cI8+c9+YJPl7PlnTaJhcX51C76/3TFw9IFf/duXuDG+J9\nmIj3oZ9NqJfu2CrvhAfvujl0/2jJUkTAe/Mpi7FDU9644tCGjYFE1ztuViTinAhGKUizM4/dPY2H\now060qZzzs7cjv5rbcnFA1eEi4ILnnjuurz1NTcPD54v8SQJ58clNpQIUnJI0LrjOZ6/zZNfUVNV\nr+N/OOK2GuGuDvpYhcI9Jgykc2nWI3bEDfF+7ZlYFMDStJZlMWcWOjdxkLYMOkBHP+HJsXONzs9m\n3HvsMqsTkVR878uvsHNHYp6XNZm4EevlKef3HBnK2RLWV5xL/Ohtd6Fff+NdYnXvjQd9YoUuxqup\nBHppsoiLE7mMoti+zFc8J81Imx5vehgWJw3zxk3es9MLfEGaz+buQchtwF215ta5ZSFx2HA/I45z\nHbMWo2hAM3Gfvf/whPWRW9zu7A1oBYO92h9yVQpRQSLsQp1smJGTsmT3Soc9qKgEeSa1lGoXrlRl\nuHx8zFQdie8eH2F9d62+5xMDRtfcJLyYXjDypVTVU0x+mTNduoXliRny6/ec+3zy9rtcVex7f3eX\nT153f3dTsORlm9IbuzH2PLi5L9bl9SOeLN25DP2MG7F7cM6k3hVGZ1RakK5kGXu3XJv1W6dv8vBV\nt/gm2RHxngslbr/o+j0+/vw+5ydfB+DLP/uIR2cqmoWGVP0mvj9hKVHfVeQe0htJhK8cv9dvMcpd\n+WXFShWhntrlzcHz+GKzvnj3iK/sunl2+X/8Bg/mbuyT+xcksVi9PuXCzuvxbW6+4I53NH4JI/Uu\nlhckCm/pRfyTLzpW8VxakqxKvrrnnpfPvvASL7vokKpYMFcO6mJZEIQd6On92TZ82NrWtvYeeyY8\nBYMh8AI867O40E5ZNDTCNM2LJadzNSsx4Kqy6Gkm6POOYS1kXpTVBOfONWzigFMVcr2kx0TJnPOx\nW31HOzN2Uof+q/MFidrJ/NbHKh7JZwvW4tk3aq6q1zmT6x0arcfygXPronBFqo7IySCjLVShmIjj\n0E841LFXbcRSycPdVQEijCmEZtsZR9wQTHZe+3hnbgyb9igF7a3CNcjVtNIVCIoVidz9XtQnnLrj\nSWqfXCIiVVkRaDwk+lKUBVkgjsa+hz9y3xuOKy5nbqfJMp8It0v5vmrifk2iYz5IEz79/McB+OIq\nYH/c1dUvGIq0pj9y72XxHveOf8393uSEp3LF9wZ4tas0XM8KdgZqkFPUMvcjzk+FTZidEikBfdXf\no+m58/NtxP4154VcEcPzeHILLt25vvj8Je8KFm8Dy0ghqwl8FtLluKcQtHfTp1BY4s8v6eva9oOI\nYOjmwHjHVQVYnbBYddiTnOC+m2fJYMqocOGRvX4Fo3m294rzzPKqIgrdPPRgwxMZRAHpxN2H522C\n/4nbAJxeOKTkaLdHKz3Vj4/2GEXu2PPhmLOHTrTmYnXO+PCDPebPxKJgcTp3gW2xuZvwZV3RE3vM\nnjngk59VTL00GKFQbgTuhu8MLPXKhQmry4JM/QcNNVy6m9Tr94hq990v77qbeSu4xVrdhzs25UJl\nuJOmIFQHXxwn1ALCrBRnm+IxQ/HoXbndYx64eHGxDtmRG3/QH3J+7lzJRN15h6PehoXJUtAIZ481\nGxKZgVphwzQhFplKENwgGmlhGbfsinK89SCz3ecFGlpA2BeMe7VibyxOSGvxKoVHdU6qCR2vVOG5\nNd7MhrvBp+gLztwfhuRz97Atz9f0lO0OtVA0taEnHkTbhvgKD8y1HVCr+e3djzPcVyv2VG2/zEmV\n4xkMQ4rQLeSnmA2EujcZMZZgazNyB1cWE9IX3fl/5WFM/VD083sJ/TvuYZlc3eMVkfv2+1KI8huq\nA+df36me47tn7tp+7eKU4Q0RwZIQhYI3x+48nhz32d1xx7ye+0wiN4dmdsnqyI3d3HXvvXhlwNtP\n1IsQjfjM827hHOzscv9VV0VYnZ7Rkz7kdx240GdvN8Wq7E0/AeVUwmjARGxYzTzAP3TPw20R4Iwm\nI/rKKQ0P96nXbr7NTy9pj9y8Xq3PKM/dtX+/tg0ftra1rb3HnglPAZy3UMcJPWEMQusx3HO70ksf\n3+W6atDRtYpMcNxQiSwvjCmEWSinM0zX4F9HVPE7APSGOaPEuZSNdvM9HzJRjWH7nB6675u9+Sa1\nyEkuZjm1pMNDVRl8WzO6K6Zmc0hrXBIw8GpGcve8lU+cqQFHNG6DNMWqAtDUAyI1XYU9n0hJq2jg\nxh0kKZPWjTEZjWkUwuwmAZ520rypWErKzoiRJU0aIrnzdezTVxekH0dE6pmMooixEpTxrtslrTci\nUMjUH/Y2naRtlrAUOU1QP8ZKV9JUSmx6GT15HcNezFogpYPkgFRYhihs8TO3o5Vd31ddMYmUdKz2\naOXCh6cnmFY4k/QaU5HIpGt3TuPJIcObLvk49R/w8E23A89XJ0wm7v4e3rzG5EpHmCPpvqJgULtr\ndev6dU5K57pfvtbS5u5aJCHMBJseDl0LT1gVnAnzEK7XmFBhU9riaUefSXimne5xK3H3f+9Fj8++\neKB7tsdnFMYEsYeJhDnpq+lseYkY3rF4GDV2Bf2A0Y7zfsIoZ3jpvrvudEeHEUEjXMwwo9L9bXYs\nA1Uo7l2mrIuNas37smdkUTBYPwBb0fjuZu6m/oagdZJFXDlUDLyO8DMhFqWUFHpgxalnE59A+nrV\ndEFQd0xGJcmO+7u46fjw+giYSF2XNNJu3LUBC7E+NUGLp4A2VDXk4vSCwLhuyP6n+ixn7mYt51/f\n3KQwagl6At4IoRan4w0oirgiiNSmOx5hLqR0VImwI4LeVef2hdMSRFDqBz6luBv9lSXQA9TxOZpe\ngNjpqaZTitpN4ovFlFQ5lWToYVdSeFKps9ePidXVGKWACD2qyuOycFn0witJxARUqsfDFJZA7dJR\nv0c6TnU8KUbxvskbGonshuoCzVnRjt11S9ZHrN51ZC/eYUJduO/LZ3PGQkBeSoehrlqihXvvrm2Y\nKyey9AZoneP53oBdzy0QtapI7TrftOKX0xk7rfIgg5T1pAvj9mmG0gHRfaoo8ETqY8KES1Gu1w/W\nlGLq6j677htuZh8D4DMjuLLrFoWDySFG4Ky6sbR1F0Ke6JwCTKAWaWtpzjpobYRp3ZxMBxGBQGue\nSvVhL8UTV6b1S6wQonEDsSoqD5YLsg9WfNiGD1vb2tbea8+IpwCmtcReSE9YAd8EIBozs4RWqtIx\nJZ6QvZ5k35umpRbEs8prBjviZfQzOtK9elXg4VboOOhYdD1Qosa0M0ytVbc0VHLX66Iikgvacfk1\njJmIf68qLL4IQAZEBAoJAusTChfgdxyOQUkrWHK9KGnF9ZBUBrF+4wsoZfycSLRraWQxSigW1hBK\nFapsZli5rudKgiarmkI0brZtaANdz6KiFDHAetljILo5b+g8jcT3iTOFGomPVVI2ryouT13SdXpe\nE7XutVVPRWgK6LyGyuB7XQdf1FFDgDnf3L+6cckwm0ek1iUwc7+hUD/KIN6nti5pbItTmo5CTbyE\n66bEero3cYsRH+e6ntFUEkA5OWJ0VZyWCju8Qb35u6CZshTl23x9jP/EeX292wHNvOvfc+c/vTyj\nUS9Gc3xCIm6MyE+wcad67vAGYyCVu5JMehvlZ79p8RSOmfKcsnYVnOKBO39qj6Zy3q0dTiF3XpPx\nA/ywY5eGuKPMUcjrLRqM1L+pYqz6btZPZpyv3Xc0/gp//9tMx7a1rW3tXy17JjwFgyXGQgs98eYP\nbcFB5uJa09QY7YTVckWoWreptKpHNSwUUwch1Uw4hknLRPG8DWM8NQH5mdCIbUlTuffqywW5vIM8\nL0mURxgMQy5UknykLsuDnZhsqe68GwWeUIOe3d2QmHpJgJmqpCjWKDutUehINa/xr7jzC4qM8IZI\nZb/g4sxmkFGfuZ3ExtCKiLMKY9oLt+PX1YpCqstdI2ZTRazpGrsW7N0VRNfGGCU+I1NilUxpBPNm\nUmHXHTVdj+rYfUfuXRAu3GeuHk4Yi9cgVmm1eXKJEUaiWVzgq3vSrlYgtGR7mdN2nakCjuS9OU3b\nJd9KHr3jdu7bL1RM9sVp4DfUi47IV/kVU2FL973F+TmRTrzvWxbK0SzyFVPR8HXyacNxjK+8TB34\nVOdqlDu6ZH/Pvd9vniNSrX89dXOoqD2WVYemTEDnWtU1gboSw8pdq3h8QOw5LyBuM2o1WrW9S5oL\nMVyFIavXnLdUHjqvKzzpkx4I8l3UWKmq25NzarGBNXmO19dnzuTR9g1W856kYLXStTWG+xojXy15\nbfbB9v5nYlHAOLYuL4jxpFvotSkrxQmlV1AvRV5R1VQz9zpIlbDJI9bCfeN5tKqPFxf7tJGrf/t+\nTWndzV/LNfRWJa1gzrmNWIkTsWhKIomFlKslS+EXekpg3vr48wzkPtPGBIK+hik0TfeA1Hihe13L\nxWuThKoVTXxVURy7B6GOTuEtN+kfye3rffURgRiQbdujJ8CLbTy8unPRfQJBnsOFwoj5mrk6Ji9t\nyw3RoccrSCWaW/srjFqmO/e0yWsadaU2qxOK0H1HNS3opRKzuTUmGx3oOPRwtCGhaN4IasqmE1yp\nMDMl+ZqKTuPUDtSrUfdo1EZ+cR4yCwX9XRjiLvzJMk7UMVhP3bW4PG/oCdq7GPdgrnBkdcH0sTum\nh37JxVe/DEAkOLctbrEv0FrbronF4znyY3wBucLlKeq0JuuS1aMAXxtAWVQMfD0yg5TmTC3Oe7oH\npOy1rlrgDS1lK6zAwzVGYKji8j652sDvf0nVErsgfsH1SURJD5QEZTghULjS2hVVLvbvjlE792Cl\nhacpqPVcVCvLbOxO5KtvFNDfaklubWtb+x3YM+EpWAtNC0VdItVvJlGIUWlqsSyZp251TG1JrVCi\n0eFHBmqxE1cmwnRc+emM6Kpc0dqnUS2/LjtykwIrvbZZ1XIylddAi9UuvV7klEpWJhpvcWFZDaSe\nvBsSTdwunz+6T2ndCh1an2jtdsKVGGPLtMUXGvEyn7J6VxRcWUKdu7DBF1SX3BKp023Q5FxIF6Bc\ntVhJn1fLhkw17yB1nz1dVxyfuJ1hXZWMFWI9thFXlKx7PujTiCTCqsxaNiV1h3mIGpbylIIwohaP\nxIPX3qB3zb0eLIQ0TBZUF87riA/GtBJZsV6LJ5m2Ngypl13pVJJ3rDip3XU5m1esF+7aH82OaER/\n155E7OxIdVreUeF7zNxloy4esZCH9GBeEErzc//4nHYguTixUp9Na/qt21VXizPmCp+8QY94LGKU\n0GCkBF5IG4R6TuyL4CczGP1d1jZUHTWb4PiL58fUMzff2mpBYZxHcHJ5SvjkdQDOT6acp+4zVyaa\nYxePefzr4nS4cYehqAWNLTc8Iq2fUZ65+9PKgzZrNqzbdtawEkHM8uSY5ZF7fblYbMhs3q89E4sC\nxoAX0EtiQp1kXfhMe+IRrCoKMTKZekmtdl9Pmfwy82h6kpzPV9SN2luTAb2ewop6yUoTtjKKo9uc\nRoxH08sZc2XOay/YiIicztbUcuHCrJMv32HZuthxfmJQxzV1U1N1rE82Iq9F9KEqhF0Z/MidXxoM\n8ff1h17M8KoAK1IHIjY0QvrMm4qFFphZ4FEfS1kq9fHEoDSXgOt69Y3ciBfAVG65v1xTnkpm/daI\nfqIqiZiBvdCwXovdaNGwit0EjOIhVYc9OGlZyl0PBJAKmphS9fNqNcNIAKZtfQKFI/XydJMxX+la\n5muf07kLlZb+ikSQ56LtM7t0sPFleUSFc8dnqkRlVU0pzMOjxwueVG6FKKoC63WcnTvUWnw7la7Q\n1FjhDYrJVQZnrsIRZjOGHXO1l1Dl6g9ZKwzMmw0Dd4DHjo4/SxPi3+1AVIevfBaAG+uUVOCzRVtQ\nqWqxE/msVX25dznbcIXe3ndVC7N/QKm8TZHnMFR4WJU0hXRPm/WG2b1S122NhxUBTuCPyEs3Xv32\nlLdThc2pwZfW5fu1bfiwta1t7T32THgKBse6u2oaksKtmFmYEMntKb2ARcdgawyFOtHK0u12yU5F\nkCu763k04t8b7DfUQp3NL1bkWuUbVQAGYY2nHWWRl+Sqzbd1wImy+mXe0qgS0YjWarxjqMSIZa/5\nRHMlzzKzUSuuCzbJtVKoMy9oCZW1T9qI/q68g2XFYMeNkeuPjl5/hydyB08jyxUpLVeDjLjTyvRq\nLtQdeiT3+7JMOfXUXNPU1CeO3yBfZbz0gkhUzAXxjtsdkw21m2V1Il3N+YJGnIHB4Qhfia3gcIe9\nHZdo3HBWlDXrufNMVqeWQh6IX50TNqoerYFMYYOQmyf2ErvssAs5R9JgzCpA8HVrM4x4OHsDd61q\nP9togUZhjiIbvDgmEatyYCCV8E1/5NCPveeeI5Nq8zphE66YrGVH4UhyM2ZauuTfPFAyuooQVw4m\nCbHCfcxtg3cpDcrrzhsz4Zz8XHOsl9L3HZdDO7tPu+eO4/zX7vHu+ZsAlCfO62qnJywfufN84cU7\nRL9fnA1+Ti49D0wOYhivBNFuW4OvxHXdgxO9ng5OWH/R/ZlvDWXdhQ/fTLz+W9uzsSh4hjCL8Lxg\no2HYxiHHekivlisWyvoOJiNio9jQk+sYJezvu8mapkOsgDmV8fCVZU8P93n8yD0gkYRHkmgIgsEO\n2gEoU/+ofYe14l2T1MSZ++7AewWALAvw1dI7PzckEiBMdw6pO1LZ/BzT0zF5blY9ni0InIfO3nCf\nyaort4UsRfEeN3pY7445/oK7s+++9Yg7n/suN/awIhSIZd2uKJWRTtYdyGdKIGr4qhlsekJW7Yp5\nB8dOeoQDQYwVs7eLlrDvJmmWVpzM3I149OBtrFS0Uq9iuSctxfu6PguP4Io79mVZkQn7n4Q7RBIn\nqf2AQr0kHUgrLkd4gau+5FGfJ3J9P7EKv0EjH/bIB4KNZ+6hivKc7KbrSzBhn9p7F4Dj05Lx0D3Q\nyeGYOulUmOTOr1seRSLO6V1jlbnwr5y1eBMHXrpx83mixK32uVr4Hy1XGOVrUj/Aizt1ppZYZcST\nr7u/We7cpJTCV3L9mP2+QqbmDsG+21BG3zUnfNN9/hfvu+P53OQm1z+rlvLrGaePJERUnlMKALWy\nl/T6hzoOdwzZIKCtlNfAY91z+ZWL7GXsd7iekOT1AafvatK9T9uGD1vb2tbeY8+Ep2BbQ7M0eD1D\noiYZ3x9xduF29ifeksl1t9LOB5bdgds1dkXNbfyApN8lF4dEiftska9YScdxcfyIUPXfRICmpA+F\nJL+KtE+zkhy4N+ZMTUAH6SsU+y4bnP4udaxdu0KnFffKZ26xelPsu6dLkBBNW29UvMjUvbe2GcvG\nrcO99CoTycH3hxN8udf51O0YZ4/eYjd237v3iYRDAZ2wJblEZMxZRaNuxa4WPaySTU289eGJOioX\n5xF3v+M7AJhECQtBYoeBu96tvcCIqjxdxQyVnPK9llLXcGcyJind8cVKjJbnOb66KHdSGO/3NHa9\nqewU65rm0iUVazWjLWyONS5RV0wfk8r7CVJLPXfntJv06Heex568kfUQlFwdXn1uQ7s/iGGkKkIQ\nXKGZigVZcnUH3jELNdDNjk4J++56fvxKn9ELzs2PxiNWAhxxrm7V7AqeKg6TMKPMBbcuS1Y/74hM\nPvWy01B+vQjYf0FQ8Ydromvu9SdzS7nn7s+t5vvxJ+7Yok87T/dg7xNcm7hr7+VLfPFOzi5vYqVd\nOSobAt3L2v8GFkSvaBc5+bGbp0ere/ye7PsB+JnZz5D0nKdQvr/o4dlYFAzgW4+2ajcZ92VzyVLg\nl7fsQ8bK/Cd+Re+6m0xhJyO/9lgcOXfQq2YEIgltqJlKR698eMq6r0496QTmXstS4I+1zVn67uY/\nqp+QafIWt2M8PRTB2yIkqft84tA9FKk/IL+u8OCXLrDLL7nvq30GyqiPrjr3dJT5RMLWt8UT8ndV\nshusqFRyvP+qK109OPoKbd89KDdvv4DfumsxaxvyS7dgLYuAqfgYrR6qpBfgiXrcjzLCIzf5T8Oz\nDZHsbFJQPnIPaSg9gthPqdUl2pYenhEi0A/pa3Ubxpawc+c7UFS2wpcLawZ2k1OxbUNedLmfc9Zi\nrcpDITdnKy4CdzyL/BG6xGRmj9q4h8J4Lf7YXedELeeZH1JJH/K89UjV9mwnDZE6JiNvRthzD9me\nxu0PQ1ZCHl5JLfWBCHrSHtZzC1m5suQdR2b3tHkhtRaW4/WMSrmIxvdom1cB+OovOv7FK69kPDl1\n9+nKW/mm0/ZwN92omR0EazzNT+9IgLw3Timuuu9o1pZgx13vsKzx1JVK6GH1uHaVsXzZ0ARq3y7g\nzSM3d75+f8G7PacHcVk8JF9sex+2trWt/Q7MfEMb9iM8CGNOgCVw+hEdwt527O3Y/wqMfctau//b\nfeiZWBQAjDG/aq39zu3Y27G3Y3+0tg0ftra1rb3HtovC1ra2tffYs7Qo/M3t2Nuxt2N/9PbM5BS2\ntrWtPRv2LHkKW9va1p4B+8gXBWPMDxhjvmaMecMY8xef8lg3jTE/b4z5qjHmK8aYP6/3J8aYnzHG\nvK7/x0/xGHxjzBeNMT+ln+8YY35Z5//3jDEfrPn9g429Y4z5+8aY14wxrxpjvvvDOndjzH+sa/5l\nY8zfMcYkT+vcjTH/gzHm2Bjz5W967zc9T+Psv9Yx/IYx5nNPYey/omv+G8aYf2iM2fmm331eY3/N\nGPOHfydjf7vsI10UjDE+8NeBHwReBn7UGPPyUxyyBv4Ta+3LwHcBf1bj/UXg56y1HwN+Tj8/Lfvz\nwKvf9PNfBv5La+0LwAXwp57i2H8N+L+stR8HPq3jeOrnboy5Dvw54DuttZ8EfOBHeHrn/j8BP/DP\nvfdbnecPAh/Tvz8N/I2nMPbPAJ+01n4K+DrweQDNvR8BXtHf/Dd6Jj5as9Z+ZP+A7wZ++pt+/jzw\n+Q9x/P8d+H7ga8BVvXcV+NpTGu8GbkL+QeCncAjvUyD4za7Ht3nsEfA2yiN90/tP/dyB68B9YIKD\n1v8U8Ief5rkDt4Ev/3bnCfx3wI/+Zp/7do39z/3u3wZ+XK/fM9+Bnwa++2nc/w/y76MOH7rJ0tkD\nvffUzRhzG/gs8MvAFWvtY/3qCET38+23/wr4C0CniLALXFprOw2fp3n+d4AT4H9U+PLfG2N6fAjn\nbq19CPwXwLvAY2AKfIEP79zhtz7PD3sO/kng//yIxn5f9lEvCh+JGWP6wE8A/5G1dvbNv7Nuyf62\nl2SMMT8EHFtrv/Dt/u73aQHwOeBvWGs/i4OVvydUeIrnPgb+LdzCdA3o8f93sT80e1rn+duZMebH\ncCHsj3/YY38Q+6gXhYfAzW/6+Ybee2pmjAlxC8KPW2v/gZH80D4AAAHMSURBVN5+Yozr49X/x09h\n6O8BftgY8w7wd3EhxF8DdowxXbfq0zz/B8ADa+0v6+e/j1skPoxz/zeAt621J9baC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3717... Generator Loss: 0.6639\n", + "Epoch 1/1... Discriminator Loss: 1.3463... Generator Loss: 0.7220\n", + "Epoch 1/1... Discriminator Loss: 1.3487... Generator Loss: 0.9054\n", + "Epoch 1/1... Discriminator Loss: 1.3769... Generator Loss: 0.7837\n", + "Epoch 1/1... Discriminator Loss: 1.5438... Generator Loss: 0.5937\n", + "Epoch 1/1... Discriminator Loss: 1.1925... Generator Loss: 0.8585\n", + "Epoch 1/1... Discriminator Loss: 1.5108... Generator Loss: 0.8107\n", + "Epoch 1/1... Discriminator Loss: 1.3479... Generator Loss: 0.8114\n", + "Epoch 1/1... Discriminator Loss: 1.2299... Generator Loss: 0.8803\n", + "Epoch 1/1... Discriminator Loss: 1.2651... Generator Loss: 0.7570\n" + ] + }, + { + "data": { + "image/png": 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yPVaczUu++0DGduCMGGfS1/NWEI/x1GfyvmzBjw4OsX2lK3O3uDRrrMecsJXx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\ncrKPxgYrzr2+LaikJlUUBVOjjHrtLu5mv0/QlXhqc4tqcguA+XbOhliBr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.4447... Generator Loss: 0.6179\n", + "Epoch 1/1... Discriminator Loss: 1.3410... Generator Loss: 0.9721\n", + "Epoch 1/1... Discriminator Loss: 1.2921... Generator Loss: 1.2052\n", + "Epoch 1/1... Discriminator Loss: 1.3283... Generator Loss: 0.9427\n", + "Epoch 1/1... Discriminator Loss: 1.3041... Generator Loss: 0.7806\n", + "Epoch 1/1... Discriminator Loss: 1.4502... Generator Loss: 0.6852\n", + "Epoch 1/1... Discriminator Loss: 1.3364... Generator Loss: 1.0475\n", + "Epoch 1/1... Discriminator Loss: 1.3068... Generator Loss: 0.9476\n", + "Epoch 1/1... Discriminator Loss: 1.2938... Generator Loss: 0.9685\n", + "Epoch 1/1... Discriminator Loss: 1.4735... Generator Loss: 0.5542\n" + ] + }, + { + "data": { + "image/png": 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SsRWlV/K6BrfduqWQ6a7HjgZatVlEqWJ5oZ6DV8kg2cQBur2YsCN74UeTa374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\nTb5cs566hddM3D1Y/zqjm4JxnwU06mwtozlHkw25TJcLbULPS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3093... Generator Loss: 0.8099\n", + "Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 0.6992\n", + "Epoch 1/1... Discriminator Loss: 1.4837... Generator Loss: 0.5886\n", + "Epoch 1/1... Discriminator Loss: 1.3110... Generator Loss: 0.9667\n", + "Epoch 1/1... Discriminator Loss: 1.4147... Generator Loss: 0.9200\n", + "Epoch 1/1... Discriminator Loss: 1.3674... Generator Loss: 0.6883\n", + "Epoch 1/1... Discriminator Loss: 1.2852... Generator Loss: 1.0330\n", + "Epoch 1/1... Discriminator Loss: 1.2894... Generator Loss: 0.9344\n", + "Epoch 1/1... Discriminator Loss: 1.1895... Generator Loss: 0.8798\n", + "Epoch 1/1... Discriminator Loss: 1.4130... Generator Loss: 0.7914\n" + ] + }, + { + "data": { + "image/png": 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p63Nnv33tSVqjNE8dl0fvyI1YzU+5txTI76LI+MKhGPy2Gp+djqavC0T9yFYV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TPs0NMKv3K8YhhJ1DouZcAe5U84fVsAKP0RGHWOz8uSSjeeVZpwV83jVSzux6rzCtNTebnncUx2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\nsk/Noel+hNVEcTmd0MjVIw0hV8SmF3i4pV5bk5nDZERHE7DNKDpFy87CK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.7511\n", + "Epoch 1/1... Discriminator Loss: 1.4302... Generator Loss: 0.6394\n", + "Epoch 1/1... Discriminator Loss: 1.3354... Generator Loss: 0.7412\n", + "Epoch 1/1... Discriminator Loss: 1.5316... Generator Loss: 0.4699\n", + "Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.7564\n", + "Epoch 1/1... Discriminator Loss: 1.3748... Generator Loss: 0.9328\n", + "Epoch 1/1... Discriminator Loss: 1.3011... Generator Loss: 0.7149\n", + "Epoch 1/1... Discriminator Loss: 1.3411... Generator Loss: 0.7330\n", + "Epoch 1/1... Discriminator Loss: 1.3431... Generator Loss: 0.7667\n", + "Epoch 1/1... Discriminator Loss: 1.5367... Generator Loss: 0.6442\n" + ] + }, + { + "data": { + "image/png": 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OTcEDP/X50bsH3EtlcV+cf4OLXNT8yDoONPSUDGPaQibs3RPp1OMAglRJUhnj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\n6e8Cv6CffwH4P/+4z9jLXvb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8jyXlrahorW12+k3ELkWMB6HYoaVX02lGXVE3hFrRyIs9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\nfGz7AKv2+UIU8I1mQKjKyWRaMZbISlUmK2WwLB2SLXMK22oH7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3587... Generator Loss: 0.7105\n", + "Epoch 1/1... Discriminator Loss: 1.4629... Generator Loss: 0.6467\n", + "Epoch 1/1... Discriminator Loss: 1.5333... Generator Loss: 0.8289\n", + "Epoch 1/1... Discriminator Loss: 1.4835... Generator Loss: 0.4824\n", + "Epoch 1/1... Discriminator Loss: 1.3330... Generator Loss: 0.9264\n", + "Epoch 1/1... Discriminator Loss: 1.4501... Generator Loss: 0.7557\n", + "Epoch 1/1... Discriminator Loss: 1.3579... Generator Loss: 0.8731\n", + "Epoch 1/1... Discriminator Loss: 1.4515... Generator Loss: 0.9340\n", + "Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.8987\n", + "Epoch 1/1... Discriminator Loss: 1.3188... Generator Loss: 0.6393\n" + ] + }, + { + "data": { + "image/png": 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9Xbtyi/sfivnwznLCjR8Iiq/bcOn2ZLdqqYOzHlkKxZuf2Yx6IVralY8slWoH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\nCazGQLJihLsqLq57aGiruEduHNZU4crokWF+HjMZa6mvSTEaAS/TjInef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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.4152... Generator Loss: 0.6163\n", + "Epoch 1/1... Discriminator Loss: 1.4929... Generator Loss: 0.6064\n", + "Epoch 1/1... Discriminator Loss: 1.5524... Generator Loss: 0.4964\n", + "Epoch 1/1... Discriminator Loss: 1.4786... Generator Loss: 0.7433\n", + "Epoch 1/1... Discriminator Loss: 1.4437... Generator Loss: 0.6628\n", + "Epoch 1/1... Discriminator Loss: 1.4178... Generator Loss: 0.7201\n", + "Epoch 1/1... Discriminator Loss: 1.2742... Generator Loss: 0.8131\n", + "Epoch 1/1... Discriminator Loss: 1.3555... Generator Loss: 0.7472\n", + "Epoch 1/1... Discriminator Loss: 1.2990... Generator Loss: 0.8521\n", + "Epoch 1/1... Discriminator Loss: 0.8481... Generator Loss: 1.3975\n" + ] + }, + { + "data": { + "image/png": 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OqcHq8pYMwyv4ylsvS1uvHJOO5GDlqT21w09vy+Y/2DWcaSlSyGN6ibDGxcTDJrL5Tcdh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\n4mLDmY//gEQPQvzuXQCq8U0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UxzSi9PpjjxPsyFgf5kM8VeK21qYcvnEXgKESpGgYEmsX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ioZnpZZ7VIbS8mC0YpL7nlke/osJCG2tpRbjYBMy+RTJ+Ig/2iP+Vn4cDbOxtn40HgoPIUU6GIR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3439... Generator Loss: 0.6907\n", + "Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.7232\n", + "Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 1.2362\n", + "Epoch 1/1... Discriminator Loss: 1.4625... Generator Loss: 0.6389\n", + "Epoch 1/1... Discriminator Loss: 1.2682... Generator Loss: 0.9903\n", + "Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 0.6646\n", + "Epoch 1/1... Discriminator Loss: 1.5688... Generator Loss: 0.5454\n", + "Epoch 1/1... Discriminator Loss: 1.1573... Generator Loss: 1.0297\n", + "Epoch 1/1... Discriminator Loss: 1.1199... Generator Loss: 1.0091\n", + "Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 1.0411\n" + ] + }, + { + "data": { + "image/png": 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4uuzfrRcBuOcdkk9Eck8Xj9gNJVJzdnDAW49kzGdHM0YdNeMGYiY58QRnKvdw\n/QnHA9EITu9XOKqFNi9ldPQsB4VojVtBhyuXNKMzTPA0r+XJ6C7VTM5AlQyo7Mr9+eHoY8EUjDG4\nxgEHXAVL8XkGW87adYViFAeETfEo78byQqSzOWNVoxflnNa2fN/qdDmUr6msXaP+WPUKpwZSffu7\n1uHKrhzAq7tbvPlYVL9kluErQxqoKjqcJIyn8qK3Sphc0bTb+YJaU17qKJrz0p6mPHfk4N7ce4Wd\npoJ5Zm/iamJVmOwSaCrtLJDnztzXKUJ5bnu7TpqrebR8n+aB+Bde2nNAfSa5lhbfaO1zmIq6O/Pf\nJG6JPXz5wT4dX02Xz7o4XxVV83SpJsM448Zl9dSfpQQrPEe/WqNV334hYF+Tr3a1xH25dUC3JevS\n273Gp2JBDbq4HpLdUD9I6OIq5uW4L2OIo5BmXUFqfZgpqvT76RHLB6LCN/0D7r6mCTkzTdI5bzPP\nFSSnX9E1K7zObUIdZxJWeJ6GcNWT7xYVy56sy+j9FKtgtGcPTjjXlOhakdJVhOWoqWnJzQm2q6nG\neYf4RMb27t4hw8d/AMBv3fs1+fv9jIWGbK+2r1BtydrHyxOuaMRgf+cSzUBe7k5T065Dn8KX/RgM\nCtAoGTsTtqww1jq3ud4RhjRqKNw9NRpqPsV2TqHnrGOvM+loDRFnGHdVNrsCAfiTaWM+bGhDG3qO\nPhaagrWQVRY/LXF4GvMNtNijtNBR7tjtN3nlsnhZb/aEi55dnLLQ5BCCFr1Y03y7LtevSwz9K7Mv\n8onbApaxdVmm3bi0AxOR3Htewmj4EIDRyTlnWjHoYDgc2fU4AC6mS7xUflfVDaFW6vW2L/Ov/rSo\nxD/zF26x3BHp4Co0fKsf09RCqt3Lr1BoirHvNShXKduOaBdx7SrRyyLB9msvsrAigdKzfVqp4gR2\nmng1qcQbPhTJ1+5e49235O9m0eByTfIKru99hYXGx5uHXyKJvwnAT70qmsanb72B2REJ9bWvTTkb\naDSnMuxuiQYRbu9wuSFqbvNFcRz23B/jloLFfObyF/E6Iv0v8SMsD1WDunKZsimSq6mOOj9OaYdi\najjG0NOoUz6/gpPJuk0uw+5cirSWOmfHXdDeVgi51h61UqVjs4Wj5mSztc8KfGBVUWt9S9tolWzz\nFEedcltX2jia9p48ekjfExPKi1VaN+vM1AyMF/tMS3EkNt6+xuH7YkLNZnKveyffwug+fuozM7a1\nE01nf4uzKUV7AAAgAElEQVReXbSGtEhpaGHWykna6r7KYqI5DZ2Mw4GYGn52iVpLxuZ6lkK1nlvb\nnwHgYrwgrMn8RuO7hOtklQHNlfmQPcCzYkNmaDXoB9DHgikAUMFkXrLUxith6K1x7h3H5fY1OSg7\nt65yfUcOU13Nh/EgX5UOcPmFDpGqXE33KiaUzWj+yDZuKqAY4QuyeF63RVCJuu6eWc4eyIvyzeOH\nRG3dmIUhVZts3SEpT8lU5XRjn1C9yXt7l7imaEvRtS3snjCI2Vg8wW4agiL3WBeiWE5bWRV4Rsek\nmZm7V3fwSxm7MRWpJmwV7Xs0tLdCVc7JjbywzW0Jt54vHvNkphGXZM7122qCdQucjtii6YM3cK/J\n+HfP5L693c8TduSlcX7i1xh9RzEMPZeFAsNs7+e0FdvRTRRmvf+Am59TX4w9ojLio3gwfIuDC3Gj\nv3X813ihdee5+TUI8NSf4xhLpmaA5z9kGmh1ZSNnOpXxdZqa8VcUdFtX9dmXCEthllHoY7RM3glc\n1gWRGp4NOpBm8qLXtwyh2udO1EHz5Wheh1CZjFvJ3ixPD0g8ESYLNyBXH4X/yMMxYvu/l4gf4Ww0\nwtdq3TRNmWhtQz++RNjWCt3hkLKQudQU4CcIcrzuKmqT0FIAn8pUa2baOiuxPWHgpZGXOwinLOby\njCSDi6Xs+7Q4Zamdqvy4QFtOsNDo7QfRxnzY0IY29Bx9PDQFa7GVZZGXpIpZ4LoQaxz/Rifix7+g\n+fUv32F5ounIHYVoO3WZaNutm81L0HoIgOdmBAq5HtfatF6Re6xyUS0dvEy4ddV7k85DuZ/vPSY/\nEklxUC0IV/VuKs1mWUbQEwlV80PymkZDojqKWo4ftvEz0Vh8hV2rmimlVjsWtrN2dplWRKVpzo42\nMWEJnn50PAfPivRwohqextCLwwNKhSOzJzL/92a/z9GhaA2OW1KLFD7ss38AR6K52BslgWobo1Ck\nec32OT3V5Jjv1En7ImF7QcVC60DyYY32a/KcfCrPKOMWHe28tPzk+zCW+Z2dWh7+lmhI25+6yY5C\nod1wRGOIHI9AcQUsEK669kzHLG/IPSbHLa5uy57UHKm/6F1yaKhG47tbmESeHXbCdYNLxwmxqSYv\naSTGNUtChTyz85xgVVcySTCqXrun1/BuypjtuUj8ZeZTfVfm/+TqE85OZJznxuK58ruGFps8rlbO\nPVgsUhaxrG3P7+BrZeciT6h15R6x9swM3Ab5WD7nJqehUPXJaEFzqDU2rQmJAsYsRUFhOLOYQDSC\nwWJBOZI9mxydM1STpl6zdOryOy2+/ED6U2sKxpirxph/aoz5njHmTWPMv6ff94wxv26MeU//7f5p\nn7GhDW3oz55+EE2hAP4Da+23jDFN4JvGmF8H/g3g/7HW/i1jzC8BvwT8xx98u4qyqtZNXfwQthvC\niV/Zu8ZnromEidu7FJE6I1ti67bGTbw9sZeb/jYs1TYeN/EuqwRauLiaHhxomWVau8Bo8ZTvhgSp\n/C4D5iqBXUJmGtOPPHU4Gm9dtRiOA/wXRUKYKsdTkNaACNNVROFV78t6QXWh+RbOXQqFY/MaTaKl\nAra2xEacTxLKUqREu97C6SkSMRGONm51wzqVK829x4mIgfMHsDhXm3XPZ6IQQv23ekQKx1b+i0P8\nt0UTih+InerOcwLFWY3fC7mh3Z6n0ymOIkL7jS5mosVP2uT0rHyPNBH7vDu7TX4h4xhsfZd0T0OE\nXp9AofCqFb4D2gkaafBc6udZFaModrjjOaf7EvbbLcSx6eQ3iBQpiWi6dvRYMlz17Vh3uk4/raqn\n6dGmqw7YeYrRSrman1FoGJHc4JUaRj0QrWnBXc4dddAueuSKkVF+1tJ4S87ArJSzFwcRqFR2az5u\nJWvVrNcJW9pgthbhq0NwhQhu60dkI0XZ8jPiXH5XdAYstdFMwzRoaOXnqZHnzYsSk8jZW5Zj0kCL\n6YylXm/p/Fyu1eWMPzyW+X8Q/amZgrX2CDjSz1NjzFtIC/qfA35SL/sfgd/gA5iCRQBIYtch0002\nJQTqye3u9clrcqi2uIH1tQrQlwNt+xWf0H62LpZQm5863R5G4bzyRYt8Ii9spfUA3vQauebAm6rO\n6UTSQbPjHokmKqVZTvxMb0oA129z2fspAILe++wFX9Lvd6ChYClxm6hSdV1Te93SpQrlxMfss0BV\n92obR0t8fYX4mrlTFgOdRx7SQjti5wUm0HqARYDRsuW0JvUHs4NjHMQx1lhE7LZ/XNaw/hmSphyK\n+vAF0ko6IodXJAGnVa/T6WhJ6bVvU9OS5Vb7lK6aNMvUozX7CzLvXe2A/PAqQ6OQdhdbHGiORPib\n/wLxA00PfuNfonxBq1i1O5fx1t3eKZ+peq7bfeIzrVFoJNweSLPVqL4qgd4lT4UpRlmNcv60sW6h\nnYA9L8YqiIpjNS2ZksARiLm0OsCq6k7LI9bqyqJ2sS5dz5uy1/XzLp2OMF5z7EFLKxwvGtQ9YUK3\nmxIBOq79+hpF2mZNAq1XCbu3aIZS4uw1U+oa7TAK2+/NO6RaRt0KLed96Z/Zm8RkS3WEey6l5k7E\nlZyRbHqOq1BrhQWnkJe/pEujIWenmmfs+ytl/Zt8GPqh+BSMMTeA14CvAbvKMACOgd0/5jfrVvSr\nsugNbWhDf/70AzMFY0wD+AfAv2+tnTzbGddaa41ZyYPn6dlW9DXftXXXCJSXZh1WpSVRKK3ZfE46\nFY45iEviUDltIc6UJJuyXAgHD5oGq04b24rI5/cAyLw6k5H0uK2sFtlUDo7KqGz+iJOhSM+T6UOq\nSDsYZ5ZScRtWZoDjlBQ1kfjblxwqc65j9kgPVNUOSorl801rLDnWiHSosjmuVnaWkxTrazHPUuZU\nVhPmGq9eeg3ypYw5igOipdwjSYaMZiKlp6ei5Szcd3AVk6E6n9O6Jveogkc4TS0CGh1QxQpT90i0\nity5jNVEjKg/BC32CfyISSGhrqxcMlVoL6cuLdKHRxmJdsHOqDOKRBU/ODrhOJdnXEsaBPHPyLxY\nORcNxaoToIVMsQBc+wgvkDld2YmwhRRb5a4CjNx/gF+XsG/lBKAh7GJucRqyhqaorfspWtQ0MAPy\npTa+iU/X8HaV26bU4jZTy8DI9U7tru7T+5hI+jcEWw06K2DeBy26igJ9XGrxUVkyW8h6j4om+Vz2\nKV9MSTNZw6JekVr9bFcp2O9yqv03mmnA2URMl8PDGaFC6FWLDvQUtGeueS8ND89boRj3KFPRes+6\nC8xCNLPcGsZzTev9kPQDMQVjjI8whP/ZWvsP9esTY8yetfbIGLMHnH7gfQBjLe3AxeqLl1Sw0Fzu\n5fycNJFFK+cNSoWBLzQqUGYTzo5kw6OBR00RivPQJ0TLmp05dfX6Tpay6NXYslR1sXCPOJvI5p6P\n51j1zh+WOS1F0FEoPzI3o2jKPawTQ6i2XDUlV7sumcWgPgGrUOBOvSJXdXD+0GU5lcOf1c+wJ3LN\nYiRjnyYlvZuagMKCqSIjL62h1pC1GB2fMNEmKQf6ch+ePORMW8OfJEPSmfpUrv0Bi3PxWzipxdUc\n/UQRlSN3xlQrC49fn7Eo5eC2mwFpLqrv/fEQ6lK1l78tL833ThI065jWl3x2tCfkw3zC4YkcxrPh\nfdJcmPYqXdtWHoEmFiUlOJlGX5IJZVfxIUfXqDfkHvOJ+FqCWkx1oE1kvG08zSdo3dzGappzOaqo\nFGoeTWRLxwnLC/l7VcSEWhRis4zKyLyraQahNhxW7/28OSc+E0Z2fPUh0YVWxHYPOHsouTOVYokm\niwmTqfx9cpIx1aSnUVLinoupkacpvuJcLgvZp+nY8viBfC7rAyZD2d8H9yfs7W3p887pnss+uGom\neImHE2h0KR9j57K29WHBwpX9mxU+58cfLmlpRT9I9MEA/x3wlrX2v37mT/8X8Av6+ReAX/7TPmND\nG9rQnz39IJrCjwN/E/iuMebb+t1/Avwt4H8zxvybwCPgr37QjawB6zj0QpdQPeSDAOpaEHUymJOt\nYLL6YJDUXVuummokWEe463Rqmc1Fdaq7GYum1psHXebTFS6fSPOBGVFkwrXH0xPOz0T6z80Cq36t\ny6VLqpGI1WLVuyEn6pHeziO2Vi3OWUCieuv8hEzb1BWamRknW/iK6pv331+rl67toPUw5GqWRG4L\nP9C2YlnBoCOYie40YjnQi9OcSvsTLLVHxtkg5fRCnpsXFe8PxAyoP8pxtcGJ+5dntN/Wvghjmdtg\n8oRpXzEs/RSjYxslC9ItEXmTWcnwVLErn8jf3yGh0DZunZOYg4FIvHvdfN3mrLyScjoThbGhzQma\nYQ2flYlmOFdTIylzIm2R50UnnHdFmwpFoaFa1MgRbWW616avjVhqyW2iSCHmgilW8TccrSj1yVh0\nZS2cqUel6X3GFqQdcYha38VR0JbsQqMarQJ7VXEwZw2yY003/0LO+VuynpNzGWORFlhHczqyC2bn\nMuds2OFU8wn8KsArZNPcTJ2kWyOibTXtli7BSFMsdxLGiZxJNw3JFyL9ey+KNuZbQ6Ad1qcnM6YK\n5rMkZaaZjqdPhlwohNyHpR8k+vA7/OEelyv657+fe1WVZZZmhMYnUZOgZXwKvf3ptOAdTSZ6YbmP\n2xWVshXKxD2TUVMsvqWZEWnTk8Tm+HPZgFmar9F2zlbIS27MQtNgs6LkbKnYhycZKwA+m1dUigC1\nco/svvwJrC+q47AZ8qIrTCrwcqyWF9pGE6+pXnRNUhofNQm74glu2k9TVvKib+12yI1CmUfiLxkP\nzsnGMvbj4Rm15qcBKKohtUDs2kVwH1f9AMelHJ6zwYBCqzkzW2Dn8jtv2OcRYhvfeucaaSbRiiL+\ndRnvwhBqJeJknhEmCmTTDXis3aSODqfrBqs1bVb7s86n+d1TYWQ3T77C4+Q3APDP9km1TsDJvkSm\nZlysYcNHScGe1rZki4RCQ6q1+Co9X5j6zEJ0KutRlFq16tYwVn7XWnS5UHOkftGhdGVtg9oVqqms\np+uufFQDGo4yjXBAoYlAaXJBOJeohClnazxGX0FrdoKXsFYrcC8qZk2ZU/XQZXkh4dJ2IesW+hAp\n8M+4hPNMBMe0rOgu5bykTgBWojm5UT9YeolAa0I6rYr0lqzxjvsqi6XC7i8WzDTdPszEjJhXKWhY\nO5ke4mlLrtPpEmehgMTLGVMtif+wtElz3tCGNvQcfTzSnJEclIui4kKxC2xi6Cvm3iTPGQ9EEj40\nKZc1+rDUvn6LBJaTVcx3xlyLWoLjEa1Si0h6OYuxqtjqYW7U68SaVzAfTxmOVxDvJbHiEziVw3iF\nw1AID92qNxmUYjH1G5/EuSJSLBxsE2sMPfRqeCpVzVycgEdvP2DvS18GoHm1TdAXieFeGFx1Ri6N\nJjx58Pjt/xeAkTflauc2ANuxjynkedgpr78nDr/7b8i/RZWTqwprCoe49zoAj0cBky3RDo6/t8v2\nNXleeKZJQ7WKYiTzOz874uSBdnnuuLw5kTEdzDKu1mVt+9sizYdnb9FRh5nvWF6+I/d98M53qbV1\nHJfeoN+U6spRrE1fihRFvqdMRtwbSdp05M2xGvlpxBmV5iE0FV/SKQdstW/I2OoxhYK9nF38FnEp\nFaiBF+FNLvTe6hhMxhjF+3eaJa7miND2sMGu7lkdctGKgo5K1/mE3FWNp5Xhe1qsdHdMXZv8sBSt\nabfdwl1qQlpVcPq+jKH4TIpBPkeOz3T0UMaUyVpOswvmqr06paHS9O/JxYAql9+ZC4huaHLWRMwS\nJ8gYajWvzcfYuvyu7qY8nsj4Z+mcPPv+uk5/LJiCBawpeeJnKC4mVVCReuq9dxweXYgdPfUsQXtV\nDScbeP/+ySpniNutBqF2GMqnPoNKATvcLeKuqpJDURHnswq/ubKjS+4pMs95WXBV1cj7rQKNslHo\naj38zhY3L0mG3Wj8hBMrD2/1JxSuqHvlpEtTS/WakWzmtVf6PHlbVM5a0+At5DDZ1vuUAy2nnUvC\nkzOPqanDIMJhO5XPxp2SasjxO99+nb/7mxKyS47kkF/4GY76NfKgXHeTCj73NtPvykHphksSPZDn\naqzPFx7nc/G/nAwzTtuapZkYCllCnLLiXGHJ378rhzUrPTpqv9euTngiXzNNE9JDTQBrX/DWSPwA\nn2vekPHkPo52A5uWM6qBYmXOzvC1XX01T6lHwjjHp4rF2E558kTGudtt0I0UyGSvJJsrOGyrj9+S\ndcx6Eoae3l+wfCJr5JjL1BVQx0Y1gkRUe7scUmooZa7RrJwhxV15CcfBgHMtBx85DkYzE+MdMZ/6\nNsdoxbVNQ1xdw6S8gLlmjoYxnp6LXGsl2skOiQK+5lXCyVD2ZHg+p12Xs9XZXxAruFBZynjmU0uq\n0ZeFl+MoGtgyTzgMVs2ScxSyFHQfP4g25sOGNrSh5+hjoSl4jmGrEQkGYEM4Zrse4Cju4DS3zBWO\nbTld0Fyog1HzGNLUEirMWXzpOpfaIlUH9oyRctLpIiXTdOVilWdvlpTqIj+9mKNIcDTaAaFmv9yq\nSlKFsaoUWv40fZ3F6a8CcGkaEFwRzt2sblJpF6a4mxJ4qlZbcUQ6SwfznqTMPmke0G99AYDavECh\nKRk6/5+M7Z2MrCnz7LS6FJGoyXlpGDwQbeTrXz3k6EQxErRTUuRFLD29mQ9ZR1PB7W2iI42ufH6C\nOVMouLGk6I7dU0JNhNm/4XDzWKSSt5WSabr5eOFznska3DuUuU0qQ62mpsRFxBvvyXgG+RwnFIfo\nG4c7XNsXiX6sKvx1QlA12cxCpploEmQlAzUZnGVJ4otZ5KYK1X7sEygUnO18m9DRmogkxzuXe8+7\nv0+jElg4oxWq7hTmOzK22qwijTVV2i/IFIE5n2RkI7lmciJrlToTpieyVqOi4OJI4ddvZGxfk4Y4\nLVdwN93yAscRCT09SSi0T2myKBjWRPq3vR7hyhyttHGQd0yi0az5MGM20ShCVLDdEedhq3WTQtd+\npCjR5+cJRqsviWvYUutqbBcnk3EWdo71VvGADweosNEUNrShDT1Hxto/Mgv5z5ReeemT9u/+7b/P\njon51l0pBqn8bf7L/+Y/A+Do/e9Q00rFeW7JtUNvovkGRVliNXwZuM46xRUDlX7vIu3gAOrhCrrN\nJ9MGB4Hv4GoDmCKDhbaY7riGVbvCmQrg7Z0mzRVuQmHBXVUtVvJj4Oh0zlydpivO67tQaAZlkluq\nVYWfhUDrP6wOPvIdOooo7PkOnvaeKAwEqkEFTY+rX5Lw1E/+4icBuP75r/BydQOAR+X/wc+2/zYA\nDQyJagK+79Nshbouigxdq9HZFsn2Yr3DZ25rk5G6C4FIq/lJxievyfdzTZOeLD3eekOk/DUvYFrT\nmv6szr1DLcAq5jweiU/o8Fj+fp4sqGn4OTWGuVYAfu6l67i69rNsTq7O3SDUVoGOJY5X6EgeaJiu\n6bkMVRKeTUvq2oX8ek+0xhsv7hMrqO5seEGnEA3y7uCMdw5E60lKy9WW+Ana+/K7xTzgYiiS+XZg\nOVdpPCtdfu1bmjuifrzKVBhFk9rqN9nXAqbRouBCsQ5C7ymCV2/lGzMOvmqhNxu1de7J0RASzWW4\n4vmk6khcaB7HsMq5qvB3/Re2mB/JGr37+F0+qw2R3iwrDk9Fazg4O/imtVbU0z+BPhbmg4MltktG\n+ZI3n4ja9r1/+sscvPddAJbZlFK98yWWUp1dxTPotC2FCK8H7lr1d1zDlkKEp05BT5NsHK2SjJoR\n5+qdrsjINXEq9QoKrbvwHIdEn5eu8pzLikhNERMaWholySaGRaLAKUWJr4de+QBFIZ2wAJaFxTVP\nodNXKdvuqhKzsOvS4tAHT1OCvQxagVYDpgEv5Z8C4NbFvwXA9eUNOnU5VZPgCN8IU6i7Hp6eXi9p\nUNd6haYjB+zLNxu8/CXJadgNMvYUfr7e7ZFXkmMw3X1nvYaE4ukfDUu4KS+Hn0Z0NDI0u6iTdMQh\nOLlw6Chq9kLXfp5WOMoIwtIyWipzLjNAmWlV0dYGNZ6uRdeNCfUebbfOXFvV25lLrI7LTuZSj2Rf\n2zqeeqvGjjrqxpGDMxSmcWkWk2q+xKm9IA7lmo4Cnew4dcpSkpvcsMWu9q6s7nnYVUWvsn3PWiJ9\npfw0Z7DU9Pg8o8z1rFroKFNXvze+cXC08rflWCJ/heFYMYplTr2ghl+TvTpXJ3G6mBNoCn4ny+hp\n784nwwlZIM7TS1PL8eRD4rApbcyHDW1oQ8/Rx0JTwC2hPWPm/DL/5KuSYXfw8JC5tlhzqxJUUqbL\nnHLVrn2V/Rg49Ovy9+12SE/r6l/ca3C9p46Yok25VHPDE3V4kXk80rb1k2LOqcaE57ZYayFHJmNV\niJaqtJpOKnql3Nc3CzKrvRuzgkQ9hjnVulfFKkq8yC2lZiAWFlaRIsdYPDVzVo1X5qWl0t1p4+Br\nMxXjSQYoQL8V87Irzq473c/Jd9YFzWq9evY3KO2/BsB5K6WpTtVxY8DuvmT3vRLLKH7stVd58c7n\n5Xmlwd+WOfluCooLsHX+GuayfF8OFXTWMdyaiqNt+sL72IFI6GlwQk3zSE47Y2aqwh4r6ElSFsx0\nZXrGIdfMw5wc4ykwiltRqFbUaYkEb/gp7ZYiX/sLahoCPKpG9BU8p92J8WK5fltj+33fX6euO8Oc\nRUs00kaesF3TtnHnEdG+SlXNlFy6BbGGC79l3uZFdVwetDLyx5pTo47dCEMWaYasG5Dq+TXVUxCZ\n7UZAS7WmazuaPRm4lFr5SKsi1MZAnR2HeCLSv7g2oan4GsGOghtfOKR9CYfee3xO3JS/e4MpX92S\n769kAdO6zukpWtyfSB8LpmC8EK/3Iu/f/yzl7HcAmNUKooHa3FGwKnZjjl2jc3Qj2eXX9uq8+oKo\nS/3tmEu6cS/f6tB+QUBWTFmu891TTcaZ4PLwQg7gyeyMdx7JZjwcQK6mQuKU0tcSeXkBijIhcxWX\nsahYpKJwVdZSqQPC9wy+puMu8qfNalemhGvWID1s+84KS4jZytQAPL245VTUtYGirbk01ZQaLhfc\nffPvyf1+SzjBnaO/grsvabtf+/2HBK6s0X4QrBndpU8FfKoptvOP9uTam3d26dxU5ONpie+I2u1V\nLlxTWPbAYh15yQrNs2/dSYk0t6LuXWJcSKLW9s0lzkht/2XI+KHk7deUCRcOazj/qrDrdu6eE+CG\nilJkSlpq2686efmFT00rAzvdLu5cGHkn2sEP5d7BjQ6zM+EWkUaD3GpOWZdn1Fo+tdoNADJ3zpVY\nq23fPWeiuSOZgpf4DUtoZAw3wzrpWPNeeiUOKz+BmkEWatqbtCxBAwBExnBrT9bw5Z2YLc3DaKkf\nKe7U8StFfm5sEcUy5uJWwOJtmdOgnlCowIla+q+7JFUfx5NhxgpvJrINrigQzyTLaV+Sa0YDTXr7\nANqYDxva0Iaeo4+FplCWDuN5jVv5X8Rt/LcA7C6v8dCTmH7YAkc5oplbulpp9xdvSrbbZ35kh1du\ni8QLnDo3Ozfkd7vQ3pPGGXZm8T1VE8cCppK6hkiz3K6e1YhV0sTHlpGy3frSMFEn0FJV+6LIaern\nSVHhaqbY3MIqmlNVFblK+pX54Zk14DB1F66G8j83AlgVxm2pH6/KDcEqbwJLr65I05XBaHx7kRUc\npDIX77v/CwDu/LeZPxLn4x/8/jcItIfAy9Fljh1RKV+u/jK3VKW88kmBc/P6Ln6gn5shgTYvMRW4\nLcllqEhZA1sjkt81bfwtUcVDW5KHUv/vTvewdUmxdooTxk3RLGZzbQEfFPi54h9QEah8MoHPtmox\nprlgq6+FVIp5YEnQQAT1XUN9Jg7P/d42qkzg7d/kXCHiUK2iMGMibYxTsVijbs+rJ/htmXfRe4/q\nZKLjk99XGbjatGZ3BmeZAtgk3noz26qNLcucUPMwUlvgGNmzfsfhi6/KunxhZ4+9tlyzVKCXKy+/\nhlGMxq3mCyysRGpM+xWODwU8Z5aUHCSyni3zoq73t5ioifbYfZ10qNotDrfORXOmGFEr5fpH/BM+\nDH0smII1FZU/4/3R9/C6ijk4HxLEq3x3B1/Rj3y35PIlOSg/98WfAODya9dpBnI4onZOL9Cqt2aE\nXxOEINPJMNrPMJhrTYIfs3NZkmOWdkCiqv90fMilnuh+40nJPNE6CA0xxn7JuR7A0lqSXHPOPYsO\nE1sZlqvYqP+0w5RqxtR9h7r6QQ4SS6nMJNYElVd3DYVmU8VuxUL7Ae40DE/0miK3XCiEfXIqYbXR\nPOHSnmDxfe3uA/r6bCeo2FUbtjG7R/+ahOfqkbzcQdLB0fCs22vgaOKVsX2wWlVaK1ghqhhtZIM7\nwGwLs3FmKX5rVcL+GBsKswiiMX3tc7inHbnG8xQN9pAUrJlN3Xfo9bUxjh/R1oY/K19MMPOoaS/G\na/U71GXb2a71QSsfi60ankYfrPpXlguPVZ5vvFPHqN8pND0SrWyNuj51rX2YL5VLTw2p+gnq9Rhf\nwWDy0xJX1f9KI1+hW7DUhLrAN+zVhFn8xCf6/PSPfgWATtdQn2okyZVz2Ny+hrcva+iNjqgrs1i4\n3yG4LuHe2qigret5NtRajMYQ5Vd0ZxXva8n8oErQvsK4ocfcKoP8kLQxHza0oQ09Rx8LTaHKLcmT\nlDO74PCxqMPZUYajyLiNpqWmMfYwCPnKpyWF9fJnhBPvdEPirrZ19x3CvvBJQ4irEOcUGWiFXtBc\nhRNKajOVSvUBV3ryu9mlBl//rnD/ebZYO+gKRQgOai7OZW0Mc69i/iza8wp9fAlTdUxWitXohhCq\ndLSxZaLmxTK2XFs5ibbkgitbPtENRe8dezxUUTo5q6jtKLZjUnEvEcldPZRrD69/j8+diuq4mKa0\nVDWZbh2zo/HtK59ecuuyrF1Noe2s60OmGLtnDlVDYb5qLs5Yog92uoBVS7NMUfamMfZY79GaYFRq\nujVQ8DgAACAASURBVIQEMzle4yyjVHwKz3u6VmOtCPVdgxalEgbxuro0qELcXPYvURi7wECrsyo0\n26XpKqR6o7POX/EmO9QWClnWUw3MmWNdUeGrucVok5zQnlLpOOPUrhv/PDkWqdt0QqqxjPlBb4Sj\ne50GCyJt5ReoaWdnsKroixyXO5dkDJ/51DWaTTFdwvnw/2fvvWItu9L8vt/a8eR4Q91QmcUq5m42\nOR1mOs20RiNNsgXZEGQBsiBAsB5kGX6wJT8ZcIAMGLZkw5AA2RYEWYJkSU8zGE3q6Z7Qze5pdmQs\nkpXvvXXjyefss+Pyw/edU6wm1U2KUrsE3AUQdXjvuXuvvdbaa33h//3/OOuyDr1ELdYA3Jn0zT87\nx+2JW1yYMk1NJZXODkGVtBc0bv07GRPlA8nijNBd9KPgnqvU8G6ZJP+AaQdtj8SmkBFzyB2+953v\nEI5kspK1A0LlLUymGU3NLK6s+lz+rGQU1tckHVexDYzyJBKHmLkuiHodqlq2F/uYXEuVFRnmtUcE\nhQy0l0KmEeet7YSu0p0fpBGjXGt81R1YXQ9x1V9MGtAWb4bKxKei34lNTqwAqIWU+5prqKjJ6dfB\n0S43Lby4LX3a/rz4m9tOlfqVBbTN4WJdJv+NP05xNRsyMikKaONtNZMvljZIVXsiio/oVGQsWlOf\ndUV01p4DT7MH/kwv4I+Iim/IZ+thJqrX6LfIW7IBFPkYZ75gRdL4TH1IrmSlThxQLuQ5eqtHpFO5\nRxAXlCpqYmvJnnPfZUE8Pi4KcpWiD8oemlzBywxTrfabH4mvv93dZqOuuoy1KUY3PTsFV7NRyeoO\n7rFS9y9UmMo5w1BIZrKjCc5YF1SSEKsSVxoc47rywrmFDOiBHeNodqGZ1ohzcTWcakwYLARy5e/n\nRYHRNHRuHa6dkU32wso2eSGmfxRFBEfSf1+lCPLy0ZLHk6yJ31Ja+tUhgWYwXNvANKUffdWRrc/H\nBHNZI4PhnL2xxjsMVHSci7ggrn8wCfpFO3UfTttpO20PtUfCUpiNCr7z2xM2DjcZVmRHXfXXuKdE\n0NN0TEeDS5+4cp7nLooQS6OjbLpFRLkmABpjDa5CdCl7OCrYYdN4yR2XB0pckTWxjgp9WHAczaWH\nTRLNdqxYjx2tiUj1FLiwfZlmTc312S61UKyNLJ5S0yj5bnRCK9d6hQWYquThKZNvXjRpK23cCxub\nPPGimJdbPyPPZk5CQpV7n4dvsjLV6Lz7W9zSakfy27h9pe5yxHoo39tg7EsQsTRO6XQk8PfM6lkc\nJQVZG/4pnKqYl6atWIKqix8K3iBPUqjJM9lKC1eVu4n6uPVF0FSDc+4F0pIQpJiyR6Ys0C22mWQC\nf27Vhgx8OYHLU/nXM2AUm+A6FmMXQJ4aZdWNtLUZiQKubEn8q/ZWk9oZCSTXty5jIwXs1EOsjn2l\n0iAJ5bmDUK0Hb0jVlb/rZ/eoqjr0yBkTurK2qmUPf03uvXtf1t7JYEygAjadNKCvllAlruH5AuNe\n1WBvP8wIjVggpRKsrEjWJpq2aGjFqFexBBWVl1c3t2wfxyp7tHduE9StqjkdvEAkCpxmQJCJpfOY\nBsTNykskGqDePT4g35WxSgpYV17QCQVmrqasEr38uHZqKZy203baHmqPhKWQO3Mm1eu8Mn+NQvNU\ne8c9kpH4kVmcUdYE/mf+w59h6/wFAFwN/JnEBUc5FvwQg2oGem2MOnymZEFjA4tgkUlS/Lae3HdH\n5IEySR8c4Spise9kxMWi/l36W6JNyb8NQNgoiFWjkMxyMpeTcELEsSpoX9BqOb9qaa/IaX1n94Qn\n2zL8F9YTNq7KaROsKy3XaoZVibJ4//vs9JWObvY231UC2oFJyTSyaRQpOBm+uqzqPE6HfLErKd7y\nWkj9rDJi996kWKT9NJAVBGs4WmXotQ2OovzccgnjimPrNiMMOs6ZnD42mGM25O+K2ZRQfe75oE+l\noWSrJ4dYR9GGWuG4WXPpx/I5cRMcTfuWAkviyfMFboKHBhXXFK7cWKdSlZOWOMZVyLC1I9yFSFDQ\nJ+guwOUybv7cwXrSt/pKDIogDPOITJmPw2qfBXVSvaVqz2OLqynLYVIQ6bpwCgg0/zxXBrBqYXH1\nlO+2QjJN36apw3Asa2Rrcx2/qjDtujybcV/DaalITnaMo5J9uXuEc0nFfIoJaU+1OhQq7tf7JJpC\nrZQcOoFc99DYJdzedxxm6Ycjbn0kNoViPmN8/XsMs28xHSwCMjm6vggdlxevSeDr8bUnyZT51paV\npjyZ4uij2MSl0AAQcb6UIrd5gfVUHFQDYNPeCKPU6HNzk3ksExBXD5jNZLDvTcfEhcKUdTMZ7NSp\n7f2cXuurxCcy6PN0xFgpxYeDfLkpOGM191oWfywTZzqWTlf6ufqnLNXH9CWtqd7l0CHVKsIkz5nG\n8oLtTqbMNqVvwxs5YwW0pwqgSt0Z6xpcKwUFLaUAr5wf4mlQsdsqU5rIM0WqYYmNcAoVge05OGdl\nURXFFKOL1MnauEr3bhUrkUeWoq80YbUTclep06shZQUyxbURxUJQJ9TIe8ty5ZyM1XObHV5SUVyn\n8PH0Rc9GlpIv196uyfj4fkGqACKTtlkYu6bWxGo1iRO3SGbyEjo1Mefz+YxCr+sbn7Qta8GMC1J1\nCUZ7GWlZafe1atOmKZHSvu8XPaqxrjPHoNNLbjS4mEKtLPfYaIaU6/Ks6dzBKUmJezRwCSsKm9dD\nz13ZwIk1bbVuyGe6ZsdNih1Zn3F5j2RBJR8p4czdhIHKCJSylGa4gOEX7KuKWMP1KNyfcKDRGOMa\nY75rjPl1/f+LxphvGmPeMcb8M2PU0Tttp+20/XvR/m1YCn8deANUFhn+J+B/tdb+U2PM3wP+MvB3\nf+QVXBfbqJL3WhTK/5/GQ6ziA9xqyHpHT53ShMOpnOhV1Y+sRT5mU83dOMTL9OSaGhKUBHMv5u6x\nBL6++9sCFz1YGXM2kRPoymqFekdO6faFkEvX5GR+czBjqOSvC53M3d7X0JorotGcmfIpMIO+uj9p\nZpfoxYULMx5abukzzWoFP1A34PzXj3FvCHeE+xlBWJrek9DRYOB8wupjMryf886xciwBo28OBjSV\nfGX3SF2iFNJFgY9TUDhyUtYOGlQ17VV8Zh/vjiLzVCBnNq0zjVWa7yinfiTjYm0dV0Ch1DhPtVB3\nRVNe8+4d0kRP48mEZE+rJFfHzFyFNFcsti4Wy+aWnJjd6ZD1FbH+6n9yyLGmZ80/spjZglshw9Oc\nfqFkp/G8z/ye6nqYd/BiJc09E+BrAI9yRnasyuTqBrglj7ypPByTkIlS+vWvj7lflrk+6Pcpx2K9\nHBwr3ZwdUUy1P46zlKZzWj6eVq66GsJ2PI+VdRnjM2sN5sryfDjpEaspd3xnh82hWF5ltWgbF96k\nU4gVV7Et3IqMUbr16rJCNcwcSuckmB42ZP798CL+oayX3Td91LjBFCwRomlhcRekQh+wfVQtyW3g\nF4H/AfgvVUruZ4E/r1/5h8B/y4/ZFOIYbt5yqPY6GISd2M3MMrIahj7uqgxkLTlPkcrCnKpC6XR+\nwnpZIuSBDQitLNIMh9lQNpPh0R7j72qkXqO+X9z8NPdVV3LWO2JyIpHz+lqNoCQ+c9WG5EsaXJn8\nO70RJR3ofpYy0VzyNIeSTkYpcKhpBHhFWanNyJIrgUhtUuOGmp2/+5sBTZWov/xlqVvYS96iUZO+\nXzhn6TwvWQn8XWo1WcSd7rc40nx8oL7+vO4QKGdiaTZn1ZHNpDm7TE+xCbVvXyPWupJ4IhHtkjui\nmEg/79yakA21z2dLXJn+gtzj0mMEnuCKU3Vnip1n6d+TDW00K5goBLfce4yjkVRMmjDAV6YmTxND\nz32my+Dz1wC44n2MqicY/6/9g2/hKc4kdwtqM3FprKcxnqRPT4lxeqOIaazQ7ZtVWiuy8axXN3G0\ndLqbq35m4GH6El85uXsTfefp9wfUG9IPd3gT9mXOxocyPqNRwTiW+W9aKOuG60w9aqHc76yK3+bl\nQzaNCtJEkChbWKMc0NZnyoqM3dc0M9KQ+b88+CTjNaHwt0ePQ0OFcN/+GDNlds4jl0zjSnkhWan9\n/kv0xrJ5T0czxgMFtRWWstZlZI5Fhbg+cPuo7sPfBv4rHlAGdIGBtXbRjR1g6/3+0BjzV4wxLxtj\nXk6T+Ufsxmk7baft31b7N7YUjDG/BBxaa79tjPnCh/37d0vRNzptW64fs39yd0G5R1Hky4rCctmj\nphVuE3ufdkVOj1CpzRzfwRg55Z1qF6OnsU0jCuXia3XmdJ7UANyqVE4GpsOGZhb6vz9lsCoR7uE0\nWsrOT22OxshwdQ/NioLbGmXysoJI3Yoss2itC7MY5op1ONYCF6+Arhwu1EzGBYX0HUxnjLTg5817\nKj+fWw4WDF43fVa/Ky7Pxedr1Fbl3u6mQ2NNTiBVMGPD1nFVDCZ2p9TbmgHwDYUvp/jJm/tEV+Ti\nwV0ZtyMsg0Ppw02GWC29X/c8Sio+kjc2yQ8eByDtiiVxdP+Iqaokx8cZenDh7x4zSFX4xZujsVou\nX5QA7Xr7Oa46UiRUa0LdCNbhpewrFJolcktzUq0eHJRlYewfzdlRte7jQYqvCtXO2LCu0oGsHHJx\nVdaIny4CdTlRImM4SnqMF6zNxZSRKnYP0x7+QhUbdVuSGUaRqWnuLOnWkllOVSnbMpUvbIWGSEtm\nR7OEtiJPUzdkrFJ+WRgzUm7RhmYIdoc95uWvAzDtdUhXdU0Oc0aHYinceW3Ivqt/pwQ/cSWlNNPA\n9jyipC+P50LJWbg0DlH6k3Mffhr4FWPMnwZKSEzh7wAtY4yn1sI2qFP/I5otLFGcYU1CpikdgyHU\nyPhj3TWGOwLueCtaoxyI7XdWiS/LpZxKqgCN2CVYlZc/zSYU+2I+D/ojZkdScfb270iXvv32HS5s\nyZv55BmP9pZcY317k9aOmNWFn7JgyM61BnqeFcuqxsBCvGANKlhCdF3LkjJ+gTy3JcjVBZmWCtY0\nGj4+V3BwW++h0fujCObqohR5RqF6gMG9NdansqkVk1eYnOgmsqjqqybYE305Rl0mfRmr5NkDuCPj\nkrTuorgwvn1PPtw7SnlH3aAhltWm9O3jgxK9iWwm/d/PufpJTc/el7G6e7fH4W15wQpSpnrrycTB\n13Riq1TBa8uCbbYVpPWpn6VWWVDLQBnZbKajPqmK3XhxSFyVv5tpHOHWcY9JJL8f2JC1UObXOjGD\newoAcn2uFeKGVS9rqGsK8UBdkP0b7I/lu+/sH7KrZvdoGLGqlau+8i9O8lSYv4B5mFLSOc2cnJWO\nbDxNNfdLfZdw8fLPCxqbKv7ay7mvKdmDeyfEyiV5Tqnqb1VmPK4Zlcc+lTPak2f91hsD9nYlbmbc\nDE1i8fJE5rTjOqAbVpsH2bGygdRXMJtxseYnpCVprf2b1tpta+0F4M8Bv2et/U+ArwB/Vr92KkV/\n2k7bv2ft3wVO4b8G/qkx5r8Hvgv8Xz++Fzlua0y8l+KpdeCu5rRrsstfefYiXU9ztM4+TU922lCZ\nmts+xGWxAvIYAg0uMklIGnIizAYHTNW0u3Mkpuq9yRGjXTkey3GH9fNi+4blMnXlZDjfvkPUk3ur\nshtpL8LRE7/sQENra0YWFK1L6BhStSZWtKCm6jucXyRozxvO1cUSGI1zhhoNWhg8FQxnFPRUzw0q\nUcmVcznVsxJxTm4WZCjdmJ6C9TTg7aH092g25vZIKkqvXV/HU6th9vM5q/cVZFOV8b7dS2lvK8vw\nFK6qOva1NZdeS0+/miXOZLzaiv+YdXsk+3ISuXNDqAzGRR3ClpywlzpV1s8pUclloaIvB9lDkuWL\nz8NZSk2rB1s2p6+m+cGx+DOzqKBckmXbvdSmW1MYu3Vpazbn3LMXqV1VJfCFcAwzxgoGC95OqUyl\n/w3js+8p1iPMSPSVqConaL3pES9EylNnycA9Dyzbii1xAplU32HJpWkPPcqJTObMH1FuyN9dmDVp\nKMdkRyPU+5WcqbqE9VqZalst3UtDgol8tzIxrAYLjk2tLvUdVjTQulayxPqsvSyh0KyULcAEP8Hs\nw6JZa78KfFU/3wR+6t/GdU/baTttP/n2aCAaU4/ooEtrZUZNscTtTpPVphTofPZLT9OtKqNwZ50K\ncjTXS7JTp/ExngYiR3f6OHp65N6QZlPSN43Hr2EnElC6ckUe+y+YVfo7Emg70wiJte68FLS4ePan\nAfiLP32N3+H3AXhVobFvD16m5i4KgyxNLXzCB9/qzh4UdI3c55zSdT2/0uZMV/7uhRc+xfSmnFz9\nrM9++VUAjPrhh3nMigrXVo1la0WeY7S/RsuRsbh58DVOVNG10FhMPJoxTxYqvYZoICbGWvg4g1As\nhZXd53ALud9Tzwl09okrRwy0Unv/hkPFl7HfXFvjmcc+LX3aPaE5U2FWFfHdLLZJu5JO3CSGqloN\nlXViPQnPb6xSXpO50mnCVfXmRVv4sdE0BqvsR4ED9yWQOlJgyKVuhwvXxBI88+TTZIqLqLY2qDTk\nfpeufg5P+RI0M4kp77KhMGcec7nYkfTek09WeTGSvrz5/Vts6am6eyy/P0wz3lL0Z24tVgV6yoHP\nlSd+Sp9FJebCe6wuipVqPuc7FwBYjab0FU+Rt8dUmvK5fUWsihcufZbpiczfZu1Jpp4wZ72Y3OT+\nuhREVSr7KAkVz3pSCDgZH9PWgPjQ9jiaq+RiyWOtIpN5mKV4WrB39+g2H6Q9EpuCMQVeGJHlc+oN\nXcQf6/KZZ0ScZGtjjbYvQJdwpURdaXI95U50TZlx/7Zcyx+TWXE7glIFq+ZnpVrBD8UlaLrKORi0\nWdkQSXln15I7YnZHEyh3JIC3UYermeS3e6+KCbvnGZRekSizDLR6MvQdShqUerztM1Ns/FNt+f3T\ntYjzT0hwKs9eI63L9Y6GEc2GmIEn6kZcvmCpKg6jHjp42rda7Yj7Sps2GE0ZLfLRync4iA3jBc28\nzQmVT+IwPMRdkyxBdDdg9aq8eBsqpV175kXcc7LYRk/+gGwiYzVPYHQkOIRqO6LQCL8NlROgyNk+\nI/dbK61QWVOorTlPnsmcNCttSmVBQJWcj8k88f4mretaAl2WB3lKrGQnXc3UnG2UePKKBCXXLp5n\nUpdnKtwJ9apsZAQ9FlnyWA8Zzze4q0oy8/wFXOUwnCW7rCg9n5/fxD2Rz4uKy5Ob4KorkU9dTHWR\nnTCsntOAb/bHAByZCU5LxnWrF9LdURdtq8TZTObduAPKyoXpnFG4cjGmtbUAJFm8HcU3bIWsNZWj\ncq9N+dLiWeRn6awgflsD09Oc/Z58HnhjfOWzr7gBY1Uw+6DttErytJ220/ZQeyQsBZtBdpBxZv0K\ncSBIsvrJU1SOxeRsX1il3pbdrlw6gzOX09/XdBVRtiwiSUcNTHdBzJpjNEJn5gZn8f2pnLpOEOKO\nxWrgwg3ckezA855PqafBnrDPOU1V3tqXk90AKlpMMgVVXqOUFijCmmSeEiuF3D2x0Ln0pRmxypzd\nn+aM9uRUSbZz6ioAkqtWxHmAFXnO8mHArC7BUXdep5vJKdbL54zHcpJqNpGpLZYpVN9lqWHYe+EN\nwgPVb1jbJQjk5NKYHtlsgh2IdVC+f5XiqqbC7s3ou3K/Ul7BrsnPi0juODVtQoXzmifGVDNFI24M\ncI/kNC519ymduQiAt1DAWY7kw80WCX3Vo7x9e06uFZrbJSXm7ToYLcZy5zU4Udm/2hBKmkacJiQD\ndUMvqc6nk1JMlWUr3se5IAHo2ttT2BTrbvv6WUZPiovZfElO+eN0tEQEBhV3+QBB1qHsfxKA9ED+\nLe3+FtmBpLuT9Bb5plgxpcyn8bRCsA+v4m6rtsRILpz44E9kHTqdGVixGjwDbStugPvTO1T3xc2Z\nNCSN7O9X4OOyljuvGMrnxVK4eC/kLU17xuMmqxW1oHjzPeP9fu3R2BSKlDQ+Yhqd0NYKsF7xfYbD\nzwEwGsSsKHTXNCcYLT21qgjE3CUKhNtxMgqpKJy1VuqSbUrMALOO8QQKvSATsW2HQst3TfAY2Uzy\n8bZ7g+S+vGzNygqh9ungs7LQ/tVvQagghI5j0HeUtu8s3YpO4DJS5SFHTXTnng9dWWzz5gyvKc+x\n7gScr8rb+QNf7lsrytTVbG0GLrFG73sHARMVhSWFhgqpFrHqSxpDFGpenZwJ+vvbKzhjBVE9M8NL\ntUx6Iv655wa4DYkHxOUMR/Uj096EfiIxA2M3KU2UUGYi/Zxv3mV2LOOzMb1CpSE45qQ5xS/ks3/m\nKq4rL7XzkNvwXnFjh3BR1cx0NiNX2v25xhnKRUA6kxfhMH+N3lDp0J1iURlPPD/k+Lb075yRPqy1\nrzBek6xNMK3QcoT2PPC3qW3IRscTbeqKRRl+TV3Cxpx0oLiJwGE+X5BpnrA/+CoAx1YwLc1ggK9V\noElRJj9R3tC2R1gV97cIU9IFuG4gB4S7tU9QFXUuv9qiqlmEev0NRieqebn3HGXNbLCp2aDDB67E\nNNmhptmJXhEvxYVGmUtyZviecf5R7dR9OG2n7bQ91B4JSyFLc453enSnq0xLEiGvv3OVewpBHjc3\nmK3LKd8cdbCRysnN5YSKerfwDv8DAMy0jx3LqRT7OeVIrQIK0Io5agsasCpo/Ty5gbZYFbXsRZKG\nnCq1Vod+rC6NGBIkObSVRXjq5tSUjuuxSolQo97GyShPZec+W5XdvuNefKCf2OuQ6UmROy0O+lII\nZidift9PhxSKKkzClLP9FwE46r3NkVKa7U969CIl/VCoXZ5BW7MdhRPQDmTcNkcfp7+gD9v5DFat\n2bQspqhpdinHQoTrlvdJD+XvbPr7VHti+mfMqHrynUwj/dfsFYYldcdO1ok1qBXcfgIbLQp4Krhb\nihf4MSlzNyhzRVWz+42IuVGXx5X+FOV1YqNFUne2uHNLXLqV4zbuuprMezXuvCaWwtpMlNen56u4\nd0Vvc5pEhLmKy9QdKn1xc9Kdu5j8S/rcf1vuG8VYV8z5mIK2Fj81/AaHO6onml+Qzvu3qWqxXtAu\nLYuynOoV3JnSBbqQHIpbXKQy9p34Iu5CSTwvE3bFVdwcfZ6hFq4lOzGTbRmX8htPynWLOxQDsXLm\n3q+R9xc6nbcJ5jLB03gGb39CR/e7P3rwtT0SmwI2J7MD9uIJDRVW6c8HOOY2AD8Id1l7/JcBMLmP\n70l2wVHhz97kPvsHsuDdVo0sksF2/TqxMt6UW3WKWN0OT8uCzQS7lG8asmR1YUZpXbIPpuKSHYvZ\ndvMVSQ+5BkbpwkQvmE/k7y61IFPp8PHUcldf2C0FuQyCG4yU8OLo0HKsakSzQ9gfyjUmykB0ueVx\noryMZypl8olEuHfiiP1EFnyUPpBzj5RGPsAnV7eqcHI6CtgZ+G/hb8riPrmbU67J5hPuifmdly+Q\nadmwKWd4yPOvXG3g9VSopJRScmRnLJQJadZ7BWdTrpEeXmfvSF2U45cotWSe2nYTdywgKuz7G6dL\nR8J18JXi/eJWnYmCd8qhPEfU22dY0vjL7C6JoxtP1iBX5FcynpIV8mKN7sqcraw9Q3Qiz5yahN6R\n6jFeXWWmLth0fkiay7NOCtl4fcdSViZqL7HUtB4+KFm8jjxrr/eyPoRLd0O+6yQhhR5eM28HxnIA\nZPhMVf9yEutLfOsPMAvux7JPouxdu3eOcNoaP4mmuErRX4u/Kn0olzCLWsTqIX5ZrruT7XJ/JLGN\nkRNiF0W+H7Cdug+n7bSdtofaI2EpWFuQJzFzk2KVYyB1fWZTOVXbvsszu7cBKKXXSBWWakpKS9bf\n4e0bEgw7012jeVlOlWS/Sa0lJ1TgF1gF+BRa4VhMmihRMyYMSBMleEmqDN9SwMrF17lzLLvu118V\nXYRpmrOz6HwORq2NV/cjrnTFZOxNEu5qkdKrt7S67acC6spKXTpbpncoP/fdAqMFMXPt2+4gpbyi\nWgmDlKGamvdPMgazBTgpZ5ovgDXK8eemlNVGD3OXSK2YW6tv0bgr5m6lucP2UMzLUSKnfPkQrKoy\n54fgqMQcRUo6lmvPezGjVQnoum/Iab47z3BGcr/65YJiItbBPE/JNYhbLqeUN+Xn5n3BzQ8+3Tvp\nk6nYSZBYFgrtkapI1zJLtVD6u8RjOpbPpY2M+ERcifFOwd5dOTXdNflZ/v0hSSDP32issbGiVHeT\nMdFInvvgziHHViyPkVEMSRJRKBjMoyCPFtZUwehYsxm9nwcgDV5lorLh5arHyrqMUUQGimWxaS6m\nJiwrgucHOXv3b8vPrCUNZU729qZsXRTrZuuKh9Gqy15f3LX8XsGsJT+bxz2GGpS9259wT7EsXlCl\n3jriw7RHYlPAGHBdjOdhkwWu25KpkOofXb/JM3/8FQDCLx1Q0boE/TVH9+7SL8trmo1HtPcvANCN\nAoal78iX7Da6ljCuovI6DiZeSJVDHsuLGVX6nJRkI7jxg3f4xj8Xf3/PyouZAhO9t7GWktpbSVHw\nzkhuMoosfTXtpoqUHE99Ol3NdlzK+ESkBBn5lK1E4gBf0TSVN7McLyQc4zmZVlQW1kElCLGpYdVf\nUJjLz9IMct38RgXc1rjGmVs1fNU5LC7NmDiaWlMuv9HkkPl9zT4kUNvR+LXNOanLC5neDykNdOOI\npJ/3nSGOpizNpEFLr9dv5EwcebmbTbC+jKdFov5ipL43+3AyibEaS6pm4J+Ra/dPxNR+ZZSwPhT3\nKQlyAq0rSc06qSpVHcVTxrF8p78rA3P75C7BWd0UHYfY1z5PBxx60ufd+Zi+isAM+joWRSYioEj2\nwSoHY6nqMTSyto6akup7zMJc4wiRv0m/rxuZP2au4r6eCfAL1TR15OUfd2ZMF8/UyyglOvEdB1MX\nN82ZNMkjcd326/J3k7HF7Ws6OJ7T03rcUVpg1QkolwK6VySewds33jPe79dO3YfTdtpO20PtHrsl\nXwAAIABJREFU0bAULBSZpZzmS4hu29Y4UUDPfFLwr16Tk6YeXOXSJ2T3zGI5iYbzlK1QagNK1lBV\ncY/CD/FV/uykHzFJJKDkKFagbs5hNWcceAHxXHZgp7zBUEExt7/yJvcHcmpUNGSfFHsolwgpD2Th\nDmZQ1UDptMhBg0ev6G7efKvFsC9Dfn5vld6+mIFemNJsSD+eVI7HW1GODgUHqUOzJ3c8iCPmqfxi\nkmQouplUEUs+sHlGTqvhSUqutRQr4yrXC5Ve+85FDrbldEwj+VnoBZQ0azEbwVAj5ART+lOJ/Hvz\nmJUtJVlRYNVjvRaHx5KpObxfo9cUs3tt43kGim9IayHTbRnbxpLX8P35fGdpTlWZtHMHumMZr4Xk\n+sk8IlEzuZKVCLRC8eRgQqhSdms2hJrc56pWc05KGfZErnWYzzi4oy5Bw+BYedbj4WBZpXuinIr3\n+zFWKyMxsKmMO0GQYxLJcj37mFiu4/5bOFrvkYQpqapVn4wKKqpWXZRm5APpf1mva0ur1NSdqV0c\nUVJZuc3mBifKtHP30CHRde2rtVUyO+DKdW/3rnNwKNc4nFvOVOQal7cvsvFZodP7g9/4nfcd8x9u\np5bCaTttp+2h9khYCsaBsOwS5ZZMi4AGUYavsmKzLOH+oWAIXjn4Hk8rOiwqJN1Yq5UIG7KLrgQO\nwbHSrpUgG0vgKKjOSVSh2U3Ft0yzBM+TkyafWKwnp8t0+BYHr/weAH/89stM9MSvhZvLPme6naYW\nUvU5Q9ditf8N3zCyi/7LifGHO3do9DUG8MYtPA2vVYsHzM9DpeRNsKwYLXZyHWZqFWU250hp6FLL\n0ndcpL8y41L4GnQEQpVr201ipoGc4nfuRqxXZQy6M+WmSAtKcwkGdhuHHB+JZZaOY5xcLQg75+6+\n6iL05AS7d79HrDkvJ/RYP6eBRj9lRUlO+/Y6+duCBVh5UoVQ3n0cvSv2mGc5E8UVN0sOiTIZ+UpU\nMU0tZY2zXlhbZY5YcUfTGSgwsWUsJ8qtMJ0u+h4xixSl2jki0GdqbYeUVZQlcFJmM+nYkQZoh1FK\nQ8MrVa+Eq0rTeeHw7J+UdTjOJfiazzL6Kyq2u9Njqkqw9WqB19JCsWmf4YlYTcfahzkx07nAq/23\nDGsd7U8xIq+K5THYGxNXpE/NmsS2VsopE9URaVZgkKt4kpOjgFRWzjRwW3yo9khsCq5jaFR9vPKU\n6L7MeOxH1MwDqflMo/Kv7R2xP5BBqSi1+rB3wrrSXZUrdbJzslkc3i+T6tvb7JylohTfuQYA7WFM\nosEZt2yZqSvRH1f4imISbk7GVDXvX12XjSmxDzYFk4H1lVXag1Ct7hUPzEwJOZQIw61ZcnWJZlO7\nHPzCcehrKDpLF6QgFmdDQVZuQU+x7PkUUiXsSCeW+8mC1FI3prOWsr7oswCOlQb/3lZK/qY8d+TP\n6Tqa06/LSzc7Mmw3ZSw281U2fkYW43yvybbSucejLmNkk+kpi7Tjumy0Bc5bWTMM1dwdzSbMjAa+\nZnv4Cm+uHgsp15WN2oNMxLvijfM8xyoltpvnZErNZpWLcOKlvKHBztUnhlxU8ZwoH3P3Te2zE1PO\ntGKwrPiGyDBXgNdqvcFGZUEGY4k0M+CYjJu+XOOtY30Zs5RQKw7ThsXUdC5dl1VPAGVPd+XfVw5+\ng/vC9cPNV97BXZM++J0yuapQmf6cM0/IS7//qoxhMp3hKYt06sL163oAhDO2ldTz4k+FDPZUdEYr\nXeLI4agkG/33d6bcHOphkUP5sgYgz5fplD4cnfOp+3DaTttpe6g9EpaCHwRsnz9LkOekM0kt5qFD\nOdUKRx8yRS/uTsZ877Z853Jbd9dhzG5ZCUrHZ/CP1dTsHZLobm1OCvyySrNpOjEqnZBqIMuNfIaJ\nnEBvnVzneC6uRsUPWeloKuuCyqe9tINZ4Bsci9L/06o41FY11WcNVVWCVmAifsnjjAaGzpShrhZI\naB1cDa711OqwuaVcVUm01FLXist5yaGjvsbdUYpV/oZ0IVIyDDjStNnUyZlZrVo8LNPXNF1p3XJ4\nrIzJjnQ+DFOqi8rCTZfmVAqlymlBuiEnU5b7WCUksZn0t1jNKBQjcTyKuK+pw7ToUS7JeM1Oakz3\n3tbxegOAs3/pRcruAxNhcZaFgcvCdIhdS6CBvVz5DTxTkJkFd4TFuyQWT3vsMq0qcWnNJ2yIu3JO\n+3bSO0E9A6qhyzxUVuYcEmW/niYBO7vj5bUBcscQa5VbUQ7xQ+VbKAUUbR1DRbGGUZNbrrgB40GP\ntBArplQOcDTVHhAQHqswjhKhbJ+b410S9pnCJgzelADt9WCMAisZnARY1Y1caFdmlTL9iczHK7fH\n3FXUpPV87MbH5X7nPoG3vqiS/GDNWPveXPFPur3wwgv2Wy9/672/0K4VFLzeEzKUm9HXmf7g+wAc\nrMhS6v/BHt/77usA9L4dMZ/KJI+SjEij+V7JoaT+YKOiL0pmiHVB1D2fkoJNxnsphycCfrniFRwo\nkcnxTBddNkd5Uzh0C6zy4WUGinzBqOtgXQX9KCQ6ye0iWEz18ZCf/asStX7mz/8CP+PJxFVDMUWv\n8XFAy2Zxl9yPxjrvV3H8PjAgcUFOCjEvfRMw0xdkXBxwOJRFetNK7OB3f+vv82pDFvRTZovtz/1H\nAPxCWOVSIMxLWzRYGJeLVRMzwmhNXkTCD0ayoG/tfYPf+B//lTz37R3eun1Xvj9XJauKobohL8el\nxza4H4kvPn1pgK+u4kmasCCRShZ6jVmB7ymUuF6muyUxjHN/4VN88QviPH/ymb/CZ7mmo7HIcjgU\n6h66uMv+W4rlZw/nXT+XljJiqhvTKLnHb/3dfwzAO++8zu/8k98G4HGdnDfCgnJdD5BalXIosZY1\nv0qsm1A4TfFcZVpWl29Y9cl7Mq7XOiE93XAPXY97yuZcTCOaCojJdWPq1GqcVZHicrPMYC7zEI9m\nfOGMjIXtdGlfE5zCn/7P/5dvW2tf4Me0U/fhtJ220/ZQeyTcB2kSdnpgt9jloWesg6lcACDNeqQK\nEz1OZKd+q7/HvgbA5hSESv83G2ek8weKwJ5KzU/V/PRLDihhhymDFysKrOKABoYSv0JdrY1FgZJf\ngZKa/qZIUb4RCjcn193f+HapXZ8ug4dg9VQ5O2ryeP7nAPip9OepK87iHGLFuD9EV2be8+E9//Oe\nZoCqERPWJycwiyBZh2lZ+t/SYpmS+RP89NPyzJ8ofYmLFTlpWrbDqvbFvOuWizuXaCyfCRxWa1JR\nOb14SPcTFwAYT/v4t2Xs2kY5F6M54V35XA5rUBJLwbUFoWZdyHKsBmlxHvBFLDIRa7UK3aacgteC\nyzT6fxGAJ7m8FO55d3OUM/NdSwu7VILU5/qh5/OoUnYW1aoesTo6yQBa6jbWFedQY0Z6T4VaVibk\ner+t8xZHOS7CNCGIVOpOLdMVp0zfVXRjDFb1PvyBR1V1QI7zgomu+7JmuJxgTGHEUqo1fPxCrKI7\nwS0O1MRaT6rEL/17KEUPDybh3WbwwrMxWDZmsrjHNz7L1/5QFt7R1z8FwI23/nfyuZJ7mFeYWBmE\nIs/J1OxMcoOrNQFG4b5O4ZEVC0n2kOG+piyrBcVEJv87tQGbOokjlfomhv2G/N6MxHcHCIxkUkD2\ng0hTlYuNwsNwRvngv3j+v+Dj1b8OwBOHAS3NWrj/uhd9sVuaH7cVvLsZPL23U7j4GkvpeE3ynvy8\n9KoAYT7Zv8Qn31QymAuwrTeprrCks3+oH8s7PKhmKFmPTRWmDXe/wLfuSfzgYPxr5EbG9gBxy4Jy\nhVIgm8Lw+EnMWLQy4/S/Y+zI/Ewzy1xv6OlGUcal5MnLVJ+egV2pben+2l/jl78kIKu1xUD9cHsf\nT/k93/qh77j4hDqGld4Vynsyf8nhHvO5mPlf3pLN1O0bJl1ZexdWW1S14nU97BLnUu7drezTvSLP\n/di6/P7IafCdl1TuvjTmqtXsxGrObKZ08Gct6xp42dHxKdwStWt6qA1mxC25RqlX8JIjgLLPDw/Y\ndV5974P/iPaR3AdjTMsY8y+MMW8aY94wxnzaGNMxxvyOMeZt/bf9Ue5x2k7bafvJto9qKfwd4Det\ntX/WGBMAFeC/Ab5srf1bxpi/AfwNRCDmRzb7ng8P/qcAeqlgBP7wy7/DH331/wDgzh3hUBhPx1TL\ni5MkI2dR/17ga2QvwWI0gq+FhWQJmEVBzTAmVk4CkxS4mpuvWohUQqyiloLNLFUVPZnmOZ5G0R3D\n8ugpsEtTVLFEtH2XzVX5YVr6HofO/wPA4fwXqCgZTFiRLzvvISN5kNO37/rRj7MabqrUfMmBsnJF\nHuQDjlWnrn9LszZmzJv3L8h3O20yX8zSC406VY3VvbdP2iXtUGohdxRMZO/RPit+VffkKpWefK4e\nKkTbFNhEfJeD2b9kR7km22Gw1J3Mi3TpRPk6mKFnyFRh+97kTUwmJ6It/RGbf/Q/A/Ar5S/R3tBx\n/CF34Ifbjxu/AojVXD/mFr25BLn3dyb4Cju+pnR7x3FE/apcsZVmFMobWm2nPPOzGoDcqXGmJbZM\nU+8+Wevjvi5rbNvUuFASS6j8YpXdPxKrAi+hdSKWibMt/247VTZdmadJFGE2ZC67pkRF+zRNDO7V\nD8fm/FEEZpvA54D/FMBamwCJMeZXgS/o1/4hIhLzYzcFLDJDS2fv3btDwf25RK9v7v09hqoJGSiZ\nnxdmrNZ0IL2Q/kwXm2MoKXQuSS1GCUms+vo2N3jqUjgjiLSmwMkMC2nGxqBEqnp9Ya4kHW6fLY3k\n3yLH5prWNHZZFlvxLCvhYqOSn336apmLH5MU2ie/ENFtCgjrbPOIRMlPg1DcoNy6S2pxa8SFer8x\ns/qymId/vGz/7O/+FgA//3yVWVU21r37v86NI7lfd1/M+d1yg0D5GgdvP0FN+RWL8ud4fEV5HI27\n3ODefY9Mx9Pkc/JAgGON1luc+5iOSyXA+BKjeONlyXb40wJP1ca9wCfWiHte8gjUXHeNXRQokul6\nmBc5qQK28sxitNbk1r23+Mf/6G8CMNz9AU/+yl8C4FPnmnpdh/LClXSgtnwOb0l0++5xXMioF9Zy\nnEkG5+TG3yeuSTr8pNgnq8nzPeuIa/ta9Zg6QmGfzvdIOtLnc+drvPixnwFENauxKUjIbCbVt43u\n8/ANqUvYWjlHuS4oXLv9SV7fElHckpcwb4g7dn5F3OdiPqecy2brt25xJpV+9GonRIeyOYemINhr\n8mHaR3EfLgJHwD8wxnzXGPN/GmOqwLq1VonL2AfW3++P3y1Ff3T04eq9T9tpO23/7tpHcR884Hng\nr1lrv2mM+TuIq7Bs1lprjHmfI+5hKfoXXnjhgVH8roDau+3kZkW6uvbUFWY3ZLd25mIWzfOYUFMO\nbp6S7SkMuAYllYEuBXaJWUjUTZjaApR7ICXHUcjzLLNEasPmJiPQ82OmajxNF4ZqUqYGUvVHXHcZ\nJGet7LG5Ln16+opYMS+8+AKPXZFTovPcz1JrSV7ZD+v4VmHMqhDs4FNowAlrQLES7w2MqUtj3s8Q\ntrx6958BMOlssaliNscXHXLlCyhWZM+uHRt6FTFrnZNjXm8LhuLpTo9xV8zkJiWKxTmiR2lhiiWA\nqrAFrhI7VILHuaiq4I2LKwyP5T73doQnsLd3mzHKsTmekmi5p80NkX4uMMvAravHeWwNSaZsyIWD\npxiCYRZw6674R//kpX/JJ88rRXtTOBfP7aySrogFec5fI1U8QQlDqC6k9z7ORGxS0kSsn4PSNscH\n8nxRXGUNpcVTLETWcjjauQ1AezNnUxm2Lz7+PKtbgptwtw5xp3II+jPBoXjNY174C3It9/geidbg\nHud/zOrT6h71UvzVRWWr8DfU1zLCkeJiKobJibqCxREltYoiG3J0/cMduh/FUtgBdqy139T//xfI\nJnFgjNkA0H8PP8I9TttpO20/4fZvbClYa/eNMfeMMVettdeBnwNe1//+IvC3+FBS9D8UEfohpGW3\nKn7Rc7+wTiWWz1YDgByEZHpaH7w2YHNb/rY7NahxgC1yVKSa6VBr25Oc6YKazdgl449jHlQ7Ttx8\naSnkyt0WAX01gNLiAUTXwDIwVtiCQmMCvirEVLeOWbkimhSl5oRQMQSuv78saCrykj5+mcLTz/gY\nPT0cfBxnocP4rj19mb81Dzn8b3zj9wEY7ZR5wxfrwBu6rC6YhgNZAl5a5u2hMAmVZh7bq8IifJR9\nns1cxrvqOniae1+MRVEU5InmOu0cm8tp5VgIchXzaWxzsSXxmO+XtXrxcMJYGYxLxQO2qDi3zHRE\nCyypPoyjOhtJViyfL7eQq6WQJDOspgjHuxPsVxUhWcjzm1lKnErfLzz+PJ97UhCbq41NzlYlXdhw\nnGVAcxHDiArD7kQskLuzb+JVJbWabgyY3pD+f1u1NONDcEuyArbiMufUUlypXMH2lS3sKKTYlNPf\nUZ5gt71KLfuiDOGVMd4dTdse1Mne3pLvXp5TOVCLpS8ox2Evp6tr3T+0pFOdm12Yrymqd3/GRDU+\nPmj7qNmHvwb8Y8083AT+ErJS/19jzF8G7gD/8Qe60g97Ge9yHwyGaijY8MdXf4nyz8kC86YC+Jjf\nGLNzLMHH8dGMllVx0chjoOXQSW9OSYODEyXj6LkZh8r9N+VBhWKcZUuzFWOWPI65eWCqm8VmY+0D\no9NCSRdVq+KyuSX3u3BFAnXnux+j1fgz0vdGB5OLSe24yYOXfoGvyR+88I4t8WC78fnRMfOHx3Gq\ntR2D/YKVTCLV/rMNEkf65EQSAIyOJtSUoq2z3uGZNeEdfKr5SbqO/J1nguXVF+CggoSiUJZsfNxM\n60u8KaYiblMQz9lsCTnL009IsPPt7w2YKSQ8TudgtewXQ2Clz7HJcRZQd404msI8yOp4LoGKtdab\nddYuiF5l43yLjWc/A8DKGX0xd9tEZTHRneEWRSTPVNgamdabZIHBaiZpscdOi4Q8lk1jWHycoiYb\nQJj0GRZCklPZE/chTAv6q7IpHkUz/kRFAoLlep9Z+Tfk3kwI+zoPXfmX7gGFuo8mmuMt5OyfvQFT\n2URblU18vfbNq3Lf7AdlUi2Zr/gJmfJRhocO9VgrO2cRr/sxH6Z9pE3BWvs94P2w1D/3Ua572k7b\nafv/rz0yiMb3nn72od+4alZvJJ/CHYlJFdTEnLrJH9Etyc/c8Btsrcvpl7oDXEc+T+/3qNblVNnR\nYqd4kvNaIifXJHcYDpVbIbEYNQVaTrhE1eX54nScLy0C69ili4I15IqLON/u8suP/yIAm8/JHtld\n/wwEYoqbSfVBVDJ3lieUmT9wbYzCeQtjcQrVcvAsRuXBCEVc5OFhezg52b+vlXPHLdKSnDrtyXlm\n65pmRU6oSTKm0xSRkWb3WTaHUpgVjNt4yhSblcDRe2d6yhdJwGgkz+HHPomSxDj5CpESgDiRT78v\nJnM3kkBrEAZkYzn5Zmm61INI0gJfLbLC2qV7sPQmjaGsaeZmvU73rFgHl9afgpJ8bvU+zcGXZY7P\n/IFYRMP6iEp8AYCdzoDed8Qy6V2q467K2tp/1rKh8zrS4rfJXZ8f7IorNT4akE0Fi1f22wxzObGf\nV7GfQcPAWCxam+SsXBEYe2X7i5RK8tzz7oSwrUHxlo6P18F60h+nXJCek8K0s876ksOjsRYyKsRK\neV6rh7+Tf5lSqoHp9m06J9L3wBkSK+pxve3zrAoX/Tr3+CDtEdoU/vXNAnPFJJzkx5jyawCMc8l8\n5qXbS6bbC1crNLeVTWnQYaKLPnANkXIQapEdx1VDRQU24kFEuy6LLc4c5ouXy7UEGq+YLbIM5gET\ncc4yHAAOdJR1udvy6IeCrWjwbQAOj2LqRrD6Ya1EtSYbhBd4OJ5Wbhp5zmwakQ2UyrtUJpstIvKW\nCmJ2umfWKa/qAlt6Gw+7D1mmYjDFGFdfPOOMCXdlLBZeSuH4GKU13x5G3O9pP3Y/zXwm9wsrlWVd\nSa41I/NkxHwkuXIzi7CaJUnzhPlUouXZZJcil3hz5imBSJEx1lhElCVLrkwfD89ZYC+Sd7mQ0nzj\n0NGS47PrZ1k5I6CvWpAxV07Ik+gGqZE+3daS61paJZrKhrUSwPWpuASzI593clGOWj3apLmIFR3J\ny/b28Fvsf1cqdKNwxHwmYzSc9KhqjYKnasMTf8j9O9KHyppPkLwkffc/Rl5Skd51u4w7GKX7t94A\np6Y10tFdvJqqYgUR61U1/dOYhW4RQ+nDY88P8O7L53Q2X7KIVVYK3GMlIGo4+D0+VDutkjxtp+20\nPdQeHUvhPWiGd0UaLXiKNqyaiJmy+VaNVpnV1mlmq/rdPZqrCg0tjSjlYj47eZvZhuy64U05aQhH\nRNeV5qvpUlFyljAElEotclNCtRp8f2EdWGJN1KfFA4s9dKCulkI6ShhoLfz8SJBr82dzGveE5q21\n/QR5R/pZvdDELeRzUgiaLT24T9FRmfUoZ241J75zCCUNtHrPE9TERXGr74ayPqgBTNS6cYBEOeR6\n8yHhAgarJ3SjHNBTXsPJUUD/Dfmcx0PoqORbqcHqiozdVKuritFkqZdRLY2YFjJPUX++FFGZTQ6w\nrjwXjp5syYRpquF3W7CYa8dYjErr+Y5ZZgEW7kPFs1xQkZwz7SlrZZnfpj9kZBbuHRxMeno9vZYN\nSa0EprNxDVRLcz4zTOb/VJ7p62sPVKcVx3F79wSVwyAeB0QauB3Pq9SMuChfb6kLugsDVb4u+yUS\nxYXnowhqYmJFxhCWxYqpLBCyaU4xUoEjv4+nMuamVMH4Mt5p4xg7k2vMMsmGNMOrzC8KIsC8E1L4\nYpnFGRwFSqgTBZTrH+7sf3Q2hfeB8S4WQkaBp+SgPsdUVSc20XLpTj1g4gl5ZjqwTN+UQQs88Ldk\nYiqra6w2VVT0KbnAuVLE5aZM0MHkLr99R/zeUemEI+VMlBdewS0qEW7iXARpkeyDWsx0Q5fPrstE\nP7NWYe9AXoRmrvqS5ZgzVy4A4NZCguA5uUZ8gtGaAa+hDD0bVyFUPcN6mblyNGZVH9RtSqOEXKPT\ntqIsVebBWOa8e3swqGgQEZCpz7MoQ7eJ6F8CfOden3sTAchs96ec2ZCNbHvlKaJIxq7uCgzark4p\npnLv49mMnTckFXj9+BZlIwu6tmJoqUBseVtcpkb1Oocj8Z0Lm+Koy+B4AdWlnZwueTND/X2nUuJ8\nSw6ArAgxyjw0b7ic0fRqeKbOxVhIdk1NTPRyGhOpuR+nOTO9R3znTU5SWReTmUNX2XNyTVuHpRLO\nvrquoyNmE5mfyOaEWm8zP5K+9ScZiaZLerOIuwNZh/WXf5v9i38AwO2XIemIK9WNdc6euM/HciGy\nOfdkhVp4XufpJrdfl83i60df5/qXhUioeE4Om8vDLa5dlveiOMlJujrvb7n4E8nAjScJrLwvfvBf\n207dh9N22k7bQ+2RsRQK+6CiDTS4qKZvFs24t6/qyO/UGGsQ1a1IJNgvgz/4gvxwd4/ZVHbom8Nd\n2qEUouR5Tr0h35+olPtkfIX7+5I/Hs63mKjkvBltkBeCe2ilVQoF1jiKf0ickyW8NjEWR/2Hkl+m\nooSNbx36XO/LfXJXqeh3C365IgUzz2/9NKxI7t5YQ6ruComcbNF0zs6BmMblnV2OhnLS3nnry8yH\ncsJuXo548RefAcBrywns+w/SEQaJ4AMEGMYL3ckMYv28sMZKbrbkiRxMIqo7YhG8/s5N6hWxCjrt\nN3nhMZGiv/yxC/Ize41vvywm7P3rb7K7I307STyKkiyvlfYWnXWxyLZWJApfKXvUArUwTIqr2ARj\nci4q/fy3JnPSYoENkX4Ok4Sv35J5KhwH11fIOw5bGnRtNbtcPSsgsfNVxVg4ITaQz/fv3ebua2L9\n7N/dI1WL5lYW09SEUJTJ2EdpzihVfodZwVz7k1mYKz5DKRoprKVQrswsatC7KxbN9+ZTsgO59x9/\n6waxrot5U4rDrv3uJ8mfkrXZ8T7NvCYXHN3e5v/+3wT7d/3GjIlS6/2Z3l+VeaocEO8pP6T/Ft2p\nZNqSkk/vQCUQE2hlC+miYz5IeyQ2hQJIAI8HL1jCnPFAzO/h8IAffE10GN74zuskx+LDHbsS6WU8\nFVUgoN3KaKkk+b3BEdMTkXAfejM0E8mR+sDGlsg02l/NDZn6stZxQHn0jDdlKxTz8p7Shge5i1Ff\nNXpX6CMzGX9wTxbTcdJjoBj9rrIcNYIRX3nj1wFIk5s86X8egDX3EieKrz+JVIvx3i43X5bneyO6\nQbMti2o2OqKrnI+jvMH2p8SkrEQXAPC82hLck5HjmEVa031QHWohUCOxG8jLv70ast6QewyDhHSg\n9R42WeoUODbh4I5sjOeuyf2O9m7y8tfFNN65t4sJZIyutS9Q3ZCXtF3LcKqyqdU1c3Dx4nmO+/LM\nvf4Eq4PYDEr4+pIWdsSijtrTVGjTr3C+o2K1hUei85RF2XJjadgSZY32N61sPEnuMBzLvE9GoyUh\nbG21jp/Jd9oY4omM/3SotRY4JOoqpjxwcgv74OXRaSbFLkFdP/WMwyUVij0uJbxzS1zTZnmAe6gb\nnWpWJPHvsf20bHSloEJ2Rubk9tsvEVd/TebHP2blqrimA++fyDwdT/jaHY1P1AwrGmtq1iJq2tOo\nCtHkw2nRn7oPp+20nbaH2qNhKRQwiwtqvmGmQbkoPmL3puDW3foOjivmfOniiI1N2UmdiZi1O9+c\ncTvXCrIY1n5VrrH+ez6/qXhxe2xZv6yPu6+mqj/n6EQ1CvOcIF5QqZklK/PISykvOBb1dImLjGJB\nrJJBprntw3nMjtZMZJl9UJPvLTIZBfU7EmSqm5A8Eiq0z//i4wxuy3Fz5xtiHRhvwNOrYgLPRm1u\nvC5Yh8HskJnm4N3LNfo3/zkA+ecVRGoWpiI4uMsTOHNTsXmR085VYZRGTX5Wrzrcj8RUlokhAAAg\nAElEQVSsnY5UPh3wSwZfVa7j1GNi5URzKxLsdKMmo6GYYL1owGwmz3E8e4OzM8EQPH5pjVJF50RP\n8/VSRE1FbaqhS6YcCaFfcFIRCzEjZ4FzXqhLz72YSE/Y43lCR6P6iYkpKdtxqxGzXpf7dAS7RFKu\nkN9WJatRSqDEEJNBwvFI5erjDE+txZke/+MiW5LsJFhCDczmxi4lARbkM44Fo+vi2kYTU1acye0+\nka6du3sT/JqqUY+VWOXxBpfOfEL6/rELZKo72coj7rwiY9FqzqlrReSJqlXv3JhQCzUrd2I4DsUS\nWq2mTF3JPrQybwm6+6Dt1FI4bafttD3UHglLAVOAm5AaC1Z2u8Q/xnOkvp98lbWuntKrlrM9STJv\nRbLDT0Y17l2U0/NCVmGrK/5kb/U+lUBOh+COw1Md8R3rerq8sTFh43tiKSQmx9Gd+GaUUo9kv3R9\nlyxb+OXSnaIw+O4i9lGQLU7j/EFNvnWhrKfR+prcdy2o8rRqHVx4vMbWswuBkNtcLsmpcvuCMO20\nJgbTl9jJ+WnMoRKCllodnlqXANZnnuqw9eQvyTUWsEprl9wKczIcPcbcwqFYnP6Bw1ZTLIpV1SMY\nRjnGlb6tdny2zqiQCfDOgVgC89SSKHNxdiwnv9eZ8dg5lY0rQaDaj3OvQXVFtRTbXc62GvpZju4r\ntU1mej87/gHXBwrzdQPQNGOZEzzVcWypoKObucyVdbtT7bC+oqjQaUGzpRoerS71deUqKMTMMWlO\nEaoGY9ujyCSwVy087umJPpzPyLXSshsuLAXD/kDW0IyMsuJQSAsyzfGGihRtO+C05fcb10psBdLn\nx56pcO6iPP/dbx5iFbJ/uStr9tyfuMq582LpBU4Vf6bIy09O+OwXZbyqbplttWKmm0p592SJrmp6\nbo4t+yqONHonojVXUSJielUNenzA9mhsCtZgMw/XOKQaaHQna8SpTFYz6S7FG9eOfp7kSGDOq6EE\nr84/P+XFLYGqnnz7DhsjFc1ofpln+7px+G+R3JdJOjGSS+/OP8+N8dcAuNSoMqtrsC+O8LUuYd2r\n0s/E3HOUTCO1Iyq6EFwjlXsAvuPycVWTqjctG+clGnx5XcA/jbWQC1elz1fP/SrpVO7RqZY5qcuz\ndlQQ9cbRmJVCNoWViyv8Z015+e/G+1yuyKLwuwllLUV2FeJqzQN25dB6S/fBNQ8i+BUvYKUrL83l\nhgSn5iVDZSE62yizpj+fRglpRV7uG7d7NBvyEio/CJVGlaeflYzE015KksgG4rX+P/beLFa2JDvP\n+2LPO+fMMw93qro1dXVXz92ySIoSB9kSDNCQCcGCX2wQFmzYEGDYhvWmFz/wwYZgw4DtBxuGQJiT\nLUAEJNGSSIpikz13sbuGruHO95755Dk57txz+GGtzNtFUq7qLqF9ZZwACjcrT+bO2BGxI9bwr//f\norZyvd3BFnMVbA3aMgeR2+bnX5QHwRlnDL8vgbisKon1YfM9Q09/b28gm1S7ZWlrzUCv1+b6NcEj\nOJMErykbwLWtl2jE8r1ApeVPZiNqBbgVNmNNPxs3IwaV4ALuHj/CUZWl47GshYW1BKrIZVyXNc2o\nFPOKkdZ53FDcfDdyaHZflXmY/wIv/LRkmtztMdfauhmu3yVui1vYVS3U/vZfXpXHGmpqX/rwAp/i\nF/99WTv+/IQMIahprwthy+TBETvrUmtR1ncYqZDlw/Vzyrel/4UbMQiWGamPln24ch+u2lW7ah9o\nz4SlYAwEXkXhpJTKPDFv34M1KShanA+hK+alDe/S70iOuY3sks7GNcpcCErr5uscKNvvRXjGhYqM\ndK+fE82UhKIne2E0/gbtvpxsUzNeodWmaY4qc1FF2TI7iV3W9FtDpgn+2pgVvuLl9Yi/cVOqNf/c\nX36Vxpe10jBUuPPFGv4ttSriBVEueery3GNRPgBgXMhnt3bP2ToWk/Pav7FPZ00qADdGF5ihCo7Y\nS6qZWB7FWLUHwjZLJtLC5KtKy7K2uFo11W002O7JydWNxAq4ubXOzs2b0t94hKdou/sHUyYnGuR1\nDc1IzW4j99lv7rLzkxIwjXOXoZ6wVWPAolAdjQSKhgbl1NrqbXaI1+U3tvZfILgj2JJWUTHNxGqK\nWhEvbUvl5s0XxcJoFzWDtlgBvRvX8FpiKQWuT+ua9C2K2oRLpWUtgqrqgqzSnLQ/xi3Fiot6HbTQ\nkHbLW5Hc+Av5t5FURJ6si0HQIFBimGo2WXL00lB4dW8tJlco8sYLQ/xA3JWw9xpxRyydbmvjKXeG\nK+aW8XOMuna2LjHLgjAX9j8h42wu+phQNSEDeW9j8w1MrfDpowqVmqS5PcV7LPd3VtZkRz9ekpV/\nJc0xhsANcExIqWW6TvkylPLwZzffIj9UCffejIYCfaLrAm0OpnPyUvy+NLFMtuRhK6pDMkfJO+aW\n9BOa031dbvuse75iTM7ilLmumdpUdHWiZ5VdMSu1taTiYl6vBF5sDY5eY6sXsn1T+tHbXdDcVtNe\nF2DrBRd00Y29BzjHWlF462t0Lh8AMF2XqP61S0P/L0k2oFe1Ca//gYwLA+rOG3LfR02Su78HQPOm\ngLTMhkGRwbhpsHIfrLErhSg/rkDJZ+qm4jT6GXFX3xt3mChQ5mJ+yHgsY2ickFCFWKya+Hbao9UT\nEz1aswRaAbgomoyV6cjvZhilcw9LWcR+FuIU8r3O2j69SOJHveiUy1rdo4XLzedkE3llU9yEZqdm\nXTc0r9UlPdI6ga2MtkK9g2YLZ6qbdlPuIx5XBEjfvcRQrsv7TT/GUVbmsm5gQxmPSmvSHz1Z0NF7\nChoVhUK6bZZh9PB5ot9pJgXt16TOZWP3r4HGH4wTYCrZFOosxDQVA2OWupyWFW21m2OtzHtdRRiF\nsTO4R1DJGNWhlj2aHfAELMbDO7xzT9ZIdFSSBPIZm8BhfiVFf9Wu2lX7GO2ZsBRE3K+gNg5GySFc\nv8RuCgy4yNcJe8J5n4QpxVDMxEQpvKrHM+7nEoTJ0x77lQRycjyayr1QJxHpUE6VRFGD4xgWYwn2\nBLnP8Wh5StQrXkbXccjVHA+Vfdk5m6IwBlyeZhl2Nlr02mJKB7M9fD6pd6e6CMYjG4l1cxF/BycX\niPJa/Sp1IlPRXddI+FnAc5cSGAuj6wRW7onmOpVGxrN37lM25SRJHn4NAL/zl0Aj9UezMcuSKGsN\noQbE1vpt2k05bdcUKr7W3CFWQpqiDXFXTPTzuymVK9iEVuDT0GyFv9BId/GI9kL62W5s4SgOYdFP\n8VQ2zfoO7UIshLqt5ryTk6jLE3uGtc/J34P3fPxjfR259G+Kib01EDepEzdY18zJrGOxKukXdtZx\ntegyjacrEpg8l5x+lrqMQxmrUV7ARL7XKB3iXXHTdvw+gZLg+Ev6O6+Fp5yILafJmWZJ0jrBWwrU\nKIO1dQwvbss9x41jHA06O3ZErRZwOr3AKWf6G/JbTt/B1BLsNEyhUDi9d5/RWOa6aa/hZGoVbYqf\nYPMethBr+uStI949EExD+92Izq6M2/nlHMd9Cn3/KO0Z2RQMrhHg7VIE1DUBvvq7RWi4LD4LQDtx\nwIqvZi5kcA7vfpX3/lAW99373+NnPitR7Vn2bcIz0SiseZ/wiXz+IpUKwO7pJrW+np9bLhNZVVnh\nUyjgyFmEtJS5Zr8lBCkP3SHuEjJc14TqJ3fYpm7I4q32X8EJZYMwHXlorNvEqtT89vl/xOgt8aP9\njS6jSzH9bp8K9PnJxRuMlEew7bzA2h//vIzF6Ah0g5g/+F9IE3E3FhOBGlfOdRYaZX/jn01Ybgqx\nFxAoY7xbx/ixjK2vKT3bHxA64qvGjqXjyoI+XD9moyv1ClHgs7Mu1x7siX8bNPYwkfTT8RsURsY4\nLtrMa5knm5fUCrhqxrIZJVkueVvAeDO2qxfkehs+17rCbXh39JBNIxunG8jvxWsNFNFONxjg9+UB\nC9yYzJPf9hZ9Ls9lXEIj/V1k7xMn8tsXk0dkqtFY9lN2ItkMM5NjFODlakYpNFJtC+CFGY7+NrVh\nXeHvn+rLAzi2OS/yt+T747+Gp5WkeB6OlsbbRZP5E638VDWw4GKT+Vuy9tovXqeMNE0+/xLDe3J/\ntVMzVWh9f136m05SylRiTXf+8CHZ63Kg2PYpj++K+9B1LLH3w20KV+7DVbtqV+0D7ZmwFH6QGn3Z\noQBDU9mA8yon0ej1hXOPcEt2xFwj1ncvvsnvH3wTgOPZCY9eF3h05Jaoghx7axGbugVONJo89h9j\nEnmd1AWVhhQLCiaaXWjUJbe6suMHvtKhGwMaJCqwlAqDPa+mnN6XPu1sHhIPJPfuq+Xv1jPCNQ0u\ndQ7ZGEhAybu8hz2SPp8eibvT2h4Tn8kJNOUtiodyf8atsRcPZCzOjhiq3Nr0G+8D0P7u2xwU0t9f\n+dpXV9RmXugSNpT/ER+tCcNP1fRNK0ot4Ak7MVMlKTk5vyS5lPeLNUMylAGd7cgJ5mX38GOxMLLU\nZ6EBuspMcBU04xQzylDp5lx1d6oZxUKucZJcMEklyvvF7T6RVmWWJxWZkuSkDTGps+EU2grMcRbU\nqu9pzBhbafAzeUK8Jma+L/8wiXLqB5KJirwRjvJCJJ2Iy5FkuZzIXQkCpWpJGBvj1HLPw3HF3TPp\nc1aWfHJTrKzPxyqJ510wOvwdAOalT3ZfaN3t/jXyjlx3Xp0QZCoGc0NU07P8XRZb4gbYgxb1hgQU\njw9OOByKhTV8/xBuKK/HA4Wy5x6zx1IQ9607f8R7c3EfYmNpX8hYZGsBi3QJuP9o7ZnYFOBP07YG\nuBjdKhqOz7yS15tJi+MnMhDZXB6Eer7g+rY+bHVIMxZTrFEZFhp5XVSWoifmnnOxLHtOOa8lXZPW\nJaG6DPPSruoEpqbk/kge3htGwES1rUU3EsBCqotxOhwxbshiG03OaCvPoaslvf7aLraSxRGVfcpM\n6hyqwSGuCui+o1mI/YuKrU/JAxE4LqUCWpJjAzr5o8kFXzuSvh0ojr7XPOXRRB7AO2fjVfYhdGuc\npbnOnKKShz5VotgsDyk1DTmdFxycSIbj4OIe5xN5YI2N+ONQxv75gaLuomtYR9WN1iFRnL2Ttlho\nNWfcDKl7Gl9Q5qLZWcVU2bSmo5rJQyWLiZvsXFNCGbONr6hHTzkhjeeQL/luzx1ydePyxmKlR5kP\nHciVAqmW8RmdPeTbd5REZ7pgXzfnTuhRauq0zKFQbca5xiIuxmPONdb0JMk5nanmZV0TqMZoMpCD\nID2qeHAi7uj46y+x9mlxV+bJhIkMEeXZGde/JJuFp0Aoz18n142pat4jUxWte7/7Fu+eyRjd3nNw\nTyWDMTqQe3p4OSU5VZqAoyEPzmUMOyNWjFs3Sh/f+eEe848rRf+fG2PeMsa8aYz5VWNMZIy5ZYz5\nujHmjjHm11UT4qpdtav2r0n7OKrTe8DfAj5hrV0YY34D+PeAvwr8XWvtrxlj/mfgl4D/6Uf5jSXN\nWVHX+GoHnkd3qCPJBS9Ukc7dGfF8U0yqV/dDHNXwc7Mx376UXb4fgb80nzUglaUOnkrKN7yAdCYn\nlG9+QEbeQq14/kdzrbgEyqeyxCt2mPEiYXqh5mVyRJbIqREaZZFOprgK5i/iu6THckLNpt/h+Pty\nL7Ou/NbJMexdyH7ajhJQFyQMYjKlOzSPYc2Vz1wqZ+LREO5M5JQblTlGf5vKhbl02lkrUQwOs7mM\nTzQd4Snb9dnkkjM1n7O5T2+jreNdQ6oRcGVwnkUP8Qr10Q4jEdQEikEMara6TgcnlclcqAtW+mNS\nrV2J7IxZJNbIk6SieyaWlTks8L8w0Wtoh21IqNbDPByTLdTSmzg4KqIyDCaUcw1A+qrzOA6o1VIM\njSVWTMOGG5A05RrzifBCArSU8y+0m0yV6+LOWUGmE2+BkWJV3n0o3zmfVXiBjP3b9/8R1Ylweax/\napeTazJGG909rN6LWcic140e7U9qP+ctgkAsmvjlS1qpVoReBpQnqlSVKT3cKOX0TNm65wVTzXJN\nCotCITiiJPghj/6PG2j0gNgY4wEN4Aj4GURXEkSK/t/5mL9x1a7aVfsxto+jJXlgjPlvgUcIF+g/\nAb4NjKy1SwjVE2Dvz/q+MeZvAn8T4Pr163/67zzdsTwMU0XHJYdtck0Bzlw5rR7ZBZvKZtwrO8x1\n549GET/3nASt4q0OtifdSpVj4Xw84e7XZfe9nGdoMRyOfUoU2vMcSg0q5hp8zOqaQF/n2JWm5WGa\n8/ZYgqCvNUIcZcLJL7RoZb6Br9RuRJ+nVlqxzuAm7ivy/n9sJfV4/PVvMzzQQFy6YMeT99OjN4gy\nOT3SbE7H0Rp6rZD7/kWOHsr4rsNCeQFM5jBSBB7nJX5HJfcWGqsISzq5XtdGBLX89tq+Q5hKIK3X\n7fPavvjJQShpyObmc1wojdssLaArP96y2+Sxnu5ei1kuJ1rUlpRt6jn0dxVPcXlXUpTAN995zE6u\ngirNmjiVNHCqllCrVT0t7Ir7JIkKygwLdnZlvPcat7icSrxm97paOc677EdfBuB733qDseJT3j89\np+XK94KmQ+ioCEyqVHphQaEACJfiB5lwOdI+z3VKF1gaiVgY/+LtDPuCjOGr7THdmaSnh40t3FO5\n7w0EmxC4n6BSFuwqOOd8JHNTG8tJIFZxdTwmWlfIukpEjMuShVodqVcy0WCLBZIl4tZxaATawY/Y\nPo770Ad+AbgFjIDfBP6tj/r9Py1F/2d8Rs2hjJTjSxE/fS97n1dUG29jS3LX5isL3n8si+7Y1gxU\nE3E6zohSeUifdyN2npOortfQQXJ9qGRxv/mNCU5DqxYdQ+gqjXzuYDQA+XDyFC66RJNXlqccXQvw\nNHfdrlv4CoSxcwFezd/6KtFtWRxRZ5P2FxUyPCqJviT5+NmhZB/a40tO/4kGsLZdpmeCQ6i9CdNz\nMbXz1JApA8rlpYzJwbxiES4p6R1SFdC9rBd4Op7OZEH7UFZWrO5Hp5vSVMq0RlRTTOS6pyPD2Yku\n0r2KYapVoEoZNhndw9GH388Dgp6yPHNJoJRodZESagDW1VqF9Szh/Qsxnw8fv8vpE9mo42zCez0J\nZu5HHeqJQNaDlgLHTE3Yk3m0vkM5Vzj2wydc/8RfAGB3/zrPvazAIK138fY2SLVasLfd4B//faH3\nOz6dEnbkM2thh3qswjZKuDOLm6T63rx4SpwDoImrVU3MwrWQypt2VvHV92UzufWFJtW29HNy+Taz\nd+VeHodyGDqtBtPH8hvWTPjuE3Ef3jm7YDSVlbZWOivVoayWjTdu1pyoyvy0Bu0yTVdAWQCFY1gs\nlrm9j1ZC/XHch58D7ltrz6y1BfD3gZ8AeupOAOwDBx/jN67aVbtqP+b2cVKSj4A/Z4xpIO7DzwLf\nAn4P+EXg1/hRpOhXzaLWGSfThK9+Rcgq35zf50Esp9hPaBXiz/zkT/PoHTGDG6djWgM5lYYP7uKu\nqWDM/iY7nxBU5Ol7cnJfnBacq+z3uFnTuNT01sCQncmufBbn+HNNYWqgspjXq5GzlehJAgyDnLNN\nOQkfV4dsLyT33PFFg7f5qRvMT+XUNW+MSI8EdXcxe4fTb38LgHemcnpGyYRbX1B0Z+xiA+nn5PGE\nR4EqG89S/oGmnoqR9P3At0QaPA23LeMHqu/gZXhqd4+8yTLVTxDLPfXdEHepYznxcfREtycXpBOx\nwmaO5WznAQCLkSBIG/WA3qa4FL3rISOFqRfDKb6vp3u7ItXTqqfJqDSZMe/IvZ5MhhRWTsrFfMrD\nREzm/XiPIJRr+66SrNiaXOXu49kGm5HyJtyY8v4fv679nxIsNBWt1GXl+YJjhXRWxxPcZRS7RMo4\ngaIuyddl0Z0fyin/+mjMuQb7pqZ8Sn9tYK5jP4ll7OMcLjWIPZsb3lbD8v5vvsNrr4nV93Kwzu2f\nVa4OJYWJT/Y53ZLfTeKC6ETSk+VFRKUu3dFty/SBjO1ID/w1AyctVVB/UpEpFdx201C2NUU9N0vu\nW5be44e1jxNT+Lox5v8EvoMM7euIO/APgV8zxvw3+t7/+qHXAkpE0l1dOhbW8vh9edB/9+zbvPUt\n1fabj5j2xOd8XixuXuq/TLj/RwAcP7EUhxKeN48MrTVZNH1/m/lQRVk0qn8neYt7mgdOWyGFMga7\nnmWhD9Air0h0c/LiJQ15sTIZrbEs7aLauMxR0hJ8picSpGi0xXR0zx/gP1IsfmNMoSIkbn6fXltm\nbPc5ZTR6q0XUlwtHF5apkqnMp5Yske+dZ+WK7OXdmQq7ljWu1mq42y7+wbIM15Bp7KPRCwiaqk7l\nic+dVRGXKrAbtgOaKqG0sx2z2Rdzfa25Qd9RRiNf+pOFBnRhzuYZwyNx1y6qCesDce/63oBQS4dT\nTx7+JFynuhSfe5pAri5PMw6Y5FonYTv4nWX1oHyvyAzVYimtnhKHslmYsEU+lIrBi3vfoqUEPI5u\nIPNkwbkn62ny5oRUmawGYUQZycExKw1Ucr3xsuT6Mmc6UXxHaUHZjwyWUHkjlxyIoVcT630kBZRa\nzXtxUXGqma1P3ApZr4QkpTOS+7xwv8rB+/LZ71+c8+AddYXjBUZx1cVJQZ7L/JW+9OFy7JEvsyHW\n4KtI2OBayMs3FOB1FybKHPYNzVR8WPu4UvR/B/g7f+Lte8CXPs51r9pVu2r/3zVj7Z8Z4/uxtmbg\n2Zc32nQKOFdMQBy3afdkJw29iuFITpXLacE412pGRR36riFbprGtXWUOosCl0syBMZZaT/dGsIT7\nuiu6ru1+i5dfkcBPEGyQTOVE/1K/TdmW0zHQ4qFvfPcJX3Lk5Pt6cEGKBNfOz89Za0o/d7aewxg5\njaeKb3CTKdNUTry8tIxmsnN72YJUIdZVKSeD67s8r1ySdbdLjvRhZmoGDc2393pcqJvz5ExCN59t\ndhiva0XezPK13/sNuQ93jeMbevpf+ykyK9f7wqvy73OvPE+mnJi/95XfplKth0ZQkYdimd1/eIed\nltzr7jXJSDRf+BwFYh1cf+Uan9/VAqTA5Tt/JNDsk/fvcf+7UvB1dKR4k/NLfvIFoS77wl/5q7z4\neamI/e/+i/+Sn3KEO+GNmz5BU+jIzhStd3s75eZNqT51gi6PHkpgtjh9wmUuVkGWubhqcu4r7Zq/\n1qTWMSzmC66rsnPR8Sh0zMfzBdGSuqwp/04XASen4rp9ZvcG0abcX2e7z+gb4vJ9UfUef/P4O5xO\nZW6++/7rVIl8b7/XpKhknRm/YqDJuULBIsNZjVXG8P3YpaWcnqXTxlUr5FPdGGdP4c0tsYKSqcOa\ncjkULRgei2X66P4Rn9FK2sd1zkx1Lf6H//ub37bWii/7/9KeCZhzbS1ZXpLVDrnVCrKZYWxkkhdl\nTpKpSk9eUpZLSXhN0ViLuncYLEbfr2rhhAXIbfVULWlJye1CUSyFYgPyUk3KKGfmy4N8Wbh4c9Vf\nfyCLZ9DLmfg35VqTKSffkMkvt85o+vKwxJ5Lp6mme1av+rPkoAzykrESaCySBZnGJZqeLJ7amVGk\nyurStHhKZhoGQ5q6WUSuYe26LJSp8llap0U7U+DRXX+1yPvRF7FKnFL/3jqzF4Qy/sjKA7i1fpty\nVzaWZH6Ep3h/p7vHmlaJXm4YmrXy/PXl4dgZhORt2WxeXO8QDSTW0GkU3P7zkmkZ3FgwVTr75ELi\nOcflI05O5PcevHmftYZWX3Yd8oZWxE7vkPyBTuAL4mc3+tuEpZLRBnPcsayRPM0J9QDo1i3mOn9R\nLtmlThDRrGVDSzaOaWspswk9Kt0sgm5BrBuup/GQyjPMR8omVbeoxvLZMs0IXdngHhWSWrw8XfDm\nVyUeMvPP0UvhOSHX1qQfs+mQrpK9aMaazdRnGslvbPltdlV42Ck3GIWigOWZHt1Q14bs14TtU4KB\n9Gez1WRdYepp+j3crmwcz0+7HPzRFcX7VbtqV+1jtGfCUqjqmlGyYNyoMYWe8o0zag3EzZOCQoMs\nWVXjKf2ZwhGwFVSaQfaMETgu0KgthdagF9UP5DfUGvEcZwVISv2SJ5r0TfMCO5ft+A827/AZIyfh\nAw3kbIQDvrP7dwF48ocRw5acqq/svkoQivvQMQmFQm3nEyl2GiYnVKn89ugyY6zS76auaKpVMdQT\nOsgMxwM5VTfdNeo1ucbm+m0G6zIu+byk0oq7wVxOre+0/yFbC3EDRoOchprr//iV32B7JgM2+/RX\neHlXwFDzheA/pkfPcfxEbnDxKCDQk6v92gmzmZzSLd+w1heL7dXPqtzcngOunOy3NrqEy0rSymFv\noUFJx9Jfk9z7oVaGJu8teHMseITizgg/lOh85Nzjd2/+stzf91tk12V+Prn/UzIua+d4rlg8ySUY\nhTaPymM2FG+QOXPCJe3+hsq2N/ZoNWQs+lUT05W5Ces2da0FdGUXVzNMc/VHnbqkN5XfuOP8Duu1\n8D5kRZ+O8yYAv7/1KwB85Z9WHFu51iDqEGnR3P6aByoTv2k8FpH0YydQjMlawX6pkm97lm1dqfPe\ngtapVgrfOMFNZR06myqhl3eIr8n3zKFL2RHXdOuRy1eTPwbgc7bDe9flfcSI+dD2TGwKYHCMS2i8\nFRmm5zvMpjIxdf20FiH2HVpqEjuqcjQZl0tcB64DrSU7kGPQPUazBPI/7VgeqkYjWAmYBpklr2RC\nz71zttQP2/Gvkyzken3l7q/Hn6d3IenGx+cHtPblAWq6Of5MHoST+IA6kAmbZPIgDGcV4+HSJaqp\nVA2r12jTU42ETGu9vdqh0VNy0SrG03hA7Ib4Vh7I0rXkDVmw7UjcgA2vyWIk123c7FG/J4vqRrHO\nnSeSlWl82eCmaoN6ijBsvcP+a+LXB7tr7O0LEMizh2SxuARZPuFT1yWGfE3Jb/3eDTzdnVuxITBL\nifuS3qaCpbotvjSROEDVFzdnOFxQFjLeyazmwmqmxvw8t8/lof/e8IS+KLTT1XkqIo0AACAASURB\nVHyac9FipCpV0y2PuhLzemPzOr4SuTSuxThjud5aLBvkoNOjpWPoJRXWiCvhFB75QA+U4YJMadvr\nQmsq2lM6y0043yJTkFj5+QGN81+UcbkvrkYy/C5uLJtm063oOeLmNbySVkdSq0U1Z08Pmb4yQV3/\nTJ/WuSp2+T0C5c+cvxCSaXYoqX0CPcyKHa18vSjwzvWzw5J6R+5/rbnLa0pxP7w/Ze+2rFv+qWzM\nH9au3IerdtWu2gfaM2EpWCAzhj18hshOWy4CEmUAxhpi3b4cz8FRufVY97SFqQhVCclxoa0ZBc+D\nueZorW/paOSnq5HlIIpWGQnwyNX0K0cwbsjpET16qgWZGQ0+vXiDvUMp6bgX/TrtCzGfaTgkrpjz\noQVHT5tczVO/zOmo6FWSxDQXSud1+0XiHTnRmzOxGC5PzrnZkMi7MUM6kYAyrGvxtRJxEZ3iT8Wi\ncW7K928frvPYSgCvVX6Ky9vS9y9GX8K9LjyOe83/ikZTzOr2KzJWf+ELLtdfkGO5/gw02iprXl7g\nB3LKzfKMdaXIs0YrSoMGlQZPQ895Gsw1hlrHuRuGODflVOxtKCHLk4r8geAKRhdjupqpuXezyc6R\nWD23Nwp2MqHWa6xrdqZ/TOXLidjubtF9Tsboxvo6rmL8/c5NMsVAbKsUPXFFZ11N/9Fi5V5M81Mi\nBUCN4xPhSAQynUc7j6jX5J5vzJucaVVmI9vickdo6p4//jcB6ITfWcHDiXxYU6j8FjQ2FEzkXuMV\npaRzFXuysfdljBhsBF6TLBXLcy26wbyvNRhlTOLJnLmJULDl6V0K5Y3wmvcJ5nKvWd+wrzyXUVnw\n+OEPx15wZSlctat21T7QnglLwQA+UBpDrenCRZphNGAYeWbFmGxxWWgasdRgkOcaQo0N+L4jgQW9\nsFFmnjh0GbTkVOy3VVsieCqrZj2X4aWceBfjGVmuNGZ+xEWgugaJvPficw850Vxn3qxYnEkgrktE\nY6H+aTcgCGTnXg/lZNts3WDjBfntM9shN+I7/8SLn18Rqd45k1PiqFmxuSbTk/ubeK4yNnUMgaYv\ny4WDkhfhFHKiBL2IXkNOoipyGWjt/Sxs0HlVzBRn/Bb9z8kXX1Ur4MbG52i0pQ+uU694BZpsUnuK\nrIydFcs1RiwU47CKAxns6rU1NY0llYOx9PqCbiz0tHv59vUVm9a7jQPmGgRt2hNSTafFvS6eCsOs\nuEfHc/o3FaXauE3nyzIWe402Xq15/EYbe0NxFrXqNGBWMaiyDY6esE7eWJJsUeNQaIlpNVvS1Hl4\n1QO5/26PZlexDllBvyP38qYjvnrdiCkVEu7EsKNszYPGFmtNsZCu7RuuteW11cImL6yxTa2uSie4\nqnvh5PcJ1hU2Xlv8C1kb07MHOpY5dSCf9coxxVAG6SJ9gt/19BqQPvjhdB+emU3BdWDi5iSqr1iZ\nP/kBhY+mJYluCkYf6MiYVWrBAUqlR3OsweqM166hUDx8ofneoLbUGnwMHB9fJ8lULhMtrS3iBWGe\n6fvygH3Oa/J2R4Arw9MRsdKMt8uYMtBIb21ZLBTPfyn3tHnNsrcvOe3nG58muRTY7dpOh4Ev5nPk\nyt85G9BSzUTr5mAV/xC0MWo+lxW4mvOuz6Vvk40pzlT1Gv0Frifvf23jV3Dflodmbe86NyLRf7Qq\nwDtNfOozhdTOalpiGRO5Bk9dM6/yMP5TGjqZA2c1WcZ9WjBqaoMmfnAIiPX+eo4AxDaS25ieZlSK\nDYqWws0PC2aaMQhnES15rmgprmAeNKlT2dza9Uu0NNDm3AzxFBTksy489YAKWkEJXkMCfNU8ga7C\nybOMfKZgtqJirvR8gQrpNkJoTWTjLLccnEqrL72KKJN+NG6pxmherWj8rjdb7KtL1+s2uLZzE4At\nLyLalfVXnWog1h9QT7XWpPFURcsrbhBNlLm8Z1Zy9vVU8SbljLmWlNuFC+quzu8l3NMAdDiFsfvD\nbQpX7sNVu2pX7QPtmbAUMOB5DkUOVtMufmCItJIt8B2WcOzC2tVrR82DwDMr+fVm6BKuBFCgVPeh\nicVXxNtSKi7sBCuBFIJgBUU9TGYrvrV5YjCaZlvoKXmWH3P2XTFP83FC8JLmkrsV+3M5ParA5fJC\nC7AqGeZPLW5QTuSkGW3dI3bFTO63fdoKOz7WKsTGo5xaC7DiJGQyEOvAxdJpabDPlgwVVr2s2urT\nI9L7nO96XPxzLQj6do9AT4zkZ94jVbGUHdUNmJzd5d5ENCMvzyI+VUmwb3dnA+vL9wI3WLFYP+Wt\nrVd0dBazMhVqU2PN0qLLiZQMJOmIO9Dez8gOhcB0mzFTdV2ODk/hTObBd2rmN1Q74lKRmUWfpupE\nXvjfYKHp4uhoHSKxkBbtEQ2FDVdWLBTfdChCqUq1aYKn1Zq+tRQbGgguJrQyhSNnct1xfEzdkjGO\nsi5BJZZV+UJAYsRtmL8lAdNO6TLfUCj9boNX+xLYvLXZxW6IVei4Lt5UgoBLl3C+9j7MZf6cUQNX\nC9fma69j9fT38g4hKn50Uywsjuc0ZjL2qZ2wUKswDy0beh92kfPE/YjlkdqejU1BeZa2PZcjT+ME\n+NglR6OtVq5CUdcrqohV6MB7arbWWLxAF0TB6ho5oHwidLWmIu428RVWHIYRvj4o8emEi1J8OVNZ\nNtvil6r1Rl502PSk0u0oPiFIRLzEJCF1Qybfjz06sUKTO/Lwb6y9RKDR9+7kpxhqmfRguE7Sl9dx\npsQb4ZuryPncP6WnEfI8zUHdH8/1GVi5tq8MzpthzDiShZt7A75S/xYAL69v4L4k1/tc9z+hh2wy\n0aY8bH53wL7mz1vXLL1YXlvPI1qqrxjR/ZQOLqnVzaqaeDUJgGNdSsWem9rB6ng2M4Ez+16XrvrR\n2ztblFpF+f1v3mdXax/SeIR3KsxLVSXmdeNagLfEb7ADWqptoj6ZloG37P6Kh9P3ZLOp6xLPaDyA\nMY66I3W4INLYVYmHp7UEU81keKM2uboiO619/Frh0ZMXMMqO/apKAPzz4HXiTFyNvfVbNF9QRqed\n5+m2ZY2ENsZTPtEqW7KO91gguBcnMBDLpteqBySlxBEazT6lL7GrdY2dTJwjojUZ43FWUZ7JBtJN\nW3jqCh/WCdnl8jG/kqK/alftqv0I7ZmwFJZxxIx6pQpT1PVSUZ2ssqAmVY1ZyXi1FK8QRg5KcYfv\nOhQa+LKmxlNToeW7eIGc+G09+Tu9zspSMMayaIopEXd8vIny+1cOU+1UpXxXtrwgU0jpYC+moR2y\n3YjyQk6YNPJoKh9hHCkXwGiEt6sFTPZbdJ6TexqmD6gK6Wemp+1gvyQMxdROQ0hzeV0YqNU0DNot\nXNXTLDSPH4dQaZR9VI6p9GTrxbfZeFFl28tDomvye0vkXhQFuIor6HsRjUhNZr+NddS0NT7WLnUx\nlymHGrOM8lrLKuJraoxyD1CmWCVRqTTj4s1nBIrfKINNTKXVoTan7srYN3sZZSqWV1e5JZqhIVAM\nSV0d0Wrpye1ZwoZWGNkDXDULy1LN9jimRlXAY6gdLSCrn2IuTJRiKtV5VMGgsJpQpRL4LPIAbyBW\nQVWf0FLE5teOBVLsNAtMLf3c2e3SdWWug1aMo8V9UdPFBMprUCsvRDZacWY69QzXV4Xq4piwr+rR\n4QlxqRWRGsCuw2yVqQjrklgzJ8POkKJaZknmLC5/TByN/ypbjSUpC1JjqTV6O6krvGU5NBBosVxV\nW8JQ05PeMmXprFKZvgvzSgYhwqGhC2iwHhN0lHLcW/q6C+qFXCshZzGT701GNZn6pEVV0tBNplJ/\npVcE3LipvIXFDt1SFmA0zzgsBEpsT8H1ZEKDmfzedOce+2c3Abj+0mfol58AoHn9AhtKmDxW8FJr\nHlMhC7SbVwQjMZkvRlO8riyabJ7RKSTVVx4LUelka06i/mlQhVBIH+6t/TbjNwVqbAYPaTbERC99\n8bOL05hORx6wi7yityWD3yEi1liLg5R0A9TK8uM5zgoAZhxWzETGOtRa51EXFarTQjlSv/+85nwp\n1HOYMSl1c8IjjeV1MW6zp27Fppr7rZ5LlmrWycbUmuzJ3ZRI3bXAaWJVPKZSwdsys4SebBp1VoKy\nJZXpHAqtSrxcULgKmFPT3st9nKH89iP7iLYeEEHTobch8ZhuRx/yhUNf87B9v0GoGpN+ERKEGmua\nujh9ub9qJr9RlyHFWONkbUueCnCqLDzsWNfezpBCyWUqZQILM5dCK1jD3CVRiLV3YHk3knU4yQxn\njt7TR2xX7sNVu2pX7QPtmbAUsEInZaxDUS8xBqw4Emr7gyy6Fl+zErGqXPiug6+mQiNwcfKlhJxd\nkagUpY+j1NgP1ETsLjJMIbtvHRQc35f3Z7MMq9Bdz3FWFO6pRtLmrmUzlYChYcT7sYi+1CcWX+mz\nrg/aNG7KaeyeqbXix7S0Tn+9F9HvaFR/sI2fSj+zl9T6aVtczVczg6ma/oMnDvn0gbw9XnCu1XeL\nWsxkc5Fj50o/v+VRKF/j6N0e55kUEtVfmLEzFrrzpsKujx8ckHgiw3f0zpzOppyeg/UNrt0Wa2Sr\nf41mT1wv38ipnPgZJw/kxBuPzulppqVxw7CjRUeBN6WaydimWrRkOFsBvY7PX+d0S3EhrsFO5MRz\nqwr/M2Lmh7mqNkcNKhXDuZO+T6yB+GwvIktlvLt7RwTKgRHZJZlmTNqXftq5C0pwU+cRaawLowwo\nF2I5DY+FI+M8OyFXfsxi6gLyeuN2i4tKXk8fybU2nBat19RS8nJmQ7HSmm6D01J5ES5juudLXIRi\nDLZy6kQsT89vUih3SNqZ4CzEgnRnOZnSCWZa4enOPMxMsTCjhEKfBzeMuabanXerBE8FhpAufGh7\nJjYFi8QKmsasAthFZShZmqJPwUle7eCZJWeeEqoCiUL7Is9QqemfFxVLNr8kh1o/76p/t+E3GauP\n268t02Vl3LzA0+fReoaGIiELzU7YWY+ttqB7vjH9ZwzPtBrSm7DZVwKYfsRaQ1Jusz1F1fktXM0o\nxM5Pkc+0WrPfJLfq+05UFn1ySUOr5dLyDMbiVhTJAVZJVGa2pEjl/XEm39+t2pxmS07IDoma82t5\nylQ3w+zR5xldFzal5I6MxYPTN4l1XB8mY+b3ZOQG+x7F7C9K/7/QZMtdUrgrUnJa88ffk8j56Dtn\ndCTIzk8cfZbzW1qjEGZ4oYydn8nDurv1MrEqT3XchxyU8vrNwfdY9+Xhnkdj9p3XAOhFSppbDHn4\nRMq909GCR0rxPhx16Klg43OTT9PoyJz1EpmbrFyQHsvYnz65y+xIdpOECR1PTfvmiDKR6xktby4T\nF8+RMd5trXPpqELW6Q65Zn5uzX4agLfCQ14e3ZR5HLuMfeWgPHuDaqJpba9go5YMxcYnpG9rrZeI\ndyXLQuyQP5DNxjm+zuhQ4hmTyZx0LBuWq9kQJ5wSqo5I0SiJIlk7/a2YzYnyibbPuZ8skYAfjaPx\nyn24alftqn2gPROWggFcayisIdUahloAtPr6Kb6+xGL1M6lmFrKsWmn8hYVLoLUK07JasS6HXkGu\nZshSOv6inHKRyMl+Wtak+r5r3NWp2aHN1kCjyJ7s/K3OJY8VtFCZc1LlYGyuuezWKrlet5mqEtV7\nl4JBuNbcJ3hVjtK8fBd3U0zDsmpSaKA0z/XkK844P5WA05PRCPSEyooxg6UvFdV4KvoRhioQ47ss\no3qzeb6idS8XJbVmAObjd6mVdXqmVGN1fs5YA3iDtrvC7W8M1mlviTXRb/p4yiScluK2VOEFN/fU\ndZm5eLsKU/fuMJlpXcmdGk/ihTR74j61NzyiXCyz/TBkcaa1CrOSuC2uUqvlYB2xeR2Ve89OHpMr\ngKiqF2ztyrj0S4hCpeybvU2hkX8Uv7EwUxINbKbzQ8xcxsiNZ/iqSTqfL1CuF7wt5WaoW8zUfQhd\nlzXNPpTZEKchfbuIl0pfGWdTAYDNq02cC8241HB6Jvfkt0uymfzeIlIci5PR2RXL0ylqClfmZJ6f\ncnIqvtLw/iWnM5kHXwFbnVsuu5VYoY0XGqu1MO1fwJKtu3bpn/4YVaev2lW7av//ax+6hRhj/jfg\n3wZOrbWf1PcGwK8DN4EHwF+31l4aSV7/94jydAL8B9ba73xoL4wKqrj1qh6/Mk8tgryy+MtiEANJ\nqcUeocYc6polLYLxHAbbyuA7djhRBp5xbukoJ7+j15rkBWPV38urGlcDF65r6WqAstuHTzz3k/J5\nTQs2ueBdI7JjB8fHeNrn24MdYtUCSNIxZ6rofKB9CFgw0FO8SltMDrVKbjOj9DWYOdQ+LByilvy9\nY1ymj+V7DgU2khO27TVWcYn5SO55FF1wOU50YAPmqhVZOxMWU/Xr3UccXAoE9/49iQccHz5eqW43\n2h22dsXvnRyUNLzPAXAWBDx3XRCJE2W7Ls8c5vckHdoZDMk0pXq8GLJQBu6e69FTFF40EXYn6xcr\npiDHBPS0mMmpDJ4yZ7l5i8ZM4jLLtOeoiDk+kXjA7HxKOJb7nnYsPRWM2W5s4ikZKzvLcc1IVCqv\nnFfkahLMkpLDg+PVb7vKqbGufnvXNmkM1dq4Ua4KnirGODrMa105zfs2J1/IXM9Ou0RabPZkOOGd\nQ/HnB+0YKpnLW09kLLc6PoEnr+PWFsXFdwFIHk95ckfVuE9HnGjfjrVY7znjc6qVtJ+c79Dd1srV\ntx3eWpd7cudQBAqF/4jto9gV/zvwPwJ/7wfe+9vA71hrf9kY87f1//9r4K8AL+h/X0Yk6L/8YT9g\nMASeg+9AuBLbeCpe4lCz5GOLfZem0nN3mtL9elaSKl32jWtdPvniTQCOT0csHivefVHQUpzBEvzk\nTBxqq9jyyqGltQ0dr836bVlgvWTAK9fkGn/QlOh86r9CcleCVmZ+ht1XTsi1Bl4q35vmC4KumODX\nO/Lg3t6+zt6miuP6d1cuQ30e0+uJfT0fyEMcZxDX8hu3O00e72pC/tDQWOL5w4qztpbqKo3bYjyn\nVtM4fqlJqkFXOw0xit9wYphrCN/quEWtiKZS2De2G+ytSyByvR3ibwjGP9zeIGjIGO6r/Pyjjce0\nlUqt6/VpNFUtywZwIp9tnsbs7WilpfIkumlKfaEB4yIjfElej98aE1yqSxD6XPTVZdMgcZItGKwr\nVLrfplYFpW6rT2+ZaPAK6rbMa+zIRh5EDUbbcs/lUYarrMxOMcVVXUzSghuJjMGOytZfehZ3yZuZ\nRvjKCZl/MWP2WOYqPVQuxqDF5Wua1WBCVcpaGIQp13Zkk11vhfQdWb/XbqihvnFJ6QsAqkwjfMRt\nXDTPsS31FS89Sg1WBkojQNPlVi3rZq8ZkvR0bLcMW9ma9mPB1jXNPoiA1oe2D3UfrLX/Arj4E2//\nAiIzDx+Um/8F4O9ZaV9DdCV3PlpXrtpVu2rPQvtRA41b1tojfX0MKJkUe8DjH/jcUor+iD/RflCK\n3nMMnuvTcWsuVnJsK+MAYwyus0wn+niaL+xqesz0UspM3ltf2+XTL4nGwLVr4A3ek44cnGErOW1D\nTfUVbYeGmt+76w473SWxRk1LrFbc2xm9G5pGfEcCS5PDJpvKe/Bd7w5RIieXjbZoqZbkoL9PPJCL\nRCpF9tzOJiaWEyoeXOfJgezsYTOkbCqRiyuYgNpmtFrLASh5LpCg5Lg4pBFo0c5sTFuH3mnKibI2\na5I1JJDVrHapNdC4HobMVCBlfbBHtyn933xB0nHpq+e09gR70XBDdrblZCN3ebEvoi399i7rTen/\npJJTcr+1z9myUsw/o6EQ8lZnm1Ll5jY+3SeMlqQ18v3qssQoj0F5Nqar+fg4OKPlyG/nUcG6I/30\nlBLv1kZOs6XpST8mP5Xzaq/bJV9iCJov0NBxHkQydyfJPdZDsQJm/SNwxH0YDDboRkpeOx+yLVMF\n+m+EQ5DI3ze7+5wqHN2c7ZFfiuU4UOXyerPkugI/2qaiowQpPTZ5vi1rK7js0F+TdRu2FSG79gLF\nhVh3QZySarq3EyQ8P1cdDSfjU119rf/6eYNXtiWV6e71wdff9h7SOJD5GXPE5kJpyNEo6oe0j519\nsNZas6wt/uG+t5KijwPPhq7LLC9WICXXPt0IPKDZUFCMH9PV12ZJaFIZTCSDunejRaz8glHgs/aK\nUnn7CwIrRstlLQ9j1/dZ09947nqX/EgmI3IGJBoPiEevsbihUvQjEVB5OT/mTKvw4o2I2Mqgr20O\n2C9lQfe3XmbtVYHB2rks1qbbJFPjLK3HdPtyjUYUEzUVFOQqcCeChro7bmONpbJI4HSxicKDM59a\n8QmRajROO5cMGloFGjcINGvRiDpsrctievX6F/j8LdmwNmJdMO2I/ickzlCki9U9lW5JpyvX2+h0\nCbQyMErVdfMidgdS2dkwrxDvNrTPLkY5Cr0ixaqkOlo7YcNjWLJFrYWUgbpNUYZVMFHc9qlijUt0\ndHxmGXFbHgRvrUuxI3yUXhqRqCSAEwT0t+TzgbpPrSRikkkcpO91iJVv09lo0VZxHVMFRLkSoJQy\nZ9WlBY0ZZB2fSKnYq6xFvCbAqpmKwniRh9XS8vW9NbZ1NcfRGpG6PM7OExqhMth4Cl4KFkQ3tL4E\naFbSn6Jzk4ZmKHYG+zhaVRppmb3XGuIoOYuZn5GqlmYRPsJuy2eDiUP1lj4nH7H9qNmHk6VboP+e\n6vsHwLUf+NyVFP1Vu2r/mrUf1VL4LURm/pf5oNz8bwH/mTHm15AA4/gH3Ix/aXMMRIFhWlaUy+J8\np8JfBgZ92FDu/SiymKWLsTytcbFqnm60O5gteb8/uqSj4h7NqEvreTkFPucIF0K8tYe7lG3PTpgO\nxfO5uDxgdyCnUfz8qxy/Jwi749+SCPorX/weTiT7oF+4tNQM7PoxnpIHN0NLd4mmS1RGfd3iZ3LK\n23SN4lx2cP96iKMwbXcmFwg98HqaLalC3EyxEkPIl9VhNsObK5nGUE6Uk9T+gHbGHF+Lv8rOkFA7\n99x+yq2eWApbW2JVhesD/EDM5DzJcdtLirEhMdonYgyqS6EVh84YwiW0eXODwNF7LUJsptWHzowy\nk/Eq52r6P2pQNSUfn1YBC61KNWXISBm9Ly4NAzWVw0R5L1o+RolxGl6fRMEnzobFjOUzndYmDSsB\nOLctFoh36OAp3ZzvGsJtmYcyWVC6cn/etEM20ACs4lfSvI37WMboaG8KrkoI2gkbypGQ9kXUZnTs\nMjDiooTjJranjNdxQaOvmYHRTeLbYk3WSj1Y4qJLGbefoDAMoszi6XjXnx5Sv6dB+JasG2/Sot5X\nEpp7LkOjgc9HCY+vi/ViDxzupsts1EdrHyUl+avAXwTWjTFPEJXpXwZ+wxjzS8BD4K/rx/8Rko68\ng6Qk/8OP0gljDFHkMUkdfF3RQeASKDdg5PusKRijNAG1MvqkWlmXjgt8Tybrewdz/DWZRKYxj8bi\n95njJs9viC+3tf4ZAMKuofaVc4+aoQqCmtqjnCl9/P07fPdbsue9d/rbAHzG/BzZRBZgI4spNmQC\nknRIpD5ww3awDZm8JdmpF4S4as4mwV1stKSLWcddaNorEBmfehHR1PeiuEuu5deld0lRitlapSkX\ngSziy0r8RT9JCLWSMWvPcFSfPJ34GDVLmy2PrtZdLP3apmNATXWTZ1h1q+rxHKtMR7YMVuXHVvH5\nef4Ee6ruWtnAH8h4lutT9KNYnpAfytlwWct8ZCdzylAfzLqH84pseqNsTjpX98HEjG/JGGzHEmvx\nvICOxgaKbop/qSS2k4SOeiitboqn5d5Gge5+bXH6WhF6EhJrnCcio1rTDNXIEo5k7JID6duJ+w4j\nK+PiHffwdU7dzxvyRF6nhzIfzmzKSVvmYX1+i3aum5AXrCgB3CYYZE6irmy8aeMYZyiurWPaq3Xv\nbDyh1HRoY76Bo+C5rC+baXFU4D1Q+v1HR4xaskFM85K2Mnw9Oh4zCXVgPmL70E3BWvs3/iV/+tk/\n47MW+E9/qB5ctat21Z6p9kzAnF3Xpdluse0VVAomqhxoak48aECp1ZN1WeGp+ehkykWYl4yeyMn1\nh8ff5+ybSzlxuDgVMzkvDhi/IQGxR1uymz8f/hSmJTn4fjDhyYXQXQ3HI/w1CebYx495cE8DQqlK\nlk9iejMJyp2536Y1FjOyzm9gNsTcNWubNH0JNJZaDNSMmqSeWBXrPM8wkHx0x+tSKny4VanMeJ3g\nWjFrq8DH1wCXxcNTaCt1jjdUk38mmZFWfYPhRI7o7PsuYaCgIS/mwsqp6S1uU+Qa+tFsQGEjfFUt\nruYuuc7DyeOMzqFYWGkvxVF35GQovzE6NBjlfqyTmjSXJdWdtFaycKbYY6wArnwhojZn2X0czT6k\nvkP0SPkqgxqj7krRHLFTSebDKeRk97s+pVKNhXmT2UxP/3pAqRwJG/YWdqSq2Xr/mRnDVBisk/yc\nUCHydX+N5kyyLoVzTJ3KGOVNcSX945i8knnqeWvMPaWHPtwmUnr8rcubAHz90a/SUdbt8+dLnHVZ\nL+1Gl66uBb+5Q6AU70Zdn7ZtURUKFS898oWsw3j2aRYTqcBNp9UKI+HdEzBdtXiM1Tmre++zMVSO\ni+wu8+8rL0fyLhvzJaX123yU9kxsCnHk88kXd7lMe+TKVWergCCSiXNyS7Yk7FhYKgUZBRonjWOH\neqJgleiSy/LrADRnLoVWPta25uFC0pPHJ7IYH7v/gEjjFm2vYqpoNJM7FCMxxap0zGEiCyWrlDgz\nPeBAN6YsnkMpn83dc1C0mW8SjBWz0teKPZwMqw/QIj9hqgCik4MmlQrMTmVt0OhDpgKt4WADq79t\n4gmOVSx+IyFyBbk2ysQsD9KITFOv9+9XdJdjWGdUmiSajB8zq2WDjI8lpbcIm+jexei44nwo171/\n9yGNuW7OO2u45/KA3JtL/NjOMzo3lI1o2CfUCsCB6RJrFsQ3czJkLJJKy/uxiAAAIABJREFUCW+d\nU9yFbu5rfeY6xs1ohtPUOoGOAV/6UYfyG4vTKamvWYa0YKh1J3k6wldX0Nx9RDZVv1uZqYaPHnF0\nopWog2q1AcbrAW4uD6FnDMrDQsOXMc6GCbmzrGcY4ypZyuLAJf6cjMV7E3H5HqcPyMdyH9s3d+gc\nS1wj3ekze6KqXbdyHM1GOaFkJLLyMVWtqMPFhEQFfefHZ3gKLqvnNVOVNnBVINXxLJG6zZ45JHXk\nnpP2Yxaqy9HyK+q3l7XCH61d1T5ctat21T7QnglLobXe5y/80r+L01vw3O//PgBvv3FEoey0djRl\neKH0aE5OvqRpsxrsqsA6yl9XuViWnIIeJtCaAeuvqMEdVSaaLCYcLmMwtsTRrMY4Lak00FbVUOvn\nlzzS/WjAqSvmenFoibRuwa1dqoZaFdmEOtOcvFav5a2aMlWzdjLAO5fTYdQ4wF0qp0wl4GTcDgvl\nCawm6Sr7kBzWONFSir4gO1HY7RM5XeeDEYXyEw5nI0y9VNHKmCmH38nhmIsHyjq9VJNqTmk8ktPl\n5PJwRSW3fT2gpSTAxltQq7bhpiN/P3g8wXaU9nz7BDOVgOD5MMBraaVex5Ap3j9b8pmcVkzUIsim\nDUKl9p+PRsRKq5ZlMZlWNiahBgwbEYkrF/HOLIEjS3j9xg7W1QDeuGZxqIrWivu37phQdSJ7jR6t\ndV0jC8hyJVHJHeZtjfar4nke5XSeyLjd23iAd66w8EZONhd3pDFQd64qeTJWK+0g5fpzcn8VPmWt\nQLvxq3h9cZWqofStPPKYHH5V7tm8z+SeUu3PxrQ1SN3djkkudP3qnLqOS7Iu9589STgK5P7O35jz\n+rpMWvuOx3jFf/7R2pWlcNWu2lX7QHsmLIVmw+cLX9gn8bqka7JPbX3pLo/+QE7js28fkSTqZ82g\noak8qwg2awoWSsEWei61nojEhlAh0XHk0dfce6YFQ9O5Q60BTL9ysfraVC4L5WfIClhoBV+9FDrp\nAkqp1Sp96k05lVxnQZ2o9Np4xGVL4iNeJO/FeQ9nqUOxe4ZXqO/MGmYmJ9RoQysAp5c4p5ouLCbU\n+xKXSIMprvr++fiSk0jGyCx1Diqf0ydyAl2mI8yyqGxRsDSxjpLvMky1qKqSMWk5LYaB3NPUXBIo\nXmJ/I2KmaEmbz3FqZU7yxSKY3QyIlay0nAV4ikYcNxeEmtOvJg6+YtYTTeml8ZRE+RbSRYH7mvz9\n/PvHhMoUZJKC9/cFpddXue7GSYKjcO1ZlLKpmhpbbBFqvGbYeoTTUnJU1YsIspBwfYnvCKiUZ6Gs\nx6Qd9bmHEM7lkUjGGouIRhSa64yLLrMlMvNmwkEpc3X3VPqY5S6ZYkguJwvOj+Q3uoP7sCFjH3gp\nzrmMrR2JtTas3yJ5pIxP7hRPi7zKcEpVhjq2NY7KIZY7y/Fpkqs1PRyOOPIkXnO8qHGOZU7O05rJ\nEtfyEdszsSm4bodO82foOZbmrkb1+1+jc+8rALxxesi9h0qMUiSMVIq9rVRUxjVkOmDjtCJSIFBa\nGWplWraeR0dpzPJldaI11Er5llOs6NaOi4pa3Ye0sqtg5LIW4/bmn+c51T78vx7+H7QvxQTPTMTU\nyIR2Ao+mpxh+I4vAt01spYCWfIcskQh33HcolXCk2xKG59Pzc6qZKk+FARuJlBxn9jGu0XJwe0yd\nSGQ5VXrzZpqwmCV6fw4T5UYcEDBV4MDhpcN9JQAJehr4K+crTMPh9JD0VHP3ZoPT+ZIibsjNLQme\nXSgNWhhHDBVXcDSZ0tIsyXbnmmyegBtElK6SlhgJrpVxSlfHoiyhk0l0/mT8ffoaVL4ox0wT6dNw\nTza/XjpbbbI4EbNMTH9OPNo9MdHHkxsMJwJJ7yjGYD4+owjFBTstLwnUHYs7Lu1IMlSN9gJHS2iN\nrwKtFy28hoz3bhpx3liKxGwzfl82va3xZ6UPxR3CXLIkh0nJw4VWeE4WBPf1IS1HlFp+nSkfZJl1\nyDMVfUkcnFrmdN25SaKCQdPKISuUx7EUF3ORJgRKHxCGJbcnWvnJMd0D6f+byYis1kA3Hy3geOU+\nXLWrdtU+0J4JSwFcPNPBIV8VAUWtmvAzYjL2T67haGAv8CPCpSCEmrKFk+FGWmXYsESqA+h7Fbmi\nH2On5CjRwJ2qR1vf0NWTYTa2nGtgcF6ap5Lq/097bx5saXrX932edz37cvfbfXuf7p5Ns6FlhIQE\nwgSJIlAYyoY4NsZUkZRdhZNylWMVfzlV/oM4ReJUOTiUSVJ2ANvBGAjGYCEWSwhJ1jb79EzPdE/f\n7rudfX/Puz354/c7d7qFBs2I6Zl2+fyquu7ps7zP8j7v8/zW79caFr7IBXflfRenPHdDMwxNRGsk\nfctG6wS57NYmcXH1JHS1YGbe3ifXKkLfq7J+agEmkpNoYdJsLCppwAH5UDP7zpzHeBpCK46YDuT0\ncNMRxars/rWmaES7r3SZaHjLIcRZOPCceEGPyXQ0p6zZoN5wwWNQprih0M69A+av6fjO7nDqomgH\n1VINbyLthGpSDCJLqqQmR1dehqLGxBtlGkVRFYpujgnVrNLYvCnMGCxChHWPOJKTcja4xlAdY/Fs\nRKJ5A2drmsXY9Sid1nj8asDBjWcBaO09xeZFyb2oNXbIjIR7x2M1/RhgpzIBmZ3ROZD4fykO8LZk\nfPV0lTDR4qhQQU4dl6lmBPqVgIlqlvP2jPWH5OR+9kui8QVhSqY0dsk85eBQvvvwuTmjTELGpckt\neqOFOSZzEkVzJi0JWbYmFrck4+/t52SLTNYRmLrcq61ItZUgJtJ07Cwdk8zldd92mGoo2jMw7vwn\nSEWf5zCbOXi2wDjW6rz0h8lf+KcAXDtqM1T1upfPQcEm6o/KYGtDl/G6emaHDiMl22jGBndFCWvL\nPp5WII6ek/fmVYdQaaiOwoxOSx+gHBY4FpkrlPVyEfmzvnaKrUByIZJ0jNWFPpoPSEQ7JmaC0Xz+\nQBGcXa9GW8ul7csO45bGplf79AeyiPOJqJ+e43H6QS2xLZTxZf3R3x/Tn0nM/6h7xJEmVPU0Pn7N\ntphqzUF5JWCgXnS/HpMpKc20fJOZL3kKWU2WQOJ5cCBzseU3uPxfSIKN46yyqeZPOEnp22dk7rqy\neY1mMbkqnKcvlMm13Nuzc1LtR1yMiTQd2ai/Jx/PcE/KNQpxk6IjG8TNVo/aliz+XrfLrifvT1Rl\nPnUqIFd++XCckihSVfWCx9yR+QzqbbyObF5HVnxRvb0pfYVk31xZZeeMmnyOxdMc5NFszHCo+Iia\nxp2bKlFH1031Gu3ZYnOecbn8QQDu+9gt7XvG4UgRoiYZR1q5O/HmZEN9uCcp2zsazRpLnsO8mhFd\nU9/A6ojWV+R3+0HKWSXB2bpsqWoilhfKOKeDnL5uoJ3DmF3lv3zlIOWa+lfWZoZpQdfvm0xXWJoP\nS1nKUu6Qe0JTmMQpn9/tMXXGlHXHfLX+DN2XxHv/2n5Cq6/cC7OYRPMGAn2v5BRxfdkGU9chVUru\nwdyl7os62J4ZIl9O6YHy/TnGMNBrzQcZ8YKQEruA4sMxBqu2hNHZmtWKjPe0ujCvMV3VU27UYaMl\n71c3ugw0rbim3IHBNKcwV6TlYgerJoM3T6kpdmNHsfUqk1WcTDkFR0N6wXUADtsDuhP1Mk9y2pmc\nbN2eqpGjgNyVeXGrVapNjah4FsfVuHmxRF9xLmepRBH8tEismZClRoHKTB1xjsO8JKqvnddAs+oc\nJbXBmzHbVarzSkhZU5CH8/4xzYAzfp02LlPymlpQpYRoI67bwC1LfyY2IFGt57BjibQIKFcqNbNy\nlnouZs40T0gKaq7NA04p/RuZhaqcsAVNOzZOxtxXkpU4YqJFRyE5Y6s5HhOY6fg6erJn3hBXoeAm\nMUxVXQ8vQFnxMk52VFNac3n+jECS3noxoqMe7RudOXWFqZt6lqAofQs7aib0hmgKAnG3yFg5TJy1\nnHpF7sP6TomizmdUVATyG3MOVDN5dRjRUqq7gxhijSRNLKzW9ewfvLnCKGOPecTfPSkHnn1oo84p\nfHarSh6aBsw0GhC4OedOSPlysbR6nAPeVDzH/WGXsdqLo+mMVI3nYtGQWl28Wcrpkub566ROkpCe\nes6boUseyHV7RzH7XVE1T4cwVOCMjpa0/p2PvI8fCSQn//fWb3L+41IMOr+ece69UldRaZwlDUTt\nvvKF3wXAOxhQURhyUyxy9U++BMBWpcKREp7u9uSB3r014dEz8sCefPI9lNelveFhSrGqlaRrVdqq\n8r/8nADwfax5huRh8aZvPXiZDz7xKAArhEy0vsL1qjSaooJv1DTsWSzgaOTA+CNqGvbr25SWwpP3\nhx0cLf1u1OW7/XmOo7ySzYLPxuaCFaqGq0lWG40qmzuaHjyU/h7svcZ7C0qis1LAr8pc/fTP/jyh\nIjnFrsVzlExXyX9NljLSkHKcQ0UXf7EQsLUm93fn/We5+ORZAB76i1K39x31Cs3id0nfafK6kmyw\nLHBBNSx1u5g//dZCfuIvvw8AXze09GbMTNO1u/0Rk5G8bo3nTHSzyVKLp3FpR01Xzzj4wYL70jKd\nL8CELUVNp6+EPiUF4vE0F9sUS5xc+Fo8h1lP+rE/alFZdLJQwhjZhD7/3Je+bK197xuPSGRpPixl\nKUu5Q+4J88F1DI2iQ6OwyUSxAkYHNby6qIzr9SZbDaXuWqszGWlxkDJGrxDSUM7Am9kM1DxYLxe5\ncFLAUmIzYE0JQmaacBBFDfbHYqLkTn5MdLLu5oxVLfe9ApXxQiORv1u1GSP/2+TzbM7h35T+9H7k\na6A4j/dfuo9sQ52Hnmgj4YkYzxcHl3swpNhUbIIsY0NPyvGuqHitUptpKu8FQY4bKTx5YUCqfI5V\nt8oJZYp+uS4w60dpRq0vGsbsixGppsTmjoefq9d64mAWNPeJnj41Q1CRz8edCQNNrOrMYg4VDGZu\nI8qLJCRNimJuCIoKekOZTM0Sz01wUoWWC8qEvjoENQpxUGzRVmo+Z1Qh/armk2BxlYjHtSloUtpc\naeujNAWNADjmmOQaN02PYer83YjwIZmDMxPJe5mXH6P0+vl5hwZwrCHY296/Q4E2X/8TLBZftdB6\nLjkb45WYtKEkM52UTM1RN8tx1FntGovvLqgJFrwEwFwTubLsmHndGAg0X6biCfUAQFEdh1EyZaaw\neMZAGCyK38bkc8U0TRKi5LZxvwlZagpLWcpS7pB7QlOADGvHPLvyHLM9xf9fPeCRc+KIurxSYHtF\nYn2VSoCZyCl1M5JwU6lomDqye24WS6zoaXRie4v7TgnWQdGZM3UlnpxlyvDsWSo3JTTXcUb09yT+\nHwUDCrrLv0CHmiL4DjU/opR/lV/80F8DYPZUkcoP/zIAj93/P+FpWKjdLTN9StObE0F6qjw0pjAR\np13XvYmvHBHzE7cop3IrwvPiGN0ZrFJ+jxKuODfJPUnnrUyaBEpj12v1SEbSt5Mai/7M6V/g4ysS\nIhuZsxjltehXDwkVes7UZ2SB2KKRhvG8eMB+WxGt5im3uuI8tVN7bA+7xqApIKyoM8xicNRmzQpz\nPEV5NuR4gYZXi1Na+1LkE/jyXafb47dLTwPwnqzOTXeRQ5IxCNX3kUCi+RCoAzPL8+PT0zeGoqJM\nZbfRCdJfwX3uhwBonPh2AM7/JXOMgKUd/8byde+/kUvBYGBvW+dL1tCg/xWmmpNy1J4w1hyZSZYf\n0yE69nVnpqNI28ZkuOpnSDKLp9qYYwxVpUBsNAL8gk6++iKiYcIklDVihwmory2Ppwy1mM6PXWK3\n9waj+MZyT2wKjgkoBztU45zXFJ34wv0FHjwrD8KOu0K1IotpGkqtA8DpkjgfV9YhLCu4STqnmInH\neW2rhq8MOqXYo5E9LO0pHkOyFeArLHh54uKp6uumPvZIF2Ywo9NRDr9Ab4bzU3z4imAzvHh1zpn/\nURb/+ks3GP2RLOLiQ0+TPynqf+GW9M2LA/pXXwBgUJpQ2pTrrdYfYgFlefph6fvWbpFCoLyFnR6d\ni58HoOrv4HUk5Xl22KO1JibKusKgPdn8MfojiVpsv+8JSp443yqlgHkkG4BfcIlV1e4r3kTBJFhd\ndPMoJo+lb7M0x9HoS8E31Iu6SNUB5gYuRUWaLrsejnrq/YIlUOTmpDU+dgQ7NU3LTepsqJlwoxVR\nOqmU62aApwlsSZIeF5zY21TtBcp3tR5Q0roLk0B/Lp7gVzovYY/+vszBUJzAawc/xNYJMZXcRdbv\nmxB7G+O54c49ozeRXBWTiqk1m86YOYq7mFsUahJrDY5iWbiOwVmk2evnaQ6OzlVoDI1QSXKKLqcV\nqGX7RIWSIwfGvh5qcTLA8TQBioxYTejQ+hi9RhzlZOatPeZL82EpS1nKHXJPaAp4Dul6hfV5mQvK\nTnyu+H5OVuX0K6zNCHTbTWxMfVNCMmuKwLO26REU5IcmK9FUrSHGUChp2m3sUHLld4nGvk1lleSk\nOBQb0wRKEiJ0OwXmJ2Vnf6Sd8IwnZspQQ53T930X7z+QEyg8/1nOt4Qgazd5itZFqYvfXIWdgkCP\nmdMyjnx8i+K6aA8bW99F2lQ6srUA+mLabGx/VNoYp+Rl+V2v+yxBJnyOJD3CguZCbHq4sTjURmfF\nKXu6sI7VlOhm+hgVTV0+7ZV4JRftpp422R2LhjTRTFFKLiU95VfLNWqeaD+zdEK5Ln2uOw5Nhchz\nNU06qBaJNB4fpvb4lLFjh0jzRdJRQuSLZlJsy8k2djKKu3LiOZOIWLkmvcBjw5NlecsmoLBp1i5M\nGIdCQdrbrJfYasrpWbOWTizt3XeqTnFVvnMulXk9vPUiKyuiKbq4xwDB1ojDEoTd/E+fkla+hGoJ\nx45Iy2FLHIwbiqfRjWLm2cJhyHFaucVSyDST07EUF5qQlrum+evaz04xZGddU8mr62wpqc17Tu/Q\nHkt71UDuuZMf4YSqPUyuYiaKrRHOqaeaFZonx4zlb1buiU3ByS3VWYpxLcUdJR4pVilqPoEdJcRa\nvhz4ZWqbanfXRb323RJhTSHB3IjQlwVmezFeLovQbXpocAHH6OeepbqpOeKzmMZU+QNnEFmx1fLA\nwVH2Ji+SPjx+sse0rGmytYhnv/yvAIgKPcpHUp0X7NxHmOjvFOHX5K9SOSs5BK4zhxUFXInHWE2y\nwhFoL//sAVaJSSp2zLAlG1O+WiJTXMVaoUG2J5vljX1JmtlcO0XjAaHvTMyUZlke7pgZc7Vbd7st\nUq0q1XpDNkshOxsypvpqiVX11K+ddPGU6cqaDume5nIoPNxobPE1P9/0XBwlvzUZmHCBGZhhNJFr\n3tbYfT6hbGTMsWfpdsRsDD2HfLERGEOeLzYD6WfoulxQ/MwHzpzloffIA9IYlqmtyQ1eeeBJChXZ\n+EtWTMx6OCNSMphCWMGxuiFlkC2eghxyZ1ERe3vE4Taj4baXRoFx9rW+pJPmzI83ELtwg1BwQHF2\ncBzDSEMmC1ydNIeG5otc3qzz6CPiP8uKJcqam0CQ0XCa2k2Z45XVAomvKe2zgLGR15NpjNFNwfcM\nM/vWSqeX5sNSlrKUO+RbpaL/h8B/CcTAK8BPWCtHqzHmk8BPAhnw09ba3/1mbVhyEjPBlCdsKVpu\ndadPTavsxkc5Q83yqvjbrOruWasqp56bE5QkypCN21hf4brCFFc944W8eHyKJZHCvA0cnKlqCp49\nJjWplItEbWVP9gY4yOmYKSZAvVKgdfnXAXjhXx/hCSA0Z3eKuDMll4lPMldM/qwnGkHl/Q/hKrmH\n610gbysF+mnIFYk3T2Uc3tU/xnlUoeTyCtlETtXx/gAK4tia9sHOxJSoaNp1byXF7Sm2QsFhoykR\nnL3xC3RH0rZNLeq0p64vNlZDVpqqgQRFzp6XuV3fWsdvSK4Hgw6DLVHHm7uiVb3WnuLO1Jm7k0Ak\nJ/BompBpUVGSREyPqe4U8q43p6/ZqzZxaGnlYMFzGDsK75blx8A2rmaV+q7l5IqMb32jTN2Vtk8+\nMWW9IlWSlc0cR0FkKCrQTRqTdhX+LD2Fp6ZpisWmC30JPNXYrGZSus4bPyLdSLWYQE2GFGZqEriW\n46crtIZcndtZBpFdOE/lc+vDyRW5Dx/4uOGJRzSNO1ohuqmanjNnpOjYwVwBYlqG8oLMJ+kwUa6S\nbJAxUE6NonXx3jDU8o3lW6Wi/xTwSWttaoz5WeCTwP9gjHkQ+FHgIeAE8HvGmEvW2m8CEmfIrUsz\nr7Gj/oDimRrOTPkcTYFcH0h37QZ+SVTGMppSWygSVTWc2O/iK3pO6FVgTR+EOAENLfo6SdPGAXmu\nJcQ51DQN2lZb+Hqj150qY8V5LNblLs7CCe1Py3WrX7nOjZ+QyMHq86t821ySZcruU/RcoRcvKWZi\n8+hxvDXZsOL138HG8r4bn8bPFe14U0p63f6MwD0r/Q2PaD0m4btw1+KMZNxea5+bG1qWreOvHzyG\nXxckoH4jwRZlfIPdiCRd2LAZvtrtW0VZYCebdYoaWSg3AtYrkte/Vm4QbS7CjwVWjJDdpDWxb0fN\nFvFNWdBrjRBvplGGdMC0rck0UeFYde9qElLip5Q0ySwZx4wXJXyuQ6zISzkGzOuJPABl41NXH4cb\nGDQbm5XkAdZKMl9u1X29INDKQx4MSpia9NPmFquuf8cWsIvcJeuz8IosNqE/Sxb5W2b+upERqsFR\ncg0mXKAuc5w2Nc+BSAlsdB02Kh5PvE/W/fsf+wCnLj8JQK82Z/g1OQDiNCe4KutsaPVZ2B8yVgRn\nJ88Z57ImE6CiJb2ONaT2rRkE3xIVvbX231trF9VDn0c4I0Go6P+FtXZurb2GMEW9/y31aClLWcq7\nKm+Ho/FvAP9SX59ENomFLKjo/0wxxhAEPid3NqAh6pIZNI4JYCrblmYo0YVKaYu6woYVG6Ke23Kd\nolK1J6UDClbVwVqGpzXoeTzFUS9xfIzwXGUey35XcDNcxeoL2+vMsusAXKw36SpO/64m/2TT+7hY\nlwKlF9//G5x75RHpm/si0Xkdfv1+NkPRPNxQkltMbQPUJAomp8jUmek6q9gNeV3yRB3MVyMcTf6Z\nF15ic/YDAIzn10hdiTRMN55hdSz7cUe50ysbNbySzFUjeg99TWo6yTrXHaX1zM3Cqc9c4cVspUBz\nR9o+s36Zcxck4cp6IauZmA8TjihotV9nTfItLjiP0p1JQtZarUhakr7ZkU9XYaAdPyJS73tvJr8f\nt3NqjvRt7oFVDogozQidxWmteQIyewAknsGWFGNg5T7WT94n426eZ64QaoXRWfK2aFCuomtHeUrp\nSJyOybiIV1HSzzDHUU0uD3I89WjmVemnX3zjBCbN38Jmui4yg6vaT+obXCUrClNLstDg/fwYw+Ns\nQ8b83suX+MBlcQ7vPPrjFNaUuXzugv+cXM/mJHUBlFnryPgn8afJWnKtvpNAKs+FY8aotUIEOP43\nKPT6M+TPtSkYY34GSIFf+hZ++1PATwFUCgH1SpFSeYWwpupSf0ZpU25WvWgor8hDXw6rFIOFK3qR\nYJQTz2XBB5UpjhEV3S03cNCy3lJ07Dk+trHilKJyKjIeEygwRSmPqGiJb+aXycoawdAagPLGiD3d\nTIL0JfqvSn9OPRlSeUWJSmyMpypoaVN8B66TSyYcYHkWd1X7YW/gaLKJdZUoZKOF8WQD8dx93Iks\ncr85IHSU6clsYOeaOFOUNoq2hL+qJdJ5n0RhzQ/pYRZYk5nFV8SpspGNq1SqUa3KYtzabOIqWakp\nlLC+mEHltQCrNdCVhm68dkpwSjc/G2OVQLZoqjQVgt8desTql1lV06BYnhM5izBdhkkWSEGWXB9M\nzzGkCzLhRQJRDuVQH7YwoqYArabUQUstGGf7RFrN6Y/l/ofVMu5MvhDaMpH6A3CL2LJcL80z3FAp\n6mPx7dS21/He4JlK1X6Y69/Y5niLrMIMlImeWQb5gvQ3dQiO+y9zuL7qsHpCo1mTFq5MJ7G5Dmoe\n+iYjnKmpkCg4TanH/lDWoevGBA0lYcbDUzxSP8+I52+/T+EbijHmryMOyO+2r9dfv2kqemvtLwC/\nALC+KDZfylKW8q7Lt7QpGGM+Dvxd4KPW2tt5rn8T+GVjzM8hjsaLwBe/2fVc16FRLxOuTylmou43\nN5vU9DRzHR87VYoxExLP1JNdVZaS4YzEagpr6qHYJLizCHeBqxb5JCWtJRjLaTcd+8z1WlHaJ50q\nnJdfpIQ4qF4b7uFOtL5fQUjcTofiWcEv2PuPDg0FPVnrraMI75Su90m3NHqiNHWF+2/i9lUzqaxj\ndAd31q5iNDUZpTc3tolSKuK62/jKVOzvWewZOcXdW3MC9ZKVu9Lfg9o1nJfFXDl1dpthW747Tnpk\n2SJebamoZlKTA4qVqsNKUU/J3Cc1Cls/tngKZJLPM1KFEc/0NE/GlrkiLkdJh1gJTsbzDJMt0LYj\nfH8BiyftFV3DtaloMckIErVnDA6Juqsya4+rIBdhiIIH6zoXG/UNQlfmLai6uNMFhp7BQWnjKrpu\nfAc31YrRLMKtL3KQc6xid9rRkFy1RdR0I1qF8uvYC7fLVB23jtY4GAvJIrU75RioJ3TMsflQci0G\nOdHHA1nf/esnuPkZmZjG9ha5rr1G/bsIPMlxSUv72C1Fpo7lfgSHAXM1QfqdiFxzFkxu6RYUSTpy\naWhquWb0f1P5VqnoP4nkvXxKkzw+b639b621zxlj/hXCZJkCf+ubRx6WspSl3EvyrVLR/+Kf8f1/\nAPyDt9IJx3EpFBsEcZlQiz285hB3pCGd3D2O0/eyKf1XxI6KfTnN2hOP+gOym6+bk1RLMqy8X4BN\nTbXtukRa2DQ9kH1qXJmQ6MldMDMW21daTiltyg58ay8j7Ssyj6b4tm3G7IuyW594KWT8VyW1OX31\nAjWFiMurY8aZZCFGX5M+VF5zGc7UEXl+xnomqEiVBzfwi2q4VuSZG4Z8AAAgAElEQVRksL2HQVF8\nsvYRk1UBTI3CnFCJZ/P2LuMT6ih9RexMO16lfSAOqeCSx1hj2/HQwVc+DFOw1AKZz1ARsfNZwH5P\nnITxxFA7VKbsYpXKtsbsZzUS9bX0W/J332sz2FVIqrRPqli0pfWQZqZOVTIiDVWuaYryanlKR9Gz\nW1mMOSbCdYVyHMAa7MIw15ik77kY1SR2u68xU7TmcZSxUZBQdWFbSHEA/LKuhdGMXi45C9PXLMPn\nFPE6mZFtyneryQqbF+TEDvqihUaFHsWChIAXeAYAMyuobwCeBvFymx/nHmS3GcSOtZTVn7NV9Chp\nLkMhkJO9O7lCb6xUf19xaTwhKe3p2Rm+FkHNc4+4JX3b68s4BoUEvyK+tul8im0qy/dRQFG5UUIH\nnOKiAmzIm5F7Is3Zcz1Wak3KZhOjnIHh/hpW0XCT0RHxTFwT817EtZaoxLO+qHjpdgH7nFKWbwbk\nU9G7YzPG9sWjfni0C2NljdYkplJ/nblGALIkx1GotGKpybqmUM/6CVeMetQ1dTR68Tz1jkQcBvZT\nnPwtiQyw9SrG/wwA5UpAGMn3x2fl4Wh95ohxKDeuduN+plvifimMvxvnolYdzhUUZjdiEfONgjr+\n0cdkrDdfIduXa0xSh8JrkjeQVSThpVqoM9WHrdb6TmaTX5A2MsNQ1dyS9TGq/rfVefXywT51xZe8\nWZwQqOd8Y6fEhaFEIpxqAT8T1b2neH+93gadiUDcu92MTJ28tfkGk6KaLnFOpm2HWsl3YXONMJHv\nfq7XJtWE67lJCbVvQ/Jj8yFbZDHZlJuHWu5d9diPJJnKeSbhzI5sjBfb76Vck4esphT2w6M9big+\nZvf6AV95UXI56lWXj1z4Xmm7PCB5Wq5BQ+pETl2cUv2O75RrbZaP96uob4nVHKto3cLUWqIF+wCv\np2YXjaGi+RbVssclPbSM4i9WKivkSEVwPXg/1pd7ymvrtJ+TKtjBJGXQkbV6YeN7AFj5Cx9mrhGu\nf9v9Z7xyS4B2bk1fpJFoFCSHSq52KEe8GVmmOS9lKUu5Q+4JTcExllKQkzl9CkUNBWaZ8IkBzvgW\nV6/LLvfaM11e0jTYVeWI2CoXmWloLt7cJJ6o0y6E0UB213H7gLwjp1velC08zfdIlWevlvj4W5oF\nNhuxouQyo5pHqSbtNBS6rLL5J7zWUifT2u+wvycq3vlzm5iXZLdudUOsohwfaHgvvZJTksJJRsNb\nVB+UUF/aewqrjsZEWY0noz6R8gGWHztNrFmMk/JNQo3PlZpz7L6iQEdqMmQliqtn5VrmGr4iGM/T\nHE/j41nqkOqRNlfAj2k9wq0oldx8iDdQrahepeuK6VINVzDqVOzHorl1JxOyRHkQhzFpU9ob9NoY\nDQEGPQsafvW1EMktJQQL0NiCR6TqvuNZJvntuQlaVKV9L3ohuZoaR7MO4SIOGc1INMcj4Xmsp0VF\nSsEWJQeM98UkWN/2+B51UNYu1FnTalU76TO5IpmlY6Xb2zv4DGsKPmP59uMciS/tvoizgPXT3qYc\nRyFxOaYJwTgw0VyG10YRIyXUeaQsv8wLc+qXxSSI0j/EKUu+X2xuMXFEo7ne/g+MtIJqvSf3pn6h\nRnVN1v1HklXizypWxzwj1IrXJIXOWKm+36TcE5tCbi2zZE4QJszHou5VqxZXU5CvvHrEb35FCgxe\nac+OuSBLikl332GdiyXNLX/JYHc0imCLxOozGPenxIks+vZNuRnDnmVDUYlXGg6NQBZVsRwSaApy\nEJZp1ORWVwNFFO5eZ3jmDwEYfXXOtgJd1HOf3kxu0qtXD/ncy9KP67qwqw148EWtNahPqL6mUYkP\nXSMayILdvyl9vP70PrXHZeGGBx2ua5n4auRw6QkFPYk2CbaV0egFRaNaaTHdF3Vx+9SENWUVusWE\nROsPUjJSBTgpahWlXzT0dH5M3xKp9367OzpGvz43LlE7L0s9vKno2dd6DA/FvErTEWZPltRz0R5V\nRXNeMS4nmtKnuSIvhVNoa4quNZZiWaHvRzEJC1an1w1zX3MaqnUYjeTBvTGb4quZs17xKWhNQJS7\nVBS70Dup5sNBzO4NBZmhTm1V5u2pLx9xvS0VptXcsF4VE3NjU1T7zL+B2ZPxlRpDoqns6nuDK8Sa\nn6AWEcbelo9gQAM8FIylo9/154a8qJWtiW5ofpf+DTEx4498DbcsCVkWh2EqVbe5n3DtJUkS+6OZ\nrJXVp3f44CeUYzTKqE51w40tYyUcxho4Tj5+c7I0H5aylKXcIfeMphAlOfWBoVQXVS4/XcNT9OSu\nZ+gtqiSrheNU1PMlURE3yxCp4ta1XUqq+q76BSK9HuMEV51kNU13HucJeUN260LWZEMjALNqQhbL\n+xumga3I3jl5jzIH5xPmvy8n35lxn8Lf1KrL/nWmWhzTbs8oqNlxRmPfD1BisyqnY34SrBwIpKM5\nbk9UvGhFFVAfCorKnBbmtLbF4VQLVvFyyWUIp5ajFVEvIyNRi3jSZKWiOQ3nDjl5VrSNg6enWKuw\ncr7B0zS9jaKYJZulIgNdDlE4paaYFOfqAWvKP1HbrtBQ6vdTSkJy61yHTJmWvaGLr3Nb2ShjFYl5\no1/gdFVezwJN0XVSVhPRzKLKiPSEtL33VJdUoeAcazCKvVlUersL26uc3BTn2qi5QkPnu7lW4X7F\n06meq+EtKi1HiqswneCekBM2q7mEQ412TEbAIiuyTEWxQC825P42HnmULJTx5+PLuJsyz+7ZKyzy\nFtzbwFQWQaSGZ6iqydOKLQU1NVaLLh+8T7AdLz8k92bgwkQZqp95NmBtSzST8qU5iWYsen6D+zfE\nAdmei6bkrLRpa2HbtLfPXkHNuAxKoazVSZoyzd5aVsA9sSkY6+DFBSqNDZRqkMpkCxuKSvX4QyM8\n9WqPSwmpljWX1basVjYpKUDIbOrSqItq6JY96grr7mRlgnUlbC3LoroY1qAr1zq3WcSrK0KNqTJS\n9brqrXKzJDdhYOTz6ugE72lcBuAZ7/c5d/RhAAJ7lcYjEp78vvfVCc8oPPl1eWiiYYKnlZ3r7/s4\nWU/TmOsz5kbswdM1ZQT69iobFwV0NK80eSyXCECy16GitR9x7SlOaDqrCZRrceUS6aY8eCvZt7N9\nTsyure6QyVAWXsFzObGuD/1F+Xvh4UtUy6d1HD7NsjywXmnAxobWtBmXsoa6zET6e9lp4qcCyroy\na7GtVdYmDxm0xZ+z7bnYqrQz0D06z4o8msjDtnm6z35RHprPxS2K6jNxHHBVN6+XJLry6COPcOaS\nzGHp/Dah5q9tnaiAmiMrJ9/PbFdNBUfMuVG7w+MVLTMvbrC2Ld+tbhb5qP8R6Vv3Jhu6+eQFMSOc\nIKJ5Qsp38hMrFLT27wPu/eRWIu8LH0+Mxar9UHFd6hqGLFdyNjQF+ZG1Ot/1XjFBvKasWX/jYb70\nh89LG6+cpP9h2ZiKvQdp+rI5ndiuMj8lYzpvpYJ1NoxY1ev+29HPwZG8Tj2HdQ3bpK6L/2cx2nwD\nWZoPS1nKUu6Qe0JTcExGudgnKRapbiklWhQT6sldXTtB83FRmSsXznLYExTd/FBO4HES0+or2MYp\nh4YvJ7BTCEk0+WOlMaGeKOCKIjxnxRo2Fi2gAOQLj/woJ1qXaEdgUzYWKa/K/bdy/3U6RW3P5NiZ\nxOkLjzapfkVOseLaedwTchImjX8v/YnOYtb1JEoneJui+ucjCHxFJVZ+yULzNcKqnP6m9jy55mkU\nHhtgR5qYU+2TdrVg5rxEMrzgZVYe+EF5nQ5YUZAVv+TjFXU+V8pcelxU0YeKovGc3r6PyrbkTZT8\niIISiHj1SzgVjQy4ESiLdVDXE9Evc14rVLdaDYpNxYgI6mytS/JWoe/TVy5Ip7coHjogXFWNplpk\neqjzkuXH3nzXdY6JfU5eVjzOQsAD9z0g47h0mWAm18UpkSiBjfXGlE+pGRrJ38ZmmYnmKRSSGzgt\nvd5OA5PJGllb8zBdje87olW4PIHTPaFzW8HZkbVQdPvH52+sDlHrcFy5tVVyOKcFcR84W6X0HrmX\nFS+isaGmTSx9v37td+gOb+kch9QKihfhNmk+LJErfz6krolfSVlyKQaDq8wGMuZGLcEWFH+kDjNN\nzbcWouStwbHdE5sCBowP1XNj/FxsVnfFp6SltZmpUCqr2jqfsdKSm5SeULrwvQkVTQQpO008XcTY\nlMBobnweUNhZkIrKpJvCJkRqiFY6zHqiiya2irMn6mW/dAOjpciJeuQr5ttwn5SHMHluh0pFgTwO\nM9hUXElukWk2ofOMqImF/6qCm8n45rmDvSVmjPfgAKtYi6kvD3Fw4MGZP5a5aF3E3daMxXYTe06z\nHvcDnHXZAGsvyviHl/uEiSTe5JkljOQB2go+xh7/n3w322B1Lup485xWojaqNBuyodleir+tdRm5\nj9GaiCBdY14XEJjGQE2qHGpKl16+f4jfU2xDP8GPlCB3q0/hhm4WGg0pXyuxq8AyfgSDeKR9zsg0\nc9ADXI18nFmTDevcB06xfkHGVKidI5tr+HndwdM+Zb6Dp/6KtCbJTcV6CvvKLNYZ45yTBzIc1Anv\nEzPHb50kX5W5SweyRpJkn2BdkpsK26vkZgEccz+WhYouXfAyyNTBMCEjL2sW53rCyQe1JuJwnaiq\nQDM3FNuxX+H567K2Ll30iaayEVTjGBZ8qmfAH2h1pael5fkp9qr/QfqeHOKqzygbJRxmsgGa1FD0\n3wKmPUvzYSlLWcrXyT2iKbikpokdlikvVP/zEe5QTmAv2cLTCje74uFM5YR1lAAlbt1iXJNT3o3B\nUUpyjxKTqpSG2Y7BacspHaiTLVlpkWnuvI1mxFpr0S+9wjTVKruhTz5QlfmM/G5enWJa6jCcxYw+\nLFBp9voGwTPiaQvKI+xFZQdWxmU/fwLUO0/4J6DjcN0zUNBKvZIkP+V79+GNJQbt1RrkDTFn8nEZ\ncmkjv3mL0VlpuzORz81wFatpM7PHrpKYorbxPKtqBrn12TEcm8nlRC2lDfIFS5NJSIaKr3jQworm\nS2BOQE3mIollLrP6a6T7BW27hK9RIFt1ybs6/sgwXzBFFyWKMspn+Kq254OMVMFQLAZXuR19Y/HU\nnV/c0BOzViIsaGlnmJFq5CAZTMn3NGX9RI/QFccsmqSUj1exBdECJk6D+kRzUpwCruKCUihhKmKm\nxvuideTrhnwmiUWkTUyohDnB7Dh5aeHI84w9JnpxcdnU3BInr1DUuQ/PbuKsyDWO9kQjeHnwAoOZ\nnPKTeZPpLZnj4blrBJqmX2hVjzXceEPWdNz1mGXSRhKVSdcVln83Ios97RuUyktNYSlLWcqfQ+4J\nTcF1CjRLFynPzzN3xN6q3zxFXhInihckoOm6JdZgRUE3lak52apR1kL93E4oaZw+jxJCrQychDlZ\nIKdGlCib8401kpFClDHFlsT+LGcbzEoSZvNnMbHy8mVd2XGL7g/jJnLKDcxXqF75b6S97leIylIF\nGdTuJ+Q75P0dOdny7BJEyvzc/nHmTykr9YkV8kxel1zRQMhnOBOFm2uC09NwaWeGHWgKa7VF+QUN\n3734lMxl6X6m6QKNZ4vm5DcA+GDto7zm/xoAG+k6uZ6gpiztJeEG/lzj8cmcbFdzFqIeZiChyll/\njl8Se35/KqFOv3CWuCMOxanvQl1OuXL7BBFit5vxlDxQNmrFlkgmIxqagTeIcyLFg3Ach62C3D9r\nUqorEr6rxlIQ5he/h9yVsKA/L2Mi0cLyXYc0kpO3ON445lU0jjhSo1cblFvS92j2NINrapefK1G9\nLiHXdLiHVRKc+eyz8vmwAsrvmRVdjIZIvaRwzPlY1fD13KbYTF43/DKlQDXEtccptIUwiGxEpSDg\nvpPGHwBwwVQYrsh6+9hj/zU7O49L2/unSNT57cRFpsplanvvk/vxbAendD8A43FEQ4l2CsnnGc8U\nO8LNqGslLLw5Tsl7YlMwToxbuknkJ4SrmljU2gdPMXBnIzyNm9tpglHwldyKGuVXLLkn6nM6y4kV\nuiwHoqkSlAYpUSKLd6xQ2M7cI9S0W9+NcYrq9+6PSDN15rkV3EA2nLnWJfgnnmK+Iqr/ePbrXL8u\nC/DixSbFz0idQHz2JGYBd1sVb7PpvhezKrp4Yg9xL8kXbMElO5L+Z0fyo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.9337... Generator Loss: 0.2622\n", + "Epoch 1/1... Discriminator Loss: 1.2560... Generator Loss: 0.8398\n", + "Epoch 1/1... Discriminator Loss: 1.2232... Generator Loss: 0.7815\n", + "Epoch 1/1... Discriminator Loss: 1.4801... Generator Loss: 0.6008\n", + "Epoch 1/1... Discriminator Loss: 1.3070... Generator Loss: 0.6860\n", + "Epoch 1/1... Discriminator Loss: 1.3470... Generator Loss: 0.7634\n", + "Epoch 1/1... Discriminator Loss: 1.5236... Generator Loss: 0.6591\n", + "Epoch 1/1... Discriminator Loss: 1.3827... Generator Loss: 0.9191\n", + "Epoch 1/1... Discriminator Loss: 1.2797... Generator Loss: 0.8605\n", + "Epoch 1/1... Discriminator Loss: 1.3069... Generator Loss: 1.0828\n" + ] + }, + { + "data": { + "image/png": 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y980uwQzLspYTeloWFJpg1uuFzJWDsoMXu8RiFQpv4lu8/1y4GX79N77Eu1PN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\n435h92X+3HdKh2KkOYxsnDKeCzFMnTRxduT2D50O675iKzQOT2en1NUyX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.4493... Generator Loss: 0.9787\n", + "Epoch 1/1... Discriminator Loss: 1.3226... Generator Loss: 0.8376\n", + "Epoch 1/1... Discriminator Loss: 1.3657... Generator Loss: 0.7043\n", + "Epoch 1/1... Discriminator Loss: 2.0819... Generator Loss: 0.2141\n", + "Epoch 1/1... Discriminator Loss: 1.4158... Generator Loss: 0.5562\n", + "Epoch 1/1... Discriminator Loss: 1.2893... Generator Loss: 0.7726\n", + "Epoch 1/1... Discriminator Loss: 1.3175... Generator Loss: 0.7764\n", + "Epoch 1/1... Discriminator Loss: 1.3040... Generator Loss: 0.9346\n", + "Epoch 1/1... Discriminator Loss: 1.5212... Generator Loss: 0.6124\n", + "Epoch 1/1... Discriminator Loss: 1.3328... Generator Loss: 1.0946\n" + ] + }, + { + "data": { + "image/png": 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mrqGqV4EuGVbdgYWtL33yV+88C8Bgo0MxlWc3nA4bm7KZWs0IJ1ltPHXdBQGOqgbBvMRs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gY5QGjzSVUhJg35/DwGLVSoLMf08UOOnZxWybrAR+eC2Xh0dg0GA235+RzvnMtv+26GFz3Jt++8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\nrGoghLMp7kC59bPqaeKv05c47MbuPSZamy/2Qip2LliSsr0pdfGlyxvs3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3074... Generator Loss: 0.8244\n", + "Epoch 1/1... Discriminator Loss: 1.5227... Generator Loss: 0.6513\n", + "Epoch 1/1... Discriminator Loss: 1.3824... Generator Loss: 0.5856\n", + "Epoch 1/1... Discriminator Loss: 1.5399... Generator Loss: 0.5881\n", + "Epoch 1/1... Discriminator Loss: 1.3308... Generator Loss: 0.8962\n", + "Epoch 1/1... Discriminator Loss: 1.4280... Generator Loss: 0.7422\n", + "Epoch 1/1... Discriminator Loss: 1.2792... Generator Loss: 0.9444\n", + "Epoch 1/1... Discriminator Loss: 1.3914... Generator Loss: 0.9070\n", + "Epoch 1/1... Discriminator Loss: 1.3808... Generator Loss: 0.7781\n", + "Epoch 1/1... Discriminator Loss: 1.4229... Generator Loss: 0.7619\n" + ] + }, + { + "data": { + "image/png": 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jAltVadYKR6+dI1YJq2wCYvWcPLt4ylLh2klmGaj68Kp6Yg6jlrEadid+g9Mx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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3483... Generator Loss: 1.0495\n", + "Epoch 1/1... Discriminator Loss: 1.3219... Generator Loss: 0.8450\n", + "Epoch 1/1... Discriminator Loss: 1.3759... Generator Loss: 0.8250\n", + "Epoch 1/1... Discriminator Loss: 1.4419... Generator Loss: 0.6523\n", + "Epoch 1/1... Discriminator Loss: 1.2803... Generator Loss: 0.8917\n", + "Epoch 1/1... Discriminator Loss: 1.3013... Generator Loss: 0.7574\n", + "Epoch 1/1... Discriminator Loss: 1.3968... Generator Loss: 0.7108\n", + "Epoch 1/1... Discriminator Loss: 1.3129... Generator Loss: 0.9058\n", + "Epoch 1/1... Discriminator Loss: 1.4153... Generator Loss: 0.8783\n", + "Epoch 1/1... Discriminator Loss: 1.3831... Generator Loss: 0.7992\n" + ] + }, + { + "data": { + "image/png": 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VT1ekirha6cFcrUta6q8umgoUNLMqYGctB92e1SyulJGpu9SvA9xrtU90fZyeHMKduEOc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\ns9kMT13h+5WFpbCQhSzkHnkoLAVjxwTZa3QOLGFPTpJucpvtifjyZ7MMu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.4230... Generator Loss: 0.5748\n", + "Epoch 1/1... Discriminator Loss: 1.3313... Generator Loss: 0.9232\n", + "Epoch 1/1... Discriminator Loss: 1.2939... Generator Loss: 0.8525\n", + "Epoch 1/1... Discriminator Loss: 1.3392... Generator Loss: 1.0006\n", + "Epoch 1/1... Discriminator Loss: 1.4827... Generator Loss: 0.5120\n", + "Epoch 1/1... Discriminator Loss: 1.2983... Generator Loss: 0.8530\n", + "Epoch 1/1... Discriminator Loss: 1.4825... Generator Loss: 0.6011\n", + "Epoch 1/1... Discriminator Loss: 1.3184... Generator Loss: 0.8947\n", + "Epoch 1/1... Discriminator Loss: 1.2802... Generator Loss: 0.8693\n", + "Epoch 1/1... Discriminator Loss: 1.4412... Generator Loss: 0.7739\n" + ] + }, + { + "data": { + "image/png": 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tqAmJdB5cbOi9ptM6j0WUyePVjlrPF0VO7z3EDeAEAzBkMzxOcQotcKYWth+JW7barXkw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\n+yD1PHomL//jNaSDJe7DQ9errz0XTMD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1fb0nrCWbDZYkinVieoc0KXJGceezLcZsI/uO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\nqaDBF9opofn9qsWaey5QUu4+iGHJtdDWgct8rOv60MSfOAT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MXsgXqx0eUqF4RlzBaQI8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\n6ppsprxGWWHUzzHswG57rsMpnqoEkboPvcUVS4UXyxJyOZ9xU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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.3356... Generator Loss: 0.7553\n", + "Epoch 1/1... Discriminator Loss: 1.4345... Generator Loss: 0.7667\n", + "Epoch 1/1... Discriminator Loss: 1.3151... Generator Loss: 0.8760\n", + "Epoch 1/1... Discriminator Loss: 1.4366... Generator Loss: 0.7039\n", + "Epoch 1/1... Discriminator Loss: 1.3346... Generator Loss: 0.9874\n", + "Epoch 1/1... Discriminator Loss: 1.3435... Generator Loss: 0.7690\n", + "Epoch 1/1... Discriminator Loss: 1.3227... Generator Loss: 0.7796\n", + "Epoch 1/1... Discriminator Loss: 1.4459... Generator Loss: 0.7080\n", + "Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 0.6979\n", + "Epoch 1/1... Discriminator Loss: 1.6802... Generator Loss: 0.5041\n" + ] + }, + { + "data": { + "image/png": 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Hb+MPdPPPlriqutapqtGDcxYP1SvsG6yjtn8UUCgzqNQL7bgpcSRMI89iLGJnt9oNJ8fC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iQaZZWmsLmrznSFkLVoXVfoKH9WMGR+b3qN5iO5voXpsB9J0c3EDvyaEvWa4bfpsOB57kYNJnRK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\na1vb2h6y9aKwtrWt7SH7fwErqDr3TXcUMwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.2873... Generator Loss: 0.8386\n", + "Epoch 1/1... Discriminator Loss: 1.3842... Generator Loss: 0.7583\n", + "Epoch 1/1... Discriminator Loss: 1.4189... Generator Loss: 0.7991\n", + "Epoch 1/1... Discriminator Loss: 1.3378... Generator Loss: 0.8856\n", + "Epoch 1/1... Discriminator Loss: 1.3755... Generator Loss: 0.8002\n", + "Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 0.8532\n", + "Epoch 1/1... Discriminator Loss: 1.3158... Generator Loss: 0.9599\n", + "Epoch 1/1... Discriminator Loss: 1.4263... Generator Loss: 0.6772\n", + "Epoch 1/1... Discriminator Loss: 1.3762... Generator Loss: 0.8854\n", + "Epoch 1/1... Discriminator Loss: 1.4065... Generator Loss: 0.7769\n" + ] + }, + { + "data": { + "image/png": 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dLraQxWuqV7wd+vTWJO4chAGOHqZynFBZjZ6o+l3ZEldtAn99A089yI899Sw/cUUiKv86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cqKOwsrHNwgpDuTPgSPk43yc0u7aLnUboyLhk4BVl3AYfT9MpbfHRY5GpJDrSwMGbFepykWfL5M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\nRbfjFt629CXTSh+EdU/TkEnhFmxRrZBJVScIFmRN98DWAp6zYk2dPkknn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1... Discriminator Loss: 1.4026... Generator Loss: 0.5969\n", + "Epoch 1/1... Discriminator Loss: 1.2856... Generator Loss: 0.8660\n", + "Epoch 1/1... Discriminator Loss: 1.3118... Generator Loss: 0.7520\n", + "Epoch 1/1... Discriminator Loss: 1.2754... Generator Loss: 0.7607\n", + "Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 1.0381\n", + "Epoch 1/1... Discriminator Loss: 1.3217... Generator Loss: 0.8141\n" + ] + } + ], "source": [ - "batch_size = None\n", - "z_dim = None\n", - "learning_rate = None\n", - "beta1 = None\n", + "batch_size = 64\n", + "z_dim = 100\n", + "learning_rate = 0.001\n", + "beta1 = 0.5\n", "\n", "\n", "\"\"\"\n", @@ -551,19 +1999,18 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "### Submitting This Project\n", - "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." + "### 提交项目\n", + "提交本项目前，确保运行所有 cells 后保存该文件。\n", + "\n", + "保存该文件为 \"dlnd_face_generation.ipynb\"， 并另存为 HTML 格式 \"File\" -> \"Download as\"。提交项目时请附带 \"helper.py\" 和 \"problem_unittests.py\" 文件。" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -581,5 +2028,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/face-generation/dlnd_face_generation.py b/face-generation/dlnd_face_generation.py new file mode 100644 index 0000000..55660c5 --- /dev/null +++ b/face-generation/dlnd_face_generation.py @@ -0,0 +1,459 @@ + +# coding: utf-8 + +# # 人脸生成（Face Generation） +# 在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。 +# ### 获取数据 +# 该项目将使用以下数据集： +# - MNIST +# - CelebA +# +# 由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。 +# +# 如果你在使用 [FloydHub](https://www.floydhub.com/), 请将 `data_dir` 设置为 "/input" 并使用 [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) "R5KrjnANiKVhLWAkpXhNBe". + +# In[1]: + +#data_dir = './data' + +# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe" +data_dir = '/input' + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +import helper + +helper.download_extract('mnist', data_dir) +helper.download_extract('celeba', data_dir) + + +# ## 探索数据（Explore the Data） +# ### MNIST +# [MNIST](http://yann.lecun.com/exdb/mnist/) 是一个手写数字的图像数据集。你可以更改 `show_n_images` 探索此数据集。 + +# In[2]: + +show_n_images = 25 + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +get_ipython().magic('matplotlib inline') +import os +from glob import glob +from matplotlib import pyplot + +mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L') +pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray') + + +# ### CelebA +# [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸，不会用不到相关说明。你可以更改 `show_n_images` 探索此数据集。 + +# In[3]: + +show_n_images = 25 + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB') +pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB')) + + +# ## 预处理数据（Preprocess the Data） +# 由于该项目的重点是建立 GANs 模型，我们将为你预处理数据。 +# +# 经过数据预处理，MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分，然后调整到 28x28 维度。 +# +# MNIST 数据集中的图像是单[通道](https://en.wikipedia.org/wiki/Channel_(digital_image%29)的黑白图像，CelebA 数据集中的图像是 [三通道的 RGB 彩色图像](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images)。 +# +# ## 建立神经网络（Build the Neural Network） +# 你将通过部署以下函数来建立 GANs 的主要组成部分: +# - `model_inputs` +# - `discriminator` +# - `generator` +# - `model_loss` +# - `model_opt` +# - `train` +# +# ### 检查 TensorFlow 版本并获取 GPU 型号 +# 检查你是否使用正确的 TensorFlow 版本，并获取 GPU 型号 + +# In[4]: + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +from distutils.version import LooseVersion +import warnings +import tensorflow as tf + +# Check TensorFlow Version +assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__) +print('TensorFlow Version: {}'.format(tf.__version__)) + +# Check for a GPU +if not tf.test.gpu_device_name(): + warnings.warn('No GPU found. Please use a GPU to train your neural network.') +else: + print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) + + +# ### 输入（Input） +# 部署 `model_inputs` 函数以创建用于神经网络的 [占位符 (TF Placeholders)](https://www.tensorflow.org/versions/r0.11/api_docs/python/io_ops/placeholders)。请创建以下占位符： +# - 输入图像占位符: 使用 `image_width`，`image_height` 和 `image_channels` 设置为 rank 4。 +# - 输入 Z 占位符: 设置为 rank 2，并命名为 `z_dim`。 +# - 学习速率占位符: 设置为 rank 0。 +# +# 返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。 +# + +# In[5]: + +import problem_unittests as tests + +def model_inputs(image_width, image_height, image_channels, z_dim): + """ + Create the model inputs + :param image_width: The input image width + :param image_height: The input image height + :param image_channels: The number of image channels + :param z_dim: The dimension of Z + :return: Tuple of (tensor of real input images, tensor of z data, learning rate) + """ + # TODO: Implement Function + input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real') + input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z') + learning_rate = tf.placeholder(tf.float32, name='learning_rate') + + return input_real, input_z, learning_rate + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_model_inputs(model_inputs) + + +# ### 辨别器（Discriminator） +# 部署 `discriminator` 函数创建辨别器神经网络以辨别 `images`。该函数应能够重复使用神经网络中的各种变量。 在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。 +# +# 该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。 + +# In[6]: + +def discriminator(images, reuse=False): + """ + Create the discriminator network + :param image: Tensor of input image(s) + :param reuse: Boolean if the weights should be reused + :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator) + """ + # TODO: Implement Function + with tf.variable_scope('discriminator', reuse=reuse): + # alpha is the param for leaky relu + alpha = 0.2 + + # Input layer is 28x28x3 + x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same') + relu1 = tf.maximum(alpha * x1, x1) + # 14x14x64 now + + x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same') + bn2 = tf.layers.batch_normalization(x2, training=True) + relu2 = tf.maximum(alpha * bn2, bn2) + # 7x7x128 now + + x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same') + bn3 = tf.layers.batch_normalization(x3, training=True) + relu3 = tf.maximum(alpha * bn3, bn3) + # 4x4x256 now + + # Flatten it + flat = tf.reshape(relu3, (-1, 4*4*256)) + logits = tf.layers.dense(flat, 1) + out = tf.sigmoid(logits) + + return out, logits + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_discriminator(discriminator, tf) + + +# ### 生成器（Generator） +# 部署 `generator` 函数以使用 `z` 生成图像。该函数应能够重复使用神经网络中的各种变量。 +# 在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 "generator" 的变量空间名来重复使用该函数中的变量。 +# +# 该函数应返回所生成的 28 x 28 x `out_channel_dim` 维度图像。 + +# In[7]: + +def generator(z, out_channel_dim, is_train=True): + """ + Create the generator network + :param z: Input z + :param out_channel_dim: The number of channels in the output image + :param is_train: Boolean if generator is being used for training + :return: The tensor output of the generator + """ + # TODO: Implement Function + with tf.variable_scope('generator', reuse=not is_train): + # alpha is the param for leaky relu + alpha = 0.2 + + # First fully connected layer + x1 = tf.layers.dense(z, 7 * 7 * 512, activation=None) + x1 = tf.reshape(x1, (-1, 7, 7, 512)) # Reshape it to start the convolutional stack + x1 = tf.layers.batch_normalization(x1, training=is_train) + x1 = tf.maximum(alpha * x1, x1) + # 7x7x512 now + + x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same') + x2 = tf.layers.batch_normalization(x2, training=is_train) + x2 = tf.maximum(alpha * x2, x2) + # 14x14x256 now + + x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same') + x3 = tf.layers.batch_normalization(x3, training=is_train) + x3 = tf.maximum(alpha * x3, x3) + # 28x28x128 now + + # Output layer + logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same') + out = tf.tanh(logits) + # 28x28x3 now + + return out + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_generator(generator, tf) + + +# ### 损失函数（Loss） +# 部署 `model_loss` 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。 +# +# 使用你已实现的函数： +# - `discriminator(images, reuse=False)` +# - `generator(z, out_channel_dim, is_train=True)` + +# In[8]: + +def model_loss(input_real, input_z, out_channel_dim): + """ + Get the loss for the discriminator and generator + :param input_real: Images from the real dataset + :param input_z: Z input + :param out_channel_dim: The number of channels in the output image + :return: A tuple of (discriminator loss, generator loss) + """ + # TODO: Implement Function + # Generator network here + g_model = generator(input_z, out_channel_dim) + + # Disriminator network here + d_model_real, d_logits_real = discriminator(input_real) + d_model_fake, d_logits_fake = discriminator(g_model, reuse=True) + + # Calculate losses + smooth = 0.1 + d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth))) + + d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake))) + + d_loss = d_loss_real + d_loss_fake + + g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake))) + + return d_loss, g_loss + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_model_loss(model_loss) + + +# ### 优化（Optimization） +# 部署 `model_opt` 函数实现对 GANs 的优化。使用 [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) 获取可训练的所有变量。通过变量空间名 `discriminator` 和 `generator` 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。 + +# In[9]: + +def model_opt(d_loss, g_loss, learning_rate, beta1): + """ + Get optimization operations + :param d_loss: Discriminator loss Tensor + :param g_loss: Generator loss Tensor + :param learning_rate: Learning Rate Placeholder + :param beta1: The exponential decay rate for the 1st moment in the optimizer + :return: A tuple of (discriminator training operation, generator training operation) + """ + # TODO: Implement Function + # Get weights and bias to update + t_vars = tf.trainable_variables() + d_vars = [var for var in t_vars if var.name.startswith('discriminator')] + g_vars = [var for var in t_vars if var.name.startswith('generator')] + + # Optimize + with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): + d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars) + g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars) + + return d_train_opt, g_train_opt + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_model_opt(model_opt, tf) + + +# ## 训练神经网络（Neural Network Training） +# ### 输出显示 +# 使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。 + +# In[10]: + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +import numpy as np + +def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode): + """ + Show example output for the generator + :param sess: TensorFlow session + :param n_images: Number of Images to display + :param input_z: Input Z Tensor + :param out_channel_dim: The number of channels in the output image + :param image_mode: The mode to use for images ("RGB" or "L") + """ + cmap = None if image_mode == 'RGB' else 'gray' + z_dim = input_z.get_shape().as_list()[-1] + example_z = np.random.uniform(-1, 1, size=[n_images, z_dim]) + + samples = sess.run( + generator(input_z, out_channel_dim, False), + feed_dict={input_z: example_z}) + + images_grid = helper.images_square_grid(samples, image_mode) + pyplot.imshow(images_grid, cmap=cmap) + pyplot.show() + + +# ### 训练 +# 部署 `train` 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数： +# - `model_inputs(image_width, image_height, image_channels, z_dim)` +# - `model_loss(input_real, input_z, out_channel_dim)` +# - `model_opt(d_loss, g_loss, learning_rate, beta1)` +# +# 使用 `show_generator_output` 函数显示 `generator` 在训练过程中的输出。 +# +# **注意**：在每个批次 (batch) 中运行 `show_generator_output` 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 `generator` 的输出。 + +# In[11]: + +def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode): + """ + Train the GAN + :param epoch_count: Number of epochs + :param batch_size: Batch Size + :param z_dim: Z dimension + :param learning_rate: Learning Rate + :param beta1: The exponential decay rate for the 1st moment in the optimizer + :param get_batches: Function to get batches + :param data_shape: Shape of the data + :param data_image_mode: The image mode to use for images ("RGB" or "L") + """ + # TODO: Build Model + _, image_width, image_height, image_channels = data_shape + input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim) + d_loss, g_loss = model_loss(input_real, input_z, image_channels) + d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1) + + steps = 0 + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for epoch_i in range(epoch_count): + for batch_images in get_batches(batch_size): + # TODO: Train Model + steps += 1 + batch_images *= 2 + + # Sample random noise for G + batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim)) + + # Run optimizers + _ = sess.run(d_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate}) + _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate}) + + if steps % 10 == 0: + # At the end of each epoch, get the losses and print them out + train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images}) + train_loss_g = g_loss.eval({input_z: batch_z}) + + print("Epoch {}/{}...".format(epoch_i + 1, epoch_count), + "Discriminator Loss: {:.4f}...".format(train_loss_d), + "Generator Loss: {:.4f}".format(train_loss_g)) + + if steps % 100 == 0: + gen_samples = sess.run(generator(input_z, image_channels, is_train=False), feed_dict={input_z: batch_z}) + _ = show_generator_output(sess, 25, input_z, image_channels, data_image_mode) + + +# ### MNIST +# 在 MNIST 上测试你的 GANs 模型。经过 2 次迭代，GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失，或接近 0。 + +# In[12]: + +batch_size = 64 +z_dim = 100 +learning_rate = 0.0002 +beta1 = 0.5 + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +epochs = 2 + +mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg'))) +with tf.Graph().as_default(): + train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches, + mnist_dataset.shape, mnist_dataset.image_mode) + + +# ### CelebA +# 在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代，或者当 GANs 开始产生真实人脸图像时停止它。 + +# In[13]: + +batch_size = 64 +z_dim = 100 +learning_rate = 0.001 +beta1 = 0.5 + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +epochs = 1 + +celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))) +with tf.Graph().as_default(): + train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches, + celeba_dataset.shape, celeba_dataset.image_mode) + + +# ### 提交项目 +# 提交本项目前，确保运行所有 cells 后保存该文件。 +# +# 保存该文件为 "dlnd_face_generation.ipynb"， 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。 diff --git a/first-neural-network/Your_first_neural_network.html b/first-neural-network/Your_first_neural_network.html new file mode 100644 index 0000000..1a63757 --- /dev/null +++ b/first-neural-network/Your_first_neural_network.html @@ -0,0 +1,18080 @@ + + + +Your_first_neural_network + + + + + + + + + + + + + + + + + + + + + +
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你的第一个神经网络

