From 45d68e823292907718570f708efef20e0c57bb34 Mon Sep 17 00:00:00 2001 From: ayushmankumar7 Date: Thu, 27 Feb 2020 21:03:07 +0530 Subject: [PATCH 1/5] tfgan_tutorial.ipynb Compatible with TF2.x --- .../tfgan_tutorial-checkpoint.ipynb | 747 ++++++++ .../colab_notebooks/tfgan_tutorial.ipynb | 1587 ++++++++--------- 2 files changed, 1461 insertions(+), 873 deletions(-) create mode 100644 tensorflow_gan/examples/colab_notebooks/.ipynb_checkpoints/tfgan_tutorial-checkpoint.ipynb diff --git a/tensorflow_gan/examples/colab_notebooks/.ipynb_checkpoints/tfgan_tutorial-checkpoint.ipynb b/tensorflow_gan/examples/colab_notebooks/.ipynb_checkpoints/tfgan_tutorial-checkpoint.ipynb new file mode 100644 index 00000000..fe75ba41 --- /dev/null +++ b/tensorflow_gan/examples/colab_notebooks/.ipynb_checkpoints/tfgan_tutorial-checkpoint.ipynb @@ -0,0 +1,747 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "9aMFvFjcoI_v" + }, + "outputs": [], + "source": [ + "# Copyright 2018 The TensorFlow GAN Authors. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "35cp5a7vN9V8" + }, + "source": [ + "# TF-GAN Tutorial\n", + "\n", + "Tutorial authors: joelshor@, westbrook@" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XSTQ5Flu7FMP" + }, + "source": [ + "## Colab Prelims\n", + "\n", + "\n", + "### Steps to run this notebook\n", + "\n", + "This notebook should be run in Colaboratory. If you are viewing this from GitHub, follow the GitHub instructions. If you are viewing this from Colaboratory, you should skip to the Colaboratory instructions.\n", + "\n", + "#### Steps from GitHub\n", + "\n", + "1. Navigate your web brower to the main Colaboratory website: https://colab.research.google.com.\n", + "1. Click the `GitHub` tab.\n", + "1. In the field marked `Enter a GitHub URL or search by organization or user`, put in the URL of this notebook in GitHub and click the magnifying glass icon next to it.\n", + "1. Run the notebook in colaboratory by following the instructions below.\n", + "\n", + "#### Steps from Colaboratory\n", + "\n", + "This colab will run much faster on GPU. To use a Google Cloud\n", + "GPU:\n", + "\n", + "1. Go to `Runtime > Change runtime type`.\n", + "1. Click `Hardware accelerator`.\n", + "1. Select `GPU` and click `Save`.\n", + "1. Click `Connect` in the upper right corner and select `Connect to hosted runtime`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "83-azWpoYsDg" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n", + "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n", + "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n", + "\u001b[33mDEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support\u001b[0m\n", + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: tensorflow-gan in /home/ayushman/.local/lib/python2.7/site-packages (2.0.0)\n", + "Requirement already satisfied: tensorflow-hub>=0.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-gan) (0.7.0)\n", + "Requirement already satisfied: tensorflow-probability>=0.7 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-gan) (0.9.0)\n", + "Requirement already satisfied: numpy>=1.12.0 in /usr/lib/python2.7/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.13.3)\n", + "Requirement already satisfied: protobuf>=3.4.0 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-hub>=0.2->tensorflow-gan) (3.11.3)\n", + "Requirement already satisfied: six>=1.10.0 in /usr/lib/python2.7/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.11.0)\n", + "Requirement already satisfied: decorator in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (4.4.1)\n", + "Requirement already satisfied: gast>=0.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (0.3.3)\n", + "Requirement already satisfied: cloudpickle>=1.2.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.3.0)\n", + "Requirement already satisfied: setuptools in /usr/lib/python2.7/dist-packages (from protobuf>=3.4.0->tensorflow-hub>=0.2->tensorflow-gan) (39.0.1)\n", + "WARNING:tensorflow:From /home/ayushman/.local/lib/python3.6/site-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n", + "\n" + ] + } + ], + "source": [ + "# Check that imports for the rest of the file work.\n", + "import tensorflow as tf\n", + "!pip install tensorflow-gan\n", + "import tensorflow_gan as tfgan\n", + "import tensorflow_datasets as tfds\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "# Allow matplotlib images to render immediately.\n", + "%matplotlib inline\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # Disable noisy outputs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b2xrX4F-OEL7" + }, + "source": [ + "## Overview\n", + "\n", + "This colab will walk you through the basics of using [TF-GAN](https://github.com/tensorflow/gan) to define, train, and evaluate Generative Adversarial Networks (GANs). We describe the library's core features as well as some extra features. This colab assumes a familiarity with TensorFlow's Python API. For more on TensorFlow, please see [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JMljl0ZwONgi" + }, + "source": [ + "## Learning objectives\n", + "\n", + "In this Colab, you will learn how to:\n", + "* Use TF-GAN Estimators to quickly train a GAN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pI8zy5Bz65pa" + }, + "source": [ + "## Unconditional MNIST with GANEstimator\n", + "\n", + "This exercise uses TF-GAN's GANEstimator and the MNIST dataset to create a GAN for generating fake handwritten digits.\n", + "\n", + "### MNIST\n", + "\n", + "The [MNIST dataset](https://wikipedia.org/wiki/MNIST_database) contains tens of thousands of images of handwritten digits. We'll use these images to train a GAN to generate fake images of handwritten digits. This task is small enough that you'll be able to train the GAN in a matter of minutes.\n", + "\n", + "### GANEstimator\n", + "\n", + "TensorFlow's Estimator API that makes it easy to train models. TF-GAN offers `GANEstimator`, an Estimator for training GANs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qxrYrU887Mns" + }, + "source": [ + "### Input Pipeline\n", + "\n", + "We set up our input pipeline by defining an `input_fn`. in the \"Train and Eval Loop\" section below we pass this function to our GANEstimator's `train` method to initiate training. The `input_fn`:\n", + "\n", + "1. Generates the random inputs for the generator.\n", + "2. Uses `tensorflow_datasets` to retrieve the MNIST data.\n", + "3. Uses the tf.data API to format the data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Zs8kdV0w7Rtq" + }, + "outputs": [], + "source": [ + "import tensorflow_datasets as tfds\n", + "import tensorflow as tf\n", + "\n", + "def input_fn(mode, params):\n", + " assert 'batch_size' in params\n", + " assert 'noise_dims' in params\n", + " bs = params['batch_size']\n", + " nd = params['noise_dims']\n", + " split = 'train' if mode == tf.estimator.ModeKeys.TRAIN else 'test'\n", + " shuffle = (mode == tf.estimator.ModeKeys.TRAIN)\n", + " just_noise = (mode == tf.estimator.ModeKeys.PREDICT)\n", + " \n", + " noise_ds = (tf.data.Dataset.from_tensors(0).repeat()\n", + " .map(lambda _: tf.compat.v1.random_normal([bs, nd])))\n", + " \n", + " if just_noise:\n", + " return noise_ds\n", + "\n", + " def _preprocess(element):\n", + " # Map [0, 255] to [-1, 1].\n", + " images = (tf.cast(element['image'], tf.float32) - 127.5) / 127.5\n", + " return images\n", + "\n", + " images_ds = (tfds.load('mnist', split=split)\n", + " .map(_preprocess)\n", + " .cache()\n", + " .repeat())\n", + " if shuffle:\n", + " images_ds = images_ds.shuffle(\n", + " buffer_size=10000, reshuffle_each_iteration=True)\n", + " images_ds = (images_ds.batch(bs, drop_remainder=True)\n", + " .prefetch(tf.data.experimental.AUTOTUNE))\n", + "\n", + " return tf.data.Dataset.zip((noise_ds, images_ds))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "t6aboJBr8Rig" + }, + "source": [ + "Download the data and sanity check the inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "height": 279 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 2639, + "status": "ok", + "timestamp": 1559656474241, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": -480 + }, + "id": "zEhgLuGo8OGc", + "outputId": "efd62ab6-6d5c-4ee3-f6ed-85447922b54e" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_gan as tfgan\n", + "import numpy as np\n", + "\n", + "params = {'batch_size': 100, 'noise_dims':64}\n", + "with tf.Graph().as_default():\n", + " ds = input_fn(tf.compat.v1.estimator.ModeKeys.TRAIN, params)\n", + " numpy_imgs = next(tfds.as_numpy(ds))[1]\n", + "img_grid = tfgan.eval.python_image_grid(numpy_imgs, grid_shape=(10, 10))\n", + "plt.axis('off')\n", + "plt.imshow(np.squeeze(img_grid))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4sAetutZ9t93" + }, + "source": [ + "### Neural Network Architecture\n", + "\n", + "To build our GAN we need two separate networks:\n", + "\n", + "* A generator that takes input noise and outputs generated MNIST digits\n", + "* A discriminator that takes images and outputs a probability of being real or fake\n", + "\n", + "We define functions that build these networks. In the GANEstimator section below we pass the builder functions to the `GANEstimator` constructor. `GANEstimator` handles hooking the generator and discriminator together into the GAN. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "oZ9n-jw_MG6C" + }, + "outputs": [], + "source": [ + "def _dense(inputs, units, l2_weight=2.5e-5):\n", + " return tf.compat.v1.layers.dense(\n", + " inputs, units, None,\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight)\n", + " )\n", + "def _batch_norm(inputs, is_training):\n", + " return tf.compat.v1.layers.batch_normalization(\n", + " inputs, momentum=0.999, epsilon=0.001, training=is_training)\n", + "\n", + "def _deconv2d(inputs, filters, kernel_size, stride, l2_weight):\n", + " return tf.compat.v1.layers.conv2d_transpose(\n", + " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", + " activation=tf.compat.v1.nn.relu, padding='same',\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", + "\n", + "def _conv2d(inputs, filters, kernel_size, stride, l2_weight):\n", + " return tf.compat.v1.layers.conv2d(\n", + " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", + " activation=None, padding='same',\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "NHkpn6ks90_R" + }, + "outputs": [], + "source": [ + "def unconditional_generator(noise, mode, weight_decay=2.5e-5):\n", + " \"\"\"Generator to produce unconditional MNIST images.\"\"\"\n", + " is_training = (mode == tf.compat.v1.estimator.ModeKeys.TRAIN)\n", + " \n", + " net = _dense(noise, 1024, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = tf.compat.v1.nn.relu(net)\n", + " \n", + " net = _dense(net, 7 * 7 * 256, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = tf.compat.v1.nn.relu(net)\n", + " \n", + " net = tf.reshape(net, [-1, 7, 7, 256])\n", + " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", + " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", + " # Make sure that generator output is in the same range as `inputs`\n", + " # ie [-1, 1].\n", + " net = _conv2d(net, 1, 4, 1, 0.0)\n", + " net = tf.tanh(net)\n", + "\n", + " return net" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "w-ZqQ4_thIrP" + }, + "outputs": [], + "source": [ + "_leaky_relu = lambda net: tf.nn.leaky_relu(net, alpha=0.01)\n", + "\n", + "def unconditional_discriminator(img, unused_conditioning, mode, weight_decay=2.5e-5):\n", + " del unused_conditioning\n", + " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", + " \n", + " net = _conv2d(img, 64, 4, 2, weight_decay)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = _conv2d(net, 128, 4, 2, weight_decay)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = tf.compat.v1.layers.flatten(net)\n", + " \n", + " net = _dense(net, 1024, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = _dense(net, 1, weight_decay)\n", + "\n", + " return net" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OhTAjxnyPS5e" + }, + "source": [ + "### Evaluating Generative Models, and evaluating GANs\n", + "\n", + "\n", + "TF-GAN provides some standard methods of evaluating generative models. In this example, we measure:\n", + "\n", + "* Inception Score: called `mnist_score` below.\n", + "* Frechet Inception Distance\n", + "\n", + "We apply a pre-trained classifier to both the real data and the generated data calculate the *Inception Score*. The Inception Score is designed to measure both quality and diversity. See [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) by Salimans et al for more information about the Inception Score.\n", + "\n", + "*Frechet Inception Distance* measures how close the generated image distribution is to the real image distribution. See [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) by Heusel et al for more information about the Frechet Inception distance." + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "1jF-FW5LPTn6" + }, + "outputs": [], + "source": [ + "from tensorflow_gan.examples.mnist import util as eval_util\n", + "import os\n", + "\n", + "def get_eval_metric_ops_fn(gan_model):\n", + " real_data_logits = tf.reduce_mean(gan_model.discriminator_real_outputs)\n", + " gen_data_logits = tf.reduce_mean(gan_model.discriminator_gen_outputs)\n", + " real_mnist_score = eval_util.mnist_score(gan_model.real_data)\n", + " generated_mnist_score = eval_util.mnist_score(gan_model.generated_data)\n", + " frechet_distance = eval_util.mnist_frechet_distance(\n", + " gan_model.real_data, gan_model.generated_data)\n", + " return {\n", + " 'real_data_logits': tf.metrics.mean(real_data_logits),\n", + " 'gen_data_logits': tf.metrics.mean(gen_data_logits),\n", + " 'real_mnist_score': tf.metrics.mean(real_mnist_score),\n", + " 'mnist_score': tf.metrics.mean(generated_mnist_score),\n", + " 'frechet_distance': tf.metrics.mean(frechet_distance),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kxF2-gWHHaej" + }, + "source": [ + "### GANEstimator\n", + "\n", + "The `GANEstimator` assembles and manages the pieces of the whole GAN model. The `GANEstimator` constructor takes the following compoonents for both the generator and discriminator:\n", + "\n", + "* Network builder functions: we defined these in the \"Neural Network Architecture\" section above.\n", + "* Loss functions: here we use the wasserstein loss for both.\n", + "* Optimizers: here we use `tf.train.AdamOptimizer` for both generator and discriminator training." + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "OBd8Vg7lHit8" + }, + "outputs": [], + "source": [ + "train_batch_size = 32 #@param\n", + "noise_dimensions = 64 #@param\n", + "generator_lr = 0.001 #@param\n", + "discriminator_lr = 0.0002 #@param\n", + "\n", + "def gen_opt():\n", + " gstep = tf.compat.v1.train.get_or_create_global_step()\n", + " base_lr = generator_lr\n", + " # Halve the learning rate at 1000 steps.\n", + " lr = tf.cond(gstep < 1000, lambda: base_lr, lambda: base_lr / 2.0)\n", + " return tf.compat.v1.train.AdamOptimizer(lr, 0.5)\n", + "\n", + "gan_estimator = tfgan.estimator.GANEstimator(\n", + " generator_fn=unconditional_generator,\n", + " discriminator_fn=unconditional_discriminator,\n", + " generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n", + " discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,\n", + " params={'batch_size': train_batch_size, 'noise_dims': noise_dimensions},\n", + " generator_optimizer=gen_opt,\n", + " discriminator_optimizer=tf.compat.v1.train.AdamOptimizer(discriminator_lr, 0.5),\n", + " get_eval_metric_ops_fn=get_eval_metric_ops_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "n1uldXfUfstT" + }, + "source": [ + "### Train and eval loop\n", + "\n", + "The `GANEstimator`'s `train()` method initiates GAN training, including the alternating generator and discriminator training phases.\n", + "\n", + "The loop in the code below calls `train()` repeatedly in order to periodically display generator output and evaluation results. But note that the code below does not manage the alternation between discriminator and generator: that's all handled automatically by `train()`." + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "colab": { + "height": 2281 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 221607, + "status": "ok", + "timestamp": 1559656706482, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": -480 + }, + "id": "AH6gcvcwHvSn", + "outputId": "a72e2218-95a8-4585-8a5c-7c4ec896ac0c" + }, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m 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\u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1360\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1361\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1362\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1363\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1365\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1366\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1368\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1369\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1350\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1351\u001b[0m return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n\u001b[0;32m-> 1352\u001b[0;31m target_list, run_metadata)\n\u001b[0m\u001b[1;32m 1353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1354\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m 1443\u001b[0m return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n\u001b[1;32m 1444\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1445\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1447\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Disable noisy output.\n", + "tf.autograph.set_verbosity(0, False)\n", + "\n", + "import time\n", + "steps_per_eval = 500 #@param\n", + "max_train_steps = 5000 #@param\n", + "batches_for_eval_metrics = 100 #@param\n", + "\n", + "# Used to track metrics.\n", + "steps = []\n", + "real_logits, fake_logits = [], []\n", + "real_mnist_scores, mnist_scores, frechet_distances = [], [], []\n", + "\n", + "cur_step = 0\n", + "start_time = time.time()\n", + "while cur_step < max_train_steps:\n", + " next_step = min(cur_step + steps_per_eval, max_train_steps)\n", + "\n", + " start = time.time()\n", + " gan_estimator.train(input_fn, max_steps=next_step)\n", + " steps_taken = next_step - cur_step\n", + " time_taken = time.time() - start\n", + " print('Time since start: %.2f min' % ((time.time() - start_time) / 60.0))\n", + " print('Trained from step %i to %i in %.2f steps / sec' % (\n", + " cur_step, next_step, steps_taken / time_taken))\n", + " cur_step = next_step\n", + " \n", + " # Calculate some metrics.\n", + " metrics = gan_estimator.evaluate(input_fn, steps=batches_for_eval_metrics)\n", + " steps.append(cur_step)\n", + " real_logits.append(metrics['real_data_logits'])\n", + " fake_logits.append(metrics['gen_data_logits'])\n", + " real_mnist_scores.append(metrics['real_mnist_score'])\n", + " mnist_scores.append(metrics['mnist_score'])\n", + " frechet_distances.append(metrics['frechet_distance'])\n", + " print('Average discriminator output on Real: %.2f Fake: %.2f' % (\n", + " real_logits[-1], fake_logits[-1]))\n", + " print('Inception Score: %.2f / %.2f Frechet Distance: %.2f' % (\n", + " mnist_scores[-1], real_mnist_scores[-1], frechet_distances[-1]))\n", + " \n", + " # Vizualize some images.\n", + " iterator = gan_estimator.predict(\n", + " input_fn, hooks=[tf.train.StopAtStepHook(num_steps=21)])\n", + " try:\n", + " imgs = np.array([next(iterator) for _ in range(20)])\n", + " except StopIteration:\n", + " pass\n", + " tiled = tfgan.eval.python_image_grid(imgs, grid_shape=(2, 10))\n", + " plt.axis('off')\n", + " plt.imshow(np.squeeze(tiled))\n", + " plt.show()\n", + " \n", + " \n", + "# Plot the metrics vs step.\n", + "plt.title('MNIST Frechet distance per step')\n", + "plt.plot(steps, frechet_distances)\n", + "plt.figure()\n", + "plt.title('MNIST Score per step')\n", + "plt.plot(steps, mnist_scores)\n", + "plt.plot(steps, real_mnist_scores)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uy1dsvWuwJeS" + }, + "source": [ + "### Next steps\n", + "\n", + "Try [this colab notebook](https://github.com/tensorflow/gan) to train a GAN on Google's Cloud TPU use TF-GAN.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "last_runtime": { + "build_target": "//learning/brain/python/client:colab_notebook", + "kind": "private" + }, + "name": "TF-GAN Tutorial", + "provenance": [ + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1571383618849 + }, + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1569547390651 + }, + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559972047311 + }, + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559900570952 + }, + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559897391264 + }, + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559752800451 + }, + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559719883868 + }, + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559717312855 + }, + { + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559641947244 + }, + { + "file_id": "14r58gghjLTBBQVoSFOBdPsvj-I1G6nbd", + "timestamp": 1549819781952 + }, + { + "file_id": "0Bz8X96EaC_2-ZW9odlhSOEFXdWs", + "timestamp": 1493398103910 + } + ] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb b/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb index 62e0a1e7..fe75ba41 100644 --- a/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb +++ b/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb @@ -1,906 +1,747 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "9aMFvFjcoI_v" - }, - "outputs": [], - "source": [ - "# Copyright 2018 The TensorFlow GAN Authors. All Rights Reserved.\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "# ==============================================================================" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "35cp5a7vN9V8" - }, - "source": [ - "# TF-GAN Tutorial\n", - "\n", - "Tutorial authors: joelshor@, westbrook@" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "XSTQ5Flu7FMP" - }, - "source": [ - "## Colab Prelims\n", - "\n", - "\n", - "### Steps to run this notebook\n", - "\n", - "This notebook should be run in Colaboratory. If you are viewing this from GitHub, follow the GitHub instructions. If you are viewing this from Colaboratory, you should skip to the Colaboratory instructions.\n", - "\n", - "#### Steps from GitHub\n", - "\n", - "1. Navigate your web brower to the main Colaboratory website: https://colab.research.google.com.\n", - "1. Click the `GitHub` tab.\n", - "1. In the field marked `Enter a GitHub URL or search by organization or user`, put in the URL of this notebook in GitHub and click the magnifying glass icon next to it.\n", - "1. Run the notebook in colaboratory by following the instructions below.\n", - "\n", - "#### Steps from Colaboratory\n", - "\n", - "This colab will run much faster on GPU. To use a Google Cloud\n", - "GPU:\n", - "\n", - "1. Go to `Runtime \u003e Change runtime type`.\n", - "1. Click `Hardware accelerator`.\n", - "1. Select `GPU` and click `Save`.\n", - "1. Click `Connect` in the upper right corner and select `Connect to hosted runtime`." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "83-azWpoYsDg" - }, - "outputs": [], - "source": [ - "# Check that imports for the rest of the file work.\n", - "import tensorflow as tf\n", - "!pip install tensorflow-gan\n", - "import tensorflow_gan as tfgan\n", - "import tensorflow_datasets as tfds\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "# Allow matplotlib images to render immediately.\n", - "%matplotlib inline\n", - "tf.logging.set_verbosity(tf.logging.ERROR) # Disable noisy outputs." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "b2xrX4F-OEL7" - }, - "source": [ - "## Overview\n", - "\n", - "This colab will walk you through the basics of using [TF-GAN](https://github.com/tensorflow/gan) to define, train, and evaluate Generative Adversarial Networks (GANs). We describe the library's core features as well as some extra features. This colab assumes a familiarity with TensorFlow's Python API. For more on TensorFlow, please see [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "JMljl0ZwONgi" - }, - "source": [ - "## Learning objectives\n", - "\n", - "In this Colab, you will learn how to:\n", - "* Use TF-GAN Estimators to quickly train a GAN" - ] - }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "9aMFvFjcoI_v" + }, + "outputs": [], + "source": [ + "# Copyright 2018 The TensorFlow GAN Authors. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "35cp5a7vN9V8" + }, + "source": [ + "# TF-GAN Tutorial\n", + "\n", + "Tutorial authors: joelshor@, westbrook@" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XSTQ5Flu7FMP" + }, + "source": [ + "## Colab Prelims\n", + "\n", + "\n", + "### Steps to run this notebook\n", + "\n", + "This notebook should be run in Colaboratory. If you are viewing this from GitHub, follow the GitHub instructions. If you are viewing this from Colaboratory, you should skip to the Colaboratory instructions.\n", + "\n", + "#### Steps from GitHub\n", + "\n", + "1. Navigate your web brower to the main Colaboratory website: https://colab.research.google.com.\n", + "1. Click the `GitHub` tab.\n", + "1. In the field marked `Enter a GitHub URL or search by organization or user`, put in the URL of this notebook in GitHub and click the magnifying glass icon next to it.\n", + "1. Run the notebook in colaboratory by following the instructions below.\n", + "\n", + "#### Steps from Colaboratory\n", + "\n", + "This colab will run much faster on GPU. To use a Google Cloud\n", + "GPU:\n", + "\n", + "1. Go to `Runtime > Change runtime type`.\n", + "1. Click `Hardware accelerator`.\n", + "1. Select `GPU` and click `Save`.\n", + "1. Click `Connect` in the upper right corner and select `Connect to hosted runtime`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "83-azWpoYsDg" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pI8zy5Bz65pa" - }, - "source": [ - "## Unconditional MNIST with GANEstimator\n", - "\n", - "This exercise uses TF-GAN's GANEstimator and the MNIST dataset to create a GAN for generating fake handwritten digits.\n", - "\n", - "### MNIST\n", - "\n", - "The [MNIST dataset](https://wikipedia.org/wiki/MNIST_database) contains tens of thousands of images of handwritten digits. We'll use these images to train a GAN to generate fake images of handwritten digits. This task is small enough that you'll be able to train the GAN in a matter of minutes.\n", - "\n", - "### GANEstimator\n", - "\n", - "TensorFlow's Estimator API that makes it easy to train models. TF-GAN offers `GANEstimator`, an Estimator for training GANs." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n", + "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n", + "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n", + "\u001b[33mDEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support\u001b[0m\n", + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: tensorflow-gan in /home/ayushman/.local/lib/python2.7/site-packages (2.0.0)\n", + "Requirement already satisfied: tensorflow-hub>=0.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-gan) (0.7.0)\n", + "Requirement already satisfied: tensorflow-probability>=0.7 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-gan) (0.9.0)\n", + "Requirement already satisfied: numpy>=1.12.0 in /usr/lib/python2.7/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.13.3)\n", + "Requirement already satisfied: protobuf>=3.4.0 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-hub>=0.2->tensorflow-gan) (3.11.3)\n", + "Requirement already satisfied: six>=1.10.0 in /usr/lib/python2.7/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.11.0)\n", + "Requirement already satisfied: decorator in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (4.4.1)\n", + "Requirement already satisfied: gast>=0.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (0.3.3)\n", + "Requirement already satisfied: cloudpickle>=1.2.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.3.0)\n", + "Requirement already satisfied: setuptools in /usr/lib/python2.7/dist-packages (from protobuf>=3.4.0->tensorflow-hub>=0.2->tensorflow-gan) (39.0.1)\n", + "WARNING:tensorflow:From /home/ayushman/.local/lib/python3.6/site-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n", + "\n" + ] + } + ], + "source": [ + "# Check that imports for the rest of the file work.\n", + "import tensorflow as tf\n", + "!pip install tensorflow-gan\n", + "import tensorflow_gan as tfgan\n", + "import tensorflow_datasets as tfds\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "# Allow matplotlib images to render immediately.\n", + "%matplotlib inline\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # Disable noisy outputs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b2xrX4F-OEL7" + }, + "source": [ + "## Overview\n", + "\n", + "This colab will walk you through the basics of using [TF-GAN](https://github.com/tensorflow/gan) to define, train, and evaluate Generative Adversarial Networks (GANs). We describe the library's core features as well as some extra features. This colab assumes a familiarity with TensorFlow's Python API. For more on TensorFlow, please see [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JMljl0ZwONgi" + }, + "source": [ + "## Learning objectives\n", + "\n", + "In this Colab, you will learn how to:\n", + "* Use TF-GAN Estimators to quickly train a GAN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pI8zy5Bz65pa" + }, + "source": [ + "## Unconditional MNIST with GANEstimator\n", + "\n", + "This exercise uses TF-GAN's GANEstimator and the MNIST dataset to create a GAN for generating fake handwritten digits.