在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。

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%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
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加载和准备数据

构建神经网络的关键一步是正确地准备数据。不同尺度级别的变量使网络难以高效地掌握正确的权重。我们在下方已经提供了加载和准备数据的代码。你很快将进一步学习这些代码！

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data_path = 'Bike-Sharing-Dataset/hour.csv'
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instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
012011-01-01101006010.240.28790.810.000031316
122011-01-01101106010.220.27270.800.000083240
232011-01-01101206010.220.27270.800.000052732
342011-01-01101306010.240.28790.750.000031013
452011-01-01101406010.240.28790.750.0000011
562011-01-01101506020.240.25760.750.0896011
672011-01-01101606010.220.27270.800.0000202
782011-01-01101706010.200.25760.860.0000123
892011-01-01101806010.240.28790.750.0000178
9102011-01-01101906010.320.34850.760.00008614
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数据简介

此数据集包含的是从 2011 年 1 月 1 日到 2012 年 12 月 31 日期间每天每小时的骑车人数。骑车用户分成临时用户和注册用户，cnt 列是骑车用户数汇总列。你可以在上方看到前几行数据。

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下图展示的是数据集中前 10 天左右的骑车人数（某些天不一定是 24 个条目，所以不是精确的 10 天）。你可以在这里看到每小时租金。这些数据很复杂！周末的骑行人数少些，工作日上下班期间是骑行高峰期。我们还可以从上方的数据中看到温度、湿度和风速信息，所有这些信息都会影响骑行人数。你需要用你的模型展示所有这些数据。

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rides[:24*10].plot(x='dteday', y='cnt')
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<matplotlib.axes._subplots.AxesSubplot at 0x2435e964278>
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虚拟变量（哑变量）

下面是一些分类变量，例如季节、天气、月份。要在我们的模型中包含这些数据，我们需要创建二进制虚拟变量。用 Pandas 库中的 get_dummies() 就可以轻松实现。

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+
+
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
+for each in dummy_fields:
+    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
+    rides = pd.concat([rides, dummies], axis=1)
+
+fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', 
+                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']
+data = rides.drop(fields_to_drop, axis=1)
+data.head()
+
+ +
+
+
+ +
+
+ + +
Out[5]:
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
yrholidaytemphumwindspeedcasualregisteredcntseason_1season_2...hr_21hr_22hr_23weekday_0weekday_1weekday_2weekday_3weekday_4weekday_5weekday_6
0000.240.810.0313161.00.0...0.00.00.00.00.00.00.00.00.01.0
1000.220.800.0832401.00.0...0.00.00.00.00.00.00.00.00.01.0
2000.220.800.0527321.00.0...0.00.00.00.00.00.00.00.00.01.0
3000.240.750.0310131.00.0...0.00.00.00.00.00.00.00.00.01.0
4000.240.750.00111.00.0...0.00.00.00.00.00.00.00.00.01.0
+

5 rows × 59 columns

+
+
+ +
+ +
+
+ +
+
+
+
+
+
+

调整目标变量

为了更轻松地训练网络，我们将对每个连续变量标准化，即转换和调整变量，使它们的均值为 0，标准差为 1。

+

我们会保存换算因子，以便当我们使用网络进行预测时可以还原数据。

+ +
+
+
+
+
+
In [6]:
+
+
+
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
+# Store scalings in a dictionary so we can convert back later
+scaled_features = {}
+for each in quant_features:
+    mean, std = data[each].mean(), data[each].std()
+    scaled_features[each] = [mean, std]
+    data.loc[:, each] = (data[each] - mean)/std
+
+ +
+
+
+ +
+
+
+
+
+
+

将数据拆分为训练、测试和验证数据集

我们将大约最后 21 天的数据保存为测试数据集，这些数据集会在训练完网络后使用。我们将使用该数据集进行预测，并与实际的骑行人数进行对比。

+ +
+
+
+
+
+
In [7]:
+
+
+
# Save data for approximately the last 21 days 
+test_data = data[-21*24:]
+
+# Now remove the test data from the data set 
+data = data[:-21*24]
+
+# Separate the data into features and targets
+target_fields = ['cnt', 'casual', 'registered']
+features, targets = data.drop(target_fields, axis=1), data[target_fields]
+test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
+
+ +
+
+
+ +
+
+
+
+
+
+

我们将数据拆分为两个数据集，一个用作训练，一个在网络训练完后用来验证网络。因为数据是有时间序列特性的，所以我们用历史数据进行训练，然后尝试预测未来数据（验证数据集）。

+ +
+
+
+
+
+
In [8]:
+
+
+
# Hold out the last 60 days or so of the remaining data as a validation set
+train_features, train_targets = features[:-60*24], targets[:-60*24]
+val_features, val_targets = features[-60*24:], targets[-60*24:]
+
+ +
+
+
+ +
+
+
+
+
+
+

开始构建网络

下面你将构建自己的网络。我们已经构建好结构和反向传递部分。你将实现网络的前向传递部分。还需要设置超参数：学习速率、隐藏单元的数量，以及训练传递数量。

+

<img src="assets/neural_network.png" width=300px>

+

该网络有两个层级，一个隐藏层和一个输出层。隐藏层级将使用 S 型函数作为激活函数。输出层只有一个节点，用于递归，节点的输出和节点的输入相同。即激活函数是 $f(x)=x$。这种函数获得输入信号，并生成输出信号，但是会考虑阈值，称为激活函数。我们完成网络的每个层级，并计算每个神经元的输出。一个层级的所有输出变成下一层级神经元的输入。这一流程叫做前向传播（forward propagation）。

+

我们在神经网络中使用权重将信号从输入层传播到输出层。我们还使用权重将错误从输出层传播回网络，以便更新权重。这叫做反向传播（backpropagation）。

+

提示：你需要为反向传播实现计算输出激活函数 ($f(x) = x$) 的导数。如果你不熟悉微积分，其实该函数就等同于等式 $y = x$。该等式的斜率是多少？也就是导数 $f(x)$。

+
+

你需要完成以下任务：

+
    +
  1. 实现 S 型激活函数。将 __init__ 中的 self.activation_function 设为你的 S 型函数。
    2. +
  2. 在 train 方法中实现前向传递。
    3. +
  3. 在 train 方法中实现反向传播算法，包括计算输出错误。
    4. +
  4. 在 run 方法中实现前向传递。
    5. +
+ +
+
+
+
+
+
In [9]:
+
+
+
class NeuralNetwork(object):
+    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
+        # Set number of nodes in input, hidden and output layers.
+        self.input_nodes = input_nodes
+        self.hidden_nodes = hidden_nodes
+        self.output_nodes = output_nodes
+
+        # Initialize weights
+        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, 
+                                       (self.input_nodes, self.hidden_nodes))
+
+        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, 
+                                       (self.hidden_nodes, self.output_nodes))
+        self.lr = learning_rate
+        
+        #### TODO: Set self.activation_function to your implemented sigmoid function ####
+        #
+        # Note: in Python, you can define a function with a lambda expression,
+        # as shown below.
+        self.activation_function = lambda x : 1/(1 + np.exp(-x))  # Replace 0 with your sigmoid calculation.
+        
+        ### If the lambda code above is not something you're familiar with,
+        # You can uncomment out the following three lines and put your 
+        # implementation there instead.
+        #
+        #def sigmoid(x):
+        #    return 0  # Replace 0 with your sigmoid calculation here
+        #self.activation_function = sigmoid
+                    
+    
+    def train(self, features, targets):
+        ''' Train the network on batch of features and targets. 
+        
+            Arguments
+            ---------
+            
+            features: 2D array, each row is one data record, each column is a feature
+            targets: 1D array of target values
+        
+        '''
+        n_records = features.shape[0]
+        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
+        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
+        for X, y in zip(features, targets):
+            #### Implement the forward pass here ####
+            ### Forward pass ###
+            # TODO: Hidden layer - Replace these values with your calculations.
+            hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer
+            hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
+
+            # TODO: Output layer - Replace these values with your calculations.
+            final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer
+            final_outputs = final_inputs # signals from final output layer
+            
+            #### Implement the backward pass here ####
+            ### Backward pass ###
+
+            # TODO: Output error - Replace this value with your calculations.
+            error = y - final_outputs # Output layer error is the difference between desired target and actual output.
+            
+            # TODO: Backpropagated error terms - Replace these values with your calculations.
+            output_error_term = error
+            
+            # TODO: Calculate the hidden layer's contribution to the error
+            hidden_error = np.dot(output_error_term, self.weights_hidden_to_output.T)
+            hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)
+
+            # Weight step (input to hidden)
+            delta_weights_i_h += hidden_error_term * X[:, None]
+            # Weight step (hidden to output)
+            delta_weights_h_o += output_error_term * hidden_outputs[:, None]
+
+        # TODO: Update the weights - Replace these values with your calculations.
+        self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step
+        self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step
+ 
+    def run(self, features):
+        ''' Run a forward pass through the network with input features 
+        
+            Arguments
+            ---------
+            features: 1D array of feature values
+        '''
+        
+        #### Implement the forward pass here ####
+        # TODO: Hidden layer - replace these values with the appropriate calculations.
+        hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer
+        hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
+        
+        # TODO: Output layer - Replace these values with the appropriate calculations.
+        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer
+        final_outputs = final_inputs # signals from final output layer 
+        
+        return final_outputs
+
+ +
+
+
+ +
+
+
+
In [10]:
+
+
+
def MSE(y, Y):
+    return np.mean((y-Y)**2)
+
+ +
+
+
+ +
+
+
+
+
+
+

单元测试

运行这些单元测试，检查你的网络实现是否正确。这样可以帮助你确保网络已正确实现，然后再开始训练网络。这些测试必须成功才能通过此项目。

+ +
+
+
+
+
+
In [12]:
+
+
+
import unittest
+
+inputs = np.array([[0.5, -0.2, 0.1]])
+targets = np.array([[0.4]])
+test_w_i_h = np.array([[0.1, -0.2],
+                       [0.4, 0.5],
+                       [-0.3, 0.2]])
+test_w_h_o = np.array([[0.3],
+                       [-0.1]])
+
+class TestMethods(unittest.TestCase):
+    
+    ##########
+    # Unit tests for data loading
+    ##########
+    
+    def test_data_path(self):
+        # Test that file path to dataset has been unaltered
+        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
+        
+    def test_data_loaded(self):
+        # Test that data frame loaded
+        self.assertTrue(isinstance(rides, pd.DataFrame))
+    
+    ##########
+    # Unit tests for network functionality
+    ##########
+
+    def test_activation(self):
+        network = NeuralNetwork(3, 2, 1, 0.5)
+        # Test that the activation function is a sigmoid
+        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1 + np.exp(-0.5))))
+
+    def test_train(self):
+        # Test that weights are updated correctly on training
+        network = NeuralNetwork(3, 2, 1, 0.5)
+        network.weights_input_to_hidden = test_w_i_h.copy()
+        network.weights_hidden_to_output = test_w_h_o.copy()
+        
+        network.train(inputs, targets)
+        self.assertTrue(np.allclose(network.weights_hidden_to_output, 
+                                    np.array([[ 0.37275328], 
+                                              [-0.03172939]])))
+        self.assertTrue(np.allclose(network.weights_input_to_hidden,
+                                    np.array([[ 0.10562014, -0.20185996], 
+                                              [0.39775194, 0.50074398], 
+                                              [-0.29887597, 0.19962801]])))
+
+    def test_run(self):
+        # Test correctness of run method
+        network = NeuralNetwork(3, 2, 1, 0.5)
+        network.weights_input_to_hidden = test_w_i_h.copy()
+        network.weights_hidden_to_output = test_w_h_o.copy()
+
+        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
+
+suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
+unittest.TextTestRunner().run(suite)
+
+ +
+
+
+ +
+
+ + +
+
+
.....
+----------------------------------------------------------------------
+Ran 5 tests in 0.016s
+
+OK
+
+
+
+ +
Out[12]:
+ + +
+
<unittest.runner.TextTestResult run=5 errors=0 failures=0>
+
+ +
+ +
+
+ +
+
+
+
+
+
+

训练网络

现在你将设置网络的超参数。策略是设置的超参数使训练集上的错误很小但是数据不会过拟合。如果网络训练时间太长，或者有太多的隐藏节点，可能就会过于针对特定训练集，无法泛化到验证数据集。即当训练集的损失降低时，验证集的损失将开始增大。

+

你还将采用随机梯度下降 (SGD) 方法训练网络。对于每次训练，都获取随机样本数据，而不是整个数据集。与普通梯度下降相比，训练次数要更多，但是每次时间更短。这样的话，网络训练效率更高。稍后你将详细了解 SGD。

+

选择迭代次数

也就是训练网络时从训练数据中抽样的批次数量。迭代次数越多，模型就与数据越拟合。但是，如果迭代次数太多，模型就无法很好地泛化到其他数据，这叫做过拟合。你需要选择一个使训练损失很低并且验证损失保持中等水平的数字。当你开始过拟合时，你会发现训练损失继续下降，但是验证损失开始上升。

+

选择学习速率

速率可以调整权重更新幅度。如果速率太大，权重就会太大，导致网络无法与数据相拟合。建议从 0.1 开始。如果网络在与数据拟合时遇到问题，尝试降低学习速率。注意，学习速率越低，权重更新的步长就越小，神经网络收敛的时间就越长。

+

选择隐藏节点数量

隐藏节点越多，模型的预测结果就越准确。尝试不同的隐藏节点的数量，看看对性能有何影响。你可以查看损失字典，寻找网络性能指标。如果隐藏单元的数量太少，那么模型就没有足够的空间进行学习，如果太多，则学习方向就有太多的选择。选择隐藏单元数量的技巧在于找到合适的平衡点。

+ +
+
+
+
+
+
In [39]:
+
+
+
import sys
+
+### Set the hyperparameters here ###
+iterations = 8000
+learning_rate = 0.5
+hidden_nodes = 20
+output_nodes = 1
+
+N_i = train_features.shape[1]
+network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
+
+losses = {'train':[], 'validation':[]}
+for ii in range(iterations):
+    # Go through a random batch of 128 records from the training data set
+    batch = np.random.choice(train_features.index, size=128)
+    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
+                             
+    network.train(X, y)
+    
+    # Printing out the training progress
+    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
+    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
+    sys.stdout.write("\rProgress: {:2.1f}".format(100 * (ii + 1)/float(iterations)) \
+                     + "% ... Training loss: " + str(train_loss)[:5] \
+                     + " ... Validation loss: " + str(val_loss)[:5])
+    sys.stdout.flush()
+    
+    losses['train'].append(train_loss)
+    losses['validation'].append(val_loss)
+
+ +
+
+
+ +
+
+ + +
+
+
Progress: 100.0% ... Training loss: 0.051 ... Validation loss: 0.130
+
+
+ +
+
+ +
+
+
+
In [76]:
+
+
+
plt.plot(losses['train'], label='Training loss')
+plt.plot(losses['validation'], label='Validation loss')
+plt.axis([0, 8000, 0, 1.6])
+plt.legend()
+_ = plt.ylim()
+
+ +
+
+
+ +
+
+ + +
+ + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+
+

检查预测结果

使用测试数据看看网络对数据建模的效果如何。如果完全错了，请确保网络中的每步都正确实现。

+ +
+
+
+
+
+
In [102]:
+
+
+
test_loss = MSE(network.run(test_features).T, test_targets['cnt'].values)
+sys.stdout.write("Test loss: " + str(test_loss)[:5])
+
+fig, ax = plt.subplots(figsize=(8,4))
+
+mean, std = scaled_features['cnt']
+predictions = network.run(test_features).T*std + mean
+ax.plot(predictions[0], label='Prediction')
+ax.plot((test_targets['cnt']*std + mean).values, label='Data')
+ax.set_xlim(right=len(predictions))
+ax.legend()
+
+dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
+dates = dates.apply(lambda d: d.strftime('%b %d'))
+ax.set_xticks(np.arange(len(dates))[12::36])
+_ = ax.set_xticklabels(dates[12::36], rotation=45)
+
+ +
+
+
+ +
+
+ + +
+
+
Test loss: 0.209
+
+
+ +
+ + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+
+

可选：思考下你的结果（我们不会评估这道题的答案）

请针对你的结果回答以下问题。模型对数据的预测效果如何？哪里出现问题了？为何出现问题呢？

+

注意：你可以通过双击该单元编辑文本。如果想要预览文本，请按 Control + Enter

+
+

请将你的答案填写在下方

验证集损失最终降低到0.13左右，测试集损失接近0.2。

+

问题： +1、矩阵相乘时维度总是出错，有时需要调整顺序，有时需要转置，甚至有时dot报错，使用*反而可以，不知有何技巧？ +2、数据预处理时删除了几个特征不知是何用意？ +3、不太理解为何输出层激活函数要使用f(x) = x？

+ +
+
+
+
+
+ + diff --git a/first-neural-network/Your_first_neural_network.ipynb b/first-neural-network/Your_first_neural_network.ipynb index f7f7bae..f37fcab 100644 --- a/first-neural-network/Your_first_neural_network.ipynb +++ b/first-neural-network/Your_first_neural_network.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -49,13 +49,276 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
012011-01-01101006010.240.28790.810.000031316
122011-01-01101106010.220.27270.800.000083240
232011-01-01101206010.220.27270.800.000052732
342011-01-01101306010.240.28790.750.000031013
452011-01-01101406010.240.28790.750.0000011
562011-01-01101506020.240.25760.750.0896011
672011-01-01101606010.220.27270.800.0000202
782011-01-01101706010.200.25760.860.0000123
892011-01-01101806010.240.28790.750.0000178
9102011-01-01101906010.320.34850.760.00008614
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" + ], + "text/plain": [ + " instant dteday season yr mnth hr holiday weekday workingday \\\n", + "0 1 2011-01-01 1 0 1 0 0 6 0 \n", + "1 2 2011-01-01 1 0 1 1 0 6 0 \n", + "2 3 2011-01-01 1 0 1 2 0 6 0 \n", + "3 4 2011-01-01 1 0 1 3 0 6 0 \n", + "4 5 2011-01-01 1 0 1 4 0 6 0 \n", + "5 6 2011-01-01 1 0 1 5 0 6 0 \n", + "6 7 2011-01-01 1 0 1 6 0 6 0 \n", + "7 8 2011-01-01 1 0 1 7 0 6 0 \n", + "8 9 2011-01-01 1 0 1 8 0 6 0 \n", + "9 10 2011-01-01 1 0 1 9 0 6 0 \n", + "\n", + " weathersit temp atemp hum windspeed casual registered cnt \n", + "0 1 0.24 0.2879 0.81 0.0000 3 13 16 \n", + "1 1 0.22 0.2727 0.80 0.0000 8 32 40 \n", + "2 1 0.22 0.2727 0.80 0.0000 5 27 32 \n", + "3 1 0.24 0.2879 0.75 0.0000 3 10 13 \n", + "4 1 0.24 0.2879 0.75 0.0000 0 1 1 \n", + "5 2 0.24 0.2576 0.75 0.0896 0 1 1 \n", + "6 1 0.22 0.2727 0.80 0.0000 2 0 2 \n", + "7 1 0.20 0.2576 0.86 0.0000 1 2 3 \n", + "8 1 0.24 0.2879 0.75 0.0000 1 7 8 \n", + "9 1 0.32 0.3485 0.76 0.0000 8 6 14 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "rides.head()" + "rides.head(10)" ] }, { @@ -71,11 +334,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/lbAB6IJwv80+vBoMgwQAzLywokHqt09Q0VCtLgYNVDQ0I7ZhkF/6UhJ0fPvb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\nncm8mpKxH8SjEfsBAPwB9hoVwIcATgawDYCdg21/A2AdAA+JyAaJvBDKL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yrholidaytemphumwindspeedcasualregisteredcntseason_1season_2...hr_21hr_22hr_23weekday_0weekday_1weekday_2weekday_3weekday_4weekday_5weekday_6
0000.240.810.0313161.00.0...0.00.00.00.00.00.00.00.00.01.0
1000.220.800.0832401.00.0...0.00.00.00.00.00.00.00.00.01.0
2000.220.800.0527321.00.0...0.00.00.00.00.00.00.00.00.01.0
3000.240.750.0310131.00.0...0.00.00.00.00.00.00.00.00.01.0
4000.240.750.00111.00.0...0.00.00.00.00.00.00.00.00.01.0
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5 rows × 59 columns

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" + ], + "text/plain": [ + " yr holiday temp hum windspeed casual registered cnt season_1 \\\n", + "0 0 0 0.24 0.81 0.0 3 13 16 1.0 \n", + "1 0 0 0.22 0.80 0.0 8 32 40 1.0 \n", + "2 0 0 0.22 0.80 0.0 5 27 32 1.0 \n", + "3 0 0 0.24 0.75 0.0 3 10 13 1.0 \n", + "4 0 0 0.24 0.75 0.0 0 1 1 1.0 \n", + "\n", + " season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \\\n", + "0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " weekday_3 weekday_4 weekday_5 weekday_6 \n", + "0 0.0 0.0 0.0 1.0 \n", + "1 0.0 0.0 0.0 1.0 \n", + "2 0.0 0.0 0.0 1.0 \n", + "3 0.0 0.0 0.0 1.0 \n", + "4 0.0 0.0 0.0 1.0 \n", + "\n", + "[5 rows x 59 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n", "for each in dummy_fields:\n", @@ -121,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -147,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -174,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -214,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -239,7 +715,7 @@ " #\n", " # Note: in Python, you can define a function with a lambda expression,\n", " # as shown below.\n", - " self.activation_function = lambda x : 0 # Replace 0 with your sigmoid calculation.\n", + " self.activation_function = lambda x : 1/(1 + np.exp(-x)) # Replace 0 with your sigmoid calculation.\n", " \n", " ### If the lambda code above is not something you're familiar with,\n", " # You can uncomment out the following three lines and put your \n", @@ -267,34 +743,34 @@ " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", " # TODO: Hidden layer - Replace these values with your calculations.\n", - " hidden_inputs = None # signals into hidden layer\n", - " hidden_outputs = None # signals from hidden layer\n", + " hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer\n", + " hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n", "\n", " # TODO: Output layer - Replace these values with your calculations.\n", - " final_inputs = None # signals into final output layer\n", - " final_outputs = None # signals from final output layer\n", + " final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n", + " final_outputs = final_inputs # signals from final output layer\n", " \n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error - Replace this value with your calculations.\n", - " error = None # Output layer error is the difference between desired target and actual output.\n", - " \n", - " # TODO: Calculate the hidden layer's contribution to the error\n", - " hidden_error = None\n", + " error = y - final_outputs # Output layer error is the difference between desired target and actual output.\n", " \n", " # TODO: Backpropagated error terms - Replace these values with your calculations.\n", - " output_error_term = None\n", - " hidden_error_term = None\n", + " output_error_term = error\n", + " \n", + " # TODO: Calculate the hidden layer's contribution to the error\n", + " hidden_error = np.dot(output_error_term, self.weights_hidden_to_output.T)\n", + " hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)\n", "\n", " # Weight step (input to hidden)\n", - " delta_weights_i_h += None\n", + " delta_weights_i_h += hidden_error_term * X[:, None]\n", " # Weight step (hidden to output)\n", - " delta_weights_h_o += None\n", + " delta_weights_h_o += output_error_term * hidden_outputs[:, None]\n", "\n", " # TODO: Update the weights - Replace these values with your calculations.\n", - " self.weights_hidden_to_output += None # update hidden-to-output weights with gradient descent step\n", - " self.weights_input_to_hidden += None # update input-to-hidden weights with gradient descent step\n", + " self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step\n", + " self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step\n", " \n", " def run(self, features):\n", " ''' Run a forward pass through the network with input features \n", @@ -306,19 +782,19 @@ " \n", " #### Implement the forward pass here ####\n", " # TODO: Hidden layer - replace these values with the appropriate calculations.\n", - " hidden_inputs = None # signals into hidden layer\n", - " hidden_outputs = None # signals from hidden layer\n", + " hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer\n", + " hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n", " \n", " # TODO: Output layer - Replace these values with the appropriate calculations.\n", - " final_inputs = None # signals into final output layer\n", - " final_outputs = None # signals from final output layer \n", + " final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n", + " final_outputs = final_inputs # signals from final output layer \n", " \n", " return final_outputs" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -339,11 +815,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ".....\n", + "----------------------------------------------------------------------\n", + "Ran 5 tests in 0.016s\n", + "\n", + "OK\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import unittest\n", "\n", @@ -376,7 +874,7 @@ " def test_activation(self):\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " # Test that the activation function is a sigmoid\n", - " self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n", + " self.assertTrue(np.all(network.activation_function(0.5) == 1/(1 + np.exp(-0.5))))\n", "\n", " def test_train(self):\n", " # Test that weights are updated correctly on training\n", @@ -432,18 +930,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progress: 100.0% ... Training loss: 0.051 ... Validation loss: 0.130" + ] + } + ], "source": [ "import sys\n", "\n", "### Set the hyperparameters here ###\n", - "iterations = 100\n", - "learning_rate = 0.1\n", - "hidden_nodes = 2\n", + "iterations = 8000\n", + "learning_rate = 0.5\n", + "hidden_nodes = 20\n", "output_nodes = 1\n", "\n", "N_i = train_features.shape[1]\n", @@ -460,7 +966,7 @@ " # Printing out the training progress\n", " train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n", " val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n", - " sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n", + " sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * (ii + 1)/float(iterations)) \\\n", " + \"% ... Training loss: \" + str(train_loss)[:5] \\\n", " + \" ... Validation loss: \" + str(val_loss)[:5])\n", " sys.stdout.flush()\n", @@ -471,14 +977,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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wQJLr/49xcXH6/vvvC9XOhx9+qLNnz8oYo3LlyqlXr16eTPO8Y613JzVfiLMd\nisLb7w9Kn2JdQ8IY01pS3UyHKmXar2uMGZA53lo7I4/m+G0HAABAidSgQQM1adJEP/74o4wxmjlz\nptq1a1fgdt5//31nv1evXufNdPmML+pKyxd2BR1HaRk34G3FvajlXZIG5HDcSGqTvmWwkvIqSBQV\nfyUAAADgMwMGDNCPP/4oa60+//xzTZkyRcHBwW6fv337dq1evdp53L9//+JIs8CGDBmiIUOG+DoN\nn3nhhRf0wgsv+DoNoETyxiUb7iw6md8ik0WdHcFClgAAAPCpPn36KDAwUMYYHT9+XHPmzCnQ+TNm\n/O+7u5o1a+rqq6/2cIYA4F3FWpCw1g50Z9HJ9C3H2RrW2hmZYp4pZB4Z53cq2ogAAACAwqlUqZJu\nuOEG57r7gi5IOWvWLEmuywPOl9kRAFAU5/2iluczFnEBAABAQWQsbmmt1cKFC3XgwAG3zlu+fLni\n4uKcx3fccUeusVu3btW//vUv3XzzzbrssssUFhamoKAgValSRc2bN9cjjzyi3377rWgDyaSgt/08\nevSonn32WTVr1kwREREqX7686tWrp3vvvVebNm0qcP+7du3SG2+8odtvv10NGjRQeHi4goKCVKlS\nJTVu3Fj333+/1q1bl2cbLVu2lJ+fnyZMmCDJ9f6MGjXKGVfm7dwxFnQhzOTkZL3zzjvq3r27atWq\npTJlyig8PFz16tXTPffco6VLl7o17qioKKffjN+j3bt364knnlCjRo0UHh6u8uXLq379+hoxYoQS\nEhLcare4bNmyRQ899JAaN26sSpUqKSQkRDExMerYsaNeeuklHT161O22Fi5cqIEDBzrvd2BgoMqW\nLauaNWuqffv2GjlypObPn6+UlJRc24iLi9PTTz+tdu3aqWrVqgoODlZISIgqV66sJk2a6I477tDb\nb7/t9r9RFJK1ls2NTVK8JKvyshrr2sJeCLMAAAC+EBMTYyXZmJgYX6eCAkhOTraRkZHWGGP9/Pzs\nK6+84tZ5AwcOdM5p3bp1rnFdu3a1xhhn8/Pzy7JlHPf397ePPfaYTUtLy7Pf22+/3Wnn448/zjHm\nrbfecmLuvffePNtbsmSJjYqKyjE/Y4wNCAiwEydOtGfOnHFiQkNDc23v/vvvd2u8xhg7YMAAe+bM\nmRzbadmyZbZzzm0rYzt3jKNGjXLiJ0yYkOf4f/jhB3vRRRflOv6M7aabbrJHjhzJs62oqCjn3P37\n99uPP/5JOiTUAAAgAElEQVTYhoWF5dpuuXLl7MKFC/Ns013uvj/WWnv27Fk7dOhQ6+/vn+e4IyMj\n7YcffphnW8ePH7edO3d2+z2fNWtWju289tprNjQ01K02rr322kK/TtYW/W91xvmS4u158LnY01tx\nL2pZqlnLDAkAAAC4LzAwUL1799aUKVMkuS7b+Mc//pHnOWfOnNHnn3/uPL7zzjtzjd29e7eMMQoM\nDNTll1+uunXrKiIiQsYYHThwQGvXrtWePXuUlpamiRMnKiUlRS+99JJHxpafVatWqWvXrjp9+rQk\n16UnLVq00OWXX66kpCStXLlSu3bt0qhRoxQaGupWm/Hx8TLGyM/PT5dddpkuu+wyVaxYUYGBgTp0\n6JDWr1+vnTt3SnK91idPntRnn32WrZ3bbrtNV155pVatWqX169fLGKNWrVopNjY2W2zbtm0LNf5F\nixapW7duSkpKkjHGGX+9evWUlJSkVatWObnOmzdPbdu21YoVK1ShQoVc27TWyhijb775Rnfffbes\ntapdu7Zatmyp8uXL6/fff9f333+v1NRU/fXXX+rVq5e2bt2qatWqFWoMBZWWlqYuXbpowYIFzpgr\nVaqk9u3bKyIiQnFxcVq2bJnOnj2rw4cPq2/fvjp+/LjuueeeHNu77bbb9N133zl3Nbn00kvVuHFj\nRUREKDk5WQcOHNCWLVv0559/5prTRx99pOHDhzv5VKhQQa1atVJMTIz8/f117Ngx/frrr/r55591\n9uzZYnldkImvKyIlZVMOMyTKP1/eAgAA+AIzJEqutWvXZvlm9qeffsozftasWVm+jT527FiusY89\n9pidPXu2PXnyZK4xs2fPtpUqVXJmSqxbty7XWE/NkDh9+rStW7eu8+3zxRdfbNevX58tbtq0aTY4\nONgGBwc7sXl9A//CCy/YDz74wB4+fDjXmGXLltk6deo4OX7++ee5xhZktkNBzklMTMwyo6FBgwZ2\ny5Yt2eJmzJhhQ0NDnbi///3vufabub2QkBBboUIF++mnn2aL27Rpk42OjnZyvO+++9waV17cnSEx\nbty4LDMQxo0bZ1NSUrLE7Nmzx3bq1MmJCwkJsRs3bszW1urVq52YiIgIu3jx4lz7/eOPP+yzzz5r\nv/vuu2zP1atXz8nn0UcftUlJSTm2ceLECfvxxx/bp556Ktd+3MEMibw31pAAAAAAvKhZs2aqV6+e\n8zi/xS3ff/99Sa4ZBT169FBYWFiusS+++KJ69OihsmXL5hrTo0cPffHFF5JcX06+/vrrBUm/UN55\n5x398ccfstaqXLlyWrRoUY6zDwYNGqQ33nhDycnJbrU7atQo9e3bVxEREbnGtG/fXgsWLFBgYKAk\nafLkyYUbRBH885//1P79+2WtVeXKlbV48WI1aNAgW1z//v313nvvOR/WPv3003zXv7DWKiUlRXPn\nzlWvXr2yPd+oUSPnPbbW6qOPPvLMoPJx5MgRTZgwwZnN8NRTT2nMmDHy9/fPEhcdHa2vv/5ajRs3\nlrVWycnJGj16dLb2fvjhB2f/kUceUceOHXPtu06dOnryySd13XXXZTl+6NAhbdu2TZJUt25dTZgw\nQUFBQTm2Ua5cOd1222165plC3VcBbqIgUQQZ/7gAAACAgsi8uOV//vOfjBm52ezbt0+LFi1yHnvq\n7hpt27ZV7dq1Za3N0n5xmTZtmiTX/58feughXXTRRbnGDho0SE2bNvVo/xdffLHatGkja61Wrlyp\npKQkj7afl7S0tCzjf+aZZ1SlSpVc42+//XZ16NDBefzmm2/m2b4xRr169VK7du1yjenRo4cqVqwo\nyVUo+OOPPwoyhEKZOXOmTp8+LWutatSooSeffDLX2JCQEE2aNEmS69/E/Pnzs112cfz4cWe/UqVK\nhcrJE23As1hDAgAAANk0e7uZ9p3c5+s0Ci2qXJTW3ZP3N8u+1K9fPz3xxBNKS0vTnj17tGjRIl17\n7bXZ4j744AOlpqZKct1V4frrr3e7j99++03r1q3Tjh07dOzYMSUlJWUpfJw6dUqStGfPHh06dEiR\nkZFFHFXODh8+rM2bNzuP87pDSIb+/ftr/fr1Bernzz//1Jo1a/Tbb7/p2LFjzofhDLt375YkpaSk\n6KeffvJ40SM3mzZt0uHDhyVJQUFB6tOnT77n3HXXXc7dNvK664a11ilI5MXPz08NGzbU8uXLJbnu\nTHLxxRe7O4RCWbJkiSRXwaRv374KCMj7o2fbtm11ySWXaPv27bLWatmyZVkKcDVq1HD2p0+frv79\n+ys4OLhAOUVFRSkgIECpqanasGGD1q5dqyuvvLJAbcCzKEgAAAAgm30n9ynhhG9vE1iaVatWTddc\nc40WLFggyXVZRk4FicyXa/Tr18+tGbpz5szR2LFjsxQB8nPw4MFiK0hs3LjR2a9UqZLq1KmT7zmt\nWrVyu/0ffvhBjz/+uFauXOn2OQcPHnQ7tqh+/PFHSa73sEGDBipXrly+57Ru3VqSq+AQFxeno0eP\nKjw8PNf4hg0b5ttm5vc380yB4pIxbkm66qqr3DqndevW2r59uyRpw4YNWQoSXbt2VUhIiM6cOaNV\nq1apXr16GjRokG666SZdccUV8vPLf/J/aGiounTpojlz5igpKUnt2rVT7969dcstt6hdu3YqX758\nAUeJoqIgAQAAgGyiykX5OoUiKQn5DxgwQAsWLJC1VrNnz9apU6dUpkwZ5/mNGzdqy5YtzmN3LtcY\nNWqUJk6cKMm9y4szZhCcOHGioOm7LTEx0cmnZs2abp3jbtybb76p+++/35kpkB9vjPdcGeOXpFq1\narl1Ts2aNeXn56e0tDRJrgJKXgWJvO7EkSFjDQ1JXrl7RGHGnflSnnOLRlWrVtXUqVN11113KSUl\nRbt27dKYMWM0ZswYlS9fXi1btlT79u3VtWvXPAs0kydP1ubNm7Vz504lJSVp+vTpmj59uvz8/NSg\nQQO1a9dO1157rTp37pzlNUPxoCABAACAbM7nyx1Ki5tvvllhYWE6fvy4Tp06pc8++yxL0WHGjBmS\nXB/kmzRpovr16+fZ3ty5czVx4kTng3nbtm01YMAANWvWTDVq1FDZsmWzfMBq1aqVVq9eLUnOB9/i\ncPLkSWc/c8ElL3ktyplh06ZNeuCBByS5XqNGjRrp7rvvVsuWLVWrVi2VL18+y4KFvXv31scffyyp\neMd7rszjd2dcGUJDQ/XXX39Jyr+Acr6tbZeampplnQ53x505Lqcx33HHHapfv76eeeYZffvtt0pJ\nSZHkeo0XLlyohQsX6sknn1SLFi30yiuvqGXLltnaiImJ0YYNGzRhwgRNmzbNKZxYa7V582Zt3rxZ\nr7/+uiIjI/X4449rxIgR593rW5qwqCUAAADgAyEhIbr11ludxxmXZ0iuD3SZ74Zw55135tveyy+/\n7Ozfd999WrZsmQYOHKiGDRsqPDw827e93polkPkShYx1K/KT8UE8Ly+//LJTWOjevbvWr1+vYcOG\nKTY2VpGRkdnunuDNWRGZZR6/O+PKcPr0aWe/pF1K4O/vn2V9B3fHnTkutzHHxsZqzpw52r9/v2bP\nnq2HHnpILVq0UGBgoIwxMsZ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0Dak/tOdQZWZkavXw1Wqa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\nWNpGMprwr4kX7XXACe7+X1U2UTxbjLvfAtwGdADfN7NRqdWKZ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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 356, + "width": 530 + } + }, + "output_type": "display_data" + } + ], "source": [ "plt.plot(losses['train'], label='Training loss')\n", "plt.plot(losses['validation'], label='Validation loss')\n", + "plt.axis([0, 8000, 0, 1.6])\n", "plt.legend()\n", "_ = plt.ylim()" ] @@ -494,12 +1017,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test loss: 0.209" + ] + }, + { + "data": { + "image/png": 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VwSUlng4PAK7iQtEs9M71TRicwU+4uF33pk2RPr255gyq02wDCAAA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\nl2ncJkNTjgb3xIDBMiaiJs5gxjGeJBxJIoMzmPHCB5f0uDWolr64MPiee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rxTyluwrqqY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\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D8Xnp56o/oy4QvL91pqMwKzcF3zJxCYJATQregBcFp6ykr732Wvj+oc5gWV5wiiMilHT3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\nX9BxLQk699tn+j1n+pbCfUsfEnXf5wE3hdbVBw7BT+RXgh9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/1l5rXZj49+I3HdcAt7fL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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 387, + "width": 705 + } + }, + "output_type": "display_data" + } + ], "source": [ + "test_loss = MSE(network.run(test_features).T, test_targets['cnt'].values)\n", + "sys.stdout.write(\"Test loss: \" + str(test_loss)[:5])\n", + "\n", "fig, ax = plt.subplots(figsize=(8,4))\n", "\n", "mean, std = scaled_features['cnt']\n", @@ -511,8 +1060,8 @@ "\n", "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n", "dates = dates.apply(lambda d: d.strftime('%b %d'))\n", - "ax.set_xticks(np.arange(len(dates))[12::24])\n", - "_ = ax.set_xticklabels(dates[12::24], rotation=45)" + "ax.set_xticks(np.arange(len(dates))[12::36])\n", + "_ = ax.set_xticklabels(dates[12::36], rotation=45)" ] }, { @@ -526,14 +1075,21 @@ "\n", "> **注意**：你可以通过双击该单元编辑文本。如果想要预览文本，请按 Control + Enter\n", "\n", - "#### 请将你的答案填写在下方\n" + "#### 请将你的答案填写在下方\n", + "\n", + "验证集损失最终降低到0.13左右，测试集损失接近0.2。\n", + "\n", + "问题：\n", + "1、矩阵相乘时维度总是出错，有时需要调整顺序，有时需要转置，甚至有时dot报错，使用*反而可以，不知有何技巧？\n", + "2、数据预处理时删除了几个特征不知是何用意？\n", + "3、不太理解为何输出层激活函数要使用f(x) = x？" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -547,7 +1103,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/image-classification/dlnd_image_classification.html b/image-classification/dlnd_image_classification.html new file mode 100644 index 0000000..87fc309 --- /dev/null +++ b/image-classification/dlnd_image_classification.html @@ -0,0 +1,19527 @@ + + + +dlnd_image_classification + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
+
+
+

图像分类

在此项目中，你将对 CIFAR-10 数据集 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully connected）的层。最后，你需要在样本图片上看到神经网络的预测结果。

+

获取数据

请运行以下单元，以下载 CIFAR-10 数据集（Python版）。

+ +
+
+
+
+
+
In [1]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+from urllib.request import urlretrieve
+from os.path import isfile, isdir
+from tqdm import tqdm
+import problem_unittests as tests
+import tarfile
+
+cifar10_dataset_folder_path = 'cifar-10-batches-py'
+
+# Use Floyd's cifar-10 dataset if present
+floyd_cifar10_location = '/input/cifar-10/python.tar.gz'
+if isfile(floyd_cifar10_location):
+    tar_gz_path = floyd_cifar10_location
+else:
+    tar_gz_path = 'cifar-10-python.tar.gz'
+
+class DLProgress(tqdm):
+    last_block = 0
+
+    def hook(self, block_num=1, block_size=1, total_size=None):
+        self.total = total_size
+        self.update((block_num - self.last_block) * block_size)
+        self.last_block = block_num
+
+if not isfile(tar_gz_path):
+    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
+        urlretrieve(
+            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
+            tar_gz_path,
+            pbar.hook)
+
+if not isdir(cifar10_dataset_folder_path):
+    with tarfile.open(tar_gz_path) as tar:
+        tar.extractall()
+        tar.close()
+
+
+tests.test_folder_path(cifar10_dataset_folder_path)
+
+ +
+
+
+ +
+
+ + +
+
+
All files found!
+
+
+
+ +
+
+ +
+
+
+
+
+
+

探索数据

该数据集分成了几部分／批次（batches），以免你的机器在计算时内存不足。CIFAR-10 数据集包含 5 个部分，名称分别为 data_batch_1、data_batch_2，以此类推。每个部分都包含以下某个类别的标签和图片：

+
    +
  • 飞机
    • +
  • 汽车
    • +
  • 鸟类
    • +
  • 猫
    • +
  • 鹿
    • +
  • 狗
    • +
  • 青蛙
    • +
  • 马
    • +
  • 船只
    • +
  • 卡车
    • +
+

了解数据集也是对数据进行预测的必经步骤。你可以通过更改 batch_id 和 sample_id 探索下面的代码单元。batch_id 是数据集一个部分的 ID（1 到 5）。sample_id 是该部分中图片和标签对（label pair）的 ID。

+

问问你自己：“可能的标签有哪些？”、“图片数据的值范围是多少？”、“标签是按顺序排列，还是随机排列的？”。思考类似的问题，有助于你预处理数据，并使预测结果更准确。

+ +
+
+
+
+
+
In [2]:
+
+
+
%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+
+import helper
+import numpy as np
+
+# Explore the dataset
+batch_id = 1
+sample_id = 5
+helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)
+
+ +
+
+
+ +
+
+ + +
+
+
+Stats of batch 1:
+Samples: 10000
+Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
+First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]
+
+Example of Image 5:
+Image - Min Value: 0 Max Value: 252
+Image - Shape: (32, 32, 3)
+Label - Label Id: 1 Name: automobile
+
+
+
+ +
+ + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+
+

实现预处理函数

标准化

在下面的单元中，实现 normalize 函数，传入图片数据 x，并返回标准化 Numpy 数组。值应该在 0 到 1 的范围内（含 0 和 1）。返回对象应该和 x 的形状一样。

+ +
+
+
+
+
+
In [3]:
+
+
+
def normalize(x):
+    """
+    Normalize a list of sample image data in the range of 0 to 1
+    : x: List of image data.  The image shape is (32, 32, 3)
+    : return: Numpy array of normalize data
+    """
+    # TODO: Implement Function
+    return np.array(x/255)
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_normalize(normalize)
+
+ +
+
+
+ +
+
+ + +
+
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+
+

One-hot 编码

和之前的代码单元一样，你将为预处理实现一个函数。这次，你将实现 one_hot_encode 函数。输入，也就是 x，是一个标签列表。实现该函数，以返回为 one_hot 编码的 Numpy 数组的标签列表。标签的可能值为 0 到 9。每次调用 one_hot_encode 时，对于每个值，one_hot 编码函数应该返回相同的编码。确保将编码映射保存到该函数外面。

+

提示：不要重复发明轮子。

+ +
+
+
+
+
+
In [4]:
+
+
+
def one_hot_encode(x):
+    """
+    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
+    : x: List of sample Labels
+    : return: Numpy array of one-hot encoded labels
+    """
+    # TODO: Implement Function
+    from tflearn.data_utils import to_categorical
+    return np.array(to_categorical(x, 10))
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_one_hot_encode(one_hot_encode)
+
+ +
+
+
+ +
+
+ + +
+
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+
+

随机化数据

之前探索数据时，你已经了解到，样本的顺序是随机的。再随机化一次也不会有什么关系，但是对于这个数据集没有必要。

+ +
+
+
+
+
+
+
+
+

预处理所有数据并保存

运行下方的代码单元，将预处理所有 CIFAR-10 数据，并保存到文件中。下面的代码还使用了 10% 的训练数据，用来验证。

+ +
+
+
+
+
+
In [5]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+# Preprocess Training, Validation, and Testing Data
+helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)
+
+ +
+
+
+ +
+
+
+
+
+
+

检查点

这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，你可以从这里开始。预处理的数据已保存到本地。

+ +
+
+
+
+
+
In [6]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import pickle
+import problem_unittests as tests
+import helper
+
+# Load the Preprocessed Validation data
+valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))
+
+ +
+
+
+ +
+
+
+
+
+
+

构建网络

对于该神经网络，你需要将每层都构建为一个函数。你看到的大部分代码都位于函数外面。要更全面地测试你的代码，我们需要你将每层放入一个函数中。这样使我们能够提供更好的反馈，并使用我们的统一测试检测简单的错误，然后再提交项目。

+

注意：如果你觉得每周很难抽出足够的时间学习这门课程，我们为此项目提供了一个小捷径。对于接下来的几个问题，你可以使用 TensorFlow Layers 或 TensorFlow Layers (contrib) 程序包中的类来构建每个层级，但是“卷积和最大池化层级”部分的层级除外。TF Layers 和 Keras 及 TFLearn 层级类似，因此很容易学会。

+

但是，如果你想充分利用这门课程，请尝试自己解决所有问题，不使用 TF Layers 程序包中的任何类。你依然可以使用其他程序包中的类，这些类和你在 TF Layers 中的类名称是一样的！例如，你可以使用 TF Neural Network 版本的 conv2d 类 tf.nn.conv2d，而不是 TF Layers 版本的 conv2d 类 tf.layers.conv2d。

+
+

我们开始吧！

+

输入

神经网络需要读取图片数据、one-hot 编码标签和丢弃保留概率（dropout keep probability）。请实现以下函数：

+
    +
  • 实现 neural_net_image_input
      +
    • 返回 TF Placeholder
      • +
    • 使用 image_shape 设置形状，部分大小设为 None
      • +
    • 使用 TF Placeholder 中的 TensorFlow name 参数对 TensorFlow 占位符 "x" 命名
      • +
    +
    • +
  • 实现 neural_net_label_input
      +
    • 返回 TF Placeholder
      • +
    • 使用 n_classes 设置形状，部分大小设为 None
      • +
    • 使用 TF Placeholder 中的 TensorFlow name 参数对 TensorFlow 占位符 "y" 命名
      • +
    +
    • +
  • 实现 neural_net_keep_prob_input
      +
    • 返回 TF Placeholder，用于丢弃保留概率
      • +
    • 使用 TF Placeholder 中的 TensorFlow name 参数对 TensorFlow 占位符 "keep_prob" 命名
      • +
    +
    • +
+

这些名称将在项目结束时，用于加载保存的模型。

+

注意：TensorFlow 中的 None 表示形状可以是动态大小。

+ +
+
+
+
+
+
In [7]:
+
+
+
import tensorflow as tf
+
+def neural_net_image_input(image_shape):
+    """
+    Return a Tensor for a batch of image input
+    : image_shape: Shape of the images
+    : return: Tensor for image input.
+    """
+    # TODO: Implement Function
+    return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')
+
+
+def neural_net_label_input(n_classes):
+    """
+    Return a Tensor for a batch of label input
+    : n_classes: Number of classes
+    : return: Tensor for label input.
+    """
+    # TODO: Implement Function
+    return tf.placeholder(tf.int32, [None, n_classes], name='y')
+
+
+def neural_net_keep_prob_input():
+    """
+    Return a Tensor for keep probability
+    : return: Tensor for keep probability.
+    """
+    # TODO: Implement Function
+    return tf.placeholder(tf.float32, name='keep_prob')
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tf.reset_default_graph()
+tests.test_nn_image_inputs(neural_net_image_input)
+tests.test_nn_label_inputs(neural_net_label_input)
+tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
+
+ +
+
+
+ +
+
+ + +
+
+
Image Input Tests Passed.
+Label Input Tests Passed.
+Keep Prob Tests Passed.
+
+
+
+ +
+
+ +
+
+
+
+
+
+

卷积和最大池化层

卷积层级适合处理图片。对于此代码单元，你应该实现函数 conv2d_maxpool 以便应用卷积然后进行最大池化：

+
    +
  • 使用 conv_ksize、conv_num_outputs 和 x_tensor 的形状创建权重（weight）和偏置（bias）。
    • +
  • 使用权重和 conv_strides 对 x_tensor 应用卷积。
      +
    • 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。
      • +
    +
    • +
  • 添加偏置
    • +
  • 向卷积中添加非线性激活（nonlinear activation）
    • +
  • 使用 pool_ksize 和 pool_strides 应用最大池化
      +
    • 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。
      • +
    +
    • +
+

注意：对于此层，请勿使用 TensorFlow Layers 或 TensorFlow Layers (contrib)，但是仍然可以使用 TensorFlow 的 Neural Network 包。对于所有其他层，你依然可以使用快捷方法。

+ +
+
+
+
+
+
In [8]:
+
+
+
def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
+    """
+    Apply convolution then max pooling to x_tensor
+    :param x_tensor: TensorFlow Tensor
+    :param conv_num_outputs: Number of outputs for the convolutional layer
+    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer
+    :param conv_strides: Stride 2-D Tuple for convolution
+    :param pool_ksize: kernal size 2-D Tuple for pool
+    :param pool_strides: Stride 2-D Tuple for pool
+    : return: A tensor that represents convolution and max pooling of x_tensor
+    """
+    # TODO: Implement Function
+    weights = tf.Variable(tf.truncated_normal(shape=[conv_ksize[0], conv_ksize[1], x_tensor.get_shape().as_list()[3], conv_num_outputs], stddev=0.1))
+    bias = tf.Variable(tf.constant(0.1, shape=[conv_num_outputs]))
+    conv = tf.nn.conv2d(input=x_tensor, filter=weights, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME') + bias
+    activate = tf.nn.relu(conv)
+    pool = tf.nn.max_pool(value=activate, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')
+    
+    return pool
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_con_pool(conv2d_maxpool)
+
+ +
+
+
+ +
+
+ + +
+
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+
+

扁平化层

实现 flatten 函数，将 x_tensor 的维度从四维张量（4-D tensor）变成二维张量。输出应该是形状（部分大小（Batch Size），扁平化图片大小（Flattened Image Size））。快捷方法：对于此层，你可以使用 TensorFlow Layers 或 TensorFlow Layers (contrib) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。

+ +
+
+
+
+
+
In [9]:
+
+
+
def flatten(x_tensor):
+    """
+    Flatten x_tensor to (Batch Size, Flattened Image Size)
+    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
+    : return: A tensor of size (Batch Size, Flattened Image Size).
+    """
+    # TODO: Implement Function
+    layer_shape = x_tensor.get_shape()
+    num_features = layer_shape[1:4].num_elements()
+    layer_flat = tf.reshape(x_tensor, [-1, num_features])
+    
+    return layer_flat
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_flatten(flatten)
+
+ +
+
+
+ +
+
+ + +
+
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+
+

完全连接的层

实现 fully_conn 函数，以向 x_tensor 应用完全连接的层级，形状为（部分大小（Batch Size），num_outputs）。快捷方法：对于此层，你可以使用 TensorFlow Layers 或 TensorFlow Layers (contrib) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。

+ +
+
+
+
+
+
In [10]:
+
+
+
def fully_conn(x_tensor, num_outputs):
+    """
+    Apply a fully connected layer to x_tensor using weight and bias
+    : x_tensor: A 2-D tensor where the first dimension is batch size.
+    : num_outputs: The number of output that the new tensor should be.
+    : return: A 2-D tensor where the second dimension is num_outputs.
+    """
+    # TODO: Implement Function
+    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))
+    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))
+    fc = tf.nn.relu(tf.matmul(x_tensor, weights) + bias)
+    
+    return fc
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_fully_conn(fully_conn)
+
+ +
+
+
+ +
+
+ + +
+
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+
+

输出层

实现 output 函数，向 x_tensor 应用完全连接的层级，形状为（部分大小（Batch Size），num_outputs）。快捷方法：对于此层，你可以使用 TensorFlow Layers 或 TensorFlow Layers (contrib) 包中的类。如果你想要更大挑战，可以仅使用其他 TensorFlow 程序包。

+

注意：该层级不应应用 Activation、softmax 或交叉熵（cross entropy）。

+ +
+
+
+
+
+
In [11]:
+
+
+
def output(x_tensor, num_outputs):
+    """
+    Apply a output layer to x_tensor using weight and bias
+    : x_tensor: A 2-D tensor where the first dimension is batch size.
+    : num_outputs: The number of output that the new tensor should be.
+    : return: A 2-D tensor where the second dimension is num_outputs.
+    """
+    # TODO: Implement Function
+    weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))
+    bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))
+    output = tf.matmul(x_tensor, weights) + bias
+    
+    return output
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_output(output)
+
+ +
+
+
+ +
+
+ + +
+
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+
+

创建卷积模型

实现函数 conv_net， 创建卷积神经网络模型。该函数传入一批图片 x，并输出对数（logits）。使用你在上方创建的层创建此模型：

+
    +
  • 应用 1、2 或 3 个卷积和最大池化层（Convolution and Max Pool layers）
    • +
  • 应用一个扁平层（Flatten Layer）
    • +
  • 应用 1、2 或 3 个完全连接层（Fully Connected Layers）
    • +
  • 应用一个输出层（Output Layer）
    • +
  • 返回输出
    • +
  • 使用 keep_prob 向模型中的一个或多个层应用 TensorFlow 的 Dropout
    • +
+ +
+
+
+
+
+
In [12]:
+
+
+
def conv_net(x, keep_prob):
+    """
+    Create a convolutional neural network model
+    : x: Placeholder tensor that holds image data.
+    : keep_prob: Placeholder tensor that hold dropout keep probability.
+    : return: Tensor that represents logits
+    """
+    # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers
+    #    Play around with different number of outputs, kernel size and stride
+    # Function Definition from Above:
+    #    conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)
+    conv_pool_1 = conv2d_maxpool(x, 64, [5, 5], [1, 1], [3, 3], [2, 2])
+    norm_layer = tf.nn.lrn(conv_pool_1, 4 , bias=1.0, alpha=0.001 / 9.0, beta=0.75)
+    conv_pool_2 = conv2d_maxpool(norm_layer, 128, [5, 5], [1, 1], [3, 3], [2, 2])
+
+    # TODO: Apply a Flatten Layer
+    # Function Definition from Above:
+    #   flatten(x_tensor)
+    flat_layer = flatten(conv_pool_2)
+
+    # TODO: Apply 1, 2, or 3 Fully Connected Layers
+    #    Play around with different number of outputs
+    # Function Definition from Above:
+    #   fully_conn(x_tensor, num_outputs)
+    fc_layer1 = fully_conn(flat_layer, 384)
+    dropout_layer_1 = tf.nn.dropout(fc_layer1, keep_prob)
+    fc_layer2 = fully_conn(dropout_layer_1, 192)
+    dropout_layer_2 = tf.nn.dropout(fc_layer2, keep_prob)
+    
+    # TODO: Apply an Output Layer
+    #    Set this to the number of classes
+    # Function Definition from Above:
+    #   output(x_tensor, num_outputs)
+    logits = output(dropout_layer_2, 10)
+    
+    # TODO: return output
+    return logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+
+##############################
+## Build the Neural Network ##
+##############################
+
+# Remove previous weights, bias, inputs, etc..
+tf.reset_default_graph()
+
+# Inputs
+x = neural_net_image_input((32, 32, 3))
+y = neural_net_label_input(10)
+keep_prob = neural_net_keep_prob_input()
+
+# Model
+logits = conv_net(x, keep_prob)
+
+# Name logits Tensor, so that is can be loaded from disk after training
+logits = tf.identity(logits, name='logits')
+
+# Loss and Optimizer
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
+optimizer = tf.train.AdamOptimizer().minimize(cost)
+
+# Accuracy
+correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
+accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
+
+tests.test_conv_net(conv_net)
+
+ +
+
+
+ +
+
+ + +
+
+
Neural Network Built!
+
+
+
+ +
+
+ +
+
+
+
+
+
+

训练神经网络

单次优化

实现函数 train_neural_network 以进行单次优化（single optimization）。该优化应该使用 optimizer 优化 session，其中 feed_dict 具有以下参数：

+
    +
  • x 表示图片输入
    • +
  • y 表示标签
    • +
  • keep_prob 表示丢弃的保留率
    • +
+

每个部分都会调用该函数，所以 tf.global_variables_initializer() 已经被调用。

+

注意：不需要返回任何内容。该函数只是用来优化神经网络。

+ +
+
+
+
+
+
In [13]:
+
+
+
def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
+    """
+    Optimize the session on a batch of images and labels
+    : session: Current TensorFlow session
+    : optimizer: TensorFlow optimizer function
+    : keep_probability: keep probability
+    : feature_batch: Batch of Numpy image data
+    : label_batch: Batch of Numpy label data
+    """
+    # TODO: Implement Function
+    session.run(optimizer, feed_dict = {keep_prob: keep_probability, x: feature_batch, y: label_batch})
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_train_nn(train_neural_network)
+
+ +
+
+
+ +
+
+ + +
+
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+
+

显示数据

实现函数 print_stats 以输出损失和验证准确率。使用全局变量 valid_features 和 valid_labels 计算验证准确率。使用保留率 1.0 计算损失和验证准确率（loss and validation accuracy）。

+ +
+
+
+
+
+
In [14]:
+
+
+
def print_stats(session, feature_batch, label_batch, cost, accuracy):
+    """
+    Print information about loss and validation accuracy
+    : session: Current TensorFlow session
+    : feature_batch: Batch of Numpy image data
+    : label_batch: Batch of Numpy label data
+    : cost: TensorFlow cost function
+    : accuracy: TensorFlow accuracy function
+    """
+    # TODO: Implement Function
+    print('Training Loss: ', end='')
+    print(session.run(cost, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0}), end='')
+    print(', Valid Accuracy: ', end='')
+    print(session.run(accuracy, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0}))
+
+ +
+
+
+ +
+
+
+
+
+
+

超参数

调试以下超参数：

+
    +
  • 设置 epochs 表示神经网络停止学习或开始过拟合的迭代次数
    • +

  • 设置 batch_size，表示机器内存允许的部分最大体积。大部分人设为以下常见内存大小：

    +
      +
    • 64
      • +
    • 128
      • +
    • 256
      • +
    • ...
      • +
    +
    • +
  • 设置 keep_probability 表示使用丢弃时保留节点的概率
    • +
+ +
+
+
+
+
+
In [15]:
+
+
+
# TODO: Tune Parameters
+epochs = 10
+batch_size = 128
+keep_probability = 0.75
+
+ +
+
+
+ +
+
+
+
+
+
+

在单个 CIFAR-10 部分上训练

我们先用单个部分，而不是用所有的 CIFAR-10 批次训练神经网络。这样可以节省时间，并对模型进行迭代，以提高准确率。最终验证准确率达到 50% 或以上之后，在下一部分对所有数据运行模型。

+ +
+
+
+
+
+
In [16]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+print('Checking the Training on a Single Batch...')
+with tf.Session() as sess:
+    # Initializing the variables
+    sess.run(tf.global_variables_initializer())
+    
+    # Training cycle
+    for epoch in range(epochs):
+        batch_i = 1
+        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
+            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
+        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
+        print_stats(sess, batch_features, batch_labels, cost, accuracy)
+
+ +
+
+
+ +
+
+ + +
+
+
Checking the Training on a Single Batch...
+Epoch  1, CIFAR-10 Batch 1:  Training Loss: 2.02859, Valid Accuracy: 0.3288
+Epoch  2, CIFAR-10 Batch 1:  Training Loss: 1.73148, Valid Accuracy: 0.3978
+Epoch  3, CIFAR-10 Batch 1:  Training Loss: 1.43727, Valid Accuracy: 0.4584
+Epoch  4, CIFAR-10 Batch 1:  Training Loss: 1.20958, Valid Accuracy: 0.488
+Epoch  5, CIFAR-10 Batch 1:  Training Loss: 1.13607, Valid Accuracy: 0.4956
+Epoch  6, CIFAR-10 Batch 1:  Training Loss: 0.906183, Valid Accuracy: 0.5094
+Epoch  7, CIFAR-10 Batch 1:  Training Loss: 0.814033, Valid Accuracy: 0.5296
+Epoch  8, CIFAR-10 Batch 1:  Training Loss: 0.686904, Valid Accuracy: 0.5408
+Epoch  9, CIFAR-10 Batch 1:  Training Loss: 0.580816, Valid Accuracy: 0.5502
+Epoch 10, CIFAR-10 Batch 1:  Training Loss: 0.497506, Valid Accuracy: 0.5612
+
+
+
+ +
+
+ +
+
+
+
+
+
+

完全训练模型

现在，单个 CIFAR-10 部分的准确率已经不错了，试试所有五个部分吧。

+ +
+
+
+
+
+
In [17]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+epochs = 8
+save_model_path = './model/image_classification'
+
+print('Training...')
+with tf.Session() as sess:
+    # Initializing the variables
+    sess.run(tf.global_variables_initializer())
+    
+    # Training cycle
+    for epoch in range(epochs):
+        # Loop over all batches
+        n_batches = 5
+        for batch_i in range(1, n_batches + 1):
+            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
+                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
+            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
+            print_stats(sess, batch_features, batch_labels, cost, accuracy)
+            
+    # Save Model
+    saver = tf.train.Saver()
+    save_path = saver.save(sess, save_model_path)
+
+ +
+
+
+ +
+
+ + +
+
+
Training...
+Epoch  1, CIFAR-10 Batch 1:  Training Loss: 2.06226, Valid Accuracy: 0.3212
+Epoch  1, CIFAR-10 Batch 2:  Training Loss: 1.57472, Valid Accuracy: 0.4404
+Epoch  1, CIFAR-10 Batch 3:  Training Loss: 1.28891, Valid Accuracy: 0.4616
+Epoch  1, CIFAR-10 Batch 4:  Training Loss: 1.4297, Valid Accuracy: 0.4844
+Epoch  1, CIFAR-10 Batch 5:  Training Loss: 1.43679, Valid Accuracy: 0.518
+Epoch  2, CIFAR-10 Batch 1:  Training Loss: 1.38818, Valid Accuracy: 0.5218
+Epoch  2, CIFAR-10 Batch 2:  Training Loss: 1.09959, Valid Accuracy: 0.5522
+Epoch  2, CIFAR-10 Batch 3:  Training Loss: 0.961711, Valid Accuracy: 0.551
+Epoch  2, CIFAR-10 Batch 4:  Training Loss: 1.12144, Valid Accuracy: 0.5856
+Epoch  2, CIFAR-10 Batch 5:  Training Loss: 1.02137, Valid Accuracy: 0.586
+Epoch  3, CIFAR-10 Batch 1:  Training Loss: 1.05762, Valid Accuracy: 0.5896
+Epoch  3, CIFAR-10 Batch 2:  Training Loss: 0.814285, Valid Accuracy: 0.6132
+Epoch  3, CIFAR-10 Batch 3:  Training Loss: 0.825072, Valid Accuracy: 0.605
+Epoch  3, CIFAR-10 Batch 4:  Training Loss: 0.865807, Valid Accuracy: 0.616
+Epoch  3, CIFAR-10 Batch 5:  Training Loss: 0.840993, Valid Accuracy: 0.636
+Epoch  4, CIFAR-10 Batch 1:  Training Loss: 0.74788, Valid Accuracy: 0.6272
+Epoch  4, CIFAR-10 Batch 2:  Training Loss: 0.652433, Valid Accuracy: 0.6234
+Epoch  4, CIFAR-10 Batch 3:  Training Loss: 0.572802, Valid Accuracy: 0.6414
+Epoch  4, CIFAR-10 Batch 4:  Training Loss: 0.697387, Valid Accuracy: 0.657
+Epoch  4, CIFAR-10 Batch 5:  Training Loss: 0.599844, Valid Accuracy: 0.6582
+Epoch  5, CIFAR-10 Batch 1:  Training Loss: 0.706912, Valid Accuracy: 0.6546
+Epoch  5, CIFAR-10 Batch 2:  Training Loss: 0.458327, Valid Accuracy: 0.6452
+Epoch  5, CIFAR-10 Batch 3:  Training Loss: 0.491401, Valid Accuracy: 0.6734
+Epoch  5, CIFAR-10 Batch 4:  Training Loss: 0.484113, Valid Accuracy: 0.6714
+Epoch  5, CIFAR-10 Batch 5:  Training Loss: 0.494395, Valid Accuracy: 0.6666
+Epoch  6, CIFAR-10 Batch 1:  Training Loss: 0.506375, Valid Accuracy: 0.6906
+Epoch  6, CIFAR-10 Batch 2:  Training Loss: 0.337756, Valid Accuracy: 0.6578
+Epoch  6, CIFAR-10 Batch 3:  Training Loss: 0.353094, Valid Accuracy: 0.687
+Epoch  6, CIFAR-10 Batch 4:  Training Loss: 0.373179, Valid Accuracy: 0.677
+Epoch  6, CIFAR-10 Batch 5:  Training Loss: 0.38469, Valid Accuracy: 0.694
+Epoch  7, CIFAR-10 Batch 1:  Training Loss: 0.428482, Valid Accuracy: 0.6932
+Epoch  7, CIFAR-10 Batch 2:  Training Loss: 0.269606, Valid Accuracy: 0.677
+Epoch  7, CIFAR-10 Batch 3:  Training Loss: 0.308877, Valid Accuracy: 0.7042
+Epoch  7, CIFAR-10 Batch 4:  Training Loss: 0.290038, Valid Accuracy: 0.688
+Epoch  7, CIFAR-10 Batch 5:  Training Loss: 0.30759, Valid Accuracy: 0.6892
+Epoch  8, CIFAR-10 Batch 1:  Training Loss: 0.376015, Valid Accuracy: 0.6916
+Epoch  8, CIFAR-10 Batch 2:  Training Loss: 0.207914, Valid Accuracy: 0.6826
+Epoch  8, CIFAR-10 Batch 3:  Training Loss: 0.22358, Valid Accuracy: 0.7052
+Epoch  8, CIFAR-10 Batch 4:  Training Loss: 0.205842, Valid Accuracy: 0.7022
+Epoch  8, CIFAR-10 Batch 5:  Training Loss: 0.201466, Valid Accuracy: 0.6862
+
+
+
+ +
+
+ +
+
+
+
+
+
+

检查点

模型已保存到本地。

+

测试模型

利用测试数据集测试你的模型。这将是最终的准确率。你的准确率应该高于 50%。如果没达到，请继续调整模型结构和参数。

+ +
+
+
+
+
+
In [18]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+
+import tensorflow as tf
+import pickle
+import helper
+import random
+
+# Set batch size if not already set
+try:
+    if batch_size:
+        pass
+except NameError:
+    batch_size = 64
+
+save_model_path = './model/image_classification'
+n_samples = 4
+top_n_predictions = 3
+
+def test_model():
+    """
+    Test the saved model against the test dataset
+    """
+
+    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))
+    loaded_graph = tf.Graph()
+
+    with tf.Session(graph=loaded_graph) as sess:
+        # Load model
+        loader = tf.train.import_meta_graph(save_model_path + '.meta')
+        loader.restore(sess, save_model_path)
+
+        # Get Tensors from loaded model
+        loaded_x = loaded_graph.get_tensor_by_name('x:0')
+        loaded_y = loaded_graph.get_tensor_by_name('y:0')
+        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
+        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
+        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
+        
+        # Get accuracy in batches for memory limitations
+        test_batch_acc_total = 0
+        test_batch_count = 0
+        
+        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
+            test_batch_acc_total += sess.run(
+                loaded_acc,
+                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})
+            test_batch_count += 1
+
+        print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))
+
+        # Print Random Samples
+        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
+        random_test_predictions = sess.run(
+            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
+            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
+        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)
+
+
+test_model()
+
+ +
+
+
+ +
+
+ + +
+
+
Testing Accuracy: 0.6764240506329114
+
+
+
+
+ +
+ + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+
+

为何准确率只有50-80%？

你可能想问，为何准确率不能更高了？首先，对于简单的 CNN 网络来说，50% 已经不低了。纯粹猜测的准确率为10%。但是，你可能注意到有人的准确率远远超过 80%。这是因为我们还没有介绍所有的神经网络知识。我们还需要掌握一些其他技巧。

+

提交项目

提交项目时，确保先运行所有单元，然后再保存记事本。将 notebook 文件另存为“dlnd_image_classification.ipynb”，再在目录 "File" -> "Download as" 另存为 HTML 格式。请在提交的项目中包含 “helper.py” 和 “problem_unittests.py” 文件。

+ +
+
+
+
+
+ + diff --git a/image-classification/dlnd_image_classification.ipynb b/image-classification/dlnd_image_classification.ipynb index dceff26..8338fe9 100644 --- a/image-classification/dlnd_image_classification.ipynb +++ b/image-classification/dlnd_image_classification.ipynb @@ -18,11 +18,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All files found!\n" + ] + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -92,11 +100,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Stats of batch 1:\n", + "Samples: 10000\n", + "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n", + "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n", + "\n", + "Example of Image 5:\n", + "Image - Min Value: 0 Max Value: 252\n", + "Image - Shape: (32, 32, 3)\n", + "Label - Label Id: 1 Name: automobile\n" + ] + }, + { + "data": { + "image/png": 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JFtoxXUvb4y2R0gxi7v98LX0AfpXauw9tF3p0G5WMgsoQ+1xq/PX1zTnZizyB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Bj56vP9WBUk9w3OKQl4gXt534pTpCuwl05AliJqZ9EBjyHUDZzSBKOuk2Lcn41Gjn4sA1QaUE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jNSp5TDAC4p/QJ8MQwXxLhhgOIT8CdPvII678XHUwp2IkBsgtPp4e0vz2GC2eYxvoRFQOqq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\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qKwxTM4gnc4iKg44TnR7IDlrczhIOeZSvU/OLAr8TGKMA8E3NrKRThk9TjTqtSqXAyAPN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\nvuJiHQ5rsCZIR1yYLxNByOIgFYCvXAmKR7eIwTIsApZhco0Y5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fNJqa3ZofMDhCkfUCbA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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 351, + "width": 353 + } + }, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", @@ -123,11 +163,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def normalize(x):\n", " \"\"\"\n", @@ -136,7 +184,7 @@ " : return: Numpy array of normalize data\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " return np.array(x/255)\n", "\n", "\n", "\"\"\"\n", @@ -158,11 +206,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def one_hot_encode(x):\n", " \"\"\"\n", @@ -171,7 +227,8 @@ " : return: Numpy array of one-hot encoded labels\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " from tflearn.data_utils import to_categorical\n", + " return np.array(to_categorical(x, 10))\n", "\n", "\n", "\"\"\"\n", @@ -200,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -224,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -279,11 +336,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image Input Tests Passed.\n", + "Label Input Tests Passed.\n", + "Keep Prob Tests Passed.\n" + ] + } + ], "source": [ "import tensorflow as tf\n", "\n", @@ -294,7 +361,7 @@ " : return: Tensor for image input.\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')\n", "\n", "\n", "def neural_net_label_input(n_classes):\n", @@ -304,7 +371,7 @@ " : return: Tensor for label input.\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " return tf.placeholder(tf.int32, [None, n_classes], name='y')\n", "\n", "\n", "def neural_net_keep_prob_input():\n", @@ -313,7 +380,7 @@ " : return: Tensor for keep probability.\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " return tf.placeholder(tf.float32, name='keep_prob')\n", "\n", "\n", "\"\"\"\n", @@ -346,11 +413,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n", " \"\"\"\n", @@ -364,7 +439,13 @@ " : return: A tensor that represents convolution and max pooling of x_tensor\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None \n", + " weights = tf.Variable(tf.truncated_normal(shape=[conv_ksize[0], conv_ksize[1], x_tensor.get_shape().as_list()[3], conv_num_outputs], stddev=0.1))\n", + " bias = tf.Variable(tf.constant(0.1, shape=[conv_num_outputs]))\n", + " conv = tf.nn.conv2d(input=x_tensor, filter=weights, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME') + bias\n", + " activate = tf.nn.relu(conv)\n", + " pool = tf.nn.max_pool(value=activate, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')\n", + " \n", + " return pool\n", "\n", "\n", "\"\"\"\n", @@ -384,11 +465,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def flatten(x_tensor):\n", " \"\"\"\n", @@ -397,7 +486,11 @@ " : return: A tensor of size (Batch Size, Flattened Image Size).\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " layer_shape = x_tensor.get_shape()\n", + " num_features = layer_shape[1:4].num_elements()\n", + " layer_flat = tf.reshape(x_tensor, [-1, num_features])\n", + " \n", + " return layer_flat\n", "\n", "\n", "\"\"\"\n", @@ -417,11 +510,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def fully_conn(x_tensor, num_outputs):\n", " \"\"\"\n", @@ -431,7 +532,11 @@ " : return: A 2-D tensor where the second dimension is num_outputs.\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n", + " bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n", + " fc = tf.nn.relu(tf.matmul(x_tensor, weights) + bias)\n", + " \n", + " return fc\n", "\n", "\n", "\"\"\"\n", @@ -453,11 +558,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def output(x_tensor, num_outputs):\n", " \"\"\"\n", @@ -467,7 +580,11 @@ " : return: A 2-D tensor where the second dimension is num_outputs.\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " weights = tf.Variable(tf.truncated_normal(shape=[x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1))\n", + " bias = tf.Variable(tf.constant(0.1, shape=[num_outputs]))\n", + " output = tf.matmul(x_tensor, weights) + bias\n", + " \n", + " return output\n", "\n", "\n", "\"\"\"\n", @@ -494,11 +611,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neural Network Built!\n" + ] + } + ], "source": [ "def conv_net(x, keep_prob):\n", " \"\"\"\n", @@ -511,27 +636,32 @@ " # Play around with different number of outputs, kernel size and stride\n", " # Function Definition from Above:\n", " # conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n", - " \n", + " conv_pool_1 = conv2d_maxpool(x, 64, [5, 5], [1, 1], [3, 3], [2, 2])\n", + " norm_layer = tf.nn.lrn(conv_pool_1, 4 , bias=1.0, alpha=0.001 / 9.0, beta=0.75)\n", + " conv_pool_2 = conv2d_maxpool(norm_layer, 128, [5, 5], [1, 1], [3, 3], [2, 2])\n", "\n", " # TODO: Apply a Flatten Layer\n", " # Function Definition from Above:\n", " # flatten(x_tensor)\n", - " \n", + " flat_layer = flatten(conv_pool_2)\n", "\n", " # TODO: Apply 1, 2, or 3 Fully Connected Layers\n", " # Play around with different number of outputs\n", " # Function Definition from Above:\n", " # fully_conn(x_tensor, num_outputs)\n", - " \n", + " fc_layer1 = fully_conn(flat_layer, 384)\n", + " dropout_layer_1 = tf.nn.dropout(fc_layer1, keep_prob)\n", + " fc_layer2 = fully_conn(dropout_layer_1, 192)\n", + " dropout_layer_2 = tf.nn.dropout(fc_layer2, keep_prob)\n", " \n", " # TODO: Apply an Output Layer\n", " # Set this to the number of classes\n", " # Function Definition from Above:\n", " # output(x_tensor, num_outputs)\n", - " \n", + " logits = output(dropout_layer_2, 10)\n", " \n", " # TODO: return output\n", - " return None\n", + " return logits\n", "\n", "\n", "\"\"\"\n", @@ -588,11 +718,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n", " \"\"\"\n", @@ -604,7 +742,7 @@ " : label_batch: Batch of Numpy label data\n", " \"\"\"\n", " # TODO: Implement Function\n", - " pass\n", + " session.run(optimizer, feed_dict = {keep_prob: keep_probability, x: feature_batch, y: label_batch})\n", "\n", "\n", "\"\"\"\n", @@ -624,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -640,7 +778,10 @@ " : accuracy: TensorFlow accuracy function\n", " \"\"\"\n", " # TODO: Implement Function\n", - " pass" + " print('Training Loss: ', end='')\n", + " print(session.run(cost, feed_dict = {x: feature_batch, y: label_batch, keep_prob: 1.0}), end='')\n", + " print(', Valid Accuracy: ', end='')\n", + " print(session.run(accuracy, feed_dict = {x: valid_features, y: valid_labels, keep_prob: 1.0}))" ] }, { @@ -662,16 +803,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Tune Parameters\n", - "epochs = None\n", - "batch_size = None\n", - "keep_probability = None" + "epochs = 10\n", + "batch_size = 128\n", + "keep_probability = 0.75" ] }, { @@ -685,11 +826,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking the Training on a Single Batch...\n", + "Epoch 1, CIFAR-10 Batch 1: Training Loss: 2.02859, Valid Accuracy: 0.3288\n", + "Epoch 2, CIFAR-10 Batch 1: Training Loss: 1.73148, Valid Accuracy: 0.3978\n", + "Epoch 3, CIFAR-10 Batch 1: Training Loss: 1.43727, Valid Accuracy: 0.4584\n", + "Epoch 4, CIFAR-10 Batch 1: Training Loss: 1.20958, Valid Accuracy: 0.488\n", + "Epoch 5, CIFAR-10 Batch 1: Training Loss: 1.13607, Valid Accuracy: 0.4956\n", + "Epoch 6, CIFAR-10 Batch 1: Training Loss: 0.906183, Valid Accuracy: 0.5094\n", + "Epoch 7, CIFAR-10 Batch 1: Training Loss: 0.814033, Valid Accuracy: 0.5296\n", + "Epoch 8, CIFAR-10 Batch 1: Training Loss: 0.686904, Valid Accuracy: 0.5408\n", + "Epoch 9, CIFAR-10 Batch 1: Training Loss: 0.580816, Valid Accuracy: 0.5502\n", + "Epoch 10, CIFAR-10 Batch 1: Training Loss: 0.497506, Valid Accuracy: 0.5612\n" + ] + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", @@ -719,16 +878,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training...\n", + "Epoch 1, CIFAR-10 Batch 1: Training Loss: 2.06226, Valid Accuracy: 0.3212\n", + "Epoch 1, CIFAR-10 Batch 2: Training Loss: 1.57472, Valid Accuracy: 0.4404\n", + "Epoch 1, CIFAR-10 Batch 3: Training Loss: 1.28891, Valid Accuracy: 0.4616\n", + "Epoch 1, CIFAR-10 Batch 4: Training Loss: 1.4297, Valid Accuracy: 0.4844\n", + "Epoch 1, CIFAR-10 Batch 5: Training Loss: 1.43679, Valid Accuracy: 0.518\n", + "Epoch 2, CIFAR-10 Batch 1: Training Loss: 1.38818, Valid Accuracy: 0.5218\n", + "Epoch 2, CIFAR-10 Batch 2: Training Loss: 1.09959, Valid Accuracy: 0.5522\n", + "Epoch 2, CIFAR-10 Batch 3: Training Loss: 0.961711, Valid Accuracy: 0.551\n", + "Epoch 2, CIFAR-10 Batch 4: Training Loss: 1.12144, Valid Accuracy: 0.5856\n", + "Epoch 2, CIFAR-10 Batch 5: Training Loss: 1.02137, Valid Accuracy: 0.586\n", + "Epoch 3, CIFAR-10 Batch 1: Training Loss: 1.05762, Valid Accuracy: 0.5896\n", + "Epoch 3, CIFAR-10 Batch 2: Training Loss: 0.814285, Valid Accuracy: 0.6132\n", + "Epoch 3, CIFAR-10 Batch 3: Training Loss: 0.825072, Valid Accuracy: 0.605\n", + "Epoch 3, CIFAR-10 Batch 4: Training Loss: 0.865807, Valid Accuracy: 0.616\n", + "Epoch 3, CIFAR-10 Batch 5: Training Loss: 0.840993, Valid Accuracy: 0.636\n", + "Epoch 4, CIFAR-10 Batch 1: Training Loss: 0.74788, Valid Accuracy: 0.6272\n", + "Epoch 4, CIFAR-10 Batch 2: Training Loss: 0.652433, Valid Accuracy: 0.6234\n", + "Epoch 4, CIFAR-10 Batch 3: Training Loss: 0.572802, Valid Accuracy: 0.6414\n", + "Epoch 4, CIFAR-10 Batch 4: Training Loss: 0.697387, Valid Accuracy: 0.657\n", + "Epoch 4, CIFAR-10 Batch 5: Training Loss: 0.599844, Valid Accuracy: 0.6582\n", + "Epoch 5, CIFAR-10 Batch 1: Training Loss: 0.706912, Valid Accuracy: 0.6546\n", + "Epoch 5, CIFAR-10 Batch 2: Training Loss: 0.458327, Valid Accuracy: 0.6452\n", + "Epoch 5, CIFAR-10 Batch 3: Training Loss: 0.491401, Valid Accuracy: 0.6734\n", + "Epoch 5, CIFAR-10 Batch 4: Training Loss: 0.484113, Valid Accuracy: 0.6714\n", + "Epoch 5, CIFAR-10 Batch 5: Training Loss: 0.494395, Valid Accuracy: 0.6666\n", + "Epoch 6, CIFAR-10 Batch 1: Training Loss: 0.506375, Valid Accuracy: 0.6906\n", + "Epoch 6, CIFAR-10 Batch 2: Training Loss: 0.337756, Valid Accuracy: 0.6578\n", + "Epoch 6, CIFAR-10 Batch 3: Training Loss: 0.353094, Valid Accuracy: 0.687\n", + "Epoch 6, CIFAR-10 Batch 4: Training Loss: 0.373179, Valid Accuracy: 0.677\n", + "Epoch 6, CIFAR-10 Batch 5: Training Loss: 0.38469, Valid Accuracy: 0.694\n", + "Epoch 7, CIFAR-10 Batch 1: Training Loss: 0.428482, Valid Accuracy: 0.6932\n", + "Epoch 7, CIFAR-10 Batch 2: Training Loss: 0.269606, Valid Accuracy: 0.677\n", + "Epoch 7, CIFAR-10 Batch 3: Training Loss: 0.308877, Valid Accuracy: 0.7042\n", + "Epoch 7, CIFAR-10 Batch 4: Training Loss: 0.290038, Valid Accuracy: 0.688\n", + "Epoch 7, CIFAR-10 Batch 5: Training Loss: 0.30759, Valid Accuracy: 0.6892\n", + "Epoch 8, CIFAR-10 Batch 1: Training Loss: 0.376015, Valid Accuracy: 0.6916\n", + "Epoch 8, CIFAR-10 Batch 2: Training Loss: 0.207914, Valid Accuracy: 0.6826\n", + "Epoch 8, CIFAR-10 Batch 3: Training Loss: 0.22358, Valid Accuracy: 0.7052\n", + "Epoch 8, CIFAR-10 Batch 4: Training Loss: 0.205842, Valid Accuracy: 0.7022\n", + "Epoch 8, CIFAR-10 Batch 5: Training Loss: 0.201466, Valid Accuracy: 0.6862\n" + ] + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", - "save_model_path = './image_classification'\n", + "epochs = 8\n", + "save_model_path = './model/image_classification'\n", "\n", "print('Training...')\n", "with tf.Session() as sess:\n", @@ -765,11 +973,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing Accuracy: 0.6764240506329114\n", + "\n" + ] + }, + { + "data": { + "image/png": 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7yGOXAp+asLxJ/Wbg39tuGpcNMizj7gFmtupzkpntSwQGa24dM7N7E8fSYJB9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3gbeTN9FVRVtEREQOhIJ2MVR50zRcswrZTq7ykgwjARWhyi20Q5XXYYfQ5HAd5lRhTh3m1GGN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\n0yAMh8w8Qk4eJw2OhHM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EByE4fPB0Xct+v+dqf8XV1R63jKDrHPc50Auha9t1bHyeRk6n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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 445, + "width": 493 + } + }, + "output_type": "display_data" + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", @@ -789,7 +1021,7 @@ "except NameError:\n", " batch_size = 64\n", "\n", - "save_model_path = './image_classification'\n", + "save_model_path = './model/image_classification'\n", "n_samples = 4\n", "top_n_predictions = 3\n", "\n", @@ -851,8 +1083,9 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, diff --git a/language-translation/dlnd_language_translation.html b/language-translation/dlnd_language_translation.html new file mode 100644 index 0000000..261ae70 --- /dev/null +++ b/language-translation/dlnd_language_translation.html @@ -0,0 +1,18349 @@ + + + +dlnd_language_translation + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
+
+

语言翻译

在此项目中，你将了解神经网络机器翻译这一领域。你将用由英语和法语语句组成的数据集，训练一个序列到序列模型（sequence to sequence model），该模型能够将新的英语句子翻译成法语。

+

获取数据

因为将整个英语语言内容翻译成法语需要大量训练时间，所以我们提供了一小部分的英语语料库。

+ +
+
+
+
+
+
In [1]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import helper
+import problem_unittests as tests
+
+source_path = 'data/small_vocab_en'
+target_path = 'data/small_vocab_fr'
+source_text = helper.load_data(source_path)
+target_text = helper.load_data(target_path)
+
+ +
+
+
+ +
+
+
+
+
+

探索数据

研究 view_sentence_range，查看并熟悉该数据的不同部分。

+ +
+
+
+
+
+
In [2]:
+
+
+
view_sentence_range = (0, 10)
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import numpy as np
+
+print('Dataset Stats')
+print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))
+
+sentences = source_text.split('\n')
+word_counts = [len(sentence.split()) for sentence in sentences]
+print('Number of sentences: {}'.format(len(sentences)))
+print('Average number of words in a sentence: {}'.format(np.average(word_counts)))
+
+print()
+print('English sentences {} to {}:'.format(*view_sentence_range))
+print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
+print()
+print('French sentences {} to {}:'.format(*view_sentence_range))
+print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Dataset Stats
+Roughly the number of unique words: 227
+Number of sentences: 137861
+Average number of words in a sentence: 13.225277634719028
+
+English sentences 0 to 10:
+new jersey is sometimes quiet during autumn , and it is snowy in april .
+the united states is usually chilly during july , and it is usually freezing in november .
+california is usually quiet during march , and it is usually hot in june .
+the united states is sometimes mild during june , and it is cold in september .
+your least liked fruit is the grape , but my least liked is the apple .
+his favorite fruit is the orange , but my favorite is the grape .
+paris is relaxing during december , but it is usually chilly in july .
+new jersey is busy during spring , and it is never hot in march .
+our least liked fruit is the lemon , but my least liked is the grape .
+the united states is sometimes busy during january , and it is sometimes warm in november .
+
+French sentences 0 to 10:
+new jersey est parfois calme pendant l' automne , et il est neigeux en avril .
+les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .
+california est généralement calme en mars , et il est généralement chaud en juin .
+les états-unis est parfois légère en juin , et il fait froid en septembre .
+votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .
+son fruit préféré est l'orange , mais mon préféré est le raisin .
+paris est relaxant en décembre , mais il est généralement froid en juillet .
+new jersey est occupé au printemps , et il est jamais chaude en mars .
+notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .
+les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .
+
+
+
+ +
+
+ +
+
+
+
+
+

实现预处理函数

文本到单词 id

和之前的 RNN 一样，你必须首先将文本转换为数字，这样计算机才能读懂。在函数 text_to_ids() 中，你需要将单词中的 source_text 和 target_text 转为 id。但是，你需要在 target_text 中每个句子的末尾，添加 <EOS> 单词 id。这样可以帮助神经网络预测句子应该在什么地方结束。

+

你可以通过以下代码获取 <EOS> 单词ID：

+
target_vocab_to_int['<EOS>']
+
+

你可以使用 source_vocab_to_int 和 target_vocab_to_int 获得其他单词 id。

+ +
+
+
+
+
+
In [3]:
+
+
+
def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):
+    """
+    Convert source and target text to proper word ids
+    :param source_text: String that contains all the source text.
+    :param target_text: String that contains all the target text.
+    :param source_vocab_to_int: Dictionary to go from the source words to an id
+    :param target_vocab_to_int: Dictionary to go from the target words to an id
+    :return: A tuple of lists (source_id_text, target_id_text)
+    """
+    # TODO: Implement Function
+    source_letter_ids = [[source_vocab_to_int.get(letter, source_vocab_to_int['<UNK>']) for letter in line.split()] for line in source_text.split('\n')]
+    target_letter_ids = [[target_vocab_to_int.get(letter, target_vocab_to_int['<UNK>']) for letter in line.split()] + [target_vocab_to_int['<EOS>']] for line in target_text.split('\n')] 
+    
+    return source_letter_ids, target_letter_ids
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_text_to_ids(text_to_ids)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

预处理所有数据并保存

运行以下代码单元，预处理所有数据，并保存到文件中。

+ +
+
+
+
+
+
In [4]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+helper.preprocess_and_save_data(source_path, target_path, text_to_ids)
+
+ +
+
+
+ +
+
+
+
+
+

检查点

这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，可以从这里继续。预处理的数据已保存到磁盘上。

+ +
+
+
+
+
+
In [5]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import numpy as np
+import helper
+
+(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()
+
+ +
+
+
+ +
+
+
+
+
+

检查 TensorFlow 版本，确认可访问 GPU

这一检查步骤，可以确保你使用的是正确版本的 TensorFlow，并且能够访问 GPU。

+ +
+
+
+
+
+
In [6]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+from distutils.version import LooseVersion
+import warnings
+import tensorflow as tf
+
+# Check TensorFlow Version
+assert LooseVersion(tf.__version__) in [LooseVersion('1.0.0'), LooseVersion('1.0.1')], 'This project requires TensorFlow version 1.0  You are using {}'.format(tf.__version__)
+print('TensorFlow Version: {}'.format(tf.__version__))
+
+# Check for a GPU
+if not tf.test.gpu_device_name():
+    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
+else:
+    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
TensorFlow Version: 1.0.1
+Default GPU Device: /gpu:0
+
+
+
+ +
+
+ +
+
+
+
+
+

构建神经网络

你将通过实现以下函数，构建出要构建一个序列到序列模型所需的组件：

+
    +
  • model_inputs
    • +
  • process_decoding_input
    • +
  • encoding_layer
    • +
  • decoding_layer_train
    • +
  • decoding_layer_infer
    • +
  • decoding_layer
    • +
  • seq2seq_model
    • +
+

输入

实现 model_inputs() 函数，为神经网络创建 TF 占位符。该函数应该创建以下占位符：

+
    +
  • 名为 “input” 的输入文本占位符，并使用 TF Placeholder 名称参数（等级（Rank）为 2）。
    • +
  • 目标占位符（等级为 2）。
    • +
  • 学习速率占位符（等级为 0）。
    • +
  • 名为 “keep_prob” 的保留率占位符，并使用 TF Placeholder 名称参数（等级为 0）。
    • +
+

在以下元祖（tuple）中返回占位符：（输入、目标、学习速率、保留率）

+ +
+
+
+
+
+
In [7]:
+
+
+
import tensorflow as tf
+def model_inputs():
+    """
+    Create TF Placeholders for input, targets, and learning rate.
+    :return: Tuple (input, targets, learning rate, keep probability)
+    """
+    # TODO: Implement Function
+    inputs = tf.placeholder(tf.int32, [None, None], name='input')
+    targets = tf.placeholder(tf.int32, [None, None], name='targets')
+    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
+    keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+    
+    return inputs, targets, learning_rate, keep_prob
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_model_inputs(model_inputs)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

处理解码输入

使用 TensorFlow 实现 process_decoding_input，以便删掉 target_data 中每个批次的最后一个单词 ID，并将 GO ID 放到每个批次的开头。

+ +
+
+
+
+
+
In [8]:
+
+
+
def process_decoding_input(target_data, target_vocab_to_int, batch_size):
+    """
+    Preprocess target data for dencoding
+    :param target_data: Target Placehoder
+    :param target_vocab_to_int: Dictionary to go from the target words to an id
+    :param batch_size: Batch Size
+    :return: Preprocessed target data
+    """
+    # TODO: Implement Function
+    ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])
+    dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)
+    
+    return dec_input
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_process_decoding_input(process_decoding_input)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

编码

实现 encoding_layer()，以使用 tf.nn.dynamic_rnn() 创建编码器 RNN 层级。

+ +
+
+
+
+
+
In [9]:
+
+
+
def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob):
+    """
+    Create encoding layer
+    :param rnn_inputs: Inputs for the RNN
+    :param rnn_size: RNN Size
+    :param num_layers: Number of layers
+    :param keep_prob: Dropout keep probability
+    :return: RNN state
+    """
+    # TODO: Implement Function
+    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
+    drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
+    enc_cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)
+    _, enc_state = tf.nn.dynamic_rnn(enc_cell, rnn_inputs, dtype=tf.float32)
+    
+    return enc_state
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_encoding_layer(encoding_layer)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

解码 - 训练

使用 tf.contrib.seq2seq.simple_decoder_fn_train() 和 tf.contrib.seq2seq.dynamic_rnn_decoder() 创建训练分对数（training logits）。将 output_fn 应用到 tf.contrib.seq2seq.dynamic_rnn_decoder() 输出上。

+ +
+
+
+
+
+
In [10]:
+
+
+
def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,
+                         output_fn, keep_prob):
+    """
+    Create a decoding layer for training
+    :param encoder_state: Encoder State
+    :param dec_cell: Decoder RNN Cell
+    :param dec_embed_input: Decoder embedded input
+    :param sequence_length: Sequence Length
+    :param decoding_scope: TenorFlow Variable Scope for decoding
+    :param output_fn: Function to apply the output layer
+    :param keep_prob: Dropout keep probability
+    :return: Train Logits
+    """
+    # TODO: Implement Function
+    # Training Decoder
+    train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state)
+    train_pred, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(
+    dec_cell, train_decoder_fn, dec_embed_input, sequence_length, scope=decoding_scope)
+    
+    # Apply output function
+    train_logits =  output_fn(train_pred)
+
+    return train_logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_decoding_layer_train(decoding_layer_train)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

解码 - 推论

使用 tf.contrib.seq2seq.simple_decoder_fn_inference() 和 tf.contrib.seq2seq.dynamic_rnn_decoder() 创建推论分对数（inference logits）。

+ +
+
+
+
+
+
In [11]:
+
+
+
def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,
+                         maximum_length, vocab_size, decoding_scope, output_fn, keep_prob):
+    """
+    Create a decoding layer for inference
+    :param encoder_state: Encoder state
+    :param dec_cell: Decoder RNN Cell
+    :param dec_embeddings: Decoder embeddings
+    :param start_of_sequence_id: GO ID
+    :param end_of_sequence_id: EOS Id
+    :param maximum_length: The maximum allowed time steps to decode
+    :param vocab_size: Size of vocabulary
+    :param decoding_scope: TensorFlow Variable Scope for decoding
+    :param output_fn: Function to apply the output layer
+    :param keep_prob: Dropout keep probability
+    :return: Inference Logits
+    """
+    # TODO: Implement Function
+    # Inference Decoder
+    infer_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference(
+        output_fn, encoder_state, dec_embeddings, start_of_sequence_id, end_of_sequence_id, 
+        maximum_length - 1, vocab_size)
+    inference_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell, infer_decoder_fn, scope=decoding_scope)
+
+    return inference_logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_decoding_layer_infer(decoding_layer_infer)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

构建解码层级

实现 decoding_layer() 以创建解码器 RNN 层级。

+
    +
  • 使用 rnn_size 和 num_layers 创建解码 RNN 单元。
    • +
  • 使用 lambda 创建输出函数，将输入，也就是分对数转换为类分对数（class logits）。
    • +
  • 使用 decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob) 函数获取训练分对数。
    • +
  • 使用 decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob) 函数获取推论分对数。
    • +
+

注意：你将需要使用 tf.variable_scope 在训练和推论分对数间分享变量。

+ +
+
+
+
+
+
In [12]:
+
+
+
def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,
+                   num_layers, target_vocab_to_int, keep_prob):
+    """
+    Create decoding layer
+    :param dec_embed_input: Decoder embedded input
+    :param dec_embeddings: Decoder embeddings
+    :param encoder_state: The encoded state
+    :param vocab_size: Size of vocabulary
+    :param sequence_length: Sequence Length
+    :param rnn_size: RNN Size
+    :param num_layers: Number of layers
+    :param target_vocab_to_int: Dictionary to go from the target words to an id
+    :param keep_prob: Dropout keep probability
+    :return: Tuple of (Training Logits, Inference Logits)
+    """
+    # TODO: Implement Function
+    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
+    dropout = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
+    dec_cell = tf.contrib.rnn.MultiRNNCell([dropout] * num_layers)
+    
+    output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, activation_fn=None, scope=decoding_scope)           
+
+    with tf.variable_scope("decoding") as decoding_scope:
+        training_decoder_output = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,
+                         output_fn, keep_prob)
+
+    with tf.variable_scope("decoding", reuse=True) as decoding_scope:
+        start_of_sequence_id = target_vocab_to_int["<GO>"]
+        end_of_sequence_id = target_vocab_to_int["<EOS>"]
+        inference_decoder_output = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, 
+                         sequence_length, vocab_size, decoding_scope, output_fn, keep_prob)
+    
+    return training_decoder_output, inference_decoder_output
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_decoding_layer(decoding_layer)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

构建神经网络

应用你在上方实现的函数，以：

+
    +
  • 向编码器的输入数据应用嵌入。
    • +
  • 使用 encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob) 编码输入。
    • +
  • 使用 process_decoding_input(target_data, target_vocab_to_int, batch_size) 函数处理目标数据。
    • +
  • 向解码器的目标数据应用嵌入。
    • +
  • 使用 decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob) 解码编码的输入数据。
    • +
+ +
+
+
+
+
+
In [13]:
+
+
+
def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,
+                  enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):
+    """
+    Build the Sequence-to-Sequence part of the neural network
+    :param input_data: Input placeholder
+    :param target_data: Target placeholder
+    :param keep_prob: Dropout keep probability placeholder
+    :param batch_size: Batch Size
+    :param sequence_length: Sequence Length
+    :param source_vocab_size: Source vocabulary size
+    :param target_vocab_size: Target vocabulary size
+    :param enc_embedding_size: Decoder embedding size
+    :param dec_embedding_size: Encoder embedding size
+    :param rnn_size: RNN Size
+    :param num_layers: Number of layers
+    :param target_vocab_to_int: Dictionary to go from the target words to an id
+    :return: Tuple of (Training Logits, Inference Logits)
+    """
+    # TODO: Implement Function
+    rnn_inputs = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size)
+    
+    encoder_state = encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)
+    
+    dec_input = process_decoding_input(target_data, target_vocab_to_int, batch_size)
+    dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size]))
+    dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)
+    
+    train_logits, inference_logits = decoding_layer(dec_embed_input, dec_embeddings, encoder_state, target_vocab_size, sequence_length,
+                       rnn_size, num_layers, target_vocab_to_int, keep_prob)
+    
+    return train_logits, inference_logits
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_seq2seq_model(seq2seq_model)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

训练神经网络

超参数

调试以下参数：

+
    +
  • 将 epochs 设为 epoch 次数。
    • +
  • 将 batch_size 设为批次大小。
    • +
  • 将 rnn_size 设为 RNN 的大小。
    • +
  • 将 num_layers 设为层级数量。
    • +
  • 将 encoding_embedding_size 设为编码器嵌入大小。
    • +
  • 将 decoding_embedding_size 设为解码器嵌入大小
    • +
  • 将 learning_rate 设为训练速率。
    • +
  • 将 keep_probability 设为丢弃保留率（Dropout keep probability）。
    • +
+ +
+
+
+
+
+
In [14]:
+
+
+
# Number of Epochs
+epochs = 5
+# Batch Size
+batch_size = 128
+# RNN Size
+rnn_size = 128
+# Number of Layers
+num_layers = 2
+# Embedding Size
+encoding_embedding_size = 100
+decoding_embedding_size = 100
+# Learning Rate
+learning_rate = 0.01
+# Dropout Keep Probability
+keep_probability = 0.8
+
+ +
+
+
+ +
+
+
+
+
+

构建图表

使用你实现的神经网络构建图表。

+ +
+
+
+
+
+
In [15]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+save_path = 'checkpoints/dev'
+(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()
+max_source_sentence_length = max([len(sentence) for sentence in source_int_text])
+
+train_graph = tf.Graph()
+with train_graph.as_default():
+    input_data, targets, lr, keep_prob = model_inputs()
+    sequence_length = tf.placeholder_with_default(max_source_sentence_length, None, name='sequence_length')
+    input_shape = tf.shape(input_data)
+    
+    train_logits, inference_logits = seq2seq_model(
+        tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int),
+        encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int)
+
+    tf.identity(inference_logits, 'logits')
+    with tf.name_scope("optimization"):
+        # Loss function
+        cost = tf.contrib.seq2seq.sequence_loss(
+            train_logits,
+            targets,
+            tf.ones([input_shape[0], sequence_length]))
+
+        # Optimizer
+        optimizer = tf.train.AdamOptimizer(lr)
+
+        # Gradient Clipping
+        gradients = optimizer.compute_gradients(cost)
+        capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]
+        train_op = optimizer.apply_gradients(capped_gradients)
+
+ +
+
+
+ +
+
+
+
+
+

训练

利用预处理的数据训练神经网络。如果很难获得低损失值，请访问我们的论坛，看看其他人是否遇到了相同的问题。

+ +
+
+
+
+
+
In [16]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import time
+
+def get_accuracy(target, logits):
+    """
+    Calculate accuracy
+    """
+    max_seq = max(target.shape[1], logits.shape[1])
+    if max_seq - target.shape[1]:
+        target = np.pad(
+            target,
+            [(0,0),(0,max_seq - target.shape[1])],
+            'constant')
+    if max_seq - logits.shape[1]:
+        logits = np.pad(
+            logits,
+            [(0,0),(0,max_seq - logits.shape[1]), (0,0)],
+            'constant')
+
+    return np.mean(np.equal(target, np.argmax(logits, 2)))
+
+train_source = source_int_text[batch_size:]
+train_target = target_int_text[batch_size:]
+
+valid_source = helper.pad_sentence_batch(source_int_text[:batch_size])
+valid_target = helper.pad_sentence_batch(target_int_text[:batch_size])
+
+with tf.Session(graph=train_graph) as sess:
+    sess.run(tf.global_variables_initializer())
+
+    for epoch_i in range(epochs):
+        for batch_i, (source_batch, target_batch) in enumerate(
+                helper.batch_data(train_source, train_target, batch_size)):
+            start_time = time.time()
+            
+            _, loss = sess.run(
+                [train_op, cost],
+                {input_data: source_batch,
+                 targets: target_batch,
+                 lr: learning_rate,
+                 sequence_length: target_batch.shape[1],
+                 keep_prob: keep_probability})
+            
+            batch_train_logits = sess.run(
+                inference_logits,
+                {input_data: source_batch, keep_prob: 1.0})
+            batch_valid_logits = sess.run(
+                inference_logits,
+                {input_data: valid_source, keep_prob: 1.0})
+                
+            train_acc = get_accuracy(target_batch, batch_train_logits)
+            valid_acc = get_accuracy(np.array(valid_target), batch_valid_logits)
+            end_time = time.time()
+            print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.3f}, Validation Accuracy: {:>6.3f}, Loss: {:>6.3f}'
+                  .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))
+
+    # Save Model
+    saver = tf.train.Saver()
+    saver.save(sess, save_path)
+    print('Model Trained and Saved')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Epoch   0 Batch    0/1077 - Train Accuracy:  0.294, Validation Accuracy:  0.305, Loss:  5.889
+Epoch   0 Batch    1/1077 - Train Accuracy:  0.221, Validation Accuracy:  0.305, Loss:  5.074
+Epoch   0 Batch    2/1077 - Train Accuracy:  0.244, Validation Accuracy:  0.335, Loss:  4.336
+Epoch   0 Batch    3/1077 - Train Accuracy:  0.275, Validation Accuracy:  0.337, Loss:  3.832
+Epoch   0 Batch    4/1077 - Train Accuracy:  0.263, Validation Accuracy:  0.336, Loss:  3.669
+Epoch   0 Batch    5/1077 - Train Accuracy:  0.295, Validation Accuracy:  0.336, Loss:  3.552
+Epoch   0 Batch    6/1077 - Train Accuracy:  0.282, Validation Accuracy:  0.342, Loss:  3.504
+Epoch   0 Batch    7/1077 - Train Accuracy:  0.268, Validation Accuracy:  0.342, Loss:  3.515
+Epoch   0 Batch    8/1077 - Train Accuracy:  0.275, Validation Accuracy:  0.341, Loss:  3.414
+Epoch   0 Batch    9/1077 - Train Accuracy:  0.283, Validation Accuracy:  0.341, Loss:  3.324
+Epoch   0 Batch   10/1077 - Train Accuracy:  0.273, Validation Accuracy:  0.363, Loss:  3.444
+Epoch   0 Batch   11/1077 - Train Accuracy:  0.334, Validation Accuracy:  0.370, Loss:  3.166
+Epoch   0 Batch   12/1077 - Train Accuracy:  0.314, Validation Accuracy:  0.380, Loss:  3.323
+Epoch   0 Batch   13/1077 - Train Accuracy:  0.353, Validation Accuracy:  0.373, Loss:  3.069
+Epoch   0 Batch   14/1077 - Train Accuracy:  0.338, Validation Accuracy:  0.377, Loss:  3.096
+Epoch   0 Batch   15/1077 - Train Accuracy:  0.333, Validation Accuracy:  0.391, Loss:  3.169
+Epoch   0 Batch   16/1077 - Train Accuracy:  0.350, Validation Accuracy:  0.392, Loss:  3.136
+Epoch   0 Batch   17/1077 - Train Accuracy:  0.341, Validation Accuracy:  0.382, Loss:  3.051
+Epoch   0 Batch   18/1077 - Train Accuracy:  0.343, Validation Accuracy:  0.407, Loss:  3.059
+Epoch   0 Batch   19/1077 - Train Accuracy:  0.361, Validation Accuracy:  0.402, Loss:  2.920
+Epoch   0 Batch   20/1077 - Train Accuracy:  0.341, Validation Accuracy:  0.400, Loss:  2.897
+Epoch   0 Batch   21/1077 - Train Accuracy:  0.330, Validation Accuracy:  0.409, Loss:  2.971
+Epoch   0 Batch   22/1077 - Train Accuracy:  0.359, Validation Accuracy:  0.417, Loss:  2.950
+Epoch   0 Batch   23/1077 - Train Accuracy:  0.354, Validation Accuracy:  0.412, Loss:  2.924
+Epoch   0 Batch   24/1077 - Train Accuracy:  0.360, Validation Accuracy:  0.413, Loss:  2.818
+Epoch   0 Batch   25/1077 - Train Accuracy:  0.361, Validation Accuracy:  0.425, Loss:  2.867
+Epoch   0 Batch   26/1077 - Train Accuracy:  0.359, Validation Accuracy:  0.420, Loss:  2.829
+Epoch   0 Batch   27/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.427, Loss:  2.581
+Epoch   0 Batch   28/1077 - Train Accuracy:  0.394, Validation Accuracy:  0.436, Loss:  2.710
+Epoch   0 Batch   29/1077 - Train Accuracy:  0.395, Validation Accuracy:  0.441, Loss:  2.660
+Epoch   0 Batch   30/1077 - Train Accuracy:  0.397, Validation Accuracy:  0.448, Loss:  2.664
+Epoch   0 Batch   31/1077 - Train Accuracy:  0.399, Validation Accuracy:  0.453, Loss:  2.683
+Epoch   0 Batch   32/1077 - Train Accuracy:  0.453, Validation Accuracy:  0.460, Loss:  2.471
+Epoch   0 Batch   33/1077 - Train Accuracy:  0.429, Validation Accuracy:  0.468, Loss:  2.467
+Epoch   0 Batch   34/1077 - Train Accuracy:  0.405, Validation Accuracy:  0.462, Loss:  2.541
+Epoch   0 Batch   35/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.460, Loss:  2.516
+Epoch   0 Batch   36/1077 - Train Accuracy:  0.425, Validation Accuracy:  0.466, Loss:  2.453
+Epoch   0 Batch   37/1077 - Train Accuracy:  0.427, Validation Accuracy:  0.480, Loss:  2.505
+Epoch   0 Batch   38/1077 - Train Accuracy:  0.378, Validation Accuracy:  0.474, Loss:  2.650
+Epoch   0 Batch   39/1077 - Train Accuracy:  0.420, Validation Accuracy:  0.480, Loss:  2.496
+Epoch   0 Batch   40/1077 - Train Accuracy:  0.410, Validation Accuracy:  0.466, Loss:  2.415
+Epoch   0 Batch   41/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.483, Loss:  2.359
+Epoch   0 Batch   42/1077 - Train Accuracy:  0.414, Validation Accuracy:  0.467, Loss:  2.375
+Epoch   0 Batch   43/1077 - Train Accuracy:  0.439, Validation Accuracy:  0.497, Loss:  2.402
+Epoch   0 Batch   44/1077 - Train Accuracy:  0.355, Validation Accuracy:  0.436, Loss:  2.547
+Epoch   0 Batch   45/1077 - Train Accuracy:  0.406, Validation Accuracy:  0.469, Loss:  2.402
+Epoch   0 Batch   46/1077 - Train Accuracy:  0.419, Validation Accuracy:  0.492, Loss:  2.394
+Epoch   0 Batch   47/1077 - Train Accuracy:  0.406, Validation Accuracy:  0.458, Loss:  2.299
+Epoch   0 Batch   48/1077 - Train Accuracy:  0.407, Validation Accuracy:  0.443, Loss:  2.303
+Epoch   0 Batch   49/1077 - Train Accuracy:  0.404, Validation Accuracy:  0.462, Loss:  2.302
+Epoch   0 Batch   50/1077 - Train Accuracy:  0.403, Validation Accuracy:  0.478, Loss:  2.336
+Epoch   0 Batch   51/1077 - Train Accuracy:  0.434, Validation Accuracy:  0.457, Loss:  2.179
+Epoch   0 Batch   52/1077 - Train Accuracy:  0.432, Validation Accuracy:  0.492, Loss:  2.245
+Epoch   0 Batch   53/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.518, Loss:  2.199
+Epoch   0 Batch   54/1077 - Train Accuracy:  0.439, Validation Accuracy:  0.523, Loss:  2.408
+Epoch   0 Batch   55/1077 - Train Accuracy:  0.479, Validation Accuracy:  0.507, Loss:  2.111
+Epoch   0 Batch   56/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.513, Loss:  2.172
+Epoch   0 Batch   57/1077 - Train Accuracy:  0.520, Validation Accuracy:  0.512, Loss:  1.935
+Epoch   0 Batch   58/1077 - Train Accuracy:  0.445, Validation Accuracy:  0.493, Loss:  2.165
+Epoch   0 Batch   59/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.532, Loss:  2.256
+Epoch   0 Batch   60/1077 - Train Accuracy:  0.487, Validation Accuracy:  0.525, Loss:  2.097
+Epoch   0 Batch   61/1077 - Train Accuracy:  0.454, Validation Accuracy:  0.509, Loss:  2.091
+Epoch   0 Batch   62/1077 - Train Accuracy:  0.464, Validation Accuracy:  0.531, Loss:  2.211
+Epoch   0 Batch   63/1077 - Train Accuracy:  0.517, Validation Accuracy:  0.535, Loss:  1.980
+Epoch   0 Batch   64/1077 - Train Accuracy:  0.446, Validation Accuracy:  0.494, Loss:  2.087
+Epoch   0 Batch   65/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.544, Loss:  2.220
+Epoch   0 Batch   66/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.528, Loss:  2.058
+Epoch   0 Batch   67/1077 - Train Accuracy:  0.497, Validation Accuracy:  0.507, Loss:  1.945
+Epoch   0 Batch   68/1077 - Train Accuracy:  0.479, Validation Accuracy:  0.534, Loss:  2.047
+Epoch   0 Batch   69/1077 - Train Accuracy:  0.516, Validation Accuracy:  0.546, Loss:  1.991
+Epoch   0 Batch   70/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.534, Loss:  2.054
+Epoch   0 Batch   71/1077 - Train Accuracy:  0.486, Validation Accuracy:  0.534, Loss:  1.950
+Epoch   0 Batch   72/1077 - Train Accuracy:  0.491, Validation Accuracy:  0.543, Loss:  1.957
+Epoch   0 Batch   73/1077 - Train Accuracy:  0.475, Validation Accuracy:  0.521, Loss:  1.968
+Epoch   0 Batch   74/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.535, Loss:  1.823
+Epoch   0 Batch   75/1077 - Train Accuracy:  0.526, Validation Accuracy:  0.539, Loss:  1.826
+Epoch   0 Batch   76/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.529, Loss:  1.857
+Epoch   0 Batch   77/1077 - Train Accuracy:  0.462, Validation Accuracy:  0.527, Loss:  1.920
+Epoch   0 Batch   78/1077 - Train Accuracy:  0.455, Validation Accuracy:  0.527, Loss:  2.023
+Epoch   0 Batch   79/1077 - Train Accuracy:  0.480, Validation Accuracy:  0.521, Loss:  1.887
+Epoch   0 Batch   80/1077 - Train Accuracy:  0.482, Validation Accuracy:  0.540, Loss:  1.808
+Epoch   0 Batch   81/1077 - Train Accuracy:  0.509, Validation Accuracy:  0.534, Loss:  1.829
+Epoch   0 Batch   82/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.542, Loss:  1.644
+Epoch   0 Batch   83/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.533, Loss:  1.863
+Epoch   0 Batch   84/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.537, Loss:  1.749
+Epoch   0 Batch   85/1077 - Train Accuracy:  0.495, Validation Accuracy:  0.535, Loss:  1.639
+Epoch   0 Batch   86/1077 - Train Accuracy:  0.489, Validation Accuracy:  0.530, Loss:  1.738
+Epoch   0 Batch   87/1077 - Train Accuracy:  0.489, Validation Accuracy:  0.525, Loss:  1.735
+Epoch   0 Batch   88/1077 - Train Accuracy:  0.509, Validation Accuracy:  0.533, Loss:  1.659
+Epoch   0 Batch   89/1077 - Train Accuracy:  0.499, Validation Accuracy:  0.548, Loss:  1.632
+Epoch   0 Batch   90/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.535, Loss:  1.699
+Epoch   0 Batch   91/1077 - Train Accuracy:  0.511, Validation Accuracy:  0.518, Loss:  1.448
+Epoch   0 Batch   92/1077 - Train Accuracy:  0.490, Validation Accuracy:  0.524, Loss:  1.569
+Epoch   0 Batch   93/1077 - Train Accuracy:  0.476, Validation Accuracy:  0.520, Loss:  1.581
+Epoch   0 Batch   94/1077 - Train Accuracy:  0.485, Validation Accuracy:  0.526, Loss:  1.457
+Epoch   0 Batch   95/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.550, Loss:  1.486
+Epoch   0 Batch   96/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.533, Loss:  1.471
+Epoch   0 Batch   97/1077 - Train Accuracy:  0.467, Validation Accuracy:  0.525, Loss:  1.476
+Epoch   0 Batch   98/1077 - Train Accuracy:  0.497, Validation Accuracy:  0.508, Loss:  1.370
+Epoch   0 Batch   99/1077 - Train Accuracy:  0.458, Validation Accuracy:  0.501, Loss:  1.478
+Epoch   0 Batch  100/1077 - Train Accuracy:  0.490, Validation Accuracy:  0.514, Loss:  1.390
+Epoch   0 Batch  101/1077 - Train Accuracy:  0.474, Validation Accuracy:  0.501, Loss:  1.334
+Epoch   0 Batch  102/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.529, Loss:  1.319
+Epoch   0 Batch  103/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.538, Loss:  1.417
+Epoch   0 Batch  104/1077 - Train Accuracy:  0.458, Validation Accuracy:  0.547, Loss:  1.394
+Epoch   0 Batch  105/1077 - Train Accuracy:  0.510, Validation Accuracy:  0.512, Loss:  1.274
+Epoch   0 Batch  106/1077 - Train Accuracy:  0.468, Validation Accuracy:  0.512, Loss:  1.405
+Epoch   0 Batch  107/1077 - Train Accuracy:  0.510, Validation Accuracy:  0.526, Loss:  1.229
+Epoch   0 Batch  108/1077 - Train Accuracy:  0.556, Validation Accuracy:  0.542, Loss:  1.149
+Epoch   0 Batch  109/1077 - Train Accuracy:  0.515, Validation Accuracy:  0.548, Loss:  1.224
+Epoch   0 Batch  110/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.543, Loss:  1.191
+Epoch   0 Batch  111/1077 - Train Accuracy:  0.496, Validation Accuracy:  0.547, Loss:  1.237
+Epoch   0 Batch  112/1077 - Train Accuracy:  0.494, Validation Accuracy:  0.531, Loss:  1.218
+Epoch   0 Batch  113/1077 - Train Accuracy:  0.475, Validation Accuracy:  0.522, Loss:  1.197
+Epoch   0 Batch  114/1077 - Train Accuracy:  0.526, Validation Accuracy:  0.529, Loss:  1.110
+Epoch   0 Batch  115/1077 - Train Accuracy:  0.504, Validation Accuracy:  0.532, Loss:  1.182
+Epoch   0 Batch  116/1077 - Train Accuracy:  0.488, Validation Accuracy:  0.524, Loss:  1.181
+Epoch   0 Batch  117/1077 - Train Accuracy:  0.459, Validation Accuracy:  0.544, Loss:  1.173
+Epoch   0 Batch  118/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.558, Loss:  1.164
+Epoch   0 Batch  119/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.550, Loss:  1.075
+Epoch   0 Batch  120/1077 - Train Accuracy:  0.482, Validation Accuracy:  0.533, Loss:  1.114
+Epoch   0 Batch  121/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.533, Loss:  1.056
+Epoch   0 Batch  122/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.539, Loss:  1.036
+Epoch   0 Batch  123/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.550, Loss:  1.013
+Epoch   0 Batch  124/1077 - Train Accuracy:  0.495, Validation Accuracy:  0.543, Loss:  1.080
+Epoch   0 Batch  125/1077 - Train Accuracy:  0.529, Validation Accuracy:  0.537, Loss:  1.015
+Epoch   0 Batch  126/1077 - Train Accuracy:  0.515, Validation Accuracy:  0.537, Loss:  0.966
+Epoch   0 Batch  127/1077 - Train Accuracy:  0.502, Validation Accuracy:  0.545, Loss:  1.023
+Epoch   0 Batch  128/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.521, Loss:  0.953
+Epoch   0 Batch  129/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.534, Loss:  1.014
+Epoch   0 Batch  130/1077 - Train Accuracy:  0.539, Validation Accuracy:  0.547, Loss:  0.926
+Epoch   0 Batch  131/1077 - Train Accuracy:  0.499, Validation Accuracy:  0.558, Loss:  0.993
+Epoch   0 Batch  132/1077 - Train Accuracy:  0.464, Validation Accuracy:  0.545, Loss:  1.016
+Epoch   0 Batch  133/1077 - Train Accuracy:  0.471, Validation Accuracy:  0.548, Loss:  0.994
+Epoch   0 Batch  134/1077 - Train Accuracy:  0.534, Validation Accuracy:  0.560, Loss:  0.927
+Epoch   0 Batch  135/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.576, Loss:  0.994
+Epoch   0 Batch  136/1077 - Train Accuracy:  0.527, Validation Accuracy:  0.565, Loss:  0.944
+Epoch   0 Batch  137/1077 - Train Accuracy:  0.573, Validation Accuracy:  0.572, Loss:  0.864
+Epoch   0 Batch  138/1077 - Train Accuracy:  0.528, Validation Accuracy:  0.570, Loss:  0.924
+Epoch   0 Batch  139/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.555, Loss:  0.947
+Epoch   0 Batch  140/1077 - Train Accuracy:  0.457, Validation Accuracy:  0.557, Loss:  0.986
+Epoch   0 Batch  141/1077 - Train Accuracy:  0.508, Validation Accuracy:  0.552, Loss:  0.943
+Epoch   0 Batch  142/1077 - Train Accuracy:  0.546, Validation Accuracy:  0.557, Loss:  0.855
+Epoch   0 Batch  143/1077 - Train Accuracy:  0.522, Validation Accuracy:  0.550, Loss:  0.941
+Epoch   0 Batch  144/1077 - Train Accuracy:  0.481, Validation Accuracy:  0.558, Loss:  0.944
+Epoch   0 Batch  145/1077 - Train Accuracy:  0.584, Validation Accuracy:  0.559, Loss:  0.890
+Epoch   0 Batch  146/1077 - Train Accuracy:  0.527, Validation Accuracy:  0.549, Loss:  0.908
+Epoch   0 Batch  147/1077 - Train Accuracy:  0.477, Validation Accuracy:  0.543, Loss:  0.933
+Epoch   0 Batch  148/1077 - Train Accuracy:  0.518, Validation Accuracy:  0.564, Loss:  0.885
+Epoch   0 Batch  149/1077 - Train Accuracy:  0.507, Validation Accuracy:  0.572, Loss:  0.904
+Epoch   0 Batch  150/1077 - Train Accuracy:  0.574, Validation Accuracy:  0.568, Loss:  0.849
+Epoch   0 Batch  151/1077 - Train Accuracy:  0.528, Validation Accuracy:  0.576, Loss:  0.812
+Epoch   0 Batch  152/1077 - Train Accuracy:  0.529, Validation Accuracy:  0.572, Loss:  0.877
+Epoch   0 Batch  153/1077 - Train Accuracy:  0.522, Validation Accuracy:  0.568, Loss:  0.907
+Epoch   0 Batch  154/1077 - Train Accuracy:  0.511, Validation Accuracy:  0.574, Loss:  0.873
+Epoch   0 Batch  155/1077 - Train Accuracy:  0.539, Validation Accuracy:  0.572, Loss:  0.864
+Epoch   0 Batch  156/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.584, Loss:  0.836
+Epoch   0 Batch  157/1077 - Train Accuracy:  0.557, Validation Accuracy:  0.574, Loss:  0.846
+Epoch   0 Batch  158/1077 - Train Accuracy:  0.536, Validation Accuracy:  0.591, Loss:  0.881
+Epoch   0 Batch  159/1077 - Train Accuracy:  0.548, Validation Accuracy:  0.581, Loss:  0.768
+Epoch   0 Batch  160/1077 - Train Accuracy:  0.532, Validation Accuracy:  0.576, Loss:  0.831
+Epoch   0 Batch  161/1077 - Train Accuracy:  0.523, Validation Accuracy:  0.566, Loss:  0.835
+Epoch   0 Batch  162/1077 - Train Accuracy:  0.534, Validation Accuracy:  0.568, Loss:  0.866
+Epoch   0 Batch  163/1077 - Train Accuracy:  0.525, Validation Accuracy:  0.571, Loss:  0.891
+Epoch   0 Batch  164/1077 - Train Accuracy:  0.533, Validation Accuracy:  0.586, Loss:  0.841
+Epoch   0 Batch  165/1077 - Train Accuracy:  0.505, Validation Accuracy:  0.587, Loss:  0.804
+Epoch   0 Batch  166/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.586, Loss:  0.824
+Epoch   0 Batch  167/1077 - Train Accuracy:  0.558, Validation Accuracy:  0.574, Loss:  0.835
+Epoch   0 Batch  168/1077 - Train Accuracy:  0.520, Validation Accuracy:  0.568, Loss:  0.844
+Epoch   0 Batch  169/1077 - Train Accuracy:  0.577, Validation Accuracy:  0.575, Loss:  0.822
+Epoch   0 Batch  170/1077 - Train Accuracy:  0.553, Validation Accuracy:  0.566, Loss:  0.861
+Epoch   0 Batch  171/1077 - Train Accuracy:  0.592, Validation Accuracy:  0.577, Loss:  0.755
+Epoch   0 Batch  172/1077 - Train Accuracy:  0.599, Validation Accuracy:  0.583, Loss:  0.743
+Epoch   0 Batch  173/1077 - Train Accuracy:  0.543, Validation Accuracy:  0.578, Loss:  0.845
+Epoch   0 Batch  174/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.580, Loss:  0.779
+Epoch   0 Batch  175/1077 - Train Accuracy:  0.598, Validation Accuracy:  0.589, Loss:  0.784
+Epoch   0 Batch  176/1077 - Train Accuracy:  0.565, Validation Accuracy:  0.605, Loss:  0.783
+Epoch   0 Batch  177/1077 - Train Accuracy:  0.537, Validation Accuracy:  0.591, Loss:  0.837
+Epoch   0 Batch  178/1077 - Train Accuracy:  0.564, Validation Accuracy:  0.582, Loss:  0.763
+Epoch   0 Batch  179/1077 - Train Accuracy:  0.586, Validation Accuracy:  0.578, Loss:  0.807
+Epoch   0 Batch  180/1077 - Train Accuracy:  0.559, Validation Accuracy:  0.581, Loss:  0.773
+Epoch   0 Batch  181/1077 - Train Accuracy:  0.555, Validation Accuracy:  0.580, Loss:  0.803
+Epoch   0 Batch  182/1077 - Train Accuracy:  0.596, Validation Accuracy:  0.570, Loss:  0.752
+Epoch   0 Batch  183/1077 - Train Accuracy:  0.567, Validation Accuracy:  0.559, Loss:  0.767
+Epoch   0 Batch  184/1077 - Train Accuracy:  0.568, Validation Accuracy:  0.566, Loss:  0.718
+Epoch   0 Batch  185/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.580, Loss:  0.760
+Epoch   0 Batch  186/1077 - Train Accuracy:  0.569, Validation Accuracy:  0.596, Loss:  0.777
+Epoch   0 Batch  187/1077 - Train Accuracy:  0.573, Validation Accuracy:  0.588, Loss:  0.741
+Epoch   0 Batch  188/1077 - Train Accuracy:  0.569, Validation Accuracy:  0.604, Loss:  0.744
+Epoch   0 Batch  189/1077 - Train Accuracy:  0.567, Validation Accuracy:  0.611, Loss:  0.724
+Epoch   0 Batch  190/1077 - Train Accuracy:  0.618, Validation Accuracy:  0.621, Loss:  0.726
+Epoch   0 Batch  191/1077 - Train Accuracy:  0.630, Validation Accuracy:  0.620, Loss:  0.663
+Epoch   0 Batch  192/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.620, Loss:  0.747
+Epoch   0 Batch  193/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.617, Loss:  0.712
+Epoch   0 Batch  194/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.596, Loss:  0.676
+Epoch   0 Batch  195/1077 - Train Accuracy:  0.561, Validation Accuracy:  0.592, Loss:  0.712
+Epoch   0 Batch  196/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.583, Loss:  0.718
+Epoch   0 Batch  197/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.586, Loss:  0.708
+Epoch   0 Batch  198/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.581, Loss:  0.667
+Epoch   0 Batch  199/1077 - Train Accuracy:  0.570, Validation Accuracy:  0.590, Loss:  0.717
+Epoch   0 Batch  200/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.599, Loss:  0.735
+Epoch   0 Batch  201/1077 - Train Accuracy:  0.597, Validation Accuracy:  0.594, Loss:  0.686
+Epoch   0 Batch  202/1077 - Train Accuracy:  0.601, Validation Accuracy:  0.592, Loss:  0.718
+Epoch   0 Batch  203/1077 - Train Accuracy:  0.563, Validation Accuracy:  0.582, Loss:  0.688
+Epoch   0 Batch  204/1077 - Train Accuracy:  0.582, Validation Accuracy:  0.596, Loss:  0.732
+Epoch   0 Batch  205/1077 - Train Accuracy:  0.584, Validation Accuracy:  0.583, Loss:  0.720
+Epoch   0 Batch  206/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.574, Loss:  0.690
+Epoch   0 Batch  207/1077 - Train Accuracy:  0.587, Validation Accuracy:  0.583, Loss:  0.712
+Epoch   0 Batch  208/1077 - Train Accuracy:  0.597, Validation Accuracy:  0.600, Loss:  0.688
+Epoch   0 Batch  209/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.608, Loss:  0.634
+Epoch   0 Batch  210/1077 - Train Accuracy:  0.613, Validation Accuracy:  0.611, Loss:  0.682
+Epoch   0 Batch  211/1077 - Train Accuracy:  0.580, Validation Accuracy:  0.626, Loss:  0.682
+Epoch   0 Batch  212/1077 - Train Accuracy:  0.609, Validation Accuracy:  0.636, Loss:  0.659
+Epoch   0 Batch  213/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.631, Loss:  0.642
+Epoch   0 Batch  214/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.620, Loss:  0.675
+Epoch   0 Batch  215/1077 - Train Accuracy:  0.579, Validation Accuracy:  0.615, Loss:  0.702
+Epoch   0 Batch  216/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.605, Loss:  0.700
+Epoch   0 Batch  217/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.596, Loss:  0.664
+Epoch   0 Batch  218/1077 - Train Accuracy:  0.582, Validation Accuracy:  0.583, Loss:  0.766
+Epoch   0 Batch  219/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.603, Loss:  0.670
+Epoch   0 Batch  220/1077 - Train Accuracy:  0.593, Validation Accuracy:  0.608, Loss:  0.685
+Epoch   0 Batch  221/1077 - Train Accuracy:  0.660, Validation Accuracy:  0.605, Loss:  0.705
+Epoch   0 Batch  222/1077 - Train Accuracy:  0.561, Validation Accuracy:  0.600, Loss:  0.712
+Epoch   0 Batch  223/1077 - Train Accuracy:  0.615, Validation Accuracy:  0.610, Loss:  0.634
+Epoch   0 Batch  224/1077 - Train Accuracy:  0.623, Validation Accuracy:  0.606, Loss:  0.677
+Epoch   0 Batch  225/1077 - Train Accuracy:  0.618, Validation Accuracy:  0.606, Loss:  0.695
+Epoch   0 Batch  226/1077 - Train Accuracy:  0.615, Validation Accuracy:  0.601, Loss:  0.668
+Epoch   0 Batch  227/1077 - Train Accuracy:  0.593, Validation Accuracy:  0.600, Loss:  0.721
+Epoch   0 Batch  228/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.605, Loss:  0.645
+Epoch   0 Batch  229/1077 - Train Accuracy:  0.630, Validation Accuracy:  0.610, Loss:  0.654
+Epoch   0 Batch  230/1077 - Train Accuracy:  0.631, Validation Accuracy:  0.621, Loss:  0.659
+Epoch   0 Batch  231/1077 - Train Accuracy:  0.589, Validation Accuracy:  0.613, Loss:  0.668
+Epoch   0 Batch  232/1077 - Train Accuracy:  0.601, Validation Accuracy:  0.615, Loss:  0.708
+Epoch   0 Batch  233/1077 - Train Accuracy:  0.621, Validation Accuracy:  0.619, Loss:  0.706
+Epoch   0 Batch  234/1077 - Train Accuracy:  0.640, Validation Accuracy:  0.616, Loss:  0.661
+Epoch   0 Batch  235/1077 - Train Accuracy:  0.633, Validation Accuracy:  0.618, Loss:  0.606
+Epoch   0 Batch  236/1077 - Train Accuracy:  0.590, Validation Accuracy:  0.622, Loss:  0.689
+Epoch   0 Batch  237/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.616, Loss:  0.619
+Epoch   0 Batch  238/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.602, Loss:  0.673
+Epoch   0 Batch  239/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.605, Loss:  0.596
+Epoch   0 Batch  240/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.611, Loss:  0.621
+Epoch   0 Batch  241/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.606, Loss:  0.611
+Epoch   0 Batch  242/1077 - Train Accuracy:  0.603, Validation Accuracy:  0.598, Loss:  0.641
+Epoch   0 Batch  243/1077 - Train Accuracy:  0.578, Validation Accuracy:  0.603, Loss:  0.677
+Epoch   0 Batch  244/1077 - Train Accuracy:  0.654, Validation Accuracy:  0.604, Loss:  0.614
+Epoch   0 Batch  245/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.604, Loss:  0.600
+Epoch   0 Batch  246/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.611, Loss:  0.644
+Epoch   0 Batch  247/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.631, Loss:  0.609
+Epoch   0 Batch  248/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.623, Loss:  0.616
+Epoch   0 Batch  249/1077 - Train Accuracy:  0.596, Validation Accuracy:  0.621, Loss:  0.615
+Epoch   0 Batch  250/1077 - Train Accuracy:  0.626, Validation Accuracy:  0.614, Loss:  0.585
+Epoch   0 Batch  251/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.588, Loss:  0.639
+Epoch   0 Batch  252/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.588, Loss:  0.620
+Epoch   0 Batch  253/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.602, Loss:  0.600
+Epoch   0 Batch  254/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.605, Loss:  0.642
+Epoch   0 Batch  255/1077 - Train Accuracy:  0.605, Validation Accuracy:  0.613, Loss:  0.625
+Epoch   0 Batch  256/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.612, Loss:  0.685
+Epoch   0 Batch  257/1077 - Train Accuracy:  0.641, Validation Accuracy:  0.614, Loss:  0.624
+Epoch   0 Batch  258/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.598, Loss:  0.613
+Epoch   0 Batch  259/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.605, Loss:  0.613
+Epoch   0 Batch  260/1077 - Train Accuracy:  0.643, Validation Accuracy:  0.606, Loss:  0.593
+Epoch   0 Batch  261/1077 - Train Accuracy:  0.625, Validation Accuracy:  0.604, Loss:  0.620
+Epoch   0 Batch  262/1077 - Train Accuracy:  0.633, Validation Accuracy:  0.609, Loss:  0.612
+Epoch   0 Batch  263/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.615, Loss:  0.601
+Epoch   0 Batch  264/1077 - Train Accuracy:  0.626, Validation Accuracy:  0.630, Loss:  0.619
+Epoch   0 Batch  265/1077 - Train Accuracy:  0.617, Validation Accuracy:  0.625, Loss:  0.626
+Epoch   0 Batch  266/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.617, Loss:  0.590
+Epoch   0 Batch  267/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.608, Loss:  0.576
+Epoch   0 Batch  268/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.623, Loss:  0.614
+Epoch   0 Batch  269/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.627, Loss:  0.658
+Epoch   0 Batch  270/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.622, Loss:  0.650
+Epoch   0 Batch  271/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.624, Loss:  0.607
+Epoch   0 Batch  272/1077 - Train Accuracy:  0.635, Validation Accuracy:  0.630, Loss:  0.633
+Epoch   0 Batch  273/1077 - Train Accuracy:  0.629, Validation Accuracy:  0.633, Loss:  0.586
+Epoch   0 Batch  274/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.623, Loss:  0.596
+Epoch   0 Batch  275/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.617, Loss:  0.576
+Epoch   0 Batch  276/1077 - Train Accuracy:  0.602, Validation Accuracy:  0.628, Loss:  0.632
+Epoch   0 Batch  277/1077 - Train Accuracy:  0.643, Validation Accuracy:  0.639, Loss:  0.557
+Epoch   0 Batch  278/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.665, Loss:  0.639
+Epoch   0 Batch  279/1077 - Train Accuracy:  0.611, Validation Accuracy:  0.671, Loss:  0.646
+Epoch   0 Batch  280/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.655, Loss:  0.627
+Epoch   0 Batch  281/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.647, Loss:  0.627
+Epoch   0 Batch  282/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.639, Loss:  0.630
+Epoch   0 Batch  283/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.631, Loss:  0.620
+Epoch   0 Batch  284/1077 - Train Accuracy:  0.600, Validation Accuracy:  0.630, Loss:  0.634
+Epoch   0 Batch  285/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.625, Loss:  0.586
+Epoch   0 Batch  286/1077 - Train Accuracy:  0.673, Validation Accuracy:  0.624, Loss:  0.567
+Epoch   0 Batch  287/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.623, Loss:  0.582
+Epoch   0 Batch  288/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.640, Loss:  0.623
+Epoch   0 Batch  289/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.643, Loss:  0.583
+Epoch   0 Batch  290/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.638, Loss:  0.617
+Epoch   0 Batch  291/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.638, Loss:  0.606
+Epoch   0 Batch  292/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.636, Loss:  0.568
+Epoch   0 Batch  293/1077 - Train Accuracy:  0.610, Validation Accuracy:  0.629, Loss:  0.611
+Epoch   0 Batch  294/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.637, Loss:  0.540
+Epoch   0 Batch  295/1077 - Train Accuracy:  0.622, Validation Accuracy:  0.616, Loss:  0.636
+Epoch   0 Batch  296/1077 - Train Accuracy:  0.696, Validation Accuracy:  0.647, Loss:  0.544
+Epoch   0 Batch  297/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.637, Loss:  0.623
+Epoch   0 Batch  298/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.659, Loss:  0.615
+Epoch   0 Batch  299/1077 - Train Accuracy:  0.641, Validation Accuracy:  0.667, Loss:  0.562
+Epoch   0 Batch  300/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.659, Loss:  0.566
+Epoch   0 Batch  301/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.643, Loss:  0.556
+Epoch   0 Batch  302/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.652, Loss:  0.564
+Epoch   0 Batch  303/1077 - Train Accuracy:  0.649, Validation Accuracy:  0.636, Loss:  0.579
+Epoch   0 Batch  304/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.598, Loss:  0.534
+Epoch   0 Batch  305/1077 - Train Accuracy:  0.656, Validation Accuracy:  0.592, Loss:  0.552
+Epoch   0 Batch  306/1077 - Train Accuracy:  0.644, Validation Accuracy:  0.607, Loss:  0.547
+Epoch   0 Batch  307/1077 - Train Accuracy:  0.640, Validation Accuracy:  0.610, Loss:  0.559
+Epoch   0 Batch  308/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.610, Loss:  0.609
+Epoch   0 Batch  309/1077 - Train Accuracy:  0.674, Validation Accuracy:  0.615, Loss:  0.521
+Epoch   0 Batch  310/1077 - Train Accuracy:  0.612, Validation Accuracy:  0.617, Loss:  0.575
+Epoch   0 Batch  311/1077 - Train Accuracy:  0.666, Validation Accuracy:  0.617, Loss:  0.530
+Epoch   0 Batch  312/1077 - Train Accuracy:  0.637, Validation Accuracy:  0.614, Loss:  0.590
+Epoch   0 Batch  313/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.624, Loss:  0.547
+Epoch   0 Batch  314/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.639, Loss:  0.552
+Epoch   0 Batch  315/1077 - Train Accuracy:  0.661, Validation Accuracy:  0.643, Loss:  0.519
+Epoch   0 Batch  316/1077 - Train Accuracy:  0.673, Validation Accuracy:  0.627, Loss:  0.522
+Epoch   0 Batch  317/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.619, Loss:  0.604
+Epoch   0 Batch  318/1077 - Train Accuracy:  0.637, Validation Accuracy:  0.617, Loss:  0.560
+Epoch   0 Batch  319/1077 - Train Accuracy:  0.639, Validation Accuracy:  0.608, Loss:  0.547
+Epoch   0 Batch  320/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.636, Loss:  0.557
+Epoch   0 Batch  321/1077 - Train Accuracy:  0.632, Validation Accuracy:  0.631, Loss:  0.547
+Epoch   0 Batch  322/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.634, Loss:  0.529
+Epoch   0 Batch  323/1077 - Train Accuracy:  0.665, Validation Accuracy:  0.622, Loss:  0.538
+Epoch   0 Batch  324/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.630, Loss:  0.547
+Epoch   0 Batch  325/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.636, Loss:  0.527
+Epoch   0 Batch  326/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.614, Loss:  0.548
+Epoch   0 Batch  327/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.610, Loss:  0.558
+Epoch   0 Batch  328/1077 - Train Accuracy:  0.685, Validation Accuracy:  0.615, Loss:  0.535
+Epoch   0 Batch  329/1077 - Train Accuracy:  0.634, Validation Accuracy:  0.630, Loss:  0.574
+Epoch   0 Batch  330/1077 - Train Accuracy:  0.658, Validation Accuracy:  0.635, Loss:  0.545
+Epoch   0 Batch  331/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.635, Loss:  0.574
+Epoch   0 Batch  332/1077 - Train Accuracy:  0.625, Validation Accuracy:  0.640, Loss:  0.505
+Epoch   0 Batch  333/1077 - Train Accuracy:  0.676, Validation Accuracy:  0.640, Loss:  0.561
+Epoch   0 Batch  334/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.640, Loss:  0.553
+Epoch   0 Batch  335/1077 - Train Accuracy:  0.684, Validation Accuracy:  0.632, Loss:  0.509
+Epoch   0 Batch  336/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.635, Loss:  0.544
+Epoch   0 Batch  337/1077 - Train Accuracy:  0.620, Validation Accuracy:  0.635, Loss:  0.549
+Epoch   0 Batch  338/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.613, Loss:  0.565
+Epoch   0 Batch  339/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.613, Loss:  0.512
+Epoch   0 Batch  340/1077 - Train Accuracy:  0.648, Validation Accuracy:  0.618, Loss:  0.547
+Epoch   0 Batch  341/1077 - Train Accuracy:  0.667, Validation Accuracy:  0.619, Loss:  0.577
+Epoch   0 Batch  342/1077 - Train Accuracy:  0.644, Validation Accuracy:  0.619, Loss:  0.511
+Epoch   0 Batch  343/1077 - Train Accuracy:  0.624, Validation Accuracy:  0.627, Loss:  0.545
+Epoch   0 Batch  344/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.636, Loss:  0.526
+Epoch   0 Batch  345/1077 - Train Accuracy:  0.706, Validation Accuracy:  0.628, Loss:  0.502
+Epoch   0 Batch  346/1077 - Train Accuracy:  0.647, Validation Accuracy:  0.636, Loss:  0.551
+Epoch   0 Batch  347/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.632, Loss:  0.491
+Epoch   0 Batch  348/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.630, Loss:  0.518
+Epoch   0 Batch  349/1077 - Train Accuracy:  0.642, Validation Accuracy:  0.627, Loss:  0.529
+Epoch   0 Batch  350/1077 - Train Accuracy:  0.646, Validation Accuracy:  0.620, Loss:  0.544
+Epoch   0 Batch  351/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.650, Loss:  0.545
+Epoch   0 Batch  352/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.655, Loss:  0.523
+Epoch   0 Batch  353/1077 - Train Accuracy:  0.635, Validation Accuracy:  0.670, Loss:  0.567
+Epoch   0 Batch  354/1077 - Train Accuracy:  0.650, Validation Accuracy:  0.661, Loss:  0.548
+Epoch   0 Batch  355/1077 - Train Accuracy:  0.652, Validation Accuracy:  0.649, Loss:  0.516
+Epoch   0 Batch  356/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.643, Loss:  0.528
+Epoch   0 Batch  357/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.644, Loss:  0.501
+Epoch   0 Batch  358/1077 - Train Accuracy:  0.636, Validation Accuracy:  0.648, Loss:  0.546
+Epoch   0 Batch  359/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.662, Loss:  0.524
+Epoch   0 Batch  360/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.648, Loss:  0.515
+Epoch   0 Batch  361/1077 - Train Accuracy:  0.680, Validation Accuracy:  0.657, Loss:  0.545
+Epoch   0 Batch  362/1077 - Train Accuracy:  0.670, Validation Accuracy:  0.636, Loss:  0.505
+Epoch   0 Batch  363/1077 - Train Accuracy:  0.651, Validation Accuracy:  0.641, Loss:  0.519
+Epoch   0 Batch  364/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.641, Loss:  0.542
+Epoch   0 Batch  365/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.636, Loss:  0.502
+Epoch   0 Batch  366/1077 - Train Accuracy:  0.638, Validation Accuracy:  0.635, Loss:  0.520
+Epoch   0 Batch  367/1077 - Train Accuracy:  0.703, Validation Accuracy:  0.645, Loss:  0.452
+Epoch   0 Batch  368/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.657, Loss:  0.506
+Epoch   0 Batch  369/1077 - Train Accuracy:  0.668, Validation Accuracy:  0.666, Loss:  0.485
+Epoch   0 Batch  370/1077 - Train Accuracy:  0.684, Validation Accuracy:  0.662, Loss:  0.483
+Epoch   0 Batch  371/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.673, Loss:  0.487
+Epoch   0 Batch  372/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.668, Loss:  0.480
+Epoch   0 Batch  373/1077 - Train Accuracy:  0.699, Validation Accuracy:  0.667, Loss:  0.463
+Epoch   0 Batch  374/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.657, Loss:  0.524
+Epoch   0 Batch  375/1077 - Train Accuracy:  0.694, Validation Accuracy:  0.662, Loss:  0.468
+Epoch   0 Batch  376/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.654, Loss:  0.471
+Epoch   0 Batch  377/1077 - Train Accuracy:  0.661, Validation Accuracy:  0.657, Loss:  0.496
+Epoch   0 Batch  378/1077 - Train Accuracy:  0.664, Validation Accuracy:  0.658, Loss:  0.473
+Epoch   0 Batch  379/1077 - Train Accuracy:  0.688, Validation Accuracy:  0.661, Loss:  0.514
+Epoch   0 Batch  380/1077 - Train Accuracy:  0.676, Validation Accuracy:  0.674, Loss:  0.480
+Epoch   0 Batch  381/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.661, Loss:  0.509
+Epoch   0 Batch  382/1077 - Train Accuracy:  0.689, Validation Accuracy:  0.672, Loss:  0.521
+Epoch   0 Batch  383/1077 - Train Accuracy:  0.688, Validation Accuracy:  0.661, Loss:  0.467
+Epoch   0 Batch  384/1077 - Train Accuracy:  0.678, Validation Accuracy:  0.654, Loss:  0.481
+Epoch   0 Batch  385/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.657, Loss:  0.477
+Epoch   0 Batch  386/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.652, Loss:  0.472
+Epoch   0 Batch  387/1077 - Train Accuracy:  0.708, Validation Accuracy:  0.662, Loss:  0.458
+Epoch   0 Batch  388/1077 - Train Accuracy:  0.681, Validation Accuracy:  0.672, Loss:  0.460
+Epoch   0 Batch  389/1077 - Train Accuracy:  0.702, Validation Accuracy:  0.681, Loss:  0.489
+Epoch   0 Batch  390/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.669, Loss:  0.494
+Epoch   0 Batch  391/1077 - Train Accuracy:  0.696, Validation Accuracy:  0.652, Loss:  0.472
+Epoch   0 Batch  392/1077 - Train Accuracy:  0.690, Validation Accuracy:  0.669, Loss:  0.463
+Epoch   0 Batch  393/1077 - Train Accuracy:  0.697, Validation Accuracy:  0.681, Loss:  0.443
+Epoch   0 Batch  394/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.698, Loss:  0.478
+Epoch   0 Batch  395/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.696, Loss:  0.442
+Epoch   0 Batch  396/1077 - Train Accuracy:  0.653, Validation Accuracy:  0.696, Loss:  0.495
+Epoch   0 Batch  397/1077 - Train Accuracy:  0.712, Validation Accuracy:  0.679, Loss:  0.460
+Epoch   0 Batch  398/1077 - Train Accuracy:  0.704, Validation Accuracy:  0.674, Loss:  0.496
+Epoch   0 Batch  399/1077 - Train Accuracy:  0.663, Validation Accuracy:  0.692, Loss:  0.478
+Epoch   0 Batch  400/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.664, Loss:  0.478
+Epoch   0 Batch  401/1077 - Train Accuracy:  0.659, Validation Accuracy:  0.639, Loss:  0.459
+Epoch   0 Batch  402/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.647, Loss:  0.449
+Epoch   0 Batch  403/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.689, Loss:  0.473
+Epoch   0 Batch  404/1077 - Train Accuracy:  0.701, Validation Accuracy:  0.670, Loss:  0.438
+Epoch   0 Batch  405/1077 - Train Accuracy:  0.717, Validation Accuracy:  0.688, Loss:  0.506
+Epoch   0 Batch  406/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.711, Loss:  0.462
+Epoch   0 Batch  407/1077 - Train Accuracy:  0.669, Validation Accuracy:  0.707, Loss:  0.490
+Epoch   0 Batch  408/1077 - Train Accuracy:  0.657, Validation Accuracy:  0.703, Loss:  0.471
+Epoch   0 Batch  409/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.707, Loss:  0.478
+Epoch   0 Batch  410/1077 - Train Accuracy:  0.662, Validation Accuracy:  0.701, Loss:  0.485
+Epoch   0 Batch  411/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.688, Loss:  0.457
+Epoch   0 Batch  412/1077 - Train Accuracy:  0.697, Validation Accuracy:  0.710, Loss:  0.438
+Epoch   0 Batch  413/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.718, Loss:  0.438
+Epoch   0 Batch  414/1077 - Train Accuracy:  0.668, Validation Accuracy:  0.710, Loss:  0.463
+Epoch   0 Batch  415/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.697, Loss:  0.420
+Epoch   0 Batch  416/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.696, Loss:  0.452
+Epoch   0 Batch  417/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.689, Loss:  0.474
+Epoch   0 Batch  418/1077 - Train Accuracy:  0.695, Validation Accuracy:  0.696, Loss:  0.449
+Epoch   0 Batch  419/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.697, Loss:  0.446
+Epoch   0 Batch  420/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.696, Loss:  0.435
+Epoch   0 Batch  421/1077 - Train Accuracy:  0.675, Validation Accuracy:  0.704, Loss:  0.465
+Epoch   0 Batch  422/1077 - Train Accuracy:  0.715, Validation Accuracy:  0.690, Loss:  0.433
+Epoch   0 Batch  423/1077 - Train Accuracy:  0.695, Validation Accuracy:  0.684, Loss:  0.465
+Epoch   0 Batch  424/1077 - Train Accuracy:  0.679, Validation Accuracy:  0.685, Loss:  0.444
+Epoch   0 Batch  425/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.670, Loss:  0.429
+Epoch   0 Batch  426/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.688, Loss:  0.442
+Epoch   0 Batch  427/1077 - Train Accuracy:  0.714, Validation Accuracy:  0.681, Loss:  0.433
+Epoch   0 Batch  428/1077 - Train Accuracy:  0.711, Validation Accuracy:  0.686, Loss:  0.416
+Epoch   0 Batch  429/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.683, Loss:  0.433
+Epoch   0 Batch  430/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.681, Loss:  0.427
+Epoch   0 Batch  431/1077 - Train Accuracy:  0.658, Validation Accuracy:  0.670, Loss:  0.429
+Epoch   0 Batch  432/1077 - Train Accuracy:  0.708, Validation Accuracy:  0.669, Loss:  0.440
+Epoch   0 Batch  433/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.680, Loss:  0.433
+Epoch   0 Batch  434/1077 - Train Accuracy:  0.707, Validation Accuracy:  0.692, Loss:  0.420
+Epoch   0 Batch  435/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.699, Loss:  0.468
+Epoch   0 Batch  436/1077 - Train Accuracy:  0.718, Validation Accuracy:  0.704, Loss:  0.412
+Epoch   0 Batch  437/1077 - Train Accuracy:  0.710, Validation Accuracy:  0.711, Loss:  0.430
+Epoch   0 Batch  438/1077 - Train Accuracy:  0.671, Validation Accuracy:  0.700, Loss:  0.426
+Epoch   0 Batch  439/1077 - Train Accuracy:  0.682, Validation Accuracy:  0.709, Loss:  0.446
+Epoch   0 Batch  440/1077 - Train Accuracy:  0.701, Validation Accuracy:  0.705, Loss:  0.441
+Epoch   0 Batch  441/1077 - Train Accuracy:  0.691, Validation Accuracy:  0.699, Loss:  0.414
+Epoch   0 Batch  442/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.701, Loss:  0.412
+Epoch   0 Batch  443/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.702, Loss:  0.403
+Epoch   0 Batch  444/1077 - Train Accuracy:  0.741, Validation Accuracy:  0.677, Loss:  0.426
+Epoch   0 Batch  445/1077 - Train Accuracy:  0.686, Validation Accuracy:  0.675, Loss:  0.439
+Epoch   0 Batch  446/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.666, Loss:  0.391
+Epoch   0 Batch  447/1077 - Train Accuracy:  0.677, Validation Accuracy:  0.670, Loss:  0.408
+Epoch   0 Batch  448/1077 - Train Accuracy:  0.691, Validation Accuracy:  0.669, Loss:  0.430
+Epoch   0 Batch  449/1077 - Train Accuracy:  0.672, Validation Accuracy:  0.686, Loss:  0.430
+Epoch   0 Batch  450/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.696, Loss:  0.410
+Epoch   0 Batch  451/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.699, Loss:  0.397
+Epoch   0 Batch  452/1077 - Train Accuracy:  0.727, Validation Accuracy:  0.691, Loss:  0.420
+Epoch   0 Batch  453/1077 - Train Accuracy:  0.725, Validation Accuracy:  0.692, Loss:  0.381
+Epoch   0 Batch  454/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.694, Loss:  0.409
+Epoch   0 Batch  455/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.690, Loss:  0.391
+Epoch   0 Batch  456/1077 - Train Accuracy:  0.738, Validation Accuracy:  0.702, Loss:  0.405
+Epoch   0 Batch  457/1077 - Train Accuracy:  0.724, Validation Accuracy:  0.694, Loss:  0.370
+Epoch   0 Batch  458/1077 - Train Accuracy:  0.683, Validation Accuracy:  0.693, Loss:  0.413
+Epoch   0 Batch  459/1077 - Train Accuracy:  0.751, Validation Accuracy:  0.689, Loss:  0.391
+Epoch   0 Batch  460/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.720, Loss:  0.425
+Epoch   0 Batch  461/1077 - Train Accuracy:  0.713, Validation Accuracy:  0.725, Loss:  0.407
+Epoch   0 Batch  462/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.722, Loss:  0.403
+Epoch   0 Batch  463/1077 - Train Accuracy:  0.693, Validation Accuracy:  0.717, Loss:  0.404
+Epoch   0 Batch  464/1077 - Train Accuracy:  0.746, Validation Accuracy:  0.716, Loss:  0.403
+Epoch   0 Batch  465/1077 - Train Accuracy:  0.714, Validation Accuracy:  0.718, Loss:  0.428
+Epoch   0 Batch  466/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.707, Loss:  0.383
+Epoch   0 Batch  467/1077 - Train Accuracy:  0.767, Validation Accuracy:  0.696, Loss:  0.393
+Epoch   0 Batch  468/1077 - Train Accuracy:  0.758, Validation Accuracy:  0.712, Loss:  0.400
+Epoch   0 Batch  469/1077 - Train Accuracy:  0.700, Validation Accuracy:  0.717, Loss:  0.404
+Epoch   0 Batch  470/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.712, Loss:  0.423
+Epoch   0 Batch  471/1077 - Train Accuracy:  0.762, Validation Accuracy:  0.721, Loss:  0.375
+Epoch   0 Batch  472/1077 - Train Accuracy:  0.722, Validation Accuracy:  0.725, Loss:  0.387
+Epoch   0 Batch  473/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.738, Loss:  0.405
+Epoch   0 Batch  474/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.739, Loss:  0.391
+Epoch   0 Batch  475/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.716, Loss:  0.390
+Epoch   0 Batch  476/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.705, Loss:  0.383
+Epoch   0 Batch  477/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.703, Loss:  0.378
+Epoch   0 Batch  478/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.713, Loss:  0.407
+Epoch   0 Batch  479/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.721, Loss:  0.404
+Epoch   0 Batch  480/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.714, Loss:  0.396
+Epoch   0 Batch  481/1077 - Train Accuracy:  0.730, Validation Accuracy:  0.708, Loss:  0.393
+Epoch   0 Batch  482/1077 - Train Accuracy:  0.704, Validation Accuracy:  0.701, Loss:  0.413
+Epoch   0 Batch  483/1077 - Train Accuracy:  0.709, Validation Accuracy:  0.713, Loss:  0.392
+Epoch   0 Batch  484/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.723, Loss:  0.385
+Epoch   0 Batch  485/1077 - Train Accuracy:  0.756, Validation Accuracy:  0.718, Loss:  0.395
+Epoch   0 Batch  486/1077 - Train Accuracy:  0.740, Validation Accuracy:  0.721, Loss:  0.377
+Epoch   0 Batch  487/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.717, Loss:  0.389
+Epoch   0 Batch  488/1077 - Train Accuracy:  0.731, Validation Accuracy:  0.709, Loss:  0.393
+Epoch   0 Batch  489/1077 - Train Accuracy:  0.739, Validation Accuracy:  0.700, Loss:  0.364
+Epoch   0 Batch  490/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.689, Loss:  0.388
+Epoch   0 Batch  491/1077 - Train Accuracy:  0.728, Validation Accuracy:  0.705, Loss:  0.379
+Epoch   0 Batch  492/1077 - Train Accuracy:  0.749, Validation Accuracy:  0.709, Loss:  0.394
+Epoch   0 Batch  493/1077 - Train Accuracy:  0.750, Validation Accuracy:  0.713, Loss:  0.357
+Epoch   0 Batch  494/1077 - Train Accuracy:  0.739, Validation Accuracy:  0.726, Loss:  0.354
+Epoch   0 Batch  495/1077 - Train Accuracy:  0.733, Validation Accuracy:  0.740, Loss:  0.359
+Epoch   0 Batch  496/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.725, Loss:  0.396
+Epoch   0 Batch  497/1077 - Train Accuracy:  0.743, Validation Accuracy:  0.735, Loss:  0.410
+Epoch   0 Batch  498/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.727, Loss:  0.372
+Epoch   0 Batch  499/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.728, Loss:  0.354
+Epoch   0 Batch  500/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.722, Loss:  0.357
+Epoch   0 Batch  501/1077 - Train Accuracy:  0.723, Validation Accuracy:  0.728, Loss:  0.364
+Epoch   0 Batch  502/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.727, Loss:  0.377
+Epoch   0 Batch  503/1077 - Train Accuracy:  0.748, Validation Accuracy:  0.733, Loss:  0.369
+Epoch   0 Batch  504/1077 - Train Accuracy:  0.745, Validation Accuracy:  0.724, Loss:  0.366
+Epoch   0 Batch  505/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.722, Loss:  0.332
+Epoch   0 Batch  506/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.707, Loss:  0.377
+Epoch   0 Batch  507/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.740, Loss:  0.361
+Epoch   0 Batch  508/1077 - Train Accuracy:  0.758, Validation Accuracy:  0.734, Loss:  0.353
+Epoch   0 Batch  509/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.719, Loss:  0.385
+Epoch   0 Batch  510/1077 - Train Accuracy:  0.736, Validation Accuracy:  0.719, Loss:  0.349
+Epoch   0 Batch  511/1077 - Train Accuracy:  0.748, Validation Accuracy:  0.735, Loss:  0.360
+Epoch   0 Batch  512/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.731, Loss:  0.355
+Epoch   0 Batch  513/1077 - Train Accuracy:  0.759, Validation Accuracy:  0.737, Loss:  0.356
+Epoch   0 Batch  514/1077 - Train Accuracy:  0.721, Validation Accuracy:  0.720, Loss:  0.379
+Epoch   0 Batch  515/1077 - Train Accuracy:  0.734, Validation Accuracy:  0.725, Loss:  0.380
+Epoch   0 Batch  516/1077 - Train Accuracy:  0.780, Validation Accuracy:  0.732, Loss:  0.347
+Epoch   0 Batch  517/1077 - Train Accuracy:  0.768, Validation Accuracy:  0.756, Loss:  0.353
+Epoch   0 Batch  518/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.756, Loss:  0.342
+Epoch   0 Batch  519/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.754, Loss:  0.342
+Epoch   0 Batch  520/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.745, Loss:  0.331
+Epoch   0 Batch  521/1077 - Train Accuracy:  0.745, Validation Accuracy:  0.752, Loss:  0.345
+Epoch   0 Batch  522/1077 - Train Accuracy:  0.720, Validation Accuracy:  0.735, Loss:  0.352
+Epoch   0 Batch  523/1077 - Train Accuracy:  0.754, Validation Accuracy:  0.743, Loss:  0.374
+Epoch   0 Batch  524/1077 - Train Accuracy:  0.781, Validation Accuracy:  0.745, Loss:  0.351
+Epoch   0 Batch  525/1077 - Train Accuracy:  0.734, Validation Accuracy:  0.736, Loss:  0.352
+Epoch   0 Batch  526/1077 - Train Accuracy:  0.737, Validation Accuracy:  0.730, Loss:  0.337
+Epoch   0 Batch  527/1077 - Train Accuracy:  0.729, Validation Accuracy:  0.723, Loss:  0.364
+Epoch   0 Batch  528/1077 - Train Accuracy:  0.746, Validation Accuracy:  0.724, Loss:  0.349
+Epoch   0 Batch  529/1077 - Train Accuracy:  0.725, Validation Accuracy:  0.728, Loss:  0.349
+Epoch   0 Batch  530/1077 - Train Accuracy:  0.761, Validation Accuracy:  0.752, Loss:  0.358
+Epoch   0 Batch  531/1077 - Train Accuracy:  0.751, Validation Accuracy:  0.731, Loss:  0.346
+Epoch   0 Batch  532/1077 - Train Accuracy:  0.717, Validation Accuracy:  0.741, Loss:  0.376
+Epoch   0 Batch  533/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.744, Loss:  0.347
+Epoch   0 Batch  534/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.729, Loss:  0.345
+Epoch   0 Batch  535/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.743, Loss:  0.329
+Epoch   0 Batch  536/1077 - Train Accuracy:  0.740, Validation Accuracy:  0.745, Loss:  0.348
+Epoch   0 Batch  537/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.762, Loss:  0.339
+Epoch   0 Batch  538/1077 - Train Accuracy:  0.788, Validation Accuracy:  0.749, Loss:  0.299
+Epoch   0 Batch  539/1077 - Train Accuracy:  0.732, Validation Accuracy:  0.742, Loss:  0.347
+Epoch   0 Batch  540/1077 - Train Accuracy:  0.773, Validation Accuracy:  0.734, Loss:  0.317
+Epoch   0 Batch  541/1077 - Train Accuracy:  0.762, Validation Accuracy:  0.739, Loss:  0.337
+Epoch   0 Batch  542/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.767, Loss:  0.325
+Epoch   0 Batch  543/1077 - Train Accuracy:  0.782, Validation Accuracy:  0.761, Loss:  0.332
+Epoch   0 Batch  544/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.773, Loss:  0.302
+Epoch   0 Batch  545/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.770, Loss:  0.349
+Epoch   0 Batch  546/1077 - Train Accuracy:  0.719, Validation Accuracy:  0.763, Loss:  0.362
+Epoch   0 Batch  547/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.777, Loss:  0.326
+Epoch   0 Batch  548/1077 - Train Accuracy:  0.776, Validation Accuracy:  0.771, Loss:  0.341
+Epoch   0 Batch  549/1077 - Train Accuracy:  0.727, Validation Accuracy:  0.761, Loss:  0.362
+Epoch   0 Batch  550/1077 - Train Accuracy:  0.731, Validation Accuracy:  0.750, Loss:  0.340
+Epoch   0 Batch  551/1077 - Train Accuracy:  0.747, Validation Accuracy:  0.743, Loss:  0.339
+Epoch   0 Batch  552/1077 - Train Accuracy:  0.771, Validation Accuracy:  0.747, Loss:  0.334
+Epoch   0 Batch  553/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.752, Loss:  0.340
+Epoch   0 Batch  554/1077 - Train Accuracy:  0.768, Validation Accuracy:  0.753, Loss:  0.322
+Epoch   0 Batch  555/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.759, Loss:  0.310
+Epoch   0 Batch  556/1077 - Train Accuracy:  0.763, Validation Accuracy:  0.762, Loss:  0.293
+Epoch   0 Batch  557/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.767, Loss:  0.314
+Epoch   0 Batch  558/1077 - Train Accuracy:  0.797, Validation Accuracy:  0.775, Loss:  0.297
+Epoch   0 Batch  559/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.766, Loss:  0.316
+Epoch   0 Batch  560/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.756, Loss:  0.305
+Epoch   0 Batch  561/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.763, Loss:  0.309
+Epoch   0 Batch  562/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.776, Loss:  0.276
+Epoch   0 Batch  563/1077 - Train Accuracy:  0.771, Validation Accuracy:  0.766, Loss:  0.319
+Epoch   0 Batch  564/1077 - Train Accuracy:  0.776, Validation Accuracy:  0.767, Loss:  0.320
+Epoch   0 Batch  565/1077 - Train Accuracy:  0.779, Validation Accuracy:  0.760, Loss:  0.319
+Epoch   0 Batch  566/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.752, Loss:  0.325
+Epoch   0 Batch  567/1077 - Train Accuracy:  0.765, Validation Accuracy:  0.755, Loss:  0.310
+Epoch   0 Batch  568/1077 - Train Accuracy:  0.808, Validation Accuracy:  0.756, Loss:  0.295
+Epoch   0 Batch  569/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.762, Loss:  0.307
+Epoch   0 Batch  570/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.762, Loss:  0.317
+Epoch   0 Batch  571/1077 - Train Accuracy:  0.790, Validation Accuracy:  0.777, Loss:  0.278
+Epoch   0 Batch  572/1077 - Train Accuracy:  0.788, Validation Accuracy:  0.779, Loss:  0.285
+Epoch   0 Batch  573/1077 - Train Accuracy:  0.765, Validation Accuracy:  0.769, Loss:  0.328
+Epoch   0 Batch  574/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.779, Loss:  0.315
+Epoch   0 Batch  575/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.785, Loss:  0.292
+Epoch   0 Batch  576/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.794, Loss:  0.302
+Epoch   0 Batch  577/1077 - Train Accuracy:  0.764, Validation Accuracy:  0.795, Loss:  0.330
+Epoch   0 Batch  578/1077 - Train Accuracy:  0.774, Validation Accuracy:  0.791, Loss:  0.304
+Epoch   0 Batch  579/1077 - Train Accuracy:  0.779, Validation Accuracy:  0.783, Loss:  0.302
+Epoch   0 Batch  580/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.786, Loss:  0.281
+Epoch   0 Batch  581/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.792, Loss:  0.282
+Epoch   0 Batch  582/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.784, Loss:  0.287
+Epoch   0 Batch  583/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.787, Loss:  0.317
+Epoch   0 Batch  584/1077 - Train Accuracy:  0.794, Validation Accuracy:  0.797, Loss:  0.289
+Epoch   0 Batch  585/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.786, Loss:  0.250
+Epoch   0 Batch  586/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.782, Loss:  0.297
+Epoch   0 Batch  587/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.786, Loss:  0.274
+Epoch   0 Batch  588/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.780, Loss:  0.273
+Epoch   0 Batch  589/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.780, Loss:  0.289
+Epoch   0 Batch  590/1077 - Train Accuracy:  0.750, Validation Accuracy:  0.781, Loss:  0.304
+Epoch   0 Batch  591/1077 - Train Accuracy:  0.805, Validation Accuracy:  0.778, Loss:  0.275
+Epoch   0 Batch  592/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.781, Loss:  0.292
+Epoch   0 Batch  593/1077 - Train Accuracy:  0.811, Validation Accuracy:  0.781, Loss:  0.293
+Epoch   0 Batch  594/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.784, Loss:  0.302
+Epoch   0 Batch  595/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.779, Loss:  0.301
+Epoch   0 Batch  596/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.786, Loss:  0.284
+Epoch   0 Batch  597/1077 - Train Accuracy:  0.775, Validation Accuracy:  0.789, Loss:  0.275
+Epoch   0 Batch  598/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.782, Loss:  0.271
+Epoch   0 Batch  599/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.767, Loss:  0.311
+Epoch   0 Batch  600/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.777, Loss:  0.284
+Epoch   0 Batch  601/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.798, Loss:  0.268
+Epoch   0 Batch  602/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.784, Loss:  0.282
+Epoch   0 Batch  603/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.781, Loss:  0.255
+Epoch   0 Batch  604/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.792, Loss:  0.299
+Epoch   0 Batch  605/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.782, Loss:  0.318
+Epoch   0 Batch  606/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.787, Loss:  0.262
+Epoch   0 Batch  607/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.785, Loss:  0.247
+Epoch   0 Batch  608/1077 - Train Accuracy:  0.795, Validation Accuracy:  0.785, Loss:  0.306
+Epoch   0 Batch  609/1077 - Train Accuracy:  0.783, Validation Accuracy:  0.795, Loss:  0.267
+Epoch   0 Batch  610/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.786, Loss:  0.287
+Epoch   0 Batch  611/1077 - Train Accuracy:  0.801, Validation Accuracy:  0.775, Loss:  0.265
+Epoch   0 Batch  612/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.772, Loss:  0.246
+Epoch   0 Batch  613/1077 - Train Accuracy:  0.784, Validation Accuracy:  0.779, Loss:  0.280
+Epoch   0 Batch  614/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.766, Loss:  0.257
+Epoch   0 Batch  615/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.771, Loss:  0.273
+Epoch   0 Batch  616/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.767, Loss:  0.284
+Epoch   0 Batch  617/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.771, Loss:  0.262
+Epoch   0 Batch  618/1077 - Train Accuracy:  0.802, Validation Accuracy:  0.778, Loss:  0.270
+Epoch   0 Batch  619/1077 - Train Accuracy:  0.789, Validation Accuracy:  0.779, Loss:  0.266
+Epoch   0 Batch  620/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.777, Loss:  0.254
+Epoch   0 Batch  621/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.780, Loss:  0.259
+Epoch   0 Batch  622/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.774, Loss:  0.280
+Epoch   0 Batch  623/1077 - Train Accuracy:  0.787, Validation Accuracy:  0.766, Loss:  0.270
+Epoch   0 Batch  624/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.771, Loss:  0.253
+Epoch   0 Batch  625/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.782, Loss:  0.259
+Epoch   0 Batch  626/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.790, Loss:  0.231
+Epoch   0 Batch  627/1077 - Train Accuracy:  0.799, Validation Accuracy:  0.787, Loss:  0.250
+Epoch   0 Batch  628/1077 - Train Accuracy:  0.808, Validation Accuracy:  0.783, Loss:  0.253
+Epoch   0 Batch  629/1077 - Train Accuracy:  0.789, Validation Accuracy:  0.788, Loss:  0.273
+Epoch   0 Batch  630/1077 - Train Accuracy:  0.820, Validation Accuracy:  0.779, Loss:  0.245
+Epoch   0 Batch  631/1077 - Train Accuracy:  0.798, Validation Accuracy:  0.784, Loss:  0.248
+Epoch   0 Batch  632/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.792, Loss:  0.251
+Epoch   0 Batch  633/1077 - Train Accuracy:  0.826, Validation Accuracy:  0.783, Loss:  0.269
+Epoch   0 Batch  634/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.793, Loss:  0.236
+Epoch   0 Batch  635/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.782, Loss:  0.271
+Epoch   0 Batch  636/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.792, Loss:  0.234
+Epoch   0 Batch  637/1077 - Train Accuracy:  0.786, Validation Accuracy:  0.800, Loss:  0.248
+Epoch   0 Batch  638/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.800, Loss:  0.237
+Epoch   0 Batch  639/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.795, Loss:  0.266
+Epoch   0 Batch  640/1077 - Train Accuracy:  0.792, Validation Accuracy:  0.790, Loss:  0.251
+Epoch   0 Batch  641/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.792, Loss:  0.248
+Epoch   0 Batch  642/1077 - Train Accuracy:  0.785, Validation Accuracy:  0.795, Loss:  0.246
+Epoch   0 Batch  643/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.788, Loss:  0.226
+Epoch   0 Batch  644/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.790, Loss:  0.249
+Epoch   0 Batch  645/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.795, Loss:  0.247
+Epoch   0 Batch  646/1077 - Train Accuracy:  0.803, Validation Accuracy:  0.789, Loss:  0.248
+Epoch   0 Batch  647/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.788, Loss:  0.246
+Epoch   0 Batch  648/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.794, Loss:  0.217
+Epoch   0 Batch  649/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.798, Loss:  0.256
+Epoch   0 Batch  650/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.790, Loss:  0.249
+Epoch   0 Batch  651/1077 - Train Accuracy:  0.853, Validation Accuracy:  0.794, Loss:  0.215
+Epoch   0 Batch  652/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.799, Loss:  0.257
+Epoch   0 Batch  653/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.795, Loss:  0.227
+Epoch   0 Batch  654/1077 - Train Accuracy:  0.830, Validation Accuracy:  0.802, Loss:  0.241
+Epoch   0 Batch  655/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.803, Loss:  0.260
+Epoch   0 Batch  656/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.797, Loss:  0.245
+Epoch   0 Batch  657/1077 - Train Accuracy:  0.840, Validation Accuracy:  0.792, Loss:  0.247
+Epoch   0 Batch  658/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.786, Loss:  0.215
+Epoch   0 Batch  659/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.784, Loss:  0.234
+Epoch   0 Batch  660/1077 - Train Accuracy:  0.812, Validation Accuracy:  0.781, Loss:  0.238
+Epoch   0 Batch  661/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.779, Loss:  0.215
+Epoch   0 Batch  662/1077 - Train Accuracy:  0.807, Validation Accuracy:  0.781, Loss:  0.235
+Epoch   0 Batch  663/1077 - Train Accuracy:  0.815, Validation Accuracy:  0.802, Loss:  0.221
+Epoch   0 Batch  664/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.806, Loss:  0.233
+Epoch   0 Batch  665/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.803, Loss:  0.202
+Epoch   0 Batch  666/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.798, Loss:  0.254
+Epoch   0 Batch  667/1077 - Train Accuracy:  0.796, Validation Accuracy:  0.814, Loss:  0.258
+Epoch   0 Batch  668/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.816, Loss:  0.230
+Epoch   0 Batch  669/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.819, Loss:  0.220
+Epoch   0 Batch  670/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.822, Loss:  0.223
+Epoch   0 Batch  671/1077 - Train Accuracy:  0.824, Validation Accuracy:  0.832, Loss:  0.251
+Epoch   0 Batch  672/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.836, Loss:  0.216
+Epoch   0 Batch  673/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.838, Loss:  0.212
+Epoch   0 Batch  674/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.836, Loss:  0.217
+Epoch   0 Batch  675/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.828, Loss:  0.240
+Epoch   0 Batch  676/1077 - Train Accuracy:  0.825, Validation Accuracy:  0.819, Loss:  0.225
+Epoch   0 Batch  677/1077 - Train Accuracy:  0.766, Validation Accuracy:  0.813, Loss:  0.256
+Epoch   0 Batch  678/1077 - Train Accuracy:  0.804, Validation Accuracy:  0.821, Loss:  0.198
+Epoch   0 Batch  679/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.823, Loss:  0.236
+Epoch   0 Batch  680/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.824, Loss:  0.210
+Epoch   0 Batch  681/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.820, Loss:  0.239
+Epoch   0 Batch  682/1077 - Train Accuracy:  0.800, Validation Accuracy:  0.823, Loss:  0.212
+Epoch   0 Batch  683/1077 - Train Accuracy:  0.822, Validation Accuracy:  0.827, Loss:  0.212
+Epoch   0 Batch  684/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.818, Loss:  0.217
+Epoch   0 Batch  685/1077 - Train Accuracy:  0.793, Validation Accuracy:  0.821, Loss:  0.234
+Epoch   0 Batch  686/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.810, Loss:  0.202
+Epoch   0 Batch  687/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.814, Loss:  0.237
+Epoch   0 Batch  688/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.814, Loss:  0.207
+Epoch   0 Batch  689/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.815, Loss:  0.200
+Epoch   0 Batch  690/1077 - Train Accuracy:  0.848, Validation Accuracy:  0.820, Loss:  0.209
+Epoch   0 Batch  691/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.824, Loss:  0.245
+Epoch   0 Batch  692/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.819, Loss:  0.204
+Epoch   0 Batch  693/1077 - Train Accuracy:  0.760, Validation Accuracy:  0.818, Loss:  0.267
+Epoch   0 Batch  694/1077 - Train Accuracy:  0.838, Validation Accuracy:  0.822, Loss:  0.240
+Epoch   0 Batch  695/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.828, Loss:  0.195
+Epoch   0 Batch  696/1077 - Train Accuracy:  0.817, Validation Accuracy:  0.816, Loss:  0.233
+Epoch   0 Batch  697/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.811, Loss:  0.214
+Epoch   0 Batch  698/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.815, Loss:  0.208
+Epoch   0 Batch  699/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.821, Loss:  0.212
+Epoch   0 Batch  700/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.817, Loss:  0.208
+Epoch   0 Batch  701/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.808, Loss:  0.239
+Epoch   0 Batch  702/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.812, Loss:  0.227
+Epoch   0 Batch  703/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.811, Loss:  0.219
+Epoch   0 Batch  704/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.820, Loss:  0.235
+Epoch   0 Batch  705/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.819, Loss:  0.250
+Epoch   0 Batch  706/1077 - Train Accuracy:  0.806, Validation Accuracy:  0.828, Loss:  0.244
+Epoch   0 Batch  707/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.832, Loss:  0.211
+Epoch   0 Batch  708/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.837, Loss:  0.216
+Epoch   0 Batch  709/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.838, Loss:  0.238
+Epoch   0 Batch  710/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.836, Loss:  0.197
+Epoch   0 Batch  711/1077 - Train Accuracy:  0.820, Validation Accuracy:  0.838, Loss:  0.229
+Epoch   0 Batch  712/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.845, Loss:  0.196
+Epoch   0 Batch  713/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.825, Loss:  0.172
+Epoch   0 Batch  714/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.815, Loss:  0.209
+Epoch   0 Batch  715/1077 - Train Accuracy:  0.830, Validation Accuracy:  0.813, Loss:  0.225
+Epoch   0 Batch  716/1077 - Train Accuracy:  0.849, Validation Accuracy:  0.831, Loss:  0.198
+Epoch   0 Batch  717/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.831, Loss:  0.206
+Epoch   0 Batch  718/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.831, Loss:  0.203
+Epoch   0 Batch  719/1077 - Train Accuracy:  0.818, Validation Accuracy:  0.837, Loss:  0.220
+Epoch   0 Batch  720/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.834, Loss:  0.215
+Epoch   0 Batch  721/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.849, Loss:  0.221
+Epoch   0 Batch  722/1077 - Train Accuracy:  0.827, Validation Accuracy:  0.843, Loss:  0.193
+Epoch   0 Batch  723/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.842, Loss:  0.207
+Epoch   0 Batch  724/1077 - Train Accuracy:  0.838, Validation Accuracy:  0.829, Loss:  0.226
+Epoch   0 Batch  725/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.822, Loss:  0.172
+Epoch   0 Batch  726/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.822, Loss:  0.201
+Epoch   0 Batch  727/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.827, Loss:  0.190
+Epoch   0 Batch  728/1077 - Train Accuracy:  0.828, Validation Accuracy:  0.841, Loss:  0.210
+Epoch   0 Batch  729/1077 - Train Accuracy:  0.814, Validation Accuracy:  0.835, Loss:  0.225
+Epoch   0 Batch  730/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.837, Loss:  0.221
+Epoch   0 Batch  731/1077 - Train Accuracy:  0.826, Validation Accuracy:  0.841, Loss:  0.190
+Epoch   0 Batch  732/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.833, Loss:  0.216
+Epoch   0 Batch  733/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.826, Loss:  0.208
+Epoch   0 Batch  734/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.844, Loss:  0.210
+Epoch   0 Batch  735/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.849, Loss:  0.202
+Epoch   0 Batch  736/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.848, Loss:  0.178
+Epoch   0 Batch  737/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.833, Loss:  0.219
+Epoch   0 Batch  738/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.838, Loss:  0.165
+Epoch   0 Batch  739/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.841, Loss:  0.183
+Epoch   0 Batch  740/1077 - Train Accuracy:  0.837, Validation Accuracy:  0.852, Loss:  0.185
+Epoch   0 Batch  741/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.850, Loss:  0.202
+Epoch   0 Batch  742/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.839, Loss:  0.186
+Epoch   0 Batch  743/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.834, Loss:  0.201
+Epoch   0 Batch  744/1077 - Train Accuracy:  0.836, Validation Accuracy:  0.840, Loss:  0.179
+Epoch   0 Batch  745/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.843, Loss:  0.188
+Epoch   0 Batch  746/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.839, Loss:  0.174
+Epoch   0 Batch  747/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.837, Loss:  0.165
+Epoch   0 Batch  748/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.842, Loss:  0.174
+Epoch   0 Batch  749/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.837, Loss:  0.191
+Epoch   0 Batch  750/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.837, Loss:  0.170
+Epoch   0 Batch  751/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.835, Loss:  0.177
+Epoch   0 Batch  752/1077 - Train Accuracy:  0.853, Validation Accuracy:  0.828, Loss:  0.164
+Epoch   0 Batch  753/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.829, Loss:  0.170
+Epoch   0 Batch  754/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.836, Loss:  0.201
+Epoch   0 Batch  755/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.850, Loss:  0.174
+Epoch   0 Batch  756/1077 - Train Accuracy:  0.839, Validation Accuracy:  0.834, Loss:  0.179
+Epoch   0 Batch  757/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.846, Loss:  0.181
+Epoch   0 Batch  758/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.846, Loss:  0.174
+Epoch   0 Batch  759/1077 - Train Accuracy:  0.842, Validation Accuracy:  0.851, Loss:  0.170
+Epoch   0 Batch  760/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.858, Loss:  0.191
+Epoch   0 Batch  761/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.863, Loss:  0.172
+Epoch   0 Batch  762/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.856, Loss:  0.167
+Epoch   0 Batch  763/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.855, Loss:  0.159
+Epoch   0 Batch  764/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.850, Loss:  0.185
+Epoch   0 Batch  765/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.840, Loss:  0.171
+Epoch   0 Batch  766/1077 - Train Accuracy:  0.816, Validation Accuracy:  0.838, Loss:  0.191
+Epoch   0 Batch  767/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.846, Loss:  0.171
+Epoch   0 Batch  768/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.867, Loss:  0.175
+Epoch   0 Batch  769/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.876, Loss:  0.186
+Epoch   0 Batch  770/1077 - Train Accuracy:  0.823, Validation Accuracy:  0.877, Loss:  0.168
+Epoch   0 Batch  771/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.871, Loss:  0.194
+Epoch   0 Batch  772/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.868, Loss:  0.153
+Epoch   0 Batch  773/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.866, Loss:  0.179
+Epoch   0 Batch  774/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.862, Loss:  0.186
+Epoch   0 Batch  775/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.865, Loss:  0.190
+Epoch   0 Batch  776/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.858, Loss:  0.160
+Epoch   0 Batch  777/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.859, Loss:  0.174
+Epoch   0 Batch  778/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.862, Loss:  0.163
+Epoch   0 Batch  779/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.856, Loss:  0.186
+Epoch   0 Batch  780/1077 - Train Accuracy:  0.809, Validation Accuracy:  0.853, Loss:  0.219
+Epoch   0 Batch  781/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.852, Loss:  0.153
+Epoch   0 Batch  782/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.854, Loss:  0.173
+Epoch   0 Batch  783/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.858, Loss:  0.182
+Epoch   0 Batch  784/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.849, Loss:  0.157
+Epoch   0 Batch  785/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.855, Loss:  0.166
+Epoch   0 Batch  786/1077 - Train Accuracy:  0.813, Validation Accuracy:  0.854, Loss:  0.180
+Epoch   0 Batch  787/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.858, Loss:  0.165
+Epoch   0 Batch  788/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.854, Loss:  0.148
+Epoch   0 Batch  789/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.836, Loss:  0.183
+Epoch   0 Batch  790/1077 - Train Accuracy:  0.832, Validation Accuracy:  0.847, Loss:  0.175
+Epoch   0 Batch  791/1077 - Train Accuracy:  0.845, Validation Accuracy:  0.849, Loss:  0.182
+Epoch   0 Batch  792/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.869, Loss:  0.185
+Epoch   0 Batch  793/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.871, Loss:  0.172
+Epoch   0 Batch  794/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.881, Loss:  0.152
+Epoch   0 Batch  795/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.876, Loss:  0.178
+Epoch   0 Batch  796/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.874, Loss:  0.168
+Epoch   0 Batch  797/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.864, Loss:  0.159
+Epoch   0 Batch  798/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.857, Loss:  0.175
+Epoch   0 Batch  799/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.853, Loss:  0.194
+Epoch   0 Batch  800/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.863, Loss:  0.172
+Epoch   0 Batch  801/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.851, Loss:  0.168
+Epoch   0 Batch  802/1077 - Train Accuracy:  0.834, Validation Accuracy:  0.855, Loss:  0.177
+Epoch   0 Batch  803/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.861, Loss:  0.174
+Epoch   0 Batch  804/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.857, Loss:  0.144
+Epoch   0 Batch  805/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.852, Loss:  0.166
+Epoch   0 Batch  806/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.851, Loss:  0.150
+Epoch   0 Batch  807/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.850, Loss:  0.154
+Epoch   0 Batch  808/1077 - Train Accuracy:  0.851, Validation Accuracy:  0.848, Loss:  0.184
+Epoch   0 Batch  809/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.839, Loss:  0.191
+Epoch   0 Batch  810/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.847, Loss:  0.146
+Epoch   0 Batch  811/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.845, Loss:  0.152
+Epoch   0 Batch  812/1077 - Train Accuracy:  0.843, Validation Accuracy:  0.846, Loss:  0.155
+Epoch   0 Batch  813/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.863, Loss:  0.159
+Epoch   0 Batch  814/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.868, Loss:  0.175
+Epoch   0 Batch  815/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.859, Loss:  0.151
+Epoch   0 Batch  816/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.863, Loss:  0.192
+Epoch   0 Batch  817/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.867, Loss:  0.173
+Epoch   0 Batch  818/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.866, Loss:  0.161
+Epoch   0 Batch  819/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.867, Loss:  0.143
+Epoch   0 Batch  820/1077 - Train Accuracy:  0.841, Validation Accuracy:  0.860, Loss:  0.144
+Epoch   0 Batch  821/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.850, Loss:  0.140
+Epoch   0 Batch  822/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.843, Loss:  0.165
+Epoch   0 Batch  823/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.847, Loss:  0.166
+Epoch   0 Batch  824/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.858, Loss:  0.167
+Epoch   0 Batch  825/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.856, Loss:  0.150
+Epoch   0 Batch  826/1077 - Train Accuracy:  0.821, Validation Accuracy:  0.858, Loss:  0.149
+Epoch   0 Batch  827/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.862, Loss:  0.154
+Epoch   0 Batch  828/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.871, Loss:  0.164
+Epoch   0 Batch  829/1077 - Train Accuracy:  0.829, Validation Accuracy:  0.865, Loss:  0.182
+Epoch   0 Batch  830/1077 - Train Accuracy:  0.864, Validation Accuracy:  0.870, Loss:  0.157
+Epoch   0 Batch  831/1077 - Train Accuracy:  0.833, Validation Accuracy:  0.875, Loss:  0.159
+Epoch   0 Batch  832/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.884, Loss:  0.152
+Epoch   0 Batch  833/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.884, Loss:  0.163
+Epoch   0 Batch  834/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.871, Loss:  0.160
+Epoch   0 Batch  835/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.876, Loss:  0.160
+Epoch   0 Batch  836/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.874, Loss:  0.172
+Epoch   0 Batch  837/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.876, Loss:  0.165
+Epoch   0 Batch  838/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.878, Loss:  0.140
+Epoch   0 Batch  839/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.880, Loss:  0.126
+Epoch   0 Batch  840/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.882, Loss:  0.132
+Epoch   0 Batch  841/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.880, Loss:  0.157
+Epoch   0 Batch  842/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.873, Loss:  0.139
+Epoch   0 Batch  843/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.873, Loss:  0.128
+Epoch   0 Batch  844/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.882, Loss:  0.132
+Epoch   0 Batch  845/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.886, Loss:  0.141
+Epoch   0 Batch  846/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.874, Loss:  0.162
+Epoch   0 Batch  847/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.869, Loss:  0.166
+Epoch   0 Batch  848/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.868, Loss:  0.138
+Epoch   0 Batch  849/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.862, Loss:  0.141
+Epoch   0 Batch  850/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.866, Loss:  0.169
+Epoch   0 Batch  851/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.849, Loss:  0.154
+Epoch   0 Batch  852/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.851, Loss:  0.171
+Epoch   0 Batch  853/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.852, Loss:  0.139
+Epoch   0 Batch  854/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.861, Loss:  0.158
+Epoch   0 Batch  855/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.862, Loss:  0.139
+Epoch   0 Batch  856/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.856, Loss:  0.145
+Epoch   0 Batch  857/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.858, Loss:  0.139
+Epoch   0 Batch  858/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.861, Loss:  0.130
+Epoch   0 Batch  859/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.866, Loss:  0.166
+Epoch   0 Batch  860/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.861, Loss:  0.156
+Epoch   0 Batch  861/1077 - Train Accuracy:  0.849, Validation Accuracy:  0.856, Loss:  0.137
+Epoch   0 Batch  862/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.864, Loss:  0.139
+Epoch   0 Batch  863/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.864, Loss:  0.136
+Epoch   0 Batch  864/1077 - Train Accuracy:  0.862, Validation Accuracy:  0.863, Loss:  0.136
+Epoch   0 Batch  865/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.870, Loss:  0.134
+Epoch   0 Batch  866/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.864, Loss:  0.148
+Epoch   0 Batch  867/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.863, Loss:  0.192
+Epoch   0 Batch  868/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.858, Loss:  0.144
+Epoch   0 Batch  869/1077 - Train Accuracy:  0.864, Validation Accuracy:  0.868, Loss:  0.145
+Epoch   0 Batch  870/1077 - Train Accuracy:  0.831, Validation Accuracy:  0.857, Loss:  0.148
+Epoch   0 Batch  871/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.856, Loss:  0.124
+Epoch   0 Batch  872/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.859, Loss:  0.150
+Epoch   0 Batch  873/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.864, Loss:  0.141
+Epoch   0 Batch  874/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.867, Loss:  0.169
+Epoch   0 Batch  875/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.862, Loss:  0.155
+Epoch   0 Batch  876/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.864, Loss:  0.144
+Epoch   0 Batch  877/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.864, Loss:  0.133
+Epoch   0 Batch  878/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.861, Loss:  0.143
+Epoch   0 Batch  879/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.865, Loss:  0.127
+Epoch   0 Batch  880/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.868, Loss:  0.152
+Epoch   0 Batch  881/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.864, Loss:  0.146
+Epoch   0 Batch  882/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.868, Loss:  0.163
+Epoch   0 Batch  883/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.859, Loss:  0.183
+Epoch   0 Batch  884/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.865, Loss:  0.131
+Epoch   0 Batch  885/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.869, Loss:  0.118
+Epoch   0 Batch  886/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.861, Loss:  0.139
+Epoch   0 Batch  887/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.861, Loss:  0.168
+Epoch   0 Batch  888/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.865, Loss:  0.134
+Epoch   0 Batch  889/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.858, Loss:  0.139
+Epoch   0 Batch  890/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.854, Loss:  0.137
+Epoch   0 Batch  891/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.854, Loss:  0.139
+Epoch   0 Batch  892/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.871, Loss:  0.128
+Epoch   0 Batch  893/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.869, Loss:  0.146
+Epoch   0 Batch  894/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.876, Loss:  0.131
+Epoch   0 Batch  895/1077 - Train Accuracy:  0.860, Validation Accuracy:  0.876, Loss:  0.128
+Epoch   0 Batch  896/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.863, Loss:  0.143
+Epoch   0 Batch  897/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.860, Loss:  0.115
+Epoch   0 Batch  898/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.862, Loss:  0.113
+Epoch   0 Batch  899/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.863, Loss:  0.147
+Epoch   0 Batch  900/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.864, Loss:  0.156
+Epoch   0 Batch  901/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.888, Loss:  0.166
+Epoch   0 Batch  902/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.885, Loss:  0.145
+Epoch   0 Batch  903/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.882, Loss:  0.130
+Epoch   0 Batch  904/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.887, Loss:  0.134
+Epoch   0 Batch  905/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.880, Loss:  0.113
+Epoch   0 Batch  906/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.881, Loss:  0.130
+Epoch   0 Batch  907/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.859, Loss:  0.132
+Epoch   0 Batch  908/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.857, Loss:  0.144
+Epoch   0 Batch  909/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.855, Loss:  0.142
+Epoch   0 Batch  910/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.851, Loss:  0.132
+Epoch   0 Batch  911/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.850, Loss:  0.129
+Epoch   0 Batch  912/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.853, Loss:  0.133
+Epoch   0 Batch  913/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.863, Loss:  0.162
+Epoch   0 Batch  914/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.862, Loss:  0.144
+Epoch   0 Batch  915/1077 - Train Accuracy:  0.854, Validation Accuracy:  0.858, Loss:  0.131
+Epoch   0 Batch  916/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.867, Loss:  0.150
+Epoch   0 Batch  917/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.867, Loss:  0.127
+Epoch   0 Batch  918/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.866, Loss:  0.119
+Epoch   0 Batch  919/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.865, Loss:  0.119
+Epoch   0 Batch  920/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.869, Loss:  0.133
+Epoch   0 Batch  921/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.870, Loss:  0.139
+Epoch   0 Batch  922/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.871, Loss:  0.145
+Epoch   0 Batch  923/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.873, Loss:  0.117
+Epoch   0 Batch  924/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.865, Loss:  0.148
+Epoch   0 Batch  925/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.863, Loss:  0.123
+Epoch   0 Batch  926/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.865, Loss:  0.125
+Epoch   0 Batch  927/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.860, Loss:  0.142
+Epoch   0 Batch  928/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.851, Loss:  0.130
+Epoch   0 Batch  929/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.849, Loss:  0.129
+Epoch   0 Batch  930/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.855, Loss:  0.120
+Epoch   0 Batch  931/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.862, Loss:  0.108
+Epoch   0 Batch  932/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.861, Loss:  0.143
+Epoch   0 Batch  933/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.862, Loss:  0.124
+Epoch   0 Batch  934/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.859, Loss:  0.109
+Epoch   0 Batch  935/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.873, Loss:  0.118
+Epoch   0 Batch  936/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.866, Loss:  0.135
+Epoch   0 Batch  937/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.862, Loss:  0.147
+Epoch   0 Batch  938/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.861, Loss:  0.137
+Epoch   0 Batch  939/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.865, Loss:  0.133
+Epoch   0 Batch  940/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.866, Loss:  0.116
+Epoch   0 Batch  941/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.866, Loss:  0.124
+Epoch   0 Batch  942/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.860, Loss:  0.125
+Epoch   0 Batch  943/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.862, Loss:  0.135
+Epoch   0 Batch  944/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.857, Loss:  0.116
+Epoch   0 Batch  945/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.862, Loss:  0.114
+Epoch   0 Batch  946/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.881, Loss:  0.121
+Epoch   0 Batch  947/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.874, Loss:  0.131
+Epoch   0 Batch  948/1077 - Train Accuracy:  0.865, Validation Accuracy:  0.868, Loss:  0.118
+Epoch   0 Batch  949/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.864, Loss:  0.100
+Epoch   0 Batch  950/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.873, Loss:  0.114
+Epoch   0 Batch  951/1077 - Train Accuracy:  0.855, Validation Accuracy:  0.876, Loss:  0.132
+Epoch   0 Batch  952/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.891, Loss:  0.107
+Epoch   0 Batch  953/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.891, Loss:  0.111
+Epoch   0 Batch  954/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.894, Loss:  0.127
+Epoch   0 Batch  955/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.886, Loss:  0.133
+Epoch   0 Batch  956/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.882, Loss:  0.134
+Epoch   0 Batch  957/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.870, Loss:  0.102
+Epoch   0 Batch  958/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.870, Loss:  0.123
+Epoch   0 Batch  959/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.868, Loss:  0.125
+Epoch   0 Batch  960/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.871, Loss:  0.111
+Epoch   0 Batch  961/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.876, Loss:  0.117
+Epoch   0 Batch  962/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.880, Loss:  0.123
+Epoch   0 Batch  963/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.872, Loss:  0.144
+Epoch   0 Batch  964/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.872, Loss:  0.101
+Epoch   0 Batch  965/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.878, Loss:  0.124
+Epoch   0 Batch  966/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.880, Loss:  0.109
+Epoch   0 Batch  967/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.881, Loss:  0.125
+Epoch   0 Batch  968/1077 - Train Accuracy:  0.846, Validation Accuracy:  0.881, Loss:  0.140
+Epoch   0 Batch  969/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.885, Loss:  0.133
+Epoch   0 Batch  970/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.886, Loss:  0.128
+Epoch   0 Batch  971/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.886, Loss:  0.122
+Epoch   0 Batch  972/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.883, Loss:  0.114
+Epoch   0 Batch  973/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.877, Loss:  0.098
+Epoch   0 Batch  974/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.873, Loss:  0.105
+Epoch   0 Batch  975/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.876, Loss:  0.112
+Epoch   0 Batch  976/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.877, Loss:  0.105
+Epoch   0 Batch  977/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.884, Loss:  0.105
+Epoch   0 Batch  978/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.886, Loss:  0.122
+Epoch   0 Batch  979/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.883, Loss:  0.127
+Epoch   0 Batch  980/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.882, Loss:  0.126
+Epoch   0 Batch  981/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.888, Loss:  0.112
+Epoch   0 Batch  982/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.888, Loss:  0.122
+Epoch   0 Batch  983/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.887, Loss:  0.121
+Epoch   0 Batch  984/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.887, Loss:  0.122
+Epoch   0 Batch  985/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.887, Loss:  0.106
+Epoch   0 Batch  986/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.886, Loss:  0.116
+Epoch   0 Batch  987/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.890, Loss:  0.101
+Epoch   0 Batch  988/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.885, Loss:  0.126
+Epoch   0 Batch  989/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.879, Loss:  0.121
+Epoch   0 Batch  990/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.878, Loss:  0.121
+Epoch   0 Batch  991/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.877, Loss:  0.119
+Epoch   0 Batch  992/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.882, Loss:  0.118
+Epoch   0 Batch  993/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.882, Loss:  0.087
+Epoch   0 Batch  994/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.114
+Epoch   0 Batch  995/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.892, Loss:  0.114
+Epoch   0 Batch  996/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.890, Loss:  0.108
+Epoch   0 Batch  997/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.894, Loss:  0.111
+Epoch   0 Batch  998/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.889, Loss:  0.107
+Epoch   0 Batch  999/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.903, Loss:  0.115
+Epoch   0 Batch 1000/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.897, Loss:  0.107
+Epoch   0 Batch 1001/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.900, Loss:  0.092
+Epoch   0 Batch 1002/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.898, Loss:  0.096
+Epoch   0 Batch 1003/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.895, Loss:  0.116
+Epoch   0 Batch 1004/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.898, Loss:  0.130
+Epoch   0 Batch 1005/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.895, Loss:  0.095
+Epoch   0 Batch 1006/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.896, Loss:  0.091
+Epoch   0 Batch 1007/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.089
+Epoch   0 Batch 1008/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.875, Loss:  0.132
+Epoch   0 Batch 1009/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.888, Loss:  0.089
+Epoch   0 Batch 1010/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.105
+Epoch   0 Batch 1011/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.882, Loss:  0.104
+Epoch   0 Batch 1012/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.887, Loss:  0.085
+Epoch   0 Batch 1013/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.890, Loss:  0.094
+Epoch   0 Batch 1014/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.898, Loss:  0.114
+Epoch   0 Batch 1015/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.895, Loss:  0.127
+Epoch   0 Batch 1016/1077 - Train Accuracy:  0.866, Validation Accuracy:  0.904, Loss:  0.128
+Epoch   0 Batch 1017/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.906, Loss:  0.112
+Epoch   0 Batch 1018/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.895, Loss:  0.101
+Epoch   0 Batch 1019/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.907, Loss:  0.119
+Epoch   0 Batch 1020/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.901, Loss:  0.102
+Epoch   0 Batch 1021/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.898, Loss:  0.099
+Epoch   0 Batch 1022/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.898, Loss:  0.092
+Epoch   0 Batch 1023/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.894, Loss:  0.111
+Epoch   0 Batch 1024/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.899, Loss:  0.130
+Epoch   0 Batch 1025/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.901, Loss:  0.113
+Epoch   0 Batch 1026/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.892, Loss:  0.113
+Epoch   0 Batch 1027/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.888, Loss:  0.101
+Epoch   0 Batch 1028/1077 - Train Accuracy:  0.858, Validation Accuracy:  0.888, Loss:  0.102
+Epoch   0 Batch 1029/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.890, Loss:  0.089
+Epoch   0 Batch 1030/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.887, Loss:  0.121
+Epoch   0 Batch 1031/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.883, Loss:  0.122
+Epoch   0 Batch 1032/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.882, Loss:  0.129
+Epoch   0 Batch 1033/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.885, Loss:  0.108
+Epoch   0 Batch 1034/1077 - Train Accuracy:  0.867, Validation Accuracy:  0.886, Loss:  0.111
+Epoch   0 Batch 1035/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.881, Loss:  0.081
+Epoch   0 Batch 1036/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.874, Loss:  0.117
+Epoch   0 Batch 1037/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.877, Loss:  0.121
+Epoch   0 Batch 1038/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.878, Loss:  0.126
+Epoch   0 Batch 1039/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.883, Loss:  0.112
+Epoch   0 Batch 1040/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.886, Loss:  0.121
+Epoch   0 Batch 1041/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.888, Loss:  0.120
+Epoch   0 Batch 1042/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.888, Loss:  0.102
+Epoch   0 Batch 1043/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.892, Loss:  0.122
+Epoch   0 Batch 1044/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.888, Loss:  0.119
+Epoch   0 Batch 1045/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.874, Loss:  0.095
+Epoch   0 Batch 1046/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.881, Loss:  0.085
+Epoch   0 Batch 1047/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.891, Loss:  0.100
+Epoch   0 Batch 1048/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.891, Loss:  0.110
+Epoch   0 Batch 1049/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.895, Loss:  0.097
+Epoch   0 Batch 1050/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.898, Loss:  0.101
+Epoch   0 Batch 1051/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.883, Loss:  0.108
+Epoch   0 Batch 1052/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.894, Loss:  0.103
+Epoch   0 Batch 1053/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.901, Loss:  0.114
+Epoch   0 Batch 1054/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.891, Loss:  0.100
+Epoch   0 Batch 1055/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.890, Loss:  0.111
+Epoch   0 Batch 1056/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.892, Loss:  0.099
+Epoch   0 Batch 1057/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.886, Loss:  0.119
+Epoch   0 Batch 1058/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.883, Loss:  0.116
+Epoch   0 Batch 1059/1077 - Train Accuracy:  0.847, Validation Accuracy:  0.876, Loss:  0.138
+Epoch   0 Batch 1060/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.876, Loss:  0.100
+Epoch   0 Batch 1061/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.880, Loss:  0.123
+Epoch   0 Batch 1062/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.879, Loss:  0.107
+Epoch   0 Batch 1063/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.885, Loss:  0.107
+Epoch   0 Batch 1064/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.107
+Epoch   0 Batch 1065/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.889, Loss:  0.096
+Epoch   0 Batch 1066/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.887, Loss:  0.089
+Epoch   0 Batch 1067/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.886, Loss:  0.116
+Epoch   0 Batch 1068/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.876, Loss:  0.091
+Epoch   0 Batch 1069/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.883, Loss:  0.079
+Epoch   0 Batch 1070/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.871, Loss:  0.100
+Epoch   0 Batch 1071/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.875, Loss:  0.105
+Epoch   0 Batch 1072/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.880, Loss:  0.106
+Epoch   0 Batch 1073/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.123
+Epoch   0 Batch 1074/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.875, Loss:  0.117
+Epoch   0 Batch 1075/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.876, Loss:  0.108
+Epoch   1 Batch    0/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.878, Loss:  0.086
+Epoch   1 Batch    1/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.875, Loss:  0.094
+Epoch   1 Batch    2/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.875, Loss:  0.111
+Epoch   1 Batch    3/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.883, Loss:  0.106
+Epoch   1 Batch    4/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.880, Loss:  0.093
+Epoch   1 Batch    5/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.889, Loss:  0.136
+Epoch   1 Batch    6/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.886, Loss:  0.103
+Epoch   1 Batch    7/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.883, Loss:  0.088
+Epoch   1 Batch    8/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.881, Loss:  0.104
+Epoch   1 Batch    9/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.885, Loss:  0.093
+Epoch   1 Batch   10/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.892, Loss:  0.098
+Epoch   1 Batch   11/1077 - Train Accuracy:  0.873, Validation Accuracy:  0.893, Loss:  0.106
+Epoch   1 Batch   12/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.902, Loss:  0.103
+Epoch   1 Batch   13/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.898, Loss:  0.121
+Epoch   1 Batch   14/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.892, Loss:  0.080
+Epoch   1 Batch   15/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.896, Loss:  0.093
+Epoch   1 Batch   16/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.899, Loss:  0.116
+Epoch   1 Batch   17/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.894, Loss:  0.091
+Epoch   1 Batch   18/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.098
+Epoch   1 Batch   19/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.889, Loss:  0.097
+Epoch   1 Batch   20/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.892, Loss:  0.086
+Epoch   1 Batch   21/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.888, Loss:  0.110
+Epoch   1 Batch   22/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.892, Loss:  0.101
+Epoch   1 Batch   23/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.896, Loss:  0.104
+Epoch   1 Batch   24/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.904, Loss:  0.096
+Epoch   1 Batch   25/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.078
+Epoch   1 Batch   26/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.902, Loss:  0.102
+Epoch   1 Batch   27/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.897, Loss:  0.088
+Epoch   1 Batch   28/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.887, Loss:  0.095
+Epoch   1 Batch   29/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.883, Loss:  0.099
+Epoch   1 Batch   30/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.892, Loss:  0.088
+Epoch   1 Batch   31/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.890, Loss:  0.088
+Epoch   1 Batch   32/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.892, Loss:  0.093
+Epoch   1 Batch   33/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.894, Loss:  0.092
+Epoch   1 Batch   34/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.892, Loss:  0.084
+Epoch   1 Batch   35/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.891, Loss:  0.090
+Epoch   1 Batch   36/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.892, Loss:  0.091
+Epoch   1 Batch   37/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.896, Loss:  0.102
+Epoch   1 Batch   38/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.898, Loss:  0.127
+Epoch   1 Batch   39/1077 - Train Accuracy:  0.863, Validation Accuracy:  0.885, Loss:  0.123
+Epoch   1 Batch   40/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.892, Loss:  0.082
+Epoch   1 Batch   41/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.892, Loss:  0.092
+Epoch   1 Batch   42/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.890, Loss:  0.106
+Epoch   1 Batch   43/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.889, Loss:  0.070
+Epoch   1 Batch   44/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.888, Loss:  0.093
+Epoch   1 Batch   45/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.891, Loss:  0.099
+Epoch   1 Batch   46/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.887, Loss:  0.107
+Epoch   1 Batch   47/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.888, Loss:  0.096
+Epoch   1 Batch   48/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.899, Loss:  0.125
+Epoch   1 Batch   49/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.908, Loss:  0.103
+Epoch   1 Batch   50/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.906, Loss:  0.090
+Epoch   1 Batch   51/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.903, Loss:  0.096
+Epoch   1 Batch   52/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.907, Loss:  0.111
+Epoch   1 Batch   53/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.897, Loss:  0.091
+Epoch   1 Batch   54/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.894, Loss:  0.130
+Epoch   1 Batch   55/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.093
+Epoch   1 Batch   56/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.889, Loss:  0.080
+Epoch   1 Batch   57/1077 - Train Accuracy:  0.850, Validation Accuracy:  0.894, Loss:  0.102
+Epoch   1 Batch   58/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.891, Loss:  0.087
+Epoch   1 Batch   59/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.889, Loss:  0.091
+Epoch   1 Batch   60/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.895, Loss:  0.079
+Epoch   1 Batch   61/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.899, Loss:  0.100
+Epoch   1 Batch   62/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.904, Loss:  0.104
+Epoch   1 Batch   63/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.903, Loss:  0.076
+Epoch   1 Batch   64/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.089
+Epoch   1 Batch   65/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.907, Loss:  0.088
+Epoch   1 Batch   66/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   1 Batch   67/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.905, Loss:  0.086
+Epoch   1 Batch   68/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.906, Loss:  0.092
+Epoch   1 Batch   69/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.912, Loss:  0.109
+Epoch   1 Batch   70/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.094
+Epoch   1 Batch   71/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.064
+Epoch   1 Batch   72/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.901, Loss:  0.097
+Epoch   1 Batch   73/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.901, Loss:  0.093
+Epoch   1 Batch   74/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.898, Loss:  0.082
+Epoch   1 Batch   75/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.902, Loss:  0.122
+Epoch   1 Batch   76/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.892, Loss:  0.076
+Epoch   1 Batch   77/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.892, Loss:  0.085
+Epoch   1 Batch   78/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.891, Loss:  0.090
+Epoch   1 Batch   79/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.890, Loss:  0.080
+Epoch   1 Batch   80/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.880, Loss:  0.089
+Epoch   1 Batch   81/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.876, Loss:  0.070
+Epoch   1 Batch   82/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.881, Loss:  0.089
+Epoch   1 Batch   83/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.878, Loss:  0.097
+Epoch   1 Batch   84/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.877, Loss:  0.095
+Epoch   1 Batch   85/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.884, Loss:  0.092
+Epoch   1 Batch   86/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.892, Loss:  0.093
+Epoch   1 Batch   87/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.893, Loss:  0.118
+Epoch   1 Batch   88/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.098
+Epoch   1 Batch   89/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.891, Loss:  0.098
+Epoch   1 Batch   90/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.902, Loss:  0.101
+Epoch   1 Batch   91/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.077
+Epoch   1 Batch   92/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.904, Loss:  0.105
+Epoch   1 Batch   93/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.891, Loss:  0.084
+Epoch   1 Batch   94/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.075
+Epoch   1 Batch   95/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.103
+Epoch   1 Batch   96/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.105
+Epoch   1 Batch   97/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.893, Loss:  0.099
+Epoch   1 Batch   98/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.890, Loss:  0.100
+Epoch   1 Batch   99/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.896, Loss:  0.098
+Epoch   1 Batch  100/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.899, Loss:  0.093
+Epoch   1 Batch  101/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.885, Loss:  0.077
+Epoch   1 Batch  102/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.883, Loss:  0.102
+Epoch   1 Batch  103/1077 - Train Accuracy:  0.856, Validation Accuracy:  0.874, Loss:  0.112
+Epoch   1 Batch  104/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.873, Loss:  0.102
+Epoch   1 Batch  105/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.887, Loss:  0.087
+Epoch   1 Batch  106/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.896, Loss:  0.114
+Epoch   1 Batch  107/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.896, Loss:  0.103
+Epoch   1 Batch  108/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.099
+Epoch   1 Batch  109/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.892, Loss:  0.087
+Epoch   1 Batch  110/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.899, Loss:  0.082
+Epoch   1 Batch  111/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.896, Loss:  0.082
+Epoch   1 Batch  112/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.900, Loss:  0.098
+Epoch   1 Batch  113/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.895, Loss:  0.087
+Epoch   1 Batch  114/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.898, Loss:  0.075
+Epoch   1 Batch  115/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.896, Loss:  0.110
+Epoch   1 Batch  116/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.896, Loss:  0.096
+Epoch   1 Batch  117/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.903, Loss:  0.090
+Epoch   1 Batch  118/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.905, Loss:  0.087
+Epoch   1 Batch  119/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.911, Loss:  0.094
+Epoch   1 Batch  120/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.915, Loss:  0.097
+Epoch   1 Batch  121/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.914, Loss:  0.092
+Epoch   1 Batch  122/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.914, Loss:  0.081
+Epoch   1 Batch  123/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.903, Loss:  0.074
+Epoch   1 Batch  124/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.892, Loss:  0.104
+Epoch   1 Batch  125/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.897, Loss:  0.091
+Epoch   1 Batch  126/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.898, Loss:  0.073
+Epoch   1 Batch  127/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.897, Loss:  0.091
+Epoch   1 Batch  128/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.892, Loss:  0.090
+Epoch   1 Batch  129/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.902, Loss:  0.111
+Epoch   1 Batch  130/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.895, Loss:  0.081
+Epoch   1 Batch  131/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.889, Loss:  0.088
+Epoch   1 Batch  132/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.902, Loss:  0.093
+Epoch   1 Batch  133/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.898, Loss:  0.078
+Epoch   1 Batch  134/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.087
+Epoch   1 Batch  135/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.896, Loss:  0.094
+Epoch   1 Batch  136/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.898, Loss:  0.086
+Epoch   1 Batch  137/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.067
+Epoch   1 Batch  138/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.900, Loss:  0.086
+Epoch   1 Batch  139/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.896, Loss:  0.110
+Epoch   1 Batch  140/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.901, Loss:  0.096
+Epoch   1 Batch  141/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.896, Loss:  0.094
+Epoch   1 Batch  142/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.891, Loss:  0.080
+Epoch   1 Batch  143/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.888, Loss:  0.101
+Epoch   1 Batch  144/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.888, Loss:  0.113
+Epoch   1 Batch  145/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.897, Loss:  0.088
+Epoch   1 Batch  146/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.891, Loss:  0.104
+Epoch   1 Batch  147/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.902, Loss:  0.088
+Epoch   1 Batch  148/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.894, Loss:  0.098
+Epoch   1 Batch  149/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.892, Loss:  0.096
+Epoch   1 Batch  150/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.898, Loss:  0.095
+Epoch   1 Batch  151/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.077
+Epoch   1 Batch  152/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.897, Loss:  0.118
+Epoch   1 Batch  153/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.891, Loss:  0.110
+Epoch   1 Batch  154/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.881, Loss:  0.079
+Epoch   1 Batch  155/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.887, Loss:  0.093
+Epoch   1 Batch  156/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.887, Loss:  0.079
+Epoch   1 Batch  157/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.894, Loss:  0.086
+Epoch   1 Batch  158/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.888, Loss:  0.111
+Epoch   1 Batch  159/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.892, Loss:  0.071
+Epoch   1 Batch  160/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.892, Loss:  0.076
+Epoch   1 Batch  161/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.893, Loss:  0.081
+Epoch   1 Batch  162/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.896, Loss:  0.103
+Epoch   1 Batch  163/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.893, Loss:  0.106
+Epoch   1 Batch  164/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.893, Loss:  0.087
+Epoch   1 Batch  165/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.903, Loss:  0.065
+Epoch   1 Batch  166/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.901, Loss:  0.102
+Epoch   1 Batch  167/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.902, Loss:  0.083
+Epoch   1 Batch  168/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.906, Loss:  0.094
+Epoch   1 Batch  169/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.108
+Epoch   1 Batch  170/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.900, Loss:  0.088
+Epoch   1 Batch  171/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.896, Loss:  0.083
+Epoch   1 Batch  172/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.901, Loss:  0.066
+Epoch   1 Batch  173/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.095
+Epoch   1 Batch  174/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.897, Loss:  0.077
+Epoch   1 Batch  175/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.896, Loss:  0.094
+Epoch   1 Batch  176/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.900, Loss:  0.085
+Epoch   1 Batch  177/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.910, Loss:  0.107
+Epoch   1 Batch  178/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.903, Loss:  0.089
+Epoch   1 Batch  179/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.896, Loss:  0.095
+Epoch   1 Batch  180/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.895, Loss:  0.079
+Epoch   1 Batch  181/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.899, Loss:  0.102
+Epoch   1 Batch  182/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.899, Loss:  0.104
+Epoch   1 Batch  183/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.894, Loss:  0.088
+Epoch   1 Batch  184/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.896, Loss:  0.090
+Epoch   1 Batch  185/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.096
+Epoch   1 Batch  186/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.888, Loss:  0.099
+Epoch   1 Batch  187/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.889, Loss:  0.071
+Epoch   1 Batch  188/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.885, Loss:  0.081
+Epoch   1 Batch  189/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.895, Loss:  0.077
+Epoch   1 Batch  190/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.892, Loss:  0.075
+Epoch   1 Batch  191/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.888, Loss:  0.072
+Epoch   1 Batch  192/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.895, Loss:  0.090
+Epoch   1 Batch  193/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.897, Loss:  0.080
+Epoch   1 Batch  194/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.897, Loss:  0.072
+Epoch   1 Batch  195/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.900, Loss:  0.074
+Epoch   1 Batch  196/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.903, Loss:  0.077
+Epoch   1 Batch  197/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.898, Loss:  0.095
+Epoch   1 Batch  198/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.898, Loss:  0.087
+Epoch   1 Batch  199/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.070
+Epoch   1 Batch  200/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.897, Loss:  0.102
+Epoch   1 Batch  201/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.893, Loss:  0.072
+Epoch   1 Batch  202/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.897, Loss:  0.082
+Epoch   1 Batch  203/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.894, Loss:  0.081
+Epoch   1 Batch  204/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.889, Loss:  0.108
+Epoch   1 Batch  205/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.880, Loss:  0.102
+Epoch   1 Batch  206/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.886, Loss:  0.079
+Epoch   1 Batch  207/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.877, Loss:  0.077
+Epoch   1 Batch  208/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.879, Loss:  0.090
+Epoch   1 Batch  209/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.880, Loss:  0.075
+Epoch   1 Batch  210/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.880, Loss:  0.094
+Epoch   1 Batch  211/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.890, Loss:  0.082
+Epoch   1 Batch  212/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.897, Loss:  0.073
+Epoch   1 Batch  213/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.866, Loss:  0.074
+Epoch   1 Batch  214/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.873, Loss:  0.074
+Epoch   1 Batch  215/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.870, Loss:  0.090
+Epoch   1 Batch  216/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.873, Loss:  0.092
+Epoch   1 Batch  217/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.877, Loss:  0.072
+Epoch   1 Batch  218/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.877, Loss:  0.102
+Epoch   1 Batch  219/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.877, Loss:  0.086
+Epoch   1 Batch  220/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.874, Loss:  0.081
+Epoch   1 Batch  221/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.896, Loss:  0.085
+Epoch   1 Batch  222/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.900, Loss:  0.078
+Epoch   1 Batch  223/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.900, Loss:  0.070
+Epoch   1 Batch  224/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.097
+Epoch   1 Batch  225/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.095
+Epoch   1 Batch  226/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.084
+Epoch   1 Batch  227/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.910, Loss:  0.104
+Epoch   1 Batch  228/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.071
+Epoch   1 Batch  229/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.891, Loss:  0.098
+Epoch   1 Batch  230/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.883, Loss:  0.092
+Epoch   1 Batch  231/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.893, Loss:  0.085
+Epoch   1 Batch  232/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.890, Loss:  0.075
+Epoch   1 Batch  233/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.892, Loss:  0.108
+Epoch   1 Batch  234/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.891, Loss:  0.093
+Epoch   1 Batch  235/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.888, Loss:  0.088
+Epoch   1 Batch  236/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.892, Loss:  0.095
+Epoch   1 Batch  237/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.905, Loss:  0.075
+Epoch   1 Batch  238/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.902, Loss:  0.080
+Epoch   1 Batch  239/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.884, Loss:  0.061
+Epoch   1 Batch  240/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.887, Loss:  0.071
+Epoch   1 Batch  241/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.886, Loss:  0.063
+Epoch   1 Batch  242/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.886, Loss:  0.065
+Epoch   1 Batch  243/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.891, Loss:  0.085
+Epoch   1 Batch  244/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.889, Loss:  0.076
+Epoch   1 Batch  245/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.907, Loss:  0.077
+Epoch   1 Batch  246/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.903, Loss:  0.072
+Epoch   1 Batch  247/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.076
+Epoch   1 Batch  248/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.905, Loss:  0.090
+Epoch   1 Batch  249/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.909, Loss:  0.080
+Epoch   1 Batch  250/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.903, Loss:  0.073
+Epoch   1 Batch  251/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.902, Loss:  0.090
+Epoch   1 Batch  252/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.909, Loss:  0.081
+Epoch   1 Batch  253/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.908, Loss:  0.080
+Epoch   1 Batch  254/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.906, Loss:  0.089
+Epoch   1 Batch  255/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.897, Loss:  0.079
+Epoch   1 Batch  256/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.894, Loss:  0.103
+Epoch   1 Batch  257/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.898, Loss:  0.087
+Epoch   1 Batch  258/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.903, Loss:  0.083
+Epoch   1 Batch  259/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.911, Loss:  0.072
+Epoch   1 Batch  260/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   1 Batch  261/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.086
+Epoch   1 Batch  262/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.907, Loss:  0.070
+Epoch   1 Batch  263/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.904, Loss:  0.071
+Epoch   1 Batch  264/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.900, Loss:  0.077
+Epoch   1 Batch  265/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.900, Loss:  0.075
+Epoch   1 Batch  266/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.899, Loss:  0.088
+Epoch   1 Batch  267/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.899, Loss:  0.076
+Epoch   1 Batch  268/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.900, Loss:  0.088
+Epoch   1 Batch  269/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.901, Loss:  0.099
+Epoch   1 Batch  270/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.897, Loss:  0.082
+Epoch   1 Batch  271/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.897, Loss:  0.072
+Epoch   1 Batch  272/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.893, Loss:  0.111
+Epoch   1 Batch  273/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.887, Loss:  0.072
+Epoch   1 Batch  274/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.898, Loss:  0.082
+Epoch   1 Batch  275/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.082
+Epoch   1 Batch  276/1077 - Train Accuracy:  0.857, Validation Accuracy:  0.896, Loss:  0.122
+Epoch   1 Batch  277/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.897, Loss:  0.072
+Epoch   1 Batch  278/1077 - Train Accuracy:  0.861, Validation Accuracy:  0.895, Loss:  0.089
+Epoch   1 Batch  279/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.890, Loss:  0.090
+Epoch   1 Batch  280/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.892, Loss:  0.090
+Epoch   1 Batch  281/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.889, Loss:  0.100
+Epoch   1 Batch  282/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.896, Loss:  0.106
+Epoch   1 Batch  283/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.900, Loss:  0.087
+Epoch   1 Batch  284/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.899, Loss:  0.089
+Epoch   1 Batch  285/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.084
+Epoch   1 Batch  286/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.081
+Epoch   1 Batch  287/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.891, Loss:  0.076
+Epoch   1 Batch  288/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.889, Loss:  0.089
+Epoch   1 Batch  289/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.892, Loss:  0.078
+Epoch   1 Batch  290/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.902, Loss:  0.109
+Epoch   1 Batch  291/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.902, Loss:  0.103
+Epoch   1 Batch  292/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.902, Loss:  0.085
+Epoch   1 Batch  293/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.908, Loss:  0.089
+Epoch   1 Batch  294/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.078
+Epoch   1 Batch  295/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.907, Loss:  0.091
+Epoch   1 Batch  296/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.907, Loss:  0.085
+Epoch   1 Batch  297/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.896, Loss:  0.094
+Epoch   1 Batch  298/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.898, Loss:  0.092
+Epoch   1 Batch  299/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.085
+Epoch   1 Batch  300/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.080
+Epoch   1 Batch  301/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.906, Loss:  0.070
+Epoch   1 Batch  302/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.898, Loss:  0.084
+Epoch   1 Batch  303/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.898, Loss:  0.094
+Epoch   1 Batch  304/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.900, Loss:  0.079
+Epoch   1 Batch  305/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.892, Loss:  0.074
+Epoch   1 Batch  306/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.901, Loss:  0.089
+Epoch   1 Batch  307/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.881, Loss:  0.072
+Epoch   1 Batch  308/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.885, Loss:  0.097
+Epoch   1 Batch  309/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.882, Loss:  0.073
+Epoch   1 Batch  310/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.884, Loss:  0.080
+Epoch   1 Batch  311/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.885, Loss:  0.074
+Epoch   1 Batch  312/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.895, Loss:  0.095
+Epoch   1 Batch  313/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.894, Loss:  0.064
+Epoch   1 Batch  314/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.895, Loss:  0.076
+Epoch   1 Batch  315/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.070
+Epoch   1 Batch  316/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.884, Loss:  0.075
+Epoch   1 Batch  317/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.890, Loss:  0.104
+Epoch   1 Batch  318/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.891, Loss:  0.068
+Epoch   1 Batch  319/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.881, Loss:  0.094
+Epoch   1 Batch  320/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.877, Loss:  0.088
+Epoch   1 Batch  321/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.879, Loss:  0.070
+Epoch   1 Batch  322/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.880, Loss:  0.073
+Epoch   1 Batch  323/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.877, Loss:  0.086
+Epoch   1 Batch  324/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.891, Loss:  0.065
+Epoch   1 Batch  325/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.883, Loss:  0.091
+Epoch   1 Batch  326/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.888, Loss:  0.073
+Epoch   1 Batch  327/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.889, Loss:  0.093
+Epoch   1 Batch  328/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.892, Loss:  0.087
+Epoch   1 Batch  329/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.889, Loss:  0.087
+Epoch   1 Batch  330/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.890, Loss:  0.086
+Epoch   1 Batch  331/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.891, Loss:  0.091
+Epoch   1 Batch  332/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.878, Loss:  0.066
+Epoch   1 Batch  333/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.882, Loss:  0.063
+Epoch   1 Batch  334/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.885, Loss:  0.074
+Epoch   1 Batch  335/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.888, Loss:  0.077
+Epoch   1 Batch  336/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.884, Loss:  0.108
+Epoch   1 Batch  337/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.880, Loss:  0.081
+Epoch   1 Batch  338/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.881, Loss:  0.106
+Epoch   1 Batch  339/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.881, Loss:  0.070
+Epoch   1 Batch  340/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.896, Loss:  0.070
+Epoch   1 Batch  341/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.901, Loss:  0.093
+Epoch   1 Batch  342/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.905, Loss:  0.069
+Epoch   1 Batch  343/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.897, Loss:  0.073
+Epoch   1 Batch  344/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.901, Loss:  0.073
+Epoch   1 Batch  345/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.903, Loss:  0.062
+Epoch   1 Batch  346/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.902, Loss:  0.082
+Epoch   1 Batch  347/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.065
+Epoch   1 Batch  348/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.077
+Epoch   1 Batch  349/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.073
+Epoch   1 Batch  350/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.914, Loss:  0.088
+Epoch   1 Batch  351/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.912, Loss:  0.073
+Epoch   1 Batch  352/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  353/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.901, Loss:  0.082
+Epoch   1 Batch  354/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.903, Loss:  0.081
+Epoch   1 Batch  355/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.898, Loss:  0.072
+Epoch   1 Batch  356/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.083
+Epoch   1 Batch  357/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.890, Loss:  0.074
+Epoch   1 Batch  358/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.894, Loss:  0.089
+Epoch   1 Batch  359/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.898, Loss:  0.076
+Epoch   1 Batch  360/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.893, Loss:  0.066
+Epoch   1 Batch  361/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.892, Loss:  0.074
+Epoch   1 Batch  362/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.887, Loss:  0.085
+Epoch   1 Batch  363/1077 - Train Accuracy:  0.876, Validation Accuracy:  0.890, Loss:  0.079
+Epoch   1 Batch  364/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.888, Loss:  0.083
+Epoch   1 Batch  365/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.885, Loss:  0.064
+Epoch   1 Batch  366/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.885, Loss:  0.069
+Epoch   1 Batch  367/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.878, Loss:  0.055
+Epoch   1 Batch  368/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.877, Loss:  0.083
+Epoch   1 Batch  369/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.880, Loss:  0.081
+Epoch   1 Batch  370/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.876, Loss:  0.074
+Epoch   1 Batch  371/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.887, Loss:  0.062
+Epoch   1 Batch  372/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.885, Loss:  0.060
+Epoch   1 Batch  373/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.887, Loss:  0.056
+Epoch   1 Batch  374/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.903, Loss:  0.085
+Epoch   1 Batch  375/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.907, Loss:  0.081
+Epoch   1 Batch  376/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.076
+Epoch   1 Batch  377/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.902, Loss:  0.069
+Epoch   1 Batch  378/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.058
+Epoch   1 Batch  379/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.083
+Epoch   1 Batch  380/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.914, Loss:  0.061
+Epoch   1 Batch  381/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.918, Loss:  0.098
+Epoch   1 Batch  382/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.925, Loss:  0.117
+Epoch   1 Batch  383/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.073
+Epoch   1 Batch  384/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.910, Loss:  0.074
+Epoch   1 Batch  385/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.917, Loss:  0.067
+Epoch   1 Batch  386/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   1 Batch  387/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.073
+Epoch   1 Batch  388/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.907, Loss:  0.065
+Epoch   1 Batch  389/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.905, Loss:  0.073
+Epoch   1 Batch  390/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.907, Loss:  0.081
+Epoch   1 Batch  391/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.908, Loss:  0.082
+Epoch   1 Batch  392/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.892, Loss:  0.084
+Epoch   1 Batch  393/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.069
+Epoch   1 Batch  394/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.899, Loss:  0.069
+Epoch   1 Batch  395/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.900, Loss:  0.082
+Epoch   1 Batch  396/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.899, Loss:  0.078
+Epoch   1 Batch  397/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.899, Loss:  0.061
+Epoch   1 Batch  398/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.902, Loss:  0.077
+Epoch   1 Batch  399/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.898, Loss:  0.067
+Epoch   1 Batch  400/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.904, Loss:  0.094
+Epoch   1 Batch  401/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.892, Loss:  0.068
+Epoch   1 Batch  402/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.896, Loss:  0.069
+Epoch   1 Batch  403/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.896, Loss:  0.091
+Epoch   1 Batch  404/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.891, Loss:  0.078
+Epoch   1 Batch  405/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.897, Loss:  0.068
+Epoch   1 Batch  406/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.896, Loss:  0.074
+Epoch   1 Batch  407/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.895, Loss:  0.083
+Epoch   1 Batch  408/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.899, Loss:  0.084
+Epoch   1 Batch  409/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.079
+Epoch   1 Batch  410/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.904, Loss:  0.085
+Epoch   1 Batch  411/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.085
+Epoch   1 Batch  412/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.903, Loss:  0.056
+Epoch   1 Batch  413/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.066
+Epoch   1 Batch  414/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.902, Loss:  0.080
+Epoch   1 Batch  415/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.899, Loss:  0.074
+Epoch   1 Batch  416/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.902, Loss:  0.076
+Epoch   1 Batch  417/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.909, Loss:  0.113
+Epoch   1 Batch  418/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.069
+Epoch   1 Batch  419/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.913, Loss:  0.061
+Epoch   1 Batch  420/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.059
+Epoch   1 Batch  421/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.915, Loss:  0.083
+Epoch   1 Batch  422/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  423/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.910, Loss:  0.086
+Epoch   1 Batch  424/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.902, Loss:  0.073
+Epoch   1 Batch  425/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.900, Loss:  0.064
+Epoch   1 Batch  426/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.901, Loss:  0.089
+Epoch   1 Batch  427/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.905, Loss:  0.070
+Epoch   1 Batch  428/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.906, Loss:  0.059
+Epoch   1 Batch  429/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.902, Loss:  0.067
+Epoch   1 Batch  430/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.905, Loss:  0.067
+Epoch   1 Batch  431/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.060
+Epoch   1 Batch  432/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.900, Loss:  0.071
+Epoch   1 Batch  433/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.902, Loss:  0.079
+Epoch   1 Batch  434/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.898, Loss:  0.064
+Epoch   1 Batch  435/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.899, Loss:  0.092
+Epoch   1 Batch  436/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.900, Loss:  0.074
+Epoch   1 Batch  437/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.900, Loss:  0.062
+Epoch   1 Batch  438/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.899, Loss:  0.071
+Epoch   1 Batch  439/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.901, Loss:  0.090
+Epoch   1 Batch  440/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.901, Loss:  0.091
+Epoch   1 Batch  441/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.909, Loss:  0.072
+Epoch   1 Batch  442/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.901, Loss:  0.081
+Epoch   1 Batch  443/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.066
+Epoch   1 Batch  444/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.906, Loss:  0.085
+Epoch   1 Batch  445/1077 - Train Accuracy:  0.859, Validation Accuracy:  0.897, Loss:  0.082
+Epoch   1 Batch  446/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.902, Loss:  0.056
+Epoch   1 Batch  447/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.901, Loss:  0.074
+Epoch   1 Batch  448/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.908, Loss:  0.093
+Epoch   1 Batch  449/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.909, Loss:  0.075
+Epoch   1 Batch  450/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.908, Loss:  0.072
+Epoch   1 Batch  451/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.914, Loss:  0.076
+Epoch   1 Batch  452/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.913, Loss:  0.078
+Epoch   1 Batch  453/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.911, Loss:  0.067
+Epoch   1 Batch  454/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.076
+Epoch   1 Batch  455/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.897, Loss:  0.072
+Epoch   1 Batch  456/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.896, Loss:  0.086
+Epoch   1 Batch  457/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.895, Loss:  0.056
+Epoch   1 Batch  458/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.897, Loss:  0.080
+Epoch   1 Batch  459/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.895, Loss:  0.069
+Epoch   1 Batch  460/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.901, Loss:  0.076
+Epoch   1 Batch  461/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.896, Loss:  0.073
+Epoch   1 Batch  462/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.895, Loss:  0.072
+Epoch   1 Batch  463/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.898, Loss:  0.080
+Epoch   1 Batch  464/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.889, Loss:  0.070
+Epoch   1 Batch  465/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.874, Loss:  0.074
+Epoch   1 Batch  466/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.879, Loss:  0.065
+Epoch   1 Batch  467/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.887, Loss:  0.080
+Epoch   1 Batch  468/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.886, Loss:  0.071
+Epoch   1 Batch  469/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.890, Loss:  0.074
+Epoch   1 Batch  470/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.893, Loss:  0.075
+Epoch   1 Batch  471/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.055
+Epoch   1 Batch  472/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.908, Loss:  0.070
+Epoch   1 Batch  473/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.907, Loss:  0.072
+Epoch   1 Batch  474/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   1 Batch  475/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.922, Loss:  0.073
+Epoch   1 Batch  476/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.917, Loss:  0.057
+Epoch   1 Batch  477/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.075
+Epoch   1 Batch  478/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.068
+Epoch   1 Batch  479/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.911, Loss:  0.087
+Epoch   1 Batch  480/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.903, Loss:  0.058
+Epoch   1 Batch  481/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.907, Loss:  0.085
+Epoch   1 Batch  482/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.903, Loss:  0.092
+Epoch   1 Batch  483/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.898, Loss:  0.071
+Epoch   1 Batch  484/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.893, Loss:  0.090
+Epoch   1 Batch  485/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.888, Loss:  0.087
+Epoch   1 Batch  486/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.889, Loss:  0.066
+Epoch   1 Batch  487/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.887, Loss:  0.062
+Epoch   1 Batch  488/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.886, Loss:  0.069
+Epoch   1 Batch  489/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.887, Loss:  0.065
+Epoch   1 Batch  490/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.897, Loss:  0.073
+Epoch   1 Batch  491/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.901, Loss:  0.076
+Epoch   1 Batch  492/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.900, Loss:  0.084
+Epoch   1 Batch  493/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.901, Loss:  0.058
+Epoch   1 Batch  494/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.902, Loss:  0.064
+Epoch   1 Batch  495/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.902, Loss:  0.072
+Epoch   1 Batch  496/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.069
+Epoch   1 Batch  497/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.891, Loss:  0.072
+Epoch   1 Batch  498/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.891, Loss:  0.074
+Epoch   1 Batch  499/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.057
+Epoch   1 Batch  500/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.900, Loss:  0.057
+Epoch   1 Batch  501/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.063
+Epoch   1 Batch  502/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.079
+Epoch   1 Batch  503/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.905, Loss:  0.064
+Epoch   1 Batch  504/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.919, Loss:  0.072
+Epoch   1 Batch  505/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.059
+Epoch   1 Batch  506/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.913, Loss:  0.079
+Epoch   1 Batch  507/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.913, Loss:  0.072
+Epoch   1 Batch  508/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.916, Loss:  0.059
+Epoch   1 Batch  509/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.914, Loss:  0.079
+Epoch   1 Batch  510/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.901, Loss:  0.072
+Epoch   1 Batch  511/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.906, Loss:  0.071
+Epoch   1 Batch  512/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.909, Loss:  0.055
+Epoch   1 Batch  513/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.900, Loss:  0.068
+Epoch   1 Batch  514/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.904, Loss:  0.080
+Epoch   1 Batch  515/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.910, Loss:  0.079
+Epoch   1 Batch  516/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.072
+Epoch   1 Batch  517/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.068
+Epoch   1 Batch  518/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   1 Batch  519/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.901, Loss:  0.072
+Epoch   1 Batch  520/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.894, Loss:  0.068
+Epoch   1 Batch  521/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.889, Loss:  0.081
+Epoch   1 Batch  522/1077 - Train Accuracy:  0.869, Validation Accuracy:  0.897, Loss:  0.081
+Epoch   1 Batch  523/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.079
+Epoch   1 Batch  524/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.899, Loss:  0.079
+Epoch   1 Batch  525/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.895, Loss:  0.075
+Epoch   1 Batch  526/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.891, Loss:  0.065
+Epoch   1 Batch  527/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.884, Loss:  0.072
+Epoch   1 Batch  528/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.888, Loss:  0.074
+Epoch   1 Batch  529/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.885, Loss:  0.075
+Epoch   1 Batch  530/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.893, Loss:  0.073
+Epoch   1 Batch  531/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.888, Loss:  0.067
+Epoch   1 Batch  532/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.897, Loss:  0.085
+Epoch   1 Batch  533/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.906, Loss:  0.069
+Epoch   1 Batch  534/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.910, Loss:  0.081
+Epoch   1 Batch  535/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.069
+Epoch   1 Batch  536/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.897, Loss:  0.071
+Epoch   1 Batch  537/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.894, Loss:  0.067
+Epoch   1 Batch  538/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.891, Loss:  0.054
+Epoch   1 Batch  539/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.877, Loss:  0.105
+Epoch   1 Batch  540/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.879, Loss:  0.063
+Epoch   1 Batch  541/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.885, Loss:  0.056
+Epoch   1 Batch  542/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.885, Loss:  0.071
+Epoch   1 Batch  543/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.891, Loss:  0.071
+Epoch   1 Batch  544/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.884, Loss:  0.052
+Epoch   1 Batch  545/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.888, Loss:  0.075
+Epoch   1 Batch  546/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.898, Loss:  0.078
+Epoch   1 Batch  547/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.902, Loss:  0.066
+Epoch   1 Batch  548/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.907, Loss:  0.077
+Epoch   1 Batch  549/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.911, Loss:  0.084
+Epoch   1 Batch  550/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.898, Loss:  0.078
+Epoch   1 Batch  551/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.080
+Epoch   1 Batch  552/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.081
+Epoch   1 Batch  553/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.913, Loss:  0.087
+Epoch   1 Batch  554/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.908, Loss:  0.068
+Epoch   1 Batch  555/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.066
+Epoch   1 Batch  556/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch  557/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.898, Loss:  0.073
+Epoch   1 Batch  558/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.900, Loss:  0.061
+Epoch   1 Batch  559/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   1 Batch  560/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.898, Loss:  0.065
+Epoch   1 Batch  561/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.066
+Epoch   1 Batch  562/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.890, Loss:  0.059
+Epoch   1 Batch  563/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.891, Loss:  0.066
+Epoch   1 Batch  564/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.891, Loss:  0.082
+Epoch   1 Batch  565/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.886, Loss:  0.075
+Epoch   1 Batch  566/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.883, Loss:  0.083
+Epoch   1 Batch  567/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.892, Loss:  0.075
+Epoch   1 Batch  568/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.894, Loss:  0.068
+Epoch   1 Batch  569/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.891, Loss:  0.073
+Epoch   1 Batch  570/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.884, Loss:  0.085
+Epoch   1 Batch  571/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.885, Loss:  0.052
+Epoch   1 Batch  572/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.885, Loss:  0.065
+Epoch   1 Batch  573/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.884, Loss:  0.079
+Epoch   1 Batch  574/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.882, Loss:  0.081
+Epoch   1 Batch  575/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.889, Loss:  0.054
+Epoch   1 Batch  576/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.896, Loss:  0.063
+Epoch   1 Batch  577/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.898, Loss:  0.078
+Epoch   1 Batch  578/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.071