\n", + "\n", + "### MNIST\n", + "\n", + "The [MNIST dataset](https://wikipedia.org/wiki/MNIST_database) contains tens of thousands of images of handwritten digits. We'll use these images to train a GAN to generate fake images of handwritten digits. This task is small enough that you'll be able to train the GAN in a matter of minutes.\n", + "\n", + "### GANEstimator\n", + "\n", + "TensorFlow's Estimator API that makes it easy to train models. TF-GAN offers `GANEstimator`, an Estimator for training GANs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qxrYrU887Mns" + }, + "source": [ + "### Input Pipeline\n", + "\n", + "We set up our input pipeline by defining an `input_fn`. in the \"Train and Eval Loop\" section below we pass this function to our GANEstimator's `train` method to initiate training. The `input_fn`:\n", + "\n", + "1. Generates the random inputs for the generator.\n", + "2. Uses `tensorflow_datasets` to retrieve the MNIST data.\n", + "3. Uses the tf.data API to format the data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Zs8kdV0w7Rtq" + }, + "outputs": [], + "source": [ + "import tensorflow_datasets as tfds\n", + "import tensorflow as tf\n", + "\n", + "def input_fn(mode, params):\n", + " assert 'batch_size' in params\n", + " assert 'noise_dims' in params\n", + " bs = params['batch_size']\n", + " nd = params['noise_dims']\n", + " split = 'train' if mode == tf.estimator.ModeKeys.TRAIN else 'test'\n", + " shuffle = (mode == tf.estimator.ModeKeys.TRAIN)\n", + " just_noise = (mode == tf.estimator.ModeKeys.PREDICT)\n", + " \n", + " noise_ds = (tf.data.Dataset.from_tensors(0).repeat()\n", + " .map(lambda _: tf.compat.v1.random_normal([bs, nd])))\n", + " \n", + " if just_noise:\n", + " return noise_ds\n", + "\n", + " def _preprocess(element):\n", + " # Map [0, 255] to [-1, 1].\n", + " images = (tf.cast(element['image'], tf.float32) - 127.5) / 127.5\n", + " return images\n", + "\n", + " images_ds = (tfds.load('mnist', split=split)\n", + " .map(_preprocess)\n", + " .cache()\n", + " .repeat())\n", + " if shuffle:\n", + " images_ds = images_ds.shuffle(\n", + " buffer_size=10000, reshuffle_each_iteration=True)\n", + " images_ds = (images_ds.batch(bs, drop_remainder=True)\n", + " .prefetch(tf.data.experimental.AUTOTUNE))\n", + "\n", + " return tf.data.Dataset.zip((noise_ds, images_ds))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "t6aboJBr8Rig" + }, + "source": [ + "Download the data and sanity check the inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "height": 279 }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "qxrYrU887Mns" - }, - "source": [ - "### Input Pipeline\n", - "\n", - "We set up our input pipeline by defining an `input_fn`. in the \"Train and Eval Loop\" section below we pass this function to our GANEstimator's `train` method to initiate training. The `input_fn`:\n", - "\n", - "1. Generates the random inputs for the generator.\n", - "2. Uses `tensorflow_datasets` to retrieve the MNIST data.\n", - "3. Uses the tf.data API to format the data." - ] + "colab_type": "code", + "executionInfo": { + "elapsed": 2639, + "status": "ok", + "timestamp": 1559656474241, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": -480 }, + "id": "zEhgLuGo8OGc", + "outputId": "efd62ab6-6d5c-4ee3-f6ed-85447922b54e" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Zs8kdV0w7Rtq" - }, - "outputs": [], - "source": [ - "import tensorflow_datasets as tfds\n", - "import tensorflow as tf\n", - "\n", - "def input_fn(mode, params):\n", - " assert 'batch_size' in params\n", - " assert 'noise_dims' in params\n", - " bs = params['batch_size']\n", - " nd = params['noise_dims']\n", - " split = 'train' if mode == tf.estimator.ModeKeys.TRAIN else 'test'\n", - " shuffle = (mode == tf.estimator.ModeKeys.TRAIN)\n", - " just_noise = (mode == tf.estimator.ModeKeys.PREDICT)\n", - " \n", - " noise_ds = (tf.data.Dataset.from_tensors(0).repeat()\n", - " .map(lambda _: tf.random_normal([bs, nd])))\n", - " \n", - " if just_noise:\n", - " return noise_ds\n", - "\n", - " def _preprocess(element):\n", - " # Map [0, 255] to [-1, 1].\n", - " images = (tf.cast(element['image'], tf.float32) - 127.5) / 127.5\n", - " return images\n", - "\n", - " images_ds = (tfds.load('mnist', split=split)\n", - " .map(_preprocess)\n", - " .cache()\n", - " .repeat())\n", - " if shuffle:\n", - " images_ds = images_ds.shuffle(\n", - " buffer_size=10000, reshuffle_each_iteration=True)\n", - " images_ds = (images_ds.batch(bs, drop_remainder=True)\n", - " .prefetch(tf.data.experimental.AUTOTUNE))\n", - "\n", - " return tf.data.Dataset.zip((noise_ds, images_ds))" + "data": { + "image/png": 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/HvfPVGGXp9m/3YwXYnBAqIIKKjCMCoGvCir4j2Gwc8pMTDE/4ohPpMGiChVU8P+Wyz8GMSKh9ISUJzHYOVNDmvCVyzbGOBwp/c0VVPA/yNUvg7k4MJSpWwfodZ7BzpldQ4OPiQkDzw821FQx5PaV6ReXWvobK/h/R07PZuxNjcEx3IakWYEkzdJ/fbusPE6aKE9WDponJpHoGcEx2Dn39BJrGk2XVzbUVDFkVpb0s04xnj2lksR5zRDMikfo0kYGcWV9yfKT+pKwOIBl146zMyWS7SmnubHZ+NKT/9/ZmxrDsbBFAKxyOcqlvgu51Hche1Nj2JsaUy6OmvKhfuoX+tA4WkNjJdQ9PAL3D/XLBTDYOdfeb8r0dF+sd5W+2Ppvkvi9P/FvzCduQXGHse51E6sjlgbbFxp4E9d5PlXkRQWTVttsXnBG2VBUc+LGtGAmJsWiPCI+1gY1RF7pWS2gl4Hc7k25tNafS7/6if9fa7ic6ZNc6rsQx3DDv9fHaCRoRpWV3O5N+bJqNJ57RlJ7gP4ZQgZFceQ2NoRU3sPAniPR5p03xNQzqFNv0yuxG3DTKPYUzmJWTc3qRUqBD98KZLvPDAZ2baPviKMYud2b0v97Mdm9wYr3kHs/pGWtS1Tech4NcGNaMHl1c/EYqN8PJFha0v3gWYbY7EKDhtYeO2AcCOMEPPeMxHPYGQNabVz8okGOhgYWm+hieRMTmZwCrZgq2QtpvV3LsSOx2CLW2ybNCuRS34WA2KO27Fn0miEICAhBxi/YSB8axLQP1xJ2vza+X9xESi2RQT1nbrAXr54agfaMcR0TQFuQz51sK6Mpsb/iKlYNZOwvqt/0mnABW8EMrUr1vNPKREorgTkX2tBu3DhcPzuFy1eFnEhxo014KtMunSVm3Dzcl5Ru5zEyE1NSPwhm3N+RvGNzlcWZrtRdH8I9dVE7zSvl6dVGbVBD3o6/zseXY5h26SwJ85sa3vs2rc/e1Bi2p5zmq6qn+bpqDD2t7mAik6NAjolM/Cts2xi5r6fe5p90Po+JEXSo7sfbV18BwO2DWHJ6NjOs/Yg9ZxcX49Tb6mhaH7M+t+lmmcGWqe0pvCFteiY9fc+/Lrt2rqZzfFe0bYw3N3ySjF11yP6risHKcwC7U6LwPDRU13upOgXw26LZ2ApmkoWqc/e6sa/eRvK0hXSYOhHbNRHY/2XHL6770aDhfL6Wt8JH4N6/7Anl2uZ+7Nq4DA0a7qpVDK3VQvfa7OQTuJuYEJbhxd56ZRvaXfssmJiRRarwT1e4dHOWto/L5htiUfGTPWRpj0dde527wfefa/PJ3hF4YWqdKGkJb199hSs/+UjuRbennGZntj3L27SU7ERPszMlktvqXHp+MYXKy0ufZxo9CSFhqBUatNz400WqiVJJj7cnxz0fuaN0tbUn0Two2rPEYdoVbAUzJt+UHmDYV0+U/PDfPw7bNRHc2urD7Fo7AeiT1JUJk0L0ckyAG60sEJAhIND7qQi4h4kSAYE/Rr5SZntnR84r9jxTk8ffT5RDpr5vnGBIj4FjSn3Pwlp/GOWzAF0PusrlKKmvSK8fva1W0cPyPqo6hm9JoahZA+URJzRo6Bg5okyO+SIMDgi5rHuxXooheEyKIKHjIrKCXA2yI/MXg0C19ojPC9o1ZoP7nwD8tbjJ804rM7Z22dzd7sWpgFXYCqb4n3wb1au3JN3Nc10LdBq61t9bF3tNg4bW5/pIqmdc99CZ14eOYUCf0XzedRBB37zHXbWKPe/9RP7r+vWet0OCdUPWL+80xn/NROSHo+g0aBQmMjk9Bo6hm3MAvWoE0qtGIEqZie79L8JjYtnVD6/85KN7/GRvqy9t101BY1DEQdQuvh0SzPaI7Wz22IWAQF6iOGWQ20gPXhmc1qNOuUnCwqYkdF1QbCgDsD3bjmXBAXptGVASaXUV1Nwq/XxBVYBcJnBw4UJYCBCFXCaAVsOpz8PgcwhJDeZSgH7zuIZ/vUtU8DKiA9Y/+ncLeO4YjeeoU5LbGtdpPjfVKt74cgr2x8Q7r7ySLRnrHOiT5ITNwEy91MifHLKbIpZv5XVowvwpoTg+Uj74c1nRxd3Nu1Wp6g0np87R/c6R/gLuiO1UHIykm3MAcqKKvV+lLSh2XRgDiy0n6bDFTze8lUr142oYCJd7mlDnkP7nyyvZsuPCITQchEfF/P5zQ7BN00LT+iz8bQG31Eq+6NRflDHRA8k9p2PtNADiF/uR0FXcX+RpXdRulhkkj/aS+hE6HC5I180BUF9MwG3bCLomdGFMSnOO5SlE8WO03FHncFql5e+fGupt13GVOfc0+RRo1body8zsc1G4SN+xzGf/SNpsmIL9sqIh0a23fDncYD2pa9wMvtHJlEr+WL4Af6WGkv57uKkKQgPp9Y3/FlKDQ6kt5eI0wl6a9Enc196cVmlpsDiE+ffdaPpdCM4/nhB/v1Pn6Bw5An9TgbjR+ucBSHbODXV/ER9owGvzGLw2j6FbcHd6BPeg5QdjydHmo9IWYHXt5Shs8Rx9CnXrVJKb5mItiD3kzHvevD0ohM/dG2O1Sf8haLVpScXWNQFigpdT53fpyz913okqtttXbvemRH22AAFBJ0psCPLqz0a/P7sTwIx0UQT5QP0NNPglDrn98y+mpZnuumHq7pQoHuypTWGbxhS2KYrKyivZkjivGdtTTuuGtbtzSp/X1d4wSvdYH4eTOu80q5OJBi0mCdL0k+T2KgSZBmSwc8grVA0rHrys3vMi0fkaJrb9g9zuTfWyLdk534kX8wTNbFV4bBD/CmrYkzCmBou/nY0cGa98OxG7X6RfUPI67lzIL8TiqvHU1gDcFWJPvGlOO8nbGwD84roXAM9do+j65nBi88WRw5eOxwxv5COud9HqemZjoM0qUlGYm+FN1zeHc65dZf5q70rXN4cD8FXV0zxY+/w9P3b1DqJAq6ZAq6YQNfvqr2XbqjB2r15I5aV3SPkwmLivvTnbUxz+qrQFzM3wJmxS31Lb5zExQuegx8IWPTcrKKdns2LJCNWPSusEurqeR0BGgae0nfFqLZXjbyoQM3wOS35bQONoDSkfBhf702gFRlRK4npX/X5Dg+o5BUtL4mbU1Q1rPf8YifNuOdbxmWguJoLGsHlGTs9m9Px6X5mXDcrK7hRxzvl6t4EGrdE+zsust2Icrp+IN6GkmYFcemuh0Srst6ecZm+OLYvatBFV6cqZWxOCmRMizkG/r/38tEa5Tx3yHa3ZtXZRqUspvWpIi4jrM5+U+n1/eTmSQDM57Qa8i/xQVOknPAdZQH0e1LbkTlcVLWsnsbjmYTRoWHC/DmtndHxh5NboSggAmuxsPEedossoMejgiZixYsyEqBGVLhK6cix1BhsvQVmDFrQa0vytsTcgyeZxb5ZftRBZ47qkN7ThSO/pxOYbp0xWHAad5su4LjhcN46KXWk4zT7B97NLzzVWxyYijxXXSdOHBtF2XDhyNKgfDcbkaNi0tznuU6WPnGpvGGVQJLYs+Cs1FBhh5qU9fQ7r02C9HlKBLhQF4ioj7Tt46YswlTITamw1bAOj55FbxTgb7iR0XsiVDuI8topcSfe/B+CA4c5U4wNxf077H56vxvcyYL8snJhlUHyWVBTFlYrHxAg6TPTDMdyGVS5HS3yPLgkBaUkIAo9SIQ+9PKmQj/mflCmZduksq+8Gc6ufvU4fVwqCtTUJX9Ul7s0wNGjo2nsYspPnDR7Og5hut2h9GG32TDJoaaaCl59yGdb+VxHnUlmP/qSjefgQj4kRdJkoDmFkGK8y59Kb5lRXKPFcqd/aawX/f6jQEHpJcduez121CuGs4RqtFfw3+Z8c1lZQwctEhfpeBRWUwN7UGLJ7G156Vh5UOKeRyRwYaHC+ZwX/HAVaNRO/W1fizmv/NhXOaWTS68ueyTGu4OWmm2UGCSOkbc5cnhjNOXemRHJ3dJCxzP0jJKxozOYbEVSPsC79zWXALxriBobhe3ywUewBJM4JZMuNU+xMiWR3ShQ7UyKRHXQ2mv3HwlmP/15WFO6uZLwTRPKGBkZVzBt6rTUALVoZX80jYUkAu1OidH+qzvqV5hnsnHKfOtza6lP6G19CQluuxUQm52iih1HsfVX1NHEFKlxmGye5Ibd7UxJ7z0elLWTAlfYEfTSWNHUuU132GMV+eZM6JZgO542z03nIn3s49t1czrZYZlRRrgAbcZ17cc3DRrN5Z5s3d7Z5M6fVWgA+vSOmFh5YvEgvOwY7Z2FlS04FrDLUzHO58l0QrqfMdXcfY80NhAbeOMnFC6faVlOD7ckd7FEgp/eKycjCDV/vFBp4s3TuLA7kKun23kQyW6STXV1m8O7T/xRyGxs+Hb6WEDv9ahifRNUxANlBZz67HEVbc8O2PSyNH9ONI2MqC6jPmSa/cqbJr3S2yKJ9bA9OhzSSZOulTkJI/jaIC++EAkX5urkeDpgYYS/JDusiqGcq9nBm6QUG20sZ5MVXabdwX3ZVktLa0wh3MuhxeiQuXxVicVZMTbsQMh+1VsaUb0ZKztcsL5LW+FM7TKO7MWW19qanpYTq5UfI67izd+n8J46UT3ikskJMRFkbH0AtA3fMy+7VjENz5/O46LrdxZ5YjIDU2WIiSXKhfjeYl9I5t6ecRkAAxLnFluzKmMkK6GjxkL0rFrE5y4HV7ZpLrtJIWBLA9kriD3+5oAD5YenVCADpw4OIej+UTs6NAOOIRBXeuk3N3rfRAAqXmlydZUPY/XR29WxG5XjjOObTc0yplR0yE1M02Qpk4aLSwp1xwUR/JN5I6oe/TU30n88lv+mEgECaOpfXZ3yA05wTpEwNJjpkXukn68GbVncAUN00PH/ZcvNJWsnHYrVRlFsx5SqfXzlNY1M5g5Lbcu/Vh6DHrfulitbKlEqSvw165JgiQZ+NY0WfTsx8bwAZGvEO1MsqjRu9a0n+nIRORZUOw+IGSm/wI9qP+ctgG89D7uGG7a9ZRDdbxazodmhTbxvFrjGDPwWv1Mchoug+v2jyHJ3ShMMaaRe9TCtW/VSWK3ln+B9k9WnGr6Nm6hQbjEF+hyL9qDprjDNsfuyYIJbfNTaVE5NfSMYYJ7SF+o2pXhrnlFey5dJXjTg/WBzGXivMxXvjWOyXhaM5G8e1jgJ2griVwqFcM6odkxhokBUP1mT9Ybguboj9CSKekL6VKZX4RCqQKZXPP6mMvL3rMKtdDyAgI77VMh5uqmKwzZIwpP70eltTrFKLpga+JmLi/ymVDOuT1yTZrDWvaIg51i6e32bMwMtErE56qJEmKfIkMoUC249EcTrPvSMRzl8y2Kbg50utk5bcmigWWS8fPxuAAq0c4aH+xdwGD2tFKUcBE5mcKgukDbcSw5oR2yMUgYNcKcwjxKU5AB5EcHdUEAc+noGVIJb0dOs0EM25BNDoP1SS13Fn2+FNjwqBoWH4O9ScbZgm7o1pwVSVR/FO3yE4R1xmac2i3dZab++FeYcrBtmf8cNbrFwTibYgn7SRQUR8Fkqz0eMkf9dg3F4ToNBZheneKARLS/K2OmAuiyJDk8uno8ZjclNaKZbm4UN6NnyduBkuxLZbhIPcHA0aTqpM+NZdmtbuY2RKJbPiD+FhoqTJ9BA8Z58wSl98r74tm50PoXz/GDfVObx6JIS4NksJUMrYfux3uvq0Qv2g7J2Kwc4Z1PUsGgwrWI3uPhsBU7ZkV2Zl9/bcGVvUO+z48CesBDFCueqBM5q/YyV/zp1Wjrq2atDg+nmBXkp2JeH2yPnU5gqW1jxCllbFugceDLe9jpWpymD7lVeE64Qb7c/nkqbOpeuoo0QsMHnhefpgDNUGhbsr9mvv4W0ZhwYtU1Jex+RPw2ok1Wnp+HxmgdBO0N1QbWQq5A72koXO5JVsSfjYFw+TExRo1TgZeHN+kkqrw+l8dyz3vE1wXpuIx91ouh7sxi6vHZLsGeyc5nLDI5331GosFNDGPJUrG2OYVDnuyU+gQKum/6WuqN6rDEh3ztzXi+5aDY4PxS3+ovRGA4KFBQ1sxQDQ1WFq5t93Y8Pnr2P520mGpxhfz1f2VwyJhVa0toolAuPsjGYMHA4q+engOiZf7s3d4dWZsvciZ5fXw8EIEfKv680AACAASURBVOVsX6diN1QfU4E7PT31FjsTLCwoDPDiSkczYvuLU6eZ94z/HZr+cRqnP9DdlBd7bAAsyNKqeFGRSUkYPOecUf24oSboNu8DbhTmYiuY6RxzdoYnt9XiOH1LdjVyX71tUK8pt69MQydxv89lmbWo/VmO3hP0pxGcqvJ1VXGIGP/qcja/3x7L34oq8m9nWRlkvyQ0WoGmyjxye+in5PYYY0Von8Tul3BGTJmArFM6V79QcDTPFIfFhjumYGmJxQfiza/R7BDdXDO9kX7jkdT3g6l+UGD3r0u5MChUd3x5TDDZvZohr1I+83gAZ7kYEPPbMb5UPeCnMepSSsY7QZLU9qr/fIJRP7coduzW+GAmfJBA029DqDrf8KHH1NMHaaYUe/ktvlUAw+skCy8n4z9zHBETZ6OUKTiwtCgDZPVDJxy6vliqJG1EEB++/yvLPN3K9HkP+wbS3CySv/LMMN+qvzpCeabnVYq8zZQLp6hneoABNZsbxeadAQ044TkXgGrHs7GeICaLBDVIRJ9Bba11yRx1qA+Diu++Ht92CbR9/nnNIvtT9Y1Lz9zEH74VyLEZ8/HYPgrP0SX/DtbHHNjg/icef4zAe+x5PPP0/72M6pyGyGA+zY8hywBwOGt4iFvTwo9mytMG2ymJajNOUNdzLFGd5mAjFG3Mu3xqD8x58Q+S3zGTXpYZLKNsznm3ex4CMqYl9MQGw6OLxuThAhn+ymya/vI+rkZKkKg75AICAjfVuaRNy9MtsZ085YUHZd+6oTAlldqf3KHH7E5FBxUKLn7kzOdtthKV5YKz8j4Lj7XG+YCMhzXlFJqDy4ILqEsYXaX1EK/JE51n0u6jD3QbbcnruKO1NKPO0kSmV9tDo9Nv4zn0jORg00uZhPAkwnHD7/a5TkVLGsfzzF7wTml4jjxN77YhdJ17AICFF1rifiyp1GBQVoYFGrQ86BdI5UNXKLxV8hqm3MON3Nr2xL26GA1azGfZGflfYDi/+a7B78/x+IZeMUqGFIhDeA0a7msUnGz8Kxo0xOZr8J55Q+/P0BYWPvP9eo5JYR3VgQLiscTz0c308VbKz/v9qq01g5ZQVW7B2bGh+FiNpV6LJH5wWU1thRi8rLNlHD7TUw36LozmnNH5xktGvjUxmBZmEYBxIpKprcT/H88z48e3ByHD+MM7xYFI9tQVhZhdOFemKK1XWC6DvF7j6M9h7Mmx5re0JqRO80B5VRy0ZTZyIuV1DWvaLqKpUsuxPBPGh46ievg5o4T+jaWtKzMxxQQZtVdpKLx5yyg2Af466wku+3TrmwAjLg7E7rr0fF1jYLbjFM1tx5DRNYfv/LcS+07Yo1fMSdfkMqD/OOr8dYZCA4XeXkqZksclQY1ODaJ6T8MiqhWULwrXWjTfHs+RBuWTkJ85IBDFoDvcjKuK1xexqO9nlsvn/Jv8J9X3nGYanmFTQflyvVcNHE1OAeXjnLZrI2AteHDZ4DXj/xovTfre03x51w/Ta2n/djMqKIVqM06w0cfwFMgKnuWl7DmL9pQsv415K6jgZeel7TkrqOB/HaM7p8LdlW4X0xmb+M9svFNBBf9fMapzJqxozGs7Yhhhm0xHC/1SlSqooILiGMU5tc39uLXVh4T2i6lpco+GC0KemDdKQ+5gz5D4qzrFucd//0sIDbzJecO4gsdyOzuSvwlC7lMHEOsa5Y5VSVjYlHbnH+q0mjbeMKwk7dL0f1aJMWFF4xI32f23+fHKSfrFpfLwLf3bZrBzKtxc+GnNIs4ErMHn13Es6/IaNb8xLBc2abU/k08eppdVGl5H3qX5R+NouCCETE0eWX1eTnXu8mLP3Dm0O2+8UYjM0oKLQ8J4e+t+7mzzxupQJXZE/UFS14VMsktkfVYVfJePpW+P4ZI/o0Cr5tfec5HbGT+TSaZUomnpj6alP5d/CiJhYVO8zpiQ0H4xvVuVPaVPplSSNPNZh8nr2pS9qTHImtQzrJ1N6nFjc13qm5owyPoWt7uoSj/pKQyO1r6x+yR1TRWsfViN2lPCjbIW9XfrBaSq1fifHEHt/mI2T6WghuSN0KKyFTB+rcfLieZsHOYyUyIzXYB7RrEZP6EmAH2s0unT5Ffd8eTCHNodHI/P1Ou43i6qIZWKv6nApUneuH5qeJ7tw76BdPn4EBpkWMnvM7ZS8Zu/gAyPP0fg80MmZS1ouDq1MXF959FlUtEIT+7hxmvfHiVTk4usUCP5Oyho15iZS+ZT39SwDDfJzqlwc+GN3Sd5w+oy9ZdNxuWLUzw/G7HsXNtUn04X3DDvcAVnLuiOy8L/5u13x2O/X78fW+5YFRzscFh6E41WHCgsrvVnsW3RQdwmfWNWVVZ51dTbfkp/D6pG5SIciS52/GGwG2kN5cjqPaCJ8zVWuRyl1fkeKNsn6/UZJ8956PI+DSHrzUAu9JsHyPkmrR77vmqJ5eaiEjdPIo260F97bun5xaVxa6sPUQGiGNur53qTtduJlUC1w/fQnC2q+9Wn7W3OZTOp8jxmZ3gWO7744Goc5eZ4b5yIR0zZe+Gn2fPLQhSPUk+ztCqCTw6jztCLeju7JOdU1KyB7+ZrvG2TQv1lk3H96jRaI2wYe3dUEPuaTWd4txEl5o6a7Nd/zhn/oTvn3pz7jDMag1sTg1k//mcAfEwtcNszDJeaadSyvscqlz/ZnGXD+8fexGGXNbdXPKADfihJNmobyopgaYnVyBsokDPs+qukTHLH8oS03aD/Ke6MCWZHo5/QYI7nrlF4jorCUnMZQHJu8dWvgni/chh31Lls+fo1rJ+obnGUm/N6bE88Jkp3zMI2jVFQpOZoJROz3LSqf2hYe+XtWmx13AZA7UVXKTSwaPkxCz+ci6PcHE3MP59PezTPlJ9i2+Okh9LCZ6PX8N47YwG40docwVbD9QtOqI860mGLqLrgieHbmcvMDL+pyExNGeAsXnSxoXWxPSH9AiwL7S++gWm6NOnSx3QcfpxqcnNuFObivl5jlB3DFw9cgFwmELxrEp4bir4DbVBDBKK4nlYJN6SJkgGkjHpWfCwqcCUt33lP75JKSc5p0/wOwiPh3MKU1OIvCnLkdrZcf9cbRTZlLpTWBjWkuvw4DRZ9QC2KzpHb2KDxchGfRMfqrV7gvk1FdA8FTZVa6m0JwTpJrHA480Fx/dNxUf2p1afsosJyx6os6+mFcEEcytY6UsoJBrC85Qq+N1CWJCfIgwHWYklbxylH+b1KK5zmnDTKBV8S93PMqWqAbcHamq+rHkEDdFn4ATUOGkfrJ0ipRq2V0bxhAhdHB1FtxzUKajrQd/leNGjZEriIyY2HoY28ULqxp7g5OZio4Nn0SupKzjQnbrawpO/Ag0yzv4i2Tzr8op89/atSmtZn6Jrt9LS8x/R0X101wu33ggmbEEqcqjr2iiw6W2RyrTCXkI7vor744oQEuX1lRkScoonyFu/2G4fNDzfY4P6n+JpMKLZr1yvnemPT567ekg9PMjnpAq3N83TD3J419Jf8GJFwmWUtA1HfviO5HWWh28V06ipT+L62Yc4pMzHl3oDGpLfN42KbxSgQb1LdEzujHSB79iYrkeRvgxjdY4+ufE4qLc/mMc3+Io/DMlkaFa9GDqXG+3mok6QrGsqrVOHSBA9MfB7wY4PNtDfPRkCGBi1e+0bgPfEy6owMve0mhjUjsccCgEfi4iJXvwzmwrBQClHTYcQYlLueLfo32ua5V9+HnpZi5LCZpRgZu7KuIcc/mMk3bwxko48TocPfBOCtc6U7JoDM3JzOFplseViXu/4WrHMX72LXCnNw2zYCj22j8Ng2igHJ7Tha/zdur62ub7OL0cIsG4DYAmiw4T1JNpb17Mju6D9xDLfh3pDyW9P7+djr1FQ8QF7XyyA72oJ87FaG4zEomo7vjKLF2T58k1aPbXV2cT3M1ihtlSmVrO4/l3n7XjfY1qqLTWlxtg+eO0bjtW8EeVoNkQFrGLFnH+lDpX/f6rt3cf04HOc3LjC/ZSump/sCkKbOpc7gSEmOKdTzZsnrS0t8zWWX2IkokFNgKS/xPc+1q29DClIsdY9fMcvn0s+BnGgxn1ei3tHNFRWZKi7kS5uHqh9ViQVH92NM9xF4jj6F5xjxL6O5eFP403856cOl/UDXvgjWPf74Sk9qT5Y291JfiKfTa32JXVyX7M4PGZFwWZKd0nA6LMdVYcG1rvZGs2myPxKbjpc42UfcHW5/46VomxteeC2TyfA3FfCcangxu9tbZ7HpeAnPUaeoMziS12d+AEBni0zCvwot5eyyUXjzFi2s4pHLBFqvnCLZTvwUC1qZifpUA5LbFXsty0UU+JLL9E8p0PsMz4/P0vdS0Z0xtl8YPd+bRJVu8bQ7/5CdKZHs2LmKuqYKHPrdLJNNbXYO27PtGFXpMpVjC+ji3JjKXRJKDAy1HTESW8GM8C/0/4HkdnZUCbqJiUxOo9khaNsYtq+J+kI8lVeEU7P3eRZ7upPTs5nRndTqhhjl2zfmJ6PaBVAnXKJzfFfsBXNCfw1D4Sp9iwvB0pL0zeIylCYvz1hN1OE0+wRdnBvTvX1/AIPa+hiFuytBSjUNZozB5TPp67GJ7cRes87W0WS+Vvzf3u4TUZ3SbfewYls1lAW9nVOTk8OdOe7Fji2cNZt+calMsBOHsGfz1dQ7PqTM80J1RgZzJ79Fjjaf/jN2laHRMl1ASi+qV+WPuhso0KqpPt14YsKPsT5hmLp7SZjcvM9plZaqcsM32imJPtXE5an7GlNQSxc/yX6tLn/5rTdWs56LLMd4jh87XqxDrTbDONeC96w7aHJE8S+ZiSm5PZrymcM54gpUeIXpvx2DpPQ9q50xNFgcwqFcUSzL08SUAdZiLxkc3Y/PWvTAte9ZvWya7TjF8Tw7hthcR17p+XMgh6lX0KClU1w3KU0vV4L3XeOrBYZvjPQk6qQrHMzylXy+wqUmD98KRKjnXeLrg23EQNCHSb1RG6D/U2gmXkqPr4nyQOHkyMVpVY1iS+5YlU3d5uK5c5RR7AEkDXXixrRgElc1om5EIUfnLyauQMXYMe+hjdY/+itpKUWrUlHryxPMmhnI9K2V2O29FYC2Y0djv+dvCiUsuAJ8FPouTuNn0ys8nm8iOuO8S1FMpPnu6CB2u4fx8z1vFMPkRlN5k0rSrEC8lmZQ4GDB5R5Kcj7X4rTF+D3yLzva8NGQeEnn2q/PZHutbWRpVYy91pG/t/giFIKgE+qPIkurgp+roC28KrmNef3FQMq+B/XAgOS/IfFX6WOVzutx3RHaFhXba5v7kf5xFkkNFwEyNOn6B26eJPZrF/xMFXiONJ5kauzbYcWeZ6jzmNx7BMoz0j7jpRT4SpkaTL5fFoN9T/Khfay4/pRdmU+iulNn6n0Kk6UtEitqONN01xU+cjhncNXMP0nyN0FcHBJWLERfVuR2diTNr4XSrICYpmueeX3ItVak97ej8Ip0xwRRlC1Lo+KtmsGlv/kFaFr4cWmYnNWvLCFQiW4pJVOTx7oHvixd2hnnVXGo06XnGo9OTKKzhSgUZozrIGFBUypdUGBztRCzHafI7tUMm8OJZW7j85ZSXkrnLE+SZgZy7s259AnsSeEN42x0W96oWzVi79qlkpzzn2JnSiSNTw+kWg/pW2Y8iWBhwYMuRWu75nfzkR8ybJNjEDf2PTMtFA1amp4ZQNXucaWfVAozk8Pps3Sy5GqsCud8RG6PpvwRGsr0dD9O9q+P+oK04WIF/00UrrXovucM8+JaUWvMPaPq7ErlPymNWR6Ybz0FoXDoo+YoL5TPFg0VvLwUJl9js09VqnPxX49ZlMb/nHOCmK6npMIxK3i5qVDfq6CCl5QK56yggpeUCud8ydmZEsn2lNNsTznN1S8NW6b4r3JjWrBOeCxxrnE1pBLnNqN6hDW3Jrx8322Fc/6HqBptnBBGxwv36XD+QbFjimpOaF71N9i2ulUjttw4RV4XaTtvP03q+8FEjZvDvlyxNNExQkLaZgnIFApS3w8msdcCltY8QtSUUKPk68oUCsYmJpCw8Nl//83JwQhmZc+gMopzypRKbk0U7257U2OYmRzO9Y8NuBMJcgQLCy7/FMSQ+Kvcfu/lu6s9ieZVf5LW+Ov+/btTopApjB9ruz/YCCp8gpyQSpdZmVS8B7oyz4GNa8Kec1LZUfx1HhOZnLXzZxqsvqdwcyFq4jwUyJlxtT031TnYxhpHiTC/VUNiJhYvnkgc5Wzw76ZpVo/OFlnYOBVvp9yxKj3f1q8i37CWCHKWJB/BWW7BnIy7dHYNRFtYgPKwI+fGhNLpW2kZLdvO73/07BgAvT6cBx8++17vTWPxmFB6pr/czo4d5w/onvdK6qh73L9aBL0sMziXX8AX18R83XPRbmWyC+K66Y7QOVjITGn8YwjVV54HYN2VP+j8/iSsNxguB3K1UJS+iAxYQ/QVDZ/1HiypUp+m9fl6w3K2ZtuLRcuPDsvt7Oha+3yxnbn1RVHDmZQ3XFg1aSZggqPcHATDerkp+3cgIKPO76OpE3KSEZ7voE2Q8O9+Clnjuuz7ZYnuecA3Y+k66iixg8Jo6DkI5zekf8aVbuZMvNkMp6eSMWK/rcXXNjuJyCv7iMIg57wzqhl31eF0/2EcTitj0BaIGfn1bKRV1csUCm6udCzz+2vuLZsMxpMFtIWoOXe9qFh72vU3+OG4GQU2Mmwvi/acZGXPvTDfeoqgepMRCsF57gndBW8lKNEaOC7J7t2M2+rjDPhyCnZxOezatJyGpnCzhS1OemqdCdbWZH+dRWNTOZ/37IIm6YlyPPtKfFf1wPNPLgVFNScuD3Ph7PB5GGvDY4CWZuIw3vuzBNSIJW7GZklmTartSeHgrRZ8FnqOaXX/4NcagZKyx+Q2NnzcfTOz5/fG8QmpHU1Lf052mEPHmCE4UPZtSvR2TlmTenAuETRaZk1eyGctelA15YRODU1ex51Pqqxn/v06+ppG28iHU01W6p6Hq+R4m2Rj95w7utnBs2VOsZ6T4cF4uyT25NjhMTC69BP04HlpW/d8ZdgYYLfqe5cZGDeQysvFWsPP7/jzedVITF9Lgzn62Yr/zpfEeguIzFej+ds4KXYgOr3/nhS2Vile6tclrjuyrLsG21+SWVNXhmUshOSbjE8NYk71cOas706t5BMo3RwAeMvqLmutpZXnPXjNh06We9iwOKqYOuD9OmbYC+bcu2mLgz7t1LcB2jPn0apUJH3fiCAzVTHtGXldL7JCtRzNs2b9F/pJVcjt7Oi58qDuueeekfzQ4Q16vD+Zd5LbPVOKFFtQoJfc4P5Wbvx8z4uuFg9QuLvq1TYppKlzqXH4WSW2siAzMaWgfRPGOh8kfX9RLx85siECAif819E4uuy1l1fWNSSyxywABi8fDy9I2dQbtZrkHFGlYe3DanjtF5Xik87VkCQH+TTLLjc3ip1i2NnSxU5Ua3D/Rax8USanA/DO1TaQcltvk3I7Ozp9fhh7wRzNE+0VLC2R9RL3ma12oJxlSh7TpfUZbquLf2m5NW24nuzA1LlDsdqknyaqNjeXn2Ne48u7fnRv1QfPYWdQJ17GekMElxZ646woii5OT/dlwohxetlXp6VzsL4lXeq04KsDG1l9/S/Sh5WP9k/C/Ka8unYKigPS9naJn9+QPSsW0MIsj+o/FfXK6fWtCP50HNH5Gj6vWrptwdIS11PmxL6ygodaDfWWjMMkS9TbzX89QFLbnkaTk8Pd4Pt0cW7Mpk7BjGx0jAxNXpnn7KWhVBSCTEb6sCCSZgfS8mwemgM1kTtIl23JbOTIa+Zi8XPh1UdlafliDV3MLWfUDx4879QSkdvZ0e2vBD60j6XxD+OK3fwSFnkS7rcBn6ND9I4/GDTnrCY3R6ZQiHKVgpytS+diJVMysXEz4ufqZ0uTl0ft/jGcRg4USX3cfzuIJV/NwtPEFBCHun91rI1JijQ9WE1ODh+/MYRB6/9g9+c/04n3sV8qTaJC7liVKyM9njl+odts+rzypqTczcI2jdnffhagxHdDCB5PiB7bLxPbuWp0C2ZUP/5CO0I9bz7fvpYApRiUcZZbcH54UXRSgxaVtoCGh0djZi6th3+a+02cmFR5MxnSBRWe4Uj932jXfgQnvyyKJE+zv4gqppB6+0fjNTYOTXa2XjYf9HvW+dJbS19GSXnHh+G2B1j2oAbVVp4rNqTd2HwRoEBdqH8/KNk5D69sSvSkcL5IOMkP1zvha3NTp259R2WNsfb2WPLVLHxMioIM3741CFLKri9bEtroCywf04NOK+fx3dSlzFhaV28bgqUl26L2POdVOYWXkyW1LaWVKa4KCzRoqfXnswEveV0velb+jRuFLx7q5dWw1jlmllbFp7deoYVNAh0sildhRLWajxwZYCqpvf8Evy+dS3Khhk4RY3D7QY3axpRaPyaS+NoStkZXYlnPjmWuLsrt3pSVfqGAgkuF+kuHlMT6CT8jl1kwc2MPXB4+EX9oWh8/0yiSC3Pw+vqB3ltTSHZOx3kn+HRegNhzatKI1AhoUrR8cqcxGS0Mq1IHuD8oiCVfFzmm75Gh1Bl1GR4Y5piPURyIZOAr/fj+0AZJ5/sezaX+X4NRZZuyrXUYPiYm/Jhel183tWHlu3OQe7hJ01fVihqq09N9MT99qdgPmt2rGQfmir2f986JeL4ged/0j9NP1X8WEI8by3B75r1yDzd2HNmsf1vLGe/VY4kbFIaNYMZbNRvhihgAFIAbgdCJRnx8OYaNe3+hV42ybbF3u38efqbiZd95w/u4I45GAieKI7EaX5ddx0EwMyNuZgM8TaJQazVcGBbKsjdrMNRGVLr/+Z7Yo3c+ORqXBP2vW4OTELSFhaBRo6jmhICMkx8HGBxwSJkazPKvZ+JjYkK4Sk6LqeOoM+qy3nOB0ii8cpV5t9uicNFv8yKAgzc8Odd8JQntF+NjYsIplYzw7l7U/PoEn/d8h6SvpcVpTRuKN7ZhdlFcf1fU/ZF7uHH1y2B+mTkDgKWZ7vjMMM7I5Gnk9pWRV6lSLrb1xaFhkWD3zUnBz00QiC+Qdhmb3xZHFjemBfNJ1SNszrZDnlH2JIf84Lrs7jQb3+Vj8Zs9jpkZdRhoncyoGy1p9sVYdnzZhiytCotD0vbFM1oaS+wP1Wkf2wOzfX8btH2cvJIt0SHzeDzM+nT8CCrtkLC1YGADiHixyFjy10FscZ5D78zX9G6n08AU6n4kBqU8lt+Gu+mo74tSH5qYiyxtFsPgeSPxnBSNtqDsc7qRnscRkGEnmBE1YR7CBBkaxOCPgAUZmly2jH0NeYLhqgAlETvdHdcNYLrX8GUQQ/nZaxMAKm0h0ZNDqdNgOLU2Cih3nxbT795rSkPTcF77ZBJ26B83aNDrIndnQGD3s9gJ5nyyuT9uyWW3o7z5gPWZAbptDvdPt2Zf8HBkJ/7GnnBUnQO4WijDLl7anN4o6XuaFn5EtJmH6WRLvS7Ep5Hb2PBgfVEU7pu0BpjtkLb1nU/YRfL3uZC0Rkyty+rTTPeXtMafu9u9OPvuXDZm1UB9P1Nv+5qHD3GbFo7btHDUiZefsfHOHyOIf2M+KROa6GV33qauaNCi0f1X9DhDk0ursClGket4HnvbzkGZZpy5mKEEPhIY792mHwCJry3hrZ93c2urDw1Piw77UKPGLq7sASHz41Yse1ADuUxgYa0/uP5pMDNr7APAdbt+66nq2EQiGhZPupCd+Ft8IMi52lPLJ1d7YHpSmhSKwTIlmQMD+evH+ezLNWeWh4+kRuT0bMa3MxYRpCzqH4M/GUflFYZvvCpYWyNTKEgZXNQ25523IC0DdeaDctvIByBjcBB/fRtKg0Uh1Pqq7PoysoD63P6kgIgmqxEQOJBrwegDbxtVKe5JFE6OjD9+kLbmKuotGqdXW5/E/Igjmzx2k6HJY1DN5ga3S/OqP3/8ugwQ9x8R/HzJdhGHiDI1mO2UduOWNa7Lru2rix3rmdQJ1avGkyzJeCeI8O/C6NrwNdRp6S98b7lpCAX+XcAnDmcJ/GocDoukOdPKa8dxkJvrnjc4MRjXQYnlohz+TyKvZEvsD14c6zSTobVa/NvNeSGpW3w51+xXfBaOkeycWW8GcnhWGGfz1XzkZpyqlHJBJkPu4Ua3bScZVSmFuuEDcPm0oEz7+pSV2qfNmFf9RJlE2Yy2kdHTfOZwDu9DwyQ7JlDMMQFc+sf/5x0TQH0/E99vU8Uk8JccxzlmfJPmjds6w3uPD981nlBzuaDVok68zBbfKnSo7keNXheM6pj3hgQxr/oJ5mQ8uwauD/9z6nsVVPCyUW49ZwUVVFA+VDhnBRW8pFQ4ZwUVvKT8Tzrntc9FSZUO5x+QGGa4YJTg50vyhgYvTWZNBf8/kOycyRsasDc1hr2pMUxMiiVxTtlyG/9tFDVrEDtyPt+n+2IiUxPfY77BTpXUz5blAStR3/1nsmrSRgaRNiIIuWftf+TzyoI2uCF7U2OoF/nfut/fGxKk2/R5Z0okbc5lc2+I8UoJE1c14rsrpyTtHC75m7Q8ZEmmJpc0dTZtzXOI7x2G4Cd9H8l/Apl/Xd49cBS/78dwoo0zf7T15pM7jYmbLr1cSO5gz8o+YZjIyi+Z4UmufhVExGehHPtsDndeMc5elcaiQKvmB6fyU9LP/sNdJyBnDARLSzZ+MZ0uVucIju6H15YxdLP+m36T9xpsW+7rSeKqRlxutxw/UwVbNyzm1kT9hOok59ZWWRjOmwuL32F2pqym3ajRklPuSkIWUJ9ZmxbhYSLmcv2Za8lcj5I3gi2NxEHWLG/fCsfkIq2f8z1rYTtHv3pAHYIcx535NFVqmXqrGWDEQsYSSBsRRNS7cwCxol6fOtS0HZ6carSeqbcbc36wV4nvETKzi4qPXzLShwURXj+UAi2MmjQBC/Qr5n+S/A5N+GbBYpoo1XQeEkHOKAAAIABJREFU8B7yw1FUJoHKwNuRk9n85XT2tAxBOCZNzqbWSUsW1hB3+a69fgzeM66RvsSCqPdD6TSr7KJ3RtdvtJ58HfU+M6MlETjMua5zTEPxnndT8t6eJaENrMfimssp0Ko5Nq+ZpORrfYj8YgEFWjkmMv3kLgC0j0rRvnM8A3vE8igBmW7/S+DRlnhGa+5Liba5H7MWhuFjKjD06mvIDxfPU668IpyRUcMR/pauM/Wz835ASYQKPCZFUAjk5ovpo9qghsjC/y6THaNPELbU2Ynav+Q7sxRWuBSpwt1U5/LpjCGSbRm6QezTZHiLQlBpmnzsVpavY6o6BlCgVev+PHeMLtfPe9lQdda/OKEkBi7bSX1TEwZc7kh6h5KnIoYIoGUODMRKpmR8ahDftXtDd7zKHDFL7Hp7yzLb+s/M3jM0efT8dgpVFpSvE+hDVscsAO6pi1cmaFr48WBPbS79HGiwsDKIjnl42RJMZGKv2fLvvniOKvvUQVHDmc0NxQTym+pcuvu0prtPa7rWbU3YfTGo1CyyP9WmSR+WKxJT+ej2owqcpvUl23keB5qIOrPR+RpMM6Ur3/e1vsnmbDtyegtoHhpHoPoxcl9Pek3dx7E8Bee/aFBMDcPkrv4KgkZ3zvGpzVEkGGfe4nqqKCd1cJOekvJ3k78VVePv7fTkdsizE3LtikIs1laS1L6zwSsBeGP3e7pjCYsD2L1hGd52d/it1xy2nd/PvKt/SbIP4jxz39KFqLUa1FoN41ObY9spSS8bhTdSGN1jJNH5GtqdGIP6wQPx734m7qaPCpr3VDYov1R99y4n77oCkOdYfrnEAyOGSRZOe1xUv6JPJ9S375Tybv2Qe9bm453rmWSXyA+9+6HcVTwwFjdOLL53/+VGmW0a3TlPLfRHnW54lX7mwEAmO4rK7z0Tu6C5d1+v8wVra+7t9OTs4Ll4m95iWb1VRE4NZUj81WJLJ4trb6TS6ZsGtVXIk+k+c1TQYQButSpg4qixZGlUuCmkK6kHDY+iQKtGg5aQ1GCSBzhLsqONvsCXQV3wGFV8aN/Rwri9R3lhIWGe/Tw6/BpOh/MP6HD+AbndjVM947PuCoFKiFCJ3/WTyCvZElDvEnKZoFfMw2jOee9dMXJb6ZIRNEZlMnJ6Z+KmMONArgXqCXZ6F3GnDqvPCf91vDLtPaZ1f4cPeg6l0c/jSFI54r77AYqaNZDXccdUJjN4Luq5ULwL3xpUn0mV4zilkqHJy8N07xmytdKHimkjReFjE5kcARmXAvIMUj0vvHVbUmF5Wbl+VR/JZP1QygxXkr/4RVUEBCbYJRNil4hSKODA/AVMT44waAe3jMFBfFJVVEOcGNu32GvySrZcXlyLdW77yFDrN7Q1SrQ2bUQQ6z7+mbZjJmJ+2PBllJsTg4gMmAfAhF/fxSVG/+GsQ+cbNDo1iOqrI9A8qrxxioZjs8xIXFWX+AhxDuO1dRJ1DAjLP8nPkxcBMDJmEM5c4Mr6BjjKI1n3sOxbTDxG1TGAyM8XoH4UTA2K6YsdiUZp59P0SupslLm8z/vxjGn4Cq99e5TwM64U/l975x3X1P39/2cWU4aigCwFIaC4EEXAqrVWrdaFo61atdY6Eamr22471FZRcY/WOuqqiraK1bpBse6igshQwIkKyoiQ5PfHleWAJDd+a3+fPH3wkCT3nrxJ7rnvdc7rXDVe8rJYLkYFk9hpDh9cC+afwPIbZgwOXI8IZfGE+TR6O5+kYktGLBuH+zTdc1rjp0UDFgSdeAOnITcrSeokR3uR1GYZ97UqWq+ehKceK/rie87gpqz/eAaecgsstxpnf3NpRHmtAc9vDFvSvrbHjRNBv3Dlo8ejPRxq3S/7/UjPHw2y/ySsJeW9e/KCIDYHC876fWIXveyourbi3Tlry4azxVo11nMNmxdXhaZ9AFIkpOzyMoo9dV4eu0/6875DIgVN3YxisyK5miK8fjRsJOITeYS2X0XyjfNR7r1ROZrNaW4c07ya03bpFFxkBXw+dDXakGZ6v4fTB5JKdXlktrYkvbiMPE0RLVdNxPMD/W6Aop3zWkgNPOSWHC4yXgGbALPyZhm6X1pv6UV6Jfdg04iZyBopAUEE+sbYULq4nUcZM4bAHyOwksjIGS4uXOvSW0LPOOfqywCsa7GU5J4LUCrMGJTWmXqj9OtB7nnIedUqt2w42+6LSMx2Gj/yJmWoDA1a6s/9x2g2628RuvpbzYyvg1us1aI9Zrg0aumCov+EJ9vw+CKOTuunEGZ9m6i1C/SyPezyi5BdvsgktbEheaEX97UqglZPwktPxwQjOOfvE6YD8Pb2kWJNGRX1zZuovq7Lt1dfYfaOFXyaeoJvjmwj4eO5nGhXE+WYBOr+EMeQ1B70jtzL/df0jw1OKxFuHIeHzCR5SStKHpYVK1WnB7gS7VOthsxjaCnbz4zIDsVxv3FXFv8vcOlivGAPmb0dAC8tf0+0rQV3fVjifpjkhUFIFI/fQBpMicd301i9A1+OXq5f1muqXm2FbFsNktovZ0R6d4McE0QqIdwIDyXho7mVngs89ib3bgoiTMoRht3tt2cJS+VjM9txubWBoXX6sMeN7X5bySwppOOWyfhE6l7TQqZswIXI2ti732VnwHJa7xa2VXznFCFJStO7QtbmzAQUEhnFWjUKiYzOw0c/k17TfL8zMT47aTl1TFmZB2MxPuUCnS3zefFsf2q8klr9CVUg8/el54ZDtLBMZ/KEcCy3iJ86yRr6cHmaGQlBK2j3eSROv6dVmh9LraxodLCI/Qtb47Ck+s/mj6zH1RBV2hKa/xKJ54fVn/80JQSDF4QurmzByZd+AMxQ7hhFwynCKqLLvYuC0LQROHHDVa96hoai/tqRyOlteKv2QTy3Fut3bvIlfMKFv30wbVA+1JjVortyeEVKh7IKiYxdhdbPxDEBBjofRa3V4LghUX9N4GrYeKsVnd33Edt4DX2DRkCC4UPR8+Nt2Wp3mbbvR2C3xTjFkdTnL+LaB9qOimTj1BkUfSJlaOJQzJfWAqDmuxl847yfTjd1kzVNLymgvry8bOB9rYqgnyfi+bG4m57BzmlzwgKrjmY0nxeB8vujqI0oMZlSrMJbYc6IBofZzLPPkZTtPcGlVjCVVsgxbIPbWBRr1Uy+2o6ERQHYZJZgVkXJBUO5OySEntYJgNzoKvoAf29swq7Rx+hsmY9WJkVcfWsBu9XGWVGvSO1F8Yw9MoICdxscJ1+ByPt8UW8rMrQ03D0GHx176ZHDIsnoKgyRQ19IJGdQLeqnih+NPJcCXzIfL7bu20Db06/rHQ1jonpKpw3K7aOfmRauCd0x+rD2WaK+mEp310DsMDnms8R9hzH6NBPPiufSOU08W7q7BgJgifHybk0Yn/9MVooJE/9rmHpOE//T3B0Sws0gIeroYtgCmie8idvnWjRnk59pHR1deC4XhEyYeNbI3d1AISfm4G+V1CBK6fnqYDSnzv2ftOWZKr4XhLVme9Zxvkg9jqZ9gDFMmqgGubsbN8aFIvMXpzqh2aN/4eD/K1K/C8HnmHmZMl7pT/rX4sItZQ61eHvPAbYcFKp5J6gkKP8YTcuZESj/EOq8ZLc3fiyzvhhlWBv+3fqHES2gqqnA0FRbeV1nzn/nwvmXF5FdoqJIK8XrYdn5Xkm9KfrB5bEkVkMo3UpotjAC968Mq6j1b3Dl41CKHDUk9YuG0v3YD8sXePTlWmQoJ/zmcedKIStym7L1s5ex3iR+P1HTPoA/1iwlufgBE+sb5kg2B2vzj9ccwd4jr50ZNoeen7QyuH1XhvkRahGD75+R+Lz1cFvp4X6y+n0hdazAVfdB44z0Iwz/YoJRSlZWxKhzzgsP6ooKr9KukXJeuYhmSyOpG/cAeX4JWS9a8engtWz13QKLoaer4V9KRVKKVdT78bQovbzY7FMUa9X4rxyH54fxlLwUSL6LArP7GqOEmYEQV6rxdsd7wUW2u8yt/gQdKXkpkCXvRtH/UjcAvvKIYcCs0wzfZFipQpuDtcmPcERz6hzFHwsxpt3/HF920etLwTv2dHUfTU5jc+oeEgIlPli3hhAL8fnCLjPjePNkJL6HEyt9/znDQ9gwZiZgjnO87ldGQ4WC/V9FkfCJBVKJhk8mjsQ8pxizi9mUXLtucDufywWhep+V92buh2BldHO+XeHMmubLedClJWaxf4t+j15HRuOZX3VZ+qqQeXtSrD2OBg2H3pzJkX51aKCIx0aqptvfoyjoHUiDFRqk+w1XcZM29uPqNEho+fNTj+lwtj/W6B+/+uqcvQSaychvJwhhR3YI57X5Ow1u61qvWJr2jsDjFHjaCoH+DWfnGRwaqE5KQZEEzruFMMhbI0NobHYPME62i3zP8cduzM6D0/FWmNM/pRs1Ys/qfONuOTOCPN8ShoQcZnTNo+yJFjJadhTYEO2jNLiNRplzKiRCkLbMSLqtUhubSo/Vd+7g3Ps8U9L6kjfOOOFmdVcZLh0CgKxcNsNOakYXq1yUCjOcZOYcb/0T5zstJGWQuHtf4KpEElqurvKY/Bhnve2qX2xBlxqJhJwqz9qX7T3BgTuGX0gAFg+TZ5a672fhXS/IMl6ydd9xf2EjNX4aWkXme20AIPGol14JC86z4lCOTuBIMwVD3whHGTuKvYUWvGp1n+z3DFdYMIpzFmuFLIoP9/cTZ6hPIa2ODWHLhb/4NPUEwaeFIHSZQy0KYz3ZrNyK3VxbI7QY5AXGWybfeN+Z5ksi6Rg+hp6urZiUbXgV6/y+rXnpbD7bs47zWZ1TAMzIaUTDNePo/UIfeniF4rtprKj2xqxayODvJ1Lz1XJlBYm5OR6Whms/bbpfG5eYcrmXbSM7iJZEGXAhm5isY8RkHWOSQ+Wc05isYzz4s54o+6XkDA9h2eVDnFA506P7ELzeM3zuKIk7jfLtv/nB258enQewbEwUModaBtkSPay9/HkoXaziADnuv4sLB1PfuYPbWxpSTxXT0hxamp9i1Xl3bGRp9LK+BYBil/ghrbEQUrtg6p/98Pm8fCgulWiQIsWQiO+9c+ZXevzJjUDODlLS4Fw8xsj1ufxZKE33B+Cz4mTlcU5jb75y/IVu6K5IXpGo1JewzRSyc0pQI1EL1iVyucFZSgNssqp8/X3PnURZBIgSMM8ZHsLRL6MBKxb164H2dGK15+iKOjGJADMpuR2V1Fivf0aN6J7ztbD9mEuMN3VV381l8gv9abxKyIt80/YKvaxvcVSloMsIcT2GMdFcSn8oI6JB2aiy3KFGK0WDRq+cMamFBZdWV96GilfJOB1sXiZZKQn058rUUM70EWRceiX3wGmx7gtPMm9Pfn4rCuWX9x67oFMmiVOyaFRTWPiQWlsTcvxNOCLM56+GG0fdzv/XCJqsHE/g0bfKnutgeZ+ibfrrM5WS+2YwR7+MRoOWIypxYtJVvo+XYW4m2qvedyhf8KhxJF10buCdt0I4PG0e9zUqMks0RDTvgdeuAma5xPFu1FoW/OEt8h2Mg7akhBZREXz+ziqubquHM8Jd/tbIEGJc5uht79bAAM6/OA94NFtEhURhRsYa3zKdXBAcSd0hW2f7194NJX7ybPz/HIMyqXJanCTQn6T2y2l48C08MWyRLMJpD/3WjOIVn/OMsv+dvAsWDLK5AZygW5RhvXHFlfkGlPc8PWlFTJbw+czz+ZWJ6L5dkzcwGPWgHNY1XoGb/DglaAj5evwzFSu3zjYslseosbVihXo1bQP48pPlxKtkdHt/ImPrvYD6zh1Su9vhuzGc1ubXkLRsbKTWisdlehyLlV44zyof0hZ3009fFyBnRAgLpwq9oe+eETScklT22p2hITgesKjgmII8yvDLHfR6jw/HrKVAW0yjLx4vU5gySUEJalx+NrwmzYC/3+FUu4XMcomjh1UeA2yuc1+ronHcUINtPo2iHob3xgumRXG4+a+4yYXd+EbrIp6ZY0qtrCjWqjHPM6zLem62UqRWVjhNv0QHy/uEdRqE7bnyO6X6+g18Im8wvOlr3PzsAbV7/IsNrYZvG28W5puAcqRue3xHP49G87BymM+QE2Wjj8wPQ4kL/wErSfkq5ckHGiZOmYj1Rt2DBR50aUlP6wQa74nAJ72ypEZh7yD+bPMjTQ+Npf4OwwM8PPqf5bXar5I0ywNNvgL7M3Kcjt3DQ4Qg19O4EVB+2Ubf7ABUP+fUvNCcjHAt/maVRw21zkq4NDOYOieg1sFMSq7orsheHbk9mpKgOmLwnvdz45zJ3zRli8dcvrrZosqyALdv2vLspIvFoy6db+qBTCJFo33oksFNyW1gRZf3DjLRobJjKreNwXdZIdbH9IviSX8NzCVyGn5y47FFJefJl8jVKPD6plj0Rpj6Vg7au944HZVgtypOb5mW/H5ClXHbfSlPFUW787sP2xtPB8w5qlKQ0a8OUHX5j6IeQfw4Zx5NzR5XjT/ytTDnZAD4/jYWeb47g7ruZ8OaF3H9/t+NHjOqc0qtrdHkGybI5eAjfBnbVrTFmcc/FEmgP1/VX8HUPl2MonlzPcgc17+MYKgCGV+E0sVK6H1azorE5Ql/x5NYluvMEFthzrp904oKr5hx8oGGsd+Mx2nHZZSZCQbpEv318mzAqtJzUisrbq1349KRWrw/uTVgnMWQTq3PkPlDHYNWlrfNmoWVtHxhquPZ17kX68wDW3ijzz4+qH0aOAaYE7AgEvev46jOMQF2L1xAaU3TqkjqU75S/tH4s3T/3rCwyFIWfj+bwVETn3g964Jxw/dmNzJYcS8n2QGaQ49hBzkWVfmDlDX04dVfDtDYrLJorxg+GbaWn2Y2MJoYGYDbviKShqrxVchwman7F7LhzY7k/hxPRM3HFd0jP4rAYa24bZQirRSZRApy4XO990Ywoz/fyACbg3TvLu4CfBSNVr96IBUJ3DKB473LHXRPk3XwhIJlt9Wqh46pO4kPSuizfTzKlQVkdLXB89frcONWNWcZvk97v39r/BUncF15weDOxKjO6dsg26A7O4Dv4hxiu9sxtc4Jmn05HrO75ZuEDl2zeMculf4pPQDxUSc/3vZjYq0LLGnfx+CKVU9CtvcEw76ZwJHP5ul1nvZ4Inte9Sd2eaPK9sZZYntevOJcj00TSRmwkBe3/cPOa/7ENVrIKZWK0E/epdYzLvirDz4RR3npwmQGj9nJMLt/KvWipWSWqOi56D3c9OiNShMDfDiKFvA4htEVBysR1IQvv1uKTCIVVdTrucvnlNrYkDStEY2bZXD2nAfusVDjrwtGV4lL+y4E+T2JXjUxdCH8YjJdrHIJ+Wo8dRY+Pxf+/xUps4LxnmAcCcv/KqVZT4PTO3GnTfXO+bR8zufOOf/rXIwK5ny/uUbLnjHx3+P7tKOcUbmyrtsLlQroPo3/lPrefxnlyntsfsXx326GiX+R9z1bP/wtXZQdk8CXkdEeT2SFr3ECsk38b2NyThMmnlNMzmnCxHOKyTlNmHhOeSbOeWVqKF+lHWN8ygUkgf5Gt38tMpT5GYeMazS4KV+kHi/LdhCDvL4HyYtaMSwpg9jsU/yRdcLgYO30r0LYniW0y+eY4YHpz5qc4SH8kXWCi3NaV39wNcjs7cgbGMyElPNlydalP5+mnmDAhWxB2tIArkeEEpN1jE2ZRxhwIbtM0e/qloai212K1Nqa/H6tye/Xmj+yThCbfYpBFzKRBOjnC0ZzTplDLTR73PkmLYGTo6MIMJPS2TKfnKbGUS4o5X7/1uycNB0PuaEaf48j8/Fi2M/bCBR57UsUZtwYG0rk7h0kd19I3xq3ykrHB3+ZgNTaWndb5ubcjPHlzNvl6WfT6u4TLQv5rPjtsxlCjKpWXMK9zN+X3E5+7Jsxlw6W9x97vaW5mkE2V1H/rEXmpP+quNPcONbec+VvlRVr/VwI+XQcvZJ7cLDlcq5ONFxSpCIX5vmxN2o+e6Pmo0FLsVbNAJvrKJfoV85StHOmfx3CpJREJiXsZ6ffVpqaycqyMo6rwC5dvFpaRT77djm1ZZZsum+88PeU4U6E1RCX7iav544yXkvCx3PpYFmESlvM4PROtIiKIGhaBF1tz3DzjaY62ZJaWzP6n0TiA1eVPZdUrMZCIuf4W7Mo6WjckDuphQWatgFkTw4l88NQve/wcnc36sosUe4YpVfh4Ucp7B3Ed7//zF+zH4+wmnPHj+g75Rq9W323kD7CGyT63wxmR5fL6Tgsi0fdIZvgpZNYMG4ecq/6BrUdhO8tb2AwyZ0XP/H1H+oe0UuyRLRz/jNsHh0si2hn8aDsuZj8mizL9WDylLHI9j5e9bc6UqeHYHOwNg9eqbyRL3d3o6OligLtAxaH9xHb9DLODhZ6p29uNaFP6956n58zPIT3927jh7rChXlLXUjYoDHcaXMbl+lxOM6PY8ZLr+pcQTrtvWZ0taocQzzi0wll1a7TXxWnWlCRlNnBBB7J549fl3JiwlxOjZtL9JZFSJs3qv7kh2QM9ECDBo+t4i4nyy0JHCusX/Y4cG4kPV1b0dO1Fbsb2xDb2Jaerq1Qxo4C4OSYKJKX6X+jcoo+yreD3qz0nMfncQSZa1GsKDSo7dqQZgw9cY4DM6KrPK7rgUs65yQbHCFU1D2IJdGz8JRbsPaeE19v7o/3smuwREUNhYqZHlt5e8h4ZPv0c85bI0O400yD36cXy+ISZU6O1N+ex1yXODoNHYHFyTRRMYsV6Zp4lzH2F2m4ZxQ+Q/S/kdzY6lemkOe7ewR+76aJCs4vfjmQmJ+iUUiEIPWxVzpwra8tJVnZXJzbmvN9hF6lT3CY3rmHN8eE0HhIIss89pY957c+HMdjIHsgfNV7o4TMjF2F1szx9qveaFATlmxcwOuJQ7Hteqnsabm7G+c+c0Z+W6GzYNa1yFD2TJ7Bx1dfJj2oaieR7XVhs3IrLY68hfvAFLQqcSM0iVzOtoyjaNDw0oQIvTR/hiVl0LdG5SD6Tol9UXxpj/SQINI2PDmNMGvhmvXdGF5phGH0CKECRxmecgt2FNjwa68X8Zbl0HXrCUbbl2qoWqK2kOmQqFPOjbGh/PXhTAb6dUJdIfUsbZ4TW1120OLYm+T1laHcbRzHzBkRQrj9POJVChosNiyb0aaCyLHP0BOiA6oVu48T+vcw3vASbhSZwfeBx+deuiKzteXCV42Y++pPtDY/TK5Gy55CB977pw8FF+3xniI4jtzVhQuTPcrOa2JWXcaGQImNGXVlltw96oQtgnPK3d3IXWJGcpNFKHeM0rmtzlFxBCkn4nxQgg26Ocep4JX09HsTrUj9H22sIDE6Pqud3mJcjzrmVXUhN/e54HaoPG57yYg+hK1Zqpdd0eF7N0tsSR3oSET/bYy2y+BySSHnimszac0w6u3UPfBb26Y5f3wwnZsaSaWc0NQ1zTkZspCh6V1x/bAEaUfjRBxKAvyJ/3weComMaW8MRppwyiA7heucwcjKKXV7n2f/E4pafPzyVr3syGo7cH6aF8ndo7mjKSL4wDgaRJVAwlnqPpK/KVmt5YJP+ZCs45opeOmQsWJ+9R4JKgnFPuU9XcZAD042mYsGDdYX9dOa9QnXLZF8q3IbIGVGTiOkOXk6J4pf/CkQS5si5AfscD56H9nFTM5/14A4n1mceWDG5bfrARd0bu+dt0IoK43xkF7T38NtnviEClGB701PSPjGSZCqvKMpYrB7G4MakfZdCImDn5xmJUVSVgVqcW59Yho5GPQepcjd3Zh36Fdc5EIm/TtHh+I54LTB9op6BBGzIAoriRnKmDEoxx6DKj7TqpApG7B17/rHnhckOMv75KC/B+HYq/oLqGviXcLtLz21lorcq35ZMZ9SNt2vzco+nVEnJj3xnKcRk3UMKeUqEKW/vzB1PLWWGy87R+7uhrq2HVu3/6T3VCRvQDAHZj59TqhvzZn8fq3LpgEA3V4fXjaMfZTv047ibyYnXiVj4rQxldYfnkmVsTMttEiRIEWCg1Tc1oYUCfc1Kr7P8cdvQzgvnu2PFAm+a8IJ+jaCbq8PN4pjBm1PxUUu7JmMXj5WlGMCWGxLIGBPOADJPRdwc1SwwbbabzqD5gn/SiU4S//5ONxE2qz6fbl3a6YLv0hlSMzN0bQPQNM+gMufhpK/04uJf26rdHyxVs0vXdrq7ZgAPcPeJuTTcXxxIxApUqRICP420uiOGbQ9lW3bV3LUgKlIzZ2P/12/F9gxNrMdAA2Py5E21mGerSdXPgnFRS6ky/+aE6zzwqCoMaLM1xtNhS5ds8cd+as39Rb59Zp6nJ5R3UCrRZN3D1/zJFLme6BBS4MpxvlyZf6+dFp/lHD7S2zOr82yN3vinmCcXE6/d9MYENOFtV6xLHk/islpY/Wu55I9OZQhdtM5qqrB8CNvlT3/T/sljx37i+dO9m6qwSzvqh006GR/Djf/FdkeJ37y3kBN6dP/3vPFxUSMG49FuoEFmBLO4pAAx5dJ0WRpACkOiYaLPT9KyqoALnQQ5myGTkXUd+7Qbko41zoIoxDXWCm2B9PQFhTS2yGMLYc3s2JDNlv6GXaDehKSQH+8Xk6jplQo/5E+yBW4VPVJDxHVc9ZYJqxKfnZDEEPe6beV/C667eVVRFv8gJKr1yi5dh1NQQH5L/iwPXQ+q+/VFdO8SoSuPc0Y+4to0LB0WBgkGE8VTn3nDgVhwlC2qZkM7y/1W5yQyOV49killsyciDlj8Rp4quxneEansuOuq8sXn560Qf8otQdcJ+ibCLYqt5VdHLmaIhruH06nxL5lx10uKSR8YiQW241TGa205zQGMns70n5tyrkOi8tGDuvv2xn8/dmuOYJyxDGUI45hvfEo6us30Ny7R0n6ZYK+iWCY7RXe2LQHmY+XUdq/LWYlm322lz3WZOi+wm6wc6ZPC2G15y46jRzN8QApvZp1BiAzTLwAxJb5s+m6eRLr2zcXbUuLcz8PAAAb40lEQVQS4E9M1jGmOJzlqEpB99dHIDls2OJPVahv5dC7TRhpJUXMdzvAtUjdo01krnUZ5boPAOkDYeX01qgQPk49xbJ6f5JZUkj79yIY4fECPV1b0ebT8dxWq1Dtql91m/LycIyOo7trYNnPIPc2SC5b8qe/MNds/dU4wr1fwuo38XU5KzL1RnOD9rjLkEiYkX6EzYm7Odt2WaWXwqxvk7a2mV7mMr4QhvJVkddAGCYPsLnO7Sj9XSNixfqyYsbyus7c2lZeGOq6upBGq8bpteVjsHOODxPuBqXFbNXeroaaqoTMx4tstQT3XWrRItUyf19eWXX44Z1cyvsfj37qhF1fJAozZPZ2lZ4rSb9Mr+VTAFgz4QedbZVkXOHdjcMo1qo58nEU0w5t5uDUKFqbF3NP84DBkyZht7p8ed9hWTxDLg5gfcPVyBr66NVuqZUVp98UBKybxr1FnYXxaIsfVHOWfugrDfookgB/tmf+TUOFouy7q/hPIZGR2G45uXqo/6tcitnbZMNTQyhlvt5ceL18sah4S51qbda4dJ9JV8vXGLpa3aP1mrNkfBlC9kJ7jrRYW/Zah7VT8HpfvymaUfYlZN6eZH1QjEwiRXpLXPTKrVAnem6egPcf4nVovH5KfziUhWHpnan5+zmjCTvVOywj3DGWsN/exXui0NaCPq15rc9+AHpsexcfdO+NvD6I55XmA9nTZB0NzYR7ZmyBHbMiBmG983E7V/90x87PDK1Mn51kIWa3NMChOL2GXufqylc3WyCVGK5wkzm1agcv1gqv72+2lrUXXFFrpaxvWHUpRL/ofBI6Skhb4UWNPyv/3Xcaafmhe3moZOCxN3FbX/21oj2ZSPLoxrC1/Fr9qPZZPhr++JBbOStVbwVF0c6Z9Zs/7zWKZYDNddKKC1F+fV6UA9zqqKLWAeNkXwTUKC9Jd32qF/I84ynt7fq7CfN6HWJ92Bw+aNaXW7+5s2bKTLwVQtv9Ptb/c7B+JRW/heHU87pB0Upnah25jlnKk7Nk3GYmQIR+9qXNG1EwvYBirZqWC9/FZ+YJI1VUrYxGK2G0Qxx/txqD1gDFd5sNNpwP0NDQTMpRlYLW5sWsyK1f9rpCombu/D7kKdV8/PJWpu3uVe2NUHPqHB9HjGLH/Fm4tXnyzkJmSSEdt0zGb+o5nQXltH//U+Xr/ivH0eDrM2gK9B8FitrnvBgVTFI/YSggZp/zUbZnHef7HH82Ln0Jpzn/rup2Vci8Pbk40pmBXQ7wSe0zrMhzJ3pRb1x/ShRdm/JZcGdoCIe/mUePXkOrvajEUvJSID3m7mGHv/0zfZ/nAbmbK3lBboz4ZhMDbIRqa2EXu5M7x0OnubxJfc8E194N5dOxq1isNM5KpAnj8EyCEEz8t3CeHcfyTu3/7WaY0BGTc/6PUZJRfW0RE88HJuc0YeI5xeSc/0MUhLVme9Zx7r8WjLye+7/dHINQvdqKuRmHuT3s+ZRrMSYm5/wfQ4OGv2bN5Ua0JTJlg3+7OXohtbEhOnoODeSW2KfoH7eb+VEoD/6sx9UtDcmeHErxy8aVezE2/1PlGGTenqQOrgsSLUGdEunucBo/s+tMqW94JklF25v3r0eKlF5NOxms1CAJ9EeaeZN7oZ5c7feAOrXyuH7dnjp/mVHr9F00IpKKrTYfpedmQfolJ6oWP+xcyHdd+qC+mFrNmbohc3LEaqOGtV6xlVLc+geHUZKZZbBdicKMGn/Zss5rFyDsIyuSs/TeR64bV8Rrg4V94wZNrvOiRTEatETfbcCuNvWe6faXRGHGxekt8F2cg/r846Uen8R/0jllTo5gY01ugCM1NugWhSPz9abhmlQ2O6+vlHcIMsanXODrqW9hu1ZEVJIBQlNPYlvMSqLvNuBFq234m1X4ejoJQevvpAraSWl3auHc23BH9Yk8wtsloxm3ZSe7ercQ7aAyJ0fsfytmab1YHi1UG/R7GkcHNjE40yNpbnNSvBaWPS7UPjAotFO27wSbGgqKfXK3AD7o5MHmL2YQbn+J3Vsaon7ROM4pc3J8rH2Zk1qyv+90hk94QWc7uu9zSmXcfTMIlb1wETr8o8I88QqanNtGLUD7VB5e/DYHHJjsGouLvJAjRa4sU3rqdPqtkSHEfSYIeVV2TuHxcRV8OuhtJPGG5XfKfLzYvG+d6J6ztHxcdai0xfR1DzE4sbuU2OxTeMaMxDfilKgY27wBweyZIXy+8SpLllxrz5mdfvh1usi6BjtpuigCz005ejmoRGGG6o+67Gr0W1mWywfXAzk70t9oQRSFvYLYM38BoH+y9dNoekLCuW6OlFwTAhLkri602XEJDRIONrV47HjRGkK52z2Ja/b0LPLXUztzr61uujO6UvxyIH/+vAQN2jJFhLl3fBgxPwK33XfRnDqns63ai+Pps7s/qUNcKj3/Wth+Pql9hgBzDZ5RyaQbpv1sNDqf68OuRr8BMPeOD5sym3PtnCPOjW6g/sWRfFcphc0LcKqVh51djuihWLeO/fl2ywY+nNMfv0n/oCko0NvGlY2NOR0SDcgYmv4yOW3uALdxJ478r0CRLePMqLkwSj8HWHrpL+rKrAAJnYaOQLH7OKAFjBfdZHMim8NFCtpYFBvFnszejr72f5Goell47O1JyG/nednmH75o2xvQfXivs3PW7JNJaP9wHHamVHo+ZaI3H4VtYp3XLnrYdzDeuD24Kd8tWYhMogCtBplEive2EShHHcOFOINiQktS0/H4PL3ssczbE0Uf9UMZEPgzoalewerPgjH19gGwLNeDPT2bUiM1FW9Kh5ypVMyDMUYQv/r8RbbnNONCz2iaX4vE4wv9wiU17QOY3XxV2RwzI8qXGo+IcxVr1ZXmoLogc3J86Jjgs/sdfHaXjyi0oc3QmMn0VnZ8Epl9PYzmmAAZY/wZcdoVlztCx3ElrC7vO2zEd/cofDL1a6/Oq7WaoiLsf4lHffNmpR/PD+PZ0CmIwyopaeONV3qh7eIEAs1kdO02kO6vvknQh2PwHX/GaPYBMvrXZYrDWYq1amLya6JcZrjKnbGZebKzToVXjUH6j77cVKsI6Kz/HDZliIwXLARBtrczXsFur25Z/tVxZYiQDnahWEX9X4RRn7y+B4WxnmzbsJQdq5fAHrey/ElD2Tlxuui2liJt6sf+sTOw/8Wm7LngfqfJ1RTRYLn+0w+jbKWUXMnkXJErPh1TkSj0U1t7Gm5mt7mqLkBz6hyaU+eo+XO8aG3SiuT3a83m0TPKHn+T9Arak4lGsy+G9fcdcVlvPOHo6rDeeJTMEkuD0ry+fUFI2t5RUJu83jLUN2+Kbk/xy4EcGD+TyyUFjI2IRLH7ONqQZozdvYu9jTchR4YUCX/4xdBwpW4rn09C5uNFbZmQoeJ/cJioNsucHFm0fSl2UgusNpcrSsx3O0DQb5OQ7j+pt02jOKck0J8mFlcY67oXpMZZtZzxcz+y1eakf2X8zebtWcfZEzWPenLhRtJ0SQROQ8RfVKWJwGL4NmoQ9RU32TN/AZszE6oMFrgYFaz7KrFUhqZ9ACk/BpPyYzB5Oxrgc8ycL1KPs/rKYQLMNSyr9ycps4K5HhH6WCL5o8hq1qQw1pO+NW6hkMhYMrDnUx1TIZGhkMhoum68Tk292sYcW6kFL+2cgMW2BArCWrNj4wpesSxApS1B+ddwvHeNAOBTp8N6F4mSNfSh7/kbbNu3kQSVhF4vv4HnG4aPyiaknGfbiZ14yGsIN43M4/yRdYKwczfxP/C2wSUqxG+lBDUhYs0Ggs2h8aLheKgMS/GSu7tVUjB3+zaOCSnjeO/Lzaz/vTMcMc6Q9u7gEBSSUxRX6CQ8Po8zyvxNUMqD3I5KvYWJS3GMjmPavgFc6eaA43EV8oynr95KSyBlVmu83636vTTtA7g9OZ+4FpVreJx/oGHm1S40s73C+JqC1GZ0jxV0sLzPnFF+7G5s8yRzAKhaeLHTf0HZ51jV6qm+c06LFg+V/u8JN7rMrsIKg0pbQpfxEXj/dpRLPwh70zUk5twdfg/nbU+29ShSCwuufKNgmO0VQMK4meNwPC8uLbGjZQFf32rC6j/aY58EKnsJPd46yBd1TrN1+EWDc2ZFOadELmfW+kUoFcLysGf0BYMucpm/L59tX8Xru8aiHFWeXFxjw1Hemp3NIl9raooXRkDu6kLg+JNlUpMAgXMiccW4OaPZXYtRPi4/qzPqxCRcdBhh+0w9Q7/jqaynahWAxSvnlsmBAvTsOxwAWV4R6nPJ/DqyM+M/u8D710K42N+Nz16oywuRR6GKy6ruV8aZWz6J/EJhROPe7CoyJ0faNhG2X4pRl+VHqi3L25Z/qepeviLJSxpyIUhQ8cvTFOE4/+nfvczejnsd/MjqXYzP0Kcv5vj99Q4+Pz7A86QgQyKrU4e7g6yEaZkBq9+liHJOrUZb5pgA287sfuyY9JICes97D5cZT/8Q1DbChZPSfRFkQbt3x2B1TdhzSy6Oo85fV/SWeHiUBscsmOVSqpguZXxWOzLDauKaZfxkbte6htdKuTUqhDon7uukIKApKODrQz2osTkfl7CnbyuVOmaTX8bj+UE8IIxC1AgX0qDxsQD8ntQYr9RT2Kem889Kg/+EMm4PC2H15zMBC5qtiqTBB7pp6Hh/WcT3vzZkd6PNUMEn2n8/ibo2Z7m2ypWUlkJQwup7jjSYpNudO29gMMkvzQckeMeMRjlGmBvK/H2506wmQz/Zxgi78qydC8Uqwscqq3RMAO/BJ6k4W//h2Fa8Fea82i8cCYbrIosb1mrUNDz4Fm3qp7HUff8TD6kvtyLi7S1smvH0WoryrNscLfAmwEzYMjgwewEatBxXwRuzJuN8RbwDzXI5WPb7sIyOpET7YZdlhO74Cexpso6etKr+wCfw/qQ1TDvXDbdwF0qysqs9XmZdjGet21S1VNbzQhibfX8jbtBMxrXtwb2B5SJX3+7fgK9CRq+k3ni/k6zzEEwq0ZTNrxvtH45PzQyy3hJ0dAtb3yex7QqEMgUWtJgdgVcVN+dHUZ9LZu/YEN5fV3n1+NgHc/n6naZ8Wrv8Wvt2zWt46DjyyX8tFw1a0kqKkOVLSV4RyMYXF1BHdoi6DxeGSv/+xbn12TS+M+a79SumLLO3Q6mwwHffcBrE678IVBHRc07PN86QDfSo0wWAq6/5oH1kmcllzQXg6REzJVcyOXHPA6l9mmBDLQwFGprJcI4S55jXI0I59sFcKq593Qy9i52OhXL+Df5utYq9+y0I/+2dKit0yXy8ONVuIS0OjcSTa0832DGTDkMikbxxk/3N1lLxWpaiIF4lo/hrZ+QFum+QJ99xpNhdmMScbreY93a1ZWPd2YCwAJSrKSK2wIPZ37+Gy3L9v0Pp4dMcUUFwBTkpKRI+rS2MKDbl12TmtIF4rNRfdNxTbsH5AdEPA1tk8LAuzdp7Tny2rw/1N2uwPHoRxV39NKckcjlXf67LnkJzfD+5LXq0Z7TY2tKVOsfox1fsdJmH/r2xCa92E6pg56osUMU44rw/B9CvGnBFrk4KZXvkdDSUf8PPYo5pTJZcaUeY3xY6WBZxbtA8/BTh1Pu9+GF0TGXq/nKDbLWa+nOrX3S3XxmPLMaO0NfHcydUxYEOc6gjM2f45Q5kfqZE8Zd+CvW1psggtvzx9LoHK73ease7KEceo5YOxZCeiFbLtFf6kzSmDif6z6KGpLLo2/KBPaj5t/hqANfVhZx7UJNRB4biO+oMymJhqGvI2kny8qYktVxCh/FjsU4TH8zy/62GUMVYWoAOkyKw+fXZ9palxXzavh+O3Srx71Vd4Z2O4WOw3GIclXZD0LQP4PMVywgwE/qITv+8wf0dzqJHO/81pNbWvHPyLL2t7wrbPIP1G87+zwl8PeqcPV0NmwP+20gC/EkaaU1t97vk3rMksd1you82YM7eLviM+3dDDU0Yh/855zRh4r+CSX3PhIn/GCbnNGHiOcXknCZMPKeYnNOEieeU59I5Xzt/jbBzN5F56yZBYuL5Q1azJtcmhPJV2jGubGxsNLtyVxfanykkb4B4UbbHbNd1Ju2bEGKzT3F1S9VVw/8veC6dU42UEXZXBKW8/xjy+h6wx83odi/NCCF5yX9oO8jFkfhJs2ms0DI/YA2atgFGMXvu67pMcjBcpkTavBFy18pSNbLaDkibN2LL37+TOHQe47Ja4x5xT2xTAZB71afnuRzcjuhfblFv55RaWXHv9WBSZgWT9duTlQ8uRgUTfFq89IN3+zTRNp7ExahgLkYFE34xmdjsU1yZGorUyor0r0NYcvkQUovHRZh0xXPDdWL8NhuxtQJBoRcY3vpg9QfqQEFYa5zibYnNPlX24xRvaxTbT6KNRTGqWuKTx6XW1kS02ivKRszvv9Bup5CgLbWx4UZ4KE3+zCHm91/YnF+LRqvHkdbfqVL6ohg6bTvFm7bJHNrTRO9z9d7nTF4YRN19UmruSwMrS869JwS0f9thA87yXHwVeVhIpHSYPhmnuYZFitwaFULCp0JkTDfXFgbZeBSZjxddtxxntH0qaSVFdNkdicVlM4p9Czjfvrys+djMdlxunW/Qe6i6tiJ26XzAuEEPRT2CiFkQRYevJ1JnobiQtdjsqit7d3FpLsp+KXXi7FnssQsQYm07jh2D5VZx0Uzbs46jQcN1tYoRHrpLTD5qo5QJ2aHsOBSA128qZAnnjKa0oW3THI1CypUxJRwMXUDYxIlVSriKVt8rRTla+IBLyh6nA7CCegDUT7DExTzXYMd8VpQ65rJcD2L6tUF57m+kVlakLFWWHXNdXcg/UU2wNTAovijiDtJnMFNQm0mwkpjhcF5/lfOKVHTMButG43JAi9XmoxSEteZg9CKxzSwj7dembK+3glL92mbxQ3EX6ZgV2ZmvrP6gKlDuHEXD6Xfh+k287wrftTGjbdb8Go2d1AIpEgal99BZW/lRjHol3RoZwiK3eH4+LW6y7rQxiWV5wrzNWDU9RtuncrhIwdY32qI+JwTT53VvyrkKvWafT6dgu8bwmNiPfP5Ag4Y9hVai21sR67FCtoghOjSlFIS1Lvu9wbrReE84gtVm4aIp/d9YJLZdUaZ+UKxV4/GdeJvZk0PFG5HK+Oh6SxpOuog6KeWZKLzLvepjJ7Wg4a/hAFyJMvxGYlTnfH/yGi4V38fvq7ui7KhzbjPnp94ApA8yXsGdayV2SLIEJW6ZsgG3+pZnqXc+14faW3TXwX0SXayEL3vMniGi7DxKA1vxesCe75XnRnpP+L9NlzOGALRZ+/LP4Mf1vQ2ykTw/kG0pjaGOA8lLW5b9aEOaIfesJ7qNANr8AroNfAe1rZDXYqhcDRjZOcOsbzO+w5tGqb3h+n0cU280J2bUdB50aSnaXvtJ4fStcYutZ/5E3aEFyrUZ/PPCCgDavheOWacM0XdSKVJuqlU0+l68WFgpeQOCiXI5LMpGbPYpVtY7ADx5TmnMxaBhSRmVHr+d8YpR7Ma1WA1AZokKt30GDu/N1bStl4rrqus08cnEcb8Cx/0Kcr2tkP2kYnvWcVqdUnN3iOGicurrN7gWZMmFbvORScS5lzgNoUB/8rxtsFl3BE3bAMZlWRpda7W+3ApVTTliBTftfk+kVZ0IFk+MYseqJWXPny8uxm61+J7k7uAQNBwj7Mzb1Eo1PAf1aSSLKJXwNIw515TVrMn9X+3pW+N4JQXCY8eUeBshsb1U2bDfqXdw3GuYmLRy2HEulz3Kx75CgrrqFwh9exxxX83DavIDDsXWM6geC4BZu1tIkZKrKTTo/FIMdm1JoD/TNq5g/4/RfJx6itvv5ZMR7l3pdd+/FXx46Qx3Bxt2J4q/ZbwgBM29ezjNjSPis3J5xsQHJYz85F2j2M/1frjgtsnBKPZKUb+ZA8DAU28bxZ5TvC0ps4KJzT5l1EWg5Hn12em/rmyeuafQio5jx6D8uHotpOq43781GjSotMWoD9QyQmufjMPqE3S/0IspDudI/tHVYDubmy1HIZHRcs1EUe0xuOfUHk/kI09BLzRlVjAXXouGLZTVNJl95x6xEe1J2luMvYHZ8GadMiALFn43mwk3w5Hv0U82oiIyW1uujG7Mici5nC8upkCjYNjxd/AwQlI0wA+DliNFSo2rxi3qdLj5rwDYrjR86NnFpTkps4JpE3xOGN4+HOIOyWgHUDbkbbButEG9nKRVE860L3f0oekvc/lHJdZbjxosC1mRt78UhNla/RhJ3R+e3S6AVqXiyj4P8IOxzfYTi2GfeV2ZJcVaNfVjxPWcomVKZN6eTOu2ju9z/Nm49CWc5pR/eDLE17IA8FeYcc/NjJoibFz4uiFJfecC8F7f4dSYfZ2GTtcwbEfzccYfGcDEFruxOpslWjvmUX687Yft8WxRdr0nHOE60DZsFNntJA8XhfKq3ffUhYz3Kj/OaXMHayPVnJG7u1FHLkwTDHVMWW0H1LdydDrWopVw3KLEF6iP4VrJh4sUSA+J+2xFLwid/8CB/jVyWPtrZcc0Nve6i6tjsqVnFABNlo1De9z4ZRcUlyxJzHfVSTFPX07muVOScaX6A3XAavPRJ67WDsloZ9Aq7qUZIZwJ/alM1T1eZWmMZpZxrat72Sq4oZz/1ovizjosKgY35WO/HVwuKaTebMMqF9wYJ2z5TBsqfsVetHPGdopiU35N6i26ILoxT6J0v3Owr+Gb2IW9gmioUND5XB+8opKQu7sRWtM41ZyfJVIbQXH9zA6/Z2K/dO9zSEY7rofk6X3+tQmh7Ht9RqU9zU8/HGG09sm96tNmpH7CY09CUeMBFh9mI3d2qvK4Mb/8Rk/rO3RPGGNwndbeI/axOb8W8pOG13ApRdSwVuZQix+uv0x6UCFVSV+KYfWU7gxftJCPaiexaeQYai/Wb/566Ydgzr8RzYsTwqmx/ghq4JdT2zihqsWf44MB42x71I/JZdY7B2n0y0iUo5JEKX2XcmNAY1bkXaL++utGKRdREWE4Kwy7DHFMoFJYzTe3Atm2oi3O6403erod7MxvdTcA0HDTOIPLM3oOOM2DF1uQ8aOG71rGk6+pvPbfw/oyNaTmhHw+jsUbL+Bxx7BFLJmyAc2sYpm6aAgu+eI/B1HOeeFTJfe+1WL1DGtaWl+8zS/3nHnL1rBlbbWVsCRxu5GU25+H8kLX09SUWvDZF29jf0q8tGIppUPlcy8tpvHSEXgNFD+XQwLFWpno6tWPUnFfs234KIO/P+fZcfS+P4Vm75zl4reNcN76bKY1DfeMwu9Tw0p9lCLbdwKvffDB94MosVEjsVKT1Gkxyq1jWLv4PtJrOThcixf1Hsmj6vCqVS5LNhln3UGUwFfPcznENDLu1oGxSV4QRHLPBZWeU+4Yhe/oU2hLjLt0U/xyIGn9hZlCxZovBhPcFNm3t1B3MO489tEY2//riKH/Xym91rq7tdTrhmpS3zNh4jnFpL5nwsR/jCp7ThMmTPx7mHpOEyaeU0zOacLEc4rJOU2YeE4xOacJE88pJuc0YeI5xeScJkw8p/w/GTbELyagP7oAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_gan as tfgan\n", + "import numpy as np\n", + "\n", + "params = {'batch_size': 100, 'noise_dims':64}\n", + "with tf.Graph().as_default():\n", + " ds = input_fn(tf.compat.v1.estimator.ModeKeys.TRAIN, params)\n", + " numpy_imgs = next(tfds.as_numpy(ds))[1]\n", + "img_grid = tfgan.eval.python_image_grid(numpy_imgs, grid_shape=(10, 10))\n", + "plt.axis('off')\n", + "plt.imshow(np.squeeze(img_grid))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4sAetutZ9t93" + }, + "source": [ + "### Neural Network Architecture\n", + "\n", + "To build our GAN we need two separate networks:\n", + "\n", + "* A generator that takes input noise and outputs generated MNIST digits\n", + "* A discriminator that takes images and outputs a probability of being real or fake\n", + "\n", + "We define functions that build these networks. In the GANEstimator section below we pass the builder functions to the `GANEstimator` constructor. `GANEstimator` handles hooking the generator and discriminator together into the GAN. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "oZ9n-jw_MG6C" + }, + "outputs": [], + "source": [ + "def _dense(inputs, units, l2_weight=2.5e-5):\n", + " return tf.compat.v1.layers.dense(\n", + " inputs, units, None,\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight)\n", + " )\n", + "def _batch_norm(inputs, is_training):\n", + " return tf.compat.v1.layers.batch_normalization(\n", + " inputs, momentum=0.999, epsilon=0.001, training=is_training)\n", + "\n", + "def _deconv2d(inputs, filters, kernel_size, stride, l2_weight):\n", + " return tf.compat.v1.layers.conv2d_transpose(\n", + " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", + " activation=tf.compat.v1.nn.relu, padding='same',\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", + "\n", + "def _conv2d(inputs, filters, kernel_size, stride, l2_weight):\n", + " return tf.compat.v1.layers.conv2d(\n", + " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", + " activation=None, padding='same',\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "NHkpn6ks90_R" + }, + "outputs": [], + "source": [ + "def unconditional_generator(noise, mode, weight_decay=2.5e-5):\n", + " \"\"\"Generator to produce unconditional MNIST images.\"\"\"\n", + " is_training = (mode == tf.compat.v1.estimator.ModeKeys.TRAIN)\n", + " \n", + " net = _dense(noise, 1024, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = tf.compat.v1.nn.relu(net)\n", + " \n", + " net = _dense(net, 7 * 7 * 256, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = tf.compat.v1.nn.relu(net)\n", + " \n", + " net = tf.reshape(net, [-1, 7, 7, 256])\n", + " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", + " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", + " # Make sure that generator output is in the same range as `inputs`\n", + " # ie [-1, 1].\n", + " net = _conv2d(net, 1, 4, 1, 0.0)\n", + " net = tf.tanh(net)\n", + "\n", + " return net" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "w-ZqQ4_thIrP" + }, + "outputs": [], + "source": [ + "_leaky_relu = lambda net: tf.nn.leaky_relu(net, alpha=0.01)\n", + "\n", + "def unconditional_discriminator(img, unused_conditioning, mode, weight_decay=2.5e-5):\n", + " del unused_conditioning\n", + " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", + " \n", + " net = _conv2d(img, 64, 4, 2, weight_decay)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = _conv2d(net, 128, 4, 2, weight_decay)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = tf.compat.v1.layers.flatten(net)\n", + " \n", + " net = _dense(net, 1024, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = _dense(net, 1, weight_decay)\n", + "\n", + " return net" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OhTAjxnyPS5e" + }, + "source": [ + "### Evaluating Generative Models, and evaluating GANs\n", + "\n", + "\n", + "TF-GAN provides some standard methods of evaluating generative models. In this example, we measure:\n", + "\n", + "* Inception Score: called `mnist_score` below.\n", + "* Frechet Inception Distance\n", + "\n", + "We apply a pre-trained classifier to both the real data and the generated data calculate the *Inception Score*. The Inception Score is designed to measure both quality and diversity. See [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) by Salimans et al for more information about the Inception Score.\n", + "\n", + "*Frechet Inception Distance* measures how close the generated image distribution is to the real image distribution. See [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) by Heusel et al for more information about the Frechet Inception distance." + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "1jF-FW5LPTn6" + }, + "outputs": [], + "source": [ + "from tensorflow_gan.examples.mnist import util as eval_util\n", + "import os\n", + "\n", + "def get_eval_metric_ops_fn(gan_model):\n", + " real_data_logits = tf.reduce_mean(gan_model.discriminator_real_outputs)\n", + " gen_data_logits = tf.reduce_mean(gan_model.discriminator_gen_outputs)\n", + " real_mnist_score = eval_util.mnist_score(gan_model.real_data)\n", + " generated_mnist_score = eval_util.mnist_score(gan_model.generated_data)\n", + " frechet_distance = eval_util.mnist_frechet_distance(\n", + " gan_model.real_data, gan_model.generated_data)\n", + " return {\n", + " 'real_data_logits': tf.metrics.mean(real_data_logits),\n", + " 'gen_data_logits': tf.metrics.mean(gen_data_logits),\n", + " 'real_mnist_score': tf.metrics.mean(real_mnist_score),\n", + " 'mnist_score': tf.metrics.mean(generated_mnist_score),\n", + " 'frechet_distance': tf.metrics.mean(frechet_distance),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kxF2-gWHHaej" + }, + "source": [ + "### GANEstimator\n", + "\n", + "The `GANEstimator` assembles and manages the pieces of the whole GAN model. The `GANEstimator` constructor takes the following compoonents for both the generator and discriminator:\n", + "\n", + "* Network builder functions: we defined these in the \"Neural Network Architecture\" section above.\n", + "* Loss functions: here we use the wasserstein loss for both.\n", + "* Optimizers: here we use `tf.train.AdamOptimizer` for both generator and discriminator training." + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "OBd8Vg7lHit8" + }, + "outputs": [], + "source": [ + "train_batch_size = 32 #@param\n", + "noise_dimensions = 64 #@param\n", + "generator_lr = 0.001 #@param\n", + "discriminator_lr = 0.0002 #@param\n", + "\n", + "def gen_opt():\n", + " gstep = tf.compat.v1.train.get_or_create_global_step()\n", + " base_lr = generator_lr\n", + " # Halve the learning rate at 1000 steps.\n", + " lr = tf.cond(gstep < 1000, lambda: base_lr, lambda: base_lr / 2.0)\n", + " return tf.compat.v1.train.AdamOptimizer(lr, 0.5)\n", + "\n", + "gan_estimator = tfgan.estimator.GANEstimator(\n", + " generator_fn=unconditional_generator,\n", + " discriminator_fn=unconditional_discriminator,\n", + " generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n", + " discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,\n", + " params={'batch_size': train_batch_size, 'noise_dims': noise_dimensions},\n", + " generator_optimizer=gen_opt,\n", + " discriminator_optimizer=tf.compat.v1.train.AdamOptimizer(discriminator_lr, 0.5),\n", + " get_eval_metric_ops_fn=get_eval_metric_ops_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "n1uldXfUfstT" + }, + "source": [ + "### Train and eval loop\n", + "\n", + "The `GANEstimator`'s `train()` method initiates GAN training, including the alternating generator and discriminator training phases.\n", + "\n", + "The loop in the code below calls `train()` repeatedly in order to periodically display generator output and evaluation results. But note that the code below does not manage the alternation between discriminator and generator: that's all handled automatically by `train()`." + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "colab": { + "height": 2281 }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "t6aboJBr8Rig" - }, - "source": [ - "Download the data and sanity check the inputs." - ] + "colab_type": "code", + "executionInfo": { + "elapsed": 221607, + "status": "ok", + "timestamp": 1559656706482, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": -480 }, + "id": "AH6gcvcwHvSn", + "outputId": "a72e2218-95a8-4585-8a5c-7c4ec896ac0c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "height": 279 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 2639, - "status": "ok", - "timestamp": 1559656474241, - "user": { - "displayName": "", - "photoUrl": "", - 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WPtqfzL5pNg/1OYEyd0KqNyd9+npuJ6Uw//JJSn1yhxk/fIRrExP92k8kKce+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\nW8/FkZlvXxSzO1vPezkZeGQ+Q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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fef6cc15750\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import tensorflow_datasets as tfds\n", - "import tensorflow_gan as tfgan\n", - "import numpy as np\n", - "\n", - "params = {'batch_size': 100, 'noise_dims':64}\n", - "with tf.Graph().as_default():\n", - " ds = input_fn(tf.estimator.ModeKeys.TRAIN, params)\n", - " numpy_imgs = next(tfds.as_numpy(ds))[1]\n", - "img_grid = tfgan.eval.python_image_grid(numpy_imgs, grid_shape=(10, 10))\n", - "plt.axis('off')\n", - "plt.imshow(np.squeeze(img_grid))\n", - "plt.show()" - ] - }, + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in 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\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_fn, hooks, steps, max_steps, saving_listeners)\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 373\u001b[0m \u001b[0msaving_listeners\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_listeners_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m 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" gan_estimator.train(input_fn, max_steps=next_step)\n", + " steps_taken = next_step - cur_step\n", + " time_taken = time.time() - start\n", + " print('Time since start: %.2f min' % ((time.time() - start_time) / 60.0))\n", + " print('Trained from step %i to %i in %.2f steps / sec' % (\n", + " cur_step, next_step, steps_taken / time_taken))\n", + " cur_step = next_step\n", + " \n", + " # Calculate some metrics.\n", + " metrics = gan_estimator.evaluate(input_fn, steps=batches_for_eval_metrics)\n", + " steps.append(cur_step)\n", + " real_logits.append(metrics['real_data_logits'])\n", + " fake_logits.append(metrics['gen_data_logits'])\n", + " real_mnist_scores.append(metrics['real_mnist_score'])\n", + " mnist_scores.append(metrics['mnist_score'])\n", + " frechet_distances.append(metrics['frechet_distance'])\n", + " print('Average discriminator output on Real: %.2f Fake: %.2f' % (\n", + " real_logits[-1], fake_logits[-1]))\n", + " print('Inception Score: %.2f / %.2f Frechet Distance: %.2f' % (\n", + " mnist_scores[-1], real_mnist_scores[-1], frechet_distances[-1]))\n", + " \n", + " # Vizualize some images.\n", + " iterator = gan_estimator.predict(\n", + " input_fn, hooks=[tf.train.StopAtStepHook(num_steps=21)])\n", + " try:\n", + " imgs = np.array([next(iterator) for _ in range(20)])\n", + " except StopIteration:\n", + " pass\n", + " tiled = tfgan.eval.python_image_grid(imgs, grid_shape=(2, 10))\n", + " plt.axis('off')\n", + " plt.imshow(np.squeeze(tiled))\n", + " plt.show()\n", + " \n", + " \n", + "# Plot the metrics vs step.\n", + "plt.title('MNIST Frechet distance per step')\n", + "plt.plot(steps, frechet_distances)\n", + "plt.figure()\n", + "plt.title('MNIST Score per step')\n", + "plt.plot(steps, mnist_scores)\n", + "plt.plot(steps, real_mnist_scores)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uy1dsvWuwJeS" + }, + "source": [ + "### Next steps\n", + "\n", + "Try [this colab notebook](https://github.com/tensorflow/gan) to train a GAN on Google's Cloud TPU use TF-GAN.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "last_runtime": { + "build_target": "//learning/brain/python/client:colab_notebook", + "kind": "private" + }, + "name": "TF-GAN Tutorial", + "provenance": [ { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4sAetutZ9t93" - }, - "source": [ - "### Neural Network Architecture\n", - "\n", - "To build our GAN we need two separate networks:\n", - "\n", - "* A generator that takes input noise and outputs generated MNIST digits\n", - "* A discriminator that takes images and outputs a probability of being real or fake\n", - "\n", - "We define functions that build these networks. In the GANEstimator section below we pass the builder functions to the `GANEstimator` constructor. `GANEstimator` handles hooking the generator and discriminator together into the GAN. \n" - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1571383618849 }, { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "oZ9n-jw_MG6C" - }, - "outputs": [], - "source": [ - "def _dense(inputs, units, l2_weight):\n", - " return tf.layers.dense(\n", - " inputs, units, None,\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", - "\n", - "def _batch_norm(inputs, is_training):\n", - " return tf.layers.batch_normalization(\n", - " inputs, momentum=0.999, epsilon=0.001, training=is_training)\n", - "\n", - "def _deconv2d(inputs, filters, kernel_size, stride, l2_weight):\n", - " return tf.layers.conv2d_transpose(\n", - " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", - " activation=tf.nn.relu, padding='same',\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", - "\n", - "def _conv2d(inputs, filters, kernel_size, stride, l2_weight):\n", - " return tf.layers.conv2d(\n", - " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", - " activation=None, padding='same',\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))" - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1569547390651 }, { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "NHkpn6ks90_R" - }, - "outputs": [], - "source": [ - "def unconditional_generator(noise, mode, weight_decay=2.5e-5):\n", - " \"\"\"Generator to produce unconditional MNIST images.\"\"\"\n", - " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", - " \n", - " net = _dense(noise, 1024, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = tf.nn.relu(net)\n", - " \n", - " net = _dense(net, 7 * 7 * 256, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = tf.nn.relu(net)\n", - " \n", - " net = tf.reshape(net, [-1, 7, 7, 256])\n", - " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", - " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", - " # Make sure that generator output is in the same range as `inputs`\n", - " # ie [-1, 1].\n", - " net = _conv2d(net, 1, 4, 1, 0.0)\n", - " net = tf.tanh(net)\n", - "\n", - " return net" - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559972047311 }, { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "w-ZqQ4_thIrP" - }, - "outputs": [], - "source": [ - "_leaky_relu = lambda net: tf.nn.leaky_relu(net, alpha=0.01)\n", - "\n", - "def unconditional_discriminator(img, unused_conditioning, mode, weight_decay=2.5e-5):\n", - " del unused_conditioning\n", - " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", - " \n", - " net = _conv2d(img, 64, 4, 2, weight_decay)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = _conv2d(net, 128, 4, 2, weight_decay)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = tf.layers.flatten(net)\n", - " \n", - " net = _dense(net, 1024, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = _dense(net, 1, weight_decay)\n", - "\n", - " return net" - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559900570952 }, { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "OhTAjxnyPS5e" - }, - "source": [ - "### Evaluating Generative Models, and evaluating GANs\n", - "\n", - "\n", - "TF-GAN provides some standard methods of evaluating generative models. In this example, we measure:\n", - "\n", - "* Inception Score: called `mnist_score` below.\n", - "* Frechet Inception Distance\n", - "\n", - "We apply a pre-trained classifier to both the real data and the generated data calculate the *Inception Score*. The Inception Score is designed to measure both quality and diversity. See [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) by Salimans et al for more information about the Inception Score.\n", - "\n", - "*Frechet Inception Distance* measures how close the generated image distribution is to the real image distribution. See [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) by Heusel et al for more information about the Frechet Inception distance." - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559897391264 }, { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "1jF-FW5LPTn6" - }, - "outputs": [], - "source": [ - "from tensorflow_gan.examples.mnist import util as eval_util\n", - "import os\n", - "\n", - "def get_eval_metric_ops_fn(gan_model):\n", - " real_data_logits = tf.reduce_mean(gan_model.discriminator_real_outputs)\n", - " gen_data_logits = tf.reduce_mean(gan_model.discriminator_gen_outputs)\n", - " real_mnist_score = eval_util.mnist_score(gan_model.real_data)\n", - " generated_mnist_score = eval_util.mnist_score(gan_model.generated_data)\n", - " frechet_distance = eval_util.mnist_frechet_distance(\n", - " gan_model.real_data, gan_model.generated_data)\n", - " return {\n", - " 'real_data_logits': tf.metrics.mean(real_data_logits),\n", - " 'gen_data_logits': tf.metrics.mean(gen_data_logits),\n", - " 'real_mnist_score': tf.metrics.mean(real_mnist_score),\n", - " 'mnist_score': tf.metrics.mean(generated_mnist_score),\n", - " 'frechet_distance': tf.metrics.mean(frechet_distance),\n", - " }" - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559752800451 }, { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "kxF2-gWHHaej" - }, - "source": [ - "### GANEstimator\n", - "\n", - "The `GANEstimator` assembles and manages the pieces of the whole GAN model. The `GANEstimator` constructor takes the following compoonents for both the generator and discriminator:\n", - "\n", - "* Network builder functions: we defined these in the \"Neural Network Architecture\" section above.