+Epoch   1 Batch  579/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.903, Loss:  0.062
+Epoch   1 Batch  580/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   1 Batch  581/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.896, Loss:  0.052
+Epoch   1 Batch  582/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.898, Loss:  0.073
+Epoch   1 Batch  583/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.898, Loss:  0.068
+Epoch   1 Batch  584/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch  585/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.048
+Epoch   1 Batch  586/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.903, Loss:  0.078
+Epoch   1 Batch  587/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.900, Loss:  0.068
+Epoch   1 Batch  588/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.896, Loss:  0.055
+Epoch   1 Batch  589/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.889, Loss:  0.066
+Epoch   1 Batch  590/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.894, Loss:  0.083
+Epoch   1 Batch  591/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.898, Loss:  0.069
+Epoch   1 Batch  592/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.888, Loss:  0.069
+Epoch   1 Batch  593/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.880, Loss:  0.094
+Epoch   1 Batch  594/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.875, Loss:  0.077
+Epoch   1 Batch  595/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.880, Loss:  0.071
+Epoch   1 Batch  596/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.875, Loss:  0.070
+Epoch   1 Batch  597/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.893, Loss:  0.059
+Epoch   1 Batch  598/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.907, Loss:  0.080
+Epoch   1 Batch  599/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.907, Loss:  0.089
+Epoch   1 Batch  600/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.900, Loss:  0.078
+Epoch   1 Batch  601/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.902, Loss:  0.074
+Epoch   1 Batch  602/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.905, Loss:  0.070
+Epoch   1 Batch  603/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.910, Loss:  0.067
+Epoch   1 Batch  604/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.912, Loss:  0.079
+Epoch   1 Batch  605/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.905, Loss:  0.089
+Epoch   1 Batch  606/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.907, Loss:  0.064
+Epoch   1 Batch  607/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.907, Loss:  0.069
+Epoch   1 Batch  608/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.075
+Epoch   1 Batch  609/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.911, Loss:  0.067
+Epoch   1 Batch  610/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.084
+Epoch   1 Batch  611/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.057
+Epoch   1 Batch  612/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.062
+Epoch   1 Batch  613/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.084
+Epoch   1 Batch  614/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.058
+Epoch   1 Batch  615/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.903, Loss:  0.069
+Epoch   1 Batch  616/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.076
+Epoch   1 Batch  617/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.922, Loss:  0.069
+Epoch   1 Batch  618/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.911, Loss:  0.067
+Epoch   1 Batch  619/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   1 Batch  620/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.067
+Epoch   1 Batch  621/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.916, Loss:  0.065
+Epoch   1 Batch  622/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.912, Loss:  0.083
+Epoch   1 Batch  623/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.911, Loss:  0.074
+Epoch   1 Batch  624/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.080
+Epoch   1 Batch  625/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.911, Loss:  0.063
+Epoch   1 Batch  626/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.904, Loss:  0.068
+Epoch   1 Batch  627/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.908, Loss:  0.069
+Epoch   1 Batch  628/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.071
+Epoch   1 Batch  629/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.908, Loss:  0.074
+Epoch   1 Batch  630/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.912, Loss:  0.063
+Epoch   1 Batch  631/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.911, Loss:  0.071
+Epoch   1 Batch  632/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.063
+Epoch   1 Batch  633/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.074
+Epoch   1 Batch  634/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   1 Batch  635/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.078
+Epoch   1 Batch  636/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   1 Batch  637/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   1 Batch  638/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   1 Batch  639/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.911, Loss:  0.091
+Epoch   1 Batch  640/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.065
+Epoch   1 Batch  641/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.916, Loss:  0.069
+Epoch   1 Batch  642/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.922, Loss:  0.083
+Epoch   1 Batch  643/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   1 Batch  644/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.920, Loss:  0.073
+Epoch   1 Batch  645/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.915, Loss:  0.080
+Epoch   1 Batch  646/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.911, Loss:  0.071
+Epoch   1 Batch  647/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   1 Batch  648/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.927, Loss:  0.046
+Epoch   1 Batch  649/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.927, Loss:  0.063
+Epoch   1 Batch  650/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.071
+Epoch   1 Batch  651/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.062
+Epoch   1 Batch  652/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.908, Loss:  0.065
+Epoch   1 Batch  653/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.067
+Epoch   1 Batch  654/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.910, Loss:  0.058
+Epoch   1 Batch  655/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.906, Loss:  0.076
+Epoch   1 Batch  656/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.067
+Epoch   1 Batch  657/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.909, Loss:  0.058
+Epoch   1 Batch  658/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.055
+Epoch   1 Batch  659/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.902, Loss:  0.073
+Epoch   1 Batch  660/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.908, Loss:  0.069
+Epoch   1 Batch  661/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.053
+Epoch   1 Batch  662/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.904, Loss:  0.075
+Epoch   1 Batch  663/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.906, Loss:  0.057
+Epoch   1 Batch  664/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.061
+Epoch   1 Batch  665/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.058
+Epoch   1 Batch  666/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.903, Loss:  0.073
+Epoch   1 Batch  667/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.911, Loss:  0.082
+Epoch   1 Batch  668/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.071
+Epoch   1 Batch  669/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.906, Loss:  0.071
+Epoch   1 Batch  670/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.908, Loss:  0.078
+Epoch   1 Batch  671/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.913, Loss:  0.069
+Epoch   1 Batch  672/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   1 Batch  673/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.062
+Epoch   1 Batch  674/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.901, Loss:  0.072
+Epoch   1 Batch  675/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.906, Loss:  0.082
+Epoch   1 Batch  676/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.907, Loss:  0.063
+Epoch   1 Batch  677/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.918, Loss:  0.091
+Epoch   1 Batch  678/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.057
+Epoch   1 Batch  679/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.907, Loss:  0.062
+Epoch   1 Batch  680/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.904, Loss:  0.061
+Epoch   1 Batch  681/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.894, Loss:  0.066
+Epoch   1 Batch  682/1077 - Train Accuracy:  0.882, Validation Accuracy:  0.903, Loss:  0.062
+Epoch   1 Batch  683/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.903, Loss:  0.057
+Epoch   1 Batch  684/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.909, Loss:  0.067
+Epoch   1 Batch  685/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.914, Loss:  0.075
+Epoch   1 Batch  686/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   1 Batch  687/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.091
+Epoch   1 Batch  688/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.066
+Epoch   1 Batch  689/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.904, Loss:  0.047
+Epoch   1 Batch  690/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.068
+Epoch   1 Batch  691/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.906, Loss:  0.085
+Epoch   1 Batch  692/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.906, Loss:  0.061
+Epoch   1 Batch  693/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.901, Loss:  0.084
+Epoch   1 Batch  694/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.885, Loss:  0.072
+Epoch   1 Batch  695/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.884, Loss:  0.049
+Epoch   1 Batch  696/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.886, Loss:  0.085
+Epoch   1 Batch  697/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.890, Loss:  0.069
+Epoch   1 Batch  698/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.900, Loss:  0.059
+Epoch   1 Batch  699/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.908, Loss:  0.056
+Epoch   1 Batch  700/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.067
+Epoch   1 Batch  701/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.900, Loss:  0.074
+Epoch   1 Batch  702/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.904, Loss:  0.087
+Epoch   1 Batch  703/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.070
+Epoch   1 Batch  704/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.910, Loss:  0.083
+Epoch   1 Batch  705/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.911, Loss:  0.074
+Epoch   1 Batch  706/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.914, Loss:  0.114
+Epoch   1 Batch  707/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.913, Loss:  0.067
+Epoch   1 Batch  708/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.909, Loss:  0.081
+Epoch   1 Batch  709/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.907, Loss:  0.089
+Epoch   1 Batch  710/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.905, Loss:  0.059
+Epoch   1 Batch  711/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.893, Loss:  0.083
+Epoch   1 Batch  712/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.896, Loss:  0.058
+Epoch   1 Batch  713/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.899, Loss:  0.056
+Epoch   1 Batch  714/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch  715/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.895, Loss:  0.074
+Epoch   1 Batch  716/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.891, Loss:  0.056
+Epoch   1 Batch  717/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.887, Loss:  0.047
+Epoch   1 Batch  718/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.887, Loss:  0.056
+Epoch   1 Batch  719/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.896, Loss:  0.084
+Epoch   1 Batch  720/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.901, Loss:  0.081
+Epoch   1 Batch  721/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.902, Loss:  0.074
+Epoch   1 Batch  722/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.904, Loss:  0.065
+Epoch   1 Batch  723/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.899, Loss:  0.075
+Epoch   1 Batch  724/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.907, Loss:  0.073
+Epoch   1 Batch  725/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.059
+Epoch   1 Batch  726/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.073
+Epoch   1 Batch  727/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   1 Batch  728/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.909, Loss:  0.078
+Epoch   1 Batch  729/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.909, Loss:  0.082
+Epoch   1 Batch  730/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.893, Loss:  0.081
+Epoch   1 Batch  731/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.899, Loss:  0.063
+Epoch   1 Batch  732/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.899, Loss:  0.072
+Epoch   1 Batch  733/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.902, Loss:  0.082
+Epoch   1 Batch  734/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.896, Loss:  0.064
+Epoch   1 Batch  735/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.901, Loss:  0.061
+Epoch   1 Batch  736/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   1 Batch  737/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.082
+Epoch   1 Batch  738/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.047
+Epoch   1 Batch  739/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.061
+Epoch   1 Batch  740/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.054
+Epoch   1 Batch  741/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.073
+Epoch   1 Batch  742/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   1 Batch  743/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.913, Loss:  0.066
+Epoch   1 Batch  744/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.077
+Epoch   1 Batch  745/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.075
+Epoch   1 Batch  746/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.054
+Epoch   1 Batch  747/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   1 Batch  748/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.059
+Epoch   1 Batch  749/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.917, Loss:  0.069
+Epoch   1 Batch  750/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.063
+Epoch   1 Batch  751/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   1 Batch  752/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.916, Loss:  0.064
+Epoch   1 Batch  753/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.060
+Epoch   1 Batch  754/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.910, Loss:  0.070
+Epoch   1 Batch  755/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.067
+Epoch   1 Batch  756/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.056
+Epoch   1 Batch  757/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   1 Batch  758/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   1 Batch  759/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.919, Loss:  0.054
+Epoch   1 Batch  760/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.069
+Epoch   1 Batch  761/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.059
+Epoch   1 Batch  762/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.912, Loss:  0.063
+Epoch   1 Batch  763/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.063
+Epoch   1 Batch  764/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.067
+Epoch   1 Batch  765/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.083
+Epoch   1 Batch  766/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.063
+Epoch   1 Batch  767/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.061
+Epoch   1 Batch  768/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.924, Loss:  0.062
+Epoch   1 Batch  769/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   1 Batch  770/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.908, Loss:  0.064
+Epoch   1 Batch  771/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.078
+Epoch   1 Batch  772/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   1 Batch  773/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.909, Loss:  0.068
+Epoch   1 Batch  774/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.067
+Epoch   1 Batch  775/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.072
+Epoch   1 Batch  776/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.924, Loss:  0.058
+Epoch   1 Batch  777/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.070
+Epoch   1 Batch  778/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.924, Loss:  0.064
+Epoch   1 Batch  779/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.920, Loss:  0.069
+Epoch   1 Batch  780/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.916, Loss:  0.091
+Epoch   1 Batch  781/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   1 Batch  782/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   1 Batch  783/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.076
+Epoch   1 Batch  784/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.053
+Epoch   1 Batch  785/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.051
+Epoch   1 Batch  786/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.922, Loss:  0.063
+Epoch   1 Batch  787/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.913, Loss:  0.065
+Epoch   1 Batch  788/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.905, Loss:  0.060
+Epoch   1 Batch  789/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.901, Loss:  0.078
+Epoch   1 Batch  790/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.907, Loss:  0.065
+Epoch   1 Batch  791/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   1 Batch  792/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.072
+Epoch   1 Batch  793/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.917, Loss:  0.061
+Epoch   1 Batch  794/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   1 Batch  795/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.068
+Epoch   1 Batch  796/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   1 Batch  797/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.928, Loss:  0.065
+Epoch   1 Batch  798/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.069
+Epoch   1 Batch  799/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.935, Loss:  0.079
+Epoch   1 Batch  800/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.064
+Epoch   1 Batch  801/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.072
+Epoch   1 Batch  802/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.068
+Epoch   1 Batch  803/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.929, Loss:  0.073
+Epoch   1 Batch  804/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   1 Batch  805/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.928, Loss:  0.067
+Epoch   1 Batch  806/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.931, Loss:  0.059
+Epoch   1 Batch  807/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.056
+Epoch   1 Batch  808/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.937, Loss:  0.090
+Epoch   1 Batch  809/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.930, Loss:  0.089
+Epoch   1 Batch  810/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.058
+Epoch   1 Batch  811/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   1 Batch  812/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.933, Loss:  0.060
+Epoch   1 Batch  813/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.929, Loss:  0.061
+Epoch   1 Batch  814/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.934, Loss:  0.084
+Epoch   1 Batch  815/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.934, Loss:  0.075
+Epoch   1 Batch  816/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.076
+Epoch   1 Batch  817/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.072
+Epoch   1 Batch  818/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.925, Loss:  0.067
+Epoch   1 Batch  819/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.925, Loss:  0.069
+Epoch   1 Batch  820/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   1 Batch  821/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  822/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.060
+Epoch   1 Batch  823/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.070
+Epoch   1 Batch  824/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.930, Loss:  0.063
+Epoch   1 Batch  825/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   1 Batch  826/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.920, Loss:  0.059
+Epoch   1 Batch  827/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   1 Batch  828/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   1 Batch  829/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.077
+Epoch   1 Batch  830/1077 - Train Accuracy:  0.874, Validation Accuracy:  0.924, Loss:  0.076
+Epoch   1 Batch  831/1077 - Train Accuracy:  0.852, Validation Accuracy:  0.919, Loss:  0.069
+Epoch   1 Batch  832/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.062
+Epoch   1 Batch  833/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.918, Loss:  0.071
+Epoch   1 Batch  834/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.068
+Epoch   1 Batch  835/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.931, Loss:  0.071
+Epoch   1 Batch  836/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.063
+Epoch   1 Batch  837/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.930, Loss:  0.086
+Epoch   1 Batch  838/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.937, Loss:  0.058
+Epoch   1 Batch  839/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   1 Batch  840/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.937, Loss:  0.055
+Epoch   1 Batch  841/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.070
+Epoch   1 Batch  842/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.053
+Epoch   1 Batch  843/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.045
+Epoch   1 Batch  844/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.932, Loss:  0.053
+Epoch   1 Batch  845/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   1 Batch  846/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.071
+Epoch   1 Batch  847/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.078
+Epoch   1 Batch  848/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.053
+Epoch   1 Batch  849/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   1 Batch  850/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.924, Loss:  0.098
+Epoch   1 Batch  851/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.076
+Epoch   1 Batch  852/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.091
+Epoch   1 Batch  853/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.076
+Epoch   1 Batch  854/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.086
+Epoch   1 Batch  855/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.906, Loss:  0.064
+Epoch   1 Batch  856/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.903, Loss:  0.070
+Epoch   1 Batch  857/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.070
+Epoch   1 Batch  858/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.059
+Epoch   1 Batch  859/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.903, Loss:  0.079
+Epoch   1 Batch  860/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.900, Loss:  0.081
+Epoch   1 Batch  861/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.900, Loss:  0.067
+Epoch   1 Batch  862/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.901, Loss:  0.067
+Epoch   1 Batch  863/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   1 Batch  864/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.898, Loss:  0.073
+Epoch   1 Batch  865/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.896, Loss:  0.069
+Epoch   1 Batch  866/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.913, Loss:  0.078
+Epoch   1 Batch  867/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.911, Loss:  0.115
+Epoch   1 Batch  868/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.069
+Epoch   1 Batch  869/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.910, Loss:  0.067
+Epoch   1 Batch  870/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.911, Loss:  0.069
+Epoch   1 Batch  871/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   1 Batch  872/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.067
+Epoch   1 Batch  873/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.918, Loss:  0.069
+Epoch   1 Batch  874/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.078
+Epoch   1 Batch  875/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.919, Loss:  0.082
+Epoch   1 Batch  876/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.929, Loss:  0.064
+Epoch   1 Batch  877/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   1 Batch  878/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   1 Batch  879/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.055
+Epoch   1 Batch  880/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.923, Loss:  0.071
+Epoch   1 Batch  881/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.912, Loss:  0.073
+Epoch   1 Batch  882/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.915, Loss:  0.075
+Epoch   1 Batch  883/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.921, Loss:  0.092
+Epoch   1 Batch  884/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.913, Loss:  0.067
+Epoch   1 Batch  885/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.042
+Epoch   1 Batch  886/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.064
+Epoch   1 Batch  887/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.083
+Epoch   1 Batch  888/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.049
+Epoch   1 Batch  889/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.900, Loss:  0.057
+Epoch   1 Batch  890/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.901, Loss:  0.069
+Epoch   1 Batch  891/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.905, Loss:  0.057
+Epoch   1 Batch  892/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.908, Loss:  0.054
+Epoch   1 Batch  893/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.903, Loss:  0.073
+Epoch   1 Batch  894/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.911, Loss:  0.069
+Epoch   1 Batch  895/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   1 Batch  896/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.064
+Epoch   1 Batch  897/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   1 Batch  898/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   1 Batch  899/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.076
+Epoch   1 Batch  900/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.917, Loss:  0.076
+Epoch   1 Batch  901/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.075
+Epoch   1 Batch  902/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.929, Loss:  0.081
+Epoch   1 Batch  903/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.062
+Epoch   1 Batch  904/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.918, Loss:  0.060
+Epoch   1 Batch  905/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.912, Loss:  0.054
+Epoch   1 Batch  906/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.904, Loss:  0.069
+Epoch   1 Batch  907/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.900, Loss:  0.078
+Epoch   1 Batch  908/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.909, Loss:  0.074
+Epoch   1 Batch  909/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.071
+Epoch   1 Batch  910/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.903, Loss:  0.065
+Epoch   1 Batch  911/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.074
+Epoch   1 Batch  912/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.061
+Epoch   1 Batch  913/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.900, Loss:  0.092
+Epoch   1 Batch  914/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.902, Loss:  0.091
+Epoch   1 Batch  915/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.898, Loss:  0.057
+Epoch   1 Batch  916/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.903, Loss:  0.086
+Epoch   1 Batch  917/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.903, Loss:  0.065
+Epoch   1 Batch  918/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.058
+Epoch   1 Batch  919/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.904, Loss:  0.051
+Epoch   1 Batch  920/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  921/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.906, Loss:  0.066
+Epoch   1 Batch  922/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.897, Loss:  0.073
+Epoch   1 Batch  923/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   1 Batch  924/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.905, Loss:  0.086
+Epoch   1 Batch  925/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.908, Loss:  0.066
+Epoch   1 Batch  926/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.908, Loss:  0.055
+Epoch   1 Batch  927/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.908, Loss:  0.077
+Epoch   1 Batch  928/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch  929/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.062
+Epoch   1 Batch  930/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.901, Loss:  0.060
+Epoch   1 Batch  931/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.905, Loss:  0.053
+Epoch   1 Batch  932/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.898, Loss:  0.067
+Epoch   1 Batch  933/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.896, Loss:  0.062
+Epoch   1 Batch  934/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.051
+Epoch   1 Batch  935/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.898, Loss:  0.055
+Epoch   1 Batch  936/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   1 Batch  937/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.917, Loss:  0.075
+Epoch   1 Batch  938/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.082
+Epoch   1 Batch  939/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.910, Loss:  0.072
+Epoch   1 Batch  940/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.044
+Epoch   1 Batch  941/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.059
+Epoch   1 Batch  942/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.908, Loss:  0.065
+Epoch   1 Batch  943/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.912, Loss:  0.063
+Epoch   1 Batch  944/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   1 Batch  945/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.057
+Epoch   1 Batch  946/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.916, Loss:  0.044
+Epoch   1 Batch  947/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.910, Loss:  0.059
+Epoch   1 Batch  948/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.059
+Epoch   1 Batch  949/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.915, Loss:  0.052
+Epoch   1 Batch  950/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   1 Batch  951/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.923, Loss:  0.068
+Epoch   1 Batch  952/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   1 Batch  953/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   1 Batch  954/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.925, Loss:  0.067
+Epoch   1 Batch  955/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.928, Loss:  0.074
+Epoch   1 Batch  956/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.933, Loss:  0.066
+Epoch   1 Batch  957/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   1 Batch  958/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.067
+Epoch   1 Batch  959/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.068
+Epoch   1 Batch  960/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.916, Loss:  0.060
+Epoch   1 Batch  961/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.918, Loss:  0.068
+Epoch   1 Batch  962/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.054
+Epoch   1 Batch  963/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.912, Loss:  0.094
+Epoch   1 Batch  964/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   1 Batch  965/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.074
+Epoch   1 Batch  966/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   1 Batch  967/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.064
+Epoch   1 Batch  968/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.917, Loss:  0.076
+Epoch   1 Batch  969/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.916, Loss:  0.085
+Epoch   1 Batch  970/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.913, Loss:  0.066
+Epoch   1 Batch  971/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.908, Loss:  0.057
+Epoch   1 Batch  972/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.059
+Epoch   1 Batch  973/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.908, Loss:  0.057
+Epoch   1 Batch  974/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   1 Batch  975/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   1 Batch  976/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.050
+Epoch   1 Batch  977/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   1 Batch  978/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.064
+Epoch   1 Batch  979/1077 - Train Accuracy:  0.880, Validation Accuracy:  0.913, Loss:  0.068
+Epoch   1 Batch  980/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   1 Batch  981/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   1 Batch  982/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.909, Loss:  0.065
+Epoch   1 Batch  983/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.063
+Epoch   1 Batch  984/1077 - Train Accuracy:  0.878, Validation Accuracy:  0.915, Loss:  0.076
+Epoch   1 Batch  985/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.911, Loss:  0.059
+Epoch   1 Batch  986/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   1 Batch  987/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.918, Loss:  0.048
+Epoch   1 Batch  988/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.913, Loss:  0.072
+Epoch   1 Batch  989/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.915, Loss:  0.073
+Epoch   1 Batch  990/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.066
+Epoch   1 Batch  991/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   1 Batch  992/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.067
+Epoch   1 Batch  993/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.044
+Epoch   1 Batch  994/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   1 Batch  995/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.066
+Epoch   1 Batch  996/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   1 Batch  997/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.912, Loss:  0.054
+Epoch   1 Batch  998/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.063
+Epoch   1 Batch  999/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   1 Batch 1000/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   1 Batch 1001/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   1 Batch 1002/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   1 Batch 1003/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.059
+Epoch   1 Batch 1004/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.917, Loss:  0.070
+Epoch   1 Batch 1005/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.050
+Epoch   1 Batch 1006/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   1 Batch 1007/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.911, Loss:  0.057
+Epoch   1 Batch 1008/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.085
+Epoch   1 Batch 1009/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.059
+Epoch   1 Batch 1010/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.925, Loss:  0.058
+Epoch   1 Batch 1011/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   1 Batch 1012/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   1 Batch 1013/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   1 Batch 1014/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.064
+Epoch   1 Batch 1015/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.929, Loss:  0.075
+Epoch   1 Batch 1016/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.922, Loss:  0.066
+Epoch   1 Batch 1017/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.059
+Epoch   1 Batch 1018/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.048
+Epoch   1 Batch 1019/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.917, Loss:  0.075
+Epoch   1 Batch 1020/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   1 Batch 1021/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.915, Loss:  0.060
+Epoch   1 Batch 1022/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   1 Batch 1023/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.910, Loss:  0.062
+Epoch   1 Batch 1024/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.906, Loss:  0.071
+Epoch   1 Batch 1025/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.912, Loss:  0.059
+Epoch   1 Batch 1026/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.080
+Epoch   1 Batch 1027/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.919, Loss:  0.067
+Epoch   1 Batch 1028/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.922, Loss:  0.061
+Epoch   1 Batch 1029/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.051
+Epoch   1 Batch 1030/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.060
+Epoch   1 Batch 1031/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.063
+Epoch   1 Batch 1032/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.064
+Epoch   1 Batch 1033/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.913, Loss:  0.057
+Epoch   1 Batch 1034/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.913, Loss:  0.055
+Epoch   1 Batch 1035/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.918, Loss:  0.036
+Epoch   1 Batch 1036/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.076
+Epoch   1 Batch 1037/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   1 Batch 1038/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.076
+Epoch   1 Batch 1039/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.929, Loss:  0.066
+Epoch   1 Batch 1040/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.928, Loss:  0.061
+Epoch   1 Batch 1041/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.928, Loss:  0.067
+Epoch   1 Batch 1042/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   1 Batch 1043/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.076
+Epoch   1 Batch 1044/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.081
+Epoch   1 Batch 1045/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.058
+Epoch   1 Batch 1046/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.046
+Epoch   1 Batch 1047/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.909, Loss:  0.055
+Epoch   1 Batch 1048/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.908, Loss:  0.050
+Epoch   1 Batch 1049/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.903, Loss:  0.046
+Epoch   1 Batch 1050/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.059
+Epoch   1 Batch 1051/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.900, Loss:  0.066
+Epoch   1 Batch 1052/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.900, Loss:  0.060
+Epoch   1 Batch 1053/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.068
+Epoch   1 Batch 1054/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.912, Loss:  0.062
+Epoch   1 Batch 1055/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.065
+Epoch   1 Batch 1056/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   1 Batch 1057/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   1 Batch 1058/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.906, Loss:  0.071
+Epoch   1 Batch 1059/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.901, Loss:  0.074
+Epoch   1 Batch 1060/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.051
+Epoch   1 Batch 1061/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.914, Loss:  0.076
+Epoch   1 Batch 1062/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.907, Loss:  0.062
+Epoch   1 Batch 1063/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.909, Loss:  0.066
+Epoch   1 Batch 1064/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.063
+Epoch   1 Batch 1065/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.909, Loss:  0.071
+Epoch   1 Batch 1066/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.909, Loss:  0.055
+Epoch   1 Batch 1067/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.919, Loss:  0.074
+Epoch   1 Batch 1068/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.910, Loss:  0.060
+Epoch   1 Batch 1069/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   1 Batch 1070/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.913, Loss:  0.070
+Epoch   1 Batch 1071/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.913, Loss:  0.058
+Epoch   1 Batch 1072/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   1 Batch 1073/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.909, Loss:  0.068
+Epoch   1 Batch 1074/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.903, Loss:  0.070
+Epoch   1 Batch 1075/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.900, Loss:  0.070
+Epoch   2 Batch    0/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.907, Loss:  0.046
+Epoch   2 Batch    1/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.910, Loss:  0.060
+Epoch   2 Batch    2/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.906, Loss:  0.068
+Epoch   2 Batch    3/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   2 Batch    4/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   2 Batch    5/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.918, Loss:  0.072
+Epoch   2 Batch    6/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   2 Batch    7/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.905, Loss:  0.052
+Epoch   2 Batch    8/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.060
+Epoch   2 Batch    9/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.930, Loss:  0.065
+Epoch   2 Batch   10/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.921, Loss:  0.062
+Epoch   2 Batch   11/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.916, Loss:  0.072
+Epoch   2 Batch   12/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   2 Batch   13/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.922, Loss:  0.063
+Epoch   2 Batch   14/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   2 Batch   15/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.060
+Epoch   2 Batch   16/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.064
+Epoch   2 Batch   17/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   2 Batch   18/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.929, Loss:  0.065
+Epoch   2 Batch   19/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.065
+Epoch   2 Batch   20/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.918, Loss:  0.060
+Epoch   2 Batch   21/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.066
+Epoch   2 Batch   22/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.930, Loss:  0.067
+Epoch   2 Batch   23/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.066
+Epoch   2 Batch   24/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.924, Loss:  0.069
+Epoch   2 Batch   25/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   2 Batch   26/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.916, Loss:  0.066
+Epoch   2 Batch   27/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.052
+Epoch   2 Batch   28/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   2 Batch   29/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.913, Loss:  0.063
+Epoch   2 Batch   30/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   2 Batch   31/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.052
+Epoch   2 Batch   32/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   2 Batch   33/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.907, Loss:  0.055
+Epoch   2 Batch   34/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.900, Loss:  0.063
+Epoch   2 Batch   35/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.900, Loss:  0.066
+Epoch   2 Batch   36/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.898, Loss:  0.056
+Epoch   2 Batch   37/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.903, Loss:  0.063
+Epoch   2 Batch   38/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.907, Loss:  0.081
+Epoch   2 Batch   39/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.902, Loss:  0.068
+Epoch   2 Batch   40/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.906, Loss:  0.049
+Epoch   2 Batch   41/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.058
+Epoch   2 Batch   42/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.075
+Epoch   2 Batch   43/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.904, Loss:  0.044
+Epoch   2 Batch   44/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.905, Loss:  0.049
+Epoch   2 Batch   45/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.908, Loss:  0.052
+Epoch   2 Batch   46/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.904, Loss:  0.063
+Epoch   2 Batch   47/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.906, Loss:  0.068
+Epoch   2 Batch   48/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.912, Loss:  0.084
+Epoch   2 Batch   49/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.904, Loss:  0.078
+Epoch   2 Batch   50/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.902, Loss:  0.058
+Epoch   2 Batch   51/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.906, Loss:  0.073
+Epoch   2 Batch   52/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.907, Loss:  0.065
+Epoch   2 Batch   53/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.058
+Epoch   2 Batch   54/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.083
+Epoch   2 Batch   55/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.911, Loss:  0.056
+Epoch   2 Batch   56/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.907, Loss:  0.044
+Epoch   2 Batch   57/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   2 Batch   58/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.901, Loss:  0.062
+Epoch   2 Batch   59/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.907, Loss:  0.050
+Epoch   2 Batch   60/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.901, Loss:  0.041
+Epoch   2 Batch   61/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.076
+Epoch   2 Batch   62/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.910, Loss:  0.067
+Epoch   2 Batch   63/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.043
+Epoch   2 Batch   64/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.901, Loss:  0.056
+Epoch   2 Batch   65/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.902, Loss:  0.054
+Epoch   2 Batch   66/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.904, Loss:  0.046
+Epoch   2 Batch   67/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.060
+Epoch   2 Batch   68/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.906, Loss:  0.073
+Epoch   2 Batch   69/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.911, Loss:  0.070
+Epoch   2 Batch   70/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.914, Loss:  0.059
+Epoch   2 Batch   71/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch   72/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   2 Batch   73/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.921, Loss:  0.052
+Epoch   2 Batch   74/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   2 Batch   75/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.074
+Epoch   2 Batch   76/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch   77/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.061
+Epoch   2 Batch   78/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.064
+Epoch   2 Batch   79/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.059
+Epoch   2 Batch   80/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.057
+Epoch   2 Batch   81/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   2 Batch   82/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.932, Loss:  0.044
+Epoch   2 Batch   83/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.928, Loss:  0.071
+Epoch   2 Batch   84/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.057
+Epoch   2 Batch   85/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.937, Loss:  0.054
+Epoch   2 Batch   86/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.057
+Epoch   2 Batch   87/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.932, Loss:  0.062
+Epoch   2 Batch   88/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.930, Loss:  0.068
+Epoch   2 Batch   89/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.925, Loss:  0.075
+Epoch   2 Batch   90/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.931, Loss:  0.057
+Epoch   2 Batch   91/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   2 Batch   92/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.919, Loss:  0.067
+Epoch   2 Batch   93/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.920, Loss:  0.051
+Epoch   2 Batch   94/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.932, Loss:  0.050
+Epoch   2 Batch   95/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.936, Loss:  0.067
+Epoch   2 Batch   96/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.940, Loss:  0.064
+Epoch   2 Batch   97/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   2 Batch   98/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.065
+Epoch   2 Batch   99/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.938, Loss:  0.068
+Epoch   2 Batch  100/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   2 Batch  101/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.932, Loss:  0.056
+Epoch   2 Batch  102/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.058
+Epoch   2 Batch  103/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.071
+Epoch   2 Batch  104/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.064
+Epoch   2 Batch  105/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   2 Batch  106/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.078
+Epoch   2 Batch  107/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.915, Loss:  0.062
+Epoch   2 Batch  108/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.919, Loss:  0.061
+Epoch   2 Batch  109/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.050
+Epoch   2 Batch  110/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   2 Batch  111/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   2 Batch  112/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.915, Loss:  0.057
+Epoch   2 Batch  113/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   2 Batch  114/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   2 Batch  115/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.912, Loss:  0.065
+Epoch   2 Batch  116/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.916, Loss:  0.061
+Epoch   2 Batch  117/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.059
+Epoch   2 Batch  118/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.917, Loss:  0.049
+Epoch   2 Batch  119/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   2 Batch  120/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.067
+Epoch   2 Batch  121/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.909, Loss:  0.052
+Epoch   2 Batch  122/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.916, Loss:  0.053
+Epoch   2 Batch  123/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   2 Batch  124/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.926, Loss:  0.077
+Epoch   2 Batch  125/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.920, Loss:  0.067
+Epoch   2 Batch  126/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   2 Batch  127/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.057
+Epoch   2 Batch  128/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.909, Loss:  0.061
+Epoch   2 Batch  129/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.894, Loss:  0.059
+Epoch   2 Batch  130/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.897, Loss:  0.057
+Epoch   2 Batch  131/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.901, Loss:  0.063
+Epoch   2 Batch  132/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.894, Loss:  0.051
+Epoch   2 Batch  133/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.899, Loss:  0.048
+Epoch   2 Batch  134/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.903, Loss:  0.046
+Epoch   2 Batch  135/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.908, Loss:  0.065
+Epoch   2 Batch  136/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.904, Loss:  0.050
+Epoch   2 Batch  137/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.904, Loss:  0.041
+Epoch   2 Batch  138/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.907, Loss:  0.054
+Epoch   2 Batch  139/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.903, Loss:  0.059
+Epoch   2 Batch  140/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   2 Batch  141/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   2 Batch  142/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.917, Loss:  0.052
+Epoch   2 Batch  143/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.062
+Epoch   2 Batch  144/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.927, Loss:  0.087
+Epoch   2 Batch  145/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.923, Loss:  0.070
+Epoch   2 Batch  146/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.926, Loss:  0.091
+Epoch   2 Batch  147/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.064
+Epoch   2 Batch  148/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.066
+Epoch   2 Batch  149/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   2 Batch  150/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   2 Batch  151/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.049
+Epoch   2 Batch  152/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.911, Loss:  0.074
+Epoch   2 Batch  153/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.075
+Epoch   2 Batch  154/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   2 Batch  155/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   2 Batch  156/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   2 Batch  157/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.042
+Epoch   2 Batch  158/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.932, Loss:  0.073
+Epoch   2 Batch  159/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   2 Batch  160/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.045
+Epoch   2 Batch  161/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.916, Loss:  0.054
+Epoch   2 Batch  162/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.076
+Epoch   2 Batch  163/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.925, Loss:  0.072
+Epoch   2 Batch  164/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   2 Batch  165/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.914, Loss:  0.047
+Epoch   2 Batch  166/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.909, Loss:  0.063
+Epoch   2 Batch  167/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.908, Loss:  0.055
+Epoch   2 Batch  168/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.908, Loss:  0.070
+Epoch   2 Batch  169/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.913, Loss:  0.078
+Epoch   2 Batch  170/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.070
+Epoch   2 Batch  171/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.051
+Epoch   2 Batch  172/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   2 Batch  173/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.921, Loss:  0.066
+Epoch   2 Batch  174/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.051
+Epoch   2 Batch  175/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.916, Loss:  0.055
+Epoch   2 Batch  176/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.912, Loss:  0.066
+Epoch   2 Batch  177/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.062
+Epoch   2 Batch  178/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.064
+Epoch   2 Batch  179/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.057
+Epoch   2 Batch  180/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.911, Loss:  0.048
+Epoch   2 Batch  181/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.063
+Epoch   2 Batch  182/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.914, Loss:  0.057
+Epoch   2 Batch  183/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.059
+Epoch   2 Batch  184/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.055
+Epoch   2 Batch  185/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   2 Batch  186/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.056
+Epoch   2 Batch  187/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch  188/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   2 Batch  189/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   2 Batch  190/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.051
+Epoch   2 Batch  191/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   2 Batch  192/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   2 Batch  193/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.903, Loss:  0.047
+Epoch   2 Batch  194/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.045
+Epoch   2 Batch  195/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   2 Batch  196/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.045
+Epoch   2 Batch  197/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   2 Batch  198/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.925, Loss:  0.059
+Epoch   2 Batch  199/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   2 Batch  200/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.065
+Epoch   2 Batch  201/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.929, Loss:  0.041
+Epoch   2 Batch  202/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   2 Batch  203/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.048
+Epoch   2 Batch  204/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.073
+Epoch   2 Batch  205/1077 - Train Accuracy:  0.891, Validation Accuracy:  0.926, Loss:  0.060
+Epoch   2 Batch  206/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   2 Batch  207/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.048
+Epoch   2 Batch  208/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   2 Batch  209/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   2 Batch  210/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   2 Batch  211/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.905, Loss:  0.045
+Epoch   2 Batch  212/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.911, Loss:  0.046
+Epoch   2 Batch  213/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.913, Loss:  0.052
+Epoch   2 Batch  214/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   2 Batch  215/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   2 Batch  216/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.056
+Epoch   2 Batch  217/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.906, Loss:  0.044
+Epoch   2 Batch  218/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.905, Loss:  0.069
+Epoch   2 Batch  219/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.901, Loss:  0.042
+Epoch   2 Batch  220/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.900, Loss:  0.051
+Epoch   2 Batch  221/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.897, Loss:  0.056
+Epoch   2 Batch  222/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.045
+Epoch   2 Batch  223/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.903, Loss:  0.043
+Epoch   2 Batch  224/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.912, Loss:  0.064
+Epoch   2 Batch  225/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   2 Batch  226/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   2 Batch  227/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.911, Loss:  0.073
+Epoch   2 Batch  228/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   2 Batch  229/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.930, Loss:  0.058
+Epoch   2 Batch  230/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   2 Batch  231/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   2 Batch  232/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.041
+Epoch   2 Batch  233/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.911, Loss:  0.075
+Epoch   2 Batch  234/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.062
+Epoch   2 Batch  235/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   2 Batch  236/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.923, Loss:  0.064
+Epoch   2 Batch  237/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   2 Batch  238/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   2 Batch  239/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   2 Batch  240/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   2 Batch  241/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.915, Loss:  0.045
+Epoch   2 Batch  242/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.909, Loss:  0.044
+Epoch   2 Batch  243/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.904, Loss:  0.055
+Epoch   2 Batch  244/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.904, Loss:  0.051
+Epoch   2 Batch  245/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.917, Loss:  0.043
+Epoch   2 Batch  246/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.044
+Epoch   2 Batch  247/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.056
+Epoch   2 Batch  248/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.045
+Epoch   2 Batch  249/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   2 Batch  250/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.056
+Epoch   2 Batch  251/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.916, Loss:  0.057
+Epoch   2 Batch  252/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.917, Loss:  0.061
+Epoch   2 Batch  253/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   2 Batch  254/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.910, Loss:  0.057
+Epoch   2 Batch  255/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.910, Loss:  0.054
+Epoch   2 Batch  256/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.906, Loss:  0.073
+Epoch   2 Batch  257/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.905, Loss:  0.055
+Epoch   2 Batch  258/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.907, Loss:  0.054
+Epoch   2 Batch  259/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.906, Loss:  0.045
+Epoch   2 Batch  260/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.901, Loss:  0.048
+Epoch   2 Batch  261/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.901, Loss:  0.057
+Epoch   2 Batch  262/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.911, Loss:  0.048
+Epoch   2 Batch  263/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.912, Loss:  0.051
+Epoch   2 Batch  264/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.053
+Epoch   2 Batch  265/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   2 Batch  266/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   2 Batch  267/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   2 Batch  268/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.927, Loss:  0.062
+Epoch   2 Batch  269/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.926, Loss:  0.082
+Epoch   2 Batch  270/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.925, Loss:  0.065
+Epoch   2 Batch  271/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.048
+Epoch   2 Batch  272/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.093
+Epoch   2 Batch  273/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   2 Batch  274/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   2 Batch  275/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.917, Loss:  0.055
+Epoch   2 Batch  276/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.910, Loss:  0.088
+Epoch   2 Batch  277/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   2 Batch  278/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.067
+Epoch   2 Batch  279/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.068
+Epoch   2 Batch  280/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.072
+Epoch   2 Batch  281/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.072
+Epoch   2 Batch  282/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.912, Loss:  0.082
+Epoch   2 Batch  283/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.919, Loss:  0.080
+Epoch   2 Batch  284/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.928, Loss:  0.062
+Epoch   2 Batch  285/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.917, Loss:  0.061
+Epoch   2 Batch  286/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053
+Epoch   2 Batch  287/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   2 Batch  288/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.904, Loss:  0.067
+Epoch   2 Batch  289/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.901, Loss:  0.056
+Epoch   2 Batch  290/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.088
+Epoch   2 Batch  291/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.922, Loss:  0.076
+Epoch   2 Batch  292/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.067
+Epoch   2 Batch  293/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.917, Loss:  0.067
+Epoch   2 Batch  294/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.917, Loss:  0.052
+Epoch   2 Batch  295/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.913, Loss:  0.067
+Epoch   2 Batch  296/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.060
+Epoch   2 Batch  297/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   2 Batch  298/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.079
+Epoch   2 Batch  299/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.921, Loss:  0.067
+Epoch   2 Batch  300/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   2 Batch  301/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.908, Loss:  0.045
+Epoch   2 Batch  302/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.901, Loss:  0.053
+Epoch   2 Batch  303/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.906, Loss:  0.067
+Epoch   2 Batch  304/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.906, Loss:  0.059
+Epoch   2 Batch  305/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.904, Loss:  0.046
+Epoch   2 Batch  306/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.075
+Epoch   2 Batch  307/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.910, Loss:  0.049
+Epoch   2 Batch  308/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.908, Loss:  0.075
+Epoch   2 Batch  309/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.053
+Epoch   2 Batch  310/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   2 Batch  311/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.058
+Epoch   2 Batch  312/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.904, Loss:  0.077
+Epoch   2 Batch  313/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.899, Loss:  0.049
+Epoch   2 Batch  314/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.903, Loss:  0.049
+Epoch   2 Batch  315/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.899, Loss:  0.049
+Epoch   2 Batch  316/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.893, Loss:  0.068
+Epoch   2 Batch  317/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.895, Loss:  0.082
+Epoch   2 Batch  318/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.898, Loss:  0.066
+Epoch   2 Batch  319/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.900, Loss:  0.071
+Epoch   2 Batch  320/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.893, Loss:  0.067
+Epoch   2 Batch  321/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.898, Loss:  0.054
+Epoch   2 Batch  322/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.903, Loss:  0.057
+Epoch   2 Batch  323/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.908, Loss:  0.066
+Epoch   2 Batch  324/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.903, Loss:  0.055
+Epoch   2 Batch  325/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   2 Batch  326/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.913, Loss:  0.059
+Epoch   2 Batch  327/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   2 Batch  328/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.070
+Epoch   2 Batch  329/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.902, Loss:  0.071
+Epoch   2 Batch  330/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.894, Loss:  0.059
+Epoch   2 Batch  331/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.886, Loss:  0.061
+Epoch   2 Batch  332/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.888, Loss:  0.049
+Epoch   2 Batch  333/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.879, Loss:  0.054
+Epoch   2 Batch  334/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.878, Loss:  0.058
+Epoch   2 Batch  335/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.878, Loss:  0.058
+Epoch   2 Batch  336/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.893, Loss:  0.096
+Epoch   2 Batch  337/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.897, Loss:  0.065
+Epoch   2 Batch  338/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.895, Loss:  0.073
+Epoch   2 Batch  339/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.909, Loss:  0.049
+Epoch   2 Batch  340/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.908, Loss:  0.061
+Epoch   2 Batch  341/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.072
+Epoch   2 Batch  342/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.922, Loss:  0.053
+Epoch   2 Batch  343/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   2 Batch  344/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.912, Loss:  0.068
+Epoch   2 Batch  345/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   2 Batch  346/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.056
+Epoch   2 Batch  347/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.050
+Epoch   2 Batch  348/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.897, Loss:  0.052
+Epoch   2 Batch  349/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.897, Loss:  0.056
+Epoch   2 Batch  350/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.904, Loss:  0.065
+Epoch   2 Batch  351/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.903, Loss:  0.060
+Epoch   2 Batch  352/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.058
+Epoch   2 Batch  353/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.902, Loss:  0.067
+Epoch   2 Batch  354/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.074
+Epoch   2 Batch  355/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.898, Loss:  0.057
+Epoch   2 Batch  356/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.896, Loss:  0.063
+Epoch   2 Batch  357/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.896, Loss:  0.057
+Epoch   2 Batch  358/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.904, Loss:  0.076
+Epoch   2 Batch  359/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.059
+Epoch   2 Batch  360/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   2 Batch  361/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.055
+Epoch   2 Batch  362/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.914, Loss:  0.066
+Epoch   2 Batch  363/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.913, Loss:  0.068
+Epoch   2 Batch  364/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.915, Loss:  0.077
+Epoch   2 Batch  365/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   2 Batch  366/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   2 Batch  367/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.908, Loss:  0.047
+Epoch   2 Batch  368/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.068
+Epoch   2 Batch  369/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.913, Loss:  0.059
+Epoch   2 Batch  370/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.061
+Epoch   2 Batch  371/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.917, Loss:  0.055
+Epoch   2 Batch  372/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   2 Batch  373/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   2 Batch  374/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.071
+Epoch   2 Batch  375/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   2 Batch  376/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   2 Batch  377/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.929, Loss:  0.053
+Epoch   2 Batch  378/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   2 Batch  379/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.068
+Epoch   2 Batch  380/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   2 Batch  381/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.929, Loss:  0.066
+Epoch   2 Batch  382/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.932, Loss:  0.078
+Epoch   2 Batch  383/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   2 Batch  384/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   2 Batch  385/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.052
+Epoch   2 Batch  386/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   2 Batch  387/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   2 Batch  388/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.913, Loss:  0.061
+Epoch   2 Batch  389/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.058
+Epoch   2 Batch  390/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.904, Loss:  0.070
+Epoch   2 Batch  391/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.912, Loss:  0.057
+Epoch   2 Batch  392/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   2 Batch  393/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.053
+Epoch   2 Batch  394/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   2 Batch  395/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   2 Batch  396/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.932, Loss:  0.054
+Epoch   2 Batch  397/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.937, Loss:  0.049
+Epoch   2 Batch  398/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.938, Loss:  0.059
+Epoch   2 Batch  399/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.938, Loss:  0.052
+Epoch   2 Batch  400/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.943, Loss:  0.071
+Epoch   2 Batch  401/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   2 Batch  402/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   2 Batch  403/1077 - Train Accuracy:  0.884, Validation Accuracy:  0.927, Loss:  0.077
+Epoch   2 Batch  404/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.929, Loss:  0.050
+Epoch   2 Batch  405/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.930, Loss:  0.065
+Epoch   2 Batch  406/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   2 Batch  407/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.062
+Epoch   2 Batch  408/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.917, Loss:  0.052
+Epoch   2 Batch  409/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   2 Batch  410/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.077
+Epoch   2 Batch  411/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.067
+Epoch   2 Batch  412/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   2 Batch  413/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   2 Batch  414/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.910, Loss:  0.071
+Epoch   2 Batch  415/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   2 Batch  416/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.052
+Epoch   2 Batch  417/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.092
+Epoch   2 Batch  418/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   2 Batch  419/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.059
+Epoch   2 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   2 Batch  421/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.070
+Epoch   2 Batch  422/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   2 Batch  423/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.923, Loss:  0.068
+Epoch   2 Batch  424/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.928, Loss:  0.062
+Epoch   2 Batch  425/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.932, Loss:  0.045
+Epoch   2 Batch  426/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.057
+Epoch   2 Batch  427/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.931, Loss:  0.060
+Epoch   2 Batch  428/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   2 Batch  429/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.048
+Epoch   2 Batch  430/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   2 Batch  431/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.050
+Epoch   2 Batch  432/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.065
+Epoch   2 Batch  433/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.065
+Epoch   2 Batch  434/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   2 Batch  435/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.075
+Epoch   2 Batch  436/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.056
+Epoch   2 Batch  437/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.046
+Epoch   2 Batch  438/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.906, Loss:  0.054
+Epoch   2 Batch  439/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.897, Loss:  0.081
+Epoch   2 Batch  440/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.898, Loss:  0.064
+Epoch   2 Batch  441/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.892, Loss:  0.058
+Epoch   2 Batch  442/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.895, Loss:  0.064
+Epoch   2 Batch  443/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.899, Loss:  0.046
+Epoch   2 Batch  444/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.904, Loss:  0.052
+Epoch   2 Batch  445/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.895, Loss:  0.061
+Epoch   2 Batch  446/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.893, Loss:  0.051
+Epoch   2 Batch  447/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.890, Loss:  0.056
+Epoch   2 Batch  448/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.894, Loss:  0.067
+Epoch   2 Batch  449/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.901, Loss:  0.060
+Epoch   2 Batch  450/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.906, Loss:  0.063
+Epoch   2 Batch  451/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   2 Batch  452/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   2 Batch  453/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   2 Batch  454/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.058
+Epoch   2 Batch  455/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   2 Batch  456/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.059
+Epoch   2 Batch  457/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.050
+Epoch   2 Batch  458/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.939, Loss:  0.062
+Epoch   2 Batch  459/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.048
+Epoch   2 Batch  460/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.061
+Epoch   2 Batch  461/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.935, Loss:  0.052
+Epoch   2 Batch  462/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   2 Batch  463/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.932, Loss:  0.064
+Epoch   2 Batch  464/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   2 Batch  465/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.062
+Epoch   2 Batch  466/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   2 Batch  467/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.908, Loss:  0.063
+Epoch   2 Batch  468/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   2 Batch  469/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   2 Batch  470/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.902, Loss:  0.052
+Epoch   2 Batch  471/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.903, Loss:  0.046
+Epoch   2 Batch  472/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.051
+Epoch   2 Batch  473/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.906, Loss:  0.054
+Epoch   2 Batch  474/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.906, Loss:  0.053
+Epoch   2 Batch  475/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   2 Batch  476/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.913, Loss:  0.041
+Epoch   2 Batch  477/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.912, Loss:  0.054
+Epoch   2 Batch  478/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.047
+Epoch   2 Batch  479/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.914, Loss:  0.054
+Epoch   2 Batch  480/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.919, Loss:  0.050
+Epoch   2 Batch  481/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.913, Loss:  0.060
+Epoch   2 Batch  482/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.064
+Epoch   2 Batch  483/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.055
+Epoch   2 Batch  484/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   2 Batch  485/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   2 Batch  486/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   2 Batch  487/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   2 Batch  488/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   2 Batch  489/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.909, Loss:  0.048
+Epoch   2 Batch  490/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.903, Loss:  0.058
+Epoch   2 Batch  491/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.905, Loss:  0.060
+Epoch   2 Batch  492/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.062
+Epoch   2 Batch  493/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.910, Loss:  0.033
+Epoch   2 Batch  494/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.910, Loss:  0.038
+Epoch   2 Batch  495/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   2 Batch  496/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   2 Batch  497/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   2 Batch  498/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.053
+Epoch   2 Batch  499/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.932, Loss:  0.044
+Epoch   2 Batch  500/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch  501/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   2 Batch  502/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   2 Batch  503/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   2 Batch  504/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   2 Batch  505/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   2 Batch  506/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.058
+Epoch   2 Batch  507/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   2 Batch  508/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.046
+Epoch   2 Batch  509/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.073
+Epoch   2 Batch  510/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   2 Batch  511/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.935, Loss:  0.042
+Epoch   2 Batch  512/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   2 Batch  513/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.928, Loss:  0.060
+Epoch   2 Batch  514/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   2 Batch  515/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   2 Batch  516/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   2 Batch  517/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.062
+Epoch   2 Batch  518/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch  519/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.047
+Epoch   2 Batch  520/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.909, Loss:  0.051
+Epoch   2 Batch  521/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   2 Batch  522/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.905, Loss:  0.057
+Epoch   2 Batch  523/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   2 Batch  524/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.054
+Epoch   2 Batch  525/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.056
+Epoch   2 Batch  526/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.039
+Epoch   2 Batch  527/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.915, Loss:  0.065
+Epoch   2 Batch  528/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.908, Loss:  0.051
+Epoch   2 Batch  529/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.908, Loss:  0.058
+Epoch   2 Batch  530/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.066
+Epoch   2 Batch  531/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.904, Loss:  0.060
+Epoch   2 Batch  532/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   2 Batch  533/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.058
+Epoch   2 Batch  534/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.919, Loss:  0.061
+Epoch   2 Batch  535/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.913, Loss:  0.047
+Epoch   2 Batch  536/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   2 Batch  537/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.038
+Epoch   2 Batch  538/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.910, Loss:  0.034
+Epoch   2 Batch  539/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.071
+Epoch   2 Batch  540/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.900, Loss:  0.045
+Epoch   2 Batch  541/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.905, Loss:  0.046
+Epoch   2 Batch  542/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.898, Loss:  0.047
+Epoch   2 Batch  543/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.896, Loss:  0.044
+Epoch   2 Batch  544/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.896, Loss:  0.038
+Epoch   2 Batch  545/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.898, Loss:  0.055
+Epoch   2 Batch  546/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.894, Loss:  0.060
+Epoch   2 Batch  547/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.894, Loss:  0.042
+Epoch   2 Batch  548/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.898, Loss:  0.060
+Epoch   2 Batch  549/1077 - Train Accuracy:  0.868, Validation Accuracy:  0.903, Loss:  0.074
+Epoch   2 Batch  550/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.906, Loss:  0.049
+Epoch   2 Batch  551/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.060
+Epoch   2 Batch  552/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.908, Loss:  0.058
+Epoch   2 Batch  553/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.906, Loss:  0.072
+Epoch   2 Batch  554/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.049
+Epoch   2 Batch  555/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.041
+Epoch   2 Batch  556/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.902, Loss:  0.048
+Epoch   2 Batch  557/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.903, Loss:  0.050
+Epoch   2 Batch  558/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   2 Batch  559/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.910, Loss:  0.049
+Epoch   2 Batch  560/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.900, Loss:  0.053
+Epoch   2 Batch  561/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.904, Loss:  0.046
+Epoch   2 Batch  562/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.908, Loss:  0.046
+Epoch   2 Batch  563/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.907, Loss:  0.053
+Epoch   2 Batch  564/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.907, Loss:  0.058
+Epoch   2 Batch  565/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.907, Loss:  0.056
+Epoch   2 Batch  566/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.049
+Epoch   2 Batch  567/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.904, Loss:  0.054
+Epoch   2 Batch  568/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.904, Loss:  0.047
+Epoch   2 Batch  569/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.903, Loss:  0.060
+Epoch   2 Batch  570/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.908, Loss:  0.066
+Epoch   2 Batch  571/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.037
+Epoch   2 Batch  572/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.912, Loss:  0.046
+Epoch   2 Batch  573/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.060
+Epoch   2 Batch  574/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   2 Batch  575/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.039
+Epoch   2 Batch  576/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   2 Batch  577/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   2 Batch  578/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   2 Batch  579/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   2 Batch  580/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   2 Batch  581/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   2 Batch  582/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.045
+Epoch   2 Batch  583/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.057
+Epoch   2 Batch  584/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.050
+Epoch   2 Batch  585/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.044
+Epoch   2 Batch  586/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.914, Loss:  0.044
+Epoch   2 Batch  587/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.910, Loss:  0.052
+Epoch   2 Batch  588/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.041
+Epoch   2 Batch  589/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.901, Loss:  0.045
+Epoch   2 Batch  590/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.902, Loss:  0.058
+Epoch   2 Batch  591/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.897, Loss:  0.055
+Epoch   2 Batch  592/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.902, Loss:  0.051
+Epoch   2 Batch  593/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.897, Loss:  0.078
+Epoch   2 Batch  594/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.894, Loss:  0.064
+Epoch   2 Batch  595/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.895, Loss:  0.048
+Epoch   2 Batch  596/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.895, Loss:  0.050
+Epoch   2 Batch  597/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.896, Loss:  0.045
+Epoch   2 Batch  598/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.897, Loss:  0.056
+Epoch   2 Batch  599/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.912, Loss:  0.061
+Epoch   2 Batch  600/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.921, Loss:  0.057
+Epoch   2 Batch  601/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   2 Batch  602/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   2 Batch  603/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   2 Batch  604/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.941, Loss:  0.065
+Epoch   2 Batch  605/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.936, Loss:  0.062
+Epoch   2 Batch  606/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   2 Batch  607/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.055
+Epoch   2 Batch  608/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.924, Loss:  0.062
+Epoch   2 Batch  609/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   2 Batch  610/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.895, Loss:  0.067
+Epoch   2 Batch  611/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.893, Loss:  0.045
+Epoch   2 Batch  612/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.889, Loss:  0.046
+Epoch   2 Batch  613/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.892, Loss:  0.061
+Epoch   2 Batch  614/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.892, Loss:  0.040
+Epoch   2 Batch  615/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.900, Loss:  0.047
+Epoch   2 Batch  616/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.907, Loss:  0.059
+Epoch   2 Batch  617/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   2 Batch  618/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.056
+Epoch   2 Batch  619/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.035
+Epoch   2 Batch  620/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.040
+Epoch   2 Batch  621/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   2 Batch  622/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.058
+Epoch   2 Batch  623/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.918, Loss:  0.064
+Epoch   2 Batch  624/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   2 Batch  625/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.042
+Epoch   2 Batch  626/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   2 Batch  627/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   2 Batch  628/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   2 Batch  629/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   2 Batch  630/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.921, Loss:  0.045
+Epoch   2 Batch  631/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   2 Batch  632/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.929, Loss:  0.038
+Epoch   2 Batch  633/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.927, Loss:  0.056
+Epoch   2 Batch  634/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   2 Batch  635/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.054
+Epoch   2 Batch  636/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.055
+Epoch   2 Batch  637/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.932, Loss:  0.050
+Epoch   2 Batch  638/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.041
+Epoch   2 Batch  639/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.930, Loss:  0.073
+Epoch   2 Batch  640/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   2 Batch  641/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   2 Batch  642/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   2 Batch  643/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   2 Batch  644/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   2 Batch  645/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.066
+Epoch   2 Batch  646/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   2 Batch  647/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   2 Batch  648/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.033
+Epoch   2 Batch  649/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   2 Batch  650/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.054
+Epoch   2 Batch  651/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.920, Loss:  0.053
+Epoch   2 Batch  652/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.054
+Epoch   2 Batch  653/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.053
+Epoch   2 Batch  654/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.049
+Epoch   2 Batch  655/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.063
+Epoch   2 Batch  656/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.057
+Epoch   2 Batch  657/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.051
+Epoch   2 Batch  658/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.040
+Epoch   2 Batch  659/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.053
+Epoch   2 Batch  660/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   2 Batch  661/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   2 Batch  662/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   2 Batch  663/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.904, Loss:  0.042
+Epoch   2 Batch  664/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.900, Loss:  0.052
+Epoch   2 Batch  665/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.899, Loss:  0.048
+Epoch   2 Batch  666/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.899, Loss:  0.063
+Epoch   2 Batch  667/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.897, Loss:  0.060
+Epoch   2 Batch  668/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.905, Loss:  0.042
+Epoch   2 Batch  669/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   2 Batch  670/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   2 Batch  671/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.908, Loss:  0.059
+Epoch   2 Batch  672/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.045
+Epoch   2 Batch  673/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.048
+Epoch   2 Batch  674/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.916, Loss:  0.051
+Epoch   2 Batch  675/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.921, Loss:  0.060
+Epoch   2 Batch  676/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.921, Loss:  0.046
+Epoch   2 Batch  677/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.068
+Epoch   2 Batch  678/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   2 Batch  679/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   2 Batch  680/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   2 Batch  681/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   2 Batch  682/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.923, Loss:  0.055
+Epoch   2 Batch  683/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.037
+Epoch   2 Batch  684/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   2 Batch  685/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   2 Batch  686/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.044
+Epoch   2 Batch  687/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.056
+Epoch   2 Batch  688/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   2 Batch  689/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   2 Batch  690/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   2 Batch  691/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   2 Batch  692/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.043
+Epoch   2 Batch  693/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.909, Loss:  0.070
+Epoch   2 Batch  694/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053
+Epoch   2 Batch  695/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.919, Loss:  0.042
+Epoch   2 Batch  696/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   2 Batch  697/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   2 Batch  698/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.046
+Epoch   2 Batch  699/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.910, Loss:  0.044
+Epoch   2 Batch  700/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   2 Batch  701/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.052
+Epoch   2 Batch  702/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   2 Batch  703/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.913, Loss:  0.055
+Epoch   2 Batch  704/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.063
+Epoch   2 Batch  705/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.061
+Epoch   2 Batch  706/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.073
+Epoch   2 Batch  707/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.928, Loss:  0.056
+Epoch   2 Batch  708/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.929, Loss:  0.057
+Epoch   2 Batch  709/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   2 Batch  710/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.040
+Epoch   2 Batch  711/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.068
+Epoch   2 Batch  712/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   2 Batch  713/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.036
+Epoch   2 Batch  714/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   2 Batch  715/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.932, Loss:  0.059
+Epoch   2 Batch  716/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.938, Loss:  0.047
+Epoch   2 Batch  717/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.939, Loss:  0.035
+Epoch   2 Batch  718/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.939, Loss:  0.053
+Epoch   2 Batch  719/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   2 Batch  720/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.053
+Epoch   2 Batch  721/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.060
+Epoch   2 Batch  722/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.042
+Epoch   2 Batch  723/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.058
+Epoch   2 Batch  724/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.056
+Epoch   2 Batch  725/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   2 Batch  726/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.052
+Epoch   2 Batch  727/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch  728/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.934, Loss:  0.058
+Epoch   2 Batch  729/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.063
+Epoch   2 Batch  730/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.932, Loss:  0.058
+Epoch   2 Batch  731/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   2 Batch  732/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.935, Loss:  0.054
+Epoch   2 Batch  733/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.933, Loss:  0.053
+Epoch   2 Batch  734/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   2 Batch  735/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.923, Loss:  0.044
+Epoch   2 Batch  736/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.046
+Epoch   2 Batch  737/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.921, Loss:  0.051
+Epoch   2 Batch  738/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.038
+Epoch   2 Batch  739/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.041
+Epoch   2 Batch  740/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.038
+Epoch   2 Batch  741/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   2 Batch  742/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.036
+Epoch   2 Batch  743/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.933, Loss:  0.056
+Epoch   2 Batch  744/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   2 Batch  745/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.053
+Epoch   2 Batch  746/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.932, Loss:  0.052
+Epoch   2 Batch  747/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.927, Loss:  0.041
+Epoch   2 Batch  748/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   2 Batch  749/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   2 Batch  750/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   2 Batch  751/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.938, Loss:  0.050
+Epoch   2 Batch  752/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.942, Loss:  0.047
+Epoch   2 Batch  753/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.055
+Epoch   2 Batch  754/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.061
+Epoch   2 Batch  755/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.942, Loss:  0.062
+Epoch   2 Batch  756/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.942, Loss:  0.049
+Epoch   2 Batch  757/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.939, Loss:  0.045
+Epoch   2 Batch  758/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.047
+Epoch   2 Batch  759/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.045
+Epoch   2 Batch  760/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   2 Batch  761/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.049
+Epoch   2 Batch  762/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   2 Batch  763/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   2 Batch  764/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   2 Batch  765/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.933, Loss:  0.060
+Epoch   2 Batch  766/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   2 Batch  767/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.938, Loss:  0.044
+Epoch   2 Batch  768/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   2 Batch  769/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.045
+Epoch   2 Batch  770/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   2 Batch  771/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.057
+Epoch   2 Batch  772/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.053
+Epoch   2 Batch  773/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   2 Batch  774/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   2 Batch  775/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.937, Loss:  0.056
+Epoch   2 Batch  776/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.947, Loss:  0.043
+Epoch   2 Batch  777/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.056
+Epoch   2 Batch  778/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.936, Loss:  0.047
+Epoch   2 Batch  779/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.057
+Epoch   2 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.947, Loss:  0.062
+Epoch   2 Batch  781/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.945, Loss:  0.044
+Epoch   2 Batch  782/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.940, Loss:  0.047
+Epoch   2 Batch  783/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.942, Loss:  0.053
+Epoch   2 Batch  784/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.938, Loss:  0.045
+Epoch   2 Batch  785/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   2 Batch  786/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.041
+Epoch   2 Batch  787/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.053
+Epoch   2 Batch  788/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   2 Batch  789/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.051
+Epoch   2 Batch  790/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   2 Batch  791/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   2 Batch  792/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   2 Batch  793/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   2 Batch  794/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.047
+Epoch   2 Batch  795/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.066
+Epoch   2 Batch  796/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   2 Batch  797/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   2 Batch  798/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   2 Batch  799/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.933, Loss:  0.078
+Epoch   2 Batch  800/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.940, Loss:  0.050
+Epoch   2 Batch  801/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.940, Loss:  0.068
+Epoch   2 Batch  802/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.058
+Epoch   2 Batch  803/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.944, Loss:  0.051
+Epoch   2 Batch  804/1077 - Train Accuracy:  0.975, Validation Accuracy:  0.944, Loss:  0.037
+Epoch   2 Batch  805/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.939, Loss:  0.048
+Epoch   2 Batch  806/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.940, Loss:  0.043
+Epoch   2 Batch  807/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch  808/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.928, Loss:  0.069
+Epoch   2 Batch  809/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.936, Loss:  0.069
+Epoch   2 Batch  810/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.944, Loss:  0.046
+Epoch   2 Batch  811/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   2 Batch  812/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   2 Batch  813/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.936, Loss:  0.045
+Epoch   2 Batch  814/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.936, Loss:  0.063
+Epoch   2 Batch  815/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   2 Batch  816/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.935, Loss:  0.062
+Epoch   2 Batch  817/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   2 Batch  818/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   2 Batch  819/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.050
+Epoch   2 Batch  820/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   2 Batch  821/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.049
+Epoch   2 Batch  822/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   2 Batch  823/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.056
+Epoch   2 Batch  824/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   2 Batch  825/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.040
+Epoch   2 Batch  826/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   2 Batch  827/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.056
+Epoch   2 Batch  828/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.048
+Epoch   2 Batch  829/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.904, Loss:  0.064
+Epoch   2 Batch  830/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.904, Loss:  0.061
+Epoch   2 Batch  831/1077 - Train Accuracy:  0.872, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   2 Batch  832/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.913, Loss:  0.049
+Epoch   2 Batch  833/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.913, Loss:  0.054
+Epoch   2 Batch  834/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.059
+Epoch   2 Batch  835/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.051
+Epoch   2 Batch  836/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   2 Batch  837/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.923, Loss:  0.062
+Epoch   2 Batch  838/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   2 Batch  839/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.036
+Epoch   2 Batch  840/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.039
+Epoch   2 Batch  841/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.924, Loss:  0.050
+Epoch   2 Batch  842/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   2 Batch  843/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   2 Batch  844/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.918, Loss:  0.039
+Epoch   2 Batch  845/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.039
+Epoch   2 Batch  846/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.063
+Epoch   2 Batch  847/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   2 Batch  848/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.045
+Epoch   2 Batch  849/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   2 Batch  850/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.913, Loss:  0.085
+Epoch   2 Batch  851/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.913, Loss:  0.062
+Epoch   2 Batch  852/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.061
+Epoch   2 Batch  853/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.055
+Epoch   2 Batch  854/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.921, Loss:  0.062
+Epoch   2 Batch  855/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   2 Batch  856/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.068
+Epoch   2 Batch  857/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.048
+Epoch   2 Batch  858/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   2 Batch  859/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.917, Loss:  0.067
+Epoch   2 Batch  860/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.057
+Epoch   2 Batch  861/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.926, Loss:  0.051
+Epoch   2 Batch  862/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   2 Batch  863/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   2 Batch  864/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   2 Batch  865/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   2 Batch  866/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.913, Loss:  0.056
+Epoch   2 Batch  867/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.908, Loss:  0.085
+Epoch   2 Batch  868/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   2 Batch  869/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   2 Batch  870/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.058
+Epoch   2 Batch  871/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   2 Batch  872/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.056
+Epoch   2 Batch  873/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.935, Loss:  0.056
+Epoch   2 Batch  874/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.931, Loss:  0.061
+Epoch   2 Batch  875/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.932, Loss:  0.058
+Epoch   2 Batch  876/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   2 Batch  877/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.046
+Epoch   2 Batch  878/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.939, Loss:  0.039
+Epoch   2 Batch  879/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.047
+Epoch   2 Batch  880/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   2 Batch  881/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.941, Loss:  0.063
+Epoch   2 Batch  882/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.941, Loss:  0.058
+Epoch   2 Batch  883/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.079
+Epoch   2 Batch  884/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.927, Loss:  0.052
+Epoch   2 Batch  885/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.920, Loss:  0.031
+Epoch   2 Batch  886/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   2 Batch  887/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.922, Loss:  0.063
+Epoch   2 Batch  888/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   2 Batch  889/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   2 Batch  890/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.911, Loss:  0.049
+Epoch   2 Batch  891/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.046
+Epoch   2 Batch  892/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.045
+Epoch   2 Batch  893/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   2 Batch  894/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   2 Batch  895/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   2 Batch  896/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.914, Loss:  0.052
+Epoch   2 Batch  897/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.907, Loss:  0.037
+Epoch   2 Batch  898/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.904, Loss:  0.044
+Epoch   2 Batch  899/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.057
+Epoch   2 Batch  900/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   2 Batch  901/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.068
+Epoch   2 Batch  902/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.057
+Epoch   2 Batch  903/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   2 Batch  904/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   2 Batch  905/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.912, Loss:  0.046
+Epoch   2 Batch  906/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.902, Loss:  0.056
+Epoch   2 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.908, Loss:  0.052
+Epoch   2 Batch  908/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.061
+Epoch   2 Batch  909/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.931, Loss:  0.061
+Epoch   2 Batch  910/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.920, Loss:  0.057
+Epoch   2 Batch  911/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.929, Loss:  0.057
+Epoch   2 Batch  912/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.933, Loss:  0.043
+Epoch   2 Batch  913/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.067
+Epoch   2 Batch  914/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.919, Loss:  0.079
+Epoch   2 Batch  915/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   2 Batch  916/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.053
+Epoch   2 Batch  917/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.901, Loss:  0.051
+Epoch   2 Batch  918/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.911, Loss:  0.048
+Epoch   2 Batch  919/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.907, Loss:  0.047
+Epoch   2 Batch  920/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.052
+Epoch   2 Batch  921/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.912, Loss:  0.056
+Epoch   2 Batch  922/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   2 Batch  923/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.923, Loss:  0.033
+Epoch   2 Batch  924/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.914, Loss:  0.071
+Epoch   2 Batch  925/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   2 Batch  926/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   2 Batch  927/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   2 Batch  928/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   2 Batch  929/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.906, Loss:  0.060
+Epoch   2 Batch  930/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.909, Loss:  0.047
+Epoch   2 Batch  931/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   2 Batch  932/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   2 Batch  933/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   2 Batch  934/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.928, Loss:  0.047
+Epoch   2 Batch  935/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   2 Batch  936/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.051
+Epoch   2 Batch  937/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.918, Loss:  0.074
+Epoch   2 Batch  938/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.058
+Epoch   2 Batch  939/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   2 Batch  940/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   2 Batch  941/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   2 Batch  942/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.059
+Epoch   2 Batch  943/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   2 Batch  944/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.927, Loss:  0.052
+Epoch   2 Batch  945/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   2 Batch  946/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.920, Loss:  0.037
+Epoch   2 Batch  947/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.916, Loss:  0.057
+Epoch   2 Batch  948/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   2 Batch  949/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.919, Loss:  0.043
+Epoch   2 Batch  950/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.039
+Epoch   2 Batch  951/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.913, Loss:  0.063
+Epoch   2 Batch  952/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.917, Loss:  0.045
+Epoch   2 Batch  953/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.046
+Epoch   2 Batch  954/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.054
+Epoch   2 Batch  955/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   2 Batch  956/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   2 Batch  957/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.914, Loss:  0.042
+Epoch   2 Batch  958/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.903, Loss:  0.048
+Epoch   2 Batch  959/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.041
+Epoch   2 Batch  960/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   2 Batch  961/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.044
+Epoch   2 Batch  962/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   2 Batch  963/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.064
+Epoch   2 Batch  964/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   2 Batch  965/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.051
+Epoch   2 Batch  966/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   2 Batch  967/1077 - Train Accuracy:  0.896, Validation Accuracy:  0.923, Loss:  0.059
+Epoch   2 Batch  968/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.922, Loss:  0.064
+Epoch   2 Batch  969/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.926, Loss:  0.062
+Epoch   2 Batch  970/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   2 Batch  971/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   2 Batch  972/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.916, Loss:  0.052
+Epoch   2 Batch  973/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.916, Loss:  0.033
+Epoch   2 Batch  974/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.037
+Epoch   2 Batch  975/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.045
+Epoch   2 Batch  976/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.037
+Epoch   2 Batch  977/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.925, Loss:  0.037
+Epoch   2 Batch  978/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   2 Batch  979/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.932, Loss:  0.056
+Epoch   2 Batch  980/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   2 Batch  981/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   2 Batch  982/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.056
+Epoch   2 Batch  983/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   2 Batch  984/1077 - Train Accuracy:  0.875, Validation Accuracy:  0.936, Loss:  0.067
+Epoch   2 Batch  985/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.938, Loss:  0.049
+Epoch   2 Batch  986/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.938, Loss:  0.055
+Epoch   2 Batch  987/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.954, Loss:  0.046
+Epoch   2 Batch  988/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.952, Loss:  0.062
+Epoch   2 Batch  989/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.938, Loss:  0.048
+Epoch   2 Batch  990/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.910, Loss:  0.061
+Epoch   2 Batch  991/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.910, Loss:  0.049
+Epoch   2 Batch  992/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.073
+Epoch   2 Batch  993/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.908, Loss:  0.040
+Epoch   2 Batch  994/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.917, Loss:  0.050
+Epoch   2 Batch  995/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   2 Batch  996/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.048
+Epoch   2 Batch  997/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   2 Batch  998/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   2 Batch  999/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.057
+Epoch   2 Batch 1000/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.946, Loss:  0.054
+Epoch   2 Batch 1001/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.940, Loss:  0.046
+Epoch   2 Batch 1002/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.930, Loss:  0.035
+Epoch   2 Batch 1003/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   2 Batch 1004/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.934, Loss:  0.069
+Epoch   2 Batch 1005/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch 1006/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.041
+Epoch   2 Batch 1007/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.934, Loss:  0.051
+Epoch   2 Batch 1008/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.932, Loss:  0.074
+Epoch   2 Batch 1009/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.928, Loss:  0.038
+Epoch   2 Batch 1010/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   2 Batch 1011/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.043
+Epoch   2 Batch 1012/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.032
+Epoch   2 Batch 1013/1077 - Train Accuracy:  0.974, Validation Accuracy:  0.917, Loss:  0.037
+Epoch   2 Batch 1014/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   2 Batch 1015/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.933, Loss:  0.058
+Epoch   2 Batch 1016/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   2 Batch 1017/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.055
+Epoch   2 Batch 1018/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.941, Loss:  0.041
+Epoch   2 Batch 1019/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.948, Loss:  0.074
+Epoch   2 Batch 1020/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.941, Loss:  0.042
+Epoch   2 Batch 1021/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   2 Batch 1022/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   2 Batch 1023/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.932, Loss:  0.052
+Epoch   2 Batch 1024/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.936, Loss:  0.066
+Epoch   2 Batch 1025/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.940, Loss:  0.055
+Epoch   2 Batch 1026/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.944, Loss:  0.052
+Epoch   2 Batch 1027/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.943, Loss:  0.049
+Epoch   2 Batch 1028/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.943, Loss:  0.048
+Epoch   2 Batch 1029/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.942, Loss:  0.045
+Epoch   2 Batch 1030/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.042
+Epoch   2 Batch 1031/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.059
+Epoch   2 Batch 1032/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.070
+Epoch   2 Batch 1033/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.062
+Epoch   2 Batch 1034/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   2 Batch 1035/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.911, Loss:  0.030
+Epoch   2 Batch 1036/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   2 Batch 1037/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.907, Loss:  0.047
+Epoch   2 Batch 1038/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.051
+Epoch   2 Batch 1039/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.907, Loss:  0.049
+Epoch   2 Batch 1040/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.058
+Epoch   2 Batch 1041/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.913, Loss:  0.050
+Epoch   2 Batch 1042/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   2 Batch 1043/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.061
+Epoch   2 Batch 1044/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.910, Loss:  0.070
+Epoch   2 Batch 1045/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   2 Batch 1046/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.914, Loss:  0.042
+Epoch   2 Batch 1047/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   2 Batch 1048/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   2 Batch 1049/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   2 Batch 1050/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   2 Batch 1051/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   2 Batch 1052/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.049
+Epoch   2 Batch 1053/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.926, Loss:  0.069
+Epoch   2 Batch 1054/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.047
+Epoch   2 Batch 1055/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   2 Batch 1056/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   2 Batch 1057/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.917, Loss:  0.059
+Epoch   2 Batch 1058/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.056
+Epoch   2 Batch 1059/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.929, Loss:  0.070
+Epoch   2 Batch 1060/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.928, Loss:  0.051
+Epoch   2 Batch 1061/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.935, Loss:  0.070
+Epoch   2 Batch 1062/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.936, Loss:  0.056
+Epoch   2 Batch 1063/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.058
+Epoch   2 Batch 1064/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   2 Batch 1065/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.043
+Epoch   2 Batch 1066/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.036
+Epoch   2 Batch 1067/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.053
+Epoch   2 Batch 1068/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.043
+Epoch   2 Batch 1069/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.037
+Epoch   2 Batch 1070/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.920, Loss:  0.043
+Epoch   2 Batch 1071/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.920, Loss:  0.046
+Epoch   2 Batch 1072/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   2 Batch 1073/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.913, Loss:  0.053
+Epoch   2 Batch 1074/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   2 Batch 1075/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   3 Batch    0/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   3 Batch    1/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.032
+Epoch   3 Batch    2/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.053
+Epoch   3 Batch    3/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   3 Batch    4/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.911, Loss:  0.043
+Epoch   3 Batch    5/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.904, Loss:  0.072
+Epoch   3 Batch    6/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.904, Loss:  0.054
+Epoch   3 Batch    7/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.906, Loss:  0.040
+Epoch   3 Batch    8/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.896, Loss:  0.051
+Epoch   3 Batch    9/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.892, Loss:  0.041
+Epoch   3 Batch   10/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.898, Loss:  0.053
+Epoch   3 Batch   11/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.905, Loss:  0.068
+Epoch   3 Batch   12/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.053
+Epoch   3 Batch   13/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.916, Loss:  0.054
+Epoch   3 Batch   14/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.912, Loss:  0.036
+Epoch   3 Batch   15/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.912, Loss:  0.044
+Epoch   3 Batch   16/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.919, Loss:  0.060
+Epoch   3 Batch   17/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   3 Batch   18/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.922, Loss:  0.067
+Epoch   3 Batch   19/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   3 Batch   20/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.040
+Epoch   3 Batch   21/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   3 Batch   22/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.933, Loss:  0.062
+Epoch   3 Batch   23/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.051
+Epoch   3 Batch   24/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   3 Batch   25/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.922, Loss:  0.038
+Epoch   3 Batch   26/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.922, Loss:  0.055
+Epoch   3 Batch   27/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.042
+Epoch   3 Batch   28/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.926, Loss:  0.053
+Epoch   3 Batch   29/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.056
+Epoch   3 Batch   30/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.036
+Epoch   3 Batch   31/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   3 Batch   32/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   3 Batch   33/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.055
+Epoch   3 Batch   34/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.050
+Epoch   3 Batch   35/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   3 Batch   36/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   3 Batch   37/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   3 Batch   38/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.930, Loss:  0.078
+Epoch   3 Batch   39/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.930, Loss:  0.062
+Epoch   3 Batch   40/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.918, Loss:  0.040
+Epoch   3 Batch   41/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.906, Loss:  0.044
+Epoch   3 Batch   42/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.908, Loss:  0.064
+Epoch   3 Batch   43/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.913, Loss:  0.028
+Epoch   3 Batch   44/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.916, Loss:  0.040
+Epoch   3 Batch   45/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.916, Loss:  0.053
+Epoch   3 Batch   46/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.046
+Epoch   3 Batch   47/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   3 Batch   48/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.087
+Epoch   3 Batch   49/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.060
+Epoch   3 Batch   50/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   3 Batch   51/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.058
+Epoch   3 Batch   52/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.065
+Epoch   3 Batch   53/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.922, Loss:  0.039
+Epoch   3 Batch   54/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.078
+Epoch   3 Batch   55/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   3 Batch   56/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.051
+Epoch   3 Batch   57/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.916, Loss:  0.059
+Epoch   3 Batch   58/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.046
+Epoch   3 Batch   59/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   3 Batch   60/1077 - Train Accuracy:  0.970, Validation Accuracy:  0.910, Loss:  0.035
+Epoch   3 Batch   61/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.060
+Epoch   3 Batch   62/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.910, Loss:  0.061
+Epoch   3 Batch   63/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   3 Batch   64/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   3 Batch   65/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   3 Batch   66/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.031
+Epoch   3 Batch   67/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   3 Batch   68/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.053
+Epoch   3 Batch   69/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.061
+Epoch   3 Batch   70/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.050
+Epoch   3 Batch   71/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.035
+Epoch   3 Batch   72/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   3 Batch   73/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.055
+Epoch   3 Batch   74/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.045
+Epoch   3 Batch   75/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.915, Loss:  0.065
+Epoch   3 Batch   76/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.926, Loss:  0.038
+Epoch   3 Batch   77/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   3 Batch   78/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.928, Loss:  0.042
+Epoch   3 Batch   79/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   3 Batch   80/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   3 Batch   81/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   3 Batch   82/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.922, Loss:  0.038
+Epoch   3 Batch   83/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.922, Loss:  0.044
+Epoch   3 Batch   84/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.043
+Epoch   3 Batch   85/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.047
+Epoch   3 Batch   86/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   3 Batch   87/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.065
+Epoch   3 Batch   88/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   3 Batch   89/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.050
+Epoch   3 Batch   90/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   3 Batch   91/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   3 Batch   92/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.910, Loss:  0.060
+Epoch   3 Batch   93/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.910, Loss:  0.038
+Epoch   3 Batch   94/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   3 Batch   95/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.054
+Epoch   3 Batch   96/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.053
+Epoch   3 Batch   97/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.050
+Epoch   3 Batch   98/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.907, Loss:  0.058
+Epoch   3 Batch   99/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.052
+Epoch   3 Batch  100/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.047
+Epoch   3 Batch  101/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.048
+Epoch   3 Batch  102/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.932, Loss:  0.042
+Epoch   3 Batch  103/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.929, Loss:  0.061
+Epoch   3 Batch  104/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   3 Batch  105/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   3 Batch  106/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.056
+Epoch   3 Batch  107/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   3 Batch  108/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   3 Batch  109/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.059
+Epoch   3 Batch  110/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.929, Loss:  0.035
+Epoch   3 Batch  111/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.922, Loss:  0.055
+Epoch   3 Batch  112/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.921, Loss:  0.047
+Epoch   3 Batch  113/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   3 Batch  114/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.911, Loss:  0.035
+Epoch   3 Batch  115/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   3 Batch  116/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.920, Loss:  0.066
+Epoch   3 Batch  117/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   3 Batch  118/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  119/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   3 Batch  120/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.940, Loss:  0.058
+Epoch   3 Batch  121/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   3 Batch  122/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   3 Batch  123/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.941, Loss:  0.045
+Epoch   3 Batch  124/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.936, Loss:  0.073
+Epoch   3 Batch  125/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   3 Batch  126/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.045
+Epoch   3 Batch  127/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  128/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.909, Loss:  0.047
+Epoch   3 Batch  129/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.066
+Epoch   3 Batch  130/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.048
+Epoch   3 Batch  131/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.901, Loss:  0.055
+Epoch   3 Batch  132/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.903, Loss:  0.040
+Epoch   3 Batch  133/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.909, Loss:  0.039
+Epoch   3 Batch  134/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.051
+Epoch   3 Batch  135/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   3 Batch  136/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   3 Batch  137/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.030
+Epoch   3 Batch  138/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   3 Batch  139/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.919, Loss:  0.062
+Epoch   3 Batch  140/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   3 Batch  141/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.913, Loss:  0.047
+Epoch   3 Batch  142/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.043
+Epoch   3 Batch  143/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.912, Loss:  0.049
+Epoch   3 Batch  144/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.917, Loss:  0.068
+Epoch   3 Batch  145/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   3 Batch  146/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.923, Loss:  0.083
+Epoch   3 Batch  147/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   3 Batch  148/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   3 Batch  149/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   3 Batch  150/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.057
+Epoch   3 Batch  151/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.046
+Epoch   3 Batch  152/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.910, Loss:  0.073
+Epoch   3 Batch  153/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.908, Loss:  0.064
+Epoch   3 Batch  154/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.905, Loss:  0.045
+Epoch   3 Batch  155/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.911, Loss:  0.056
+Epoch   3 Batch  156/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   3 Batch  157/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.909, Loss:  0.041
+Epoch   3 Batch  158/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.903, Loss:  0.065
+Epoch   3 Batch  159/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.040
+Epoch   3 Batch  160/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.047
+Epoch   3 Batch  161/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.913, Loss:  0.041
+Epoch   3 Batch  162/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.913, Loss:  0.063
+Epoch   3 Batch  163/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.064
+Epoch   3 Batch  164/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   3 Batch  165/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   3 Batch  166/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.917, Loss:  0.056
+Epoch   3 Batch  167/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   3 Batch  168/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.919, Loss:  0.071
+Epoch   3 Batch  169/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.064
+Epoch   3 Batch  170/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.065
+Epoch   3 Batch  171/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   3 Batch  172/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.914, Loss:  0.038
+Epoch   3 Batch  173/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.908, Loss:  0.052
+Epoch   3 Batch  174/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.040
+Epoch   3 Batch  175/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.059
+Epoch   3 Batch  176/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   3 Batch  177/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.057
+Epoch   3 Batch  178/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  179/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.047
+Epoch   3 Batch  180/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   3 Batch  181/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.058
+Epoch   3 Batch  182/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.935, Loss:  0.051
+Epoch   3 Batch  183/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   3 Batch  184/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.046
+Epoch   3 Batch  185/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.935, Loss:  0.054
+Epoch   3 Batch  186/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.933, Loss:  0.064
+Epoch   3 Batch  187/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.932, Loss:  0.039
+Epoch   3 Batch  188/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.052
+Epoch   3 Batch  189/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.044
+Epoch   3 Batch  190/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.051
+Epoch   3 Batch  191/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.932, Loss:  0.043
+Epoch   3 Batch  192/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.069
+Epoch   3 Batch  193/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   3 Batch  194/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   3 Batch  195/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.927, Loss:  0.034
+Epoch   3 Batch  196/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.927, Loss:  0.041
+Epoch   3 Batch  197/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  198/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.062
+Epoch   3 Batch  199/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  200/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.932, Loss:  0.059
+Epoch   3 Batch  201/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.927, Loss:  0.040
+Epoch   3 Batch  202/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.927, Loss:  0.049
+Epoch   3 Batch  203/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   3 Batch  204/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.074
+Epoch   3 Batch  205/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.932, Loss:  0.077
+Epoch   3 Batch  206/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   3 Batch  207/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   3 Batch  208/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   3 Batch  209/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.927, Loss:  0.030
+Epoch   3 Batch  210/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   3 Batch  211/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   3 Batch  212/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.043
+Epoch   3 Batch  213/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.043
+Epoch   3 Batch  214/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   3 Batch  215/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.927, Loss:  0.051
+Epoch   3 Batch  216/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.927, Loss:  0.052
+Epoch   3 Batch  217/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  218/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.069
+Epoch   3 Batch  219/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.041
+Epoch   3 Batch  220/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.928, Loss:  0.042
+Epoch   3 Batch  221/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.065
+Epoch   3 Batch  222/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   3 Batch  223/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.927, Loss:  0.039
+Epoch   3 Batch  224/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   3 Batch  225/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   3 Batch  226/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.052
+Epoch   3 Batch  227/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.908, Loss:  0.056
+Epoch   3 Batch  228/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   3 Batch  229/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.921, Loss:  0.048
+Epoch   3 Batch  230/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   3 Batch  231/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.065
+Epoch   3 Batch  232/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.909, Loss:  0.040
+Epoch   3 Batch  233/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.908, Loss:  0.065
+Epoch   3 Batch  234/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.927, Loss:  0.067
+Epoch   3 Batch  235/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.051
+Epoch   3 Batch  236/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.061
+Epoch   3 Batch  237/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.917, Loss:  0.045
+Epoch   3 Batch  238/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   3 Batch  239/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.922, Loss:  0.038
+Epoch   3 Batch  240/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   3 Batch  241/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.032
+Epoch   3 Batch  242/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.924, Loss:  0.040
+Epoch   3 Batch  243/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.060
+Epoch   3 Batch  244/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   3 Batch  245/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   3 Batch  246/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   3 Batch  247/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  248/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   3 Batch  249/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   3 Batch  250/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   3 Batch  251/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   3 Batch  252/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   3 Batch  253/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   3 Batch  254/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  255/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.928, Loss:  0.047
+Epoch   3 Batch  256/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.920, Loss:  0.076
+Epoch   3 Batch  257/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.043
+Epoch   3 Batch  258/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   3 Batch  259/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.922, Loss:  0.040
+Epoch   3 Batch  260/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   3 Batch  261/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.904, Loss:  0.059
+Epoch   3 Batch  262/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.899, Loss:  0.048
+Epoch   3 Batch  263/1077 - Train Accuracy:  0.973, Validation Accuracy:  0.898, Loss:  0.033
+Epoch   3 Batch  264/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.895, Loss:  0.044
+Epoch   3 Batch  265/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.896, Loss:  0.048
+Epoch   3 Batch  266/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.901, Loss:  0.054
+Epoch   3 Batch  267/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.903, Loss:  0.039
+Epoch   3 Batch  268/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.909, Loss:  0.058
+Epoch   3 Batch  269/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   3 Batch  270/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.060
+Epoch   3 Batch  271/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.040
+Epoch   3 Batch  272/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.073
+Epoch   3 Batch  273/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.921, Loss:  0.043
+Epoch   3 Batch  274/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   3 Batch  275/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   3 Batch  276/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.923, Loss:  0.073
+Epoch   3 Batch  277/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   3 Batch  278/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.929, Loss:  0.062
+Epoch   3 Batch  279/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.074
+Epoch   3 Batch  280/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.926, Loss:  0.061
+Epoch   3 Batch  281/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.065
+Epoch   3 Batch  282/1077 - Train Accuracy:  0.885, Validation Accuracy:  0.929, Loss:  0.080
+Epoch   3 Batch  283/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.068
+Epoch   3 Batch  284/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.061
+Epoch   3 Batch  285/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   3 Batch  286/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.051
+Epoch   3 Batch  287/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  288/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   3 Batch  289/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   3 Batch  290/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.935, Loss:  0.074
+Epoch   3 Batch  291/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.934, Loss:  0.076
+Epoch   3 Batch  292/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   3 Batch  293/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.932, Loss:  0.072
+Epoch   3 Batch  294/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   3 Batch  295/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.055
+Epoch   3 Batch  296/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   3 Batch  297/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.068
+Epoch   3 Batch  298/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.065
+Epoch   3 Batch  299/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   3 Batch  300/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.042
+Epoch   3 Batch  301/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   3 Batch  302/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.906, Loss:  0.046
+Epoch   3 Batch  303/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   3 Batch  304/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   3 Batch  305/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.918, Loss:  0.046
+Epoch   3 Batch  306/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.916, Loss:  0.058
+Epoch   3 Batch  307/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   3 Batch  308/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.912, Loss:  0.059
+Epoch   3 Batch  309/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   3 Batch  310/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.916, Loss:  0.055
+Epoch   3 Batch  311/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   3 Batch  312/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.062
+Epoch   3 Batch  313/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.040
+Epoch   3 Batch  314/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch  315/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.922, Loss:  0.039
+Epoch   3 Batch  316/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.925, Loss:  0.051
+Epoch   3 Batch  317/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.920, Loss:  0.059
+Epoch   3 Batch  318/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.915, Loss:  0.042
+Epoch   3 Batch  319/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.920, Loss:  0.068
+Epoch   3 Batch  320/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.917, Loss:  0.066
+Epoch   3 Batch  321/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   3 Batch  322/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.054
+Epoch   3 Batch  323/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   3 Batch  324/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.037
+Epoch   3 Batch  325/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   3 Batch  326/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   3 Batch  327/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.055
+Epoch   3 Batch  328/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.064
+Epoch   3 Batch  329/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.919, Loss:  0.068
+Epoch   3 Batch  330/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.910, Loss:  0.058
+Epoch   3 Batch  331/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.058
+Epoch   3 Batch  332/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.903, Loss:  0.040
+Epoch   3 Batch  333/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.903, Loss:  0.053
+Epoch   3 Batch  334/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   3 Batch  335/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.905, Loss:  0.052
+Epoch   3 Batch  336/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.900, Loss:  0.067
+Epoch   3 Batch  337/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.065
+Epoch   3 Batch  338/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   3 Batch  339/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.909, Loss:  0.039
+Epoch   3 Batch  340/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   3 Batch  341/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.909, Loss:  0.068
+Epoch   3 Batch  342/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   3 Batch  343/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.055
+Epoch   3 Batch  344/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   3 Batch  345/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch  346/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.916, Loss:  0.052
+Epoch   3 Batch  347/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.907, Loss:  0.033
+Epoch   3 Batch  348/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.900, Loss:  0.039
+Epoch   3 Batch  349/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.906, Loss:  0.053
+Epoch   3 Batch  350/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   3 Batch  351/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   3 Batch  352/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   3 Batch  353/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.915, Loss:  0.067
+Epoch   3 Batch  354/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.920, Loss:  0.062
+Epoch   3 Batch  355/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.041
+Epoch   3 Batch  356/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.057
+Epoch   3 Batch  357/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   3 Batch  358/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   3 Batch  359/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.051
+Epoch   3 Batch  360/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   3 Batch  361/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.050
+Epoch   3 Batch  362/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.050
+Epoch   3 Batch  363/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.064
+Epoch   3 Batch  364/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.925, Loss:  0.060
+Epoch   3 Batch  365/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.919, Loss:  0.047
+Epoch   3 Batch  366/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.913, Loss:  0.052
+Epoch   3 Batch  367/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.912, Loss:  0.038
+Epoch   3 Batch  368/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.055
+Epoch   3 Batch  369/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.060
+Epoch   3 Batch  370/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   3 Batch  371/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   3 Batch  372/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  373/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.037
+Epoch   3 Batch  374/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   3 Batch  375/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.046
+Epoch   3 Batch  376/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   3 Batch  377/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   3 Batch  378/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   3 Batch  379/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.933, Loss:  0.057
+Epoch   3 Batch  380/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.045
+Epoch   3 Batch  381/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.947, Loss:  0.055
+Epoch   3 Batch  382/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.947, Loss:  0.073
+Epoch   3 Batch  383/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.947, Loss:  0.046
+Epoch   3 Batch  384/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.947, Loss:  0.031
+Epoch   3 Batch  385/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.956, Loss:  0.046
+Epoch   3 Batch  386/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.954, Loss:  0.042
+Epoch   3 Batch  387/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.953, Loss:  0.038
+Epoch   3 Batch  388/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.955, Loss:  0.050
+Epoch   3 Batch  389/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.945, Loss:  0.047
+Epoch   3 Batch  390/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.945, Loss:  0.056
+Epoch   3 Batch  391/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.941, Loss:  0.054
+Epoch   3 Batch  392/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.941, Loss:  0.052
+Epoch   3 Batch  393/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.044
+Epoch   3 Batch  394/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.936, Loss:  0.045
+Epoch   3 Batch  395/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.045
+Epoch   3 Batch  396/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   3 Batch  397/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.927, Loss:  0.048
+Epoch   3 Batch  398/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.054
+Epoch   3 Batch  399/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.946, Loss:  0.057
+Epoch   3 Batch  400/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.943, Loss:  0.055
+Epoch   3 Batch  401/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.943, Loss:  0.048
+Epoch   3 Batch  402/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.939, Loss:  0.044
+Epoch   3 Batch  403/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.938, Loss:  0.077
+Epoch   3 Batch  404/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.938, Loss:  0.052
+Epoch   3 Batch  405/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   3 Batch  406/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.928, Loss:  0.039
+Epoch   3 Batch  407/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.935, Loss:  0.074
+Epoch   3 Batch  408/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.936, Loss:  0.052
+Epoch   3 Batch  409/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.941, Loss:  0.062
+Epoch   3 Batch  410/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.936, Loss:  0.062
+Epoch   3 Batch  411/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.934, Loss:  0.058
+Epoch   3 Batch  412/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.042
+Epoch   3 Batch  413/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   3 Batch  414/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.940, Loss:  0.044
+Epoch   3 Batch  415/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.052
+Epoch   3 Batch  416/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.943, Loss:  0.046
+Epoch   3 Batch  417/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.943, Loss:  0.078
+Epoch   3 Batch  418/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.945, Loss:  0.046
+Epoch   3 Batch  419/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.948, Loss:  0.045
+Epoch   3 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.946, Loss:  0.042
+Epoch   3 Batch  421/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.940, Loss:  0.061
+Epoch   3 Batch  422/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.940, Loss:  0.042
+Epoch   3 Batch  423/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.936, Loss:  0.068
+Epoch   3 Batch  424/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.935, Loss:  0.047
+Epoch   3 Batch  425/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   3 Batch  426/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  427/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.933, Loss:  0.047
+Epoch   3 Batch  428/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.932, Loss:  0.036
+Epoch   3 Batch  429/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   3 Batch  430/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.941, Loss:  0.047
+Epoch   3 Batch  431/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.041
+Epoch   3 Batch  432/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.951, Loss:  0.048
+Epoch   3 Batch  433/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.943, Loss:  0.057
+Epoch   3 Batch  434/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.938, Loss:  0.044
+Epoch   3 Batch  435/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.056
+Epoch   3 Batch  436/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   3 Batch  437/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.918, Loss:  0.034
+Epoch   3 Batch  438/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.916, Loss:  0.049
+Epoch   3 Batch  439/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.916, Loss:  0.057
+Epoch   3 Batch  440/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.920, Loss:  0.057
+Epoch   3 Batch  441/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.926, Loss:  0.046
+Epoch   3 Batch  442/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.925, Loss:  0.060
+Epoch   3 Batch  443/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   3 Batch  444/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.925, Loss:  0.037
+Epoch   3 Batch  445/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.925, Loss:  0.057
+Epoch   3 Batch  446/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   3 Batch  447/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   3 Batch  448/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.069
+Epoch   3 Batch  449/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch  450/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.054
+Epoch   3 Batch  451/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   3 Batch  452/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   3 Batch  453/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.920, Loss:  0.053
+Epoch   3 Batch  454/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   3 Batch  455/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.926, Loss:  0.057
+Epoch   3 Batch  456/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   3 Batch  457/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.923, Loss:  0.042
+Epoch   3 Batch  458/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.055
+Epoch   3 Batch  459/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   3 Batch  460/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.923, Loss:  0.061
+Epoch   3 Batch  461/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   3 Batch  462/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  463/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  464/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   3 Batch  465/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.916, Loss:  0.056
+Epoch   3 Batch  466/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   3 Batch  467/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.058
+Epoch   3 Batch  468/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.923, Loss:  0.057
+Epoch   3 Batch  469/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   3 Batch  470/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   3 Batch  471/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   3 Batch  472/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.909, Loss:  0.049
+Epoch   3 Batch  473/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.909, Loss:  0.046
+Epoch   3 Batch  474/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.916, Loss:  0.047
+Epoch   3 Batch  475/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.051
+Epoch   3 Batch  476/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.917, Loss:  0.039
+Epoch   3 Batch  477/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  478/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.934, Loss:  0.052
+Epoch   3 Batch  479/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   3 Batch  480/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.934, Loss:  0.054
+Epoch   3 Batch  481/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.935, Loss:  0.064
+Epoch   3 Batch  482/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.935, Loss:  0.065
+Epoch   3 Batch  483/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.051
+Epoch   3 Batch  484/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   3 Batch  485/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   3 Batch  486/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.046
+Epoch   3 Batch  487/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   3 Batch  488/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.927, Loss:  0.053
+Epoch   3 Batch  489/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   3 Batch  490/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.931, Loss:  0.051
+Epoch   3 Batch  491/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.056
+Epoch   3 Batch  492/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.921, Loss:  0.059
+Epoch   3 Batch  493/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.921, Loss:  0.042
+Epoch   3 Batch  494/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   3 Batch  495/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   3 Batch  496/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   3 Batch  497/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   3 Batch  498/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   3 Batch  499/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.039
+Epoch   3 Batch  500/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   3 Batch  501/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   3 Batch  502/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.935, Loss:  0.053
+Epoch   3 Batch  503/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   3 Batch  504/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.939, Loss:  0.042
+Epoch   3 Batch  505/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.935, Loss:  0.036
+Epoch   3 Batch  506/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.939, Loss:  0.059
+Epoch   3 Batch  507/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.937, Loss:  0.055
+Epoch   3 Batch  508/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.941, Loss:  0.046
+Epoch   3 Batch  509/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.941, Loss:  0.069
+Epoch   3 Batch  510/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   3 Batch  511/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.936, Loss:  0.048
+Epoch   3 Batch  512/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.930, Loss:  0.056
+Epoch   3 Batch  513/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   3 Batch  514/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.934, Loss:  0.055
+Epoch   3 Batch  515/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   3 Batch  516/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   3 Batch  517/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.925, Loss:  0.063
+Epoch   3 Batch  518/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.047
+Epoch   3 Batch  519/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.046
+Epoch   3 Batch  520/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   3 Batch  521/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.913, Loss:  0.058
+Epoch   3 Batch  522/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.921, Loss:  0.063
+Epoch   3 Batch  523/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.054
+Epoch   3 Batch  524/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   3 Batch  525/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.061
+Epoch   3 Batch  526/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   3 Batch  527/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.915, Loss:  0.056
+Epoch   3 Batch  528/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.056
+Epoch   3 Batch  529/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   3 Batch  530/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.937, Loss:  0.063
+Epoch   3 Batch  531/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.935, Loss:  0.057
+Epoch   3 Batch  532/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.931, Loss:  0.066
+Epoch   3 Batch  533/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.058
+Epoch   3 Batch  534/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.059
+Epoch   3 Batch  535/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   3 Batch  536/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   3 Batch  537/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.039
+Epoch   3 Batch  538/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.928, Loss:  0.037
+Epoch   3 Batch  539/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.061
+Epoch   3 Batch  540/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   3 Batch  541/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   3 Batch  542/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.923, Loss:  0.052
+Epoch   3 Batch  543/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   3 Batch  544/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.918, Loss:  0.035
+Epoch   3 Batch  545/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   3 Batch  546/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.924, Loss:  0.056
+Epoch   3 Batch  547/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.925, Loss:  0.045
+Epoch   3 Batch  548/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.930, Loss:  0.067
+Epoch   3 Batch  549/1077 - Train Accuracy:  0.879, Validation Accuracy:  0.926, Loss:  0.069
+Epoch   3 Batch  550/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   3 Batch  551/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   3 Batch  552/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   3 Batch  553/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.070
+Epoch   3 Batch  554/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.914, Loss:  0.043
+Epoch   3 Batch  555/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   3 Batch  556/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   3 Batch  557/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   3 Batch  558/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.901, Loss:  0.038
+Epoch   3 Batch  559/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.908, Loss:  0.046
+Epoch   3 Batch  560/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.904, Loss:  0.049
+Epoch   3 Batch  561/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.042
+Epoch   3 Batch  562/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.916, Loss:  0.046
+Epoch   3 Batch  563/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.904, Loss:  0.057
+Epoch   3 Batch  564/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.901, Loss:  0.057
+Epoch   3 Batch  565/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.907, Loss:  0.068
+Epoch   3 Batch  566/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.902, Loss:  0.057
+Epoch   3 Batch  567/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.060
+Epoch   3 Batch  568/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.907, Loss:  0.041
+Epoch   3 Batch  569/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.908, Loss:  0.049
+Epoch   3 Batch  570/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.071
+Epoch   3 Batch  571/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.913, Loss:  0.041
+Epoch   3 Batch  572/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   3 Batch  573/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.918, Loss:  0.070
+Epoch   3 Batch  574/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.920, Loss:  0.054
+Epoch   3 Batch  575/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.036
+Epoch   3 Batch  576/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   3 Batch  577/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.925, Loss:  0.060
+Epoch   3 Batch  578/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   3 Batch  579/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.921, Loss:  0.052
+Epoch   3 Batch  580/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.046
+Epoch   3 Batch  581/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.041
+Epoch   3 Batch  582/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   3 Batch  583/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.054
+Epoch   3 Batch  584/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   3 Batch  585/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.921, Loss:  0.032
+Epoch   3 Batch  586/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  587/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.921, Loss:  0.063
+Epoch   3 Batch  588/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.921, Loss:  0.043
+Epoch   3 Batch  589/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   3 Batch  590/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.921, Loss:  0.069
+Epoch   3 Batch  591/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.055
+Epoch   3 Batch  592/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.055
+Epoch   3 Batch  593/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.932, Loss:  0.073
+Epoch   3 Batch  594/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   3 Batch  595/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   3 Batch  596/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.053
+Epoch   3 Batch  597/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   3 Batch  598/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.051
+Epoch   3 Batch  599/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   3 Batch  600/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.916, Loss:  0.055
+Epoch   3 Batch  601/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.057
+Epoch   3 Batch  602/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   3 Batch  603/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.917, Loss:  0.054
+Epoch   3 Batch  604/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.058
+Epoch   3 Batch  605/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.078
+Epoch   3 Batch  606/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.933, Loss:  0.041
+Epoch   3 Batch  607/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.042
+Epoch   3 Batch  608/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.065
+Epoch   3 Batch  609/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.050
+Epoch   3 Batch  610/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  611/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   3 Batch  612/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.905, Loss:  0.042
+Epoch   3 Batch  613/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.905, Loss:  0.067
+Epoch   3 Batch  614/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.906, Loss:  0.041
+Epoch   3 Batch  615/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.912, Loss:  0.047
+Epoch   3 Batch  616/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   3 Batch  617/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   3 Batch  618/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.931, Loss:  0.054
+Epoch   3 Batch  619/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   3 Batch  620/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   3 Batch  621/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.930, Loss:  0.059
+Epoch   3 Batch  622/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.916, Loss:  0.054
+Epoch   3 Batch  623/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   3 Batch  624/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.912, Loss:  0.051
+Epoch   3 Batch  625/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.912, Loss:  0.052
+Epoch   3 Batch  626/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.915, Loss:  0.051
+Epoch   3 Batch  627/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   3 Batch  628/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   3 Batch  629/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   3 Batch  630/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.926, Loss:  0.040
+Epoch   3 Batch  631/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.931, Loss:  0.054
+Epoch   3 Batch  632/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.935, Loss:  0.045
+Epoch   3 Batch  633/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   3 Batch  634/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.933, Loss:  0.041
+Epoch   3 Batch  635/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   3 Batch  636/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   3 Batch  637/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   3 Batch  638/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.937, Loss:  0.051
+Epoch   3 Batch  639/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.938, Loss:  0.068
+Epoch   3 Batch  640/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   3 Batch  641/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   3 Batch  642/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   3 Batch  643/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   3 Batch  644/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  645/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.061
+Epoch   3 Batch  646/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   3 Batch  647/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.931, Loss:  0.057
+Epoch   3 Batch  648/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.931, Loss:  0.031
+Epoch   3 Batch  649/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   3 Batch  650/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.040
+Epoch   3 Batch  651/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   3 Batch  652/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.936, Loss:  0.065
+Epoch   3 Batch  653/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.934, Loss:  0.054
+Epoch   3 Batch  654/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  655/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.924, Loss:  0.058
+Epoch   3 Batch  656/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   3 Batch  657/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.929, Loss:  0.045
+Epoch   3 Batch  658/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.934, Loss:  0.033
+Epoch   3 Batch  659/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.929, Loss:  0.053
+Epoch   3 Batch  660/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.938, Loss:  0.034
+Epoch   3 Batch  661/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   3 Batch  662/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   3 Batch  663/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.927, Loss:  0.045
+Epoch   3 Batch  664/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   3 Batch  665/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.038
+Epoch   3 Batch  666/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.052
+Epoch   3 Batch  667/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   3 Batch  668/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.911, Loss:  0.060
+Epoch   3 Batch  669/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   3 Batch  670/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.924, Loss:  0.057
+Epoch   3 Batch  671/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.933, Loss:  0.061
+Epoch   3 Batch  672/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.937, Loss:  0.046
+Epoch   3 Batch  673/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.941, Loss:  0.046
+Epoch   3 Batch  674/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  675/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.932, Loss:  0.059
+Epoch   3 Batch  676/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   3 Batch  677/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.067
+Epoch   3 Batch  678/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   3 Batch  679/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.045
+Epoch   3 Batch  680/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   3 Batch  681/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   3 Batch  682/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   3 Batch  683/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   3 Batch  684/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   3 Batch  685/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   3 Batch  686/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.914, Loss:  0.047
+Epoch   3 Batch  687/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.075
+Epoch   3 Batch  688/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.907, Loss:  0.050
+Epoch   3 Batch  689/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.035
+Epoch   3 Batch  690/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   3 Batch  691/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.067
+Epoch   3 Batch  692/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   3 Batch  693/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.063
+Epoch   3 Batch  694/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.917, Loss:  0.062
+Epoch   3 Batch  695/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   3 Batch  696/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   3 Batch  697/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   3 Batch  698/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.901, Loss:  0.051
+Epoch   3 Batch  699/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.908, Loss:  0.063
+Epoch   3 Batch  700/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   3 Batch  701/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.916, Loss:  0.051
+Epoch   3 Batch  702/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.924, Loss:  0.073
+Epoch   3 Batch  703/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  704/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.929, Loss:  0.075
+Epoch   3 Batch  705/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.934, Loss:  0.066
+Epoch   3 Batch  706/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.931, Loss:  0.081
+Epoch   3 Batch  707/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.933, Loss:  0.058
+Epoch   3 Batch  708/1077 - Train Accuracy:  0.883, Validation Accuracy:  0.932, Loss:  0.065
+Epoch   3 Batch  709/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.933, Loss:  0.067
+Epoch   3 Batch  710/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.935, Loss:  0.048
+Epoch   3 Batch  711/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.064
+Epoch   3 Batch  712/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  713/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.048
+Epoch   3 Batch  714/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   3 Batch  715/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.059
+Epoch   3 Batch  716/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.048
+Epoch   3 Batch  717/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.923, Loss:  0.044
+Epoch   3 Batch  718/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.039
+Epoch   3 Batch  719/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  720/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.927, Loss:  0.058
+Epoch   3 Batch  721/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   3 Batch  722/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.048
+Epoch   3 Batch  723/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.938, Loss:  0.049
+Epoch   3 Batch  724/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   3 Batch  725/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.039
+Epoch   3 Batch  726/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   3 Batch  727/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   3 Batch  728/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.926, Loss:  0.071
+Epoch   3 Batch  729/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.922, Loss:  0.065
+Epoch   3 Batch  730/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.066
+Epoch   3 Batch  731/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.918, Loss:  0.053
+Epoch   3 Batch  732/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.918, Loss:  0.059
+Epoch   3 Batch  733/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.904, Loss:  0.046
+Epoch   3 Batch  734/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.054
+Epoch   3 Batch  735/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.911, Loss:  0.047
+Epoch   3 Batch  736/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.041
+Epoch   3 Batch  737/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.063
+Epoch   3 Batch  738/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.039
+Epoch   3 Batch  739/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   3 Batch  740/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   3 Batch  741/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   3 Batch  742/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  743/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.932, Loss:  0.053
+Epoch   3 Batch  744/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.941, Loss:  0.053
+Epoch   3 Batch  745/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.058
+Epoch   3 Batch  746/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   3 Batch  747/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.039
+Epoch   3 Batch  748/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   3 Batch  749/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.045
+Epoch   3 Batch  750/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.937, Loss:  0.048
+Epoch   3 Batch  751/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.937, Loss:  0.053
+Epoch   3 Batch  752/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.048
+Epoch   3 Batch  753/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   3 Batch  754/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.060
+Epoch   3 Batch  755/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.058
+Epoch   3 Batch  756/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   3 Batch  757/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   3 Batch  758/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   3 Batch  759/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.927, Loss:  0.042
+Epoch   3 Batch  760/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.938, Loss:  0.059
+Epoch   3 Batch  761/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.942, Loss:  0.048
+Epoch   3 Batch  762/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.939, Loss:  0.043
+Epoch   3 Batch  763/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.049
+Epoch   3 Batch  764/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.941, Loss:  0.053
+Epoch   3 Batch  765/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.058
+Epoch   3 Batch  766/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.942, Loss:  0.043
+Epoch   3 Batch  767/1077 - Train Accuracy:  0.971, Validation Accuracy:  0.939, Loss:  0.046
+Epoch   3 Batch  768/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.939, Loss:  0.051
+Epoch   3 Batch  769/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   3 Batch  770/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.919, Loss:  0.049
+Epoch   3 Batch  771/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.917, Loss:  0.050
+Epoch   3 Batch  772/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.048
+Epoch   3 Batch  773/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   3 Batch  774/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.063
+Epoch   3 Batch  775/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.057
+Epoch   3 Batch  776/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.045
+Epoch   3 Batch  777/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   3 Batch  778/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   3 Batch  779/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.918, Loss:  0.062
+Epoch   3 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.916, Loss:  0.077
+Epoch   3 Batch  781/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.935, Loss:  0.041
+Epoch   3 Batch  782/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.939, Loss:  0.045
+Epoch   3 Batch  783/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.945, Loss:  0.053
+Epoch   3 Batch  784/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.945, Loss:  0.040
+Epoch   3 Batch  785/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.947, Loss:  0.037
+Epoch   3 Batch  786/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.947, Loss:  0.043
+Epoch   3 Batch  787/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.055
+Epoch   3 Batch  788/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.045
+Epoch   3 Batch  789/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.938, Loss:  0.060
+Epoch   3 Batch  790/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.933, Loss:  0.053
+Epoch   3 Batch  791/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   3 Batch  792/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.060
+Epoch   3 Batch  793/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.932, Loss:  0.044
+Epoch   3 Batch  794/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.931, Loss:  0.037
+Epoch   3 Batch  795/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.068
+Epoch   3 Batch  796/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.930, Loss:  0.037
+Epoch   3 Batch  797/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.923, Loss:  0.052
+Epoch   3 Batch  798/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.053
+Epoch   3 Batch  799/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   3 Batch  800/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.041
+Epoch   3 Batch  801/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.913, Loss:  0.056
+Epoch   3 Batch  802/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   3 Batch  803/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.912, Loss:  0.061
+Epoch   3 Batch  804/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.912, Loss:  0.040
+Epoch   3 Batch  805/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.913, Loss:  0.043
+Epoch   3 Batch  806/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.059
+Epoch   3 Batch  807/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.905, Loss:  0.044
+Epoch   3 Batch  808/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.905, Loss:  0.068
+Epoch   3 Batch  809/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.901, Loss:  0.056
+Epoch   3 Batch  810/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.900, Loss:  0.043
+Epoch   3 Batch  811/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.051
+Epoch   3 Batch  812/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.910, Loss:  0.050
+Epoch   3 Batch  813/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.054
+Epoch   3 Batch  814/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   3 Batch  815/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   3 Batch  816/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.910, Loss:  0.056
+Epoch   3 Batch  817/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.920, Loss:  0.061
+Epoch   3 Batch  818/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.925, Loss:  0.056
+Epoch   3 Batch  819/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   3 Batch  820/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.927, Loss:  0.048
+Epoch   3 Batch  821/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   3 Batch  822/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.918, Loss:  0.040
+Epoch   3 Batch  823/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   3 Batch  824/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.064
+Epoch   3 Batch  825/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.919, Loss:  0.043
+Epoch   3 Batch  826/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.923, Loss:  0.048
+Epoch   3 Batch  827/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   3 Batch  828/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.051
+Epoch   3 Batch  829/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.918, Loss:  0.069
+Epoch   3 Batch  830/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.920, Loss:  0.059
+Epoch   3 Batch  831/1077 - Train Accuracy:  0.881, Validation Accuracy:  0.915, Loss:  0.052
+Epoch   3 Batch  832/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.915, Loss:  0.044
+Epoch   3 Batch  833/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.920, Loss:  0.057
+Epoch   3 Batch  834/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.927, Loss:  0.045
+Epoch   3 Batch  835/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   3 Batch  836/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.923, Loss:  0.047
+Epoch   3 Batch  837/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   3 Batch  838/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.923, Loss:  0.046
+Epoch   3 Batch  839/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.037
+Epoch   3 Batch  840/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   3 Batch  841/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   3 Batch  842/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.935, Loss:  0.037
+Epoch   3 Batch  843/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.934, Loss:  0.050
+Epoch   3 Batch  844/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.945, Loss:  0.044
+Epoch   3 Batch  845/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.950, Loss:  0.048
+Epoch   3 Batch  846/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.940, Loss:  0.059
+Epoch   3 Batch  847/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.944, Loss:  0.057
+Epoch   3 Batch  848/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.944, Loss:  0.039
+Epoch   3 Batch  849/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.940, Loss:  0.051
+Epoch   3 Batch  850/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.940, Loss:  0.078
+Epoch   3 Batch  851/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.935, Loss:  0.063
+Epoch   3 Batch  852/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.933, Loss:  0.053
+Epoch   3 Batch  853/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.059
+Epoch   3 Batch  854/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.935, Loss:  0.052
+Epoch   3 Batch  855/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   3 Batch  856/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.050
+Epoch   3 Batch  857/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.055
+Epoch   3 Batch  858/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   3 Batch  859/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.054
+Epoch   3 Batch  860/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   3 Batch  861/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.936, Loss:  0.061
+Epoch   3 Batch  862/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.936, Loss:  0.049
+Epoch   3 Batch  863/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.037
+Epoch   3 Batch  864/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   3 Batch  865/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.048
+Epoch   3 Batch  866/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.065
+Epoch   3 Batch  867/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.926, Loss:  0.095
+Epoch   3 Batch  868/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.063
+Epoch   3 Batch  869/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   3 Batch  870/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   3 Batch  871/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.036
+Epoch   3 Batch  872/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   3 Batch  873/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.926, Loss:  0.051
+Epoch   3 Batch  874/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   3 Batch  875/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.067
+Epoch   3 Batch  876/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.043
+Epoch   3 Batch  877/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.933, Loss:  0.043
+Epoch   3 Batch  878/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.043
+Epoch   3 Batch  879/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   3 Batch  880/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.933, Loss:  0.049
+Epoch   3 Batch  881/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.050
+Epoch   3 Batch  882/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   3 Batch  883/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.063
+Epoch   3 Batch  884/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   3 Batch  885/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.923, Loss:  0.036
+Epoch   3 Batch  886/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.914, Loss:  0.041
+Epoch   3 Batch  887/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.908, Loss:  0.061
+Epoch   3 Batch  888/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.908, Loss:  0.048
+Epoch   3 Batch  889/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   3 Batch  890/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.914, Loss:  0.053
+Epoch   3 Batch  891/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.045
+Epoch   3 Batch  892/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.912, Loss:  0.042
+Epoch   3 Batch  893/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   3 Batch  894/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   3 Batch  895/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   3 Batch  896/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.916, Loss:  0.053
+Epoch   3 Batch  897/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   3 Batch  898/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.925, Loss:  0.040
+Epoch   3 Batch  899/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   3 Batch  900/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.066
+Epoch   3 Batch  901/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.935, Loss:  0.067
+Epoch   3 Batch  902/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.933, Loss:  0.055
+Epoch   3 Batch  903/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.933, Loss:  0.054
+Epoch   3 Batch  904/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.939, Loss:  0.043
+Epoch   3 Batch  905/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.933, Loss:  0.038
+Epoch   3 Batch  906/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   3 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   3 Batch  908/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.052
+Epoch   3 Batch  909/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.052
+Epoch   3 Batch  910/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   3 Batch  911/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   3 Batch  912/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.047
+Epoch   3 Batch  913/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.078
+Epoch   3 Batch  914/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.067
+Epoch   3 Batch  915/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.916, Loss:  0.045
+Epoch   3 Batch  916/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.912, Loss:  0.050
+Epoch   3 Batch  917/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   3 Batch  918/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   3 Batch  919/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   3 Batch  920/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.917, Loss:  0.043
+Epoch   3 Batch  921/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.918, Loss:  0.058
+Epoch   3 Batch  922/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   3 Batch  923/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.920, Loss:  0.037
+Epoch   3 Batch  924/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   3 Batch  925/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.919, Loss:  0.050
+Epoch   3 Batch  926/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.916, Loss:  0.032
+Epoch   3 Batch  927/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.919, Loss:  0.064
+Epoch   3 Batch  928/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  929/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   3 Batch  930/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.050
+Epoch   3 Batch  931/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.046
+Epoch   3 Batch  932/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.905, Loss:  0.055
+Epoch   3 Batch  933/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.911, Loss:  0.049
+Epoch   3 Batch  934/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   3 Batch  935/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   3 Batch  936/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   3 Batch  937/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.058
+Epoch   3 Batch  938/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.920, Loss:  0.056
+Epoch   3 Batch  939/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   3 Batch  940/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.926, Loss:  0.041
+Epoch   3 Batch  941/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.042
+Epoch   3 Batch  942/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.927, Loss:  0.053
+Epoch   3 Batch  943/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   3 Batch  944/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   3 Batch  945/1077 - Train Accuracy:  0.969, Validation Accuracy:  0.930, Loss:  0.041
+Epoch   3 Batch  946/1077 - Train Accuracy:  0.973, Validation Accuracy:  0.927, Loss:  0.029
+Epoch   3 Batch  947/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   3 Batch  948/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   3 Batch  949/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   3 Batch  950/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   3 Batch  951/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.926, Loss:  0.055
+Epoch   3 Batch  952/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.929, Loss:  0.037
+Epoch   3 Batch  953/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.928, Loss:  0.037
+Epoch   3 Batch  954/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   3 Batch  955/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.926, Loss:  0.073
+Epoch   3 Batch  956/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   3 Batch  957/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.919, Loss:  0.034
+Epoch   3 Batch  958/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.042
+Epoch   3 Batch  959/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   3 Batch  960/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.919, Loss:  0.044
+Epoch   3 Batch  961/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.920, Loss:  0.041
+Epoch   3 Batch  962/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   3 Batch  963/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.919, Loss:  0.066
+Epoch   3 Batch  964/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.920, Loss:  0.044
+Epoch   3 Batch  965/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.920, Loss:  0.064
+Epoch   3 Batch  966/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.043
+Epoch   3 Batch  967/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.914, Loss:  0.067
+Epoch   3 Batch  968/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.057
+Epoch   3 Batch  969/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.916, Loss:  0.074
+Epoch   3 Batch  970/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   3 Batch  971/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.916, Loss:  0.060
+Epoch   3 Batch  972/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   3 Batch  973/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   3 Batch  974/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.929, Loss:  0.035
+Epoch   3 Batch  975/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  976/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.931, Loss:  0.039
+Epoch   3 Batch  977/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.932, Loss:  0.038
+Epoch   3 Batch  978/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.047
+Epoch   3 Batch  979/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   3 Batch  980/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   3 Batch  981/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   3 Batch  982/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.936, Loss:  0.048
+Epoch   3 Batch  983/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.936, Loss:  0.049
+Epoch   3 Batch  984/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.933, Loss:  0.066
+Epoch   3 Batch  985/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   3 Batch  986/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.936, Loss:  0.049
+Epoch   3 Batch  987/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   3 Batch  988/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.932, Loss:  0.066
+Epoch   3 Batch  989/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.933, Loss:  0.055
+Epoch   3 Batch  990/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.930, Loss:  0.059
+Epoch   3 Batch  991/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   3 Batch  992/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   3 Batch  993/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.040
+Epoch   3 Batch  994/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   3 Batch  995/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.048
+Epoch   3 Batch  996/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.052
+Epoch   3 Batch  997/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.922, Loss:  0.045
+Epoch   3 Batch  998/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   3 Batch  999/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.054
+Epoch   3 Batch 1000/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.041
+Epoch   3 Batch 1001/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.925, Loss:  0.035
+Epoch   3 Batch 1002/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.928, Loss:  0.036
+Epoch   3 Batch 1003/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.933, Loss:  0.050
+Epoch   3 Batch 1004/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   3 Batch 1005/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   3 Batch 1006/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   3 Batch 1007/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.930, Loss:  0.047
+Epoch   3 Batch 1008/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.925, Loss:  0.063
+Epoch   3 Batch 1009/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.927, Loss:  0.038
+Epoch   3 Batch 1010/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   3 Batch 1011/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.927, Loss:  0.037
+Epoch   3 Batch 1012/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.924, Loss:  0.037
+Epoch   3 Batch 1013/1077 - Train Accuracy:  0.971, Validation Accuracy:  0.923, Loss:  0.034
+Epoch   3 Batch 1014/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   3 Batch 1015/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   3 Batch 1016/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   3 Batch 1017/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   3 Batch 1018/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.921, Loss:  0.042
+Epoch   3 Batch 1019/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   3 Batch 1020/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.923, Loss:  0.040
+Epoch   3 Batch 1021/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   3 Batch 1022/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   3 Batch 1023/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch 1024/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.931, Loss:  0.068
+Epoch   3 Batch 1025/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.050
+Epoch   3 Batch 1026/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.938, Loss:  0.053
+Epoch   3 Batch 1027/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.934, Loss:  0.048
+Epoch   3 Batch 1028/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   3 Batch 1029/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.931, Loss:  0.038
+Epoch   3 Batch 1030/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.935, Loss:  0.043
+Epoch   3 Batch 1031/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   3 Batch 1032/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   3 Batch 1033/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   3 Batch 1034/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   3 Batch 1035/1077 - Train Accuracy:  0.975, Validation Accuracy:  0.929, Loss:  0.029
+Epoch   3 Batch 1036/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.929, Loss:  0.046
+Epoch   3 Batch 1037/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.930, Loss:  0.038
+Epoch   3 Batch 1038/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.935, Loss:  0.067
+Epoch   3 Batch 1039/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.938, Loss:  0.050
+Epoch   3 Batch 1040/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.938, Loss:  0.053
+Epoch   3 Batch 1041/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   3 Batch 1042/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.935, Loss:  0.041
+Epoch   3 Batch 1043/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.054
+Epoch   3 Batch 1044/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.937, Loss:  0.053
+Epoch   3 Batch 1045/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   3 Batch 1046/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.929, Loss:  0.035
+Epoch   3 Batch 1047/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.048
+Epoch   3 Batch 1048/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.942, Loss:  0.051
+Epoch   3 Batch 1049/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.045
+Epoch   3 Batch 1050/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.037
+Epoch   3 Batch 1051/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.934, Loss:  0.053
+Epoch   3 Batch 1052/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.939, Loss:  0.046
+Epoch   3 Batch 1053/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.941, Loss:  0.045
+Epoch   3 Batch 1054/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.941, Loss:  0.053
+Epoch   3 Batch 1055/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.940, Loss:  0.045
+Epoch   3 Batch 1056/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.943, Loss:  0.046
+Epoch   3 Batch 1057/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.936, Loss:  0.055
+Epoch   3 Batch 1058/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.936, Loss:  0.052
+Epoch   3 Batch 1059/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   3 Batch 1060/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   3 Batch 1061/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   3 Batch 1062/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   3 Batch 1063/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.929, Loss:  0.061
+Epoch   3 Batch 1064/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.930, Loss:  0.046
+Epoch   3 Batch 1065/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   3 Batch 1066/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.039
+Epoch   3 Batch 1067/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.938, Loss:  0.057
+Epoch   3 Batch 1068/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.940, Loss:  0.043
+Epoch   3 Batch 1069/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.937, Loss:  0.035
+Epoch   3 Batch 1070/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.937, Loss:  0.041
+Epoch   3 Batch 1071/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.940, Loss:  0.048
+Epoch   3 Batch 1072/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.934, Loss:  0.040
+Epoch   3 Batch 1073/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   3 Batch 1074/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   3 Batch 1075/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.051
+Epoch   4 Batch    0/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   4 Batch    1/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.036
+Epoch   4 Batch    2/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   4 Batch    3/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch    4/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.034
+Epoch   4 Batch    5/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.069
+Epoch   4 Batch    6/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   4 Batch    7/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   4 Batch    8/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.914, Loss:  0.041
+Epoch   4 Batch    9/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.912, Loss:  0.045
+Epoch   4 Batch   10/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   4 Batch   11/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.919, Loss:  0.058
+Epoch   4 Batch   12/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   4 Batch   13/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   4 Batch   14/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.039
+Epoch   4 Batch   15/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.917, Loss:  0.038
+Epoch   4 Batch   16/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.060
+Epoch   4 Batch   17/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   4 Batch   18/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   4 Batch   19/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   4 Batch   20/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.037
+Epoch   4 Batch   21/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.926, Loss:  0.054
+Epoch   4 Batch   22/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.061
+Epoch   4 Batch   23/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.048
+Epoch   4 Batch   24/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.042
+Epoch   4 Batch   25/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   4 Batch   26/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.916, Loss:  0.056
+Epoch   4 Batch   27/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.916, Loss:  0.037
+Epoch   4 Batch   28/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch   29/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   4 Batch   30/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.038
+Epoch   4 Batch   31/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   4 Batch   32/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.048
+Epoch   4 Batch   33/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   4 Batch   34/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   4 Batch   35/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   4 Batch   36/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   4 Batch   37/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   4 Batch   38/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.936, Loss:  0.060
+Epoch   4 Batch   39/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.926, Loss:  0.061
+Epoch   4 Batch   40/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.928, Loss:  0.040
+Epoch   4 Batch   41/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.038
+Epoch   4 Batch   42/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   4 Batch   43/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.029
+Epoch   4 Batch   44/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.926, Loss:  0.030
+Epoch   4 Batch   45/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.043
+Epoch   4 Batch   46/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   4 Batch   47/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.924, Loss:  0.048
+Epoch   4 Batch   48/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.063
+Epoch   4 Batch   49/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.060
+Epoch   4 Batch   50/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   4 Batch   51/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   4 Batch   52/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.051
+Epoch   4 Batch   53/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.935, Loss:  0.042
+Epoch   4 Batch   54/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.931, Loss:  0.085
+Epoch   4 Batch   55/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   4 Batch   56/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.042
+Epoch   4 Batch   57/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   4 Batch   58/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.911, Loss:  0.037
+Epoch   4 Batch   59/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   4 Batch   60/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.035
+Epoch   4 Batch   61/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.917, Loss:  0.051
+Epoch   4 Batch   62/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.913, Loss:  0.045
+Epoch   4 Batch   63/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.917, Loss:  0.035
+Epoch   4 Batch   64/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.913, Loss:  0.040
+Epoch   4 Batch   65/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   4 Batch   66/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.909, Loss:  0.026
+Epoch   4 Batch   67/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.909, Loss:  0.039
+Epoch   4 Batch   68/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.909, Loss:  0.056
+Epoch   4 Batch   69/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.906, Loss:  0.060
+Epoch   4 Batch   70/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.051
+Epoch   4 Batch   71/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.914, Loss:  0.028
+Epoch   4 Batch   72/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   4 Batch   73/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.909, Loss:  0.043
+Epoch   4 Batch   74/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   4 Batch   75/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.062
+Epoch   4 Batch   76/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.914, Loss:  0.032
+Epoch   4 Batch   77/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   4 Batch   78/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.038
+Epoch   4 Batch   79/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.929, Loss:  0.035
+Epoch   4 Batch   80/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   4 Batch   81/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.043
+Epoch   4 Batch   82/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.923, Loss:  0.045
+Epoch   4 Batch   83/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch   84/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch   85/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch   86/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.917, Loss:  0.046
+Epoch   4 Batch   87/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.908, Loss:  0.054
+Epoch   4 Batch   88/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.908, Loss:  0.047
+Epoch   4 Batch   89/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.913, Loss:  0.046
+Epoch   4 Batch   90/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   4 Batch   91/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.908, Loss:  0.035
+Epoch   4 Batch   92/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   4 Batch   93/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch   94/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.918, Loss:  0.034
+Epoch   4 Batch   95/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch   96/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   4 Batch   97/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.918, Loss:  0.050
+Epoch   4 Batch   98/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   4 Batch   99/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   4 Batch  100/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   4 Batch  101/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.048
+Epoch   4 Batch  102/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.042
+Epoch   4 Batch  103/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.940, Loss:  0.049
+Epoch   4 Batch  104/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   4 Batch  105/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   4 Batch  106/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.053
+Epoch   4 Batch  107/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.928, Loss:  0.041
+Epoch   4 Batch  108/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.050
+Epoch   4 Batch  109/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   4 Batch  110/1077 - Train Accuracy:  0.976, Validation Accuracy:  0.931, Loss:  0.028
+Epoch   4 Batch  111/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   4 Batch  112/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.929, Loss:  0.041
+Epoch   4 Batch  113/1077 - Train Accuracy:  0.892, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   4 Batch  114/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.037
+Epoch   4 Batch  115/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch  116/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.054
+Epoch   4 Batch  117/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.914, Loss:  0.034
+Epoch   4 Batch  118/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.916, Loss:  0.041
+Epoch   4 Batch  119/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.914, Loss:  0.037
+Epoch   4 Batch  120/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   4 Batch  121/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   4 Batch  122/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.917, Loss:  0.036
+Epoch   4 Batch  123/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.910, Loss:  0.036
+Epoch   4 Batch  124/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.910, Loss:  0.052
+Epoch   4 Batch  125/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.914, Loss:  0.058
+Epoch   4 Batch  126/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.035
+Epoch   4 Batch  127/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   4 Batch  128/1077 - Train Accuracy:  0.966, Validation Accuracy:  0.916, Loss:  0.043
+Epoch   4 Batch  129/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.914, Loss:  0.048
+Epoch   4 Batch  130/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  131/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   4 Batch  132/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.913, Loss:  0.038
+Epoch   4 Batch  133/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.918, Loss:  0.037
+Epoch   4 Batch  134/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.918, Loss:  0.039
+Epoch   4 Batch  135/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.046
+Epoch   4 Batch  136/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.914, Loss:  0.041
+Epoch   4 Batch  137/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.913, Loss:  0.036
+Epoch   4 Batch  138/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.906, Loss:  0.043
+Epoch   4 Batch  139/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.906, Loss:  0.052
+Epoch   4 Batch  140/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.906, Loss:  0.047
+Epoch   4 Batch  141/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   4 Batch  142/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.913, Loss:  0.037
+Epoch   4 Batch  143/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.041
+Epoch   4 Batch  144/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.906, Loss:  0.059
+Epoch   4 Batch  145/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.907, Loss:  0.052
+Epoch   4 Batch  146/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.906, Loss:  0.078
+Epoch   4 Batch  147/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.906, Loss:  0.046
+Epoch   4 Batch  148/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.906, Loss:  0.050
+Epoch   4 Batch  149/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.903, Loss:  0.043
+Epoch   4 Batch  150/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.909, Loss:  0.049
+Epoch   4 Batch  151/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.909, Loss:  0.041
+Epoch   4 Batch  152/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.070
+Epoch   4 Batch  153/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.907, Loss:  0.067
+Epoch   4 Batch  154/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.911, Loss:  0.044
+Epoch   4 Batch  155/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.915, Loss:  0.042
+Epoch   4 Batch  156/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.039
+Epoch   4 Batch  157/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   4 Batch  158/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.912, Loss:  0.066
+Epoch   4 Batch  159/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.917, Loss:  0.039
+Epoch   4 Batch  160/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.046
+Epoch   4 Batch  161/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.926, Loss:  0.042
+Epoch   4 Batch  162/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   4 Batch  163/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.060
+Epoch   4 Batch  164/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.927, Loss:  0.045
+Epoch   4 Batch  165/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.044
+Epoch   4 Batch  166/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.060
+Epoch   4 Batch  167/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.931, Loss:  0.047
+Epoch   4 Batch  168/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.929, Loss:  0.062
+Epoch   4 Batch  169/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.929, Loss:  0.059
+Epoch   4 Batch  170/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   4 Batch  171/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.917, Loss:  0.055
+Epoch   4 Batch  172/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.917, Loss:  0.044
+Epoch   4 Batch  173/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch  174/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.922, Loss:  0.042
+Epoch   4 Batch  175/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.921, Loss:  0.049
+Epoch   4 Batch  176/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.922, Loss:  0.057
+Epoch   4 Batch  177/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   4 Batch  178/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   4 Batch  179/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.924, Loss:  0.058
+Epoch   4 Batch  180/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.043
+Epoch   4 Batch  181/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   4 Batch  182/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   4 Batch  183/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   4 Batch  184/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.904, Loss:  0.050
+Epoch   4 Batch  185/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.911, Loss:  0.060
+Epoch   4 Batch  186/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.909, Loss:  0.053
+Epoch   4 Batch  187/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.908, Loss:  0.038
+Epoch   4 Batch  188/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.908, Loss:  0.049
+Epoch   4 Batch  189/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.909, Loss:  0.040
+Epoch   4 Batch  190/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   4 Batch  191/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.924, Loss:  0.039
+Epoch   4 Batch  192/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   4 Batch  193/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   4 Batch  194/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.931, Loss:  0.036
+Epoch   4 Batch  195/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.930, Loss:  0.039
+Epoch   4 Batch  196/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.935, Loss:  0.039
+Epoch   4 Batch  197/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.933, Loss:  0.059
+Epoch   4 Batch  198/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.932, Loss:  0.048
+Epoch   4 Batch  199/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.933, Loss:  0.051
+Epoch   4 Batch  200/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.933, Loss:  0.057
+Epoch   4 Batch  201/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.040
+Epoch   4 Batch  202/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.934, Loss:  0.045
+Epoch   4 Batch  203/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   4 Batch  204/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.069
+Epoch   4 Batch  205/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.930, Loss:  0.069
+Epoch   4 Batch  206/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.928, Loss:  0.045
+Epoch   4 Batch  207/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   4 Batch  208/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch  209/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.916, Loss:  0.042
+Epoch   4 Batch  210/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   4 Batch  211/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.915, Loss:  0.049
+Epoch   4 Batch  212/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.917, Loss:  0.036
+Epoch   4 Batch  213/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.911, Loss:  0.040
+Epoch   4 Batch  214/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.915, Loss:  0.044
+Epoch   4 Batch  215/1077 - Train Accuracy:  0.890, Validation Accuracy:  0.918, Loss:  0.060
+Epoch   4 Batch  216/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   4 Batch  217/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.912, Loss:  0.042
+Epoch   4 Batch  218/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   4 Batch  219/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.042
+Epoch   4 Batch  220/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.043
+Epoch   4 Batch  221/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.056
+Epoch   4 Batch  222/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.925, Loss:  0.052
+Epoch   4 Batch  223/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.931, Loss:  0.049
+Epoch   4 Batch  224/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.046
+Epoch   4 Batch  225/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.922, Loss:  0.062
+Epoch   4 Batch  226/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   4 Batch  227/1077 - Train Accuracy:  0.871, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   4 Batch  228/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.917, Loss:  0.047
+Epoch   4 Batch  229/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   4 Batch  230/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   4 Batch  231/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   4 Batch  232/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.919, Loss:  0.041
+Epoch   4 Batch  233/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   4 Batch  234/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.919, Loss:  0.052
+Epoch   4 Batch  235/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.047
+Epoch   4 Batch  236/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.064
+Epoch   4 Batch  237/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.912, Loss:  0.055
+Epoch   4 Batch  238/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.903, Loss:  0.047
+Epoch   4 Batch  239/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.036
+Epoch   4 Batch  240/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.920, Loss:  0.043
+Epoch   4 Batch  241/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.922, Loss:  0.036
+Epoch   4 Batch  242/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.922, Loss:  0.049
+Epoch   4 Batch  243/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   4 Batch  244/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   4 Batch  245/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.920, Loss:  0.037
+Epoch   4 Batch  246/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   4 Batch  247/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   4 Batch  248/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.052
+Epoch   4 Batch  249/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.922, Loss:  0.046
+Epoch   4 Batch  250/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.916, Loss:  0.038
+Epoch   4 Batch  251/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   4 Batch  252/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   4 Batch  253/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.903, Loss:  0.048
+Epoch   4 Batch  254/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.900, Loss:  0.062
+Epoch   4 Batch  255/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.905, Loss:  0.045
+Epoch   4 Batch  256/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.902, Loss:  0.058
+Epoch   4 Batch  257/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.898, Loss:  0.050
+Epoch   4 Batch  258/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.898, Loss:  0.057
+Epoch   4 Batch  259/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.896, Loss:  0.041
+Epoch   4 Batch  260/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.906, Loss:  0.036
+Epoch   4 Batch  261/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.905, Loss:  0.066
+Epoch   4 Batch  262/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.896, Loss:  0.040
+Epoch   4 Batch  263/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.042
+Epoch   4 Batch  264/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.910, Loss:  0.052
+Epoch   4 Batch  265/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.920, Loss:  0.043
+Epoch   4 Batch  266/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.924, Loss:  0.048
+Epoch   4 Batch  267/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.928, Loss:  0.036
+Epoch   4 Batch  268/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   4 Batch  269/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.932, Loss:  0.062
+Epoch   4 Batch  270/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.050
+Epoch   4 Batch  271/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   4 Batch  272/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.932, Loss:  0.080
+Epoch   4 Batch  273/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.941, Loss:  0.046
+Epoch   4 Batch  274/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.936, Loss:  0.057
+Epoch   4 Batch  275/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.936, Loss:  0.050
+Epoch   4 Batch  276/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.924, Loss:  0.080
+Epoch   4 Batch  277/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.038
+Epoch   4 Batch  278/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.926, Loss:  0.067
+Epoch   4 Batch  279/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.924, Loss:  0.063
+Epoch   4 Batch  280/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.058
+Epoch   4 Batch  281/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.926, Loss:  0.059
+Epoch   4 Batch  282/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.935, Loss:  0.074
+Epoch   4 Batch  283/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.063
+Epoch   4 Batch  284/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.940, Loss:  0.053
+Epoch   4 Batch  285/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.936, Loss:  0.056
+Epoch   4 Batch  286/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  287/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.045
+Epoch   4 Batch  288/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.925, Loss:  0.061
+Epoch   4 Batch  289/1077 - Train Accuracy:  0.964, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch  290/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.922, Loss:  0.071
+Epoch   4 Batch  291/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.070
+Epoch   4 Batch  292/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   4 Batch  293/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   4 Batch  294/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   4 Batch  295/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.921, Loss:  0.069
+Epoch   4 Batch  296/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   4 Batch  297/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.912, Loss:  0.058
+Epoch   4 Batch  298/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.912, Loss:  0.072
+Epoch   4 Batch  299/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.062
+Epoch   4 Batch  300/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   4 Batch  301/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.044
+Epoch   4 Batch  302/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   4 Batch  303/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.913, Loss:  0.054
+Epoch   4 Batch  304/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   4 Batch  305/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.923, Loss:  0.046
+Epoch   4 Batch  306/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.912, Loss:  0.059
+Epoch   4 Batch  307/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch  308/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   4 Batch  309/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.923, Loss:  0.041
+Epoch   4 Batch  310/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   4 Batch  311/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.930, Loss:  0.047
+Epoch   4 Batch  312/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.062
+Epoch   4 Batch  313/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.930, Loss:  0.035
+Epoch   4 Batch  314/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.045
+Epoch   4 Batch  315/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   4 Batch  316/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.934, Loss:  0.051
+Epoch   4 Batch  317/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.930, Loss:  0.056
+Epoch   4 Batch  318/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.036
+Epoch   4 Batch  319/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.071
+Epoch   4 Batch  320/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.929, Loss:  0.058
+Epoch   4 Batch  321/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.043
+Epoch   4 Batch  322/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.933, Loss:  0.040
+Epoch   4 Batch  323/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.929, Loss:  0.044
+Epoch   4 Batch  324/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.046
+Epoch   4 Batch  325/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   4 Batch  326/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.042
+Epoch   4 Batch  327/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.933, Loss:  0.056
+Epoch   4 Batch  328/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.931, Loss:  0.062
+Epoch   4 Batch  329/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.060
+Epoch   4 Batch  330/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.921, Loss:  0.048
+Epoch   4 Batch  331/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   4 Batch  332/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.038
+Epoch   4 Batch  333/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   4 Batch  334/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.907, Loss:  0.049
+Epoch   4 Batch  335/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.900, Loss:  0.050
+Epoch   4 Batch  336/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.909, Loss:  0.080
+Epoch   4 Batch  337/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.914, Loss:  0.050
+Epoch   4 Batch  338/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.918, Loss:  0.070
+Epoch   4 Batch  339/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.041
+Epoch   4 Batch  340/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   4 Batch  341/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.933, Loss:  0.064
+Epoch   4 Batch  342/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.933, Loss:  0.051
+Epoch   4 Batch  343/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.930, Loss:  0.050
+Epoch   4 Batch  344/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   4 Batch  345/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.930, Loss:  0.034
+Epoch   4 Batch  346/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   4 Batch  347/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.924, Loss:  0.033
+Epoch   4 Batch  348/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   4 Batch  349/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   4 Batch  350/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.050
+Epoch   4 Batch  351/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.905, Loss:  0.046
+Epoch   4 Batch  352/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.903, Loss:  0.043
+Epoch   4 Batch  353/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.903, Loss:  0.056
+Epoch   4 Batch  354/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.067
+Epoch   4 Batch  355/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch  356/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   4 Batch  357/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.918, Loss:  0.042
+Epoch   4 Batch  358/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   4 Batch  359/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.916, Loss:  0.043
+Epoch   4 Batch  360/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.039
+Epoch   4 Batch  361/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   4 Batch  362/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   4 Batch  363/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   4 Batch  364/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.923, Loss:  0.057
+Epoch   4 Batch  365/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.929, Loss:  0.044
+Epoch   4 Batch  366/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   4 Batch  367/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.915, Loss:  0.040
+Epoch   4 Batch  368/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.913, Loss:  0.051
+Epoch   4 Batch  369/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.909, Loss:  0.048
+Epoch   4 Batch  370/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   4 Batch  371/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  372/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.919, Loss:  0.041
+Epoch   4 Batch  373/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.919, Loss:  0.036
+Epoch   4 Batch  374/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.059
+Epoch   4 Batch  375/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.049
+Epoch   4 Batch  376/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   4 Batch  377/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   4 Batch  378/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.931, Loss:  0.041
+Epoch   4 Batch  379/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.933, Loss:  0.062
+Epoch   4 Batch  380/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.041
+Epoch   4 Batch  381/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.057
+Epoch   4 Batch  382/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.930, Loss:  0.068
+Epoch   4 Batch  383/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.931, Loss:  0.046
+Epoch   4 Batch  384/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.041
+Epoch   4 Batch  385/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.924, Loss:  0.047
+Epoch   4 Batch  386/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.055
+Epoch   4 Batch  387/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.929, Loss:  0.044
+Epoch   4 Batch  388/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.937, Loss:  0.059
+Epoch   4 Batch  389/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.932, Loss:  0.049
+Epoch   4 Batch  390/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.929, Loss:  0.064
+Epoch   4 Batch  391/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.059
+Epoch   4 Batch  392/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.929, Loss:  0.050
+Epoch   4 Batch  393/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.923, Loss:  0.044
+Epoch   4 Batch  394/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   4 Batch  395/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   4 Batch  396/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   4 Batch  397/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   4 Batch  398/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   4 Batch  399/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   4 Batch  400/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.066
+Epoch   4 Batch  401/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.918, Loss:  0.041
+Epoch   4 Batch  402/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.918, Loss:  0.042
+Epoch   4 Batch  403/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.919, Loss:  0.077
+Epoch   4 Batch  404/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.915, Loss:  0.046
+Epoch   4 Batch  405/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   4 Batch  406/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.913, Loss:  0.049
+Epoch   4 Batch  407/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.904, Loss:  0.073
+Epoch   4 Batch  408/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.904, Loss:  0.048
+Epoch   4 Batch  409/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.906, Loss:  0.062
+Epoch   4 Batch  410/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.914, Loss:  0.061
+Epoch   4 Batch  411/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.914, Loss:  0.060
+Epoch   4 Batch  412/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   4 Batch  413/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   4 Batch  414/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.049
+Epoch   4 Batch  415/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.916, Loss:  0.058
+Epoch   4 Batch  416/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.042
+Epoch   4 Batch  417/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.920, Loss:  0.085
+Epoch   4 Batch  418/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.051
+Epoch   4 Batch  419/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.052
+Epoch   4 Batch  420/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.920, Loss:  0.041
+Epoch   4 Batch  421/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.062
+Epoch   4 Batch  422/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch  423/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.070
+Epoch   4 Batch  424/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.923, Loss:  0.054
+Epoch   4 Batch  425/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.923, Loss:  0.039
+Epoch   4 Batch  426/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.922, Loss:  0.055
+Epoch   4 Batch  427/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  428/1077 - Train Accuracy:  0.961, Validation Accuracy:  0.919, Loss:  0.036
+Epoch   4 Batch  429/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.917, Loss:  0.043
+Epoch   4 Batch  430/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.912, Loss:  0.043
+Epoch   4 Batch  431/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.920, Loss:  0.047
+Epoch   4 Batch  432/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.055
+Epoch   4 Batch  433/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   4 Batch  434/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.914, Loss:  0.036
+Epoch   4 Batch  435/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.909, Loss:  0.059
+Epoch   4 Batch  436/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.909, Loss:  0.041
+Epoch   4 Batch  437/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  438/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch  439/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   4 Batch  440/1077 - Train Accuracy:  0.886, Validation Accuracy:  0.913, Loss:  0.059
+Epoch   4 Batch  441/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   4 Batch  442/1077 - Train Accuracy:  0.901, Validation Accuracy:  0.911, Loss:  0.060
+Epoch   4 Batch  443/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.913, Loss:  0.041
+Epoch   4 Batch  444/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.916, Loss:  0.047
+Epoch   4 Batch  445/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.912, Loss:  0.055
+Epoch   4 Batch  446/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.907, Loss:  0.038
+Epoch   4 Batch  447/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.907, Loss:  0.047
+Epoch   4 Batch  448/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.909, Loss:  0.067
+Epoch   4 Batch  449/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.905, Loss:  0.066
+Epoch   4 Batch  450/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.916, Loss:  0.045
+Epoch   4 Batch  451/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.910, Loss:  0.053
+Epoch   4 Batch  452/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   4 Batch  453/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.907, Loss:  0.049
+Epoch   4 Batch  454/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.057
+Epoch   4 Batch  455/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.910, Loss:  0.064
+Epoch   4 Batch  456/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.061
+Epoch   4 Batch  457/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.912, Loss:  0.050
+Epoch   4 Batch  458/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.910, Loss:  0.068
+Epoch   4 Batch  459/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.912, Loss:  0.054
+Epoch   4 Batch  460/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.907, Loss:  0.060
+Epoch   4 Batch  461/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.908, Loss:  0.048
+Epoch   4 Batch  462/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.906, Loss:  0.053
+Epoch   4 Batch  463/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.909, Loss:  0.058
+Epoch   4 Batch  464/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   4 Batch  465/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.905, Loss:  0.062
+Epoch   4 Batch  466/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   4 Batch  467/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.923, Loss:  0.049
+Epoch   4 Batch  468/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.062
+Epoch   4 Batch  469/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.924, Loss:  0.051
+Epoch   4 Batch  470/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.054
+Epoch   4 Batch  471/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.912, Loss:  0.037
+Epoch   4 Batch  472/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch  473/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.054
+Epoch   4 Batch  474/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.920, Loss:  0.053
+Epoch   4 Batch  475/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.922, Loss:  0.051
+Epoch   4 Batch  476/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.922, Loss:  0.040
+Epoch   4 Batch  477/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   4 Batch  478/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.915, Loss:  0.048
+Epoch   4 Batch  479/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.920, Loss:  0.071
+Epoch   4 Batch  480/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   4 Batch  481/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.060
+Epoch   4 Batch  482/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.061
+Epoch   4 Batch  483/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   4 Batch  484/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.926, Loss:  0.059
+Epoch   4 Batch  485/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.925, Loss:  0.068
+Epoch   4 Batch  486/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.920, Loss:  0.045
+Epoch   4 Batch  487/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.915, Loss:  0.042
+Epoch   4 Batch  488/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.915, Loss:  0.061
+Epoch   4 Batch  489/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   4 Batch  490/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch  491/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.914, Loss:  0.067
+Epoch   4 Batch  492/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.922, Loss:  0.060
+Epoch   4 Batch  493/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.915, Loss:  0.047
+Epoch   4 Batch  494/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.051
+Epoch   4 Batch  495/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   4 Batch  496/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.911, Loss:  0.071
+Epoch   4 Batch  497/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.904, Loss:  0.053
+Epoch   4 Batch  498/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.921, Loss:  0.060
+Epoch   4 Batch  499/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.923, Loss:  0.055
+Epoch   4 Batch  500/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.927, Loss:  0.054
+Epoch   4 Batch  501/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.934, Loss:  0.053
+Epoch   4 Batch  502/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.935, Loss:  0.049
+Epoch   4 Batch  503/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.939, Loss:  0.052
+Epoch   4 Batch  504/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.938, Loss:  0.055
+Epoch   4 Batch  505/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.932, Loss:  0.046
+Epoch   4 Batch  506/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.931, Loss:  0.084
+Epoch   4 Batch  507/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.931, Loss:  0.064
+Epoch   4 Batch  508/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.932, Loss:  0.047
+Epoch   4 Batch  509/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.076
+Epoch   4 Batch  510/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.919, Loss:  0.061
+Epoch   4 Batch  511/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.918, Loss:  0.049
+Epoch   4 Batch  512/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.908, Loss:  0.047
+Epoch   4 Batch  513/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.915, Loss:  0.054
+Epoch   4 Batch  514/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.915, Loss:  0.063
+Epoch   4 Batch  515/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.915, Loss:  0.055
+Epoch   4 Batch  516/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.906, Loss:  0.056
+Epoch   4 Batch  517/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.913, Loss:  0.060
+Epoch   4 Batch  518/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.912, Loss:  0.069
+Epoch   4 Batch  519/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.916, Loss:  0.062
+Epoch   4 Batch  520/1077 - Train Accuracy:  0.965, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   4 Batch  521/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.060
+Epoch   4 Batch  522/1077 - Train Accuracy:  0.870, Validation Accuracy:  0.918, Loss:  0.069
+Epoch   4 Batch  523/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.059
+Epoch   4 Batch  524/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.917, Loss:  0.068
+Epoch   4 Batch  525/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.914, Loss:  0.062
+Epoch   4 Batch  526/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.909, Loss:  0.052
+Epoch   4 Batch  527/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.909, Loss:  0.060
+Epoch   4 Batch  528/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   4 Batch  529/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.914, Loss:  0.058
+Epoch   4 Batch  530/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.909, Loss:  0.070
+Epoch   4 Batch  531/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.910, Loss:  0.055
+Epoch   4 Batch  532/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.910, Loss:  0.072
+Epoch   4 Batch  533/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.898, Loss:  0.056
+Epoch   4 Batch  534/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.898, Loss:  0.059
+Epoch   4 Batch  535/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.907, Loss:  0.061
+Epoch   4 Batch  536/1077 - Train Accuracy:  0.887, Validation Accuracy:  0.911, Loss:  0.057
+Epoch   4 Batch  537/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   4 Batch  538/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.921, Loss:  0.041
+Epoch   4 Batch  539/1077 - Train Accuracy:  0.893, Validation Accuracy:  0.922, Loss:  0.070
+Epoch   4 Batch  540/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   4 Batch  541/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.917, Loss:  0.061
+Epoch   4 Batch  542/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.913, Loss:  0.056
+Epoch   4 Batch  543/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.914, Loss:  0.056
+Epoch   4 Batch  544/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.914, Loss:  0.051
+Epoch   4 Batch  545/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.908, Loss:  0.056
+Epoch   4 Batch  546/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.904, Loss:  0.056
+Epoch   4 Batch  547/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.911, Loss:  0.041
+Epoch   4 Batch  548/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.909, Loss:  0.064
+Epoch   4 Batch  549/1077 - Train Accuracy:  0.888, Validation Accuracy:  0.910, Loss:  0.080
+Epoch   4 Batch  550/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.908, Loss:  0.055
+Epoch   4 Batch  551/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.912, Loss:  0.068
+Epoch   4 Batch  552/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.058
+Epoch   4 Batch  553/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.919, Loss:  0.068
+Epoch   4 Batch  554/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   4 Batch  555/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.922, Loss:  0.043
+Epoch   4 Batch  556/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch  557/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   4 Batch  558/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.928, Loss:  0.046
+Epoch   4 Batch  559/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   4 Batch  560/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.055
+Epoch   4 Batch  561/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.925, Loss:  0.047
+Epoch   4 Batch  562/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.930, Loss:  0.055
+Epoch   4 Batch  563/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   4 Batch  564/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.921, Loss:  0.056
+Epoch   4 Batch  565/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.925, Loss:  0.066
+Epoch   4 Batch  566/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   4 Batch  567/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.929, Loss:  0.057
+Epoch   4 Batch  568/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   4 Batch  569/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.926, Loss:  0.071
+Epoch   4 Batch  570/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.057
+Epoch   4 Batch  571/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.926, Loss:  0.053
+Epoch   4 Batch  572/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   4 Batch  573/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.929, Loss:  0.065
+Epoch   4 Batch  574/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   4 Batch  575/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.033
+Epoch   4 Batch  576/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.069
+Epoch   4 Batch  577/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.055
+Epoch   4 Batch  578/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.930, Loss:  0.051
+Epoch   4 Batch  579/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.054
+Epoch   4 Batch  580/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.044
+Epoch   4 Batch  581/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.939, Loss:  0.048
+Epoch   4 Batch  582/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.938, Loss:  0.063
+Epoch   4 Batch  583/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.056
+Epoch   4 Batch  584/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.928, Loss:  0.060
+Epoch   4 Batch  585/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   4 Batch  586/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.056
+Epoch   4 Batch  587/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.925, Loss:  0.066
+Epoch   4 Batch  588/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.927, Loss:  0.053
+Epoch   4 Batch  589/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.059
+Epoch   4 Batch  590/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.927, Loss:  0.072
+Epoch   4 Batch  591/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.055
+Epoch   4 Batch  592/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.928, Loss:  0.062
+Epoch   4 Batch  593/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.923, Loss:  0.084
+Epoch   4 Batch  594/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.922, Loss:  0.067
+Epoch   4 Batch  595/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   4 Batch  596/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.059
+Epoch   4 Batch  597/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.927, Loss:  0.061
+Epoch   4 Batch  598/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.934, Loss:  0.062
+Epoch   4 Batch  599/1077 - Train Accuracy:  0.889, Validation Accuracy:  0.934, Loss:  0.080
+Epoch   4 Batch  600/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.932, Loss:  0.060
+Epoch   4 Batch  601/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.931, Loss:  0.067
+Epoch   4 Batch  602/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.936, Loss:  0.056
+Epoch   4 Batch  603/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   4 Batch  604/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.072
+Epoch   4 Batch  605/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.940, Loss:  0.072
+Epoch   4 Batch  606/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.935, Loss:  0.043
+Epoch   4 Batch  607/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.939, Loss:  0.052
+Epoch   4 Batch  608/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.064
+Epoch   4 Batch  609/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.938, Loss:  0.052
+Epoch   4 Batch  610/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.061
+Epoch   4 Batch  611/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.935, Loss:  0.052
+Epoch   4 Batch  612/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.931, Loss:  0.042
+Epoch   4 Batch  613/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.935, Loss:  0.062
+Epoch   4 Batch  614/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   4 Batch  615/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.937, Loss:  0.048
+Epoch   4 Batch  616/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.044
+Epoch   4 Batch  617/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.937, Loss:  0.048
+Epoch   4 Batch  618/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.938, Loss:  0.054
+Epoch   4 Batch  619/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.948, Loss:  0.043
+Epoch   4 Batch  620/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.944, Loss:  0.047
+Epoch   4 Batch  621/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.939, Loss:  0.056
+Epoch   4 Batch  622/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.939, Loss:  0.058
+Epoch   4 Batch  623/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.937, Loss:  0.071
+Epoch   4 Batch  624/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.053
+Epoch   4 Batch  625/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.934, Loss:  0.047
+Epoch   4 Batch  626/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.934, Loss:  0.057
+Epoch   4 Batch  627/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   4 Batch  628/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.931, Loss:  0.054
+Epoch   4 Batch  629/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.931, Loss:  0.064
+Epoch   4 Batch  630/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   4 Batch  631/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.942, Loss:  0.050
+Epoch   4 Batch  632/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.946, Loss:  0.043
+Epoch   4 Batch  633/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.941, Loss:  0.048
+Epoch   4 Batch  634/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  635/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.941, Loss:  0.059
+Epoch   4 Batch  636/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.938, Loss:  0.042
+Epoch   4 Batch  637/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.934, Loss:  0.051
+Epoch   4 Batch  638/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.934, Loss:  0.056
+Epoch   4 Batch  639/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.935, Loss:  0.075
+Epoch   4 Batch  640/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.928, Loss:  0.053
+Epoch   4 Batch  641/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.927, Loss:  0.042
+Epoch   4 Batch  642/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.917, Loss:  0.057
+Epoch   4 Batch  643/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch  644/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   4 Batch  645/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.920, Loss:  0.066
+Epoch   4 Batch  646/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   4 Batch  647/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.923, Loss:  0.056
+Epoch   4 Batch  648/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.920, Loss:  0.039
+Epoch   4 Batch  649/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.914, Loss:  0.055
+Epoch   4 Batch  650/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   4 Batch  651/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   4 Batch  652/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.919, Loss:  0.058
+Epoch   4 Batch  653/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.060
+Epoch   4 Batch  654/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   4 Batch  655/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.919, Loss:  0.058
+Epoch   4 Batch  656/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.920, Loss:  0.048
+Epoch   4 Batch  657/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   4 Batch  658/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.916, Loss:  0.037
+Epoch   4 Batch  659/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.914, Loss:  0.047
+Epoch   4 Batch  660/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.044
+Epoch   4 Batch  661/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.914, Loss:  0.046
+Epoch   4 Batch  662/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.919, Loss:  0.048
+Epoch   4 Batch  663/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.039
+Epoch   4 Batch  664/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.924, Loss:  0.055
+Epoch   4 Batch  665/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.928, Loss:  0.039
+Epoch   4 Batch  666/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.065
+Epoch   4 Batch  667/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.063
+Epoch   4 Batch  668/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   4 Batch  669/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.915, Loss:  0.043
+Epoch   4 Batch  670/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.055
+Epoch   4 Batch  671/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.052
+Epoch   4 Batch  672/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   4 Batch  673/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.926, Loss:  0.049
+Epoch   4 Batch  674/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.929, Loss:  0.039
+Epoch   4 Batch  675/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.926, Loss:  0.060
+Epoch   4 Batch  676/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.921, Loss:  0.042
+Epoch   4 Batch  677/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   4 Batch  678/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.925, Loss:  0.035
+Epoch   4 Batch  679/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.049
+Epoch   4 Batch  680/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.926, Loss:  0.058
+Epoch   4 Batch  681/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.929, Loss:  0.054
+Epoch   4 Batch  682/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   4 Batch  683/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   4 Batch  684/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   4 Batch  685/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.925, Loss:  0.065
+Epoch   4 Batch  686/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.044
+Epoch   4 Batch  687/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.058
+Epoch   4 Batch  688/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.047
+Epoch   4 Batch  689/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.035
+Epoch   4 Batch  690/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  691/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.063
+Epoch   4 Batch  692/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.040
+Epoch   4 Batch  693/1077 - Train Accuracy:  0.894, Validation Accuracy:  0.918, Loss:  0.064
+Epoch   4 Batch  694/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.918, Loss:  0.055
+Epoch   4 Batch  695/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   4 Batch  696/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.911, Loss:  0.065
+Epoch   4 Batch  697/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.911, Loss:  0.050
+Epoch   4 Batch  698/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.910, Loss:  0.046
+Epoch   4 Batch  699/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.910, Loss:  0.042
+Epoch   4 Batch  700/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.911, Loss:  0.039
+Epoch   4 Batch  701/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.911, Loss:  0.063
+Epoch   4 Batch  702/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.911, Loss:  0.061
+Epoch   4 Batch  703/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.920, Loss:  0.049
+Epoch   4 Batch  704/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.920, Loss:  0.057
+Epoch   4 Batch  705/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.067
+Epoch   4 Batch  706/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.921, Loss:  0.089
+Epoch   4 Batch  707/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.919, Loss:  0.059
+Epoch   4 Batch  708/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.057
+Epoch   4 Batch  709/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   4 Batch  710/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.926, Loss:  0.036
+Epoch   4 Batch  711/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.930, Loss:  0.058
+Epoch   4 Batch  712/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.925, Loss:  0.041
+Epoch   4 Batch  713/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   4 Batch  714/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   4 Batch  715/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   4 Batch  716/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.932, Loss:  0.041
+Epoch   4 Batch  717/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.927, Loss:  0.044
+Epoch   4 Batch  718/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.047
+Epoch   4 Batch  719/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.939, Loss:  0.063
+Epoch   4 Batch  720/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.939, Loss:  0.053
+Epoch   4 Batch  721/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.065
+Epoch   4 Batch  722/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.940, Loss:  0.037
+Epoch   4 Batch  723/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.944, Loss:  0.059
+Epoch   4 Batch  724/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.939, Loss:  0.051
+Epoch   4 Batch  725/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.926, Loss:  0.039
+Epoch   4 Batch  726/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   4 Batch  727/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.926, Loss:  0.046
+Epoch   4 Batch  728/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.926, Loss:  0.062
+Epoch   4 Batch  729/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.920, Loss:  0.059
+Epoch   4 Batch  730/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.915, Loss:  0.069
+Epoch   4 Batch  731/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.911, Loss:  0.054
+Epoch   4 Batch  732/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.912, Loss:  0.062
+Epoch   4 Batch  733/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.916, Loss:  0.070
+Epoch   4 Batch  734/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   4 Batch  735/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.919, Loss:  0.039
+Epoch   4 Batch  736/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.923, Loss:  0.030
+Epoch   4 Batch  737/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   4 Batch  738/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.925, Loss:  0.042
+Epoch   4 Batch  739/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  740/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.925, Loss:  0.044
+Epoch   4 Batch  741/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.915, Loss:  0.053
+Epoch   4 Batch  742/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.916, Loss:  0.031
+Epoch   4 Batch  743/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.917, Loss:  0.052
+Epoch   4 Batch  744/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.046
+Epoch   4 Batch  745/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.926, Loss:  0.054
+Epoch   4 Batch  746/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.929, Loss:  0.047
+Epoch   4 Batch  747/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.936, Loss:  0.047
+Epoch   4 Batch  748/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.940, Loss:  0.047
+Epoch   4 Batch  749/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.938, Loss:  0.052
+Epoch   4 Batch  750/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.934, Loss:  0.032
+Epoch   4 Batch  751/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.929, Loss:  0.049
+Epoch   4 Batch  752/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   4 Batch  753/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.922, Loss:  0.044
+Epoch   4 Batch  754/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.931, Loss:  0.059
+Epoch   4 Batch  755/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.932, Loss:  0.063
+Epoch   4 Batch  756/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   4 Batch  757/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.930, Loss:  0.044
+Epoch   4 Batch  758/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.039
+Epoch   4 Batch  759/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.935, Loss:  0.037
+Epoch   4 Batch  760/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.931, Loss:  0.061
+Epoch   4 Batch  761/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   4 Batch  762/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.048
+Epoch   4 Batch  763/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.931, Loss:  0.049
+Epoch   4 Batch  764/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.932, Loss:  0.042
+Epoch   4 Batch  765/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   4 Batch  766/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   4 Batch  767/1077 - Train Accuracy:  0.962, Validation Accuracy:  0.917, Loss:  0.042
+Epoch   4 Batch  768/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.920, Loss:  0.052
+Epoch   4 Batch  769/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.916, Loss:  0.052
+Epoch   4 Batch  770/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.918, Loss:  0.038
+Epoch   4 Batch  771/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.914, Loss:  0.046
+Epoch   4 Batch  772/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.901, Loss:  0.050
+Epoch   4 Batch  773/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.048
+Epoch   4 Batch  774/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.920, Loss:  0.050
+Epoch   4 Batch  775/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  776/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  777/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  778/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.926, Loss:  0.047
+Epoch   4 Batch  779/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.943, Loss:  0.054
+Epoch   4 Batch  780/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.937, Loss:  0.070
+Epoch   4 Batch  781/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.933, Loss:  0.044
+Epoch   4 Batch  782/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.922, Loss:  0.041
+Epoch   4 Batch  783/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.053
+Epoch   4 Batch  784/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.933, Loss:  0.037
+Epoch   4 Batch  785/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   4 Batch  786/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.920, Loss:  0.036
+Epoch   4 Batch  787/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.918, Loss:  0.047
+Epoch   4 Batch  788/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.922, Loss:  0.047
+Epoch   4 Batch  789/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.918, Loss:  0.043
+Epoch   4 Batch  790/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.912, Loss:  0.053
+Epoch   4 Batch  791/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.927, Loss:  0.051
+Epoch   4 Batch  792/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.931, Loss:  0.058
+Epoch   4 Batch  793/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.040
+Epoch   4 Batch  794/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.937, Loss:  0.044
+Epoch   4 Batch  795/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.931, Loss:  0.051
+Epoch   4 Batch  796/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.931, Loss:  0.043
+Epoch   4 Batch  797/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.931, Loss:  0.047
+Epoch   4 Batch  798/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.936, Loss:  0.053
+Epoch   4 Batch  799/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.930, Loss:  0.064
+Epoch   4 Batch  800/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.933, Loss:  0.049
+Epoch   4 Batch  801/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.935, Loss:  0.052
+Epoch   4 Batch  802/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.931, Loss:  0.052
+Epoch   4 Batch  803/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.942, Loss:  0.045
+Epoch   4 Batch  804/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.929, Loss:  0.038
+Epoch   4 Batch  805/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.936, Loss:  0.047
+Epoch   4 Batch  806/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.934, Loss:  0.045
+Epoch   4 Batch  807/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.934, Loss:  0.043
+Epoch   4 Batch  808/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.932, Loss:  0.062
+Epoch   4 Batch  809/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.928, Loss:  0.073
+Epoch   4 Batch  810/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.928, Loss:  0.039
+Epoch   4 Batch  811/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.929, Loss:  0.050
+Epoch   4 Batch  812/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.922, Loss:  0.037
+Epoch   4 Batch  813/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.919, Loss:  0.055
+Epoch   4 Batch  814/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.917, Loss:  0.060
+Epoch   4 Batch  815/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.922, Loss:  0.048
+Epoch   4 Batch  816/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.926, Loss:  0.059
+Epoch   4 Batch  817/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.929, Loss:  0.049
+Epoch   4 Batch  818/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.929, Loss:  0.051
+Epoch   4 Batch  819/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.933, Loss:  0.052
+Epoch   4 Batch  820/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.933, Loss:  0.037
+Epoch   4 Batch  821/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.929, Loss:  0.055
+Epoch   4 Batch  822/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.929, Loss:  0.052
+Epoch   4 Batch  823/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.927, Loss:  0.057
+Epoch   4 Batch  824/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.927, Loss:  0.056
+Epoch   4 Batch  825/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   4 Batch  826/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.927, Loss:  0.044
+Epoch   4 Batch  827/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.928, Loss:  0.049
+Epoch   4 Batch  828/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.926, Loss:  0.040
+Epoch   4 Batch  829/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.064
+Epoch   4 Batch  830/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.924, Loss:  0.061
+Epoch   4 Batch  831/1077 - Train Accuracy:  0.877, Validation Accuracy:  0.919, Loss:  0.051
+Epoch   4 Batch  832/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.924, Loss:  0.052
+Epoch   4 Batch  833/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.919, Loss:  0.046
+Epoch   4 Batch  834/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.924, Loss:  0.049
+Epoch   4 Batch  835/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.934, Loss:  0.049
+Epoch   4 Batch  836/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.929, Loss:  0.040
+Epoch   4 Batch  837/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.933, Loss:  0.063
+Epoch   4 Batch  838/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.930, Loss:  0.046
+Epoch   4 Batch  839/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.938, Loss:  0.033
+Epoch   4 Batch  840/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.944, Loss:  0.037
+Epoch   4 Batch  841/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.938, Loss:  0.058
+Epoch   4 Batch  842/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.940, Loss:  0.043
+Epoch   4 Batch  843/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.042
+Epoch   4 Batch  844/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.933, Loss:  0.037
+Epoch   4 Batch  845/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.933, Loss:  0.031
+Epoch   4 Batch  846/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.933, Loss:  0.067
+Epoch   4 Batch  847/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.931, Loss:  0.053
+Epoch   4 Batch  848/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.926, Loss:  0.045
+Epoch   4 Batch  849/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.926, Loss:  0.043
+Epoch   4 Batch  850/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.926, Loss:  0.074
+Epoch   4 Batch  851/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.925, Loss:  0.061
+Epoch   4 Batch  852/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.929, Loss:  0.067
+Epoch   4 Batch  853/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.939, Loss:  0.049
+Epoch   4 Batch  854/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.933, Loss:  0.055
+Epoch   4 Batch  855/1077 - Train Accuracy:  0.908, Validation Accuracy:  0.931, Loss:  0.048
+Epoch   4 Batch  856/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.941, Loss:  0.043
+Epoch   4 Batch  857/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.941, Loss:  0.050
+Epoch   4 Batch  858/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  859/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.065
+Epoch   4 Batch  860/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   4 Batch  861/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.934, Loss:  0.044
+Epoch   4 Batch  862/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.943, Loss:  0.053
+Epoch   4 Batch  863/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.945, Loss:  0.046
+Epoch   4 Batch  864/1077 - Train Accuracy:  0.913, Validation Accuracy:  0.931, Loss:  0.045
+Epoch   4 Batch  865/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.930, Loss:  0.053
+Epoch   4 Batch  866/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.926, Loss:  0.060
+Epoch   4 Batch  867/1077 - Train Accuracy:  0.900, Validation Accuracy:  0.922, Loss:  0.092
+Epoch   4 Batch  868/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.921, Loss:  0.058
+Epoch   4 Batch  869/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.938, Loss:  0.046
+Epoch   4 Batch  870/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.938, Loss:  0.042
+Epoch   4 Batch  871/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.939, Loss:  0.034
+Epoch   4 Batch  872/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.943, Loss:  0.051
+Epoch   4 Batch  873/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.935, Loss:  0.038
+Epoch   4 Batch  874/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.942, Loss:  0.065
+Epoch   4 Batch  875/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.942, Loss:  0.055
+Epoch   4 Batch  876/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  877/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.936, Loss:  0.050
+Epoch   4 Batch  878/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.938, Loss:  0.043
+Epoch   4 Batch  879/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.943, Loss:  0.039
+Epoch   4 Batch  880/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.939, Loss:  0.051
+Epoch   4 Batch  881/1077 - Train Accuracy:  0.918, Validation Accuracy:  0.941, Loss:  0.056
+Epoch   4 Batch  882/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.933, Loss:  0.041
+Epoch   4 Batch  883/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.933, Loss:  0.065
+Epoch   4 Batch  884/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.933, Loss:  0.048
+Epoch   4 Batch  885/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.931, Loss:  0.035
+Epoch   4 Batch  886/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.918, Loss:  0.046
+Epoch   4 Batch  887/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.910, Loss:  0.069
+Epoch   4 Batch  888/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.910, Loss:  0.040
+Epoch   4 Batch  889/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.913, Loss:  0.048
+Epoch   4 Batch  890/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.916, Loss:  0.050
+Epoch   4 Batch  891/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.913, Loss:  0.042
+Epoch   4 Batch  892/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.909, Loss:  0.042
+Epoch   4 Batch  893/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.910, Loss:  0.048
+Epoch   4 Batch  894/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.039
+Epoch   4 Batch  895/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.921, Loss:  0.044
+Epoch   4 Batch  896/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.926, Loss:  0.052
+Epoch   4 Batch  897/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   4 Batch  898/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.925, Loss:  0.049
+Epoch   4 Batch  899/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.934, Loss:  0.063
+Epoch   4 Batch  900/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.934, Loss:  0.055
+Epoch   4 Batch  901/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.935, Loss:  0.068
+Epoch   4 Batch  902/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.945, Loss:  0.052
+Epoch   4 Batch  903/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.942, Loss:  0.046
+Epoch   4 Batch  904/1077 - Train Accuracy:  0.897, Validation Accuracy:  0.942, Loss:  0.057
+Epoch   4 Batch  905/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.942, Loss:  0.034
+Epoch   4 Batch  906/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.942, Loss:  0.049
+Epoch   4 Batch  907/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.942, Loss:  0.045
+Epoch   4 Batch  908/1077 - Train Accuracy:  0.925, Validation Accuracy:  0.940, Loss:  0.051
+Epoch   4 Batch  909/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.934, Loss:  0.057
+Epoch   4 Batch  910/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.934, Loss:  0.050
+Epoch   4 Batch  911/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.938, Loss:  0.049
+Epoch   4 Batch  912/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.938, Loss:  0.042
+Epoch   4 Batch  913/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.938, Loss:  0.072
+Epoch   4 Batch  914/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.917, Loss:  0.083
+Epoch   4 Batch  915/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.918, Loss:  0.038
+Epoch   4 Batch  916/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   4 Batch  917/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.918, Loss:  0.040
+Epoch   4 Batch  918/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.919, Loss:  0.039
+Epoch   4 Batch  919/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.907, Loss:  0.036
+Epoch   4 Batch  920/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.912, Loss:  0.037
+Epoch   4 Batch  921/1077 - Train Accuracy:  0.899, Validation Accuracy:  0.917, Loss:  0.053
+Epoch   4 Batch  922/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.919, Loss:  0.053
+Epoch   4 Batch  923/1077 - Train Accuracy:  0.954, Validation Accuracy:  0.924, Loss:  0.040
+Epoch   4 Batch  924/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.930, Loss:  0.060
+Epoch   4 Batch  925/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.936, Loss:  0.043
+Epoch   4 Batch  926/1077 - Train Accuracy:  0.907, Validation Accuracy:  0.936, Loss:  0.048
+Epoch   4 Batch  927/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.930, Loss:  0.055
+Epoch   4 Batch  928/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.930, Loss:  0.055
+Epoch   4 Batch  929/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.933, Loss:  0.046
+Epoch   4 Batch  930/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.937, Loss:  0.037
+Epoch   4 Batch  931/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.940, Loss:  0.039
+Epoch   4 Batch  932/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.940, Loss:  0.040
+Epoch   4 Batch  933/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.936, Loss:  0.045
+Epoch   4 Batch  934/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.042
+Epoch   4 Batch  935/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.047
+Epoch   4 Batch  936/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.051
+Epoch   4 Batch  937/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.058
+Epoch   4 Batch  938/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.919, Loss:  0.061
+Epoch   4 Batch  939/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.922, Loss:  0.056
+Epoch   4 Batch  940/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.928, Loss:  0.043
+Epoch   4 Batch  941/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.928, Loss:  0.038
+Epoch   4 Batch  942/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   4 Batch  943/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.927, Loss:  0.044
+Epoch   4 Batch  944/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   4 Batch  945/1077 - Train Accuracy:  0.959, Validation Accuracy:  0.942, Loss:  0.044
+Epoch   4 Batch  946/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.936, Loss:  0.030
+Epoch   4 Batch  947/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.931, Loss:  0.056
+Epoch   4 Batch  948/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.941, Loss:  0.040
+Epoch   4 Batch  949/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.941, Loss:  0.040
+Epoch   4 Batch  950/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.941, Loss:  0.045
+Epoch   4 Batch  951/1077 - Train Accuracy:  0.905, Validation Accuracy:  0.945, Loss:  0.060
+Epoch   4 Batch  952/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.946, Loss:  0.042
+Epoch   4 Batch  953/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.946, Loss:  0.042
+Epoch   4 Batch  954/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.946, Loss:  0.053
+Epoch   4 Batch  955/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.956, Loss:  0.056
+Epoch   4 Batch  956/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.961, Loss:  0.058
+Epoch   4 Batch  957/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.942, Loss:  0.031
+Epoch   4 Batch  958/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.939, Loss:  0.050
+Epoch   4 Batch  959/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.928, Loss:  0.052
+Epoch   4 Batch  960/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.928, Loss:  0.048
+Epoch   4 Batch  961/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.043
+Epoch   4 Batch  962/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.928, Loss:  0.041
+Epoch   4 Batch  963/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.066
+Epoch   4 Batch  964/1077 - Train Accuracy:  0.960, Validation Accuracy:  0.924, Loss:  0.043
+Epoch   4 Batch  965/1077 - Train Accuracy:  0.924, Validation Accuracy:  0.921, Loss:  0.053
+Epoch   4 Batch  966/1077 - Train Accuracy:  0.920, Validation Accuracy:  0.934, Loss:  0.042
+Epoch   4 Batch  967/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.934, Loss:  0.053
+Epoch   4 Batch  968/1077 - Train Accuracy:  0.895, Validation Accuracy:  0.930, Loss:  0.061
+Epoch   4 Batch  969/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.930, Loss:  0.064
+Epoch   4 Batch  970/1077 - Train Accuracy:  0.950, Validation Accuracy:  0.936, Loss:  0.050
+Epoch   4 Batch  971/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.931, Loss:  0.044
+Epoch   4 Batch  972/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   4 Batch  973/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.929, Loss:  0.037
+Epoch   4 Batch  974/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.933, Loss:  0.035
+Epoch   4 Batch  975/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.932, Loss:  0.050
+Epoch   4 Batch  976/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.931, Loss:  0.038
+Epoch   4 Batch  977/1077 - Train Accuracy:  0.957, Validation Accuracy:  0.934, Loss:  0.032
+Epoch   4 Batch  978/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.938, Loss:  0.048
+Epoch   4 Batch  979/1077 - Train Accuracy:  0.919, Validation Accuracy:  0.934, Loss:  0.042
+Epoch   4 Batch  980/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.935, Loss:  0.046
+Epoch   4 Batch  981/1077 - Train Accuracy:  0.912, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   4 Batch  982/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.930, Loss:  0.041
+Epoch   4 Batch  983/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.939, Loss:  0.047
+Epoch   4 Batch  984/1077 - Train Accuracy:  0.903, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   4 Batch  985/1077 - Train Accuracy:  0.944, Validation Accuracy:  0.935, Loss:  0.044
+Epoch   4 Batch  986/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.936, Loss:  0.046
+Epoch   4 Batch  987/1077 - Train Accuracy:  0.910, Validation Accuracy:  0.936, Loss:  0.033
+Epoch   4 Batch  988/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.940, Loss:  0.068
+Epoch   4 Batch  989/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.938, Loss:  0.066
+Epoch   4 Batch  990/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.938, Loss:  0.053
+Epoch   4 Batch  991/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.925, Loss:  0.050
+Epoch   4 Batch  992/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.928, Loss:  0.057
+Epoch   4 Batch  993/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.925, Loss:  0.037
+Epoch   4 Batch  994/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.919, Loss:  0.044
+Epoch   4 Batch  995/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.921, Loss:  0.051
+Epoch   4 Batch  996/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.922, Loss:  0.052
+Epoch   4 Batch  997/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.922, Loss:  0.050
+Epoch   4 Batch  998/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.913, Loss:  0.049
+Epoch   4 Batch  999/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.911, Loss:  0.049
+Epoch   4 Batch 1000/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.908, Loss:  0.041
+Epoch   4 Batch 1001/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.907, Loss:  0.041
+Epoch   4 Batch 1002/1077 - Train Accuracy:  0.968, Validation Accuracy:  0.907, Loss:  0.035
+Epoch   4 Batch 1003/1077 - Train Accuracy:  0.943, Validation Accuracy:  0.911, Loss:  0.050
+Epoch   4 Batch 1004/1077 - Train Accuracy:  0.956, Validation Accuracy:  0.917, Loss:  0.051
+Epoch   4 Batch 1005/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.917, Loss:  0.044
+Epoch   4 Batch 1006/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.916, Loss:  0.056
+Epoch   4 Batch 1007/1077 - Train Accuracy:  0.948, Validation Accuracy:  0.916, Loss:  0.039
+Epoch   4 Batch 1008/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.904, Loss:  0.059
+Epoch   4 Batch 1009/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.911, Loss:  0.040
+Epoch   4 Batch 1010/1077 - Train Accuracy:  0.952, Validation Accuracy:  0.906, Loss:  0.054
+Epoch   4 Batch 1011/1077 - Train Accuracy:  0.923, Validation Accuracy:  0.906, Loss:  0.038
+Epoch   4 Batch 1012/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.912, Loss:  0.048
+Epoch   4 Batch 1013/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.912, Loss:  0.039
+Epoch   4 Batch 1014/1077 - Train Accuracy:  0.915, Validation Accuracy:  0.914, Loss:  0.062
+Epoch   4 Batch 1015/1077 - Train Accuracy:  0.909, Validation Accuracy:  0.918, Loss:  0.053
+Epoch   4 Batch 1016/1077 - Train Accuracy:  0.914, Validation Accuracy:  0.919, Loss:  0.049
+Epoch   4 Batch 1017/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.057
+Epoch   4 Batch 1018/1077 - Train Accuracy:  0.932, Validation Accuracy:  0.912, Loss:  0.049
+Epoch   4 Batch 1019/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.915, Loss:  0.073
+Epoch   4 Batch 1020/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.914, Loss:  0.040
+Epoch   4 Batch 1021/1077 - Train Accuracy:  0.929, Validation Accuracy:  0.918, Loss:  0.044
+Epoch   4 Batch 1022/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.919, Loss:  0.045
+Epoch   4 Batch 1023/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.922, Loss:  0.057
+Epoch   4 Batch 1024/1077 - Train Accuracy:  0.904, Validation Accuracy:  0.921, Loss:  0.059
+Epoch   4 Batch 1025/1077 - Train Accuracy:  0.921, Validation Accuracy:  0.921, Loss:  0.050
+Epoch   4 Batch 1026/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.920, Loss:  0.055
+Epoch   4 Batch 1027/1077 - Train Accuracy:  0.911, Validation Accuracy:  0.920, Loss:  0.046
+Epoch   4 Batch 1028/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.925, Loss:  0.046
+Epoch   4 Batch 1029/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.929, Loss:  0.040
+Epoch   4 Batch 1030/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.924, Loss:  0.046
+Epoch   4 Batch 1031/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.928, Loss:  0.054
+Epoch   4 Batch 1032/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.928, Loss:  0.061
+Epoch   4 Batch 1033/1077 - Train Accuracy:  0.922, Validation Accuracy:  0.934, Loss:  0.060
+Epoch   4 Batch 1034/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.926, Loss:  0.050
+Epoch   4 Batch 1035/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.926, Loss:  0.032
+Epoch   4 Batch 1036/1077 - Train Accuracy:  0.931, Validation Accuracy:  0.927, Loss:  0.060
+Epoch   4 Batch 1037/1077 - Train Accuracy:  0.906, Validation Accuracy:  0.927, Loss:  0.053
+Epoch   4 Batch 1038/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.923, Loss:  0.051
+Epoch   4 Batch 1039/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.927, Loss:  0.050
+Epoch   4 Batch 1040/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.927, Loss:  0.068
+Epoch   4 Batch 1041/1077 - Train Accuracy:  0.902, Validation Accuracy:  0.925, Loss:  0.047
+Epoch   4 Batch 1042/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.926, Loss:  0.041
+Epoch   4 Batch 1043/1077 - Train Accuracy:  0.949, Validation Accuracy:  0.937, Loss:  0.052
+Epoch   4 Batch 1044/1077 - Train Accuracy:  0.933, Validation Accuracy:  0.930, Loss:  0.057
+Epoch   4 Batch 1045/1077 - Train Accuracy:  0.934, Validation Accuracy:  0.925, Loss:  0.047
+Epoch   4 Batch 1046/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.929, Loss:  0.040
+Epoch   4 Batch 1047/1077 - Train Accuracy:  0.974, Validation Accuracy:  0.919, Loss:  0.036
+Epoch   4 Batch 1048/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.915, Loss:  0.040
+Epoch   4 Batch 1049/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.913, Loss:  0.040
+Epoch   4 Batch 1050/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.909, Loss:  0.036
+Epoch   4 Batch 1051/1077 - Train Accuracy:  0.947, Validation Accuracy:  0.919, Loss:  0.056
+Epoch   4 Batch 1052/1077 - Train Accuracy:  0.955, Validation Accuracy:  0.918, Loss:  0.051
+Epoch   4 Batch 1053/1077 - Train Accuracy:  0.946, Validation Accuracy:  0.923, Loss:  0.053
+Epoch   4 Batch 1054/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.918, Loss:  0.045
+Epoch   4 Batch 1055/1077 - Train Accuracy:  0.963, Validation Accuracy:  0.928, Loss:  0.044
+Epoch   4 Batch 1056/1077 - Train Accuracy:  0.942, Validation Accuracy:  0.924, Loss:  0.044
+Epoch   4 Batch 1057/1077 - Train Accuracy:  0.941, Validation Accuracy:  0.922, Loss:  0.058
+Epoch   4 Batch 1058/1077 - Train Accuracy:  0.930, Validation Accuracy:  0.930, Loss:  0.054
+Epoch   4 Batch 1059/1077 - Train Accuracy:  0.898, Validation Accuracy:  0.939, Loss:  0.055
+Epoch   4 Batch 1060/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.935, Loss:  0.040
+Epoch   4 Batch 1061/1077 - Train Accuracy:  0.926, Validation Accuracy:  0.931, Loss:  0.056
+Epoch   4 Batch 1062/1077 - Train Accuracy:  0.916, Validation Accuracy:  0.926, Loss:  0.054
+Epoch   4 Batch 1063/1077 - Train Accuracy:  0.928, Validation Accuracy:  0.931, Loss:  0.068
+Epoch   4 Batch 1064/1077 - Train Accuracy:  0.951, Validation Accuracy:  0.940, Loss:  0.050
+Epoch   4 Batch 1065/1077 - Train Accuracy:  0.938, Validation Accuracy:  0.935, Loss:  0.050
+Epoch   4 Batch 1066/1077 - Train Accuracy:  0.940, Validation Accuracy:  0.929, Loss:  0.032
+Epoch   4 Batch 1067/1077 - Train Accuracy:  0.927, Validation Accuracy:  0.927, Loss:  0.059
+Epoch   4 Batch 1068/1077 - Train Accuracy:  0.937, Validation Accuracy:  0.927, Loss:  0.041
+Epoch   4 Batch 1069/1077 - Train Accuracy:  0.953, Validation Accuracy:  0.923, Loss:  0.038
+Epoch   4 Batch 1070/1077 - Train Accuracy:  0.936, Validation Accuracy:  0.924, Loss:  0.041
+Epoch   4 Batch 1071/1077 - Train Accuracy:  0.917, Validation Accuracy:  0.924, Loss:  0.042
+Epoch   4 Batch 1072/1077 - Train Accuracy:  0.958, Validation Accuracy:  0.924, Loss:  0.045
+Epoch   4 Batch 1073/1077 - Train Accuracy:  0.945, Validation Accuracy:  0.925, Loss:  0.053
+Epoch   4 Batch 1074/1077 - Train Accuracy:  0.935, Validation Accuracy:  0.925, Loss:  0.054
+Epoch   4 Batch 1075/1077 - Train Accuracy:  0.939, Validation Accuracy:  0.923, Loss:  0.062
+Model Trained and Saved
+
+
+
+ +
+
+ +
+
+
+
+
+