\n", - "* Loss functions: here we use the wasserstein loss for both.\n", - "* Optimizers: here we use `tf.train.AdamOptimizer` for both generator and discriminator training." - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559719883868 }, { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "OBd8Vg7lHit8" - }, - "outputs": [], - "source": [ - "train_batch_size = 32 #@param\n", - "noise_dimensions = 64 #@param\n", - "generator_lr = 0.001 #@param\n", - "discriminator_lr = 0.0002 #@param\n", - "\n", - "def gen_opt():\n", - " gstep = tf.train.get_or_create_global_step()\n", - " base_lr = generator_lr\n", - " # Halve the learning rate at 1000 steps.\n", - " lr = tf.cond(gstep \u003c 1000, lambda: base_lr, lambda: base_lr / 2.0)\n", - " return tf.train.AdamOptimizer(lr, 0.5)\n", - "\n", - "gan_estimator = tfgan.estimator.GANEstimator(\n", - " generator_fn=unconditional_generator,\n", - " discriminator_fn=unconditional_discriminator,\n", - " generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n", - " discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,\n", - " params={'batch_size': train_batch_size, 'noise_dims': noise_dimensions},\n", - " generator_optimizer=gen_opt,\n", - " discriminator_optimizer=tf.train.AdamOptimizer(discriminator_lr, 0.5),\n", - " get_eval_metric_ops_fn=get_eval_metric_ops_fn)" - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559717312855 }, { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "n1uldXfUfstT" - }, - "source": [ - "### Train and eval loop\n", - "\n", - "The `GANEstimator`'s `train()` method initiates GAN training, including the alternating generator and discriminator training phases.\n", - "\n", - "The loop in the code below calls `train()` repeatedly in order to periodically display generator output and evaluation results. But note that the code below does not manage the alternation between discriminator and generator: that's all handled automatically by `train()`." - ] + "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", + "timestamp": 1559641947244 }, { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "height": 2281 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 221607, - "status": "ok", - "timestamp": 1559656706482, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "AH6gcvcwHvSn", - "outputId": "a72e2218-95a8-4585-8a5c-7c4ec896ac0c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 0.32 min\n", - "Trained from step 0 to 500 in 25.67 steps / sec\n", - "Average discriminator output on Real: -10.51 Fake: -10.17\n", - "Inception Score: 5.97 / 8.38 Frechet Distance: 98.58\n" - ] - }, - { - "data": { - "image/png": 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hQJpURqocfekk+1szbyKVXZEOFNFoHOJxQKE8GzQd5TQJihWC0ydQZoA/XcM2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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fef7ca32a10\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 0.69 min\n", - "Trained from step 500 to 1000 in 33.82 steps / sec\n", - "Average discriminator output on Real: -6.00 Fake: -5.53\n", - "Inception Score: 6.61 / 8.38 Frechet Distance: 69.39\n" - ] - }, - { - "data": { - "image/png": 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YySTOmqOQM3R27D/Fj8U+vvbn72fZPQ0UhccJb4vRH0oXTNyyRp6+iRC9m6uo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\nif9zQzEbt96PuLSalhPN/NMLzQx6A1O+6BoY+GM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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee56571790\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 1.05 min\n", - "Trained from step 1000 to 1500 in 33.28 steps / sec\n", - "Average discriminator output on Real: -20.26 Fake: -20.98\n", - "Inception Score: 7.15 / 8.38 Frechet Distance: 58.26\n" - ] - }, - { - "data": { - "image/png": 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+dzaVIVyFaL0dnHlgH31n+oi9hgfCpaIbBl27ujnqOk59QQFlBVaEOxcUM3Lj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\n5edPDvDo7ACyrr/fpw9glc0I2YQ+NZ4aZJQmK3J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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee3f248e10\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 1.43 min\n", - "Trained from step 1500 to 2000 in 29.00 steps / sec\n", - "Average discriminator output on Real: -28.74 Fake: -29.66\n", - "Inception Score: 7.35 / 8.38 Frechet Distance: 54.52\n" - ] - }, - { - "data": { - "image/png": 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iClmw+2csg9bRIC3H2qmpURENzahu/7msXkYHsUf6c9Y4ZWjMy9CIm4ZEhMxg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\nLkmSNIf8L+JFEmJm2STdAAAAAElFTkSuQmCC\n", - "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee53e1a310\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 1.79 min\n", - "Trained from step 2000 to 2500 in 33.04 steps / sec\n", - "Average discriminator output on Real: -27.47 Fake: -28.54\n", - "Inception Score: 7.17 / 8.38 Frechet Distance: 62.29\n" - ] - }, - { - "data": { - "image/png": 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jIZrqamncbab8aiPClc6GJXaG4nOgLYe+kV6UYBCnsLDaPZNr17iZs3YmomTW\nWJq2d4jo/kb+4ZV6Opv7iLyLE14gcBvtGK3pCINCaDhCb9MIQxHfu56Iykw2PllSwXUbrkZZtwY1\nEmTgWA2PDPdTm+ICc7quI3Sda+1FfHZrESWrFyNll4BkQAuHiB6vZ4+3jRZtYnbkpKbSEhq46Pc5\nFQvpRTMYzZrJK/fsxR+aeLG394tBkrGmmbFX2hGJMMfPJum++I/wvvmfJ46RKZn5uICBUIKnHj/I\ngP+dF5GHhpNU+aOUrcnEee08eOnwRY952Yi6JEkYJAk9qfJuwXfeaACB4PDOUzzZEeBTtzgwXv9x\n3F+7mS81lmB79CX+/jE/nkjq4k7fjeqMQgqWbITCYtr3HOLe0dqUr/o9ES/P7DvPidf6eNU7lpXw\nx2FfJoPEurxRMswqsUN7aXz5OQ77Lt4eNx6EEHx+kWBlWRrC6kRHQCJB8uQBTvSdpTPqndQKiZqu\n8UTdEIOevTzS0YnxY7dj3LiVj21z8LFYBLXxLPEnHic+HENZno4yfy5SyRyEMxM9HkEUVmEuXYjZ\nux255d2fcA2NzqiHqKKAYqZ51MYrnW8UkXv768cKrQm+5S5gy+c3kXbTSnRdI9DdxwPfP8vR9jZG\nU2R6eTMmdLYQJ7O8GpFbjJAN6JqGGozStn2YzrZhQpeoOUqZNYvZ9jzaJI1/i7cSToHP6f3iUEzk\nlczAdONVaIkojyR6OK1feEefCjwRP8eebGShnsXibVn86Kp0rnp0mJ5QBPUCC5nTYMFscSLScxGu\ngnGNeVmIuiwkPnrzZr668QoavvMbvjLYSuACX7QkJMwGIy6TnbgisaNnAPVphU+W12CrWopcuYIb\nNseQurv54iuTL+qSkPiL22awOj/EiRdO8tDDdZNi7tB1nbbwEF0R7wVjeA2SjCszi/S//TPkwmwe\nfjHJ9mZtSmzpAoHDaMW8YTNy2ZzX09M1Ir4Qjz/oo707RGIS6t38MZqucWw4xNpnGzAf/G/+++vX\nsuTKKxHpOcQ1N+2HFV5NOmhqHeb68/tZd9UwUvUCtGEPQ/9zhI5BA02d9e8ZbaDrY8d71duLHpnB\nnMIE1y2J89QrFgKx8FuuuNVgojozh3/bmknltjtxVFaCrjFwoplnv7ub+7qO059IbdQFjH0nJqtC\n8c1OrLNyEWY7Y7WnffibjvHtUA8tKYq2Gc/crlmdxqaMAU49fxx/PDRlPh9ZSGxxmfhWWQaqvZim\nbz5JZ00qB7CgAAAgAElEQVQroUkOj34+3Am7JL7YP5u5y7P5qUvwv2LNnEu8XaNkISH8HvAPI2Bc\n2bWXhaiDICPNQdWqKjK+fj1r7tmFFNaIo9GbDBJQo1Q53Ny62oR9zhxMkkTiXB96q5fS+QJjesaY\nndloIhR0MtTlmPQZpxkN/L8FWVyxqJzD5/w8/MIZ9na0Ttp4cV19R0fcbJPCPxS4cZYUozeepK7+\nNA2jU9MMwSRkrrPNJCt3FsKehhACPRoi0tvEzoEehmITS7i6GCKqRrM/hhTo54GHazG6Kpm/PhfF\nacQ0W+XRQ610xaLUjAoeah3G6K4jFPQTqx8iEJWoi/ne0wygoxOMR1ADHohHscwsZumVq/mPhjAj\nowZe1XUG0cnXDKyYmc/yu9exqDwbU1EpOhqtB+p57jf7eKD+GN1x33tGy4wHgySRbnNiXjwHye0G\n2YCOjjYySvToUc4HhghNcTeoN0g32cipqOC8VeEX52omNW/hj8m3uJi7dBFF16wi5B/mns5G2sKB\nCSc7vRc+LcbLnk6GT4dYHsxhbcyCQb9wspMvGSaSjKDHw+jRIJIQXGxVjctC1DVdY+BsN20HWyjY\ntJjPqXaMgQiJZJRhLUpYwEyrk3ULHZiKCtG1JPHKfpI9o1jLnAh3LmgaWkc9Z8/XsqN3cgXNIZtZ\n4S7ixtvWEGiBl15p5pXGNgbHGR42ESQhyHHb2XRFCUY9ykt7GznV0D3uqIaLxSILbsyRyHJYwWAE\nQBsZJXLgIGf93QRT5LS+GDRd48UzLYjtr3CHLFgxN4+0668g5/TLdEb7qPXGaPIPYJCG8UQCCMCi\nmIjy/oQuqamEX2shUV6NcW4ZWSuWc1PcSiRiYo6aZESSyBQmynPdFG6sAMWI1nye/SdbeP6leg4c\nPcfZWOoTot7AIhmZYcvBUFiGsNgRQkKPhxnuG2b3kVF84QuXWJgKbp/lpDri42RXhL1Dk2f2+GMk\nIVhfYmf90hKEKxPfs6/yqq8P7xTdnwNqhL3eAdpqE2ApJiCJC1rsZgk7meaMsf+ERsZKJnNxJ93L\nQtR1dM6fbuSBxPOsNq5j45VFGEmMxdSa7QizbSzW0+KEWBiSCUzznJiqNYjHUds6SPh16utqeOG1\nYxyNTk59aIBsyczKrCJuWrcUa3EhTzx2kEM1jQzHp+4GfTNug5XZuYVYVpUTO3mWxw50UNOXmqJh\n74VAYDHKLCyJ4LRJIGR0XSc2HKDvpSaG/b4p7w/6Bh2RYbbv3oc7y8qKJXdiX3MVn1zSjf9knI5g\nCF2HkBoloSWRhUye0YlHD77vCJ1DB5uZUXaG8mwnhoIC7B8rxiFJbFaTCFkGWUHXQY1G6djXQPPB\nHfx2Xx0vtYy8pU7QZGCXFKqMmRicWWAYK7+rjwzRW9/Iw01xIsmpF3TBWBnqW9dU4BoN0Xa8BX98\nau5TgIVKGtsWlrB4biZDfcO8+MxrDAcmVrf8YolpCdrCw+wwWIkbDJg1I9HkWCc0AxJlpgxuXDyP\n8tKZEFfR+3sZT+2qy0LUAc7FvdSd3kveN07w4BdW4M7OIj27CGNmEVFzklhohITcjz7Sj6ypyIoF\n0In1dxLZeQhfi+DnwwGeC3onpSIfgFMobHYVcffq5VR/cjadDz/NAx1NnI6FL0nfUwHMTcvkmpnV\naK5Cmn/wG841NTMyRQ+LJARmkwnj7Ewkm2nM9KIlCQQFp1sziCUvbS9YT9RP52Av3t5BXLnZXLXa\nzPZWBV/URkSNMRSLISNRqNhYacrmVFJjkPd32vqJZxTbocN8NsdA5oarkbIKXm8OIkBXIRFHDQQZ\nbujlof/zOA/0nKEz7ktZA4h3QiBIl2WWWI0oivH1hVYj2tFN32vHOBvunRSTz3thlA2sKCrEtf5K\nXtvTztHhzikb2yKbuLuogtXLNhBxuzn60hH+vneY8BSaft4gqauc8rUzN60Ql0UmShhdlrGabdyV\nuYRtX7mKnKoCAjU1DJ1rGZd56rIRdRgLXez1Bdj6X6+Qa3PxZ6Y8SpUMjsg6Z1UvTcE+ZCHINaWT\no6Sh6ir14V6GAiOgC2KaSnwS47LvsBTzua1rmXvXfEb6uvjYnn4aPZNTAfD9YDIYmb8im80fLyag\nS/zNQJSGaHJK56MZDMgbroKM12tC6zpRXaPLIC7y0Dg5HNzbxM/0F/nGX2+m9f4BWtr7ORP3o+tj\nmdsug4W/t1RSrAq8qs7p9/l3w4kYPzk2SFw9xLckFenmz77+GzFmDx3sxHOgll3fP8/Pho/hSUam\nxnEtBNkujS1XBDHZzSBJ6PEorY1xXtk/Fo0x1QgEboeZn3yhmlxbiN96GzgV7puSsRUhs9RVSvW3\ntpKxZBYv7zzPT39xklD80kT+wJhlIt/g4EtLsllTbUQqKkSqXIixoBKDw4k+3MG+093cczQ+LjPZ\nZSXqMHbYiCZURiNhHlMHsEpeRnQVbyJMQleJqQlGQ2GaxQCqphJW4yS1JKSga8m7kWVJY97WAmZt\nyaFzMM5//+AYnR7flER2vBMJNYlqtSObrMSP7KHF25OyxKv3g6brxJJxdN8QvFGzQ9fpTwZ5PNBA\n7BKZXt5Md8jDL/e/wo7mk0TbvbS/qUyzrsNoMsp3A2cxAgPaxT3o3miYX53uZGeXB/GrY6//VIyV\ntk3ESAbCjA5FGFGnRtDH0BEmA0pOOkIxgZDQI35qRtt5OnDxiSypoFS28Ve2uWQ40/m3+4+w41DT\nlDS8FggybCa+87FiyuYUkDx8ktbnd3Ha2zHpY78Xx/0dDKbNxbZ0JfKcRWCxgSSReOxBfvzSKX5T\n00v36PhKF1x2ov4GoUSUJjWJEAJN10hqKpIQJDWNGGPdjN56lJ3ch+YL183jquuWMxRS2PHQEZ45\ne45Q/NLt0mEsTfvVk018NRgm0NXOcCg8ZUkcMLbjCIRj3PtoPZ/JnUdhpQ1Ghwi3nKEzOoJ6Ca/N\nG8S1JH3+Ufr8F05+Suoarer4wgpVXaM/FKM/FIPO1BRtmyiarjM4orHzmM7mbTFMRhM9LzVzfnc9\nPYmp36UXG9O5tryS9Tcu5ZmXOthxpJnOYe+UPDd2g4kF7lLmXL0Ja1Y2TzTv44n6HgLJ1Dd+v1hG\nE2F+faKNw6NJRMZ5QAdNQ22o51jHEE3++Liv0WUr6glNJTFFKe7vh7VLZjAjTWHn7nqe2HeMrojn\nUk8JgPrWHhraeqc0NOzNROJJfnegnrB7B4UzC9EDIzSePn1ZfXd/avT4I/zicCuNv34ak91K70v1\nHKhpuiT3SFVpDrdsW0Taqnwe+e3LNA9NfhnoN9B0nVAszoljAbynD/G7ow0cG730gg5jG6KjrcMc\nbU19kTuhT7bn5l2QlfxLNfRF869fuoYqOcqOIw3ce2pg0pyxH2QkMVZad6KtyC7l6efDhED8Pnll\nYs1ZxncOloTE7VfM56u3rqMvGuML//wA3sDUZHq/gUkYuCa9gnOJYbqiI5NWf+hSoL5DBcdpUX+f\nTIvN1GCUDaiaNqWhZtO8O0bZMK7eBUbZQJElk1xzGu1RD30hz5SaBz/sTIv6ZYxBklG1y8ECPc00\nb0USgixrOiORwLjMJrKQkIVEUtcuWcLTh5V3EvXL1qb+p8S0oE9zuaLpOr5oiOQ4i26p+vSpa6qZ\nFvXLgGlBn+ZyJnoJSj1MM36mRf0Djt1gpkCxMguJbmTaEz6CamzadjnNBZEQ5JszWFRup3HQR2O/\nf9pX9CHjAynqAoFdNpKpKNgsSdoCKkl9rNDSmKd/6sk0G8gxKXhDMgPJEJM9i5mKkbSsNAqy81ht\nz2OrpHBIMvPs+dOc9HYzPEUFvT5olGXZsGdkgKoR9Hhp9b17p6MPE3ZZYo7TxuqqFXx+Sxr37DlN\n80DggnW9p0kdiiQjCwkJgVUykGtSkWXoj2kMx9SU338fGFEXgIxAksBiNLPKXsgn8/NZWeXhU/sC\neCIJfIkw/liYeDJB4mLrVU4AoyzzkdluvjyriO3HbXyv7xChFHY+ejPi9fH+X0EJGz+7HuuGKjSj\nDUw25sgSS/7awH8cjfFCrH8sLlnVQBYkVI3JenYlBLI0lnYPgD6WranqY/G4l4tkKELix7cvYu2t\nN6OOBtj3m4f55M4eRqNT18dVAAYhjTWWFwItqaNOwUZEEhJL0m08sq4c+z98GrXhKMndJyd1QZMQ\nSAIkCcY+OWNF+sYyB9E1HU3nskhSmywUIZFnyyDNYsMqmVhodfGNEh9pTvjPtii/bh1hOORP6T3w\ngRB1k6www+rmE3IhK1dEca8ux1Yxn/S0TMwE+cXVAyTOn0XLzCIxGOLw84f5av3glMxNkQ18Y+Ns\n7rrlStzzlnNzzyg//NwJQv7Ui7pRNlDksPOz9dlU3foRrPkZnNzXwuNP1dGre/nebCOzty3lX9at\n4Ns2B5o1n9CvH8V68xK+8PM9nGxJ/TUxSQprXeV8YX6S0llOMBlI9gYYPhfh/lEb52NehuNBAokI\n4UluRvBuOISB7zrmUrl8GyK7gAMnTnDPiSThxNTUezcbjOi6znIlk09XlLH4E7mQns3On9fzcG0t\nZyJv77iUY0kHYCAysVaAkhBUW3LYNn8Ftr+6HmGU2PuEl5azk5eIYzIoXGUt5uaydBYv0hGusc8i\nyqohGUOta6DneC9PNtp4xFNLVL/0JSVSiUDgMFn4pqWA9bfOx7V5HrK7AJPBiNuqIGlxvhxNsvHZ\nWvb96lUeZ4Qmf+9Y3Xtdm5D59LIX9WJTBpvmVHDzRxcyO2MWmUNHMKUbEPowhENgMFDoFPh6k4Rr\nBtjnHeV3fVOXDr3aVszyeSvJWbgYdIH16EuIRGocS7KQ0Bk7vmWaHCzLcfHpFXYWzivgp7uOcG7A\nT1/XMC1tHsLEuLtLIq0zgEFXubIyj49ssKP+2XUYF1bxt6/08KP+EPtDqUn+MMkKC/Iz+PrVZeTO\nW0dlcTpOlx0kgTbqJ9zeS4Zux3PyIJHeXkJDMYYGJWqDZrYHmohNYQszk6xQmpnL+i+tI63Yji6g\nvDyLz15VScXjMveGm4mkeD6SkJCEwCBkKu153JarUbx2PvkLFlPmzsTlCqCeOk6dv53uxNvLNjuM\nFrYYM4gk42xnYqK+0ZjLHUvmcsUdlchZmST3Pc+LjWdoCKamb6xBkklTrMwxOrnOaCN/vRnjzBIK\niuYxIz+bTJcOJgXUJDjTQU2izZxJ0YJhnC2jNN0X5MxgJyH10i36qcYuGfiaqYjr797GjE2VGAvd\nYLQCOsQjoGSQKcnYbrBQUJbLKpFgpOEIP3mmllMdg0Qm0Lf2shf1mJZkOByit9tHma+fp5qH6IqM\nkJQAIY11PBKC8Ml+osMJaqJhjkxRBTZJCK6Z46CysgTS3XSea+KXO44Tiadm1/GG6UJDJ0+ycmXu\nTNZfOZPTh4d4ds9JTg8MkXxTUsjOIVC8jchCorV/mLZhL/aZs/nC8hVs2LqMF4eGOXSqYcJHbrti\nZkV1KZ+6dik3XlGB7s4C3yiDZz3U9gYZjI+yIaFRNTvCsZhMb9hIQWE6V26YzTI9h4Jn/TzQPUhP\niq7Tu1FhcHBlUQkLt1WTmxdhdHcN1nkzyZ3tZuONa8jT7Tzx1AC9QX9KTBHl1myWL59FSWkGyX4f\n4eN9LLhtA+tzBTmLK0CYaD/VwK/2dBM/d4q9vYN4X3+ADZKMw2BihSGTBUsz2GA1crh1CBrHPx8h\nBEtzjWxcmkfOklKigSA7X2zkUG83w8mJl2heZc5lUWUGRVWFlOTMYpU1DXeVQNiNCIuNaF+IkYMj\n2F1x1LCKMiMdyeVASk/HkVfIgnI/dwsr9zz8IjXt7VNSkM4tW1iZUUjVTfORPJ0M1YxwotPL6URq\n6vdYZSNz0/O4YcMsZlxdjWl2MSQTqH09RI7U0DpooCBdJZA0EwxJOLOtbLh+EdocO5ozn/uePszh\ns01Expn9etmL+kAiwO7WBpp/O4THkMFzqp+asJdg4tLWcJARFJvdrLiiiILyHAgE6Tx3np+1BIiq\nqbFTvnEES6hJ8jKMLJ2bRzS7gN+9cpKOQT8J9e2imFCTJIAjHR6OdHhwOduoLi5n+bIySk60kXe2\nl+74+Kq/wdjpYaHTxidXVHLzltX4230cOnuaWGsdHQe97GseoUsPMmDOpGB+jF2tRs74wlRXS3y+\nyMqK+SVU1Gayd2hkUkVdFhLzSnO4deYcthaWoeaZeemlV+g5GqNilYeFH11H5twyigli2WVGCgVQ\nJ2CFMQrBugwL65av5Lo711FRLBPYe5rOOkFeURa9diPn20bw1dRR+8IBftTnI66OlUmWhUSRYqLK\nnUH+3CKuj6exeIVMIhhl78DEagyZZSO51S4yl5UgrOmEak/x6xMeWkdCKanPc/PcUm66oYr8K8oQ\nGTkkR33Ut4UY7ejB7z2L53QP4TODpOdG0SISpupsbEV5WHNn4sorpDLHyC0fWc2Qz0P4mRC1nT0T\nntOFkIQgw2ClxGlkWY6TG8oWsObLd2DoOEnHK73c+/I5Tp9LjahnyGY2ZBRRdEslxlwnxKOMtvXS\n+vIxzj+1g1P9TmanB/BGjYyGzGTOdDPPFGaJbOeGTatQVZVoKMDhpq5xmQYve1EHiKhxmkJDPFli\nwmXKwD0QI+h9Z1Efc9xJk1pUymJQuLlgFnkrr0Lkl6CeOYF0/CiZwojBoGPQIYFO4vX99pgbRKDq\nKknt4mpxOCUjFQuyqNjsprGugSc85/C+z0pzwUCYH/zr0/z7z77C6oLZ1GbVs71nAqIuyVybZuIa\nl51Qj4+D3/45X+jtZDSZRNf/4Bb9u4gX9v7hffVH+vAEAzzxVwqSTUZS5HHXFHk3BGCXTRQUZPPX\nH1vHteuWUlcX5n/+6TG2e+qI6Ulu9sEXM4tYOzsLXVPxxyMkJ1jsymmU+dGauRR97Tr0rHw8xw/R\ncbKGFq9Ey7/t5DlDkr3BXnqibxUOi2Qkz2bijuxcvrhyAe5PL8L72im8nR28dkLjROPE5pVpsuNc\nugLDwkWoI378ew9Q42klkILoKCEEZavSyF5TRTg9m+H6erz7XuJnO6I0hv00BwfwxoNjjUMGQJFl\ntKYBsiyd5JjqWeE2809r07F99at84RNraertoq6rf9yJTu+GJCRm23L4zEwXty9VMC6xI5tl9Nxi\nCrekURDywLnUjJUpSWyyWDHPmAdmG2p/O3XPH+EXvz7G44EBYskueHPHzTPg/MpB/iNtLtf87PPc\ntqka32A3J1r7iY3j5PKBEHWAdLuZB/7xRtzuDP72Zzv51XPeCx6XJSGwKRZyLem0+vsnxbsvCQmX\nzciXNuvkFzgRsoywKczKL+L/2LM4btSYmZRoFjHapThJXcOnRZGQGIz7GIiMEoq/fwfdjZZibixY\nSYso4e9+vJNA+P1/0XFdZZ+3gZHAECs3uTk5lMf2X9WN96MTVxP0DlgY6NQYyu3iq0NDjCYT792w\nWQfd6kSuWoYt5CetxYPRFyOW4rraJsnAtZnV/M1/fp451TmcffoU9//geR4aPoPK2L0wIlRGZCCZ\nQO9sRtY1hGBC0UHCZsX+zbuRstx0/HA3v3zoBf472IaugaZpJH+/sL+V5Rml3F0ms/H6hVi33UAo\novLTR15le2szPREfyQlGcVWbc8mzZKJrKr4WDycfixMKJVLSgUkgOP3wAGXOJk5qJ/juL/bQ7u8n\nkdTR9T983jfu89jrbfT6Qh76w14MIScdh3KY8xUZ2V1AlquITIuT/nDqewwnNZVRNUpvT5LBuE6e\npRnDahA5Mxh97Bn8z55JyTiSkHC5dBZfEcDoyhhra3j+LKdO7mH7aDeJd1iwArrK30ZamDVQx4rC\nSuxZJWRaHPQEL/6k9oERdSHJmDNzseQX8Ln8WahpLfxq9O3NezVdJ5yI0qUOTVq4Vp7Bzp2ZC0i/\nZhtyTh4ICZFTiOumLWzaaGSlyYwpFiUa9RFpayT62jk6axS+6x8lkIig6tpY67f3+WDNuWM+c64r\n4lR7CyeG20le5AlE03W0oBfJXIopIxOTrExITPcRYo4W4GZTGj+clcafN8UZjsWRJYGu87a+pC6z\ng1vWz+crn90EioPI/jYCgyPEL2A+Gi8CwSzFyedzq9nyL7dRUplH9Ln9HHl4J095Wn4v6ABno4Oc\niwywuDvCr37bx2goBclaQoBiRH11BzvrTrMr4Seu8XoDlz8gC4ksk5M1ubP43F+uITe3iNwMBxY5\nSuvho/z3A+d5puU4AxE/iQnev7IksSIpUSxbEZJMf8zPo7qfKKkyD2r8OtDM+cd1kpIgHE/gUOx4\nkoF3zdPQX//HnG+m5NOFSA4LeiJCKB4gPAEH4XvRERriV1EfL4XMLNhfwndu9+FA5VhI53g8NVIo\nBAwGFB4/Zee2YBiL1TYWPID+joIOYwvfaCJCIuRHSAZsVhdug50ePsSinogkOHnfOVZ/pZjyLctY\n4B3B/ex+PNG3Rw6ouoaaIrv2H6NIBmZ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aRLOs6fgKnJINv+jAjEYAE6PpAk2d1bTErz5d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\n", - "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee3f2306d0\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 2.14 min\n", - "Trained from step 2500 to 3000 in 33.26 steps / sec\n", - "Average discriminator output on Real: 1.75 Fake: 2.23\n", - "Inception Score: 7.38 / 8.38 Frechet Distance: 55.90\n" - ] - }, - { - "data": { - "image/png": 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x2IU/FpqSBfZ0aEgOJwP8x6sHWZPIYulVgsbmLGavmUvp/gCR1Mikiv29vlip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\nnznt/hiKlmQVEkWWkPOsxT8DLJ6DmW/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BoSNsdkBgtJ+kr6+N/ujE+4e8G+WubKpK5+Oq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\nCfLiZYiU9GQjq+E+Gl86wO6DhzkTufSk8ZuRhYRLseOWHEQ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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee3d4299d0\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 2.51 min\n", - "Trained from step 3000 to 3500 in 31.49 steps / sec\n", - "Average discriminator output on Real: -2.78 Fake: -13.50\n", - "Inception Score: 7.38 / 8.38 Frechet Distance: 58.81\n" - ] - }, - { - "data": { - "image/png": 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B7HgIafZCbm50cavTg1AdY45CLYHw5XHVTZdw5YIyrGNNaCd6ad3soMkSJBWF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\nXu91uxpNNDEmMbuGLEBYAoSBYR2YYZt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bKGKHus1ms40idqjbbDbbKGKHus1m\ns40idqjbbDbbKGKHus1ms40idqjbbDbbKGKHus1ms40idqjbbDbbKGKHus1ms40idqjbbDbbKGKH\nus1ms40i/x/HKN8JI5IGjgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee48486ad0\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 2.86 min\n", - "Trained from step 3500 to 4000 in 32.85 steps / sec\n", - "Average discriminator output on Real: 84.46 Fake: 79.25\n", - "Inception Score: 7.63 / 8.38 Frechet Distance: 56.05\n" - ] - }, - { - "data": { - "image/png": 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CCAlLCIqdMYqdMZjk+9IZkocZeTWIsqlYwSh+LTyhJ8mz4ZQUVLsTVAf6GWMt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\nHl7c1cj9o0EiqTevLzMQD9DS5GWgLUE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KnNm4i0tQ0tMwWhsJvv0q52Njn8sOMJ8zi1Ai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\nt97+DqmbmQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee235bc310\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 3.20 min\n", - "Trained from step 4000 to 4500 in 33.85 steps / sec\n", - "Average discriminator output on Real: 139.49 Fake: 130.57\n", - "Inception Score: 7.48 / 8.38 Frechet Distance: 59.30\n" - ] - }, - { - "data": { - "image/png": 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2RkUmRePmufl8ZtMyZq9bCTYnIuYmb4YVTBZEMoHRWEP0XBtvN1moGemm7+gZ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\n4WvS4X2RmKrQPTDGc88dY+0MFzUzc5BmFeAqkClKGqVMgCx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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee3e309510\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time since start: 3.58 min\n", - "Trained from step 4500 to 5000 in 32.00 steps / sec\n", - "Average discriminator output on Real: 197.43 Fake: 191.78\n", - "Inception Score: 7.44 / 8.38 Frechet Distance: 55.80\n" - ] - }, - { - "data": { - "image/png": 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63nWUzDKix3iq7RAv/MkJvuVu4JY/v5Wbbt2AT9G56TuDk77DDTi9VBSpFJW/\nsTtJxlFMC+kDiKys9uSz4IG1VN21CAwds7+H5OHDpEJjkx7UMKvUy7//3nzkujmYXU0kt+1CLijH\nNX3BePkMxpPwEAKhOij3FvJAYCZ/nz5AaBLcth9JUTcsk7gJx/UE3w4149lhsah1Gw886GPxVQ2s\nd9/K1m8m2D2SudovkhCUeHLx33Mb8pwGkruO0PPQ0xwfa89I+7C0oaObJmERZ0siioQgZRuY2CzN\n9/KdVRUEGxaCZXLi8ePsf+IkI8nM1vooUL3cXDWP+75xPSWNDYhADvZwN2e27+Zvv7uTQ70X6Dcm\n393gUZw05hbzv2YJZj/wSZyLG8frhkViWOc68CtOQopJykxj2eMRD37ZyRd8s6iyBQ33zaf0ujk4\nFCejPZ08+SdHiUfeXdTlhgaUq65COH3YehqrqRkxPIo90guag5TlZOO3T/JY91Fa+/sYjcWY7lHx\n5OhIfi9xOUyaD0cziJSR5nERojLWy3XOmYiaGcCOSR8nlIwRLSvDsXo66Drm68+zq/so7Rlyxb0b\nn/MJlvu9CIeL5Mk2mr7xU77cfoKTI5FJNTokIVD8uYg5S0AS9P3oKE/u6yawWOf+htcRucVI5TMR\nijqeiS0kCmeXccvX5rD3XyJs7WliTJ/YzuEjKeqmZXLYGKOLJF1GDBEWDLc04fiZGwzB7PkFfOmW\nMo78vAU9mRlR90oyXwsGmFZRjdBkmntDbGpPEc/QYaQN49s1G6L8x3eqcuayqmEBDb9/PbLTSeQX\nm9j02gG2DXZmNDXcr7pZWlPG5++oomzBdJRADuhJhve1cfinx9l+4QKj6eikWX8CgUvVcMkas4v9\n/OV1dSzYsB7n7LkIv2/8mvwicu/6NP+0KkksEcMc6cZORBGeAI6CKmaoOXgSUfx1eUjDQ8Re24k5\nmIDU+/Ane3xIBaUIJIwjJ3hsxyCnE71E1RMkLZNY2qDj1Cjn46PE3ohmaU7a/GVbP3972sPVZprj\nKnQI8YElAcH47sqrubg5V2Jm0IMdjWFfaB4PNJjkeZW5cqgorUOungaGgdnazVBohNh77IomE7eQ\nedBdy5p715GzoA5Mg6FQmFc645waik76WZMsZByqAzQN9BSBOU6utNxowSSEQ0h1CxHyeFEzKzYG\n6QSyy0XpupX8N3cRnzixhyO7+jjSmqDDjjNqxBhKXFxEzEdS1G2g24jRZyXe+H+boXSEXaebmNPc\nwIprZrIbG2qVAAAgAElEQVTo+uX4f32IaGpyt1cCQY7iYmVeBdffu5a88iKM0+dpOnySHdHolPoK\nZUliblWQG9fVITfMQN/6Ks+/to+XLrRO6sHPW8nVvKypKuS+ddOYc+0yJH8QgOZdzWx+ah8bjzUx\nmpo8QQdocBeyalEVdXMqqaooY9XiMqSGBaBqYBrjRcWMFK7Z87hqNtimgR0ZgUQE27YglUY/0c7r\nvTHaLrQw1nSeyL6ThBKChP4+SgYIMV46V8jgdHByxOaV8wMMpkKkDP1tyw4M6QbPD4aZfyjENaV+\n7tGDlLernIubbEv1v80gl84KZzFB2ckoJl1WnJ7k6LghgA32+LmMR3YwPVjI9dc2cFv9NMrrSmg+\n38Pr+7ozsm4VSUbxBBD+fAzdpu2sRjhiTlkzG4HA53Zxx63zqbhmGUpZEfZAN/1nD/PrSD/JDMzD\nqzjId/rHy1GbJp6V85hbXzxeT7+gEBQN68xBXj82RmggRK0rxZxpfpTp9SxcWo09zcns6XGu6Dfo\nt5NE9BijF47y060n6Rh6f1FKH0lRhzfiUM3/2M4KBMIabxUm/Hk46xex0l3Ma2NxRibJevYrLmq8\nDpaWFXDNFVdTcN86JDvOyd3H2LH/GOcTU7utnKG5uXp2BYvX12Im07Q9d5DHz/dwJB7LmJUuCcHS\n4iCfXFnPdTcsQ6qdPx6qNtTFrk37eWTHAY6m+ifdP1vjyufOdctYc8dSpNzScYE1dMwzZ2nqHaZj\naAgjFcPOKUFTNEoUL0JIOPQ0SmSE9o5TjG49xk86UxyPxgilYxdV/6Wra4jDB06xsL4Ief585rRI\ntEWS6F1JhsTb32vbtomkDX7cEmfJ8lKub6hg/uEULx7qZHvqnetrXwqzXIWsrCnFKnHTJKVoCXVi\nC7CT8fGKpk4PQU8ui4um8fFPzMSlqpw5184vt57mqTNjGYkYs7HHq1RKMnoajnR5CCXElAUy+mWN\nxmAJNZ9YhKu6BCSZwQs9HNp1lCPRrox853zZTa2aA7aNkCQoqUH4cgn1hultSzF25gj9B1/h0a39\nDI3YLMizWT3diWteG/7S6eQ4gxQG/awrAznHBbn5jDwZ57W95+ngMhf130YgkCSJuWous5VcEAKX\npHK/VsFJ0c0IExd1RZKZX1jCZ2YWcdvaarSPX401lqLr2D4ePtHCLwdjJIyp21YKBHfkF3DHzEak\nqtlEz7ez8ayT5pF4RueRp3q4dV4+1163CGXJ+vHwzXiInoO72dVygBOJvow8LINmnHZTpS0qIccG\nEaaBHhsl/sgv+MHeC2wcTBIzkqQMnRynl/XOSlRZI9e08Zsmz0khepOjjCQvzYe6fd8ZpJEQ3/rE\nPKqu+Rif/rSfGs3Pi9tOsz/Uzbm+IdK2Rdo2MKz/SPO2sYmlBrByFuO+Zh3uvEE4dGHS79FRO8ri\nuTKr19ew1l+AFg8TcApEagzh9SNyi8GZg55SGUgnSGz6Nd/feJwnT/YTTmcmUixHcpFjSdixEHpK\n5ZCUICym5lxBEoJKT4AHahfgqWlEON2Yg4McPtrPrw5krlSEz5YIRHVami8gF9rjBexaT9G0uYUt\n28OctiLsHT1P+o0qkAfGBD9pU8jZ1ku18yRzJB8r3UkaipK45hXBinWcfvgcY33vP379shB1GxvT\nMsfdLEKAkDAEHNRkwtLkhJHlO/3ct76UO+66EnXmYsyREQa++gP+qKmZnaEQyfezhZ9EvJqTwjXF\nBFcUYHV1Mfa9f+MXI0P0W5kN5bzZW8u8NXegLFsKCEjHMfa+wNe/v52XTnRmLP38yFgbX/qHf8P7\nby5ynV68spPO2BCJRJy0aY73eHyD/tgYv4yFGH/1jbevM2BCD3IkneDFMxc4+u0BdpQUUrBgBSvv\nnsuyZQ4uHCvip48P0WonOBPvpS85RsIY76aUq8o8UV9Ow7KrEMFczqdb2alKE74fb+XwWCt/9ose\nyjYeZaanhHmam8/UDpJz1zqkqmKIR4lv2sOZR1r4amKQC5EhIqkEaTNzIttvRumPDWC1nybRdI7X\nR04xMgXF1MYRFNQGue6vF6N5NcAm/uwmzjzxPPvCk+v6+m2OJvs5sf815AdeH28IA+MJaKaFZb7Z\nYOY/1qGNTdrU6Y+NMRgLcQjBo8JG6gQOt8JPD2DpJvpFRPJdFqIOMDdQyQ33r6Px441gGSSiwzwf\nPsvQJJUdHU5G+NfXz/CL433o6tMkEzH0jgFak0mSpjmluXGarDLLU0rJrFWIynr6T3Xz4gk/I6ne\nCbVBey8kIXHVtV7qZ3tB0bD1FEZvJ4OPnaWzpZdIKnM1dyxs0rrOmGEQiY3Xw05b+juel5hveJQn\nE9OyGRhL8Mf/spO/+bKfGbNqkOvnUFNYxRcXGqRkhYRtoGNjGTrYJorTR0N+IVq+l82/PMijT+xg\nd3jyC1lZ2ERTKVr1AXrDo+wTEs8OGMhNwwiXEywLKxIjMZii3dJJTUHVxv5EiO+/uptnDx7BSCRo\nHY1hZLjB9Zss0vK5L3cuakX9eAOXyAgdvTYdww4MO3NzsADLstAvoSb+bwT/zaVr2W984sVxWYi6\nEIJ7ZjpYPS8XX46K1dOKfmA3XdEhkpO0iHTL4NzAGC2DoXF//hSW53wriiRz1/X1zF9Yg903QM+W\nTTw9PMBYKpGxeQnApWjkVwVx57rBSDN6rot9P9rBUwebOBeOZD4vgPGGC5ZtTKAv0MRIGybbzrby\njYde4J7yetYsaiB3WT6lwSTC6cLWDYTmBB3seBJDSnH66VO8MtrKzqNNnGrtmHDI2jthY5OydFKW\nTgjoSQPhqSlu93bolkH3SIjukanvumTIMimnC+H0gQBj13Y2nz7IlsTwBxp9NBVcFqIO0B4d45kd\nB5DOdWOHR4icPU04Mbm+s6msbPduGJbJ+dAgT2/eiTUwQuue/RxOJEiYk5vJ+laEEFjDYazhQToG\n02z+9UFefGEnm+ID77t110cdG5twOs4Le88Q1fo52dpJTlshdjyK0DRsPT3ess80sBNxDFuhadMw\nW6J99OrRSUkDz/LedBlRNradIfnwc5i2hb5zC8+fbaM5Qy/UDxPCnup83d9CVks/qKGzXAKarPCN\neXnMXb2Iw8MSv3zuKKciU1e7PkuWi0EgUGU5Y3VtPmhMvedt//5fWtTFG7U4prpaXJYsWbJMlHcS\n9cvG/XIpZMU8S5YslxuTH1uVJUuWLFk+MLKiniVLliyXEf+l3S8Xi0vRKJE9TPO5KK6RsXWD3g5o\nikToM+LZyIYPKQKBIskYlpl1uf0Xp6EkQG1BgD1nhwmlM9+E/IMgK+rvE7/sZEZNNRuKa7i+tIAF\n1zohnmD/Vounu7o5NHSeof5+zkf1KW8AkOXtcckauZJCvteFv6aUhJHGDI/QNRRmKJqa8sdZIJAl\nCQmBYVtTEtef5T/wyw5umV/HDUun8fl/3UVET2Q0We+DIivq7wMZwar8er78x/ex5rZl2AhMc7x4\n2PIbLZZbaYy9O9jxyBPcu6OHSCIxJYIhIZAlgZDHk+GxbWxzvDzv1Oa4vjuKLCFJErYNhjE11rIi\nJGb6S7nbn88dC6sp+4ffG+8cv/sF/uKHm3ho5wWSF1HQ61KREEiAJEs4NQd+hxuHpDIaizCail2W\nluKHlVX+KlYUzyIdcNKXGL1sX6pZUX8P3ELh93Pmc89f38WMdXOww0M07zvGT35whJm2i9kkmXZv\nI4E1M1n23/+UZ9ft4vb//TRjscz2YNRklRvcldyxdjZz7qwHlx97qJOeH5/iqdZBXkmN0hsf+cB3\nDU5F42tfuJXbb1pDU/Mgf/eNRzkZas94ItcdgRncc1MDi66sJ1BdP16HQ3EgLd7AAzsj6MdCfH8s\nc32IJCEIqB7m+8q403Az7w/mELhiHnKwgOiJLvZ9/Vf8lX6asDV1ReDeDoHAqWgUuAL0x0dJm8Zl\n+6JZcWMly1d4OHy6mVh66ndqU8VlI+qarCIJgWGZaJJCuTsfVUjcVWxQVepAriomZAbZ91wH241+\nehMh9PdRQkAFrpAkqmIdvP6LVrYcb+dCVxunTw0QRCaISVGyjZuSV3PXzcuZPncu6/NPsyV1jrEM\nFi+61l3JvXdtYNXtS8ipywNZheR0KvLnUnD0AKt2nmX/kSAv2iH6EmPv67tmgmtclawub6ChoZZA\nQuNTso9vyRqjdjJj6dqykHDIGrmyQXCsHWNHO4lRF77P3obk81O1uJjq00HknZPT6PetqJLMckcO\nd1VVMPNj9dTWLSN3bjFaUS5oLtJuF84HFzLt4RGahvtJZLgJsxCCCkcOS135zLdNjgsHFYbFtMVe\ngo1FyLmFCFPl2Yf281r/OYYnqV7ShwkhBN7KckQqyNCWwSm10oUQlDiCzHEWMENz0nhrPlpNA8KX\nC7ZN+nwnXT/byv8d7SMyCYlSH3lRVyWZfEeAStXP7IBBcV0OjpmzKMwpQxYS64MGRfkKUlEuMcvL\n3LIeFvUe4+HXj3G2d/A9a2qnbIuXYl0cfT7G4f4YR9pHCOlxTMuk841rHCcjlO4JcOfiUrS8chY5\nizkktTFGBkX92vlccesScufVjndSAXD78V6Rz5xKN9W19cxaNkbVQDMPbzxM+9ggKWtqK6YIBBvK\nFOrzHAhFJaDarMgzmWbmcTI8QCJDHXBsbI4m+vnZ0QSb2y38owZrqGT6/RaSkFAdBoqmZ8xS02SV\nWfXF3HLjQvJuXIBUPh1kBSQJISS0giDlNy/jD3STf39mG6c6ui+qtvvFIEsSqxwBrmmczaIr5lCr\nJ1nqDFCkuimZ6cVblwu+AHpSJ2cgzsmnuhnunzxRl4SET3FS7cihwZQpKU7iKvdimzbdHWO8OKgz\nlo5lfEfplDVUl5fmmMTm9sz17X0rsiSx3uvlynn1zFi8iEp/Hg3rClFKKhBuH2Cjd/TQaSm88osd\nHBnuIjbB/g8fSVGXhUSFqtGgOnC6NKY1zqQuWMnyIpu6xeUoSxYhFA3zfBvRYWgxbeL9AjdQUldE\nmVrGDn8HbQOh93yYklj8LNELO3rf8ZqUqZPobsdqOo26YhrzAzo+xYYMaJaMoMqZy7wb51E4q3S8\n16FtY9smJGPYpoFUWIa/bBrz1yWZ1lpOvHOAhw+G6IpMrairskL9ogB5FR4QgrQwGXJbqNL4ripT\nWLbNiXgfTSeGEEIw2+tj1fIchKoBkOxMkOhKZERICjUf8+fUc8XNi8i7YTFS6XSwTWzLQCBjS4Dm\nxFNZxyc/7WZsbJhHXo5yumdo0ucCME8N8sl5c7nxjlUErpkN6QSl7gCSJzi+dt7IqpbSCVasziGw\nWYGLqEyrSQqz8hxUFAXQJTejZ0NItkWg2EbL96L4c8jx5jPXX81iU6W2JoqnIQdbtzh3qgf13Bi/\n3nGAUCKW0UJbDkVFkRTOWzrbzKnZibgUB8tnFPO56XVsWLsI1/oFCG8uQnMhfmv9q5VlFH3yKu6N\nanhf2c7BvnaGzEs3CD9yoq5JCuX5OdxTXMIDOfmoAYWcz61Crq5HOL2QNkiFIgx1tjD6k0e5cEJl\nawK6FUEpGpWGjNuyGLCiWJPZCDiexhqJoKAztzSMt9mADKwdp6Jyd91civPLQHMBYFsmZnSMrnNN\nGNExnJ48gmWVeIry8VXP4Gs3V7Oro53eaGLKwi4lIch3+nCtWolcU40dS9DdOcojXRpHxloyZqW/\niY1NytSRhUROqZtZ95aguFXsVILuDouensld+oLxCKmramby+c/fwbIbFiMUbfzFkUqCmSYV1jGT\nNrJTw1HoQyqo4ot3L+VCdydne0cm3SUgCcFnyqZz9X3XEbxpwXihMRjfMSDgzb6klomZiNHT1kcq\ncXG/S5HTzx8vr+Gu6+cTlks48b0zaLbNjHUm/sVViOo6RE4pki8PbBMsa9xVKASzr07wvzpaOXa6\nnTM9nZk/uJYVTKSM9u59E4essKA4n/99/zrmrVuLVFyNkNXfveiNl5hQHXjKKnngr++kQdf48ZZX\neG2oleHkpQUVfOREfbq/nK99ajU3374KtagagT1eHS82itXfirH/IKdfOso/ntXYNtBGPJ3CsMdv\njfhNtZfx6BBrkjbgspBQK0tRFs0hbalsOlvIcKyFyVZ1gcDvc/K5L8+guDYPIY//fHZoiOHtG/m9\n/7uDnlCYJc4y7rt9Ddd+cS2SLxd5wy0U/boHb2eUUDo2qXN6J1Qhs9BbSaCgFuHyEX9lL83/8jDP\nDrViTKE/M+jwUFFSj9y4GlQHducZXgv1sMme3IqWTknl3vxGvvCl9dSvrh0XLsbF3tac2KNjtH5v\nG10vNBFoLGfxP9+C8OUjVc1GzduLIp2e1IYVAoHf4WHm19ZRsH42wuF++wttGzs2xuiJ/fzRj07T\n1Htx62Olq4K6NbejXr+EPKGwZsN6EBKSDEjjLw5sCzudxBpohcgIoqASKa8cIUlI9ngTc1nIkMGC\nyrplYBppSrQgtc58umKZ2RnBuB6U+j385I4qSpevQCqqAuntpPbN9SfGb5PqZPlf30jj/DgPP5ng\nb44MEdMvvnTyR0bUBYIcp5ev/tltbNiwGC0vgJ0YJXX2BOd+2MZT8UGakkOMDPcxNDhCT0IiYiTf\nItyZ2d5Zto3ldIM3gBkdZdC2yITH1iErlPgLcc5ZjuQNYNs29mgfp/bs42++u5eTPT0kDZ3NkThV\nW6KszQvjfuCTCH8Bd6ll9Mvn2S4yb61LQiKoOLlWd5InO7HTSTYPDPKPfaNTKugA04WXq9QSRKAQ\nhETs+QO0HDxKR2J40saoUXw8mNvAtVdC1axqZF8OAvsNtxjYkREOfnsHP3l1B3uGOsnd38KVfx7n\nT775CbwBH4uD0zjkKmFPtGPS5iTEeLKcll+M9IbvFssa78ITGYbhbuKbT9BxaIRtwwY/GzhCc+cw\nMf3i/LmbwxdY9dSTzG7fiTa9GKmxEdJJTM31mx3KSGeSbT9t5heRMwQUJ5+6soY1116BqKjHSkaJ\nGZlfk5XOPILILCpO88nFEttfERmL8vEqTmblVONdvhY5vxhkFSEEtm1hJ6KYW57lH1/p4txgivXT\nA3zi+tkoS68ez2NwufFcexMfi7tJdz3D13t6LnqeHwlRl4QgV3PwF7V+1sytIpjjYWjfeQ68cIDn\nuo8wcGiYM0aSYTtF0khPyfbqt/GqTry+PFAdpFpOszfRRSgDoWoeSWOmqwjVmweSTNfGY2zbspNn\n246zvbmDWDqJDYxacfb3hth0NsLHLBOhqMzymhRqJvYU9ExwySp1+SWsuHcegTI/Pa82cWjjMU7G\n3l/j3MnCqahMX1TNFXcvBEUFM81jTQPs7hud1IPJXBWuLNGouf0GtLIy7HQC20iDbWMODTL084M8\numk3r/S202vGcI4lSO3dy+dfcOC54RZWXFHGiaYS9m/umjRxEwgCihtFdSIkGTsdJ9zUxclfHue1\nRDux2AiRli6GuiK0JWxOpUcuSeQG9SgPnzzH7u525DwPoug4mMb4wbBpgGWSCJu0nxjhlDFGwOHh\nyqUl4HDwxj4Gw7YyHkY5mIoQ7T2Pv6aKslnlSK+dxbQyM6Zhm0TsNOQVIjQHArDGBjh/4hQ/fP4w\nY02H2dUSYiRu0t7qIyXBZxdfBZIYb1adk0t+QR7TXPIljf+hF3UhBJWFOXxy/Tw+PrOSYEnR+N9t\nHTUdx9Hdz4ikEcMkYaQ/kEYWxZqfEmcu8ZDFyZebORnpJ5aBSJMCp+CmEhmHIrD1FHsOHueR53aw\nK9n3nw6ZLiQMdowluDkdB9lHTkEKr89GjkgZD+cq8Wnct7iY6lsXouU42XfsHLuPnSM5hY25JSGx\npjqXG69soGT1dDBNrLbTbOrrozkxeSGEqiQTLM2j6o4lqI1LQRXYkWHs3k66mtt45uB5RjY28fJI\nL71vRDWkTJ3u8BC9z58ksHQDZRUOyiudSEKaVIs1bRlYbzbBtmFsKMzW5w7waPQsEWvcAJporXEb\nm4ORBAcjCegYA7rf9Xq/5kbkFo//Z5lY4RGiqVjGLfWRdJSxky2ky9xIOUGcskbMyoyFk7YM+pNj\nmJFRbD2Frac4feQYP3/iVX7yynEiqfhvXmG6qeEUNvx23ICeoiepczwtX54+9TzZyfrqafz3B29C\nFFa/YXUkyZ2dz/q8ZSzdYvFiS4Dd0W5am1u40N1L60VuISfK7FwPszwSA+0D/PqVHkaiMcxJ7gQk\n+P/bu8/gOM4zweP/t8NEDAYZRCACCZAAQYIBpJgsKlC0JIqyRUXLVjhbOu/urbW73gtb3juffbe+\n0ganqnVYW9LqrJMsyQpW5FIkxSSKOYMgEokcB5gBZjB5prvvwyh5LYoSOQOxUP2r4jdOdZPT8/Qb\nnud5oShb4cZlFiyKwAj7ORoe4kji44/nmtQiDARH0b1DSKXzsFVYseVZUEYkEhn8/ThkCw0lRdy3\naT5KaSWGZ5Ajvi6OJScyd9GPkWdxcse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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee24589910\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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GQkICrl27htWrV0OlUsHR0RHx8fEYOHAgAOhc1hElJZUWVSjc3fujqOimuWO0YIm5mEk/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\nnL3iwruMgAAAAABJRU5ErkJggg==\n"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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fef6862e310\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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VFYmmzZ07F0lJSQaN12IxeHt7o7i4GHl5eQgICEBubi7KysrQvXt30XLjxo3D999/jzFj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\nMXQwWRfLsfOHk8j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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7fee200ff950\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "# Disable noisy output.\n", - "tf.autograph.set_verbosity(0, False)\n", - "\n", - "import time\n", - "steps_per_eval = 500 #@param\n", - "max_train_steps = 5000 #@param\n", - "batches_for_eval_metrics = 100 #@param\n", - "\n", - "# Used to track metrics.\n", - "steps = []\n", - "real_logits, fake_logits = [], []\n", - "real_mnist_scores, mnist_scores, frechet_distances = [], [], []\n", - "\n", - "cur_step = 0\n", - "start_time = time.time()\n", - "while cur_step \u003c max_train_steps:\n", - " next_step = min(cur_step + steps_per_eval, max_train_steps)\n", - "\n", - " start = time.time()\n", - " gan_estimator.train(input_fn, max_steps=next_step)\n", - " steps_taken = next_step - cur_step\n", - " time_taken = time.time() - start\n", - " print('Time since start: %.2f min' % ((time.time() - start_time) / 60.0))\n", - " print('Trained from step %i to %i in %.2f steps / sec' % (\n", - " cur_step, next_step, steps_taken / time_taken))\n", - " cur_step = next_step\n", - " \n", - " # Calculate some metrics.\n", - " metrics = gan_estimator.evaluate(input_fn, steps=batches_for_eval_metrics)\n", - " steps.append(cur_step)\n", - " real_logits.append(metrics['real_data_logits'])\n", - " fake_logits.append(metrics['gen_data_logits'])\n", - " real_mnist_scores.append(metrics['real_mnist_score'])\n", - " mnist_scores.append(metrics['mnist_score'])\n", - " frechet_distances.append(metrics['frechet_distance'])\n", - " print('Average discriminator output on Real: %.2f Fake: %.2f' % (\n", - " real_logits[-1], fake_logits[-1]))\n", - " print('Inception Score: %.2f / %.2f Frechet Distance: %.2f' % (\n", - " mnist_scores[-1], real_mnist_scores[-1], frechet_distances[-1]))\n", - " \n", - " # Vizualize some images.\n", - " iterator = gan_estimator.predict(\n", - " input_fn, hooks=[tf.train.StopAtStepHook(num_steps=21)])\n", - " try:\n", - " imgs = np.array([next(iterator) for _ in range(20)])\n", - " except StopIteration:\n", - " pass\n", - " tiled = tfgan.eval.python_image_grid(imgs, grid_shape=(2, 10))\n", - " plt.axis('off')\n", - " plt.imshow(np.squeeze(tiled))\n", - " plt.show()\n", - " \n", - " \n", - "# Plot the metrics vs step.\n", - "plt.title('MNIST Frechet distance per step')\n", - "plt.plot(steps, frechet_distances)\n", - "plt.figure()\n", - "plt.title('MNIST Score per step')\n", - "plt.plot(steps, mnist_scores)\n", - "plt.plot(steps, real_mnist_scores)\n", - "plt.show()" - ] + "file_id": "14r58gghjLTBBQVoSFOBdPsvj-I1G6nbd", + "timestamp": 1549819781952 }, { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "uy1dsvWuwJeS" - }, - "source": [ - "### Next steps\n", - "\n", - "Try [this colab notebook](https://github.com/tensorflow/gan) to train a GAN on Google's Cloud TPU use TF-GAN.\n", - "\n", - "\n" - ] - } - ], - 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"nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } From 67f81af46d73dc59ae71356f71b4a576887d9d78 Mon Sep 17 00:00:00 2001 From: ayushmankumar7 Date: Thu, 27 Feb 2020 21:10:08 +0530 Subject: [PATCH 2/5] Code Migrated to TF2.x --- .../tfgan_tutorial-checkpoint.ipynb | 747 ------------------ 1 file changed, 747 deletions(-) delete mode 100644 tensorflow_gan/examples/colab_notebooks/.ipynb_checkpoints/tfgan_tutorial-checkpoint.ipynb diff --git a/tensorflow_gan/examples/colab_notebooks/.ipynb_checkpoints/tfgan_tutorial-checkpoint.ipynb b/tensorflow_gan/examples/colab_notebooks/.ipynb_checkpoints/tfgan_tutorial-checkpoint.ipynb deleted file mode 100644 index fe75ba41..00000000 --- a/tensorflow_gan/examples/colab_notebooks/.ipynb_checkpoints/tfgan_tutorial-checkpoint.ipynb +++ /dev/null @@ -1,747 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "9aMFvFjcoI_v" - }, - "outputs": [], - "source": [ - "# Copyright 2018 The TensorFlow GAN Authors. All Rights Reserved.\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "# ==============================================================================" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "35cp5a7vN9V8" - }, - "source": [ - "# TF-GAN Tutorial\n", - "\n", - "Tutorial authors: joelshor@, westbrook@" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "XSTQ5Flu7FMP" - }, - "source": [ - "## Colab Prelims\n", - "\n", - "\n", - "### Steps to run this notebook\n", - "\n", - "This notebook should be run in Colaboratory. If you are viewing this from GitHub, follow the GitHub instructions. If you are viewing this from Colaboratory, you should skip to the Colaboratory instructions.\n", - "\n", - "#### Steps from GitHub\n", - "\n", - "1. Navigate your web brower to the main Colaboratory website: https://colab.research.google.com.\n", - "1. Click the `GitHub` tab.\n", - "1. In the field marked `Enter a GitHub URL or search by organization or user`, put in the URL of this notebook in GitHub and click the magnifying glass icon next to it.\n", - "1. Run the notebook in colaboratory by following the instructions below.\n", - "\n", - "#### Steps from Colaboratory\n", - "\n", - "This colab will run much faster on GPU. To use a Google Cloud\n", - "GPU:\n", - "\n", - "1. Go to `Runtime > Change runtime type`.\n", - "1. Click `Hardware accelerator`.\n", - "1. Select `GPU` and click `Save`.\n", - "1. Click `Connect` in the upper right corner and select `Connect to hosted runtime`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "83-azWpoYsDg" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n", - "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n", - "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n", - "\u001b[33mDEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support\u001b[0m\n", - "Defaulting to user installation because normal site-packages is not writeable\n", - "Requirement already satisfied: tensorflow-gan in /home/ayushman/.local/lib/python2.7/site-packages (2.0.0)\n", - "Requirement already satisfied: tensorflow-hub>=0.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-gan) (0.7.0)\n", - "Requirement already satisfied: tensorflow-probability>=0.7 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-gan) (0.9.0)\n", - "Requirement already satisfied: numpy>=1.12.0 in /usr/lib/python2.7/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.13.3)\n", - "Requirement already satisfied: protobuf>=3.4.0 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-hub>=0.2->tensorflow-gan) (3.11.3)\n", - "Requirement already satisfied: six>=1.10.0 in /usr/lib/python2.7/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.11.0)\n", - "Requirement already satisfied: decorator in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (4.4.1)\n", - "Requirement already satisfied: gast>=0.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (0.3.3)\n", - "Requirement already satisfied: cloudpickle>=1.2.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.3.0)\n", - "Requirement already satisfied: setuptools in /usr/lib/python2.7/dist-packages (from protobuf>=3.4.0->tensorflow-hub>=0.2->tensorflow-gan) (39.0.1)\n", - "WARNING:tensorflow:From /home/ayushman/.local/lib/python3.6/site-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n", - "\n" - ] - } - ], - "source": [ - "# Check that imports for the rest of the file work.\n", - "import tensorflow as tf\n", - "!pip install tensorflow-gan\n", - "import tensorflow_gan as tfgan\n", - "import tensorflow_datasets as tfds\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "# Allow matplotlib images to render immediately.\n", - "%matplotlib inline\n", - "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # Disable noisy outputs." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "b2xrX4F-OEL7" - }, - "source": [ - "## Overview\n", - "\n", - "This colab will walk you through the basics of using [TF-GAN](https://github.com/tensorflow/gan) to define, train, and evaluate Generative Adversarial Networks (GANs). We describe the library's core features as well as some extra features. This colab assumes a familiarity with TensorFlow's Python API. For more on TensorFlow, please see [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "JMljl0ZwONgi" - }, - "source": [ - "## Learning objectives\n", - "\n", - "In this Colab, you will learn how to:\n", - "* Use TF-GAN Estimators to quickly train a GAN" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pI8zy5Bz65pa" - }, - "source": [ - "## Unconditional MNIST with GANEstimator\n", - "\n", - "This exercise uses TF-GAN's GANEstimator and the MNIST dataset to create a GAN for generating fake handwritten digits.\n", - "\n", - "### MNIST\n", - "\n", - "The [MNIST dataset](https://wikipedia.org/wiki/MNIST_database) contains tens of thousands of images of handwritten digits. We'll use these images to train a GAN to generate fake images of handwritten digits. This task is small enough that you'll be able to train the GAN in a matter of minutes.\n", - "\n", - "### GANEstimator\n", - "\n", - "TensorFlow's Estimator API that makes it easy to train models. TF-GAN offers `GANEstimator`, an Estimator for training GANs." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "qxrYrU887Mns" - }, - "source": [ - "### Input Pipeline\n", - "\n", - "We set up our input pipeline by defining an `input_fn`. in the \"Train and Eval Loop\" section below we pass this function to our GANEstimator's `train` method to initiate training. The `input_fn`:\n", - "\n", - "1. Generates the random inputs for the generator.\n", - "2. Uses `tensorflow_datasets` to retrieve the MNIST data.\n", - "3. Uses the tf.data API to format the data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Zs8kdV0w7Rtq" - }, - "outputs": [], - "source": [ - "import tensorflow_datasets as tfds\n", - "import tensorflow as tf\n", - "\n", - "def input_fn(mode, params):\n", - " assert 'batch_size' in params\n", - " assert 'noise_dims' in params\n", - " bs = params['batch_size']\n", - " nd = params['noise_dims']\n", - " split = 'train' if mode == tf.estimator.ModeKeys.TRAIN else 'test'\n", - " shuffle = (mode == tf.estimator.ModeKeys.TRAIN)\n", - " just_noise = (mode == tf.estimator.ModeKeys.PREDICT)\n", - " \n", - " noise_ds = (tf.data.Dataset.from_tensors(0).repeat()\n", - " .map(lambda _: tf.compat.v1.random_normal([bs, nd])))\n", - " \n", - " if just_noise:\n", - " return noise_ds\n", - "\n", - " def _preprocess(element):\n", - " # Map [0, 255] to [-1, 1].\n", - " images = (tf.cast(element['image'], tf.float32) - 127.5) / 127.5\n", - " return images\n", - "\n", - " images_ds = (tfds.load('mnist', split=split)\n", - " .map(_preprocess)\n", - " .cache()\n", - " .repeat())\n", - " if shuffle:\n", - " images_ds = images_ds.shuffle(\n", - " buffer_size=10000, reshuffle_each_iteration=True)\n", - " images_ds = (images_ds.batch(bs, drop_remainder=True)\n", - " .prefetch(tf.data.experimental.AUTOTUNE))\n", - "\n", - " return tf.data.Dataset.zip((noise_ds, images_ds))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "t6aboJBr8Rig" - }, - "source": [ - "Download the data and sanity check the inputs." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "height": 279 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 2639, - "status": "ok", - "timestamp": 1559656474241, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "zEhgLuGo8OGc", - "outputId": 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/HvfPVGGXp9m/3YwXYnBAqIIKKjCMCoGvCir4j2Gwc8pMTDE/4ohPpMGiChVU8P+Wyz8GMSKh9ISUJzHYOVNDmvCVyzbGOBwp/c0VVPA/yNUvg7k4MJSpWwfodZ7BzpldQ4OPiQkDzw821FQx5PaV6ReXWvobK/h/R07PZuxNjcEx3IakWYEkzdJ/fbusPE6aKE9WDponJpHoGcEx2Dn39BJrGk2XVzbUVDFkVpb0s04xnj2lksR5zRDMikfo0kYGcWV9yfKT+pKwOIBl146zMyWS7SmnubHZ+NKT/9/ZmxrDsbBFAKxyOcqlvgu51Hche1Nj2JsaUy6OmvKhfuoX+tA4WkNjJdQ9PAL3D/XLBTDYOdfeb8r0dF+sd5W+2Ppvkvi9P/FvzCduQXGHse51E6sjlgbbFxp4E9d5PlXkRQWTVttsXnBG2VBUc+LGtGAmJsWiPCI+1gY1RF7pWS2gl4Hc7k25tNafS7/6if9fa7ic6ZNc6rsQx3DDv9fHaCRoRpWV3O5N+bJqNJ57RlJ7gP4ZQgZFceQ2NoRU3sPAniPR5p03xNQzqFNv0yuxG3DTKPYUzmJWTc3qRUqBD98KZLvPDAZ2baPviKMYud2b0v97Mdm9wYr3kHs/pGWtS1Tech4NcGNaMHl1c/EYqN8PJFha0v3gWYbY7EKDhtYeO2AcCOMEPPeMxHPYGQNabVz8okGOhgYWm+hieRMTmZwCrZgq2QtpvV3LsSOx2CLW2ybNCuRS34WA2KO27Fn0miEICAhBxi/YSB8axLQP1xJ2vza+X9xESi2RQT1nbrAXr54agfaMcR0TQFuQz51sK6Mpsb/iKlYNZOwvqt/0mnABW8EMrUr1vNPKREorgTkX2tBu3DhcPzuFy1eFnEhxo014KtMunSVm3Dzcl5Ru5zEyE1NSPwhm3N+RvGNzlcWZrtRdH8I9dVE7zSvl6dVGbVBD3o6/zseXY5h26SwJ85sa3vs2rc/e1Bi2p5zmq6qn+bpqDD2t7mAik6NAjolM/Cts2xi5r6fe5p90Po+JEXSo7sfbV18BwO2DWHJ6NjOs/Yg9ZxcX49Tb6mhaH7M+t+lmmcGWqe0pvCFteiY9fc+/Lrt2rqZzfFe0bYw3N3ySjF11yP6risHKcwC7U6LwPDRU13upOgXw26LZ2ApmkoWqc/e6sa/eRvK0hXSYOhHbNRHY/2XHL6770aDhfL6Wt8JH4N6/7Anl2uZ+7Nq4DA0a7qpVDK3VQvfa7OQTuJuYEJbhxd56ZRvaXfssmJiRRarwT1e4dHOWto/L5htiUfGTPWRpj0dde527wfefa/PJ3hF4YWqdKGkJb199hSs/+UjuRbennGZntj3L27SU7ERPszMlktvqXHp+MYXKy0ufZxo9CSFhqBUatNz400WqiVJJj7cnxz0fuaN0tbUn0Two2rPEYdoVbAUzJt+UHmDYV0+U/PDfPw7bNRHc2urD7Fo7AeiT1JUJk0L0ckyAG60sEJAhIND7qQi4h4kSAYE/Rr5SZntnR84r9jxTk8ffT5RDpr5vnGBIj4FjSn3Pwlp/GOWzAF0PusrlKKmvSK8fva1W0cPyPqo6hm9JoahZA+URJzRo6Bg5okyO+SIMDgi5rHuxXooheEyKIKHjIrKCXA2yI/MXg0C19ojPC9o1ZoP7nwD8tbjJ804rM7Z22dzd7sWpgFXYCqb4n3wb1au3JN3Nc10LdBq61t9bF3tNg4bW5/pIqmdc99CZ14eOYUCf0XzedRBB37zHXbWKPe/9RP7r+vWet0OCdUPWL+80xn/NROSHo+g0aBQmMjk9Bo6hm3MAvWoE0qtGIEqZie79L8JjYtnVD6/85KN7/GRvqy9t101BY1DEQdQuvh0SzPaI7Wz22IWAQF6iOGWQ20gPXhmc1qNOuUnCwqYkdF1QbCgDsD3bjmXBAXptGVASaXUV1Nwq/XxBVYBcJnBw4UJYCBCFXCaAVsOpz8PgcwhJDeZSgH7zuIZ/vUtU8DKiA9Y/+ncLeO4YjeeoU5LbGtdpPjfVKt74cgr2x8Q7r7ySLRnrHOiT5ITNwEy91MifHLKbIpZv5XVowvwpoTg+Uj74c1nRxd3Nu1Wp6g0np87R/c6R/gLuiO1UHIykm3MAcqKKvV+lLSh2XRgDiy0n6bDFTze8lUr142oYCJd7mlDnkP7nyyvZsuPCITQchEfF/P5zQ7BN00LT+iz8bQG31Eq+6NRflDHRA8k9p2PtNADiF/uR0FXcX+RpXdRulhkkj/aS+hE6HC5I180BUF9MwG3bCLomdGFMSnOO5SlE8WO03FHncFql5e+fGupt13GVOfc0+RRo1body8zsc1G4SN+xzGf/SNpsmIL9sqIh0a23fDncYD2pa9wMvtHJlEr+WL4Af6WGkv57uKkKQgPp9Y3/FlKDQ6kt5eI0wl6a9Enc196cVmlpsDiE+ffdaPpdCM4/nhB/v1Pn6Bw5An9TgbjR+ucBSHbODXV/ER9owGvzGLw2j6FbcHd6BPeg5QdjydHmo9IWYHXt5Shs8Rx9CnXrVJKb5mItiD3kzHvevD0ohM/dG2O1Sf8haLVpScXWNQFigpdT53fpyz913okqtttXbvemRH22AAFBJ0psCPLqz0a/P7sTwIx0UQT5QP0NNPglDrn98y+mpZnuumHq7pQoHuypTWGbxhS2KYrKyivZkjivGdtTTuuGtbtzSp/X1d4wSvdYH4eTOu80q5OJBi0mCdL0k+T2KgSZBmSwc8grVA0rHrys3vMi0fkaJrb9g9zuTfWyLdk534kX8wTNbFV4bBD/CmrYkzCmBou/nY0cGa98OxG7X6RfUPI67lzIL8TiqvHU1gDcFWJPvGlOO8nbGwD84roXAM9do+j65nBi88WRw5eOxwxv5COud9HqemZjoM0qUlGYm+FN1zeHc65dZf5q70rXN4cD8FXV0zxY+/w9P3b1DqJAq6ZAq6YQNfvqr2XbqjB2r15I5aV3SPkwmLivvTnbUxz+qrQFzM3wJmxS31Lb5zExQuegx8IWPTcrKKdns2LJCNWPSusEurqeR0BGgae0nfFqLZXjbyoQM3wOS35bQONoDSkfBhf702gFRlRK4npX/X5Dg+o5BUtL4mbU1Q1rPf8YifNuOdbxmWguJoLGsHlGTs9m9Px6X5mXDcrK7hRxzvl6t4EGrdE+zsust2Icrp+IN6GkmYFcemuh0Srst6ecZm+OLYvatBFV6cqZWxOCmRMizkG/r/38tEa5Tx3yHa3ZtXZRqUspvWpIi4jrM5+U+n1/eTmSQDM57Qa8i/xQVOknPAdZQH0e1LbkTlcVLWsnsbjmYTRoWHC/DmtndHxh5NboSggAmuxsPEedossoMejgiZixYsyEqBGVLhK6cix1BhsvQVmDFrQa0vytsTcgyeZxb5ZftRBZ47qkN7ThSO/pxOYbp0xWHAad5su4LjhcN46KXWk4zT7B97NLzzVWxyYijxXXSdOHBtF2XDhyNKgfDcbkaNi0tznuU6WPnGpvGGVQJLYs+Cs1FBhh5qU9fQ7r02C9HlKBLhQF4ioj7Tt46YswlTITamw1bAOj55FbxTgb7iR0XsiVDuI8topcSfe/B+CA4c5U4wNxf077H56vxvcyYL8snJhlUHyWVBTFlYrHxAg6TPTDMdyGVS5HS3yPLgkBaUkIAo9SIQ+9PKmQj/mflCmZduksq+8Gc6ufvU4fVwqCtTUJX9Ul7s0wNGjo2nsYspPnDR7Og5hut2h9GG32TDJoaaaCl59yGdb+VxHnUlmP/qSjefgQj4kRdJkoDmFkGK8y59Kb5lRXKPFcqd/aawX/f6jQEHpJcduez121CuGs4RqtFfw3+Z8c1lZQwctEhfpeBRWUwN7UGLJ7G156Vh5UOKeRyRwYaHC+ZwX/HAVaNRO/W1fizmv/NhXOaWTS68ueyTGu4OWmm2UGCSOkbc5cnhjNOXemRHJ3dJCxzP0jJKxozOYbEVSPsC79zWXALxriBobhe3ywUewBJM4JZMuNU+xMiWR3ShQ7UyKRHXQ2mv3HwlmP/15WFO6uZLwTRPKGBkZVzBt6rTUALVoZX80jYUkAu1OidH+qzvqV5hnsnHKfOtza6lP6G19CQluuxUQm52iih1HsfVX1NHEFKlxmGye5Ibd7UxJ7z0elLWTAlfYEfTSWNHUuU132GMV+eZM6JZgO542z03nIn3s49t1czrZYZlRRrgAbcZ17cc3DRrN5Z5s3d7Z5M6fVWgA+vSOmFh5YvEgvOwY7Z2FlS04FrDLUzHO58l0QrqfMdXcfY80NhAbeOMnFC6faVlOD7ckd7FEgp/eKycjCDV/vFBp4s3TuLA7kKun23kQyW6STXV1m8O7T/xRyGxs+Hb6WEDv9ahifRNUxANlBZz67HEVbc8O2PSyNH9ONI2MqC6jPmSa/cqbJr3S2yKJ9bA9OhzSSZOulTkJI/jaIC++EAkX5urkeDpgYYS/JDusiqGcq9nBm6QUG20sZ5MVXabdwX3ZVktLa0wh3MuhxeiQuXxVicVZMTbsQMh+1VsaUb0ZKztcsL5LW+FM7TKO7MWW19qanpYTq5UfI67izd+n8J46UT3ikskJMRFkbH0AtA3fMy+7VjENz5/O46LrdxZ5YjIDU2WIiSXKhfjeYl9I5t6ecRkAAxLnFluzKmMkK6GjxkL0rFrE5y4HV7ZpLrtJIWBLA9kriD3+5oAD5YenVCADpw4OIej+UTs6NAOOIRBXeuk3N3rfRAAqXmlydZUPY/XR29WxG5XjjOObTc0yplR0yE1M02Qpk4aLSwp1xwUR/JN5I6oe/TU30n88lv+mEgECaOpfXZ3yA05wTpEwNJjpkXukn68GbVncAUN00PH/ZcvNJWsnHYrVRlFsx5SqfXzlNY1M5g5Lbcu/Vh6DHrfulitbKlEqSvw165JgiQZ+NY0WfTsx8bwAZGvEO1MsqjRu9a0n+nIRORZUOw+IGSm/wI9qP+ctgG89D7uGG7a9ZRDdbxazodmhTbxvFrjGDPwWv1Mchoug+v2jyHJ3ShMMaaRe9TCtW/VSWK3ln+B9k9WnGr6Nm6hQbjEF+hyL9qDprjDNsfuyYIJbfNTaVE5NfSMYYJ7SF+o2pXhrnlFey5dJXjTg/WBzGXivMxXvjWOyXhaM5G8e1jgJ2griVwqFcM6odkxhokBUP1mT9Ybguboj9CSKekL6VKZX4RCqQKZXPP6mMvL3rMKtdDyAgI77VMh5uqmKwzZIwpP70eltTrFKLpga+JmLi/ymVDOuT1yTZrDWvaIg51i6e32bMwMtErE56qJEmKfIkMoUC249EcTrPvSMRzl8y2Kbg50utk5bcmigWWS8fPxuAAq0c4aH+xdwGD2tFKUcBE5mcKgukDbcSw5oR2yMUgYNcKcwjxKU5AB5EcHdUEAc+noGVIJb0dOs0EM25BNDoP1SS13Fn2+FNjwqBoWH4O9ScbZgm7o1pwVSVR/FO3yE4R1xmac2i3dZab++FeYcrBtmf8cNbrFwTibYgn7SRQUR8Fkqz0eMkf9dg3F4ToNBZheneKARLS/K2OmAuiyJDk8uno8ZjclNaKZbm4UN6NnyduBkuxLZbhIPcHA0aTqpM+NZdmtbuY2RKJbPiD+FhoqTJ9BA8Z58wSl98r74tm50PoXz/GDfVObx6JIS4NksJUMrYfux3uvq0Qv2g7J2Kwc4Z1PUsGgwrWI3uPhsBU7ZkV2Zl9/bcGVvUO+z48CesBDFCueqBM5q/YyV/zp1Wjrq2atDg+nmBXkp2JeH2yPnU5gqW1jxCllbFugceDLe9jpWpymD7lVeE64Qb7c/nkqbOpeuoo0QsMHnhefpgDNUGhbsr9mvv4W0ZhwYtU1Jex+RPw2ok1Wnp+HxmgdBO0N1QbWQq5A72koXO5JVsSfjYFw+TExRo1TgZeHN+kkqrw+l8dyz3vE1wXpuIx91ouh7sxi6vHZLsGeyc5nLDI5331GosFNDGPJUrG2OYVDnuyU+gQKum/6WuqN6rDEh3ztzXi+5aDY4PxS3+ovRGA4KFBQ1sxQDQ1WFq5t93Y8Pnr2P520mGpxhfz1f2VwyJhVa0toolAuPsjGYMHA4q+engOiZf7s3d4dWZsvciZ5fXw8EIEfKv680AACAASURBVOVsX6diN1QfU4E7PT31FjsTLCwoDPDiSkczYvuLU6eZ94z/HZr+cRqnP9DdlBd7bAAsyNKqeFGRSUkYPOecUf24oSboNu8DbhTmYiuY6RxzdoYnt9XiOH1LdjVyX71tUK8pt69MQydxv89lmbWo/VmO3hP0pxGcqvJ1VXGIGP/qcja/3x7L34oq8m9nWRlkvyQ0WoGmyjxye+in5PYYY0Von8Tul3BGTJmArFM6V79QcDTPFIfFhjumYGmJxQfiza/R7BDdXDO9kX7jkdT3g6l+UGD3r0u5MChUd3x5TDDZvZohr1I+83gAZ7kYEPPbMb5UPeCnMepSSsY7QZLU9qr/fIJRP7coduzW+GAmfJBA029DqDrf8KHH1NMHaaYUe/ktvlUAw+skCy8n4z9zHBETZ6OUKTiwtCgDZPVDJxy6vliqJG1EEB++/yvLPN3K9HkP+wbS3CySv/LMMN+qvzpCeabnVYq8zZQLp6hneoABNZsbxeadAQ044TkXgGrHs7GeICaLBDVIRJ9Bba11yRx1qA+Diu++Ht92CbR9/nnNIvtT9Y1Lz9zEH74VyLEZ8/HYPgrP0SX/DtbHHNjg/icef4zAe+x5PPP0/72M6pyGyGA+zY8hywBwOGt4iFvTwo9mytMG2ymJajNOUNdzLFGd5mAjFG3Mu3xqD8x58Q+S3zGTXpYZLKNsznm3ex4CMqYl9MQGw6OLxuThAhn+ymya/vI+rkZKkKg75AICAjfVuaRNy9MtsZ085YUHZd+6oTAlldqf3KHH7E5FBxUKLn7kzOdtthKV5YKz8j4Lj7XG+YCMhzXlFJqDy4ILqEsYXaX1EK/JE51n0u6jD3QbbcnruKO1NKPO0kSmV9tDo9Nv4zn0jORg00uZhPAkwnHD7/a5TkVLGsfzzF7wTml4jjxN77YhdJ17AICFF1rifiyp1GBQVoYFGrQ86BdI5UNXKLxV8hqm3MON3Nr2xL26GA1azGfZGflfYDi/+a7B78/x+IZeMUqGFIhDeA0a7msUnGz8Kxo0xOZr8J55Q+/P0BYWPvP9eo5JYR3VgQLiscTz0c308VbKz/v9qq01g5ZQVW7B2bGh+FiNpV6LJH5wWU1thRi8rLNlHD7TUw36LozmnNH5xktGvjUxmBZmEYBxIpKprcT/H88z48e3ByHD+MM7xYFI9tQVhZhdOFemKK1XWC6DvF7j6M9h7Mmx5re0JqRO80B5VRy0ZTZyIuV1DWvaLqKpUsuxPBPGh46ievg5o4T+jaWtKzMxxQQZtVdpKLx5yyg2Af466wku+3TrmwAjLg7E7rr0fF1jYLbjFM1tx5DRNYfv/LcS+07Yo1fMSdfkMqD/OOr8dYZCA4XeXkqZksclQY1ODaJ6T8MiqhWULwrXWjTfHs+RBuWTkJ85IBDFoDvcjKuK1xexqO9nlsvn/Jv8J9X3nGYanmFTQflyvVcNHE1OAeXjnLZrI2AteHDZ4DXj/xovTfre03x51w/Ta2n/djMqKIVqM06w0cfwFMgKnuWl7DmL9pQsv415K6jgZeel7TkrqOB/HaM7p8LdlW4X0xmb+M9svFNBBf9fMapzJqxozGs7Yhhhm0xHC/1SlSqooILiGMU5tc39uLXVh4T2i6lpco+GC0KemDdKQ+5gz5D4qzrFucd//0sIDbzJecO4gsdyOzuSvwlC7lMHEOsa5Y5VSVjYlHbnH+q0mjbeMKwk7dL0f1aJMWFF4xI32f23+fHKSfrFpfLwLf3bZrBzKtxc+GnNIs4ErMHn13Es6/IaNb8xLBc2abU/k08eppdVGl5H3qX5R+NouCCETE0eWX1eTnXu8mLP3Dm0O2+8UYjM0oKLQ8J4e+t+7mzzxupQJXZE/UFS14VMsktkfVYVfJePpW+P4ZI/o0Cr5tfec5HbGT+TSaZUomnpj6alP5d/CiJhYVO8zpiQ0H4xvVuVPaVPplSSNPNZh8nr2pS9qTHImtQzrJ1N6nFjc13qm5owyPoWt7uoSj/pKQyO1r6x+yR1TRWsfViN2lPCjbIW9XfrBaSq1fifHEHt/mI2T6WghuSN0KKyFTB+rcfLieZsHOYyUyIzXYB7RrEZP6EmAH2s0unT5Ffd8eTCHNodHI/P1Ou43i6qIZWKv6nApUneuH5qeJ7tw76BdPn4EBpkWMnvM7ZS8Zu/gAyPP0fg80MmZS1ouDq1MXF959FlUtEIT+7hxmvfHiVTk4usUCP5Oyho15iZS+ZT39SwDDfJzqlwc+GN3Sd5w+oy9ZdNxuWLUzw/G7HsXNtUn04X3DDvcAVnLuiOy8L/5u13x2O/X78fW+5YFRzscFh6E41WHCgsrvVnsW3RQdwmfWNWVVZ51dTbfkp/D6pG5SIciS52/GGwG2kN5cjqPaCJ8zVWuRyl1fkeKNsn6/UZJ8956PI+DSHrzUAu9JsHyPkmrR77vmqJ5eaiEjdPIo260F97bun5xaVxa6sPUQGiGNur53qTtduJlUC1w/fQnC2q+9Wn7W3OZTOp8jxmZ3gWO7744Goc5eZ4b5yIR0zZe+Gn2fPLQhSPUk+ztCqCTw6jztCLeju7JOdU1KyB7+ZrvG2TQv1lk3H96jRaI2wYe3dUEPuaTWd4txEl5o6a7Nd/zhn/oTvn3pz7jDMag1sTg1k//mcAfEwtcNszDJeaadSyvscqlz/ZnGXD+8fexGGXNbdXPKADfihJNmobyopgaYnVyBsokDPs+qukTHLH8oS03aD/Ke6MCWZHo5/QYI7nrlF4jorCUnMZQHJu8dWvgni/chh31Lls+fo1rJ+obnGUm/N6bE88Jkp3zMI2jVFQpOZoJROz3LSqf2hYe+XtWmx13AZA7UVXKTSwaPkxCz+ci6PcHE3MP59PezTPlJ9i2+Okh9LCZ6PX8N47YwG40docwVbD9QtOqI860mGLqLrgieHbmcvMDL+pyExNGeAsXnSxoXWxPSH9AiwL7S++gWm6NOnSx3QcfpxqcnNuFObivl5jlB3DFw9cgFwmELxrEp4bir4DbVBDBKK4nlYJN6SJkgGkjHpWfCwqcCUt33lP75JKSc5p0/wOwiPh3MKU1OIvCnLkdrZcf9cbRTZlLpTWBjWkuvw4DRZ9QC2KzpHb2KDxchGfRMfqrV7gvk1FdA8FTZVa6m0JwTpJrHA480Fx/dNxUf2p1afsosJyx6os6+mFcEEcytY6UsoJBrC85Qq+N1CWJCfIgwHWYklbxylH+b1KK5zmnDTKBV8S93PMqWqAbcHamq+rHkEDdFn4ATUOGkfrJ0ipRq2V0bxhAhdHB1FtxzUKajrQd/leNGjZEriIyY2HoY28ULqxp7g5OZio4Nn0SupKzjQnbrawpO/Ag0yzv4i2Tzr8op89/atSmtZn6Jrt9LS8x/R0X101wu33ggmbEEqcqjr2iiw6W2RyrTCXkI7vor744oQEuX1lRkScoonyFu/2G4fNDzfY4P6n+JpMKLZr1yvnemPT567ekg9PMjnpAq3N83TD3J419Jf8GJFwmWUtA1HfviO5HWWh28V06ipT+L62Yc4pMzHl3oDGpLfN42KbxSgQb1LdEzujHSB79iYrkeRvgxjdY4+ufE4qLc/mMc3+Io/DMlkaFa9GDqXG+3mok6QrGsqrVOHSBA9MfB7wY4PNtDfPRkCGBi1e+0bgPfEy6owMve0mhjUjsccCgEfi4iJXvwzmwrBQClHTYcQYlLueLfo32ua5V9+HnpZi5LCZpRgZu7KuIcc/mMk3bwxko48TocPfBOCtc6U7JoDM3JzOFplseViXu/4WrHMX72LXCnNw2zYCj22j8Ng2igHJ7Tha/zdur62ub7OL0cIsG4DYAmiw4T1JNpb17Mju6D9xDLfh3pDyW9P7+djr1FQ8QF7XyyA72oJ87FaG4zEomo7vjKLF2T58k1aPbXV2cT3M1ihtlSmVrO4/l3n7XjfY1qqLTWlxtg+eO0bjtW8EeVoNkQFrGLFnH+lDpX/f6rt3cf04HOc3LjC/ZSump/sCkKbOpc7gSEmOKdTzZsnrS0t8zWWX2IkokFNgKS/xPc+1q29DClIsdY9fMcvn0s+BnGgxn1ei3tHNFRWZKi7kS5uHqh9ViQVH92NM9xF4jj6F5xjxL6O5eFP403856cOl/UDXvgjWPf74Sk9qT5Y291JfiKfTa32JXVyX7M4PGZFwWZKd0nA6LMdVYcG1rvZGs2myPxKbjpc42UfcHW5/46VomxteeC2TyfA3FfCcangxu9tbZ7HpeAnPUaeoMziS12d+AEBni0zCvwot5eyyUXjzFi2s4pHLBFqvnCLZTvwUC1qZifpUA5LbFXsty0UU+JLL9E8p0PsMz4/P0vdS0Z0xtl8YPd+bRJVu8bQ7/5CdKZHs2LmKuqYKHPrdLJNNbXYO27PtGFXpMpVjC+ji3JjKXRJKDAy1HTESW8GM8C/0/4HkdnZUCbqJiUxOo9khaNsYtq+J+kI8lVeEU7P3eRZ7upPTs5nRndTqhhjl2zfmJ6PaBVAnXKJzfFfsBXNCfw1D4Sp9iwvB0pL0zeIylCYvz1hN1OE0+wRdnBvTvX1/AIPa+hiFuytBSjUNZozB5TPp67GJ7cRes87W0WS+Vvzf3u4TUZ3SbfewYls1lAW9nVOTk8OdOe7Fji2cNZt+calMsBOHsGfz1dQ7PqTM80J1RgZzJ79Fjjaf/jN2laHRMl1ASi+qV+WPuhso0KqpPt14YsKPsT5hmLp7SZjcvM9plZaqcsM32imJPtXE5an7GlNQSxc/yX6tLn/5rTdWs56LLMd4jh87XqxDrTbDONeC96w7aHJE8S+ZiSm5PZrymcM54gpUeIXpvx2DpPQ9q50xNFgcwqFcUSzL08SUAdZiLxkc3Y/PWvTAte9ZvWya7TjF8Tw7hthcR17p+XMgh6lX0KClU1w3KU0vV4L3XeOrBYZvjPQk6qQrHMzylXy+wqUmD98KRKjnXeLrg23EQNCHSb1RG6D/U2gmXkqPr4nyQOHkyMVpVY1iS+5YlU3d5uK5c5RR7AEkDXXixrRgElc1om5EIUfnLyauQMXYMe+hjdY/+itpKUWrUlHryxPMmhnI9K2V2O29FYC2Y0djv+dvCiUsuAJ8FPouTuNn0ys8nm8iOuO8S1FMpPnu6CB2u4fx8z1vFMPkRlN5k0rSrEC8lmZQ4GDB5R5Kcj7X4rTF+D3yLzva8NGQeEnn2q/PZHutbWRpVYy91pG/t/giFIKgE+qPIkurgp+roC28KrmNef3FQMq+B/XAgOS/IfFX6WOVzutx3RHaFhXba5v7kf5xFkkNFwEyNOn6B26eJPZrF/xMFXiONJ5kauzbYcWeZ6jzmNx7BMoz0j7jpRT4SpkaTL5fFoN9T/Khfay4/pRdmU+iulNn6n0Kk6UtEitqONN01xU+cjhncNXMP0nyN0FcHBJWLERfVuR2diTNr4XSrICYpmueeX3ItVak97ej8Ip0xwRRlC1Lo+KtmsGlv/kFaFr4cWmYnNWvLCFQiW4pJVOTx7oHvixd2hnnVXGo06XnGo9OTKKzhSgUZozrIGFBUypdUGBztRCzHafI7tUMm8OJZW7j85ZSXkrnLE+SZgZy7s259AnsSeEN42x0W96oWzVi79qlkpzzn2JnSiSNTw+kWg/pW2Y8iWBhwYMuRWu75nfzkR8ybJNjEDf2PTMtFA1amp4ZQNXucaWfVAozk8Pps3Sy5GqsCud8RG6PpvwRGsr0dD9O9q+P+oK04WIF/00UrrXovucM8+JaUWvMPaPq7ErlPymNWR6Ybz0FoXDoo+YoL5TPFg0VvLwUJl9js09VqnPxX49ZlMb/nHOCmK6npMIxK3i5qVDfq6CCl5QK56yggpeUCud8ydmZEsn2lNNsTznN1S8NW6b4r3JjWrBOeCxxrnE1pBLnNqN6hDW3Jrx8322Fc/6HqBptnBBGxwv36XD+QbFjimpOaF71N9i2ulUjttw4RV4XaTtvP03q+8FEjZvDvlyxNNExQkLaZgnIFApS3w8msdcCltY8QtSUUKPk68oUCsYmJpCw8Nl//83JwQhmZc+gMopzypRKbk0U7257U2OYmRzO9Y8NuBMJcgQLCy7/FMSQ+Kvcfu/lu6s9ieZVf5LW+Ov+/btTopApjB9ruz/YCCp8gpyQSpdZmVS8B7oyz4GNa8Kec1LZUfx1HhOZnLXzZxqsvqdwcyFq4jwUyJlxtT031TnYxhpHiTC/VUNiJhYvnkgc5Wzw76ZpVo/OFlnYOBVvp9yxKj3f1q8i37CWCHKWJB/BWW7BnIy7dHYNRFtYgPKwI+fGhNLpW2kZLdvO73/07BgAvT6cBx8++17vTWPxmFB6pr/czo4d5w/onvdK6qh73L9aBL0sMziXX8AX18R83XPRbmWyC+K66Y7QOVjITGn8YwjVV54HYN2VP+j8/iSsNxguB3K1UJS+iAxYQ/QVDZ/1HiypUp+m9fl6w3K2ZtuLRcuPDsvt7Oha+3yxnbn1RVHDmZQ3XFg1aSZggqPcHATDerkp+3cgIKPO76OpE3KSEZ7voE2Q8O9+Clnjuuz7ZYnuecA3Y+k66iixg8Jo6DkI5zekf8aVbuZMvNkMp6eSMWK/rcXXNjuJyCv7iMIg57wzqhl31eF0/2EcTitj0BaIGfn1bKRV1csUCm6udCzz+2vuLZsMxpMFtIWoOXe9qFh72vU3+OG4GQU2Mmwvi/acZGXPvTDfeoqgepMRCsF57gndBW8lKNEaOC7J7t2M2+rjDPhyCnZxOezatJyGpnCzhS1OemqdCdbWZH+dRWNTOZ/37IIm6YlyPPtKfFf1wPNPLgVFNScuD3Ph7PB5GGvDY4CWZuIw3vuzBNSIJW7GZklmTartSeHgrRZ8FnqOaXX/4NcagZKyx+Q2NnzcfTOz5/fG8QmpHU1Lf052mEPHmCE4UPZtSvR2TlmTenAuETRaZk1eyGctelA15YRODU1ex51Pqqxn/v06+ppG28iHU01W6p6Hq+R4m2Rj95w7utnBs2VOsZ6T4cF4uyT25NjhMTC69BP04HlpW/d8ZdgYYLfqe5cZGDeQysvFWsPP7/jzedVITF9Lgzn62Yr/zpfEeguIzFej+ds4KXYgOr3/nhS2Vile6tclrjuyrLsG21+SWVNXhmUshOSbjE8NYk71cOas706t5BMo3RwAeMvqLmutpZXnPXjNh06We9iwOKqYOuD9OmbYC+bcu2mLgz7t1LcB2jPn0apUJH3fiCAzVTHtGXldL7JCtRzNs2b9F/pJVcjt7Oi58qDuueeekfzQ4Q16vD+Zd5LbPVOKFFtQoJfc4P5Wbvx8z4uuFg9QuLvq1TYppKlzqXH4WSW2siAzMaWgfRPGOh8kfX9RLx85siECAif819E4uuy1l1fWNSSyxywABi8fDy9I2dQbtZrkHFGlYe3DanjtF5Xik87VkCQH+TTLLjc3ip1i2NnSxU5Ua3D/Rax8USanA/DO1TaQcltvk3I7Ozp9fhh7wRzNE+0VLC2R9RL3ma12oJxlSh7TpfUZbquLf2m5NW24nuzA1LlDsdqknyaqNjeXn2Ne48u7fnRv1QfPYWdQJ17GekMElxZ646woii5OT/dlwohxetlXp6VzsL4lXeq04KsDG1l9/S/Sh5WP9k/C/Ka8unYKigPS9naJn9+QPSsW0MIsj+o/FfXK6fWtCP50HNH5Gj6vWrptwdIS11PmxL6ygodaDfWWjMMkS9TbzX89QFLbnkaTk8Pd4Pt0cW7Mpk7BjGx0jAxNXpnn7KWhVBSCTEb6sCCSZgfS8mwemgM1kTtIl23JbOTIa+Zi8XPh1UdlafliDV3MLWfUDx4879QSkdvZ0e2vBD60j6XxD+OK3fwSFnkS7rcBn6ND9I4/GDTnrCY3R6ZQiHKVgpytS+diJVMysXEz4ufqZ0uTl0ft/jGcRg4USX3cfzuIJV/NwtPEFBCHun91rI1JijQ9WE1ODh+/MYRB6/9g9+c/04n3sV8qTaJC7liVKyM9njl+odts+rzypqTczcI2jdnffhagxHdDCB5PiB7bLxPbuWp0C2ZUP/5CO0I9bz7fvpYApRiUcZZbcH54UXRSgxaVtoCGh0djZi6th3+a+02cmFR5MxnSBRWe4Uj932jXfgQnvyyKJE+zv4gqppB6+0fjNTYOTXa2XjYf9HvW+dJbS19GSXnHh+G2B1j2oAbVVp4rNqTd2HwRoEBdqH8/KNk5D69sSvSkcL5IOMkP1zvha3NTp259R2WNsfb2WPLVLHxMioIM3741CFLKri9bEtroCywf04NOK+fx3dSlzFhaV28bgqUl26L2POdVOYWXkyW1LaWVKa4KCzRoqfXnswEveV0velb+jRuFLx7q5dWw1jlmllbFp7deoYVNAh0sildhRLWajxwZYCqpvf8Evy+dS3Khhk4RY3D7QY3axpRaPyaS+NoStkZXYlnPjmWuLsrt3pSVfqGAgkuF+kuHlMT6CT8jl1kwc2MPXB4+EX9oWh8/0yiSC3Pw+vqB3ltTSHZOx3kn+HRegNhzatKI1AhoUrR8cqcxGS0Mq1IHuD8oiCVfFzmm75Gh1Bl1GR4Y5piPURyIZOAr/fj+0AZJ5/sezaX+X4NRZZuyrXUYPiYm/Jhel183tWHlu3OQe7hJ01fVihqq09N9MT99qdgPmt2rGQfmir2f986JeL4ged/0j9NP1X8WEI8by3B75r1yDzd2HNmsf1vLGe/VY4kbFIaNYMZbNRvhihgAFIAbgdCJRnx8OYaNe3+hV42ybbF3u38efqbiZd95w/u4I45GAieKI7EaX5ddx0EwMyNuZgM8TaJQazVcGBbKsjdrMNRGVLr/+Z7Yo3c+ORqXBP2vW4OTELSFhaBRo6jmhICMkx8HGBxwSJkazPKvZ+JjYkK4Sk6LqeOoM+qy3nOB0ii8cpV5t9uicNFv8yKAgzc8Odd8JQntF+NjYsIplYzw7l7U/PoEn/d8h6SvpcVpTRuKN7ZhdlFcf1fU/ZF7uHH1y2B+mTkDgKWZ7vjMMM7I5Gnk9pWRV6lSLrb1xaFhkWD3zUnBz00QiC+Qdhmb3xZHFjemBfNJ1SNszrZDnlH2JIf84Lrs7jQb3+Vj8Zs9jpkZdRhoncyoGy1p9sVYdnzZhiytCotD0vbFM1oaS+wP1Wkf2wOzfX8btH2cvJIt0SHzeDzM+nT8CCrtkLC1YGADiHixyFjy10FscZ5D78zX9G6n08AU6n4kBqU8lt+Gu+mo74tSH5qYiyxtFsPgeSPxnBSNtqDsc7qRnscRkGEnmBE1YR7CBBkaxOCPgAUZmly2jH0NeYLhqgAlETvdHdcNYLrX8GUQQ/nZaxMAKm0h0ZNDqdNgOLU2Cih3nxbT795rSkPTcF77ZBJ26B83aNDrIndnQGD3s9gJ5nyyuT9uyWW3o7z5gPWZAbptDvdPt2Zf8HBkJ/7GnnBUnQO4WijDLl7anN4o6XuaFn5EtJmH6WRLvS7Ep5Hb2PBgfVEU7pu0BpjtkLb1nU/YRfL3uZC0Rkyty+rTTPeXtMafu9u9OPvuXDZm1UB9P1Nv+5qHD3GbFo7btHDUiZefsfHOHyOIf2M+KROa6GV33qauaNCi0f1X9DhDk0ursClGket4HnvbzkGZZpy5mKEEPhIY792mHwCJry3hrZ93c2urDw1Piw77UKPGLq7sASHz41Yse1ADuUxgYa0/uP5pMDNr7APAdbt+66nq2EQiGhZPupCd+Ft8IMi52lPLJ1d7YHpSmhSKwTIlmQMD+evH+ezLNWeWh4+kRuT0bMa3MxYRpCzqH4M/GUflFYZvvCpYWyNTKEgZXNQ25523IC0DdeaDctvIByBjcBB/fRtKg0Uh1Pqq7PoysoD63P6kgIgmqxEQOJBrwegDbxtVKe5JFE6OjD9+kLbmKuotGqdXW5/E/Igjmzx2k6HJY1DN5ga3S/OqP3/8ugwQ9x8R/HzJdhGHiDI1mO2UduOWNa7Lru2rix3rmdQJ1avGkyzJeCeI8O/C6NrwNdRp6S98b7lpCAX+XcAnDmcJ/GocDoukOdPKa8dxkJvrnjc4MRjXQYnlohz+TyKvZEvsD14c6zSTobVa/NvNeSGpW3w51+xXfBaOkeycWW8GcnhWGGfz1XzkZpyqlHJBJkPu4Ua3bScZVSmFuuEDcPm0oEz7+pSV2qfNmFf9RJlE2Yy2kdHTfOZwDu9DwyQ7JlDMMQFc+sf/5x0TQH0/E99vU8Uk8JccxzlmfJPmjds6w3uPD981nlBzuaDVok68zBbfKnSo7keNXheM6pj3hgQxr/oJ5mQ8uwauD/9z6nsVVPCyUW49ZwUVVFA+VDhnBRW8pFQ4ZwUVvKT8Tzrntc9FSZUO5x+QGGa4YJTg50vyhgYvTWZNBf8/kOycyRsasDc1hr2pMUxMiiVxTtlyG/9tFDVrEDtyPt+n+2IiUxPfY77BTpXUz5blAStR3/1nsmrSRgaRNiIIuWftf+TzyoI2uCF7U2OoF/nfut/fGxKk2/R5Z0okbc5lc2+I8UoJE1c14rsrpyTtHC75m7Q8ZEmmJpc0dTZtzXOI7x2G4Cd9H8l/Apl/Xd49cBS/78dwoo0zf7T15pM7jYmbLr1cSO5gz8o+YZjIyi+Z4UmufhVExGehHPtsDndeMc5elcaiQKvmB6fyU9LP/sNdJyBnDARLSzZ+MZ0uVucIju6H15YxdLP+m36T9xpsW+7rSeKqRlxutxw/UwVbNyzm1kT9hOok59ZWWRjOmwuL32F2pqym3ajRklPuSkIWUJ9ZmxbhYSLmcv2Za8lcj5I3gi2NxEHWLG/fCsfkIq2f8z1rYTtHv3pAHYIcx535NFVqmXqrGWDEQsYSSBsRRNS7cwCxol6fOtS0HZ6carSeqbcbc36wV4nvETKzi4qPXzLShwURXj+UAi2MmjQBC/Qr5n+S/A5N+GbBYpoo1XQeEkHOKAAAIABJREFU8B7yw1FUJoHKwNuRk9n85XT2tAxBOCZNzqbWSUsW1hB3+a69fgzeM66RvsSCqPdD6TSr7KJ3RtdvtJ58HfU+M6MlETjMua5zTEPxnndT8t6eJaENrMfimssp0Ko5Nq+ZpORrfYj8YgEFWjkmMv3kLgC0j0rRvnM8A3vE8igBmW7/S+DRlnhGa+5Liba5H7MWhuFjKjD06mvIDxfPU668IpyRUcMR/pauM/Wz835ASYQKPCZFUAjk5ovpo9qghsjC/y6THaNPELbU2Ynav+Q7sxRWuBSpwt1U5/LpjCGSbRm6QezTZHiLQlBpmnzsVpavY6o6BlCgVev+PHeMLtfPe9lQdda/OKEkBi7bSX1TEwZc7kh6h5KnIoYIoGUODMRKpmR8ahDftXtDd7zKHDFL7Hp7yzLb+s/M3jM0efT8dgpVFpSvE+hDVscsAO6pi1cmaFr48WBPbS79HGiwsDKIjnl42RJMZGKv2fLvvniOKvvUQVHDmc0NxQTym+pcuvu0prtPa7rWbU3YfTGo1CyyP9WmSR+WKxJT+ej2owqcpvUl23keB5qIOrPR+RpMM6Ur3/e1vsnmbDtyegtoHhpHoPoxcl9Pek3dx7E8Bee/aFBMDcPkrv4KgkZ3zvGpzVEkGGfe4nqqKCd1cJOekvJ3k78VVePv7fTkdsizE3LtikIs1laS1L6zwSsBeGP3e7pjCYsD2L1hGd52d/it1xy2nd/PvKt/SbIP4jxz39KFqLUa1FoN41ObY9spSS8bhTdSGN1jJNH5GtqdGIP6wQPx734m7qaPCpr3VDYov1R99y4n77oCkOdYfrnEAyOGSRZOe1xUv6JPJ9S375Tybv2Qe9bm453rmWSXyA+9+6HcVTwwFjdOLL53/+VGmW0a3TlPLfRHnW54lX7mwEAmO4rK7z0Tu6C5d1+v8wVra+7t9OTs4Ll4m95iWb1VRE4NZUj81WJLJ4trb6TS6ZsGtVXIk+k+c1TQYQButSpg4qixZGlUuCmkK6kHDY+iQKtGg5aQ1GCSBzhLsqONvsCXQV3wGFV8aN/Rwri9R3lhIWGe/Tw6/BpOh/MP6HD+AbndjVM947PuCoFKiFCJ3/WTyCvZElDvEnKZoFfMw2jOee9dMXJb6ZIRNEZlMnJ6Z+KmMONArgXqCXZ6F3GnDqvPCf91vDLtPaZ1f4cPeg6l0c/jSFI54r77AYqaNZDXccdUJjN4Luq5ULwL3xpUn0mV4zilkqHJy8N07xmytdKHimkjReFjE5kcARmXAvIMUj0vvHVbUmF5Wbl+VR/JZP1QygxXkr/4RVUEBCbYJRNil4hSKODA/AVMT44waAe3jMFBfFJVVEOcGNu32GvySrZcXlyLdW77yFDrN7Q1SrQ2bUQQ6z7+mbZjJmJ+2PBllJsTg4gMmAfAhF/fxSVG/+GsQ+cbNDo1iOqrI9A8qrxxioZjs8xIXFWX+AhxDuO1dRJ1DAjLP8nPkxcBMDJmEM5c4Mr6BjjKI1n3sOxbTDxG1TGAyM8XoH4UTA2K6YsdiUZp59P0SupslLm8z/vxjGn4Cq99e5TwM64U/l975x3X1P39/2cWU4aigCwFIaC4EEXAqrVWrdaFo61atdY6Eamr22471FZRcY/WOuqqiraK1bpBse6igshQwIkKyoiQ5PfHleWAJDd+a3+fPH3wkCT3nrxJ7rnvdc7rXDVe8rJYLkYFk9hpDh9cC+afwPIbZgwOXI8IZfGE+TR6O5+kYktGLBuH+zTdc1rjp0UDFgSdeAOnITcrSeokR3uR1GYZ97UqWq+ehKceK/rie87gpqz/eAaecgsstxpnf3NpRHmtAc9vDFvSvrbHjRNBv3Dlo8ejPRxq3S/7/UjPHw2y/ySsJeW9e/KCIDYHC876fWIXveyourbi3Tlry4azxVo11nMNmxdXhaZ9AFIkpOzyMoo9dV4eu0/6875DIgVN3YxisyK5miK8fjRsJOITeYS2X0XyjfNR7r1ROZrNaW4c07ya03bpFFxkBXw+dDXakGZ6v4fTB5JKdXlktrYkvbiMPE0RLVdNxPMD/W6Aop3zWkgNPOSWHC4yXgGbALPyZhm6X1pv6UV6Jfdg04iZyBopAUEE+sbYULq4nUcZM4bAHyOwksjIGS4uXOvSW0LPOOfqywCsa7GU5J4LUCrMGJTWmXqj9OtB7nnIedUqt2w42+6LSMx2Gj/yJmWoDA1a6s/9x2g2628RuvpbzYyvg1us1aI9Zrg0aumCov+EJ9vw+CKOTuunEGZ9m6i1C/SyPezyi5BdvsgktbEheaEX97UqglZPwktPxwQjOOfvE6YD8Pb2kWJNGRX1zZuovq7Lt1dfYfaOFXyaeoJvjmwj4eO5nGhXE+WYBOr+EMeQ1B70jtzL/df0jw1OKxFuHIeHzCR5SStKHpYVK1WnB7gS7VOthsxjaCnbz4zIDsVxv3FXFv8vcOlivGAPmb0dAC8tf0+0rQV3fVjifpjkhUFIFI/fQBpMicd301i9A1+OXq5f1muqXm2FbFsNktovZ0R6d4McE0QqIdwIDyXho7mVngs89ib3bgoiTMoRht3tt2cJS+VjM9txubWBoXX6sMeN7X5bySwppOOWyfhE6l7TQqZswIXI2ti732VnwHJa7xa2VXznFCFJStO7QtbmzAQUEhnFWjUKiYzOw0c/k17TfL8zMT47aTl1TFmZB2MxPuUCnS3zefFsf2q8klr9CVUg8/el54ZDtLBMZ/KEcCy3iJ86yRr6cHmaGQlBK2j3eSROv6dVmh9LraxodLCI/Qtb47Ck+s/mj6zH1RBV2hKa/xKJ54fVn/80JQSDF4QurmzByZd+AMxQ7hhFwynCKqLLvYuC0LQROHHDVa96hoai/tqRyOlteKv2QTy3Fut3bvIlfMKFv30wbVA+1JjVortyeEVKh7IKiYxdhdbPxDEBBjofRa3V4LghUX9N4GrYeKsVnd33Edt4DX2DRkCC4UPR8+Nt2Wp3mbbvR2C3xTjFkdTnL+LaB9qOimTj1BkUfSJlaOJQzJfWAqDmuxl847yfTjd1kzVNLymgvry8bOB9rYqgnyfi+bG4m57BzmlzwgKrjmY0nxeB8vujqI0oMZlSrMJbYc6IBofZzLPPkZTtPcGlVjCVVsgxbIPbWBRr1Uy+2o6ERQHYZJZgVkXJBUO5OySEntYJgNzoKvoAf29swq7Rx+hsmY9WJkVcfWsBu9XGWVGvSO1F8Yw9MoICdxscJ1+ByPt8UW8rMrQ03D0GHx176ZHDIsnoKgyRQ19IJGdQLeqnih+NPJcCXzIfL7bu20Db06/rHQ1jonpKpw3K7aOfmRauCd0x+rD2WaK+mEp310DsMDnms8R9hzH6NBPPiufSOU08W7q7BgJgifHybk0Yn/9MVooJE/9rmHpOE//T3B0Sws0gIeroYtgCmie8idvnWjRnk59pHR1deC4XhEyYeNbI3d1AISfm4G+V1CBK6fnqYDSnzv2ftOWZKr4XhLVme9Zxvkg9jqZ9gDFMmqgGubsbN8aFIvMXpzqh2aN/4eD/K1K/C8HnmHmZMl7pT/rX4sItZQ61eHvPAbYcFKp5J6gkKP8YTcuZESj/EOq8ZLc3fiyzvhhlWBv+3fqHES2gqqnA0FRbeV1nzn/nwvmXF5FdoqJIK8XrYdn5Xkm9KfrB5bEkVkMo3UpotjAC968Mq6j1b3Dl41CKHDUk9YuG0v3YD8sXePTlWmQoJ/zmcedKIStym7L1s5ex3iR+P1HTPoA/1iwlufgBE+sb5kg2B2vzj9ccwd4jr50ZNoeen7QyuH1XhvkRahGD75+R+Lz1cFvp4X6y+n0hdazAVfdB44z0Iwz/YoJRSlZWxKhzzgsP6ooKr9KukXJeuYhmSyOpG/cAeX4JWS9a8engtWz13QKLoaer4V9KRVKKVdT78bQovbzY7FMUa9X4rxyH54fxlLwUSL6LArP7GqOEmYEQV6rxdsd7wUW2u8yt/gQdKXkpkCXvRtH/UjcAvvKIYcCs0wzfZFipQpuDtcmPcERz6hzFHwsxpt3/HF920etLwTv2dHUfTU5jc+oeEgIlPli3hhAL8fnCLjPjePNkJL6HEyt9/znDQ9gwZiZgjnO87ldGQ4WC/V9FkfCJBVKJhk8mjsQ8pxizi9mUXLtucDufywWhep+V92buh2BldHO+XeHMmubLedClJWaxf4t+j15HRuOZX3VZ+qqQeXtSrD2OBg2H3pzJkX51aKCIx0aqptvfoyjoHUiDFRqk+w1XcZM29uPqNEho+fNTj+lwtj/W6B+/+uqcvQSaychvJwhhR3YI57X5Ow1u61qvWJr2jsDjFHjaCoH+DWfnGRwaqE5KQZEEzruFMMhbI0NobHYPME62i3zP8cduzM6D0/FWmNM/pRs1Ys/qfONuOTOCPN8ShoQcZnTNo+yJFjJadhTYEO2jNLiNRplzKiRCkLbMSLqtUhubSo/Vd+7g3Ps8U9L6kjfOOOFmdVcZLh0CgKxcNsNOakYXq1yUCjOcZOYcb/0T5zstJGWQuHtf4KpEElqurvKY/Bhnve2qX2xBlxqJhJwqz9qX7T3BgTuGX0gAFg+TZ5a672fhXS/IMl6ydd9xf2EjNX4aWkXme20AIPGol14JC86z4lCOTuBIMwVD3whHGTuKvYUWvGp1n+z3DFdYMIpzFmuFLIoP9/cTZ6hPIa2ODWHLhb/4NPUEwaeFIHSZQy0KYz3ZrNyK3VxbI7QY5AXGWybfeN+Z5ksi6Rg+hp6urZiUbXgV6/y+rXnpbD7bs47zWZ1TAMzIaUTDNePo/UIfeniF4rtprKj2xqxayODvJ1Lz1XJlBYm5OR6Whms/bbpfG5eYcrmXbSM7iJZEGXAhm5isY8RkHWOSQ+Wc05isYzz4s54o+6XkDA9h2eVDnFA506P7ELzeM3zuKIk7jfLtv/nB258enQewbEwUModaBtkSPay9/HkoXaziADnuv4sLB1PfuYPbWxpSTxXT0hxamp9i1Xl3bGRp9LK+BYBil/ghrbEQUrtg6p/98Pm8fCgulWiQIsWQiO+9c+ZXevzJjUDODlLS4Fw8xsj1ufxZKE33B+Cz4mTlcU5jb75y/IVu6K5IXpGo1JewzRSyc0pQI1EL1iVyucFZSgNssqp8/X3PnURZBIgSMM8ZHsLRL6MBKxb164H2dGK15+iKOjGJADMpuR2V1Fivf0aN6J7ztbD9mEuMN3VV381l8gv9abxKyIt80/YKvaxvcVSloMsIcT2GMdFcSn8oI6JB2aiy3KFGK0WDRq+cMamFBZdWV96GilfJOB1sXiZZKQn058rUUM70EWRceiX3wGmx7gtPMm9Pfn4rCuWX9x67oFMmiVOyaFRTWPiQWlsTcvxNOCLM56+GG0fdzv/XCJqsHE/g0bfKnutgeZ+ibfrrM5WS+2YwR7+MRoOWIypxYtJVvo+XYW4m2qvedyhf8KhxJF10buCdt0I4PG0e9zUqMks0RDTvgdeuAma5xPFu1FoW/OEt8h2Mg7akhBZREXz+ziqubquHM8Jd/tbIEGJc5uht79bAAM6/OA94NFtEhURhRsYa3zKdXBAcSd0hW2f7194NJX7ybPz/HIMyqXJanCTQn6T2y2l48C08MWyRLMJpD/3WjOIVn/OMsv+dvAsWDLK5AZygW5RhvXHFlfkGlPc8PWlFTJbw+czz+ZWJ6L5dkzcwGPWgHNY1XoGb/DglaAj5evwzFSu3zjYslseosbVihXo1bQP48pPlxKtkdHt/ImPrvYD6zh1Su9vhuzGc1ubXkLRsbKTWisdlehyLlV44zyof0hZ3009fFyBnRAgLpwq9oe+eETScklT22p2hITgesKjgmII8yvDLHfR6jw/HrKVAW0yjLx4vU5gySUEJalx+NrwmzYC/3+FUu4XMcomjh1UeA2yuc1+ronHcUINtPo2iHob3xgumRXG4+a+4yYXd+EbrIp6ZY0qtrCjWqjHPM6zLem62UqRWVjhNv0QHy/uEdRqE7bnyO6X6+g18Im8wvOlr3PzsAbV7/IsNrYZvG28W5puAcqRue3xHP49G87BymM+QE2Wjj8wPQ4kL/wErSfkq5ckHGiZOmYj1Rt2DBR50aUlP6wQa74nAJ72ypEZh7yD+bPMjTQ+Npf4OwwM8PPqf5bXar5I0ywNNvgL7M3Kcjt3DQ4Qg19O4EVB+2Ubf7ABUP+fUvNCcjHAt/maVRw21zkq4NDOYOieg1sFMSq7orsheHbk9mpKgOmLwnvdz45zJ3zRli8dcvrrZosqyALdv2vLspIvFoy6db+qBTCJFo33oksFNyW1gRZf3DjLRobJjKreNwXdZIdbH9IviSX8NzCVyGn5y47FFJefJl8jVKPD6plj0Rpj6Vg7au944HZVgtypOb5mW/H5ClXHbfSlPFUW787sP2xtPB8w5qlKQ0a8OUHX5j6IeQfw4Zx5NzR5XjT/ytTDnZAD4/jYWeb47g7ruZ8OaF3H9/t+NHjOqc0qtrdHkGybI5eAjfBnbVrTFmcc/FEmgP1/VX8HUPl2MonlzPcgc17+MYKgCGV+E0sVK6H1azorE5Ql/x5NYluvMEFthzrp904oKr5hx8oGGsd+Mx2nHZZSZCQbpEv318mzAqtJzUisrbq1349KRWrw/uTVgnMWQTq3PkPlDHYNWlrfNmoWVtHxhquPZ17kX68wDW3ijzz4+qH0aOAaYE7AgEvev46jOMQF2L1xAaU3TqkjqU75S/tH4s3T/3rCwyFIWfj+bwVETn3g964Jxw/dmNzJYcS8n2QGaQ49hBzkWVfmDlDX04dVfDtDYrLJorxg+GbaWn2Y2MJoYGYDbviKShqrxVchwman7F7LhzY7k/hxPRM3HFd0jP4rAYa24bZQirRSZRApy4XO990Ywoz/fyACbg3TvLu4CfBSNVr96IBUJ3DKB473LHXRPk3XwhIJlt9Wqh46pO4kPSuizfTzKlQVkdLXB89frcONWNWcZvk97v39r/BUncF15weDOxKjO6dsg26A7O4Dv4hxiu9sxtc4Jmn05HrO75ZuEDl2zeMculf4pPQDxUSc/3vZjYq0LLGnfx+CKVU9CtvcEw76ZwJHP5ul1nvZ4Inte9Sd2eaPK9sZZYntevOJcj00TSRmwkBe3/cPOa/7ENVrIKZWK0E/epdYzLvirDz4RR3npwmQGj9nJMLt/KvWipWSWqOi56D3c9OiNShMDfDiKFvA4htEVBysR1IQvv1uKTCIVVdTrucvnlNrYkDStEY2bZXD2nAfusVDjrwtGV4lL+y4E+T2JXjUxdCH8YjJdrHIJ+Wo8dRY+Pxf+/xUps4LxnmAcCcv/KqVZT4PTO3GnTfXO+bR8zufOOf/rXIwK5ny/uUbLnjHx3+P7tKOcUbmyrtsLlQroPo3/lPrefxnlyntsfsXx326GiX+R9z1bP/wtXZQdk8CXkdEeT2SFr3ECsk38b2NyThMmnlNMzmnCxHOKyTlNmHhOeSbOeWVqKF+lHWN8ygUkgf5Gt38tMpT5GYeMazS4KV+kHi/LdhCDvL4HyYtaMSwpg9jsU/yRdcLgYO30r0LYniW0y+eY4YHpz5qc4SH8kXWCi3NaV39wNcjs7cgbGMyElPNlydalP5+mnmDAhWxB2tIArkeEEpN1jE2ZRxhwIbtM0e/qloai212K1Nqa/H6tye/Xmj+yThCbfYpBFzKRBOjnC0ZzTplDLTR73PkmLYGTo6MIMJPS2TKfnKbGUS4o5X7/1uycNB0PuaEaf48j8/Fi2M/bCBR57UsUZtwYG0rk7h0kd19I3xq3ykrHB3+ZgNTaWndb5ubcjPHlzNvl6WfT6u4TLQv5rPjtsxlCjKpWXMK9zN+X3E5+7Jsxlw6W9x97vaW5mkE2V1H/rEXmpP+quNPcONbec+VvlRVr/VwI+XQcvZJ7cLDlcq5ONFxSpCIX5vmxN2o+e6Pmo0FLsVbNAJvrKJfoV85StHOmfx3CpJREJiXsZ6ffVpqaycqyMo6rwC5dvFpaRT77djm1ZZZsum+88PeU4U6E1RCX7iav544yXkvCx3PpYFmESlvM4PROtIiKIGhaBF1tz3DzjaY62ZJaWzP6n0TiA1eVPZdUrMZCIuf4W7Mo6WjckDuphQWatgFkTw4l88NQve/wcnc36sosUe4YpVfh4Ucp7B3Ed7//zF+zH4+wmnPHj+g75Rq9W323kD7CGyT63wxmR5fL6Tgsi0fdIZvgpZNYMG4ecq/6BrUdhO8tb2AwyZ0XP/H1H+oe0UuyRLRz/jNsHh0si2hn8aDsuZj8mizL9WDylLHI9j5e9bc6UqeHYHOwNg9eqbyRL3d3o6OligLtAxaH9xHb9DLODhZ6p29uNaFP6956n58zPIT3927jh7rChXlLXUjYoDHcaXMbl+lxOM6PY8ZLr+pcQTrtvWZ0taocQzzi0wll1a7TXxWnWlCRlNnBBB7J549fl3JiwlxOjZtL9JZFSJs3qv7kh2QM9ECDBo+t4i4nyy0JHCusX/Y4cG4kPV1b0dO1Fbsb2xDb2Jaerq1Qxo4C4OSYKJKX6X+jcoo+yreD3qz0nMfncQSZa1GsKDSo7dqQZgw9cY4DM6KrPK7rgUs65yQbHCFU1D2IJdGz8JRbsPaeE19v7o/3smuwREUNhYqZHlt5e8h4ZPv0c85bI0O400yD36cXy+ISZU6O1N+ex1yXODoNHYHFyTRRMYsV6Zp4lzH2F2m4ZxQ+Q/S/kdzY6lemkOe7ewR+76aJCs4vfjmQmJ+iUUiEIPWxVzpwra8tJVnZXJzbmvN9hF6lT3CY3rmHN8eE0HhIIss89pY957c+HMdjIHsgfNV7o4TMjF2F1szx9qveaFATlmxcwOuJQ7Hteqnsabm7G+c+c0Z+W6GzYNa1yFD2TJ7Bx1dfJj2oaieR7XVhs3IrLY68hfvAFLQqcSM0iVzOtoyjaNDw0oQIvTR/hiVl0LdG5SD6Tol9UXxpj/SQINI2PDmNMGvhmvXdGF5phGH0CKECRxmecgt2FNjwa68X8Zbl0HXrCUbbl2qoWqK2kOmQqFPOjbGh/PXhTAb6dUJdIfUsbZ4TW1120OLYm+T1laHcbRzHzBkRQrj9POJVChosNiyb0aaCyLHP0BOiA6oVu48T+vcw3vASbhSZwfeBx+deuiKzteXCV42Y++pPtDY/TK5Gy55CB977pw8FF+3xniI4jtzVhQuTPcrOa2JWXcaGQImNGXVlltw96oQtgnPK3d3IXWJGcpNFKHeM0rmtzlFxBCkn4nxQgg26Ocep4JX09HsTrUj9H22sIDE6Pqud3mJcjzrmVXUhN/e54HaoPG57yYg+hK1Zqpdd0eF7N0tsSR3oSET/bYy2y+BySSHnimszac0w6u3UPfBb26Y5f3wwnZsaSaWc0NQ1zTkZspCh6V1x/bAEaUfjRBxKAvyJ/3weComMaW8MRppwyiA7heucwcjKKXV7n2f/E4pafPzyVr3syGo7cH6aF8ndo7mjKSL4wDgaRJVAwlnqPpK/KVmt5YJP+ZCs45opeOmQsWJ+9R4JKgnFPuU9XcZAD042mYsGDdYX9dOa9QnXLZF8q3IbIGVGTiOkOXk6J4pf/CkQS5si5AfscD56H9nFTM5/14A4n1mceWDG5bfrARd0bu+dt0IoK43xkF7T38NtnviEClGB701PSPjGSZCqvKMpYrB7G4MakfZdCImDn5xmJUVSVgVqcW59Yho5GPQepcjd3Zh36Fdc5EIm/TtHh+I54LTB9op6BBGzIAoriRnKmDEoxx6DKj7TqpApG7B17/rHnhckOMv75KC/B+HYq/oLqGviXcLtLz21lorcq35ZMZ9SNt2vzco+nVEnJj3xnKcRk3UMKeUqEKW/vzB1PLWWGy87R+7uhrq2HVu3/6T3VCRvQDAHZj59TqhvzZn8fq3LpgEA3V4fXjaMfZTv047ibyYnXiVj4rQxldYfnkmVsTMttEiRIEWCg1Tc1oYUCfc1Kr7P8cdvQzgvnu2PFAm+a8IJ+jaCbq8PN4pjBm1PxUUu7JmMXj5WlGMCWGxLIGBPOADJPRdwc1SwwbbabzqD5gn/SiU4S//5ONxE2qz6fbl3a6YLv0hlSMzN0bQPQNM+gMufhpK/04uJf26rdHyxVs0vXdrq7ZgAPcPeJuTTcXxxIxApUqRICP420uiOGbQ9lW3bV3LUgKlIzZ2P/12/F9gxNrMdAA2Py5E21mGerSdXPgnFRS6ky/+aE6zzwqCoMaLM1xtNhS5ds8cd+as39Rb59Zp6nJ5R3UCrRZN3D1/zJFLme6BBS4MpxvlyZf6+dFp/lHD7S2zOr82yN3vinmCcXE6/d9MYENOFtV6xLHk/islpY/Wu55I9OZQhdtM5qqrB8CNvlT3/T/sljx37i+dO9m6qwSzvqh006GR/Djf/FdkeJ37y3kBN6dP/3vPFxUSMG49FuoEFmBLO4pAAx5dJ0WRpACkOiYaLPT9KyqoALnQQ5myGTkXUd+7Qbko41zoIoxDXWCm2B9PQFhTS2yGMLYc3s2JDNlv6GXaDehKSQH+8Xk6jplQo/5E+yBW4VPVJDxHVc9ZYJqxKfnZDEEPe6beV/C667eVVRFv8gJKr1yi5dh1NQQH5L/iwPXQ+q+/VFdO8SoSuPc0Y+4to0LB0WBgkGE8VTn3nDgVhwlC2qZkM7y/1W5yQyOV49killsyciDlj8Rp4quxneEansuOuq8sXn560Qf8otQdcJ+ibCLYqt5VdHLmaIhruH06nxL5lx10uKSR8YiQW241TGa205zQGMns70n5tyrkOi8tGDuvv2xn8/dmuOYJyxDGUI45hvfEo6us30Ny7R0n6ZYK+iWCY7RXe2LQHmY+XUdq/LWYlm322lz3WZOi+wm6wc6ZPC2G15y46jRzN8QApvZp1BiAzTLwAxJb5s+m6eRLr2zcXbUuLcz8PAAAb40lEQVQS4E9M1jGmOJzlqEpB99dHIDls2OJPVahv5dC7TRhpJUXMdzvAtUjdo01krnUZ5boPAOkDYeX01qgQPk49xbJ6f5JZUkj79yIY4fECPV1b0ebT8dxWq1Dtql91m/LycIyOo7trYNnPIPc2SC5b8qe/MNds/dU4wr1fwuo38XU5KzL1RnOD9rjLkEiYkX6EzYm7Odt2WaWXwqxvk7a2mV7mMr4QhvJVkddAGCYPsLnO7Sj9XSNixfqyYsbyus7c2lZeGOq6upBGq8bpteVjsHOODxPuBqXFbNXeroaaqoTMx4tstQT3XWrRItUyf19eWXX44Z1cyvsfj37qhF1fJAozZPZ2lZ4rSb9Mr+VTAFgz4QedbZVkXOHdjcMo1qo58nEU0w5t5uDUKFqbF3NP84DBkyZht7p8ed9hWTxDLg5gfcPVyBr66NVuqZUVp98UBKybxr1FnYXxaIsfVHOWfugrDfookgB/tmf+TUOFouy7q/hPIZGR2G45uXqo/6tcitnbZMNTQyhlvt5ceL18sah4S51qbda4dJ9JV8vXGLpa3aP1mrNkfBlC9kJ7jrRYW/Zah7VT8HpfvymaUfYlZN6eZH1QjEwiRXpLXPTKrVAnem6egPcf4nVovH5KfziUhWHpnan5+zmjCTvVOywj3DGWsN/exXui0NaCPq15rc9+AHpsexcfdO+NvD6I55XmA9nTZB0NzYR7ZmyBHbMiBmG983E7V/90x87PDK1Mn51kIWa3NMChOL2GXufqylc3WyCVGK5wkzm1agcv1gqv72+2lrUXXFFrpaxvWHUpRL/ofBI6Skhb4UWNPyv/3Xcaafmhe3moZOCxN3FbX/21oj2ZSPLoxrC1/Fr9qPZZPhr++JBbOStVbwVF0c6Z9Zs/7zWKZYDNddKKC1F+fV6UA9zqqKLWAeNkXwTUKC9Jd32qF/I84ynt7fq7CfN6HWJ92Bw+aNaXW7+5s2bKTLwVQtv9Ptb/c7B+JRW/heHU87pB0Upnah25jlnKk7Nk3GYmQIR+9qXNG1EwvYBirZqWC9/FZ+YJI1VUrYxGK2G0Qxx/txqD1gDFd5sNNpwP0NDQTMpRlYLW5sWsyK1f9rpCombu/D7kKdV8/PJWpu3uVe2NUHPqHB9HjGLH/Fm4tXnyzkJmSSEdt0zGb+o5nQXltH//U+Xr/ivH0eDrM2gK9B8FitrnvBgVTFI/YSggZp/zUbZnHef7HH82Ln0Jpzn/rup2Vci8Pbk40pmBXQ7wSe0zrMhzJ3pRb1x/ShRdm/JZcGdoCIe/mUePXkOrvajEUvJSID3m7mGHv/0zfZ/nAbmbK3lBboz4ZhMDbIRqa2EXu5M7x0OnubxJfc8E194N5dOxq1isNM5KpAnj8EyCEEz8t3CeHcfyTu3/7WaY0BGTc/6PUZJRfW0RE88HJuc0YeI5xeSc/0MUhLVme9Zx7r8WjLye+7/dHINQvdqKuRmHuT3s+ZRrMSYm5/wfQ4OGv2bN5Ua0JTJlg3+7OXohtbEhOnoODeSW2KfoH7eb+VEoD/6sx9UtDcmeHErxy8aVezE2/1PlGGTenqQOrgsSLUGdEunucBo/s+tMqW94JklF25v3r0eKlF5NOxms1CAJ9EeaeZN7oZ5c7feAOrXyuH7dnjp/mVHr9F00IpKKrTYfpedmQfolJ6oWP+xcyHdd+qC+mFrNmbohc3LEaqOGtV6xlVLc+geHUZKZZbBdicKMGn/Zss5rFyDsIyuSs/TeR64bV8Rrg4V94wZNrvOiRTEatETfbcCuNvWe6faXRGHGxekt8F2cg/r846Uen8R/0jllTo5gY01ugCM1NugWhSPz9abhmlQ2O6+vlHcIMsanXODrqW9hu1ZEVJIBQlNPYlvMSqLvNuBFq234m1X4ejoJQevvpAraSWl3auHc23BH9Yk8wtsloxm3ZSe7ercQ7aAyJ0fsfytmab1YHi1UG/R7GkcHNjE40yNpbnNSvBaWPS7UPjAotFO27wSbGgqKfXK3AD7o5MHmL2YQbn+J3Vsaon7ROM4pc3J8rH2Zk1qyv+90hk94QWc7uu9zSmXcfTMIlb1wETr8o8I88QqanNtGLUD7VB5e/DYHHJjsGouLvJAjRa4sU3rqdPqtkSHEfSYIeVV2TuHxcRV8OuhtJPGG5XfKfLzYvG+d6J6ztHxcdai0xfR1DzE4sbuU2OxTeMaMxDfilKgY27wBweyZIXy+8SpLllxrz5mdfvh1usi6BjtpuigCz005ejmoRGGG6o+67Gr0W1mWywfXAzk70t9oQRSFvYLYM38BoH+y9dNoekLCuW6OlFwTAhLkri602XEJDRIONrV47HjRGkK52z2Ja/b0LPLXUztzr61uujO6UvxyIH/+vAQN2jJFhLl3fBgxPwK33XfRnDqns63ai+Pps7s/qUNcKj3/Wth+Pql9hgBzDZ5RyaQbpv1sNDqf68OuRr8BMPeOD5sym3PtnCPOjW6g/sWRfFcphc0LcKqVh51djuihWLeO/fl2ywY+nNMfv0n/oCko0NvGlY2NOR0SDcgYmv4yOW3uALdxJ478r0CRLePMqLkwSj8HWHrpL+rKrAAJnYaOQLH7OKAFjBfdZHMim8NFCtpYFBvFnszejr72f5Goell47O1JyG/nednmH75o2xvQfXivs3PW7JNJaP9wHHamVHo+ZaI3H4VtYp3XLnrYdzDeuD24Kd8tWYhMogCtBplEive2EShHHcOFOINiQktS0/H4PL3ssczbE0Uf9UMZEPgzoalewerPgjH19gGwLNeDPT2bUiM1FW9Kh5ypVMyDMUYQv/r8RbbnNONCz2iaX4vE4wv9wiU17QOY3XxV2RwzI8qXGo+IcxVr1ZXmoLogc3J86Jjgs/sdfHaXjyi0oc3QmMn0VnZ8Epl9PYzmmAAZY/wZcdoVlztCx3ElrC7vO2zEd/cofDL1a6/Oq7WaoiLsf4lHffNmpR/PD+PZ0CmIwyopaeONV3qh7eIEAs1kdO02kO6vvknQh2PwHX/GaPYBMvrXZYrDWYq1amLya6JcZrjKnbGZebKzToVXjUH6j77cVKsI6Kz/HDZliIwXLARBtrczXsFur25Z/tVxZYiQDnahWEX9X4RRn7y+B4WxnmzbsJQdq5fAHrey/ElD2Tlxuui2liJt6sf+sTOw/8Wm7LngfqfJ1RTRYLn+0w+jbKWUXMnkXJErPh1TkSj0U1t7Gm5mt7mqLkBz6hyaU+eo+XO8aG3SiuT3a83m0TPKHn+T9Arak4lGsy+G9fcdcVlvPOHo6rDeeJTMEkuD0ry+fUFI2t5RUJu83jLUN2+Kbk/xy4EcGD+TyyUFjI2IRLH7ONqQZozdvYu9jTchR4YUCX/4xdBwpW4rn09C5uNFbZmQoeJ/cJioNsucHFm0fSl2UgusNpcrSsx3O0DQb5OQ7j+pt02jOKck0J8mFlcY67oXpMZZtZzxcz+y1eakf2X8zebtWcfZEzWPenLhRtJ0SQROQ8RfVKWJwGL4NmoQ9RU32TN/AZszE6oMFrgYFaz7KrFUhqZ9ACk/BpPyYzB5Oxrgc8ycL1KPs/rKYQLMNSyr9ycps4K5HhH6WCL5o8hq1qQw1pO+NW6hkMhYMrDnUx1TIZGhkMhoum68Tk292sYcW6kFL+2cgMW2BArCWrNj4wpesSxApS1B+ddwvHeNAOBTp8N6F4mSNfSh7/kbbNu3kQSVhF4vv4HnG4aPyiaknGfbiZ14yGsIN43M4/yRdYKwczfxP/C2wSUqxG+lBDUhYs0Ggs2h8aLheKgMS/GSu7tVUjB3+zaOCSnjeO/Lzaz/vTMcMc6Q9u7gEBSSUxRX6CQ8Po8zyvxNUMqD3I5KvYWJS3GMjmPavgFc6eaA43EV8oynr95KSyBlVmu83636vTTtA7g9OZ+4FpVreJx/oGHm1S40s73C+JqC1GZ0jxV0sLzPnFF+7G5s8yRzAKhaeLHTf0HZ51jV6qm+c06LFg+V/u8JN7rMrsIKg0pbQpfxEXj/dpRLPwh70zUk5twdfg/nbU+29ShSCwuufKNgmO0VQMK4meNwPC8uLbGjZQFf32rC6j/aY58EKnsJPd46yBd1TrN1+EWDc2ZFOadELmfW+kUoFcLysGf0BYMucpm/L59tX8Xru8aiHFWeXFxjw1Hemp3NIl9raooXRkDu6kLg+JNlUpMAgXMiccW4OaPZXYtRPi4/qzPqxCRcdBhh+0w9Q7/jqaynahWAxSvnlsmBAvTsOxwAWV4R6nPJ/DqyM+M/u8D710K42N+Nz16oywuRR6GKy6ruV8aZWz6J/EJhROPe7CoyJ0faNhG2X4pRl+VHqi3L25Z/qepeviLJSxpyIUhQ8cvTFOE4/+nfvczejnsd/MjqXYzP0Kcv5vj99Q4+Pz7A86QgQyKrU4e7g6yEaZkBq9+liHJOrUZb5pgA287sfuyY9JICes97D5cZT/8Q1DbChZPSfRFkQbt3x2B1TdhzSy6Oo85fV/SWeHiUBscsmOVSqpguZXxWOzLDauKaZfxkbte6htdKuTUqhDon7uukIKApKODrQz2osTkfl7CnbyuVOmaTX8bj+UE8IIxC1AgX0qDxsQD8ntQYr9RT2Kem889Kg/+EMm4PC2H15zMBC5qtiqTBB7pp6Hh/WcT3vzZkd6PNUMEn2n8/ibo2Z7m2ypWUlkJQwup7jjSYpNudO29gMMkvzQckeMeMRjlGmBvK/H2506wmQz/Zxgi78qydC8Uqwscqq3RMAO/BJ6k4W//h2Fa8Fea82i8cCYbrIosb1mrUNDz4Fm3qp7HUff8TD6kvtyLi7S1smvH0WoryrNscLfAmwEzYMjgwewEatBxXwRuzJuN8RbwDzXI5WPb7sIyOpET7YZdlhO74Cexpso6etKr+wCfw/qQ1TDvXDbdwF0qysqs9XmZdjGet21S1VNbzQhibfX8jbtBMxrXtwb2B5SJX3+7fgK9CRq+k3ni/k6zzEEwq0ZTNrxvtH45PzQyy3hJ0dAtb3yex7QqEMgUWtJgdgVcVN+dHUZ9LZu/YEN5fV3n1+NgHc/n6naZ8Wrv8Wvt2zWt46DjyyX8tFw1a0kqKkOVLSV4RyMYXF1BHdoi6DxeGSv/+xbn12TS+M+a79SumLLO3Q6mwwHffcBrE678IVBHRc07PN86QDfSo0wWAq6/5oH1kmcllzQXg6REzJVcyOXHPA6l9mmBDLQwFGprJcI4S55jXI0I59sFcKq593Qy9i52OhXL+Df5utYq9+y0I/+2dKit0yXy8ONVuIS0OjcSTa0832DGTDkMikbxxk/3N1lLxWpaiIF4lo/hrZ+QFum+QJ99xpNhdmMScbreY93a1ZWPd2YCwAJSrKSK2wIPZ37+Gy3L9v0Pp4dMcUUFwBTkpKRI+rS2MKDbl12TmtIF4rNRfdNxTbsH5AdEPA1tk8LAuzdp7Tny2rw/1N2uwPHoRxV39NKckcjlXf67LnkJzfD+5LXq0Z7TY2tKVOsfox1fsdJmH/r2xCa92E6pg56osUMU44rw/B9CvGnBFrk4KZXvkdDSUf8PPYo5pTJZcaUeY3xY6WBZxbtA8/BTh1Pu9+GF0TGXq/nKDbLWa+nOrX3S3XxmPLMaO0NfHcydUxYEOc6gjM2f45Q5kfqZE8Zd+CvW1psggtvzx9LoHK73ease7KEceo5YOxZCeiFbLtFf6kzSmDif6z6KGpLLo2/KBPaj5t/hqANfVhZx7UJNRB4biO+oMymJhqGvI2kny8qYktVxCh/FjsU4TH8zy/62GUMVYWoAOkyKw+fXZ9palxXzavh+O3Srx71Vd4Z2O4WOw3GIclXZD0LQP4PMVywgwE/qITv+8wf0dzqJHO/81pNbWvHPyLL2t7wrbPIP1G87+zwl8PeqcPV0NmwP+20gC/EkaaU1t97vk3rMksd1you82YM7eLviM+3dDDU0Yh/855zRh4r+CSX3PhIn/GCbnNGHiOcXknCZMPKeYnNOEieeU59I5Xzt/jbBzN5F56yZBYuL5Q1azJtcmhPJV2jGubGxsNLtyVxfanykkb4B4UbbHbNd1Ju2bEGKzT3F1S9VVw/8veC6dU42UEXZXBKW8/xjy+h6wx83odi/NCCF5yX9oO8jFkfhJs2ms0DI/YA2atgFGMXvu67pMcjBcpkTavBFy18pSNbLaDkibN2LL37+TOHQe47Ja4x5xT2xTAZB71afnuRzcjuhfblFv55RaWXHv9WBSZgWT9duTlQ8uRgUTfFq89IN3+zTRNp7ExahgLkYFE34xmdjsU1yZGorUyor0r0NYcvkQUovHRZh0xXPDdWL8NhuxtQJBoRcY3vpg9QfqQEFYa5zibYnNPlX24xRvaxTbT6KNRTGqWuKTx6XW1kS02ivKRszvv9Bup5CgLbWx4UZ4KE3+zCHm91/YnF+LRqvHkdbfqVL6ohg6bTvFm7bJHNrTRO9z9d7nTF4YRN19UmruSwMrS869JwS0f9thA87yXHwVeVhIpHSYPhmnuYZFitwaFULCp0JkTDfXFgbZeBSZjxddtxxntH0qaSVFdNkdicVlM4p9Czjfvrys+djMdlxunW/Qe6i6tiJ26XzAuEEPRT2CiFkQRYevJ1JnobiQtdjsqit7d3FpLsp+KXXi7FnssQsQYm07jh2D5VZx0Uzbs46jQcN1tYoRHrpLTD5qo5QJ2aHsOBSA128qZAnnjKa0oW3THI1CypUxJRwMXUDYxIlVSriKVt8rRTla+IBLyh6nA7CCegDUT7DExTzXYMd8VpQ65rJcD2L6tUF57m+kVlakLFWWHXNdXcg/UU2wNTAovijiDtJnMFNQm0mwkpjhcF5/lfOKVHTMButG43JAi9XmoxSEteZg9CKxzSwj7dembK+3glL92mbxQ3EX6ZgV2ZmvrP6gKlDuHEXD6Xfh+k287wrftTGjbdb8Go2d1AIpEgal99BZW/lRjHol3RoZwiK3eH4+LW6y7rQxiWV5wrzNWDU9RtuncrhIwdY32qI+JwTT53VvyrkKvWafT6dgu8bwmNiPfP5Ag4Y9hVai21sR67FCtoghOjSlFIS1Lvu9wbrReE84gtVm4aIp/d9YJLZdUaZ+UKxV4/GdeJvZk0PFG5HK+Oh6SxpOuog6KeWZKLzLvepjJ7Wg4a/hAFyJMvxGYlTnfH/yGi4V38fvq7ui7KhzbjPnp94ApA8yXsGdayV2SLIEJW6ZsgG3+pZnqXc+14faW3TXwX0SXayEL3vMniGi7DxKA1vxesCe75XnRnpP+L9NlzOGALRZ+/LP4Mf1vQ2ykTw/kG0pjaGOA8lLW5b9aEOaIfesJ7qNANr8AroNfAe1rZDXYqhcDRjZOcOsbzO+w5tGqb3h+n0cU280J2bUdB50aSnaXvtJ4fStcYutZ/5E3aEFyrUZ/PPCCgDavheOWacM0XdSKVJuqlU0+l68WFgpeQOCiXI5LMpGbPYpVtY7ADx5TmnMxaBhSRmVHr+d8YpR7Ma1WA1AZokKt30GDu/N1bStl4rrqus08cnEcb8Cx/0Kcr2tkP2kYnvWcVqdUnN3iOGicurrN7gWZMmFbvORScS5lzgNoUB/8rxtsFl3BE3bAMZlWRpda7W+3ApVTTliBTftfk+kVZ0IFk+MYseqJWXPny8uxm61+J7k7uAQNBwj7Mzb1Eo1PAf1aSSLKJXwNIw515TVrMn9X+3pW+N4JQXCY8eUeBshsb1U2bDfqXdw3GuYmLRy2HEulz3Kx75CgrrqFwh9exxxX83DavIDDsXWM6geC4BZu1tIkZKrKTTo/FIMdm1JoD/TNq5g/4/RfJx6itvv5ZMR7l3pdd+/FXx46Qx3Bxt2J4q/ZbwgBM29ezjNjSPis3J5xsQHJYz85F2j2M/1frjgtsnBKPZKUb+ZA8DAU28bxZ5TvC0ps4KJzT5l1EWg5Hn12em/rmyeuafQio5jx6D8uHotpOq43781GjSotMWoD9QyQmufjMPqE3S/0IspDudI/tHVYDubmy1HIZHRcs1EUe0xuOfUHk/kI09BLzRlVjAXXouGLZTVNJl95x6xEe1J2luMvYHZ8GadMiALFn43mwk3w5Hv0U82oiIyW1uujG7Mici5nC8upkCjYNjxd/AwQlI0wA+DliNFSo2rxi3qdLj5rwDYrjR86NnFpTkps4JpE3xOGN4+HOIOyWgHUDbkbbButEG9nKRVE860L3f0oekvc/lHJdZbjxosC1mRt78UhNla/RhJ3R+e3S6AVqXiyj4P8IOxzfYTi2GfeV2ZJcVaNfVjxPWcomVKZN6eTOu2ju9z/Nm49CWc5pR/eDLE17IA8FeYcc/NjJoibFz4uiFJfecC8F7f4dSYfZ2GTtcwbEfzccYfGcDEFruxOpslWjvmUX687Yft8WxRdr0nHOE60DZsFNntJA8XhfKq3ffUhYz3Kj/OaXMHayPVnJG7u1FHLkwTDHVMWW0H1LdydDrWopVw3KLEF6iP4VrJh4sUSA+J+2xFLwid/8CB/jVyWPtrZcc0Nve6i6tjsqVnFABNlo1De9z4ZRcUlyxJzHfVSTFPX07muVOScaX6A3XAavPRJ67WDsloZ9Aq7qUZIZwJ/alM1T1eZWmMZpZxrat72Sq4oZz/1ovizjosKgY35WO/HVwuKaTebMMqF9wYJ2z5TBsqfsVetHPGdopiU35N6i26ILoxT6J0v3Owr+Gb2IW9gmioUND5XB+8opKQu7sRWtM41ZyfJVIbQXH9zA6/Z2K/dO9zSEY7rofk6X3+tQmh7Ht9RqU9zU8/HGG09sm96tNmpH7CY09CUeMBFh9mI3d2qvK4Mb/8Rk/rO3RPGGNwndbeI/axOb8W8pOG13ApRdSwVuZQix+uv0x6UCFVSV+KYfWU7gxftJCPaiexaeQYai/Wb/566Ydgzr8RzYsTwqmx/ghq4JdT2zihqsWf44MB42x71I/JZdY7B2n0y0iUo5JEKX2XcmNAY1bkXaL++utGKRdREWE4Kwy7DHFMoFJYzTe3Atm2oi3O6403erod7MxvdTcA0HDTOIPLM3oOOM2DF1uQ8aOG71rGk6+pvPbfw/oyNaTmhHw+jsUbL+Bxx7BFLJmyAc2sYpm6aAgu+eI/B1HOeeFTJfe+1WL1DGtaWl+8zS/3nHnL1rBlbbWVsCRxu5GU25+H8kLX09SUWvDZF29jf0q8tGIppUPlcy8tpvHSEXgNFD+XQwLFWpno6tWPUnFfs234KIO/P+fZcfS+P4Vm75zl4reNcN76bKY1DfeMwu9Tw0p9lCLbdwKvffDB94MosVEjsVKT1Gkxyq1jWLv4PtJrOThcixf1Hsmj6vCqVS5LNhln3UGUwFfPcznENDLu1oGxSV4QRHLPBZWeU+4Yhe/oU2hLjLt0U/xyIGn9hZlCxZovBhPcFNm3t1B3MO489tEY2//riKH/Xym91rq7tdTrhmpS3zNh4jnFpL5nwsR/jCp7ThMmTPx7mHpOEyaeU0zOacLEc4rJOU2YeE4xOacJE88pJuc0YeI5xeScJkw8p/w/GTbELyagP7oAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import tensorflow_datasets as tfds\n", - "import tensorflow_gan as tfgan\n", - "import numpy as np\n", - "\n", - "params = {'batch_size': 100, 'noise_dims':64}\n", - "with tf.Graph().as_default():\n", - " ds = input_fn(tf.compat.v1.estimator.ModeKeys.TRAIN, params)\n", - " numpy_imgs = next(tfds.as_numpy(ds))[1]\n", - "img_grid = tfgan.eval.python_image_grid(numpy_imgs, grid_shape=(10, 10))\n", - "plt.axis('off')\n", - "plt.imshow(np.squeeze(img_grid))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4sAetutZ9t93" - }, - "source": [ - "### Neural Network Architecture\n", - "\n", - "To build our GAN we need two separate networks:\n", - "\n", - "* A generator that takes input noise and outputs generated MNIST digits\n", - "* A discriminator that takes images and outputs a probability of being real or fake\n", - "\n", - "We define functions that build these networks. In the GANEstimator section below we pass the builder functions to the `GANEstimator` constructor. `GANEstimator` handles hooking the generator and discriminator together into the GAN. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "oZ9n-jw_MG6C" - }, - "outputs": [], - "source": [ - "def _dense(inputs, units, l2_weight=2.5e-5):\n", - " return tf.compat.v1.layers.dense(\n", - " inputs, units, None,\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight)\n", - " )\n", - "def _batch_norm(inputs, is_training):\n", - " return tf.compat.v1.layers.batch_normalization(\n", - " inputs, momentum=0.999, epsilon=0.001, training=is_training)\n", - "\n", - "def _deconv2d(inputs, filters, kernel_size, stride, l2_weight):\n", - " return tf.compat.v1.layers.conv2d_transpose(\n", - " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", - " activation=tf.compat.v1.nn.relu, padding='same',\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", - "\n", - "def _conv2d(inputs, filters, kernel_size, stride, l2_weight):\n", - " return tf.compat.v1.layers.conv2d(\n", - " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", - " activation=None, padding='same',\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "NHkpn6ks90_R" - }, - "outputs": [], - "source": [ - "def unconditional_generator(noise, mode, weight_decay=2.5e-5):\n", - " \"\"\"Generator to produce unconditional MNIST images.\"\"\"\n", - " is_training = (mode == tf.compat.v1.estimator.ModeKeys.TRAIN)\n", - " \n", - " net = _dense(noise, 1024, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = tf.compat.v1.nn.relu(net)\n", - " \n", - " net = _dense(net, 7 * 7 * 256, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = tf.compat.v1.nn.relu(net)\n", - " \n", - " net = tf.reshape(net, [-1, 7, 7, 256])\n", - " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", - " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", - " # Make sure that generator output is in the same range as `inputs`\n", - " # ie [-1, 1].\n", - " net = _conv2d(net, 1, 4, 1, 0.0)\n", - " net = tf.tanh(net)\n", - "\n", - " return net" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "w-ZqQ4_thIrP" - }, - "outputs": [], - "source": [ - "_leaky_relu = lambda net: tf.nn.leaky_relu(net, alpha=0.01)\n", - "\n", - "def unconditional_discriminator(img, unused_conditioning, mode, weight_decay=2.5e-5):\n", - " del unused_conditioning\n", - " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", - " \n", - " net = _conv2d(img, 64, 4, 2, weight_decay)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = _conv2d(net, 128, 4, 2, weight_decay)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = tf.compat.v1.layers.flatten(net)\n", - " \n", - " net = _dense(net, 1024, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = _dense(net, 1, weight_decay)\n", - "\n", - " return net" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "OhTAjxnyPS5e" - }, - "source": [ - "### Evaluating Generative Models, and evaluating GANs\n", - "\n", - "\n", - "TF-GAN provides some standard methods of evaluating generative models. In this example, we measure:\n", - "\n", - "* Inception Score: called `mnist_score` below.\n", - "* Frechet Inception Distance\n", - "\n", - "We apply a pre-trained classifier to both the real data and the generated data calculate the *Inception Score*. The Inception Score is designed to measure both quality and diversity. See [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) by Salimans et al for more information about the Inception Score.\n", - "\n", - "*Frechet Inception Distance* measures how close the generated image distribution is to the real image distribution. See [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) by Heusel et al for more information about the Frechet Inception distance." - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "1jF-FW5LPTn6" - }, - "outputs": [], - "source": [ - "from tensorflow_gan.examples.mnist import util as eval_util\n", - "import os\n", - "\n", - "def get_eval_metric_ops_fn(gan_model):\n", - " real_data_logits = tf.reduce_mean(gan_model.discriminator_real_outputs)\n", - " gen_data_logits = tf.reduce_mean(gan_model.discriminator_gen_outputs)\n", - " real_mnist_score = eval_util.mnist_score(gan_model.real_data)\n", - " generated_mnist_score = eval_util.mnist_score(gan_model.generated_data)\n", - " frechet_distance = eval_util.mnist_frechet_distance(\n", - " gan_model.real_data, gan_model.generated_data)\n", - " return {\n", - " 'real_data_logits': tf.metrics.mean(real_data_logits),\n", - " 'gen_data_logits': tf.metrics.mean(gen_data_logits),\n", - " 'real_mnist_score': tf.metrics.mean(real_mnist_score),\n", - " 'mnist_score': tf.metrics.mean(generated_mnist_score),\n", - " 'frechet_distance': tf.metrics.mean(frechet_distance),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "kxF2-gWHHaej" - }, - "source": [ - "### GANEstimator\n", - "\n", - "The `GANEstimator` assembles and manages the pieces of the whole GAN model. The `GANEstimator` constructor takes the following compoonents for both the generator and discriminator:\n", - "\n", - "* Network builder functions: we defined these in the \"Neural Network Architecture\" section above.\n", - "* Loss functions: here we use the wasserstein loss for both.\n", - "* Optimizers: here we use `tf.train.AdamOptimizer` for both generator and discriminator training." - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "OBd8Vg7lHit8" - }, - "outputs": [], - "source": [ - "train_batch_size = 32 #@param\n", - "noise_dimensions = 64 #@param\n", - "generator_lr = 0.001 #@param\n", - "discriminator_lr = 0.0002 #@param\n", - "\n", - "def gen_opt():\n", - " gstep = tf.compat.v1.train.get_or_create_global_step()\n", - " base_lr = generator_lr\n", - " # Halve the learning rate at 1000 steps.\n", - " lr = tf.cond(gstep < 1000, lambda: base_lr, lambda: base_lr / 2.0)\n", - " return tf.compat.v1.train.AdamOptimizer(lr, 0.5)\n", - "\n", - "gan_estimator = tfgan.estimator.GANEstimator(\n", - " generator_fn=unconditional_generator,\n", - " discriminator_fn=unconditional_discriminator,\n", - " generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n", - " discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,\n", - " params={'batch_size': train_batch_size, 'noise_dims': noise_dimensions},\n", - " generator_optimizer=gen_opt,\n", - " discriminator_optimizer=tf.compat.v1.train.AdamOptimizer(discriminator_lr, 0.5),\n", - " get_eval_metric_ops_fn=get_eval_metric_ops_fn)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "n1uldXfUfstT" - }, - "source": [ - "### Train and eval loop\n", - "\n", - "The `GANEstimator`'s `train()` method initiates GAN training, including the alternating generator and discriminator training phases.\n", - "\n", - "The loop in the code below calls `train()` repeatedly in order to periodically display generator output and evaluation results. But note that the code below does not manage the alternation between discriminator and generator: that's all handled automatically by `train()`." - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": { - "colab": { - "height": 2281 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 221607, - "status": "ok", - "timestamp": 1559656706482, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": -480 - }, - "id": "AH6gcvcwHvSn", - "outputId": "a72e2218-95a8-4585-8a5c-7c4ec896ac0c" - }, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mgan_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnext_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0msteps_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext_step\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcur_step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mtime_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1365\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1366\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1368\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1369\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1350\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1351\u001b[0m return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n\u001b[0;32m-> 1352\u001b[0;31m target_list, run_metadata)\n\u001b[0m\u001b[1;32m 1353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1354\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m 1443\u001b[0m return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n\u001b[1;32m 1444\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1445\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1447\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# Disable noisy output.\n", - "tf.autograph.set_verbosity(0, False)\n", - "\n", - "import time\n", - "steps_per_eval = 500 #@param\n", - "max_train_steps = 5000 #@param\n", - "batches_for_eval_metrics = 100 #@param\n", - "\n", - "# Used to track metrics.\n", - "steps = []\n", - "real_logits, fake_logits = [], []\n", - "real_mnist_scores, mnist_scores, frechet_distances = [], [], []\n", - "\n", - "cur_step = 0\n", - "start_time = time.time()\n", - "while cur_step < max_train_steps:\n", - " next_step = min(cur_step + steps_per_eval, max_train_steps)\n", - "\n", - " start = time.time()\n", - " gan_estimator.train(input_fn, max_steps=next_step)\n", - " steps_taken = next_step - cur_step\n", - " time_taken = time.time() - start\n", - " print('Time since start: %.2f min' % ((time.time() - start_time) / 60.0))\n", - " print('Trained from step %i to %i in %.2f steps / sec' % (\n", - " cur_step, next_step, steps_taken / time_taken))\n", - " cur_step = next_step\n", - " \n", - " # Calculate some metrics.