保存参数

保存 batch_size 和 save_path 参数以进行推论（for inference）。

+ +
+
+
+
+
+
In [17]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+# Save parameters for checkpoint
+helper.save_params(save_path)
+
+ +
+
+
+ +
+
+
+
+
+

检查点

+
+
+
+
+
+
In [18]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import tensorflow as tf
+import numpy as np
+import helper
+import problem_unittests as tests
+
+_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()
+load_path = helper.load_params()
+
+ +
+
+
+ +
+
+
+
+
+

句子到序列

要向模型提供要翻译的句子，你首先需要预处理该句子。实现函数 sentence_to_seq() 以预处理新的句子。

+
    +
  • 将句子转换为小写形式
    • +
  • 使用 vocab_to_int 将单词转换为 id
      +
    • 如果单词不在词汇表中，将其转换为<UNK> 单词 id
      • +
    +
    • +
+ +
+
+
+
+
+
In [19]:
+
+
+
def sentence_to_seq(sentence, vocab_to_int):
+    """
+    Convert a sentence to a sequence of ids
+    :param sentence: String
+    :param vocab_to_int: Dictionary to go from the words to an id
+    :return: List of word ids
+    """
+    # TODO: Implement Function
+    word_ids = [vocab_to_int.get(word, vocab_to_int["<UNK>"]) for word in sentence.lower().split()]
+    
+    return word_ids
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_sentence_to_seq(sentence_to_seq)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

翻译

将 translate_sentence 从英语翻译成法语。

+ +
+
+
+
+
+
In [20]:
+
+
+
translate_sentence = 'he saw a old yellow truck .'
+
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)
+
+loaded_graph = tf.Graph()
+with tf.Session(graph=loaded_graph) as sess:
+    # Load saved model
+    loader = tf.train.import_meta_graph(load_path + '.meta')
+    loader.restore(sess, load_path)
+
+    input_data = loaded_graph.get_tensor_by_name('input:0')
+    logits = loaded_graph.get_tensor_by_name('logits:0')
+    keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
+
+    translate_logits = sess.run(logits, {input_data: [translate_sentence], keep_prob: 1.0})[0]
+
+print('Input')
+print('  Word Ids:      {}'.format([i for i in translate_sentence]))
+print('  English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))
+
+print('\nPrediction')
+print('  Word Ids:      {}'.format([i for i in np.argmax(translate_logits, 1)]))
+print('  French Words: {}'.format([target_int_to_vocab[i] for i in np.argmax(translate_logits, 1)]))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Input
+  Word Ids:      [226, 218, 43, 30, 138, 202, 171]
+  English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']
+
+Prediction
+  Word Ids:      [286, 17, 192, 281, 138, 94, 89, 60, 1]
+  French Words: ['il', 'a', 'vu', 'un', 'petit', 'camion', 'jaune', '.', '<EOS>']
+
+
+
+ +
+
+ +
+
+
+
+
+

不完美的翻译

你可能注意到了，某些句子的翻译质量比其他的要好。因为你使用的数据集只有 227 个英语单词，但实际生活中有数千个单词，只有使用这些单词的句子结果才会比较理想。对于此项目，不需要达到完美的翻译。但是，如果你想创建更好的翻译模型，则需要更好的数据。

+

你可以使用 WMT10 French-English corpus 语料库训练模型。该数据集拥有更多的词汇，讨论的话题也更丰富。但是，训练时间要好多天的时间，所以确保你有 GPU 并且对于我们提供的数据集，你的神经网络性能很棒。提交此项目后，别忘了研究下 WMT10 语料库。

+

提交项目

提交项目时，确保先运行所有单元，然后再保存记事本。保存记事本文件为 “dlnd_language_translation.ipynb”，再通过菜单中的“文件” ->“下载为”将其另存为 HTML 格式。提交的项目文档中需包含“helper.py”和“problem_unittests.py”文件。