\n", - " metrics = gan_estimator.evaluate(input_fn, steps=batches_for_eval_metrics)\n", - " steps.append(cur_step)\n", - " real_logits.append(metrics['real_data_logits'])\n", - " fake_logits.append(metrics['gen_data_logits'])\n", - " real_mnist_scores.append(metrics['real_mnist_score'])\n", - " mnist_scores.append(metrics['mnist_score'])\n", - " frechet_distances.append(metrics['frechet_distance'])\n", - " print('Average discriminator output on Real: %.2f Fake: %.2f' % (\n", - " real_logits[-1], fake_logits[-1]))\n", - " print('Inception Score: %.2f / %.2f Frechet Distance: %.2f' % (\n", - " mnist_scores[-1], real_mnist_scores[-1], frechet_distances[-1]))\n", - " \n", - " # Vizualize some images.\n", - " iterator = gan_estimator.predict(\n", - " input_fn, hooks=[tf.train.StopAtStepHook(num_steps=21)])\n", - " try:\n", - " imgs = np.array([next(iterator) for _ in range(20)])\n", - " except StopIteration:\n", - " pass\n", - " tiled = tfgan.eval.python_image_grid(imgs, grid_shape=(2, 10))\n", - " plt.axis('off')\n", - " plt.imshow(np.squeeze(tiled))\n", - " plt.show()\n", - " \n", - " \n", - "# Plot the metrics vs step.\n", - "plt.title('MNIST Frechet distance per step')\n", - "plt.plot(steps, frechet_distances)\n", - "plt.figure()\n", - "plt.title('MNIST Score per step')\n", - "plt.plot(steps, mnist_scores)\n", - "plt.plot(steps, real_mnist_scores)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "uy1dsvWuwJeS" - }, - "source": [ - "### Next steps\n", - "\n", - "Try [this colab notebook](https://github.com/tensorflow/gan) to train a GAN on Google's Cloud TPU use TF-GAN.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "last_runtime": { - "build_target": "//learning/brain/python/client:colab_notebook", - "kind": "private" - }, - "name": "TF-GAN Tutorial", - "provenance": [ - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1571383618849 - }, - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1569547390651 - }, - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559972047311 - }, - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559900570952 - }, - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559897391264 - }, - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559752800451 - }, - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559719883868 - }, - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559717312855 - }, - { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559641947244 - }, - { - "file_id": "14r58gghjLTBBQVoSFOBdPsvj-I1G6nbd", - "timestamp": 1549819781952 - }, - { - "file_id": "0Bz8X96EaC_2-ZW9odlhSOEFXdWs", - "timestamp": 1493398103910 - } - ] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} From 10c7e0334cc57d130c11e10f1b03a121c34328fd Mon Sep 17 00:00:00 2001 From: ayushmankumar7 Date: Fri, 28 Feb 2020 22:48:29 +0530 Subject: [PATCH 3/5] Code Migrated to TF2.x --- .../colab_notebooks/tfgan_tutorial.ipynb | 1619 +++++++++-------- 1 file changed, 904 insertions(+), 715 deletions(-) diff --git a/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb b/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb index fe75ba41..ab2439aa 100644 --- a/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb +++ b/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb @@ -1,747 +1,936 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "9aMFvFjcoI_v" - }, - "outputs": [], - "source": [ - "# Copyright 2018 The TensorFlow GAN Authors. All Rights Reserved.\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "# ==============================================================================" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "35cp5a7vN9V8" - }, - "source": [ - "# TF-GAN Tutorial\n", - "\n", - "Tutorial authors: joelshor@, westbrook@" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "XSTQ5Flu7FMP" - }, - "source": [ - "## Colab Prelims\n", - "\n", - "\n", - "### Steps to run this notebook\n", - "\n", - "This notebook should be run in Colaboratory. If you are viewing this from GitHub, follow the GitHub instructions. If you are viewing this from Colaboratory, you should skip to the Colaboratory instructions.\n", - "\n", - "#### Steps from GitHub\n", - "\n", - "1. Navigate your web brower to the main Colaboratory website: https://colab.research.google.com.\n", - "1. Click the `GitHub` tab.\n", - "1. In the field marked `Enter a GitHub URL or search by organization or user`, put in the URL of this notebook in GitHub and click the magnifying glass icon next to it.\n", - "1. Run the notebook in colaboratory by following the instructions below.\n", - "\n", - "#### Steps from Colaboratory\n", - "\n", - "This colab will run much faster on GPU. To use a Google Cloud\n", - "GPU:\n", - "\n", - "1. Go to `Runtime > Change runtime type`.\n", - "1. Click `Hardware accelerator`.\n", - "1. Select `GPU` and click `Save`.\n", - "1. Click `Connect` in the upper right corner and select `Connect to hosted runtime`." - ] + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TF-GAN Tutorial", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "accelerator": "GPU" }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "83-azWpoYsDg" - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n", - "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n", - "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n", - "\u001b[33mDEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support\u001b[0m\n", - "Defaulting to user installation because normal site-packages is not writeable\n", - "Requirement already satisfied: tensorflow-gan in /home/ayushman/.local/lib/python2.7/site-packages (2.0.0)\n", - "Requirement already satisfied: tensorflow-hub>=0.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-gan) (0.7.0)\n", - "Requirement already satisfied: tensorflow-probability>=0.7 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-gan) (0.9.0)\n", - "Requirement already satisfied: numpy>=1.12.0 in /usr/lib/python2.7/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.13.3)\n", - "Requirement already satisfied: protobuf>=3.4.0 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-hub>=0.2->tensorflow-gan) (3.11.3)\n", - "Requirement already satisfied: six>=1.10.0 in /usr/lib/python2.7/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.11.0)\n", - "Requirement already satisfied: decorator in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (4.4.1)\n", - "Requirement already satisfied: gast>=0.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (0.3.3)\n", - "Requirement already satisfied: cloudpickle>=1.2.2 in /home/ayushman/.local/lib/python2.7/site-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.3.0)\n", - "Requirement already satisfied: setuptools in /usr/lib/python2.7/dist-packages (from protobuf>=3.4.0->tensorflow-hub>=0.2->tensorflow-gan) (39.0.1)\n", - "WARNING:tensorflow:From /home/ayushman/.local/lib/python3.6/site-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n", - "\n" - ] - } - ], - "source": [ - "# Check that imports for the rest of the file work.\n", - "import tensorflow as tf\n", - "!pip install tensorflow-gan\n", - "import tensorflow_gan as tfgan\n", - "import tensorflow_datasets as tfds\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "# Allow matplotlib images to render immediately.\n", - "%matplotlib inline\n", - "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # Disable noisy outputs." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "b2xrX4F-OEL7" - }, - "source": [ - "## Overview\n", - "\n", - "This colab will walk you through the basics of using [TF-GAN](https://github.com/tensorflow/gan) to define, train, and evaluate Generative Adversarial Networks (GANs). We describe the library's core features as well as some extra features. This colab assumes a familiarity with TensorFlow's Python API. For more on TensorFlow, please see [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "JMljl0ZwONgi" - }, - "source": [ - "## Learning objectives\n", - "\n", - "In this Colab, you will learn how to:\n", - "* Use TF-GAN Estimators to quickly train a GAN" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pI8zy5Bz65pa" - }, - "source": [ - "## Unconditional MNIST with GANEstimator\n", - "\n", - "This exercise uses TF-GAN's GANEstimator and the MNIST dataset to create a GAN for generating fake handwritten digits.\n", - "\n", - "### MNIST\n", - "\n", - "The [MNIST dataset](https://wikipedia.org/wiki/MNIST_database) contains tens of thousands of images of handwritten digits. We'll use these images to train a GAN to generate fake images of handwritten digits. This task is small enough that you'll be able to train the GAN in a matter of minutes.\n", - "\n", - "### GANEstimator\n", - "\n", - "TensorFlow's Estimator API that makes it easy to train models. TF-GAN offers `GANEstimator`, an Estimator for training GANs." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "qxrYrU887Mns" - }, - "source": [ - "### Input Pipeline\n", - "\n", - "We set up our input pipeline by defining an `input_fn`. in the \"Train and Eval Loop\" section below we pass this function to our GANEstimator's `train` method to initiate training. The `input_fn`:\n", - "\n", - "1. Generates the random inputs for the generator.\n", - "2. Uses `tensorflow_datasets` to retrieve the MNIST data.\n", - "3. Uses the tf.data API to format the data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Zs8kdV0w7Rtq" - }, - "outputs": [], - "source": [ - "import tensorflow_datasets as tfds\n", - "import tensorflow as tf\n", - "\n", - "def input_fn(mode, params):\n", - " assert 'batch_size' in params\n", - " assert 'noise_dims' in params\n", - " bs = params['batch_size']\n", - " nd = params['noise_dims']\n", - " split = 'train' if mode == tf.estimator.ModeKeys.TRAIN else 'test'\n", - " shuffle = (mode == tf.estimator.ModeKeys.TRAIN)\n", - " just_noise = (mode == tf.estimator.ModeKeys.PREDICT)\n", - " \n", - " noise_ds = (tf.data.Dataset.from_tensors(0).repeat()\n", - " .map(lambda _: tf.compat.v1.random_normal([bs, nd])))\n", - " \n", - " if just_noise:\n", - " return noise_ds\n", - "\n", - " def _preprocess(element):\n", - " # Map [0, 255] to [-1, 1].\n", - " images = (tf.cast(element['image'], tf.float32) - 127.5) / 127.5\n", - " return images\n", - "\n", - " images_ds = (tfds.load('mnist', split=split)\n", - " .map(_preprocess)\n", - " .cache()\n", - " .repeat())\n", - " if shuffle:\n", - " images_ds = images_ds.shuffle(\n", - " buffer_size=10000, reshuffle_each_iteration=True)\n", - " images_ds = (images_ds.batch(bs, drop_remainder=True)\n", - " .prefetch(tf.data.experimental.AUTOTUNE))\n", - "\n", - " return tf.data.Dataset.zip((noise_ds, images_ds))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "t6aboJBr8Rig" - }, - "source": [ - "Download the data and sanity check the inputs." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "height": 279 + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "9aMFvFjcoI_v", + "colab": {} + }, + "source": [ + "# Copyright 2018 The TensorFlow GAN Authors. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ], + "execution_count": 0, + "outputs": [] }, - "colab_type": "code", - "executionInfo": { - "elapsed": 2639, - "status": "ok", - "timestamp": 1559656474241, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": -480 + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "35cp5a7vN9V8" + }, + "source": [ + "# TF-GAN Tutorial\n", + "\n", + "Tutorial authors: joelshor@, westbrook@" + ] }, - "id": "zEhgLuGo8OGc", - "outputId": "efd62ab6-6d5c-4ee3-f6ed-85447922b54e" - }, - "outputs": [ { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XSTQ5Flu7FMP" + }, + "source": [ + "## Colab Prelims\n", + "\n", + "\n", + "### Steps to run this notebook\n", + "\n", + "This notebook should be run in Colaboratory. If you are viewing this from GitHub, follow the GitHub instructions. If you are viewing this from Colaboratory, you should skip to the Colaboratory instructions.\n", + "\n", + "#### Steps from GitHub\n", + "\n", + "1. Navigate your web brower to the main Colaboratory website: https://colab.research.google.com.\n", + "1. Click the `GitHub` tab.\n", + "1. In the field marked `Enter a GitHub URL or search by organization or user`, put in the URL of this notebook in GitHub and click the magnifying glass icon next to it.\n", + "1. Run the notebook in colaboratory by following the instructions below.\n", + "\n", + "#### Steps from Colaboratory\n", + "\n", + "This colab will run much faster on GPU. To use a Google Cloud\n", + "GPU:\n", + "\n", + "1. Go to `Runtime > Change runtime type`.\n", + "1. Click `Hardware accelerator`.\n", + "1. Select `GPU` and click `Save`.\n", + "1. Click `Connect` in the upper right corner and select `Connect to hosted runtime`." ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import tensorflow_datasets as tfds\n", - "import tensorflow_gan as tfgan\n", - "import numpy as np\n", - "\n", - "params = {'batch_size': 100, 'noise_dims':64}\n", - "with tf.Graph().as_default():\n", - " ds = input_fn(tf.compat.v1.estimator.ModeKeys.TRAIN, params)\n", - " numpy_imgs = next(tfds.as_numpy(ds))[1]\n", - "img_grid = tfgan.eval.python_image_grid(numpy_imgs, grid_shape=(10, 10))\n", - "plt.axis('off')\n", - "plt.imshow(np.squeeze(img_grid))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4sAetutZ9t93" - }, - "source": [ - "### Neural Network Architecture\n", - "\n", - "To build our GAN we need two separate networks:\n", - "\n", - "* A generator that takes input noise and outputs generated MNIST digits\n", - "* A discriminator that takes images and outputs a probability of being real or fake\n", - "\n", - "We define functions that build these networks. In the GANEstimator section below we pass the builder functions to the `GANEstimator` constructor. `GANEstimator` handles hooking the generator and discriminator together into the GAN. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "oZ9n-jw_MG6C" - }, - "outputs": [], - "source": [ - "def _dense(inputs, units, l2_weight=2.5e-5):\n", - " return tf.compat.v1.layers.dense(\n", - " inputs, units, None,\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight)\n", - " )\n", - "def _batch_norm(inputs, is_training):\n", - " return tf.compat.v1.layers.batch_normalization(\n", - " inputs, momentum=0.999, epsilon=0.001, training=is_training)\n", - "\n", - "def _deconv2d(inputs, filters, kernel_size, stride, l2_weight):\n", - " return tf.compat.v1.layers.conv2d_transpose(\n", - " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", - " activation=tf.compat.v1.nn.relu, padding='same',\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", - "\n", - "def _conv2d(inputs, filters, kernel_size, stride, l2_weight):\n", - " return tf.compat.v1.layers.conv2d(\n", - " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", - " activation=None, padding='same',\n", - " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", - " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "NHkpn6ks90_R" - }, - "outputs": [], - "source": [ - "def unconditional_generator(noise, mode, weight_decay=2.5e-5):\n", - " \"\"\"Generator to produce unconditional MNIST images.\"\"\"\n", - " is_training = (mode == tf.compat.v1.estimator.ModeKeys.TRAIN)\n", - " \n", - " net = _dense(noise, 1024, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = tf.compat.v1.nn.relu(net)\n", - " \n", - " net = _dense(net, 7 * 7 * 256, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = tf.compat.v1.nn.relu(net)\n", - " \n", - " net = tf.reshape(net, [-1, 7, 7, 256])\n", - " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", - " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", - " # Make sure that generator output is in the same range as `inputs`\n", - " # ie [-1, 1].\n", - " net = _conv2d(net, 1, 4, 1, 0.0)\n", - " net = tf.tanh(net)\n", - "\n", - " return net" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "w-ZqQ4_thIrP" - }, - "outputs": [], - "source": [ - "_leaky_relu = lambda net: tf.nn.leaky_relu(net, alpha=0.01)\n", - "\n", - "def unconditional_discriminator(img, unused_conditioning, mode, weight_decay=2.5e-5):\n", - " del unused_conditioning\n", - " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", - " \n", - " net = _conv2d(img, 64, 4, 2, weight_decay)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = _conv2d(net, 128, 4, 2, weight_decay)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = tf.compat.v1.layers.flatten(net)\n", - " \n", - " net = _dense(net, 1024, weight_decay)\n", - " net = _batch_norm(net, is_training)\n", - " net = _leaky_relu(net)\n", - " \n", - " net = _dense(net, 1, weight_decay)\n", - "\n", - " return net" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "OhTAjxnyPS5e" - }, - "source": [ - "### Evaluating Generative Models, and evaluating GANs\n", - "\n", - "\n", - "TF-GAN provides some standard methods of evaluating generative models. In this example, we measure:\n", - "\n", - "* Inception Score: called `mnist_score` below.\n", - "* Frechet Inception Distance\n", - "\n", - "We apply a pre-trained classifier to both the real data and the generated data calculate the *Inception Score*. The Inception Score is designed to measure both quality and diversity. See [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) by Salimans et al for more information about the Inception Score.\n", - "\n", - "*Frechet Inception Distance* measures how close the generated image distribution is to the real image distribution. See [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) by Heusel et al for more information about the Frechet Inception distance." - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "1jF-FW5LPTn6" - }, - "outputs": [], - "source": [ - "from tensorflow_gan.examples.mnist import util as eval_util\n", - "import os\n", - "\n", - "def get_eval_metric_ops_fn(gan_model):\n", - " real_data_logits = tf.reduce_mean(gan_model.discriminator_real_outputs)\n", - " gen_data_logits = tf.reduce_mean(gan_model.discriminator_gen_outputs)\n", - " real_mnist_score = eval_util.mnist_score(gan_model.real_data)\n", - " generated_mnist_score = eval_util.mnist_score(gan_model.generated_data)\n", - " frechet_distance = eval_util.mnist_frechet_distance(\n", - " gan_model.real_data, gan_model.generated_data)\n", - " return {\n", - " 'real_data_logits': tf.metrics.mean(real_data_logits),\n", - " 'gen_data_logits': tf.metrics.mean(gen_data_logits),\n", - " 'real_mnist_score': tf.metrics.mean(real_mnist_score),\n", - " 'mnist_score': tf.metrics.mean(generated_mnist_score),\n", - " 'frechet_distance': tf.metrics.mean(frechet_distance),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "kxF2-gWHHaej" - }, - "source": [ - "### GANEstimator\n", - "\n", - "The `GANEstimator` assembles and manages the pieces of the whole GAN model. The `GANEstimator` constructor takes the following compoonents for both the generator and discriminator:\n", - "\n", - "* Network builder functions: we defined these in the \"Neural Network Architecture\" section above.\n", - "* Loss functions: here we use the wasserstein loss for both.\n", - "* Optimizers: here we use `tf.train.AdamOptimizer` for both generator and discriminator training." - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "OBd8Vg7lHit8" - }, - "outputs": [], - "source": [ - "train_batch_size = 32 #@param\n", - "noise_dimensions = 64 #@param\n", - "generator_lr = 0.001 #@param\n", - "discriminator_lr = 0.0002 #@param\n", - "\n", - "def gen_opt():\n", - " gstep = tf.compat.v1.train.get_or_create_global_step()\n", - " base_lr = generator_lr\n", - " # Halve the learning rate at 1000 steps.\n", - " lr = tf.cond(gstep < 1000, lambda: base_lr, lambda: base_lr / 2.0)\n", - " return tf.compat.v1.train.AdamOptimizer(lr, 0.5)\n", - "\n", - "gan_estimator = tfgan.estimator.GANEstimator(\n", - " generator_fn=unconditional_generator,\n", - " discriminator_fn=unconditional_discriminator,\n", - " generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n", - " discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,\n", - " params={'batch_size': train_batch_size, 'noise_dims': noise_dimensions},\n", - " generator_optimizer=gen_opt,\n", - " discriminator_optimizer=tf.compat.v1.train.AdamOptimizer(discriminator_lr, 0.5),\n", - " get_eval_metric_ops_fn=get_eval_metric_ops_fn)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "n1uldXfUfstT" - }, - "source": [ - "### Train and eval loop\n", - "\n", - "The `GANEstimator`'s `train()` method initiates GAN training, including the alternating generator and discriminator training phases.\n", - "\n", - "The loop in the code below calls `train()` repeatedly in order to periodically display generator output and evaluation results. But note that the code below does not manage the alternation between discriminator and generator: that's all handled automatically by `train()`." - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": { - "colab": { - "height": 2281 }, - "colab_type": "code", - "executionInfo": { - "elapsed": 221607, - "status": "ok", - "timestamp": 1559656706482, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": -480 + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "83-azWpoYsDg", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 270 + }, + "outputId": "c25b57da-0b4f-49d1-8004-36599a96d291" + }, + "source": [ + "# Check that imports for the rest of the file work.\n", + "import tensorflow as tf\n", + "!pip install tensorflow-gan\n", + "import tensorflow_gan as tfgan\n", + "import tensorflow_datasets as tfds\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "# Allow matplotlib images to render immediately.\n", + "%matplotlib inline\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # Disable noisy outputs." + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "

\n", + "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.
\n", + "We recommend you upgrade now \n", + "or ensure your notebook will continue to use TensorFlow 1.x via the %tensorflow_version 1.x magic:\n", + "more info.

\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Requirement already satisfied: tensorflow-gan in /usr/local/lib/python3.6/dist-packages (2.0.0)\n", + "Requirement already satisfied: tensorflow-hub>=0.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gan) (0.7.0)\n", + "Requirement already satisfied: tensorflow-probability>=0.7 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gan) (0.7.0)\n", + "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.17.5)\n", + "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.12.0)\n", + "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (3.10.0)\n", + "Requirement already satisfied: cloudpickle>=0.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.2.2)\n", + "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (4.4.1)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.4.0->tensorflow-hub>=0.2->tensorflow-gan) (45.1.0)\n", + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n", + "\n" + ], + "name": "stdout" + } + ] }, - "id": "AH6gcvcwHvSn", - "outputId": "a72e2218-95a8-4585-8a5c-7c4ec896ac0c" - }, - "outputs": [ { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mgan_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnext_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0msteps_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext_step\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcur_step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mtime_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1350\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1351\u001b[0m return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n\u001b[0;32m-> 1352\u001b[0;31m target_list, run_metadata)\n\u001b[0m\u001b[1;32m 1353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1354\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m 1443\u001b[0m return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n\u001b[1;32m 1444\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1445\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1447\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# Disable noisy output.\n", - "tf.autograph.set_verbosity(0, False)\n", - "\n", - "import time\n", - "steps_per_eval = 500 #@param\n", - "max_train_steps = 5000 #@param\n", - "batches_for_eval_metrics = 100 #@param\n", - "\n", - "# Used to track metrics.\n", - "steps = []\n", - "real_logits, fake_logits = [], []\n", - "real_mnist_scores, mnist_scores, frechet_distances = [], [], []\n", - "\n", - "cur_step = 0\n", - "start_time = time.time()\n", - "while cur_step < max_train_steps:\n", - " next_step = min(cur_step + steps_per_eval, max_train_steps)\n", - "\n", - " start = time.time()\n", - " gan_estimator.train(input_fn, max_steps=next_step)\n", - " steps_taken = next_step - cur_step\n", - " time_taken = time.time() - start\n", - " print('Time since start: %.2f min' % ((time.time() - start_time) / 60.0))\n", - " print('Trained from step %i to %i in %.2f steps / sec' % (\n", - " cur_step, next_step, steps_taken / time_taken))\n", - " cur_step = next_step\n", - " \n", - " # Calculate some metrics.\n", - " metrics = gan_estimator.evaluate(input_fn, steps=batches_for_eval_metrics)\n", - " steps.append(cur_step)\n", - " real_logits.append(metrics['real_data_logits'])\n", - " fake_logits.append(metrics['gen_data_logits'])\n", - " real_mnist_scores.append(metrics['real_mnist_score'])\n", - " mnist_scores.append(metrics['mnist_score'])\n", - " frechet_distances.append(metrics['frechet_distance'])\n", - " print('Average discriminator output on Real: %.2f Fake: %.2f' % (\n", - " real_logits[-1], fake_logits[-1]))\n", - " print('Inception Score: %.2f / %.2f Frechet Distance: %.2f' % (\n", - " mnist_scores[-1], real_mnist_scores[-1], frechet_distances[-1]))\n", - " \n", - " # Vizualize some images.\n", - " iterator = gan_estimator.predict(\n", - " input_fn, hooks=[tf.train.StopAtStepHook(num_steps=21)])\n", - " try:\n", - " imgs = np.array([next(iterator) for _ in range(20)])\n", - " except StopIteration:\n", - " pass\n", - " tiled = tfgan.eval.python_image_grid(imgs, grid_shape=(2, 10))\n", - " plt.axis('off')\n", - " plt.imshow(np.squeeze(tiled))\n", - " plt.show()\n", - " \n", - " \n", - "# Plot the metrics vs step.\n", - "plt.title('MNIST Frechet distance per step')\n", - "plt.plot(steps, frechet_distances)\n", - "plt.figure()\n", - "plt.title('MNIST Score per step')\n", - "plt.plot(steps, mnist_scores)\n", - "plt.plot(steps, real_mnist_scores)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "uy1dsvWuwJeS" - }, - "source": [ - "### Next steps\n", - "\n", - "Try [this colab notebook](https://github.com/tensorflow/gan) to train a GAN on Google's Cloud TPU use TF-GAN.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "last_runtime": { - "build_target": "//learning/brain/python/client:colab_notebook", - "kind": "private" - }, - "name": "TF-GAN Tutorial", - "provenance": [ + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b2xrX4F-OEL7" + }, + "source": [ + "## Overview\n", + "\n", + "This colab will walk you through the basics of using [TF-GAN](https://github.com/tensorflow/gan) to define, train, and evaluate Generative Adversarial Networks (GANs). We describe the library's core features as well as some extra features. This colab assumes a familiarity with TensorFlow's Python API. For more on TensorFlow, please see [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JMljl0ZwONgi" + }, + "source": [ + "## Learning objectives\n", + "\n", + "In this Colab, you will learn how to:\n", + "* Use TF-GAN Estimators to quickly train a GAN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pI8zy5Bz65pa" + }, + "source": [ + "## Unconditional MNIST with GANEstimator\n", + "\n", + "This exercise uses TF-GAN's GANEstimator and the MNIST dataset to create a GAN for generating fake handwritten digits.\n", + "\n", + "### MNIST\n", + "\n", + "The [MNIST dataset](https://wikipedia.org/wiki/MNIST_database) contains tens of thousands of images of handwritten digits. We'll use these images to train a GAN to generate fake images of handwritten digits. This task is small enough that you'll be able to train the GAN in a matter of minutes.\n", + "\n", + "### GANEstimator\n", + "\n", + "TensorFlow's Estimator API that makes it easy to train models. TF-GAN offers `GANEstimator`, an Estimator for training GANs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qxrYrU887Mns" + }, + "source": [ + "### Input Pipeline\n", + "\n", + "We set up our input pipeline by defining an `input_fn`. in the \"Train and Eval Loop\" section below we pass this function to our GANEstimator's `train` method to initiate training. The `input_fn`:\n", + "\n", + "1. Generates the random inputs for the generator.\n", + "2. Uses `tensorflow_datasets` to retrieve the MNIST data.\n", + "3. Uses the tf.data API to format the data." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "Zs8kdV0w7Rtq", + "colab": {} + }, + "source": [ + "import tensorflow_datasets as tfds\n", + "import tensorflow as tf\n", + "\n", + "def input_fn(mode, params):\n", + " assert 'batch_size' in params\n", + " assert 'noise_dims' in params\n", + " bs = params['batch_size']\n", + " nd = params['noise_dims']\n", + " split = 'train' if mode == tf.estimator.ModeKeys.TRAIN else 'test'\n", + " shuffle = (mode == tf.estimator.ModeKeys.TRAIN)\n", + " just_noise = (mode == tf.estimator.ModeKeys.PREDICT)\n", + " \n", + " noise_ds = (tf.data.Dataset.from_tensors(0).repeat()\n", + " .map(lambda _: tf.compat.v1.random_normal([bs, nd])))\n", + " \n", + " if just_noise:\n", + " return noise_ds\n", + "\n", + " def _preprocess(element):\n", + " # Map [0, 255] to [-1, 1].