+ +
+
+
+
+
+ + + + + + diff --git a/language-translation/dlnd_language_translation.ipynb b/language-translation/dlnd_language_translation.ipynb index 4fad7ba..c10e6e3 100644 --- a/language-translation/dlnd_language_translation.ipynb +++ b/language-translation/dlnd_language_translation.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "source": [ "# 语言翻译\n", @@ -19,11 +17,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -41,10 +37,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 探索数据\n", "\n", @@ -53,13 +46,44 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset Stats\n", + "Roughly the number of unique words: 227\n", + "Number of sentences: 137861\n", + "Average number of words in a sentence: 13.225277634719028\n", + "\n", + "English sentences 0 to 10:\n", + "new jersey is sometimes quiet during autumn , and it is snowy in april .\n", + "the united states is usually chilly during july , and it is usually freezing in november .\n", + "california is usually quiet during march , and it is usually hot in june .\n", + "the united states is sometimes mild during june , and it is cold in september .\n", + "your least liked fruit is the grape , but my least liked is the apple .\n", + "his favorite fruit is the orange , but my favorite is the grape .\n", + "paris is relaxing during december , but it is usually chilly in july .\n", + "new jersey is busy during spring , and it is never hot in march .\n", + "our least liked fruit is the lemon , but my least liked is the grape .\n", + "the united states is sometimes busy during january , and it is sometimes warm in november .\n", + "\n", + "French sentences 0 to 10:\n", + "new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n", + "les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n", + "california est généralement calme en mars , et il est généralement chaud en juin .\n", + "les états-unis est parfois légère en juin , et il fait froid en septembre .\n", + "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n", + "son fruit préféré est l'orange , mais mon préféré est le raisin .\n", + "paris est relaxant en décembre , mais il est généralement froid en juillet .\n", + "new jersey est occupé au printemps , et il est jamais chaude en mars .\n", + "notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n", + "les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n" + ] + } + ], "source": [ "view_sentence_range = (0, 10)\n", "\n", @@ -86,10 +110,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 实现预处理函数\n", "\n", @@ -109,13 +130,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n", " \"\"\"\n", @@ -127,7 +152,10 @@ " :return: A tuple of lists (source_id_text, target_id_text)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None\n", + " source_letter_ids = [[source_vocab_to_int.get(letter, source_vocab_to_int['']) for letter in line.split()] for line in source_text.split('\\n')]\n", + " target_letter_ids = [[target_vocab_to_int.get(letter, target_vocab_to_int['']) for letter in line.split()] + [target_vocab_to_int['']] for line in target_text.split('\\n')] \n", + " \n", + " return source_letter_ids, target_letter_ids\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -137,10 +165,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 预处理所有数据并保存\n", "\n", @@ -149,11 +174,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -165,10 +188,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# 检查点\n", "\n", @@ -177,11 +197,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -196,10 +214,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 检查 TensorFlow 版本，确认可访问 GPU\n", "\n", @@ -208,13 +223,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TensorFlow Version: 1.0.1\n", + "Default GPU Device: /gpu:0\n" + ] + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", @@ -236,10 +256,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 构建神经网络\n", "\n", @@ -267,21 +284,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ + "import tensorflow as tf\n", "def model_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, and learning rate.\n", " :return: Tuple (input, targets, learning rate, keep probability)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None, None, None\n", + " inputs = tf.placeholder(tf.int32, [None, None], name='input')\n", + " targets = tf.placeholder(tf.int32, [None, None], name='targets')\n", + " learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n", + " keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n", + " \n", + " return inputs, targets, learning_rate, keep_prob\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -291,10 +318,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 处理解码输入\n", "\n", @@ -303,13 +327,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def process_decoding_input(target_data, target_vocab_to_int, batch_size):\n", " \"\"\"\n", @@ -320,7 +348,10 @@ " :return: Preprocessed target data\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n", + " dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['']), ending], 1)\n", + " \n", + " return dec_input\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -330,10 +361,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 编码\n", "\n", @@ -342,13 +370,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob):\n", " \"\"\"\n", @@ -360,7 +392,12 @@ " :return: RNN state\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n", + " drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n", + " enc_cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)\n", + " _, enc_state = tf.nn.dynamic_rnn(enc_cell, rnn_inputs, dtype=tf.float32)\n", + " \n", + " return enc_state\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -370,10 +407,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 解码 - 训练\n", "\n", @@ -382,13 +416,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n", " output_fn, keep_prob):\n", @@ -404,7 +442,15 @@ " :return: Train Logits\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " # Training Decoder\n", + " train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state)\n", + " train_pred, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(\n", + " dec_cell, train_decoder_fn, dec_embed_input, sequence_length, scope=decoding_scope)\n", + " \n", + " # Apply output function\n", + " train_logits = output_fn(train_pred)\n", + "\n", + " return train_logits\n", "\n", "\n", "\"\"\"\n", @@ -415,10 +461,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 解码 - 推论\n", "\n", @@ -427,13 +470,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,\n", " maximum_length, vocab_size, decoding_scope, output_fn, keep_prob):\n", @@ -452,7 +499,13 @@ " :return: Inference Logits\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " # Inference Decoder\n", + " infer_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference(\n", + " output_fn, encoder_state, dec_embeddings, start_of_sequence_id, end_of_sequence_id, \n", + " maximum_length - 1, vocab_size)\n", + " inference_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell, infer_decoder_fn, scope=decoding_scope)\n", + "\n", + " return inference_logits\n", "\n", "\n", "\"\"\"\n", @@ -463,10 +516,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 构建解码层级\n", "\n", @@ -482,13 +532,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,\n", " num_layers, target_vocab_to_int, keep_prob):\n", @@ -506,7 +560,23 @@ " :return: Tuple of (Training Logits, Inference Logits)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None\n", + " lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n", + " dropout = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n", + " dec_cell = tf.contrib.rnn.MultiRNNCell([dropout] * num_layers)\n", + " \n", + " output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, activation_fn=None, scope=decoding_scope) \n", + "\n", + " with tf.variable_scope(\"decoding\") as decoding_scope:\n", + " training_decoder_output = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n", + " output_fn, keep_prob)\n", + "\n", + " with tf.variable_scope(\"decoding\", reuse=True) as decoding_scope:\n", + " start_of_sequence_id = target_vocab_to_int[\"\"]\n", + " end_of_sequence_id = target_vocab_to_int[\"\"]\n", + " inference_decoder_output = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, \n", + " sequence_length, vocab_size, decoding_scope, output_fn, keep_prob)\n", + " \n", + " return training_decoder_output, inference_decoder_output\n", "\n", "\n", "\"\"\"\n", @@ -517,10 +587,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 构建神经网络\n", "\n", @@ -535,13 +602,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,\n", " enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):\n", @@ -562,7 +633,18 @@ " :return: Tuple of (Training Logits, Inference Logits)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " rnn_inputs = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size)\n", + " \n", + " encoder_state = encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)\n", + " \n", + " dec_input = process_decoding_input(target_data, target_vocab_to_int, batch_size)\n", + " dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size]))\n", + " dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n", + " \n", + " train_logits, inference_logits = decoding_layer(dec_embed_input, dec_embeddings, encoder_state, target_vocab_size, sequence_length,\n", + " rnn_size, num_layers, target_vocab_to_int, keep_prob)\n", + " \n", + " return train_logits, inference_logits\n", "\n", "\n", "\"\"\"\n", @@ -573,10 +655,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 训练神经网络\n", "\n", @@ -596,37 +675,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ "# Number of Epochs\n", - "epochs = None\n", + "epochs = 5\n", "# Batch Size\n", - "batch_size = None\n", + "batch_size = 128\n", "# RNN Size\n", - "rnn_size = None\n", + "rnn_size = 128\n", "# Number of Layers\n", - "num_layers = None\n", + "num_layers = 2\n", "# Embedding Size\n", - "encoding_embedding_size = None\n", - "decoding_embedding_size = None\n", + "encoding_embedding_size = 100\n", + "decoding_embedding_size = 100\n", "# Learning Rate\n", - "learning_rate = None\n", + "learning_rate = 0.01\n", "# Dropout Keep Probability\n", - "keep_probability = None" + "keep_probability = 0.8" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 构建图表\n", "\n", @@ -635,11 +709,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -679,10 +751,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 训练\n", "\n", @@ -691,14 +760,5765 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 Batch 0/1077 - Train Accuracy: 0.294, Validation Accuracy: 0.305, Loss: 5.889\n", + "Epoch 0 Batch 1/1077 - Train Accuracy: 0.221, Validation Accuracy: 0.305, Loss: 5.074\n", + "Epoch 0 Batch 2/1077 - Train Accuracy: 0.244, Validation Accuracy: 0.335, Loss: 4.336\n", + "Epoch 0 Batch 3/1077 - Train Accuracy: 0.275, Validation Accuracy: 0.337, Loss: 3.832\n", + "Epoch 0 Batch 4/1077 - Train Accuracy: 0.263, Validation Accuracy: 0.336, Loss: 3.669\n", + "Epoch 0 Batch 5/1077 - Train Accuracy: 0.295, Validation Accuracy: 0.336, Loss: 3.552\n", + "Epoch 0 Batch 6/1077 - Train Accuracy: 0.282, Validation Accuracy: 0.342, Loss: 3.504\n", + "Epoch 0 Batch 7/1077 - Train Accuracy: 0.268, Validation Accuracy: 0.342, Loss: 3.515\n", + "Epoch 0 Batch 8/1077 - Train Accuracy: 0.275, Validation Accuracy: 0.341, Loss: 3.414\n", + "Epoch 0 Batch 9/1077 - Train Accuracy: 0.283, Validation Accuracy: 0.341, Loss: 3.324\n", + "Epoch 0 Batch 10/1077 - Train Accuracy: 0.273, Validation Accuracy: 0.363, Loss: 3.444\n", + "Epoch 0 Batch 11/1077 - Train Accuracy: 0.334, Validation Accuracy: 0.370, Loss: 3.166\n", + "Epoch 0 Batch 12/1077 - Train Accuracy: 0.314, Validation Accuracy: 0.380, Loss: 3.323\n", + "Epoch 0 Batch 13/1077 - Train Accuracy: 0.353, Validation Accuracy: 0.373, Loss: 3.069\n", + "Epoch 0 Batch 14/1077 - Train Accuracy: 0.338, Validation Accuracy: 0.377, Loss: 3.096\n", + "Epoch 0 Batch 15/1077 - Train Accuracy: 0.333, Validation Accuracy: 0.391, Loss: 3.169\n", + "Epoch 0 Batch 16/1077 - Train Accuracy: 0.350, Validation Accuracy: 0.392, Loss: 3.136\n", + "Epoch 0 Batch 17/1077 - Train Accuracy: 0.341, Validation Accuracy: 0.382, Loss: 3.051\n", + "Epoch 0 Batch 18/1077 - Train Accuracy: 0.343, Validation Accuracy: 0.407, Loss: 3.059\n", + "Epoch 0 Batch 19/1077 - Train Accuracy: 0.361, Validation Accuracy: 0.402, Loss: 2.920\n", + "Epoch 0 Batch 20/1077 - Train Accuracy: 0.341, Validation Accuracy: 0.400, Loss: 2.897\n", + "Epoch 0 Batch 21/1077 - Train Accuracy: 0.330, Validation Accuracy: 0.409, Loss: 2.971\n", + "Epoch 0 Batch 22/1077 - Train Accuracy: 0.359, Validation Accuracy: 0.417, Loss: 2.950\n", + "Epoch 0 Batch 23/1077 - Train Accuracy: 0.354, Validation Accuracy: 0.412, Loss: 2.924\n", + "Epoch 0 Batch 24/1077 - Train Accuracy: 0.360, Validation Accuracy: 0.413, Loss: 2.818\n", + "Epoch 0 Batch 25/1077 - Train Accuracy: 0.361, Validation Accuracy: 0.425, Loss: 2.867\n", + "Epoch 0 Batch 26/1077 - Train Accuracy: 0.359, Validation Accuracy: 0.420, Loss: 2.829\n", + "Epoch 0 Batch 27/1077 - Train Accuracy: 0.410, Validation Accuracy: 0.427, Loss: 2.581\n", + "Epoch 0 Batch 28/1077 - Train Accuracy: 0.394, Validation Accuracy: 0.436, Loss: 2.710\n", + "Epoch 0 Batch 29/1077 - Train Accuracy: 0.395, Validation Accuracy: 0.441, Loss: 2.660\n", + "Epoch 0 Batch 30/1077 - Train Accuracy: 0.397, Validation Accuracy: 0.448, Loss: 2.664\n", + "Epoch 0 Batch 31/1077 - Train Accuracy: 0.399, Validation Accuracy: 0.453, Loss: 2.683\n", + "Epoch 0 Batch 32/1077 - Train Accuracy: 0.453, Validation Accuracy: 0.460, Loss: 2.471\n", + "Epoch 0 Batch 33/1077 - Train Accuracy: 0.429, Validation Accuracy: 0.468, Loss: 2.467\n", + "Epoch 0 Batch 34/1077 - Train Accuracy: 0.405, Validation Accuracy: 0.462, Loss: 2.541\n", + "Epoch 0 Batch 35/1077 - Train Accuracy: 0.410, Validation Accuracy: 0.460, Loss: 2.516\n", + "Epoch 0 Batch 36/1077 - Train Accuracy: 0.425, Validation Accuracy: 0.466, Loss: 2.453\n", + "Epoch 0 Batch 37/1077 - Train Accuracy: 0.427, Validation Accuracy: 0.480, Loss: 2.505\n", + "Epoch 0 Batch 38/1077 - Train Accuracy: 0.378, Validation Accuracy: 0.474, Loss: 2.650\n", + "Epoch 0 Batch 39/1077 - Train Accuracy: 0.420, Validation Accuracy: 0.480, Loss: 2.496\n", + "Epoch 0 Batch 40/1077 - Train Accuracy: 0.410, Validation Accuracy: 0.466, Loss: 2.415\n", + "Epoch 0 Batch 41/1077 - Train Accuracy: 0.455, Validation Accuracy: 0.483, Loss: 2.359\n", + "Epoch 0 Batch 42/1077 - Train Accuracy: 0.414, Validation Accuracy: 0.467, Loss: 2.375\n", + "Epoch 0 Batch 43/1077 - Train Accuracy: 0.439, Validation Accuracy: 0.497, Loss: 2.402\n", + "Epoch 0 Batch 44/1077 - Train Accuracy: 0.355, Validation Accuracy: 0.436, Loss: 2.547\n", + "Epoch 0 Batch 45/1077 - Train Accuracy: 0.406, Validation Accuracy: 0.469, Loss: 2.402\n", + "Epoch 0 Batch 46/1077 - Train Accuracy: 0.419, Validation Accuracy: 0.492, Loss: 2.394\n", + "Epoch 0 Batch 47/1077 - Train Accuracy: 0.406, Validation Accuracy: 0.458, Loss: 2.299\n", + "Epoch 0 Batch 48/1077 - Train Accuracy: 0.407, Validation Accuracy: 0.443, Loss: 2.303\n", + "Epoch 0 Batch 49/1077 - Train Accuracy: 0.404, Validation Accuracy: 0.462, Loss: 2.302\n", + "Epoch 0 Batch 50/1077 - Train Accuracy: 0.403, Validation Accuracy: 0.478, Loss: 2.336\n", + "Epoch 0 Batch 51/1077 - Train Accuracy: 0.434, Validation Accuracy: 0.457, Loss: 2.179\n", + "Epoch 0 Batch 52/1077 - Train Accuracy: 0.432, Validation Accuracy: 0.492, Loss: 2.245\n", + "Epoch 0 Batch 53/1077 - Train Accuracy: 0.474, Validation Accuracy: 0.518, Loss: 2.199\n", + "Epoch 0 Batch 54/1077 - Train Accuracy: 0.439, Validation Accuracy: 0.523, Loss: 2.408\n", + "Epoch 0 Batch 55/1077 - Train Accuracy: 0.479, Validation Accuracy: 0.507, Loss: 2.111\n", + "Epoch 0 Batch 56/1077 - Train Accuracy: 0.455, Validation Accuracy: 0.513, Loss: 2.172\n", + "Epoch 0 Batch 57/1077 - Train Accuracy: 0.520, Validation Accuracy: 0.512, Loss: 1.935\n", + "Epoch 0 Batch 58/1077 - Train Accuracy: 0.445, Validation Accuracy: 0.493, Loss: 2.165\n", + "Epoch 0 Batch 59/1077 - Train Accuracy: 0.462, Validation Accuracy: 0.532, Loss: 2.256\n", + "Epoch 0 Batch 60/1077 - Train Accuracy: 0.487, Validation Accuracy: 0.525, Loss: 2.097\n", + "Epoch 0 Batch 61/1077 - Train Accuracy: 0.454, Validation Accuracy: 0.509, Loss: 2.091\n", + "Epoch 0 Batch 62/1077 - Train Accuracy: 0.464, Validation Accuracy: 0.531, Loss: 2.211\n", + "Epoch 0 Batch 63/1077 - Train Accuracy: 0.517, Validation Accuracy: 0.535, Loss: 1.980\n", + "Epoch 0 Batch 64/1077 - Train Accuracy: 0.446, Validation Accuracy: 0.494, Loss: 2.087\n", + "Epoch 0 Batch 65/1077 - Train Accuracy: 0.462, Validation Accuracy: 0.544, Loss: 2.220\n", + "Epoch 0 Batch 66/1077 - Train Accuracy: 0.477, Validation Accuracy: 0.528, Loss: 2.058\n", + "Epoch 0 Batch 67/1077 - Train Accuracy: 0.497, Validation Accuracy: 0.507, Loss: 1.945\n", + "Epoch 0 Batch 68/1077 - Train Accuracy: 0.479, Validation Accuracy: 0.534, Loss: 2.047\n", + "Epoch 0 Batch 69/1077 - Train Accuracy: 0.516, Validation Accuracy: 0.546, Loss: 1.991\n", + "Epoch 0 Batch 70/1077 - Train Accuracy: 0.474, Validation Accuracy: 0.534, Loss: 2.054\n", + "Epoch 0 Batch 71/1077 - Train Accuracy: 0.486, Validation Accuracy: 0.534, Loss: 1.950\n", + "Epoch 0 Batch 72/1077 - Train Accuracy: 0.491, Validation Accuracy: 0.543, Loss: 1.957\n", + "Epoch 0 Batch 73/1077 - Train Accuracy: 0.475, Validation Accuracy: 0.521, Loss: 1.968\n", + "Epoch 0 Batch 74/1077 - Train Accuracy: 0.507, Validation Accuracy: 0.535, Loss: 1.823\n", + "Epoch 0 Batch 75/1077 - Train Accuracy: 0.526, Validation Accuracy: 0.539, Loss: 1.826\n", + "Epoch 0 Batch 76/1077 - Train Accuracy: 0.502, Validation Accuracy: 0.529, Loss: 1.857\n", + "Epoch 0 Batch 77/1077 - Train Accuracy: 0.462, Validation Accuracy: 0.527, Loss: 1.920\n", + "Epoch 0 Batch 78/1077 - Train Accuracy: 0.455, Validation Accuracy: 0.527, Loss: 2.023\n", + "Epoch 0 Batch 79/1077 - Train Accuracy: 0.480, Validation Accuracy: 0.521, Loss: 1.887\n", + "Epoch 0 Batch 80/1077 - 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Train Accuracy: 0.800, Validation Accuracy: 0.823, Loss: 0.212\n", + "Epoch 0 Batch 683/1077 - Train Accuracy: 0.822, Validation Accuracy: 0.827, Loss: 0.212\n", + "Epoch 0 Batch 684/1077 - Train Accuracy: 0.837, Validation Accuracy: 0.818, Loss: 0.217\n", + "Epoch 0 Batch 685/1077 - Train Accuracy: 0.793, Validation Accuracy: 0.821, Loss: 0.234\n", + "Epoch 0 Batch 686/1077 - Train Accuracy: 0.833, Validation Accuracy: 0.810, Loss: 0.202\n", + "Epoch 0 Batch 687/1077 - Train Accuracy: 0.834, Validation Accuracy: 0.814, Loss: 0.237\n", + "Epoch 0 Batch 688/1077 - Train Accuracy: 0.846, Validation Accuracy: 0.814, Loss: 0.207\n", + "Epoch 0 Batch 689/1077 - Train Accuracy: 0.874, Validation Accuracy: 0.815, Loss: 0.200\n", + "Epoch 0 Batch 690/1077 - Train Accuracy: 0.848, Validation Accuracy: 0.820, Loss: 0.209\n", + "Epoch 0 Batch 691/1077 - Train Accuracy: 0.839, Validation Accuracy: 0.824, Loss: 0.245\n", + "Epoch 0 Batch 692/1077 - Train Accuracy: 0.828, Validation Accuracy: 0.819, Loss: 0.204\n", + "Epoch 0 Batch 693/1077 - Train Accuracy: 0.760, Validation Accuracy: 0.818, Loss: 0.267\n", + "Epoch 0 Batch 694/1077 - Train Accuracy: 0.838, Validation Accuracy: 0.822, Loss: 0.240\n", + "Epoch 0 Batch 695/1077 - Train Accuracy: 0.841, Validation Accuracy: 0.828, Loss: 0.195\n", + "Epoch 0 Batch 696/1077 - Train Accuracy: 0.817, Validation Accuracy: 0.816, Loss: 0.233\n", + "Epoch 0 Batch 697/1077 - Train Accuracy: 0.847, Validation Accuracy: 0.811, Loss: 0.214\n", + "Epoch 0 Batch 698/1077 - Train Accuracy: 0.827, Validation Accuracy: 0.815, Loss: 0.208\n", + "Epoch 0 Batch 699/1077 - Train Accuracy: 0.839, Validation Accuracy: 0.821, Loss: 0.212\n", + "Epoch 0 Batch 700/1077 - Train Accuracy: 0.845, Validation Accuracy: 0.817, Loss: 0.208\n", + "Epoch 0 Batch 701/1077 - Train Accuracy: 0.852, Validation Accuracy: 0.808, Loss: 0.239\n", + "Epoch 0 Batch 702/1077 - Train Accuracy: 0.837, Validation Accuracy: 0.812, Loss: 0.227\n", + "Epoch 0 Batch 703/1077 - Train Accuracy: 0.842, Validation Accuracy: 0.811, Loss: 0.219\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 Batch 704/1077 - Train Accuracy: 0.845, Validation Accuracy: 0.820, Loss: 0.235\n", + "Epoch 0 Batch 705/1077 - Train Accuracy: 0.846, Validation Accuracy: 0.819, Loss: 0.250\n", + "Epoch 0 Batch 706/1077 - Train Accuracy: 0.806, Validation Accuracy: 0.828, Loss: 0.244\n", + "Epoch 0 Batch 707/1077 - Train Accuracy: 0.854, Validation Accuracy: 0.832, Loss: 0.211\n", + "Epoch 0 Batch 708/1077 - Train Accuracy: 0.821, Validation Accuracy: 0.837, Loss: 0.216\n", + "Epoch 0 Batch 709/1077 - Train Accuracy: 0.834, Validation Accuracy: 0.838, Loss: 0.238\n", + "Epoch 0 Batch 710/1077 - Train Accuracy: 0.851, Validation Accuracy: 0.836, Loss: 0.197\n", + "Epoch 0 Batch 711/1077 - Train Accuracy: 0.820, Validation Accuracy: 0.838, Loss: 0.229\n", + "Epoch 0 Batch 712/1077 - Train Accuracy: 0.854, Validation Accuracy: 0.845, Loss: 0.196\n", + "Epoch 0 Batch 713/1077 - Train Accuracy: 0.839, Validation Accuracy: 0.825, Loss: 0.172\n", + "Epoch 0 Batch 714/1077 - Train Accuracy: 0.829, Validation Accuracy: 0.815, Loss: 0.209\n", + "Epoch 0 Batch 715/1077 - 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Train Accuracy: 0.902, Validation Accuracy: 0.909, Loss: 0.078\n", + "Epoch 1 Batch 729/1077 - Train Accuracy: 0.905, Validation Accuracy: 0.909, Loss: 0.082\n", + "Epoch 1 Batch 730/1077 - Train Accuracy: 0.918, Validation Accuracy: 0.893, Loss: 0.081\n", + "Epoch 1 Batch 731/1077 - Train Accuracy: 0.913, Validation Accuracy: 0.899, Loss: 0.063\n", + "Epoch 1 Batch 732/1077 - Train Accuracy: 0.897, Validation Accuracy: 0.899, Loss: 0.072\n", + "Epoch 1 Batch 733/1077 - Train Accuracy: 0.913, Validation Accuracy: 0.902, Loss: 0.082\n", + "Epoch 1 Batch 734/1077 - Train Accuracy: 0.949, Validation Accuracy: 0.896, Loss: 0.064\n", + "Epoch 1 Batch 735/1077 - Train Accuracy: 0.910, Validation Accuracy: 0.901, Loss: 0.061\n", + "Epoch 1 Batch 736/1077 - Train Accuracy: 0.943, Validation Accuracy: 0.914, Loss: 0.053\n", + "Epoch 1 Batch 737/1077 - Train Accuracy: 0.915, Validation Accuracy: 0.914, Loss: 0.082\n", + "Epoch 1 Batch 738/1077 - Train Accuracy: 0.933, Validation Accuracy: 0.913, Loss: 0.047\n", + "Epoch 1 Batch 739/1077 - 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Train Accuracy: 0.921, Validation Accuracy: 0.909, Loss: 0.060\n", + "Epoch 1 Batch 773/1077 - Train Accuracy: 0.896, Validation Accuracy: 0.909, Loss: 0.068\n", + "Epoch 1 Batch 774/1077 - Train Accuracy: 0.929, Validation Accuracy: 0.909, Loss: 0.067\n", + "Epoch 1 Batch 775/1077 - Train Accuracy: 0.923, Validation Accuracy: 0.907, Loss: 0.072\n", + "Epoch 1 Batch 776/1077 - Train Accuracy: 0.916, Validation Accuracy: 0.924, Loss: 0.058\n", + "Epoch 1 Batch 777/1077 - Train Accuracy: 0.930, Validation Accuracy: 0.926, Loss: 0.070\n", + "Epoch 1 Batch 778/1077 - Train Accuracy: 0.906, Validation Accuracy: 0.924, Loss: 0.064\n", + "Epoch 1 Batch 779/1077 - Train Accuracy: 0.908, Validation Accuracy: 0.920, Loss: 0.069\n", + "Epoch 1 Batch 780/1077 - Train Accuracy: 0.889, Validation Accuracy: 0.916, Loss: 0.091\n", + "Epoch 1 Batch 781/1077 - Train Accuracy: 0.936, Validation Accuracy: 0.923, Loss: 0.054\n", + "Epoch 1 Batch 782/1077 - Train Accuracy: 0.914, Validation Accuracy: 0.917, Loss: 0.053\n", + "Epoch 1 Batch 783/1077 - 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Train Accuracy: 0.911, Validation Accuracy: 0.909, Loss: 0.059\n", + "Epoch 1 Batch 949/1077 - Train Accuracy: 0.965, Validation Accuracy: 0.915, Loss: 0.052\n", + "Epoch 1 Batch 950/1077 - Train Accuracy: 0.942, Validation Accuracy: 0.917, Loss: 0.058\n", + "Epoch 1 Batch 951/1077 - Train Accuracy: 0.900, Validation Accuracy: 0.923, Loss: 0.068\n", + "Epoch 1 Batch 952/1077 - Train Accuracy: 0.932, Validation Accuracy: 0.928, Loss: 0.055\n", + "Epoch 1 Batch 953/1077 - Train Accuracy: 0.933, Validation Accuracy: 0.930, Loss: 0.049\n", + "Epoch 1 Batch 954/1077 - Train Accuracy: 0.912, Validation Accuracy: 0.925, Loss: 0.067\n", + "Epoch 1 Batch 955/1077 - Train Accuracy: 0.905, Validation Accuracy: 0.928, Loss: 0.074\n", + "Epoch 1 Batch 956/1077 - Train Accuracy: 0.905, Validation Accuracy: 0.933, Loss: 0.066\n", + "Epoch 1 Batch 957/1077 - Train Accuracy: 0.929, Validation Accuracy: 0.922, Loss: 0.050\n", + "Epoch 1 Batch 958/1077 - Train Accuracy: 0.932, Validation Accuracy: 0.914, Loss: 0.067\n", + "Epoch 1 Batch 959/1077 - 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Train Accuracy: 0.944, Validation Accuracy: 0.928, Loss: 0.057\n", + "Epoch 2 Batch 775/1077 - Train Accuracy: 0.916, Validation Accuracy: 0.937, Loss: 0.056\n", + "Epoch 2 Batch 776/1077 - Train Accuracy: 0.947, Validation Accuracy: 0.947, Loss: 0.043\n", + "Epoch 2 Batch 777/1077 - Train Accuracy: 0.943, Validation Accuracy: 0.936, Loss: 0.056\n", + "Epoch 2 Batch 778/1077 - Train Accuracy: 0.929, Validation Accuracy: 0.936, Loss: 0.047\n", + "Epoch 2 Batch 779/1077 - Train Accuracy: 0.923, Validation Accuracy: 0.935, Loss: 0.057\n", + "Epoch 2 Batch 780/1077 - Train Accuracy: 0.912, Validation Accuracy: 0.947, Loss: 0.062\n", + "Epoch 2 Batch 781/1077 - Train Accuracy: 0.933, Validation Accuracy: 0.945, Loss: 0.044\n", + "Epoch 2 Batch 782/1077 - Train Accuracy: 0.918, Validation Accuracy: 0.940, Loss: 0.047\n", + "Epoch 2 Batch 783/1077 - Train Accuracy: 0.901, Validation Accuracy: 0.942, Loss: 0.053\n", + "Epoch 2 Batch 784/1077 - Train Accuracy: 0.941, Validation Accuracy: 0.938, Loss: 0.045\n", + "Epoch 2 Batch 785/1077 - 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Train Accuracy: 0.958, Validation Accuracy: 0.927, Loss: 0.048\n", + "Epoch 3 Batch 821/1077 - Train Accuracy: 0.937, Validation Accuracy: 0.923, Loss: 0.054\n", + "Epoch 3 Batch 822/1077 - Train Accuracy: 0.961, Validation Accuracy: 0.918, Loss: 0.040\n", + "Epoch 3 Batch 823/1077 - Train Accuracy: 0.922, Validation Accuracy: 0.922, Loss: 0.058\n", + "Epoch 3 Batch 824/1077 - Train Accuracy: 0.927, Validation Accuracy: 0.922, Loss: 0.064\n", + "Epoch 3 Batch 825/1077 - Train Accuracy: 0.943, Validation Accuracy: 0.919, Loss: 0.043\n", + "Epoch 3 Batch 826/1077 - Train Accuracy: 0.913, Validation Accuracy: 0.923, Loss: 0.048\n", + "Epoch 3 Batch 827/1077 - Train Accuracy: 0.941, Validation Accuracy: 0.926, Loss: 0.047\n", + "Epoch 3 Batch 828/1077 - Train Accuracy: 0.937, Validation Accuracy: 0.922, Loss: 0.051\n", + "Epoch 3 Batch 829/1077 - Train Accuracy: 0.914, Validation Accuracy: 0.918, Loss: 0.069\n", + "Epoch 3 Batch 830/1077 - Train Accuracy: 0.898, Validation Accuracy: 0.920, Loss: 0.059\n", + "Epoch 3 Batch 831/1077 - 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Train Accuracy: 0.949, Validation Accuracy: 0.910, Loss: 0.040\n", + "Epoch 4 Batch 889/1077 - Train Accuracy: 0.940, Validation Accuracy: 0.913, Loss: 0.048\n", + "Epoch 4 Batch 890/1077 - Train Accuracy: 0.950, Validation Accuracy: 0.916, Loss: 0.050\n", + "Epoch 4 Batch 891/1077 - Train Accuracy: 0.919, Validation Accuracy: 0.913, Loss: 0.042\n", + "Epoch 4 Batch 892/1077 - Train Accuracy: 0.950, Validation Accuracy: 0.909, Loss: 0.042\n", + "Epoch 4 Batch 893/1077 - Train Accuracy: 0.952, Validation Accuracy: 0.910, Loss: 0.048\n", + "Epoch 4 Batch 894/1077 - Train Accuracy: 0.945, Validation Accuracy: 0.908, Loss: 0.039\n", + "Epoch 4 Batch 895/1077 - Train Accuracy: 0.939, Validation Accuracy: 0.921, Loss: 0.044\n", + "Epoch 4 Batch 896/1077 - Train Accuracy: 0.935, Validation Accuracy: 0.926, Loss: 0.052\n", + "Epoch 4 Batch 897/1077 - Train Accuracy: 0.924, Validation Accuracy: 0.929, Loss: 0.043\n", + "Epoch 4 Batch 898/1077 - Train Accuracy: 0.920, Validation Accuracy: 0.925, Loss: 0.049\n", + "Epoch 4 Batch 899/1077 - 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Train Accuracy: 0.940, Validation Accuracy: 0.928, Loss: 0.054\n", + "Epoch 4 Batch 1032/1077 - Train Accuracy: 0.941, Validation Accuracy: 0.928, Loss: 0.061\n", + "Epoch 4 Batch 1033/1077 - Train Accuracy: 0.922, Validation Accuracy: 0.934, Loss: 0.060\n", + "Epoch 4 Batch 1034/1077 - Train Accuracy: 0.906, Validation Accuracy: 0.926, Loss: 0.050\n", + "Epoch 4 Batch 1035/1077 - Train Accuracy: 0.953, Validation Accuracy: 0.926, Loss: 0.032\n", + "Epoch 4 Batch 1036/1077 - Train Accuracy: 0.931, Validation Accuracy: 0.927, Loss: 0.060\n", + "Epoch 4 Batch 1037/1077 - Train Accuracy: 0.906, Validation Accuracy: 0.927, Loss: 0.053\n", + "Epoch 4 Batch 1038/1077 - Train Accuracy: 0.926, Validation Accuracy: 0.923, Loss: 0.051\n", + "Epoch 4 Batch 1039/1077 - Train Accuracy: 0.928, Validation Accuracy: 0.927, Loss: 0.050\n", + "Epoch 4 Batch 1040/1077 - Train Accuracy: 0.934, Validation Accuracy: 0.927, Loss: 0.068\n", + "Epoch 4 Batch 1041/1077 - Train Accuracy: 0.902, Validation Accuracy: 0.925, Loss: 0.047\n", + "Epoch 4 Batch 1042/1077 - Train Accuracy: 0.934, Validation Accuracy: 0.926, Loss: 0.041\n", + "Epoch 4 Batch 1043/1077 - Train Accuracy: 0.949, Validation Accuracy: 0.937, Loss: 0.052\n", + "Epoch 4 Batch 1044/1077 - Train Accuracy: 0.933, Validation Accuracy: 0.930, Loss: 0.057\n", + "Epoch 4 Batch 1045/1077 - Train Accuracy: 0.934, Validation Accuracy: 0.925, Loss: 0.047\n", + "Epoch 4 Batch 1046/1077 - Train Accuracy: 0.938, Validation Accuracy: 0.929, Loss: 0.040\n", + "Epoch 4 Batch 1047/1077 - Train Accuracy: 0.974, Validation Accuracy: 0.919, Loss: 0.036\n", + "Epoch 4 Batch 1048/1077 - Train Accuracy: 0.951, Validation Accuracy: 0.915, Loss: 0.040\n", + "Epoch 4 Batch 1049/1077 - Train Accuracy: 0.939, Validation Accuracy: 0.913, Loss: 0.040\n", + "Epoch 4 Batch 1050/1077 - Train Accuracy: 0.938, Validation Accuracy: 0.909, Loss: 0.036\n", + "Epoch 4 Batch 1051/1077 - Train Accuracy: 0.947, Validation Accuracy: 0.919, Loss: 0.056\n", + "Epoch 4 Batch 1052/1077 - Train Accuracy: 0.955, Validation Accuracy: 0.918, Loss: 0.051\n", + "Epoch 4 Batch 1053/1077 - Train Accuracy: 0.946, Validation Accuracy: 0.923, Loss: 0.053\n", + "Epoch 4 Batch 1054/1077 - Train Accuracy: 0.926, Validation Accuracy: 0.918, Loss: 0.045\n", + "Epoch 4 Batch 1055/1077 - Train Accuracy: 0.963, Validation Accuracy: 0.928, Loss: 0.044\n", + "Epoch 4 Batch 1056/1077 - Train Accuracy: 0.942, Validation Accuracy: 0.924, Loss: 0.044\n", + "Epoch 4 Batch 1057/1077 - Train Accuracy: 0.941, Validation Accuracy: 0.922, Loss: 0.058\n", + "Epoch 4 Batch 1058/1077 - Train Accuracy: 0.930, Validation Accuracy: 0.930, Loss: 0.054\n", + "Epoch 4 Batch 1059/1077 - Train Accuracy: 0.898, Validation Accuracy: 0.939, Loss: 0.055\n", + "Epoch 4 Batch 1060/1077 - Train Accuracy: 0.953, Validation Accuracy: 0.935, Loss: 0.040\n", + "Epoch 4 Batch 1061/1077 - Train Accuracy: 0.926, Validation Accuracy: 0.931, Loss: 0.056\n", + "Epoch 4 Batch 1062/1077 - Train Accuracy: 0.916, Validation Accuracy: 0.926, Loss: 0.054\n", + "Epoch 4 Batch 1063/1077 - Train Accuracy: 0.928, Validation Accuracy: 0.931, Loss: 0.068\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4 Batch 1064/1077 - Train Accuracy: 0.951, Validation Accuracy: 0.940, Loss: 0.050\n", + "Epoch 4 Batch 1065/1077 - Train Accuracy: 0.938, Validation Accuracy: 0.935, Loss: 0.050\n", + "Epoch 4 Batch 1066/1077 - Train Accuracy: 0.940, Validation Accuracy: 0.929, Loss: 0.032\n", + "Epoch 4 Batch 1067/1077 - Train Accuracy: 0.927, Validation Accuracy: 0.927, Loss: 0.059\n", + "Epoch 4 Batch 1068/1077 - Train Accuracy: 0.937, Validation Accuracy: 0.927, Loss: 0.041\n", + "Epoch 4 Batch 1069/1077 - Train Accuracy: 0.953, Validation Accuracy: 0.923, Loss: 0.038\n", + "Epoch 4 Batch 1070/1077 - Train Accuracy: 0.936, Validation Accuracy: 0.924, Loss: 0.041\n", + "Epoch 4 Batch 1071/1077 - Train Accuracy: 0.917, Validation Accuracy: 0.924, Loss: 0.042\n", + "Epoch 4 Batch 1072/1077 - Train Accuracy: 0.958, Validation Accuracy: 0.924, Loss: 0.045\n", + "Epoch 4 Batch 1073/1077 - Train Accuracy: 0.945, Validation Accuracy: 0.925, Loss: 0.053\n", + "Epoch 4 Batch 1074/1077 - Train Accuracy: 0.935, Validation Accuracy: 0.925, Loss: 0.054\n", + "Epoch 4 Batch 1075/1077 - Train Accuracy: 0.939, Validation Accuracy: 0.923, Loss: 0.062\n", + "Model Trained and Saved\n" + ] + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", @@ -766,10 +6586,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### 保存参数\n", "\n", @@ -778,11 +6595,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -795,21 +6610,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# 检查点" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -827,10 +6637,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 句子到序列\n", "\n", @@ -843,13 +6650,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def sentence_to_seq(sentence, vocab_to_int):\n", " \"\"\"\n", @@ -859,7 +6670,9 @@ " :return: List of word ids\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " word_ids = [vocab_to_int.get(word, vocab_to_int[\"\"]) for word in sentence.lower().split()]\n", + " \n", + " return word_ids\n", "\n", "\n", "\"\"\"\n", @@ -870,10 +6683,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 翻译\n", "\n", @@ -882,13 +6692,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input\n", + " Word Ids: [226, 218, 43, 30, 138, 202, 171]\n", + " English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n", + "\n", + "Prediction\n", + " Word Ids: [286, 17, 192, 281, 138, 94, 89, 60, 1]\n", + " French Words: ['il', 'a', 'vu', 'un', 'petit', 'camion', 'jaune', '.', '']\n" + ] + } + ], "source": [ "translate_sentence = 'he saw a old yellow truck .'\n", "\n", @@ -921,10 +6741,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## 不完美的翻译\n", "\n", @@ -940,24 +6757,25 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.5.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/language-translation/dlnd_language_translation.py b/language-translation/dlnd_language_translation.py new file mode 100644 index 0000000..4eef684 --- /dev/null +++ b/language-translation/dlnd_language_translation.py @@ -0,0 +1,697 @@ + +# coding: utf-8 + +# # 语言翻译 +# +# 在此项目中，你将了解神经网络机器翻译这一领域。你将用由英语和法语语句组成的数据集，训练一个序列到序列模型（sequence to sequence model），该模型能够将新的英语句子翻译成法语。 +# +# ## 获取数据 +# +# 因为将整个英语语言内容翻译成法语需要大量训练时间，所以我们提供了一小部分的英语语料库。 +# + +# In[1]: + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +import helper +import problem_unittests as tests + +source_path = 'data/small_vocab_en' +target_path = 'data/small_vocab_fr' +source_text = helper.load_data(source_path) +target_text = helper.load_data(target_path) + + +# ## 探索数据 +# +# 研究 view_sentence_range，查看并熟悉该数据的不同部分。 +# + +# In[2]: + + +view_sentence_range = (0, 10) + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +import numpy as np + +print('Dataset Stats') +print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()}))) + +sentences = source_text.split('\n') +word_counts = [len(sentence.split()) for sentence in sentences] +print('Number of sentences: {}'.format(len(sentences))) +print('Average number of words in a sentence: {}'.format(np.average(word_counts))) + +print() +print('English sentences {} to {}:'.format(*view_sentence_range)) +print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) +print() +print('French sentences {} to {}:'.format(*view_sentence_range)) +print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) + + +# ## 实现预处理函数 +# +# ### 文本到单词 id +# +# 和之前的 RNN 一样，你必须首先将文本转换为数字，这样计算机才能读懂。在函数 `text_to_ids()` 中，你需要将单词中的 `source_text` 和 `target_text` 转为 id。但是，你需要在 `target_text` 中每个句子的末尾，添加 `` 单词 id。这样可以帮助神经网络预测句子应该在什么地方结束。 +# +# +# 你可以通过以下代码获取 ` ` 单词ID： +# +# ```python +# target_vocab_to_int[''] +# ``` +# +# 你可以使用 `source_vocab_to_int` 和 `target_vocab_to_int` 获得其他单词 id。 +# + +# In[3]: + + +def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int): + """ + Convert source and target text to proper word ids + :param source_text: String that contains all the source text. + :param target_text: String that contains all the target text. + :param source_vocab_to_int: Dictionary to go from the source words to an id + :param target_vocab_to_int: Dictionary to go from the target words to an id + :return: A tuple of lists (source_id_text, target_id_text) + """ + # TODO: Implement Function + source_letter_ids = [[source_vocab_to_int.get(letter, source_vocab_to_int['']) for letter in line.split()] for line in source_text.split('\n')] + target_letter_ids = [[target_vocab_to_int.get(letter, target_vocab_to_int['']) for letter in line.split()] + [target_vocab_to_int['']] for line in target_text.split('\n')] + + return source_letter_ids, target_letter_ids + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_text_to_ids(text_to_ids) + + +# ### 预处理所有数据并保存 +# +# 运行以下代码单元，预处理所有数据，并保存到文件中。 +# + +# In[4]: + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +helper.preprocess_and_save_data(source_path, target_path, text_to_ids) + + +# # 检查点 +# +# 这是你的第一个检查点。如果你什么时候决定再回到该记事本，或需要重新启动该记事本，可以从这里继续。预处理的数据已保存到磁盘上。 + +# In[5]: + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +import numpy as np +import helper + +(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() + + +# ### 检查 TensorFlow 版本，确认可访问 GPU +# +# 这一检查步骤，可以确保你使用的是正确版本的 TensorFlow，并且能够访问 GPU。 +# + +# In[6]: + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +from distutils.version import LooseVersion +import warnings +import tensorflow as tf + +# Check TensorFlow Version +assert LooseVersion(tf.__version__) in [LooseVersion('1.0.0'), LooseVersion('1.0.1')], 'This project requires TensorFlow version 1.0 You are using {}'.format(tf.__version__) +print('TensorFlow Version: {}'.format(tf.__version__)) + +# Check for a GPU +if not tf.test.gpu_device_name(): + warnings.warn('No GPU found. Please use a GPU to train your neural network.') +else: + print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) + + +# ## 构建神经网络 +# +# 你将通过实现以下函数，构建出要构建一个序列到序列模型所需的组件： +# +# - `model_inputs` +# - `process_decoding_input` +# - `encoding_layer` +# - `decoding_layer_train` +# - `decoding_layer_infer` +# - `decoding_layer` +# - `seq2seq_model` +# +# ### 输入 +# +# 实现 `model_inputs()` 函数，为神经网络创建 TF 占位符。该函数应该创建以下占位符： +# +# - 名为 “input” 的输入文本占位符，并使用 TF Placeholder 名称参数（等级（Rank）为 2）。 +# - 目标占位符（等级为 2）。 +# - 学习速率占位符（等级为 0）。 +# - 名为 “keep_prob” 的保留率占位符，并使用 TF Placeholder 名称参数（等级为 0）。 +# +# 在以下元祖（tuple）中返回占位符：（输入、目标、学习速率、保留率） +# + +# In[7]: + + +import tensorflow as tf +def model_inputs(): + """ + Create TF Placeholders for input, targets, and learning rate. + :return: Tuple (input, targets, learning rate, keep probability) + """ + # TODO: Implement Function + inputs = tf.placeholder(tf.int32, [None, None], name='input') + targets = tf.placeholder(tf.int32, [None, None], name='targets') + learning_rate = tf.placeholder(tf.float32, name='learning_rate') + keep_prob = tf.placeholder(tf.float32, name='keep_prob') + + return inputs, targets, learning_rate, keep_prob + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_model_inputs(model_inputs) + + +# ### 处理解码输入 +# +# 使用 TensorFlow 实现 `process_decoding_input`，以便删掉 `target_data` 中每个批次的最后一个单词 ID，并将 GO ID 放到每个批次的开头。 + +# In[8]: + + +def process_decoding_input(target_data, target_vocab_to_int, batch_size): + """ + Preprocess target data for dencoding + :param target_data: Target Placehoder + :param target_vocab_to_int: Dictionary to go from the target words to an id + :param batch_size: Batch Size + :return: Preprocessed target data + """ + # TODO: Implement Function + ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1]) + dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['']), ending], 1) + + return dec_input + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_process_decoding_input(process_decoding_input) + + +# ### 编码 +# +# 实现 `encoding_layer()`，以使用 [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn) 创建编码器 RNN 层级。 + +# In[9]: + + +def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob): + """ + Create encoding layer + :param rnn_inputs: Inputs for the RNN + :param rnn_size: RNN Size + :param num_layers: Number of layers + :param keep_prob: Dropout keep probability + :return: RNN state + """ + # TODO: Implement Function + lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) + drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) + enc_cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers) + _, enc_state = tf.nn.dynamic_rnn(enc_cell, rnn_inputs, dtype=tf.float32) + + return enc_state + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_encoding_layer(encoding_layer) + + +# ### 解码 - 训练 +# +# 使用 [`tf.contrib.seq2seq.simple_decoder_fn_train()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建训练分对数（training logits）。将 `output_fn` 应用到 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 输出上。 + +# In[10]: + + +def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, + output_fn, keep_prob): + """ + Create a decoding layer for training + :param encoder_state: Encoder State + :param dec_cell: Decoder RNN Cell + :param dec_embed_input: Decoder embedded input + :param sequence_length: Sequence Length + :param decoding_scope: TenorFlow Variable Scope for decoding + :param output_fn: Function to apply the output layer + :param keep_prob: Dropout keep probability + :return: Train Logits + """ + # TODO: Implement Function + # Training Decoder + train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state) + train_pred, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder( + dec_cell, train_decoder_fn, dec_embed_input, sequence_length, scope=decoding_scope) + + # Apply output function + train_logits = output_fn(train_pred) + + return train_logits + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_decoding_layer_train(decoding_layer_train) + + +# ### 解码 - 推论 +# +# 使用 [`tf.contrib.seq2seq.simple_decoder_fn_inference()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_inference) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建推论分对数（inference logits）。 + +# In[11]: + + +def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, + maximum_length, vocab_size, decoding_scope, output_fn, keep_prob): + """ + Create a decoding layer for inference + :param encoder_state: Encoder state + :param dec_cell: Decoder RNN Cell + :param dec_embeddings: Decoder embeddings + :param start_of_sequence_id: GO ID + :param end_of_sequence_id: EOS Id + :param maximum_length: The maximum allowed time steps to decode + :param vocab_size: Size of vocabulary + :param decoding_scope: TensorFlow Variable Scope for decoding + :param output_fn: Function to apply the output layer + :param keep_prob: Dropout keep probability + :return: Inference Logits + """ + # TODO: Implement Function + # Inference Decoder + infer_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference( + output_fn, encoder_state, dec_embeddings, start_of_sequence_id, end_of_sequence_id, + maximum_length - 1, vocab_size) + inference_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell, infer_decoder_fn, scope=decoding_scope) + + return inference_logits + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_decoding_layer_infer(decoding_layer_infer) + + +# ### 构建解码层级 +# +# 实现 `decoding_layer()` 以创建解码器 RNN 层级。 +# +# - 使用 `rnn_size` 和 `num_layers` 创建解码 RNN 单元。 +# - 使用 [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) 创建输出函数，将输入，也就是分对数转换为类分对数（class logits）。 +# - 使用 `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)` 函数获取训练分对数。 +# - 使用 `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)` 函数获取推论分对数。 +# +# 注意：你将需要使用 [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 在训练和推论分对数间分享变量。 + +# In[12]: + + +def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, + num_layers, target_vocab_to_int, keep_prob): + """ + Create decoding layer + :param dec_embed_input: Decoder embedded input + :param dec_embeddings: Decoder embeddings + :param encoder_state: The encoded state + :param vocab_size: Size of vocabulary + :param sequence_length: Sequence Length + :param rnn_size: RNN Size + :param num_layers: Number of layers + :param target_vocab_to_int: Dictionary to go from the target words to an id + :param keep_prob: Dropout keep probability + :return: Tuple of (Training Logits, Inference Logits) + """ + # TODO: Implement Function + lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) + dropout = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) + dec_cell = tf.contrib.rnn.MultiRNNCell([dropout] * num_layers) + + output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, activation_fn=None, scope=decoding_scope) + + with tf.variable_scope("decoding") as decoding_scope: + training_decoder_output = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, + output_fn, keep_prob) + + with tf.variable_scope("decoding", reuse=True) as decoding_scope: + start_of_sequence_id = target_vocab_to_int[""] + end_of_sequence_id = target_vocab_to_int[""] + inference_decoder_output = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, + sequence_length, vocab_size, decoding_scope, output_fn, keep_prob) + + return training_decoder_output, inference_decoder_output + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_decoding_layer(decoding_layer) + + +# ### 构建神经网络 +# +# 应用你在上方实现的函数，以： +# +# - 向编码器的输入数据应用嵌入。 +# - 使用 `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)` 编码输入。 +# - 使用 `process_decoding_input(target_data, target_vocab_to_int, batch_size)` 函数处理目标数据。 +# - 向解码器的目标数据应用嵌入。 +# - 使用 `decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)` 解码编码的输入数据。 + +# In[13]: + + +def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size, + enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int): + """ + Build the Sequence-to-Sequence part of the neural network + :param input_data: Input placeholder + :param target_data: Target placeholder + :param keep_prob: Dropout keep probability placeholder + :param batch_size: Batch Size + :param sequence_length: Sequence Length + :param source_vocab_size: Source vocabulary size + :param target_vocab_size: Target vocabulary size + :param enc_embedding_size: Decoder embedding size + :param dec_embedding_size: Encoder embedding size + :param rnn_size: RNN Size + :param num_layers: Number of layers + :param target_vocab_to_int: Dictionary to go from the target words to an id + :return: Tuple of (Training Logits, Inference Logits) + """ + # TODO: Implement Function + rnn_inputs = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size) + + encoder_state = encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob) + + dec_input = process_decoding_input(target_data, target_vocab_to_int, batch_size) + dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size])) + dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input) + + train_logits, inference_logits = decoding_layer(dec_embed_input, dec_embeddings, encoder_state, target_vocab_size, sequence_length, + rnn_size, num_layers, target_vocab_to_int, keep_prob) + + return train_logits, inference_logits + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_seq2seq_model(seq2seq_model) + + +# ## 训练神经网络 +# +# ### 超参数 +# +# 调试以下参数： +# +# - 将 `epochs` 设为 epoch 次数。 +# - 将 `batch_size` 设为批次大小。 +# - 将 `rnn_size` 设为 RNN 的大小。 +# - 将 `num_layers` 设为层级数量。 +# - 将 `encoding_embedding_size` 设为编码器嵌入大小。 +# - 将 `decoding_embedding_size` 设为解码器嵌入大小 +# - 将 `learning_rate` 设为训练速率。 +# - 将 `keep_probability` 设为丢弃保留率（Dropout keep probability）。 + +# In[14]: + + +# Number of Epochs +epochs = 5 +# Batch Size +batch_size = 128 +# RNN Size +rnn_size = 128 +# Number of Layers +num_layers = 2 +# Embedding Size +encoding_embedding_size = 100 +decoding_embedding_size = 100 +# Learning Rate +learning_rate = 0.01 +# Dropout Keep Probability +keep_probability = 0.8 + + +# ### 构建图表 +# +# 使用你实现的神经网络构建图表。 + +# In[15]: + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +save_path = 'checkpoints/dev' +(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() +max_source_sentence_length = max([len(sentence) for sentence in source_int_text]) + +train_graph = tf.Graph() +with train_graph.as_default(): + input_data, targets, lr, keep_prob = model_inputs() + sequence_length = tf.placeholder_with_default(max_source_sentence_length, None, name='sequence_length') + input_shape = tf.shape(input_data) + + train_logits, inference_logits = seq2seq_model( + tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int), + encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int) + + tf.identity(inference_logits, 'logits') + with tf.name_scope("optimization"): + # Loss function + cost = tf.contrib.seq2seq.sequence_loss( + train_logits, + targets, + tf.ones([input_shape[0], sequence_length])) + + # Optimizer + optimizer = tf.train.AdamOptimizer(lr) + + # Gradient Clipping + gradients = optimizer.compute_gradients(cost) + capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None] + train_op = optimizer.apply_gradients(capped_gradients) + + +# ### 训练 +# +# 利用预处理的数据训练神经网络。如果很难获得低损失值，请访问我们的论坛，看看其他人是否遇到了相同的问题。 + +# In[16]: + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +import time + +def get_accuracy(target, logits): + """ + Calculate accuracy + """ + max_seq = max(target.shape[1], logits.shape[1]) + if max_seq - target.shape[1]: + target = np.pad( + target, + [(0,0),(0,max_seq - target.shape[1])], + 'constant') + if max_seq - logits.shape[1]: + logits = np.pad( + logits, + [(0,0),(0,max_seq - logits.shape[1]), (0,0)], + 'constant') + + return np.mean(np.equal(target, np.argmax(logits, 2))) + +train_source = source_int_text[batch_size:] +train_target = target_int_text[batch_size:] + +valid_source = helper.pad_sentence_batch(source_int_text[:batch_size]) +valid_target = helper.pad_sentence_batch(target_int_text[:batch_size]) + +with tf.Session(graph=train_graph) as sess: + sess.run(tf.global_variables_initializer()) + + for epoch_i in range(epochs): + for batch_i, (source_batch, target_batch) in enumerate( + helper.batch_data(train_source, train_target, batch_size)): + start_time = time.time() + + _, loss = sess.run( + [train_op, cost], + {input_data: source_batch, + targets: target_batch, + lr: learning_rate, + sequence_length: target_batch.shape[1], + keep_prob: keep_probability}) + + batch_train_logits = sess.run( + inference_logits, + {input_data: source_batch, keep_prob: 1.0}) + batch_valid_logits = sess.run( + inference_logits, + {input_data: valid_source, keep_prob: 1.0}) + + train_acc = get_accuracy(target_batch, batch_train_logits) + valid_acc = get_accuracy(np.array(valid_target), batch_valid_logits) + end_time = time.time() + print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.3f}, Validation Accuracy: {:>6.3f}, Loss: {:>6.3f}' + .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss)) + + # Save Model + saver = tf.train.Saver() + saver.save(sess, save_path) + print('Model Trained and Saved') + + +# ### 保存参数 +# +# 保存 `batch_size` 和 `save_path` 参数以进行推论（for inference）。 + +# In[17]: + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +# Save parameters for checkpoint +helper.save_params(save_path) + + +# # 检查点 + +# In[18]: + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +import tensorflow as tf +import numpy as np +import helper +import problem_unittests as tests + +_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess() +load_path = helper.load_params() + + +# ## 句子到序列 +# +# 要向模型提供要翻译的句子，你首先需要预处理该句子。实现函数 `sentence_to_seq()` 以预处理新的句子。 +# +# - 将句子转换为小写形式 +# - 使用 `vocab_to_int` 将单词转换为 id +# - 如果单词不在词汇表中，将其转换为`` 单词 id + +# In[19]: + + +def sentence_to_seq(sentence, vocab_to_int): + """ + Convert a sentence to a sequence of ids + :param sentence: String + :param vocab_to_int: Dictionary to go from the words to an id + :return: List of word ids + """ + # TODO: Implement Function + word_ids = [vocab_to_int.get(word, vocab_to_int[""]) for word in sentence.lower().split()] + + return word_ids + + +""" +DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE +""" +tests.test_sentence_to_seq(sentence_to_seq) + + +# ## 翻译 +# +# 将 `translate_sentence` 从英语翻译成法语。 + +# In[20]: + + +translate_sentence = 'he saw a old yellow truck .' + + +""" +DON'T MODIFY ANYTHING IN THIS CELL +""" +translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int) + +loaded_graph = tf.Graph() +with tf.Session(graph=loaded_graph) as sess: + # Load saved model + loader = tf.train.import_meta_graph(load_path + '.meta') + loader.restore(sess, load_path) + + input_data = loaded_graph.get_tensor_by_name('input:0') + logits = loaded_graph.get_tensor_by_name('logits:0') + keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0') + + translate_logits = sess.run(logits, {input_data: [translate_sentence], keep_prob: 1.0})[0] + +print('Input') +print(' Word Ids: {}'.format([i for i in translate_sentence])) +print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence])) + +print('\nPrediction') +print(' Word Ids: {}'.format([i for i in np.argmax(translate_logits, 1)])) +print(' French Words: {}'.format([target_int_to_vocab[i] for i in np.argmax(translate_logits, 1)])) + + +# ## 不完美的翻译 +# +# 你可能注意到了，某些句子的翻译质量比其他的要好。因为你使用的数据集只有 227 个英语单词，但实际生活中有数千个单词，只有使用这些单词的句子结果才会比较理想。对于此项目，不需要达到完美的翻译。但是，如果你想创建更好的翻译模型，则需要更好的数据。 +# +# 你可以使用 [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar) 语料库训练模型。该数据集拥有更多的词汇，讨论的话题也更丰富。但是，训练时间要好多天的时间，所以确保你有 GPU 并且对于我们提供的数据集，你的神经网络性能很棒。提交此项目后，别忘了研究下 WMT10 语料库。 +# +# +# ## 提交项目 +# +# 提交项目时，确保先运行所有单元，然后再保存记事本。保存记事本文件为 “dlnd_language_translation.ipynb”，再通过菜单中的“文件” ->“下载为”将其另存为 HTML 格式。提交的项目文档中需包含“helper.py”和“problem_unittests.py”文件。 +# diff --git a/tv-script-generation/dlnd_tv_script_generation.html b/tv-script-generation/dlnd_tv_script_generation.html new file mode 100644 index 0000000..b6367a1 --- /dev/null +++ b/tv-script-generation/dlnd_tv_script_generation.html @@ -0,0 +1,13077 @@ + + + +dlnd_tv_script_generation + + + + + + + + + + + + + + + + + + + +
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+ +
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+

TV Script Generation

In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at Moe's Tavern.

+

Get the Data

The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like "Moe's Cavern", "Flaming Moe's", "Uncle Moe's Family Feed-Bag", etc..

+ +
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In [1]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import helper
+
+data_dir = './data/simpsons/moes_tavern_lines.txt'
+text = helper.load_data(data_dir)
+# Ignore notice, since we don't use it for analysing the data
+text = text[81:]
+
+ +
+
+
+ +
+
+
+
+
+

Explore the Data

Play around with view_sentence_range to view different parts of the data.

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+
In [2]:
+
+
+
view_sentence_range = (0, 10)
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import numpy as np
+
+print('Dataset Stats')
+print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))
+scenes = text.split('\n\n')
+print('Number of scenes: {}'.format(len(scenes)))
+sentence_count_scene = [scene.count('\n') for scene in scenes]
+print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))
+
+sentences = [sentence for scene in scenes for sentence in scene.split('\n')]
+print('Number of lines: {}'.format(len(sentences)))
+word_count_sentence = [len(sentence.split()) for sentence in sentences]
+print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))
+
+print()
+print('The sentences {} to {}:'.format(*view_sentence_range))
+print('\n'.join(text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Dataset Stats
+Roughly the number of unique words: 11492
+Number of scenes: 262
+Average number of sentences in each scene: 15.248091603053435
+Number of lines: 4257
+Average number of words in each line: 11.50434578341555
+
+The sentences 0 to 10:
+Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.
+Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.
+Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?
+Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.
+Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self.
+Homer_Simpson: I got my problems, Moe. Give me another one.
+Moe_Szyslak: Homer, hey, you should not drink to forget your problems.
+Barney_Gumble: Yeah, you should only drink to enhance your social skills.
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+ +
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+ +
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+
+

Implement Preprocessing Functions

The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:

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    +
  • Lookup Table
    • +
  • Tokenize Punctuation
    • +
+

Lookup Table

To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:

+
    +
  • Dictionary to go from the words to an id, we'll call vocab_to_int
    • +
  • Dictionary to go from the id to word, we'll call int_to_vocab
    • +
+

Return these dictionaries in the following tuple (vocab_to_int, int_to_vocab)

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+
In [3]:
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+
+
import numpy as np
+import problem_unittests as tests
+from collections import Counter
+
+def create_lookup_tables(text):
+    """
+    Create lookup tables for vocabulary
+    :param text: The text of tv scripts split into words
+    :return: A tuple of dicts (vocab_to_int, int_to_vocab)
+    """
+    # TODO: Implement Function
+    word_counts = Counter(text)
+    sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)
+    int_to_vocab = {i: word for i, word in enumerate(sorted_vocab)}
+    vocab_to_int = {word: i for i, word in int_to_vocab.items()}
+
+    return vocab_to_int, int_to_vocab
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_create_lookup_tables(create_lookup_tables)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
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+ +
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+ +
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+
+

Tokenize Punctuation

We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word "bye" and "bye!".

+

Implement the function token_lookup to return a dict that will be used to tokenize symbols like "!" into "||Exclamation_Mark||". Create a dictionary for the following symbols where the symbol is the key and value is the token:

+
    +
  • Period ( . )
    • +
  • Comma ( , )
    • +
  • Quotation Mark ( " )
    • +
  • Semicolon ( ; )
    • +
  • Exclamation mark ( ! )
    • +
  • Question mark ( ? )
    • +
  • Left Parentheses ( ( )
    • +
  • Right Parentheses ( ) )
    • +
  • Dash ( -- )
    • +
  • Return ( \n )
    • +
+

This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token "dash", try using something like "||dash||".

+ +
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+
In [4]:
+
+
+
def token_lookup():
+    """
+    Generate a dict to turn punctuation into a token.
+    :return: Tokenize dictionary where the key is the punctuation and the value is the token
+    """
+    # TODO: Implement Function
+    dict = {}
+    dict['.'] = '||Period||'
+    dict[','] = '||Comma||'
+    dict['"'] = '||Quotation_Mark||'
+    dict[';'] = '||Semicolon||'
+    dict['!'] = '||Exclamation_Mark||'
+    dict['?'] = '||Question_Mark||'
+    dict['('] = '||Left_Parentheses||'
+    dict[')'] = '||Right_Parentheses||'
+    dict['--'] = '||Dash||'
+    dict['\n'] = '||Return||'
+    
+    return dict
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_tokenize(token_lookup)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
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+ +
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+ +
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+
+
+

Preprocess all the data and save it

Running the code cell below will preprocess all the data and save it to file.

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+
In [5]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+# Preprocess Training, Validation, and Testing Data
+helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)
+
+ +
+
+
+ +
+
+
+
+
+

Check Point

This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.

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+
In [6]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import helper
+import numpy as np
+import problem_unittests as tests
+
+int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
+
+ +
+
+
+ +
+
+
+
+
+

Build the Neural Network

You'll build the components necessary to build a RNN by implementing the following functions below:

+
    +
  • get_inputs
    • +
  • get_init_cell
    • +
  • get_embed
    • +
  • build_rnn
    • +
  • build_nn
    • +
  • get_batches
    • +
+

Check the Version of TensorFlow and Access to GPU

+
+
+
+
+
+
In [7]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+from distutils.version import LooseVersion
+import warnings
+import tensorflow as tf
+
+# Check TensorFlow Version
+assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'
+print('TensorFlow Version: {}'.format(tf.__version__))
+
+# Check for a GPU
+if not tf.test.gpu_device_name():
+    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
+else:
+    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
TensorFlow Version: 1.0.1
+Default GPU Device: /gpu:0
+
+
+
+ +
+
+ +
+
+
+
+
+

Input

Implement the get_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders:

+
    +
  • Input text placeholder named "input" using the TF Placeholder name parameter.
    • +
  • Targets placeholder
    • +
  • Learning Rate placeholder
    • +
+

Return the placeholders in the following tuple (Input, Targets, LearningRate)

+ +
+
+
+
+
+
In [8]:
+
+
+
def get_inputs():
+    """
+    Create TF Placeholders for input, targets, and learning rate.
+    :return: Tuple (input, targets, learning rate)
+    """
+    # TODO: Implement Function
+    inputs = tf.placeholder(tf.int32, [None, None], name='input')
+    targets = tf.placeholder(tf.int32, [None, None], name='targets')
+    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
+    
+    return inputs, targets, learning_rate
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_get_inputs(get_inputs)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

Build RNN Cell and Initialize

Stack one or more BasicLSTMCells in a MultiRNNCell.

+
    +
  • The Rnn size should be set using rnn_size
    • +
  • Initalize Cell State using the MultiRNNCell's zero_state() function
      +
    • Apply the name "initial_state" to the initial state using tf.identity()
      • +
    +
    • +
+

Return the cell and initial state in the following tuple (Cell, InitialState)

+ +
+
+
+
+
+
In [9]:
+
+
+
def get_init_cell(batch_size, rnn_size):
+    """
+    Create an RNN Cell and initialize it.
+    :param batch_size: Size of batches
+    :param rnn_size: Size of RNNs
+    :return: Tuple (cell, initialize state)
+    """
+    # TODO: Implement Function
+    num_layers = 2
+    # keep_prob = 0.7
+    
+    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
+    # drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
+    cell = tf.contrib.rnn.MultiRNNCell([lstm] * num_layers)
+    initial_state = tf.identity(cell.zero_state(batch_size, tf.float32), name='initial_state')
+
+    return cell, initial_state
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_get_init_cell(get_init_cell)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

Word Embedding

Apply embedding to input_data using TensorFlow. Return the embedded sequence.

+ +
+
+
+
+
+
In [10]:
+
+
+
def get_embed(input_data, vocab_size, embed_dim):
+    """
+    Create embedding for <input_data>.
+    :param input_data: TF placeholder for text input.
+    :param vocab_size: Number of words in vocabulary.
+    :param embed_dim: Number of embedding dimensions
+    :return: Embedded input.
+    """
+    # TODO: Implement Function
+    embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))
+    embed = tf.nn.embedding_lookup(embedding, input_data)
+    
+    return embed
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_get_embed(get_embed)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

Build RNN

You created a RNN Cell in the get_init_cell() function. Time to use the cell to create a RNN.

+ +

Return the outputs and final_state state in the following tuple (Outputs, FinalState)

+ +
+
+
+
+
+
In [11]:
+
+
+
def build_rnn(cell, inputs):
+    """
+    Create a RNN using a RNN Cell
+    :param cell: RNN Cell
+    :param inputs: Input text data
+    :return: Tuple (Outputs, Final State)
+    """
+    # TODO: Implement Function
+    outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
+    final_state = tf.identity(final_state, name='final_state')
+    
+    return outputs, final_state
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_build_rnn(build_rnn)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

Build the Neural Network

Apply the functions you implemented above to:

+
    +
  • Apply embedding to input_data using your get_embed(input_data, vocab_size, embed_dim) function.
    • +
  • Build RNN using cell and your build_rnn(cell, inputs) function.
    • +
  • Apply a fully connected layer with a linear activation and vocab_size as the number of outputs.
    • +
+

Return the logits and final state in the following tuple (Logits, FinalState)

+ +
+
+
+
+
+
In [12]:
+
+
+
def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):
+    """
+    Build part of the neural network
+    :param cell: RNN cell
+    :param rnn_size: Size of rnns
+    :param input_data: Input data
+    :param vocab_size: Vocabulary size
+    :param embed_dim: Number of embedding dimensions
+    :return: Tuple (Logits, FinalState)
+    """
+    # TODO: Implement Function
+    embed_data = get_embed(input_data, vocab_size, embed_dim)
+    outputs, final_state = build_rnn(cell, embed_data)
+    logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)
+    
+    return logits, final_state
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_build_nn(build_nn)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

Batches

Implement get_batches to create batches of input and targets using int_text. The batches should be a Numpy array with the shape (number of batches, 2, batch size, sequence length). Each batch contains two elements:

+
    +
  • The first element is a single batch of input with the shape [batch size, sequence length]
    • +
  • The second element is a single batch of targets with the shape [batch size, sequence length]
    • +
+

If you can't fill the last batch with enough data, drop the last batch.

+

For exmple, get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3) would return a Numpy array of the following:

+ +
[
+  # First Batch
+  [
+    # Batch of Input
+    [[ 1  2  3], [ 7  8  9]],
+    # Batch of targets
+    [[ 2  3  4], [ 8  9 10]]
+  ],
+
+  # Second Batch
+  [
+    # Batch of Input
+    [[ 4  5  6], [10 11 12]],
+    # Batch of targets
+    [[ 5  6  7], [11 12 13]]
+  ]
+]
+ +
+
+
+
+
+
In [13]:
+
+
+
def get_batches(int_text, batch_size, seq_length):
+    """
+    Return batches of input and target
+    :param int_text: Text with the words replaced by their ids
+    :param batch_size: The size of batch
+    :param seq_length: The length of sequence
+    :return: Batches as a Numpy array
+    """
+    # TODO: Implement Function
+    n_batches = len(int_text) // (batch_size * seq_length)
+    
+    x = np.array(int_text[:n_batches * batch_size * seq_length])
+    y = np.array(int_text[1:n_batches * batch_size * seq_length + 1])
+    y[-1] = x[0]
+    x_batches = np.split(x.reshape(batch_size, -1), n_batches, 1)
+    y_batches = np.split(y.reshape(batch_size, -1), n_batches, 1)
+    
+    return np.array(list(zip(x_batches, y_batches)))
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_get_batches(get_batches)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

Neural Network Training

Hyperparameters

Tune the following parameters:

+
    +
  • Set num_epochs to the number of epochs.
    • +
  • Set batch_size to the batch size.
    • +
  • Set rnn_size to the size of the RNNs.
    • +
  • Set embed_dim to the size of the embedding.
    • +
  • Set seq_length to the length of sequence.
    • +
  • Set learning_rate to the learning rate.
    • +
  • Set show_every_n_batches to the number of batches the neural network should print progress.
    • +
+ +
+
+
+
+
+
In [14]:
+
+
+
# Number of Epochs
+num_epochs = 100
+# Batch Size
+batch_size = 128
+# RNN Size
+rnn_size = 256
+# Embedding Dimension Size
+embed_dim = 200
+# Sequence Length
+seq_length = 32
+# Learning Rate
+learning_rate = 0.01
+# Show stats for every n number of batches
+show_every_n_batches = 100
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+save_dir = './save'
+
+ +
+
+
+ +
+
+
+
+
+

Build the Graph

Build the graph using the neural network you implemented.

+ +
+
+
+
+
+
In [15]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+from tensorflow.contrib import seq2seq
+
+train_graph = tf.Graph()
+with train_graph.as_default():
+    vocab_size = len(int_to_vocab)
+    input_text, targets, lr = get_inputs()
+    input_data_shape = tf.shape(input_text)
+    cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
+    logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)
+
+    # Probabilities for generating words
+    probs = tf.nn.softmax(logits, name='probs')
+
+    # Loss function
+    cost = seq2seq.sequence_loss(
+        logits,
+        targets,
+        tf.ones([input_data_shape[0], input_data_shape[1]]))
+
+    # Optimizer
+    optimizer = tf.train.AdamOptimizer(lr)
+
+    # Gradient Clipping
+    gradients = optimizer.compute_gradients(cost)
+    capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]
+    train_op = optimizer.apply_gradients(capped_gradients)
+
+ +
+
+
+ +
+
+
+
+
+

Train

Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem.

+ +
+
+
+
+
+
In [16]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+batches = get_batches(int_text, batch_size, seq_length)
+
+with tf.Session(graph=train_graph) as sess:
+    sess.run(tf.global_variables_initializer())
+
+    for epoch_i in range(num_epochs):
+        state = sess.run(initial_state, {input_text: batches[0][0]})
+
+        for batch_i, (x, y) in enumerate(batches):
+            feed = {
+                input_text: x,
+                targets: y,
+                initial_state: state,
+                lr: learning_rate}
+            train_loss, state, _ = sess.run([cost, final_state, train_op], feed)
+
+            # Show every <show_every_n_batches> batches
+            if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:
+                print('Epoch {:>3} Batch {:>4}/{}   train_loss = {:.3f}'.format(
+                    epoch_i,
+                    batch_i,
+                    len(batches),
+                    train_loss))
+
+    # Save Model
+    saver = tf.train.Saver()
+    saver.save(sess, save_dir)
+    print('Model Trained and Saved')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Epoch   0 Batch    0/16   train_loss = 8.822
+Epoch   6 Batch    4/16   train_loss = 5.457
+Epoch  12 Batch    8/16   train_loss = 4.972
+Epoch  18 Batch   12/16   train_loss = 4.116
+Epoch  25 Batch    0/16   train_loss = 3.257
+Epoch  31 Batch    4/16   train_loss = 2.535
+Epoch  37 Batch    8/16   train_loss = 2.091
+Epoch  43 Batch   12/16   train_loss = 1.477
+Epoch  50 Batch    0/16   train_loss = 1.024
+Epoch  56 Batch    4/16   train_loss = 0.852
+Epoch  62 Batch    8/16   train_loss = 0.566
+Epoch  68 Batch   12/16   train_loss = 0.429
+Epoch  75 Batch    0/16   train_loss = 0.395
+Epoch  81 Batch    4/16   train_loss = 0.316
+Epoch  87 Batch    8/16   train_loss = 0.201
+Epoch  93 Batch   12/16   train_loss = 0.206
+Model Trained and Saved
+
+
+
+ +
+
+ +
+
+
+
+
+

Save Parameters

Save seq_length and save_dir for generating a new TV script.

+ +
+
+
+
+
+
In [17]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+# Save parameters for checkpoint
+helper.save_params((seq_length, save_dir))
+
+ +
+
+
+ +
+
+
+
+
+

Checkpoint

+
+
+
+
+
+
In [18]:
+
+
+
"""
+DON'T MODIFY ANYTHING IN THIS CELL
+"""
+import tensorflow as tf
+import numpy as np
+import helper
+import problem_unittests as tests
+
+_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
+seq_length, load_dir = helper.load_params()
+
+ +
+
+
+ +
+
+
+
+
+

Implement Generate Functions

Get Tensors

Get tensors from loaded_graph using the function get_tensor_by_name(). Get the tensors using the following names:

+
    +
  • "input:0"
    • +
  • "initial_state:0"
    • +
  • "final_state:0"
    • +
  • "probs:0"
    • +
+

Return the tensors in the following tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)

+ +
+
+
+
+
+
In [19]:
+
+
+
def get_tensors(loaded_graph):
+    """
+    Get input, initial state, final state, and probabilities tensor from <loaded_graph>
+    :param loaded_graph: TensorFlow graph loaded from file
+    :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
+    """
+    # TODO: Implement Function
+    InputTensor = loaded_graph.get_tensor_by_name('input:0')
+    InitialStateTensor = loaded_graph.get_tensor_by_name('initial_state:0')
+    FinalStateTensor = loaded_graph.get_tensor_by_name('final_state:0')
+    ProbsTensor = loaded_graph.get_tensor_by_name('probs:0')
+    
+    return InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_get_tensors(get_tensors)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

Choose Word

Implement the pick_word() function to select the next word using probabilities.

+ +
+
+
+
+
+
In [20]:
+
+
+
def pick_word(probabilities, int_to_vocab):
+    """
+    Pick the next word in the generated text
+    :param probabilities: Probabilites of the next word
+    :param int_to_vocab: Dictionary of word ids as the keys and words as the values
+    :return: String of the predicted word
+    """
+    # TODO: Implement Function
+    return int_to_vocab[int(np.argmax(probabilities))]
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+tests.test_pick_word(pick_word)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Tests Passed
+
+
+
+ +
+
+ +
+
+
+
+
+

Generate TV Script

This will generate the TV script for you. Set gen_length to the length of TV script you want to generate.

+ +
+
+
+
+
+
In [21]:
+
+
+
gen_length = 800
+# homer_simpson, moe_szyslak, or Barney_Gumble
+prime_word = 'moe_szyslak'
+
+"""
+DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
+"""
+loaded_graph = tf.Graph()
+with tf.Session(graph=loaded_graph) as sess:
+    # Load saved model
+    loader = tf.train.import_meta_graph(load_dir + '.meta')
+    loader.restore(sess, load_dir)
+
+    # Get Tensors from loaded model
+    input_text, initial_state, final_state, probs = get_tensors(loaded_graph)
+
+    # Sentences generation setup
+    gen_sentences = [prime_word + ':']
+    prev_state = sess.run(initial_state, {input_text: np.array([[1]])})
+
+    # Generate sentences
+    for n in range(gen_length):
+        # Dynamic Input
+        dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]
+        dyn_seq_length = len(dyn_input[0])
+
+        # Get Prediction
+        probabilities, prev_state = sess.run(
+            [probs, final_state],
+            {input_text: dyn_input, initial_state: prev_state})
+        
+        pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)
+
+        gen_sentences.append(pred_word)
+    
+    # Remove tokens
+    tv_script = ' '.join(gen_sentences)
+    for key, token in token_dict.items():
+        ending = ' ' if key in ['\n', '(', '"'] else ''
+        tv_script = tv_script.replace(' ' + token.lower(), key)
+    tv_script = tv_script.replace('\n ', '\n')
+    tv_script = tv_script.replace('( ', '(')
+        
+    print(tv_script)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
moe_szyslak:(eyeing homer's ass) oh yeah, that would look so good on me.
+
+
+moe_szyslak:(hostile) hey homer, i told you not to come out.
+
+
+lisa_simpson: your love of you, stupid more funny.
+moe_szyslak: i gotta join a girl for moe's bar it'll let him.
+moe_szyslak: so, uh, you got it, shouldn't it be. i'm talkin' malfeasance here.
+bart_simpson: sir, well, thank you, i've never seen this again.
+homer_simpson:(chuckles) oh yeah.
+bart_simpson:(finishing in) you know about that!
+duffman:(small frustrated noise) that's good...(then) just said that was i could do it?
+homer_simpson: i don't pay you to have. i'm talkin' about you.
+moe_szyslak: okay, well, you're the love of jeff!
+lenny_leonard:(reading)" i don't know.
+moe_szyslak: sorry, it's probably gonna do worse...(sighs)
+moe_szyslak: that's the big day of innocent.(sadly) are you doing?
+moe_szyslak: and now, homer.
+lisa_simpson: my barney is, moe.
+moe_szyslak: yeah. but who was great. now, i can't believe marge comes down.
+moe_szyslak:(tough) homer, i can't talk on the future.
+homer_simpson: all right! the fire twins!
+homer_simpson:(moans) yeah, that. it's my love!
+homer_simpson:(ominous) it's it.
+moe_szyslak: my sweet friend? he makes 'em nice in the world here.
+
+
+moe_szyslak: i got a big shot who again... that's the grammy judges.(laughs) all right. you're a free beer.
+lenny_leonard: are you home? i'm just a guy, that's a yes!
+homer_simpson:(chuckles)
+moe_szyslak: oh my god...
+thanks for that...
+moe_szyslak: oh, no.(as beer) it's all day.
+lenny_leonard: hey, moe! i wrote a little girl!
+duffman:(laughs) we gotta get you a job?
+homer_simpson: wow.(laughs)
+homer_simpson:(grunt) hey!(laughs)
+marge_simpson:(party laugh) oh, no!
+homer_simpson:(chuckles) hey, maggie, i'm not, barney.
+homer_simpson: guys, i love you, barney!
+homer_simpson:(flatly) yeah.
+marge_simpson:(chanting) are you done to the little girl you're disappointing.
+lisa_simpson:(worried) hey, you know what about you, huh?!
+moe_szyslak: you will have to go home from a way to be nine of a jar.
+carl_carlson: and the second way, a lot of people bad-mouth you and me...
+homer_simpson:(to moe) wow, that's a coaster.
+homer_simpson: hey, i've got a lot to mull.
+moe_szyslak: hey, hey, hey, hey! hey ain't work?
+moe_szyslak: that's the beauty part. you need a pal. i gotta go.
+
+
+moe_szyslak:(laughs) the cop,(points off pain) lisa_simpson: moe, moe. but then you can say the most poor bucks-- it's?
+moe_szyslak: it's true, a" forget-me-shot in my old man.
+moe_szyslak: okay, this is like a!
+homer_simpson:(amazed) him...
+moe_szyslak:(to barney) i think when i've gonna get some professional help. no one could you no real good.
+moe_szyslak: ah, this is a pal. gotta be how how much that how you know a" business problems...
+
+
+moe_szyslak: he's the greatest gift of all, so a little bit. it was all the other day / a problem. that's sweet.
+moe_szyslak:(ominous) one, for you...
+homer_simpson:(to homer) come on, sweet...
+moe_szyslak: oh, boy...
+fat_tony:(sings) full-blooded...
+barney_gumble:(realizing) yeah, i was sure the little girl.
+bart_simpson: oh, you know, that was philip glass.
+david_byrne: yeah, have you go, ain't you little worried.
+
+
+
+ +
+
+ +
+
+
+
+
+

The TV Script is Nonsensical

It's ok if the TV script doesn't make any sense. We trained on less than a megabyte of text. In order to get good results, you'll have to use a smaller vocabulary or get more data. Luckly there's more data! As we mentioned in the begging of this project, this is a subset of another dataset. We didn't have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.

+

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_tv_script_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

+ +
+
+
+
+
+ + + + + + diff --git a/tv-script-generation/dlnd_tv_script_generation.ipynb b/tv-script-generation/dlnd_tv_script_generation.ipynb index ecc4005..ce2a97b 100644 --- a/tv-script-generation/dlnd_tv_script_generation.ipynb +++ b/tv-script-generation/dlnd_tv_script_generation.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at [Moe's Tavern](https://simpsonswiki.com/wiki/Moe's_Tavern).\n", @@ -15,11 +12,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -36,10 +31,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Explore the Data\n", "Play around with `view_sentence_range` to view different parts of the data." @@ -47,13 +39,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset Stats\n", + "Roughly the number of unique words: 11492\n", + "Number of scenes: 262\n", + "Average number of sentences in each scene: 15.248091603053435\n", + "Number of lines: 4257\n", + "Average number of words in each line: 11.50434578341555\n", + "\n", + "The sentences 0 to 10:\n", + "Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.\n", + "Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.\n", + "Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?\n", + "Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.\n", + "Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self.\n", + "Homer_Simpson: I got my problems, Moe. Give me another one.\n", + "Moe_Szyslak: Homer, hey, you should not drink to forget your problems.\n", + "Barney_Gumble: Yeah, you should only drink to enhance your social skills.\n", + "\n", + "\n" + ] + } + ], "source": [ "view_sentence_range = (0, 10)\n", "\n", @@ -81,10 +94,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Implement Preprocessing Functions\n", "The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:\n", @@ -101,16 +111,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "import numpy as np\n", "import problem_unittests as tests\n", + "from collections import Counter\n", "\n", "def create_lookup_tables(text):\n", " \"\"\"\n", @@ -119,8 +134,12 @@ " :return: A tuple of dicts (vocab_to_int, int_to_vocab)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None\n", + " word_counts = Counter(text)\n", + " sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)\n", + " int_to_vocab = {i: word for i, word in enumerate(sorted_vocab)}\n", + " vocab_to_int = {word: i for i, word in int_to_vocab.items()}\n", "\n", + " return vocab_to_int, int_to_vocab\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -130,10 +149,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Tokenize Punctuation\n", "We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word \"bye\" and \"bye!\".\n", @@ -155,13 +171,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def token_lookup():\n", " \"\"\"\n", @@ -169,7 +189,19 @@ " :return: Tokenize dictionary where the key is the punctuation and the value is the token\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", + " dict = {}\n", + " dict['.'] = '||Period||'\n", + " dict[','] = '||Comma||'\n", + " dict['\"'] = '||Quotation_Mark||'\n", + " dict[';'] = '||Semicolon||'\n", + " dict['!'] = '||Exclamation_Mark||'\n", + " dict['?'] = '||Question_Mark||'\n", + " dict['('] = '||Left_Parentheses||'\n", + " dict[')'] = '||Right_Parentheses||'\n", + " dict['--'] = '||Dash||'\n", + " dict['\\n'] = '||Return||'\n", + " \n", + " return dict\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -179,10 +211,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." @@ -190,11 +219,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -207,10 +234,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." @@ -218,11 +242,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -238,10 +260,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a RNN by implementing the following functions below:\n", @@ -257,11 +276,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TensorFlow Version: 1.0.1\n", + "Default GPU Device: /gpu:0\n" + ] + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", @@ -296,13 +322,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def get_inputs():\n", " \"\"\"\n", @@ -310,8 +340,11 @@ " :return: Tuple (input, targets, learning rate)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None, None\n", - "\n", + " inputs = tf.placeholder(tf.int32, [None, None], name='input')\n", + " targets = tf.placeholder(tf.int32, [None, None], name='targets')\n", + " learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n", + " \n", + " return inputs, targets, learning_rate\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -321,10 +354,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Build RNN Cell and Initialize\n", "Stack one or more [`BasicLSTMCells`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell) in a [`MultiRNNCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell).\n", @@ -337,13 +367,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def get_init_cell(batch_size, rnn_size):\n", " \"\"\"\n", @@ -353,8 +387,15 @@ " :return: Tuple (cell, initialize state)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None\n", + " num_layers = 2\n", + " # keep_prob = 0.7\n", + " \n", + " lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n", + " # drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n", + " cell = tf.contrib.rnn.MultiRNNCell([lstm] * num_layers)\n", + " initial_state = tf.identity(cell.zero_state(batch_size, tf.float32), name='initial_state')\n", "\n", + " return cell, initial_state\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -364,10 +405,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Word Embedding\n", "Apply embedding to `input_data` using TensorFlow. Return the embedded sequence." @@ -375,13 +413,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def get_embed(input_data, vocab_size, embed_dim):\n", " \"\"\"\n", @@ -392,8 +434,10 @@ " :return: Embedded input.\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", - "\n", + " embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))\n", + " embed = tf.nn.embedding_lookup(embedding, input_data)\n", + " \n", + " return embed\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -403,10 +447,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Build RNN\n", "You created a RNN Cell in the `get_init_cell()` function. Time to use the cell to create a RNN.\n", @@ -418,13 +459,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def build_rnn(cell, inputs):\n", " \"\"\"\n", @@ -434,8 +479,10 @@ " :return: Tuple (Outputs, Final State)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None\n", - "\n", + " outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)\n", + " final_state = tf.identity(final_state, name='final_state')\n", + " \n", + " return outputs, final_state\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -445,10 +492,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", @@ -461,13 +505,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):\n", " \"\"\"\n", @@ -480,8 +528,11 @@ " :return: Tuple (Logits, FinalState)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None\n", - "\n", + " embed_data = get_embed(input_data, vocab_size, embed_dim)\n", + " outputs, final_state = build_rnn(cell, embed_data)\n", + " logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)\n", + " \n", + " return logits, final_state\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -491,10 +542,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Batches\n", "Implement `get_batches` to create batches of input and targets using `int_text`. The batches should be a Numpy array with the shape `(number of batches, 2, batch size, sequence length)`. Each batch contains two elements:\n", @@ -527,13 +575,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def get_batches(int_text, batch_size, seq_length):\n", " \"\"\"\n", @@ -544,8 +596,15 @@ " :return: Batches as a Numpy array\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", - "\n", + " n_batches = len(int_text) // (batch_size * seq_length)\n", + " \n", + " x = np.array(int_text[:n_batches * batch_size * seq_length])\n", + " y = np.array(int_text[1:n_batches * batch_size * seq_length + 1])\n", + " y[-1] = x[0]\n", + " x_batches = np.split(x.reshape(batch_size, -1), n_batches, 1)\n", + " y_batches = np.split(y.reshape(batch_size, -1), n_batches, 1)\n", + " \n", + " return np.array(list(zip(x_batches, y_batches)))\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -555,10 +614,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", @@ -575,28 +631,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ "# Number of Epochs\n", - "num_epochs = None\n", + "num_epochs = 100\n", "# Batch Size\n", - "batch_size = None\n", + "batch_size = 128\n", "# RNN Size\n", - "rnn_size = None\n", + "rnn_size = 256\n", "# Embedding Dimension Size\n", - "embed_dim = None\n", + "embed_dim = 200\n", "# Sequence Length\n", - "seq_length = None\n", + "seq_length = 32\n", "# Learning Rate\n", - "learning_rate = None\n", + "learning_rate = 0.01\n", "# Show stats for every n number of batches\n", - "show_every_n_batches = None\n", + "show_every_n_batches = 100\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -606,10 +660,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." @@ -617,11 +668,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -658,10 +707,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the [forms](https://discussions.udacity.com/) to see if anyone is having the same problem." @@ -669,13 +715,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0 Batch 0/16 train_loss = 8.822\n", + "Epoch 6 Batch 4/16 train_loss = 5.457\n", + "Epoch 12 Batch 8/16 train_loss = 4.972\n", + "Epoch 18 Batch 12/16 train_loss = 4.116\n", + "Epoch 25 Batch 0/16 train_loss = 3.257\n", + "Epoch 31 Batch 4/16 train_loss = 2.535\n", + "Epoch 37 Batch 8/16 train_loss = 2.091\n", + "Epoch 43 Batch 12/16 train_loss = 1.477\n", + "Epoch 50 Batch 0/16 train_loss = 1.024\n", + "Epoch 56 Batch 4/16 train_loss = 0.852\n", + "Epoch 62 Batch 8/16 train_loss = 0.566\n", + "Epoch 68 Batch 12/16 train_loss = 0.429\n", + "Epoch 75 Batch 0/16 train_loss = 0.395\n", + "Epoch 81 Batch 4/16 train_loss = 0.316\n", + "Epoch 87 Batch 8/16 train_loss = 0.201\n", + "Epoch 93 Batch 12/16 train_loss = 0.206\n", + "Model Trained and Saved\n" + ] + } + ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", @@ -712,10 +778,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Save Parameters\n", "Save `seq_length` and `save_dir` for generating a new TV script." @@ -723,11 +786,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -740,21 +801,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Checkpoint" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -772,10 +828,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Implement Generate Functions\n", "### Get Tensors\n", @@ -790,13 +843,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def get_tensors(loaded_graph):\n", " \"\"\"\n", @@ -805,8 +862,12 @@ " :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None, None, None, None\n", - "\n", + " InputTensor = loaded_graph.get_tensor_by_name('input:0')\n", + " InitialStateTensor = loaded_graph.get_tensor_by_name('initial_state:0')\n", + " FinalStateTensor = loaded_graph.get_tensor_by_name('final_state:0')\n", + " ProbsTensor = loaded_graph.get_tensor_by_name('probs:0')\n", + " \n", + " return InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -816,10 +877,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Choose Word\n", "Implement the `pick_word()` function to select the next word using `probabilities`." @@ -827,13 +885,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], "source": [ "def pick_word(probabilities, int_to_vocab):\n", " \"\"\"\n", @@ -843,8 +905,7 @@ " :return: String of the predicted word\n", " \"\"\"\n", " # TODO: Implement Function\n", - " return None\n", - "\n", + " return int_to_vocab[int(np.argmax(probabilities))]\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", @@ -854,10 +915,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Generate TV Script\n", "This will generate the TV script for you. Set `gen_length` to the length of TV script you want to generate." @@ -865,15 +923,86 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "moe_szyslak:(eyeing homer's ass) oh yeah, that would look so good on me.\n", + "\n", + "\n", + "moe_szyslak:(hostile) hey homer, i told you not to come out.\n", + "\n", + "\n", + "lisa_simpson: your love of you, stupid more funny.\n", + "moe_szyslak: i gotta join a girl for moe's bar it'll let him.\n", + "moe_szyslak: so, uh, you got it, shouldn't it be. i'm talkin' malfeasance here.\n", + "bart_simpson: sir, well, thank you, i've never seen this again.\n", + "homer_simpson:(chuckles) oh yeah.\n", + "bart_simpson:(finishing in) you know about that!\n", + "duffman:(small frustrated noise) that's good...(then) just said that was i could do it?\n", + "homer_simpson: i don't pay you to have. i'm talkin' about you.\n", + "moe_szyslak: okay, well, you're the love of jeff!\n", + "lenny_leonard:(reading)\" i don't know.\n", + "moe_szyslak: sorry, it's probably gonna do worse...(sighs)\n", + "moe_szyslak: that's the big day of innocent.(sadly) are you doing?\n", + "moe_szyslak: and now, homer.\n", + "lisa_simpson: my barney is, moe.\n", + "moe_szyslak: yeah. but who was great. now, i can't believe marge comes down.\n", + "moe_szyslak:(tough) homer, i can't talk on the future.\n", + "homer_simpson: all right! the fire twins!\n", + "homer_simpson:(moans) yeah, that. it's my love!\n", + "homer_simpson:(ominous) it's it.\n", + "moe_szyslak: my sweet friend? he makes 'em nice in the world here.\n", + "\n", + "\n", + "moe_szyslak: i got a big shot who again... that's the grammy judges.(laughs) all right. you're a free beer.\n", + "lenny_leonard: are you home? i'm just a guy, that's a yes!\n", + "homer_simpson:(chuckles)\n", + "moe_szyslak: oh my god...\n", + "thanks for that...\n", + "moe_szyslak: oh, no.(as beer) it's all day.\n", + "lenny_leonard: hey, moe! i wrote a little girl!\n", + "duffman:(laughs) we gotta get you a job?\n", + "homer_simpson: wow.(laughs)\n", + "homer_simpson:(grunt) hey!(laughs)\n", + "marge_simpson:(party laugh) oh, no!\n", + "homer_simpson:(chuckles) hey, maggie, i'm not, barney.\n", + "homer_simpson: guys, i love you, barney!\n", + "homer_simpson:(flatly) yeah.\n", + "marge_simpson:(chanting) are you done to the little girl you're disappointing.\n", + "lisa_simpson:(worried) hey, you know what about you, huh?!\n", + "moe_szyslak: you will have to go home from a way to be nine of a jar.\n", + "carl_carlson: and the second way, a lot of people bad-mouth you and me...\n", + "homer_simpson:(to moe) wow, that's a coaster.\n", + "homer_simpson: hey, i've got a lot to mull.\n", + "moe_szyslak: hey, hey, hey, hey! hey ain't work?\n", + "moe_szyslak: that's the beauty part. you need a pal. i gotta go.\n", + "\n", + "\n", + "moe_szyslak:(laughs) the cop,(points off pain) lisa_simpson: moe, moe. but then you can say the most poor bucks-- it's?\n", + "moe_szyslak: it's true, a\" forget-me-shot in my old man.\n", + "moe_szyslak: okay, this is like a!\n", + "homer_simpson:(amazed) him...\n", + "moe_szyslak:(to barney) i think when i've gonna get some professional help. no one could you no real good.\n", + "moe_szyslak: ah, this is a pal. gotta be how how much that how you know a\" business problems...\n", + "\n", + "\n", + "moe_szyslak: he's the greatest gift of all, so a little bit. it was all the other day / a problem. that's sweet.\n", + "moe_szyslak:(ominous) one, for you...\n", + "homer_simpson:(to homer) come on, sweet...\n", + "moe_szyslak: oh, boy...\n", + "fat_tony:(sings) full-blooded...\n", + "barney_gumble:(realizing) yeah, i was sure the little girl.\n", + "bart_simpson: oh, you know, that was philip glass.\n", + "david_byrne: yeah, have you go, ain't you little worried.\n" + ] + } + ], "source": [ - "gen_length = 200\n", + "gen_length = 800\n", "# homer_simpson, moe_szyslak, or Barney_Gumble\n", "prime_word = 'moe_szyslak'\n", "\n", @@ -921,10 +1050,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# The TV Script is Nonsensical\n", "It's ok if the TV script doesn't make any sense. We trained on less than a megabyte of text. In order to get good results, you'll have to use a smaller vocabulary or get more data. Luckly there's more data! As we mentioned in the begging of this project, this is a subset of [another dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data). We didn't have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.\n", @@ -934,6 +1060,7 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -949,13 +1076,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" - }, - "widgets": { - "state": {}, - "version": "1.1.2" + "version": "3.5.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 }