\n", + " images = (tf.cast(element['image'], tf.float32) - 127.5) / 127.5\n", + " return images\n", + "\n", + " images_ds = (tfds.load('mnist', split=split)\n", + " .map(_preprocess)\n", + " .cache()\n", + " .repeat())\n", + " if shuffle:\n", + " images_ds = images_ds.shuffle(\n", + " buffer_size=10000, reshuffle_each_iteration=True)\n", + " images_ds = (images_ds.batch(bs, drop_remainder=True)\n", + " .prefetch(tf.data.experimental.AUTOTUNE))\n", + "\n", + " return tf.data.Dataset.zip((noise_ds, images_ds))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "t6aboJBr8Rig" + }, + "source": [ + "Download the data and sanity check the inputs." + ] + }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1571383618849 + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "zEhgLuGo8OGc", + "outputId": "7e87aa29-411f-46ce-d0d0-95320511fa18", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 285 + } + }, + "source": [ + "import matplotlib.pyplot as plt\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_gan as tfgan\n", + "import numpy as np\n", + "\n", + "params = {'batch_size': 100, 'noise_dims':64}\n", + "with tf.Graph().as_default():\n", + " ds = input_fn(tf.compat.v1.estimator.ModeKeys.TRAIN, params)\n", + " numpy_imgs = next(tfds.as_numpy(ds))[1]\n", + "img_grid = tfgan.eval.python_image_grid(numpy_imgs, grid_shape=(10, 10))\n", + "plt.axis('off')\n", + "plt.imshow(np.squeeze(img_grid))\n", + "plt.show()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING: Entity . at 0x7f4eb3decc80> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: expected exactly one node node, found []\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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NXeHXlBnxMWy89xcFnVu80d+V16Ml+1MiVGqRR1OjMP3LjGeD9HtjPZoapbpx\na4pWxhm/1IfU2X4k7GjCxLh4pMet6R6dybNBPhrp8LyKwo6erEr8i0Mpl+kSnUXqHD+9fWHjTJP4\ncXgQ+DTRS3sme87jMCNc9Xjgqww/+9nmDDl9vPXyOwCkC5/QUqJg/Zi+Orel8G3KnW3NUFQzwcUg\nmykDDuihh7phsfQO9wrNqCOSsGD1hjfyO9LH+/LV3Qi2Bi99ESigUJUA3Gp7jN2Lv+eruxFIj+se\nJ67rd6/xPue9eX5EjF+KsaD8LYl+8V0o6JGrU/5l0q7G/NBsB+2NSwdM66sobVERVfc1k7H66s1U\n2DIPq87PNmcASkmG6sLWpHPMTO7CwzZ5ZQTVNKGwoyd9lh9nePUYBlv7s+XeX5gIRAzoNhr5FfXr\ndJZEWK0auW1cyAvMZJHLLvwlyugYkUCI7b7xuHwYjTw7+5VtCDzcECgUWK5NJMjyT3rZtdL7nuPK\nxHOYi4T0vTmIpAsW2O0p1mQq/OYJHereYrbZDe7LcsvN3dWE+GU+3B60BgD7HQE4zCh/y0dvMiUz\nhuyt0DABttkfIM9H+5zOxx/4csX3pzKG+W/j7iJX1XN9jZ41hcbc2OCmk2ECbNy4nKvPLek9aToi\n0+oIgZ+eOmpsmMIqVcgY50v6ASdCbp7mxLq1nGv6m8owpci4WpDHvi4rSB/0+lmKIjIaedQNLtxX\njlq3VpXNzNGVGkLw3jgLccd72M4NQxFxTfUQd7zHhjPtgNIFnbXF36c4R7Yiw3wVGm+l7Haty27K\n163J7+rFifVrGbz8EHt9HTWS43822IfWH4Xzdd0gAC7lw/TPA6l1OYOPD+7EVyIjtsdqWsRMUclR\n6oJIICTX6s1kprzM2eC1dN6j2+iZNdIXjwhX6usgfiy2sqTH0UgCbFoB2RhxgXl3LvFEruBgLy+g\n4oTslxHVNmN6+Bk6GJ8lurAAh5ApWB0SYJKSg+Jicb6p0MiIkNuhZHbIo9Ym9dq2GJcOUXDzvdW4\nL5yK7dwwUn53I9L7Z7oN+ECzYPqXOJljie03l1UxzwIDQxQeztyeJiam3Qaey0OZlPwuqQNrAdrn\nD5ecObUOnIAJmmf+6NUh9KCFUspyTPVkBDVraHSu8dhUvq6rTIly/XMsn0wKwHRrOLKYOB7Lqqre\nV1TeXVdkCjnGSQZ6aas8zgavVT0v6SzSFIGHUjTrob8U0bGa2rcjkRC3uBZnMkvPajwl0PeHOa9U\nSigPWXoGi8aOoN2UiXzSfhBOEyIw3nehjGHGftOMI7kmOC1Qf5mjyM6mT2yvUq9d8v4RgLu9TTTq\n58v0q5pO0vRiyZj7O+0J2XPaclkAACAASURBVPMjMe2Ua9xO82Zxzztbp8R+QGWYoPRJaIPejFNg\nYMjZcYsBGJvUFkXG66UzSrLDebvqucP70ary3+njfaknKh6BP0t9r8y5bzvaTGkAHo/2pc1PyuyZ\n79vvoMG+BO074e5IdOvNnL9QXL4+a4QvsYUFNFh9WasmRX9exuT380jvJpZ7XFivLvv7LGPqgfeR\nxaqvOyzPyUHRTZlof3L4YrL7FS8LWrW5XtFpanNg4iIKO7VA3taDi17KJOF70lx62vtT60fdQwFL\nemZbB07Quh2dI4TkJ6zY47zzxTrUGCkykrNrcGeBhVYXZeNNk2lYqJy63fnWlxsjgkodT5ppr3by\n7uswStdvJGVOH29s58ToxRHkGCFhWYMgult4kvCVL22Nz7GyiQUmNaohi76lcXv3ulRHiIDYgas4\n21NMayMpIkEUXbqOQZ6nvX5QeQgkEp7steSvJjv5LqOZVteBPCcHn2+mEf7Jck6sKL4G1lidhBTo\n69ldK9Gwnl2Hs//QLxzdrJzZOB8fh+OoopuTfvwcRU4g0H7UBB1HzucDfTjisq+Ug0iMiCMu+4gd\nWL6cfkV887A1AIdGLiZzlC+Jv7lzZXhZdTVd1hsvU3eTdiNGRZwNXltqnaEthR09Wd4gjBO5Johc\nHdk7Ygl3pIYc3bCKH49sQuRkr3GbtpsT6RPfFTkK/I0KkaNAppDz/d6N5HfRvf5MSW4tb8pfTXZy\nX5bDmTHap3LVDQql1WdTGXanC0KEGAhECF/8U5hqKPf+D6DLcgZ0NM7Ud5Wh0wnSHBKkOWTKc5Fq\nGU594iflVMBabMy5hUFc8/+xTIZ7slT9TPW/m5cDDnS5YyaOUrorPrrel9ixZjgYSPA0VF6YwY9b\nwhPNVdWlySkUdn1Gz67DAXA6MgHv+YE4GRjSthx1cl2I76EcOfpc/UDnPNxam8PI6S+k24AP6NR/\nFD1u9USOnPSlAsT1NZfEKayl9MLGF+ZzLLcK61v9ROxaL0TV9RMwUvI6aHBGt5mZTsbpNC4C73mB\nBDp1INCpA8N7jqfjlMm0vDxY47bqXMnlYE5ZFbjRiR1Uz985PFOX7pbi1+d1QKZ7XkZOH2+OpkaV\ncgDoesckXSkEfNHrF2IGF+up+s6bzAVfU41LPBQhz87mgV8NnI5OwGnsJczWh/F1ehM+q31Vb9s9\nRTVHbhbmU3eC7nVdAGQPHiIIvYIg9AqiUQImJ7fjbLNtPOiqmSYPQO5HyrVsz99mscLBhVZGecR2\nX8ODrfrRviq6DkYmttHpBg1vItlaIOD5YVv+dN+plxStkvUz9BmE4Hr2fWwHa6b2XpKcPt6lPLIl\nGZnYRhUlpAsCDzcOhPzMO1MmYfK77nVJ45f5cHNgcKnvReTmzPajP9L81CQcRkRq3bbQxISbQY2I\n77wOx70T32wdVaGI9QmnqSYQ0vyPqThPuKr23m9IyiX6xXcjv61S7UBgYEjsMg9u9VlF+2sDqNY7\nVW21wJKUvB40/f7fWK2Ul0mZ48sZ9124nh6j76bfGuKX+VRomFDaja4LosdPWZjujsle3fSOipAb\nl1U0ynKvSY5cRr0D2pVrLOLux02J77wO4M0XOJbLiMhrgInQgJudVyPUQNUuWZpLRnBD1c+KwgKc\n51yj042+nHLfiaCKdls1tnOKAzjOhTfSqo2X0atxCjzdCBq/ho1PLXGeo1udlLeZkpEfoJzG2u8I\noHXgBFoHTtBbuF7MTAt2bWkHcv2kRZvcUzrnBS0aq14bOe8AazK9qfqbblsI5z9YCkD76/10akdd\nPvxroOp50tfqqe/ldW/JrUIzRAXyUjUy5Tk5KJYqA2vi5jhXdPorKXlD1nbr7GX0l2wtEPDB9hBa\nG0kZfaorTsn6udsXIUeOQKqdZH551N2pfXjW3UWujJyj/EJaB07AYY/+5T6E1arxbZftbG7mppGC\n36uw2fOIUd06su73NfySpZzajjG9x7vjuiJBe/Gw1Dl+VBVcxj1sJA0nPvhbFBacJ1yl3x892e24\nH58GiagjrZZXU0QH4xw6BK9m4xNr9tz3AOD2g9rIXvjY5Iaar+SU+5raF1mqCK3WnAIDQwSuShnE\npPkivmuym1ZGmXidDcB+9C2t5uwVUbTm/PmpBUu29dVLoPqhlMt0GjkO8QndKxr/l5F28OT3n4Ko\nKpDQw7WdbsWmtEBkVgvhbgm7HffjuXJauTUxKyKve0vSfETI7XK50VZZc14XH0nRHve58EYaj5x6\nU99Lm+GHbe/b7HY4XOr154p8Btm303sWQUjKJVZmOvLH4JbIr9/Ua9uVaI/Ay53p23/jXeNcOkT3\nRdIp4Z/u0r+W/7w0ZiX6pdrZ2uywO4bj8bG4fJiE7NH/Xx/Dm6ZSGrMSvfKsdTpdaY4jlytV/N4Q\n/0mZkkoq+TdQaZyVVPKWUmmclfz7EAgQVnv7A991pXLNqQPpE3w5+flSTASGPJHnEZJdHKny2/0W\n0D8PWcZjrdrOPmLHCfcdQHHFqp4W+skeEVapQsLspnh2usFGmz94Ji/Ab+uHOHwfq5eCwiVDLotY\nmenI8VZWGqljlEfWSF/++iYIIQKafz+Zess021p7OsSHRtOus87qDHIUegkxFXi54772Ot+al/67\nO8X0RtxR++JWOo2cRUHOFeF80YAFdy6RMVZzGUpxPXOy+ymDykNSLqkeCV/rJmkptrLEPKy6Sg7x\nUMpl4n5u/voTyyF8XhBheTVw3jOJXrNmsd2lgeqxz+kAj36qrVW7ub1actp9F0KEtPp8Kr19etHi\nwihub/XQqr2XubnEjahxy1WGWU1oyLURK1gYcUgv7ZfkaoEM5z/Gkyc3AAPd6tQkf+JH4NydCFE6\nN7dNW8LToZpJTp75PphVVqdw/nMMTdZN0ak/RWS6Vi1jmADHXPdy5ztfZcaLQPMAGq2NU1y/Ht12\nlyNuLBQh8HTj6VAfltQPx1OCxtVh5K09sD2QxYkVQUxK8cF9wxQWpruzP7smnu213+uM/8GHEcfP\nsdH6lEoOUY6CyHeCid2s+R10f3ZNfhjUH8fJ58uEv3W43p8pDicRW1po3K5ADquybOkyfBy1NoUh\nTUrGesoTfvLdWPymltqJX6XO9uNmD2Wmi/vZMfSdMgOvpdMAcDXUfZUjbOxS6uezOU44vn+Js0M9\nkGs5aorq1KHt1VxOT1rMsGrFGTkuBhIYof4WztMhPnzx0INOAYHYD4vEeoF+UuXSm1d8gd8cHsyB\nmD95MlTzrB+d9jmzRvjy17dBFR5vFjxFY/Hj5E/8+Hn8D4y8PBqbufnIbsUjrmfOjS9suNkzGCFC\nHshy6bR+jtrRQoWdWqgy351CAnCaUDpULSTlEmH5Ir7r0KtCyY3yuD/Lj/pLyu+DyNWRfcd/ZUTC\nu2T6aze1LUnshhZM8z3+os6JdiR96se1SUE02hRI9TvKXEkAsY0Ve0P3AtpHyTya6MuWj5biZGBI\n79b9yGtoxpEt68iU5zHCyl+rNoVGRmyJO0FNoTLU8rkin/b/m4n55khCbis/91ZXB1C9y+slUHJ7\nteTUqjV0tSg7S5K+48n/NqwjIteOEDfNdZqKpFYBnsrz6PrRTB67Cdg2dDnNDItXjpcKZHxu1xJe\nsrk3kpVSY0sYvVt0o9c7g2iyfgqt5wTS/MII1XHr/ZpdlGnT/dgTsJhhP0/Hsl80slvxAMgys7Dd\nI6fnzT4A3JFWxTpEvbuwwrcpS9cGc7VARqcxAThPLl9JYV58b40ME6D+stdnXxTIdCuJTkt3so/Y\nEdtlLVuXdNGpqYa/pdE+MICGn4epDBNAtlmOHDktFmk/zdv7yWKcDAz5LsMN6Z0EjBKUa9eaQqPX\nnFkx8rw8un88i3P5Qtw2TGaoVx/qrA5DYKw01qfyPJ6fNFerraQeclXV8ZdpuPAWHhI542vEazwj\nUfgXJzk8lefhu+lDqm8Lp+GnYcy1bckXj5qqjnsaihCaqJ/1ovM8Rno/DdmteKznh/K4Rw6XW24B\nIEdRoFG4ncjNmVrdUrAVG2FzuHSSriI/H8OMXBbY7iNZmsuUFZNQRL6+BL2ohil585/iZihm9oRJ\nGB6JKJP3J+2gHClSM8omer+WV2SLJHepA0BasOaSIqC806d85Mf6Xas55b4TOXJqbdJeFhNAFncH\n472l82EfTPHjd6c9xBdKqf+XdtNOhX8z6ouMCc6yZ/PxdsrfdS8Zl98CAXTyrJpuDed/ds2w+SJU\nVXE6YZJSEzjwXjcaLFZv9jTE84JqrVqSjDG+rLM6Q2S+Uv7k/lypRv2LH1KcandHKsbmi9L92bOj\ntUbtlUSvWymn/ZS6QSdyTeg6Y7pG58YPr8Uyh9/4OM0LwksnQWeM9aXqDw/wkMjpvmYO9X5Q7wt5\nMKgRJxrvQo4cg2Nls2SERkaYfqH0phU+0S2f8WVEbR+TKc/D8Llm8TOiRk5kH7Hj1Ko1RE5dSX2R\nMUIECBHybLB+6ngIjYyQt1U6l85/vByRQEDPPTNQXHr9Da88bk8UEJxlz5GR/jjMVK69FVIp4myl\nMQisG+il3wBJn/lxZdJKUmQ5xGxzff0JKJ2AnlUSyh05P/loK3IUjP5pCnLkhDRfj8BLf2LWNhvi\n8IgYpvrdAsv6ap+rl60UsV1DBh36i+8eteNWO+WUw7RBBnGLfBEWgtUf+Yj+rFhMS2xjxcxe+3E1\nFPLnWm9qoxwh7n7jS/TIIOQl0pksv1F/DfvYU3kXPJ1rovSWvZjrC42MeLjTmn1NN2EuMuZSPnpR\nWCjJhRZbaX1lFKYHNUvFmn9wK3KFkD7x3bhzyA6L74r/3keLBJxdcpk21/pTY1wB0qRktdsV1TDl\n5gJX4vqvenGhnHtxRMiFfIFWOYjCxi7sP7oVuETrjwIxvVR+G3JD3S+zuBXeXOqzjOrCy4CAAM8+\n1H2k3rUgTUpmfnR3er6Y1RVxZ5Ev3atE0DEgEOsDofTd2Ju95/ezb+8mevb5AC7opn8EIHv0iPq9\nHxGTUIibgSF2vyQRp+aOmM4jp9jKkowgMUOqPSBsuRcp493xP/eIfSd30NQ3DpMUwSsN82UeN5fx\nye2rfHL7KueHLyl1rOPkyRr1rf4JEXelebQ3ziN9vyOPJvpS2NGT/bfPEd58O/ueuzLkTmeN2lSH\n2LVeJEtzMV6uufNmftehfNltMPlt00oZJoDdnDAcd0/kpPsOEoeqX2hHZF4X79OPiOm/EjkKGm2d\njLzEvxrCfK00hFI6K4tWRRbIMf2lYuO2WauZYHURiV/6kvKxH0+H+BDXbzXVX6xfR99rp3GgfYNv\nRMppbUt3xFaWxK305sawIFp9PhWjA8obszQ5hYCktggRktL+nw9y0PiWJhCLed7LE8NnMgyOXeTG\nF/WIb7oOEBD6TbEYVYfofkg6JVCXdLXaXRL1LmPabiS2x2oAogukXCmoyuGnTfi67iXaXxtAFQ0F\nk6rtCGeUcBadPj5LePPtUMJR5xQSgOvc28R8Zcdag3aA5op25RG71ovobsF0nPUh1Y5qPhrJYuJe\nedxx6nka501la8ByPv9OvVvwrU/s2FP7INcLFEyfNQnz8cV6r3ue16VP1YfsWbGUIemTEZ5V6ggJ\njYxemZcrqmFKl+HKm8ferFd7eK/+0JTqqPdZiJzsSVlkyLkWmzEWKG/qypFeOUV2OBCA82QtJE0v\nXEOOgvW7VhOR14CeVTLLXceHHmmCfNwpsh11q0dTkmeDfagl/AswpFX1WG506lfuMutlNN5KqXmu\nFous9lNNIKSqULlOm53mzeFDXtj9L1KvidYiB1v2nd5Fh+v9Me58V2/tFvF8gDdHly2n18hAxCe1\nS7x+FOBLll8+pqY5XGixlVn3fbj6RTMkGk5n1UVUw5Snv5px2n1XudsCL5PbqyV/rApmSUZjNp5s\nz+5ey6ktKqTNqam4zs9AejcRQYvG7Nu3GYDmwdMQemXxrfvvrHBwqbDdwLhYupgob2htZwVS7dey\nxpfwtS/XRwfRq9NQtYSws4/Ycdp91+v/JkUBbS6PwsXsIcPMw3A3TNe5IlhJPCPlLKgbqda2UtLn\nflwLUG4nxhbmMXVUIMLTkYjr16PAqT63+xsS13e16v3TUn2J8yqd86y3lLFM/8eMoxXy1h6M2/g7\nTgYPiR1kRcP4ML3JaRSR2rU+6bJcHoXWxxr9G+cTOxESgQGSyDtapT3dn+VH6IylSATFNVeuZTbA\n6PhVTeMu1KbLuQTG1ziOXM0VSXI/KUKEzDa7wewBNxBigNPJABxHXabIL6m4eJ2eFl50ic4iKnAl\n+YpC3A9NwekV0iVFhglQ/XZ2mb9XZFYL97YvZgHp6pXmeNkw5SgYldCR8Egn4voUX+DGAkMiPJXl\nOyLyFXyc1APQrPzH6yjPs1seVl+FwgslVCcDI45s20ijTYFUafqYCM+NZd5//JAnNqi3VtZqpS6q\nbcbdLkZ0Nkmj99ipGMbrf5RIH+9Lje6ptN75IfZ6iuR4Ga8+ygW/LFPzL1Zs0YBLM1cCSsO8L8vl\nmVzEsUa/M/pUBx75aa4I8XSID9W3Vzz9swyvyqQal5EjxOnwBJx4/dSorVMc8hK3zegCeYUSmMvP\nvcvEbnF4/jQDp0/V37Yx/P4R+W2Vz8UWDchuakH1j5LYbncQQGud3Sbrp2A9PxRHzvPu3nHc+0BG\nnZrKm4LxEuV6XpKeizxKv+Ukdh7zZ8Fw7WVCb3wQXOEx20VX1B7EtDJOgZER+4YuwUQgURUc0ici\nB1sMej/iD7fddF40Xu/t64PUVcXK3t9luPH76vYYZEOb6eFstjlBz6bDNa53afKgEMlppbjxnUN2\nqteFvpl0t4nmi7qnkCPEeVcgjRYnoc6OXGiiLVjBM3kBNwqr8OmHFZejcxofgc/kaTRc9fq1vc/l\nIcp1PPCR1SFG/DwWgEbW9/nDsbhWyJk89eNp1z+xopnRPUaEK2VV7eYX35QNjl/C/njZc/Q9WwOw\n2/0cgxEixFaWannEvecFcv7Lig2yCIej43HKUX/5pPGa82GgHxfmriRZmsuQuR9iulX/ynOekXK+\nqHuJFZkuHG/8ZrxmwmaN2LR/HbVFxtpnJghF5QYiCKtUYX/sGTrd6Ivk46oa7R/em+9HoWMuMe02\nqEY8IUKCs+w55mutlYiWwNNN6z3MVyGqWZNHW+oQ6lFcIW52mjdX5jbD8Kh+1Rf/bjLG+DJ6VggH\nfWzV/syfD/DmzA+rS73WbspEAKocjKpQX0tvGkIT4+LpZvKEZuEjseyn/y8c4Lu753E1FOK2fQr2\nH+rf+EG5R7f84EZsxUZ6SRuq5P8fC+5c4stug1/rQdcVvcXW3smvy+Gcalh/9ea0v05muzI6oRNO\n3765D0V+/Sbf3P/31fqs5O/jCzvPN26Yr6JSfa+SSv5h/rZaKZVUUol+qDTOSip5S3nrjPPBVD8O\npVwm/YAT+d30W3G5kkr+Tbx1xlnv7BNGJb5DePPt/LFuDTPiY5C1007jp5JK/s28dcapiIwms68E\nt7OjAehgnMNnG39EVEOLZOh/gJw+3sQv8+FoalSpx9uAqE4dCjq3wDysOkdTo5h35zLCZvqpJfl3\n8OSQA/YR2isr/NtQ2zhl7Zvz472/ENs1fIPdUSJNe4DtkGs0C1bKZvgbFfJgiznCKlX00v6CO8Vq\nfvo0nKOpUZwNXsvtQWvKHDMPq17OGa9HVMOU7CN29LyRobWoFyhF0wp/NeLIptWstz5BoUJGC4mM\nIb8e49FE3RQNX0bWvjmpc/y4N8+Po6lRpM7xI7+r7kuU9Og6VBPnIZDoJzFe4d+MtL2uHEq5zKGU\nywibqpe8/SpSP/Tj4b7yEwYK3tPsM1B7K0VoYoLC1ZYNe9byzrlAHKamVphTJzAwJK9jU4xPRyPP\nydGoQy9zf6Yfl2atBOCBLJcxWmQfiK0sabQvha/NyyZUCxFyT5rLkHmzqfmT5jIg5mHVy61k/XIB\n3aKbgCaFdUU1TDkQfYqIfAVCgRxPQxFyFAgRqDLrDQQiChWyVwZS/JoUSp5Cjt/hGTj+XIjgnLIv\nIgdb9pz+DZcDgVonm4sbWhMzvw5L/XfQzaRY5mRNlh3p0qp8VrtY1WJ2mjcxnprJgOQetcVAJEPc\n8R53vvXlxoggerUbgCxOuxzRIsbE3qVPlbIaVy6nxuKgRVytqIYpMYuduNl1FV5LplF/aXHoYX5X\nL2av2EJH42flag/rvJUiz8lBcSmaQgVEt9nE+NAwsvuXTtDN6eNNTh9vDI/X4uiGVTwa1rSC1tTH\nckscIxLeBcBcpF3B2xsL6pUxTNcTE/BYrZSEtBRLeG6lua5oRYYJaJW8/DKpI9yQo2DmJ4EMDRuH\nHMWL9Gjl/65/jsVj4SS6DB/3ynZMhAb0/fhDnCZEqAwToLC+9kp+AA8n+9Eh5Dq33l1HN5MnfJ3e\nhGbhI2kWPpIj3ZoS0aEeXzzUXWv3fpZ2s46KUPg1xd3wPgAuJ8ficnIsnW70BcDsD82nzcJmjehy\nLoGbXZUyPaUMs4sXK1atpKOx5vnCGge+v/vbbGKGBtHFJJP2P/zAxvluCAVy5AohgTWVeW1KhXL9\nIHv0iEefe/LkpzxMhUYVxrOqy+iETmT4Z+LIi4RdZegjVX01L2H3smGOTGzDufBG3B60htQ2Ahz2\naN1NxFaWjJt4gEZbJ2O3I4xqO6C3VS8Sh1pjceoZXLiGPerd4V0OBOK0rXQYZNYIX4Z8fBghQoS5\nmrseLMOrssfyBwwESnXBRlsmY/hEoJKRKRofdx/0Z8FoZT+jMiyRkKDR75lme4LPtg5X/axuKter\nSGlbBQcD5dS4KENHaGLCwjB3zHZdRY4y5U1uZ4Ei4vVSJZmNqyuV+8rhwQd5OBsoPyPX3yfjWEHS\nQXlo/K3Yzw7D77PJBGc6c71AwpSacUyvmcCUmnEEZzoTnOmMgUCEECGGT/UTYCQ+eYlHMuWXkvFB\nS63b6XmzD1m9y78fnW22Tet2i3jg+1QrLZ7ySBxqzXjTBOzmFE+1pUnJSukSDbVtrENK/5w23Y8h\nHx9mYo042l0bgNNnmmvlrLE6rTLM92L6YPdx2Gv1nQwXaDZSy9t60NHkAZZ/5gIgM5GTKc9FUFCo\ncX9fR247NwJrRXB7gwNjYu8SeD6UgG17Xi/2JRThP6N4VjYqobTsTZTfJgDWZTngvEGzpAWtUsZq\nbQ7j6ObqHKXsFkfyXD+mBK5EjpxqO/QftF7n4hOtR+X9LnsgClyOTuQb/930qfqQovtTq8hh1CJW\nrXZy+nhzNlgpUm2/I6Bcg9SXkeoDoxDlxRO7zoub3VYBEarZTU6+IUbeLhopQTyc5Aco339floup\nYS6cqcOtI47Y7E0noU9tDJ9Co8ExhDQMolAhY/idbtwJBPtzr267JHd7SGh5bgINTytHtxbN4zmf\nb8bDDpaI8i30khGV+b4vHpOiCLJYAxgR3WYT96S5zE7sw+3fHakX8eobjsisFgvrKctYvHNtEAYr\nzXgS6AICmBi4V/W+rQu7YHpFs/7qvZBR3fYpAJzPN3jNO9Xn+QBvrMTKP0ybxFqnMZcQJhdPEmI7\nr33xTPjCoQLZ4bXVMs6ShjkysU25Rviy06fIU2u/IwAHNbV0lL3TfQpXkiq1yzrnwltsIXVzPmPG\nTMPguHoGWndVKHyqfF5fZMwO+yPKHwKPQGDZ9xsIRMr32EN31M8Aau4Tx8X4hgCkfOzHmLpb6WSc\nTaevgug8Svs832pJcjLledQUGnHuf8UVC2ILC+gfMR6rZUJEUXHUy3l9kn/i2OJ6QSfdd3B+pQHe\nkkJV8SmAycntMDtxV63825LodZ/z/iw/drgop4ezvpqolzbFDa0xHJ9WSgpEG/Zk1yqlOFf0r1Ah\n47fndWm4Vr3sg9Q2xQbzwPf105T4ZT78bHOmQkOuCLMbUuQoyBijv20Oi77R+C2YSh+3jnS38KSn\nhRcrMl0wFxny86blyNqrH+zR6UZfChWarf2jCzS7PLvXvsLQZhdIn+DLlSlBDKz6hN3Pa9Nl+Di1\nbyTlUev8Q5Kkpa+nLx56MH34RKwHXEMQekXtXQabDaWvG29J2Sn3nWdmSNMelHn9deg1K2V/ilIV\nocuoAI0+vKIc0deRKc/Db8csHLc80VhloLx+gtJ5pUk+Z9GWSEXT2SJKjrCg2RZKEaLq1dkXc4oL\n+QK+HDxKLzqq5VEkxLUw3Z3QprpVAivJw8l+XPhkJemyXN7XYgtM2sGT9MYS6kblklvHkNPLV9Mk\naLJG2sXlEbfCm1v9Vql+fm/EeK0F3kA5tZU6W6l+FoReISTlktKj/vtkHKe82gn0xrNSFL7KbRP3\ns2M0vqu1kChl9rc/M3+ljmxNoRExQ4JZe2C99h1FaZBF/xZnvJkImZKGOTKxjVZtyJ4+JSCpLS0l\nCo3LBGiCwOn569+kIc4XDdg3ZxEA3b+erVUb4hOXqLc8FOHpSDKdlM4nkzTdnYw7eyj3zZsvUwa5\n3BmimxnIMh4jCL2ieugLvRlnwjTlh6ZI0D6KZ8GR/uT0KOTd6H6q157I87gvy1U9nEIC6Ll4jlbt\ni2qYcvfXJqWmtee6OmjVlr9P+WvfnD7epaKORia2UWv6WxExPzRGjpzuNm9GdULh25T93sqIpl+i\ntfeEl+ThZD8+rHuK+iJjMuV51F6nW40XgNwGyil0nb80nx6WRFy/HrcL6+C5ZApWG5SfqatDis79\nK0nJshk+zWMR1TbTqh29GKewWSOO+q6iX1xPnNamanx+59VziC0s4OaAYPZFn+QPt92qY8MGTGSc\ndSvVw2lCBOYrtZvWyG0tuda6WK7Q9cQEpMmafTFFo2B5wQfmYdVLjZj2OwJ0MkyANH8FQoRcf6q/\neiNFCCQSvty6CVuxEcnSXOxW6Gd3+sInK1WG2fOTWXpps/5p5f+PWqlXVawiLPc94ZPzfam/NFRV\nZbu20XO9hYYCPOhW7FzH2QAAIABJREFUrBXUrtYtBEbaxQPr7K0VNmvEtF27aSCWoBghQpqkWRk9\nUNY/mflNRY6PqxW8rhlDbqYyrNoliu5HbT4MxPEVMpQV8cD3KfbLArg9aE25cbkjE9twd5ErJnvO\na+SZLY+nQ3yI67uKNtcGUPU93cLVQFl7ppHfHa5eaUgrrxjWW58AwC9yGLW6x6LTZy0QEBvsRWyv\n1eQoCnjny5nUXheGqY6fgap5PWlyzDH/A2eTB2ye/R5Wa68DsNH6FO+2DUBySD9KkjfeWUfRdfbL\nJ90xTtYyNFLXjtzpZ0oH4xzkoFFhnb8bmaLYte16YgIuB29oJSQNyj1MewLKBLi3DpyAyZ7zFUpP\nasKdRb5s6R9ERD6YzhBp3ddSCGCHQwg4FEdxue6djOuSBxq7+V9G5OpIbC+l8lznj2dQ+xfdp7Ll\nIdTxgxh49QPCm29nyvQ4ZgxUVmUPzrKnys1HOn8GLxOWJylTclETdDbOBQN/BfS7r/km8DJOAJRO\nBZfJ8VpJTJbEYUY4nWc0I36Zj+pnfRilZ6ScheZXKVRcKhHorx+RKYcNaezpo6yNcl+Wy7tbZuM0\n/xLSQv3VBQFeWdRIF25LczHbG63TjarGsmrc3ZyHrdiIZQ1CKVTIWLujK9Z39C9c/tMjf3SpwVMp\n8FVJJf8wlQJflVTyL6PSOCup5C2l0jgrqeQt5T9jnAXveXF/rysGf9Yn5Xc3xFaW/3SXKqnklbwR\n4xSIxcjbepD4m/aaNxWR8rsbR1OjNBKmEkgkHNwQTKTXVvY5HuSK9xbq7nx9LO/biMjVkcUJ4Sov\n8X8FUc2aPJzkR+IC5SNpV2NCUi6RMU6/+kdvEzpvpYhqmPKwfyNQQN2Q2wiqmHBjTl1ie6zG6Zj+\ny/dFev9M11s9UMTcVr+Ptc3wODcWu9HKbPVeFxPZYHWaZnMm02CR/lzoR1Oj8L3SD9MeiSikbyYW\nNrlLHVwNDDDM+m9MevK7ePF4wnO+b7yLZpIQTIXFgflyYNXcFYypMY0Gi99MDdd/Ep2N88drh6gp\nPKkUnfqyeOclvjAf15naVYwuD5GbM1uPbKZX22HI4jWrci1NSaXhoFRVkvaeRnW4F2lG1LQgWmYF\n6iX2E6BQIeNMk9/oIfKDN2Cczwb5cGnWSppfGIG1lgWFH+13xnyuAPn1mwCsTDyHvdiYJkGTsVp8\nQfubymvkY+JWeBPXbzUdx0wAeG1d16wRvpz5dgWUqK4dlleNDFlVALpXyQCgqSFINYy8E1s04Oa3\n5mz234y/RHlVuPwSiN1H2l0HGeN8OT8/GJFAiO+sAPJqCTF5JAeFgrReyj1kUZIRDffnIAhTPzBe\nJ+MUW1lSU1g6bjBZmkv3tXOodz4fcab2aTgvU1jLhKpCicaGWREHEhrzVd0ocuvoL6F5TZYdATV0\nD7OriEe98jiYY4r1xAyto1l86idyx6g42N9ebMxtaS4Nf01BqqVhSt/x5EFLCRbfln/DyOvekgM9\nf+BEbnW1iy0/cSj9vXz1qDmRfe2R3kkAoHuK0jhjCuRYnM5Tu69Ph/rw0YJf6GbyhHxFIQNu92CN\n7V7ODFnM+x9pntZWhBwFcoWMM99XXET30iBl5TJ10ck44wKLc9jemRAAgMFzKZZ/6n+K0XPNSS7k\n68+Q5BdqQEswfqS/OIs3aZgAN9tuotFf79MwTbsYWIHH/7V33mFRXVsffqdQFRBRVBRRQEBsKCIt\naowt9q5Ro4m9UGwxmmrLNYk1imA3xt4Se4+xg70rVQQUURRRpDPl++PIAKE4zRvzXd48PplyZs/m\nzFln7732Wr/VgB9rrGEAjqrncI0B86di/UC73yz1c2+OzlnI7TxTZtwYVaLxOX13FxcDIz6bMxIr\n1BydRELA/wNZNh2PTsRp9GUoJA4mfuMuSZBZIDl5Te3+npkvGE+He71hoTWGRy7zqa8f67YuQ9zY\nBcWtCLXbyqfK+qu0fTaO5GaSkt9v8ZSTjXZq3K7WxpndtQV/DZoPCHKV7eee4eBPH2J8QP0TVRpR\nqzyobvsC807CulLSwJkAy+04/DUCRzUV595GpdZCDqnh639HEFRWjxb8nvEAh+8ztV4qxEw1oKLI\nqMhzgGoX0tDmLIjcG7Br9nzMxab8MPgzDMNKHhVn1DhKshys7mao3bb94gh6bOyHSCbH6UFBuxJz\nc55+0gDFm+nu4rj2GKJeskVmb09i8kIZM2EiJvuvgkL4nOj8Dfam16fHtrPsdq2qdh/zUeblYrLn\nEnZ7Sn7/4Xc+0Ai2pXqCBnMerb0KuebiIjqyX1nd48z8YA4kXuX+fN08aNvbh1CvkiBVKalmzff7\ntzD3ubNWYr+FkTg7kjLCm+73UthUfwMec/ww1yIzpSwanB1WanlxbXn0tQ8nQ1aw1qku8siSJRjL\nRCzh+X4nIlsLSnDKK3dI/dybyNbr6ObWEeWVOxo3mdnLk/37NiAGuti1KHUtJXF1oobEVEi4vqD+\niC9PTUUeHYvsgWBAEnNz3K8r2B1+gtAZSwH4ePg4DNurZ5jiChWY8vNmJtbxwWTvpSLr49yOzfnM\nPJoRFgm8GqxfL/jTAB/Cxixk6hNPIltpplSv9chpsfMajR0DyKkq/JEfe91kiY0grTa3xxbWr2mv\n3YUEuBsK04N6l404eN0OdyMYsbottbzSNfqBCxO12oOLH/+ClVi4oSTIEBbtekYSXlHvbc74fDNp\nCvXXVX9H3roJl5qtUT1P/dwbcX/h5nd/oiMG9a2paiaoITy+ZIPFmzh7y/WlT0GfDcxEjIjO10ZR\nQ1a6ZEzkVxXYll6V6scfa7VOfjLRhzTXPBwdnjDD+oTqdbfVE7CPVr9Npas9robHgOLJ9QoDsc4a\nVaUx3X8rxiIp55d5UDlDM4eT1sapzMul9uyCdcp9wGnVWB50XU2fCqkE16uMcaTm7ab380QiusGv\ntU8hEYlZYhMGiKgUI2fWll8ZP9+fqss196qd7riY6DxTFr5qxt1XNVjnsIvTS5fj4ulXRBtWV2zO\n6nfUfDXYC3ejc4yJ7wGkaNVGr+DjRZ6H/afAaXHv8785MAptH3deX7rgV8QHG5ErISO99ERiibMj\nB1oG89mMKVg+0O4c1zyczJWp2988K5jo1Z4ZqpGxi9Ozea0wQGRgiLJwFo5IRNa4VK36pg59Kj6n\nwZmR1P1V879fY+NUtG7KQz8ZhJthN6OoE8F5eSZ5XYSR1GRyIsoDJbVQMjmdPdi0YjE1JNfoaCNc\nFGJjYw7cF76j5oQYvhk1mqontPuRRxURmEpiCL5I69oReOAQi63b4Tz2Lops7Uend8WF+Stw3DwV\nh6na30D8Kj1E/mZRueCFM8Mq3VDNIDrX1K68Yqd6vkT+2JDoPsshETa/tmbm8T4YpUiw2y8EeBgv\nTsbJwFitGjTxs3y4OXIJ4x+24ZFXgaaRKDsH/0Tht5tofQJ7A+1GOHl4NFP8/DgetwIFCm7mwsBz\no7ndZiUGois8l2fxVK6/0fPVYC/Ozgum6dIA6v6snbNNY+OcuHYrrgbP6X2quI5PVk3tpR6S3Q2o\nJjFRFegBSBnQlGEJhsQurI/5nxFIX+pvawZA9iCeDQs7Ez0nmLZtx2B0UD+Z8PpCYlWZVa9scPqP\n9onhAM1njCOlmRzHzblIb97H4qIToywesi1dc+dHPoqMDJy/ucdHx8ZiOCmJAy5/MLi3kGxNodIt\nCg1dTZ0r32IV9qrnsviHxL2RNppysi+7nfZq3WejQ5eZ/sSDWdVCaWpoQMRHa8hUyhka25nkBfZM\nXrCF1PoidC02KZJKaRQoKCXWXhOh9W+nsXF2MMnALfhLaoUUvxt8+csG1eOkvXZUR319Htvj6fzc\nrwHrj32Iwxtpi5f1ITrIFfNdF/QWzPB3qu6PgTnvqHEdCf/JgbgtLti+1G1rymp1GPkSUwqgjuFz\nAF7KTXVqV/H6taAmfwA+6h/As2YibJol0aZaFN9WEZxMrqdHYI/6ZRZdDZ+Q1fOTEhUEwsNrgZNO\nXeaOu4L+DT4jsZ1wRmrtikOW+BgTnsMC+HnARlYt9ECeqv1UN+an5uyvFUx4Xh7ylOKVzNRFK2+t\n+YPijpQXw7zpYCK4yj+83Y+amzVccF64xdnGxjh8IRim1LYWP/bejOXNd7ceAMC6MjLkGKS/O+lJ\nbUic5sPRDr9Qd7Pmgmlvo72JUHtkeYR2kp0lUXHHBepOD8OoQxy/3RA8ntOfuuPwmXoK/XYzQhEj\nxtHAiBPBy8nqWVQJUOTegJ/bbVdJmuqC/G4k1ZeEUn1JKLLEgvM76eRAupi+4nUb3e4Ap/ov4Fau\nnEC/AJ3a0cohdGZBMEk/Z/FXpj19KyZgJDJAzDV+z6jMes9mVEzVLWzvyQQftkxaiIuBEavuauFV\nKoS0RnXWXNyFtaRglBgS1xaAehWT+b7KVrp9NBBxpH72T42+S0J+SrdKaB435ByoGkSPqH7I30TE\n6BOJSIxcqSA3Qr+l9fKJbrcGl835jjb1JVA+Du/FofqC8uKJ4OUoggsPAsKSQ//+9QKcl2dCF3ja\nQkzdP7Rr48me+gwKr4tJxwcYodsySetbUA2JCYPNklQu6HRlDr999IFO04F8bk4LwcXAiAmPdc84\nkCU9ofP1kTQKG8rtN9WpNtY5wcY6J+hlfg3P7/203vIpib1O+xFX0G26OKvqTdIVOWTOr6mnXhVF\nrhQucYdt725WYnVT87AG489k9IjsWer7L+Q5uO4MwHdmoC5dKxXxa6EEQ/OWmkcJgaBEucVtHa8O\n1dBLfzQeObt3/pSB249zPk0o4JIhMyL0qjMu30Ugf6kfcV6HHWPZ0D2EW/9pggnaq5flY91DONnT\nKF7QVu1wMjUYkdCGtbVPEvkfV+oF6ib21ezQBJz0JNVYGuKUtHcyEjVd6E+NzZqvk2WJj5H0q4xb\nwASqeidxvOEO1XtznjXj0IoPcFzxblT9AEh5yapXdehT9SprzJqgeK2+OJfY1JTEmUqcDAypuTlS\nLz6ScoGv/0Fkf9ZmfO1TLJgxSO8RUv92Yud5EzE4mA/9x2G6W/0b7NNAHy5PC2JHujUbCtVNUYfS\nBL70XgKwnPcfabsEVmGPuZ4En/8/USkcBj5oj9nZGI1GP+urwpR4/rIBVEM/iR/lI2c55fzDlEtj\nllPOv4xy4yynnPeUcuMsR2ek9nVYn3AOo9PVid7QjPT+XoiaN/ynu/Wvp9whVI7OhM+sTGWJETsd\n9wsZWW3htSKXj66OpNbIZOTPtcum+V/nvXEIvRziLejGiJSgFGGQAZZRMtrOOsfWPR+qjrP/JVwv\ngQ76IL98veuOgDJL0GtC+hF7TpUiaTEkrj2pvtrHaqr1/f08qbhT8z3afIlK691RJPdyIsUzj4hO\nQiB895oeeu0jwFf3b/GjQ2OtPitq3pCYT4S828iBIUhEYtyv9qdKtyid+vRsrDdh3y1FSoFcSZoi\nm2ZHJlB/SmSpxbNKcwj948Ypcm/A+G27cTNKpprE6E1pOgXZShlP5QrspIZFjm9wehQOg94eSH1/\nQdGM9t96h+BVKBG92eXBZMQI+Qd2h/KQ/qV5xku+cb6Q5zBo1EQMj14RvnuhF5IsEXW+1WzD/PkY\nby58v6zU91MV2XidCKT+jGRk8Q817m9pyNs0I2G0nIGuV/i2yi3ylHK6RfRF2i5B6zYz+nhyYqnw\nt+jbOJXeTdiyI4SekyZTYZf6NxLFB27s2BqCgajk5OquNdUX3yqJ6A3NiG4rJLV/ndyMQ3Gu3Gix\nCYAe0V2Qd0svMbBBb/ucK+LPUUtqUuS1FlcGU2NqHvIo9bVk8xm+9QAdTV8BBZYTm5fHL8ltVc8v\nPK6Dc5VkNtY9goVZ5lvbzOnsQfjA4ipohaNhrnhsgjfXTM4neTyVyxg1aiIGx66o13Fxwd2xssSI\nvIoSDAFxk/r81iuEJoa59Djph/SEekafOM2HCwGLAOGiaXFlMLky4Tt+bLybTqavsRQbE9l+Fd2C\nhqKmbM5biV7ixaXeC7F8k995NtsAb2MFm5y28DnaqdHJ2zRj5+KFiDGh7Z2+mKAfxcR8RGE3sRAb\nk15DgrpJio++8mHtqCAqiguuM69rA3nx1JyKVplca7GRmI1NcRyifYy188IsZjdpxLNcM6KmumJz\n6ho7I63oVzGFvfUO0r1KL82ijjTtwOhPA2gS9hkTHvuSqshGgYILzTcSPqWypk0BYCzKK/K82S8B\njJ48ibgWWap/tSZmkJqjfrxqrlnJKmilYSQyoLbUhPVrfiGvQ3O1PqPwLXlKFTHOjOZGcgxEEhRS\n9dUCO/a/gJHIgOlPPGg3dhzV+sZSq89davW5y0/fDqXtnb6qY2O/0J+r4HjPBSrD/DmlPj86NKbJ\n+eHczLV6yydLp3PQKSpLjFCgwGSGmdbtRIW0KPW9JHkWVe6olxz/6Csfrvkvwd0Iml/+lCYrAujZ\nohtV+8bjNPIK1RcKs7Ng7y1a9xVAcTOcSz6VuO+RjeSUIHT31am+b/lU6eg8rd2XeJk1r+w52LOF\nViMnCOp60d+YcK/1WgxEEnakWzBnzWBs5oUS/VszItutBmBYfFue+bzU6jtKQ2RgyP64gumn62Z/\ntWRLooM8Ce8tTNsarwuk7qzLKBVKOF6DfS67+TPLjOAuXdUOqn/0tQ92qyKLOE8k9eux4uiv1JAU\nnamMSGjDU2/div+WhEgqZdS9KKpLXzJjxEiNJCc73knDz7LkDKJGZ0biMCxSI6WJr+7f4nS6C6FN\nDIu91/JWNvde1yDFVz3fg6SKFVSrQuR0U+qNvFeiAFvqwXqkXalaTN1DV35+cJFGhgYsSXXkT59a\nJa4730kQQlbPFogRsz7WW2vDBCG/zmHoHXp82I9Zz1zpUeE55wIXEr3enettg1GgoGtED1500Z9u\nbT5KPVR1vjV8Kcqj1Xmxz4F9LrsBOJzaRKNsl1pzQ4t7NUWiIoaZp5SzO6MyUcHq14nRhMhlzehZ\n4SXjl/hrZJhAEcNse3sAz+QFBnC71Rqi17mo3ZbEsS6VxFkMrHQZeZuSZVRGVz+tdnvy5ynI70bi\nOOR6iYYpqWaNV7U4DN9B+ZxGhgakK3PY8207jaup62ScNlNjUKAg71gVXZoBQCmTIY+O5VIvJxam\nNMRYJCW8/QqMRcIULmWb7XvjpQWoEF906rzPZTehTbeqntev8Bhp9Wp6/c4HMjlrnerqXNZdUs0a\nkUdBkSmxsTHpR+yJ6racFrP8qB6kubdWjJijmRY02BJIhY9jGVH7A7rX9KBb/1GIEfOb1zq124qY\nZE19QzHDwodgeCuuyHvp/TxpaPKI4WeHadzH0sirZ8PCGrqdU3GFCqSM8CZlRPE0x9cKeYnKDm9D\n62nt89HehM5YStdPRiM+W/IiWuTegLjuFiwcvA65UsyiBx0w6hD31k7le0EBIvPkOBsIhjAsroPa\nUxl1kFha8nBdDa612AhAuiKHT1sPUkn+v41nY72RffySafWPATDYLIU8ZdFw6Z4+PbX2rErMzfnp\n1jHqlyBqNeuZG5fd1Ftbm5yuxu+Oh8s8xnW9H3W+eTfpWFVDK7HW7ji+MwOxWq3ed+S1c+fob6tU\nz0cktKGZeQJ+lYQZmq6e1XwkVayocziDJTbntf6tDiVeQ4ac2DzBf2IjFanEu3OUMjpMDCjTq6z3\nrBTf0VcQI8bg5n1V9L60pg3KiqZEjq2CpHoWd1sJd8s8pZzP4zqhCLGmsKR+ScTP9kaM4OEcFt+W\np1/VJWVKJhfdt/BbnT9p4ReAdbDu6wKJpSUJa2243kLQPcpR5uG1YQp1YtW/QKuuCIMVsAEhRah/\nYjKKQj7hJmsDqftM/cI1f0eelsaX/UYRPVjwSUb0K/BAz6h6g552b7+YooM8iXZcTpYyl0ZH/DFM\nMsB+eyott1xnmpWgN5ujlGF1693umsXkybA+nax2pofBn1eLnMu1tU+iKPKKfsh2q8MSG+EmoMv2\n1NdPPLnjLvTu2ThvTn6ziIoiI/pG99Rou6cwWhvn1ee2KGqEUudELleShcTrb50OvtkWEaY581Nc\nWX31A6qdMMBi0wVM1NBdFbu+RoGC7hG9kPTNQJx6nWoXjXGd5c/EbgfYO20eXY2+pMYi7Q1UYlWZ\nhNU1uO4pGGamMpfP7vfU68hxMceAujtSUWS+feunLJSXb+P4ZiLRZcdwDu4smB7m1bJCVMYFldeh\nOVd7LmZAbHeSF9jjtPcSEnNzKh8RM80qnDhZJmtf+DDH+gbr5y2kc3d/HD+/q5d1eGHm1DzAQ3lF\njf0SXnMnYNs/Fi/LB0y1Uk+L6J9iT3gTVamQ6jujeDFdTkUpuFV6xFUtV49aG6fBEiuersxhsc1Z\nsCn63ppX9oRs6obdygicUjTb3J/ZeD8grDGtUgVjUWRnYz8tjIO/edPvaATbAxcwKn4SFX7X7o70\nrLsz1z0LNvs9Nk6m7lf6ndLtSPFUldnTF5JrkbT4MYDFk1bga5xHzBgx9c6XfvyT0dnkoeR1y+eY\n8Jy8du5s+zUIc7ExXtc/oeqIl6BQ4vTVeOo3i+dyq2A8N4yj7sC3j/YHEgt+VwORhCRZOr6nBPkQ\nyRMjZBYybE5ISOqaSy3pVQ5laJaADGAdHEpOMJyvXgfjP/NUTqeP7/VVuz7KfwMFSq59GELToIk4\nT79D5X1yakuFrb8Aq1Ba/TxVq/KC/3iE0N+Rt2nGwU0ric3LY8zEiSUupOtdNmKhzTmtIk8ye3vy\nV1CI6vn0Jx6q6YiuHEgsmIp98F0glddpb/BS21q8bibc9XLHvGB43VDamkZRu1AASPdOg8usivXg\nJ2+ufboYgNbXh5J214qap2UYHdZd/qSwXyA/qqs0vK9+StWeMTqJnsVsbMp3Hgf5cVcfHFc+RPbw\nkdZtFabw2lbbdazY2JiUAU354bs1tDURvMEdwntiFGjMrmObMBJJha2U1nVKlMp8b8P3SqLwXbmk\nE5bTyYPja1ZofDIVLZsy57c1uBvBK0U2LfZOxnnFK72NcIWNU+Mbh1iC2MSY6NmNMLFPo3mNh6yy\nPVXioXlKOa8UuXxeW/t6krpidrYKm+0FJ1NZxjntiTfRPavrbEwHEq/i8VMA1YL0uw+pD+PMR1qr\nJs9WmpKSWhGX71ORxcbxcFdDbnsLy6fSpE/+VTIlM5KbMsNaMNC0gV5FdG5eDvFm/qzlGrsGxGZm\nJATKcDcSvLI+m7+g3vSwdyq1qAkK38Yc2LYGOPPWYxtvD8Rhyj8rMZL+URrd3UcQ84kpSstcwtut\nVL23Kc2WH053x2l9NuJbMSgy9TPKuXwSwes/bIpozb5PyB4lYtkFLCko9FdnRh4rdtox1iKetb8s\nImC3r9rtvZcjZ2FyOnlg/U0sG+seAeDPLDOyFYbMnzUIi83qXaAiIyP2xxbccXu0H4j8nm4ZCCWh\n08iJEBN8fPUKAL5+2pwjW4Q9M9uDz5CHR+uvo/9CGl8TcSO1FnFXauG0TH/TWmldO3z3RjDV6h4J\nsiyG+00WVOz1iEgqJWZDQyJbr6PVpPFU3FH0uv1XTWsLk7+fCkLkifnAVI2DEZ4G+HB5epDqub72\nyMr5/4G0Tm0+PHCXiZZRPJVn8emYSXpZl6vLv9Y49YHS142DO9YCUP/0CLVSzsop57/F/7RxllPO\n+0y5+l455fzLKDfOcvTCorgwjj6+Qfd75XpB+qLcOMvRmcTpPjgZGJKnlDPaIo4Hc3UvQPVPsD7h\nHFNi7v7T3VCh8z7n0cc3yFHm0XH0eL1UhhabmYFCgSIjQ+e2/tukDfRi5Y+/FMsicdo7Dqfxurnn\nn0z04crUoCKvzXrmxrU2VbRKpXsxzBt5rxccdFuL76lA/mi5nL67Jmpc3j4k/hy1pcKetOtGf5yC\n4rF/fk3Detb/LFI7W8y2ZDCiaXetit1m9C0okJX0gYjlXdfS1iRHVWrRacd46k0vOZe0LHR2COVH\n87gvCqDGQt2iN1JGeWP5ySMy8wzI3V6Nyr++w4pSekbc2IV9hzcXee1kljFtTITsf122b5L9fVg2\neRktjISfI12RoxKp6tZ1KMrrmt3tJfXsmXh4P21MsosEc5zLNmaeQ6MyPlkUxQdubN4ajKXYmI7D\nxmB48pbeg+b/G8RsakpEmzVa/0aFI9pKo+Gv/qUKvr3TCKE8pZzqYUVHupwuHijFIoz3v33EEEml\nPPqiBXvHzVPFjka55LJ8/IfcH1ZXdZw45SWypCda9TF+tjfm7s9RKoXzULmr/oIQRB6NqLAoSfV8\nd0ZlgqZ/QsX7aRz/NYK51dQUDSsBibk5n4w5TgsjJb+kOrFpbUeqLw7l0Vc+3PAPensDJSCPjmXS\nulGsGrmMIYf9Gdf6TwItI7iZZadRO8lf5GApNgbAOPE1cj0bZna3FhxbEYwUCS6nhyOKM8H2z1yt\nlBJLI22gF1daL6JXdB8g6a3Hl8VzeRZ5QO9ZUzF7JOR2xneVcKvXEo4MmU/n3C+pPVv9AUwn4xTE\nsK6So5QhCi2ayVBx6iOMJXm83v/2diRVrLgeEAQUBHU7GRiy2CYUjhb8MeMftSKheInNstt2dsR+\n00MO2CzDQCRRJUM3mu2P3fe6j8wiIyNmbl+P+xtRN59v/al69gmmMRdRAH+c9mJuf+2NM2lIQyZX\nPkmqIps/G5pR/U0Fq0w72Vs+WTa15oYye24znI1vcvMvW7CM4NwLB+CZ2m2ccl9LYdVEfSG1rUXM\nGFtODJ2PGBMUKLnXei20hkeDs3imMGJm845aTUH/TtWxcdzOM0U+0RJtjFNQlLjKL6lOnBzQHPnd\nyCI1X10eOTOpRVtCap0hu2Ze6Q2VgNbGqWjdlO+XC7mFn/QZA9wGBP2XFX9tpIbEhJ4t+wDP39rW\noNNFL95Ga/2p8AiqrAojfrbgXMgzUxLRP5jxF1tx/kATbOeUfQfKN8plNXeRp5Qz4bEvV5JtUSpF\nVO4ahR1h5HRJX9o+AAAgAElEQVTy4LWtFLOH2mVqJO914VJzYSrb4Oww6n5yi8qEFUkobtAsTuN2\nC3Ppa2F09Nn2BQ6EITY2JnKlK1HtBMHmxHYW2Gip5ii1r8Oes79zONOMnp2HorgZrtHnf33VkABL\nPYcVejVmzMZddDJ9TbuxkzE+eBUUciSWlmR8UI+Erkq6ud/g+8vH8fvJn+r7YpE9earVV0mqVqWr\n9S1+9v0YRZJ2+aLKy8J1P9EyCrOd2TyXCWqDa0Nb4TrnEXuOCYp+Hw8ZjdNfml1jWhtnzCcG+Brn\nMfrhh4huRqFEmJ5K12apRKnUlfvoXzFZ9fj39CpFRrT8xyKpFBf8iOgfTPro43yY/gXVF5duoE9b\nV2VXjS3kKYXRMm5wTSyjCi6knE4eTFy6lS6mrzicacbigMEYHtHs5OUb5gNZNnV/Kf6+yMgIcwP1\nFefKwmFnOuLGLjisfcA+m9Wq1+XG2rUndnPl/tcSnsuzWBwwFsObmt+cNgV3ZPQ390oUaNaaC7dY\nENuRTg13kVFdgvGbNDN5airG+y/htB8iAfdEuDBjGS0z/bDYpJ1xPunryMJdjtRJ0m0GdSLLlLjc\nKoywKBDhntbtLnQrOMb43iM0netoZZwvhntzuesCwJgzYQ2o2UlwKqTVlnLZUbt1EMDVHFgzthcS\niiu/KWUyHHZlQ3+oKDZCZlJCA4X76CHDQCTBQCRhwmPfYln48b2hi+krxIjoYprObFspmii1Zvb2\nBK7SOPRzvGzj4MKtYsekd3Njv11Isde1QZKeQ9Dhdao1+StFNp/5DKB20iWtPKNx30i47bOe+lum\n4nBEu4uz6vIwnk6XUVtasnEqvZugMJKoNFzVRTq/Mp6Bg8iog0a/iSZIqlkzzP8QR9q6aGw0f2fW\nd8MweiVnWSPhPFwP1N4GCqOVt9binBWb6x4r9XNfP23OrWbqXzL53q5Hsix6LfySaktLHxGjl3oS\n2Ue44Mvyrh19fIM8pRwxIjxn+hURlqp32YglNmEoUHIsqwIfm2SiQFnisSWR7OfDpa+D6DBibKmj\nrcTcnGrHlKyyPYXznvHU89NctSG9vxenFhdVrnc9PQKnyUlaT+Xy6XgnjQDLaJ2TAIStFBNafeFH\ndmUxlXomcsz1j2LHjX74IYmT7BGFaa+pVJgDiVdx/n089QK1UAo0NuarexfwNVKQrsyhosgIx/1j\ncRqr23aXuEIFIkOciWy3mg1pNVlwpz0As5rso1cFYX3czc4Tpazo7UCv4XtpE6rjfHw0Dc4OU/1b\n+6q26v07w+tr0yy1pCZcnqb+XUfcpPTvyVPKyVPKUaCkxeiCRVn8bG/m1TiLAiVO+8exwH8ICpSq\nY982DEnq12P7l/MBMDpZfLTMJ3K2K6tsT3Em2xDn5eoLoopNTZHUs6feZSO2Lligel2BAu/v/bEf\nfFNnwwTYGPIxBiIJIvcGOrcFcGZBMJe+DirRMAFW2Z5iyw7dZxFiU1MmxQhr4zoHtFRWkEjwNVKQ\nJM+k3XeT8br+CXe7ll6jRu2+VbJQCaAvuNOe2v1uU7vfbZZ9OYBHsixAWOqo3Z42nVBevUu9z69S\n95Nbqn+Lbwu1TVz+GqmxY6FPTJcizyWWlmp97olv6cd5zfbHQCRBjIglNuc5kHiVQ4nXuD1iGV8k\ntaLRWn+cxl7C8MhlvGYVHBs2q+wfKfwLC+pKhYVeWZvKp3sLhrX6SWv1lRbEEmqfUrL31E4W24QW\nEZSuv8cfq7VhUMZMRxOsQ0Kpu280nut0y9DpcnGc2sdaiI159anX2w8sg/vfN6GtSSYDYztifEG7\n7bDHo5oA0PpMAJV/DUOyrTJGIt13Fc12CP6FqU88sQ8o8KOY7LnE5ldCmY/sPeprPOslfE9kYMjK\n5pvYnVEZ58mPNL6A8rpmMDC2o+p5zk4z4n7wRunrVuzYamEiYvIEo5B3KD0yptrJZCY89lWNivkj\nY75zqLDTqdqposeWhYGp4A7/+w2l2Pe/MaxMWfFyAiUhqV+PWqEmLKt5TvXajnRr1WOX2fotBgRQ\n77ccplld5+VQ7cPtai8Wq0aFfMY+bI37wgBGJLTBad84+sV0Vr2XWl971X5Fy6bM6yNoDL/6quTS\nBupQv18E53PEOE9JBODpRzLSlZpF75REQzNBoSG6d41is5vTAcI5rl9J/VmPzreLwmFl3Tt2QvFM\ncz0exevXvG75mh7VOmG8U8mR+ruhPlCiqPdV8vfWqv9c+oUvj7rPfQ/oSklrqvvFjr381AlsBCm7\nlJHeWK0ped251Ws1ICF1iR2mFA+IeD7am8PfLSBdIaLzl5OLSKyURvwsH26PFM5hgzPDqbMU1dqs\nf2JyWR/VidxZr/D8ZSI2G7SP7BKF3aTrii+LBEQ88kqnBqE8XQROXKJ9oWD4Wie0C1SIWexFRP9g\nMpW5dBgRiOE57UJFczp7sLnOSprP9cf6aSiyP2vTwfwuAxw+BLQ30KjVHhywElQsStK/lV6LwenA\nWDo1u612mzqPnEcmz1M91lUoS/40mex+Ik5klV1R7IEsG//ED/TmXAAhYkiMCAORBMq4uQ+8MAqA\ntNrF1dYTp/mw65v5WIqN8Vn7hVqGCZBrV3BROH6Xpvq7xG5CTZRf02whSz9bMiB4UXOO1eFUwz3U\n3qR9jZt8bEKLjpyipg0Qm5oiNjUlaYoPoy3iVO9pG90T0V9wjOUoFRpveRVGnKtAgZKX7rlIXJ04\nVn8P8eMdNI571ZTI/7gS1XUFJw+WXPulJHQeOau8mb6NSGgD6F75Sv40mZ8mDGVKEwNa97rGEpui\nwqwd7vXm9TYbYf2lZxwPjOFul+AynUKi+6bQCrZNXMCQjCmq19Pbp3PbtyDKqfZM9UejShcMQXDs\nsePUVuRvOmDAJUDC0vU9qZmmW9yy2M2VyIkm+DjdZ62dUOA1PDdPL84l8emiURD7D2wo9Oyszu2L\nmjYArvJIlsWg6V9gjvbiZgZ/XmVKkhc/+v5Oto8BjvvG4hJ+950G6sf+5M3VXgsBI+x+UH/rS2/q\nezd2NaQG+pEtNDp4mVoHIW6ZOT2MPi7ynnHmcwwz3o2gsN0e+POjSrQYc537a0o+pu6cazhVHkdU\nj+WEzSzuPPo1zZagX3tio8G5qL49gra9+nKi4a5iG/puF4Zitz5G7TIGJdKiEYofU4l02agKdB8R\n356En50wQT9iVu9Sl6nNBqGPXVZ9ie1W3a+xaz8149P5oRiiwGncJb0oMBo/NOCRLItaUhNyOhWI\nu21duRgz8QWeyRX4rA2gtkz9/uslK8V1oz/20/89GSRv48UBJ70Gxv/TpB6sx1m3LYgR0+Z2Pyp8\nHPtPd0ltUj/zZu+c+Yz0GYDsUeI/3Z0yyejrycklRbeLPgoYj/nVx2XWYSnXECrnX0nCDB9ujQ76\nf62YWK4hVE45/zLKR85yyvmHKR85yynnX0a5cZZTznvKe1nIqJxyCiM2NubZ4Kak+OaBEipGGmIz\nT7/Vxt5H9Dpy7n50ifsLdQtsLolqYeYcfXyDmMX6b1tX0vt50unuSw4kXiUo/jwNr4qR2tbSW/tS\nO1sSdjbiUOI1vom9UWQPTVsye3ly9PGNIv+qhZnr7fzmJxkcSrxGykjt43bFTeozLDKePffPcn72\nUqI6riTi4+VcmbCEepf1L4/yvvHej5wxi704aifELN4fsIKOk4oHw/9TiM3M+ODri/hVuo/LDj+M\nn4m55bcM50A/HKbqpwrWZ8fP0KvCCxTAnGHDMH2coltAAnA2eGWx1zbYnQG7MzgwFsdJ2kfgSBo4\nI36TLO9yejgOv13VOvrmia8lvSqWHFv8U43TdO4RiMle9YIoJFWrEv5DHUzjDMiqXxAKGdl2dRFt\nKQDnE6MwfGCM3Qz9jM45nTx44i2lUvNnfOd0gDlRXbHoHPPWz+lt5Ixa6cHhzCo4/aR7rGZhfL20\n03b5bxA515W51tdoeXMATt/cotbcUHrFdCZ8kO65gQAxi7zoVeEFMXk5jH/UirGrf0dhUXbccVlk\n9vKkWph5mcfcH7DirceURc11jzibLaXd8NE4Do/Qm1TmlMcfMCXJi+Uv6wFCqXv/+duRVFFPKyFx\ndRUiuoZwzX8JkW1XE952JeFtV6JA8SZjqeC/yLaruTlyCRInB637m9PJg1eHHHG+YkDjOTfIqSEj\nb29Vvp8/jAtuu4ha/fYZkN62Uh7uaohYrKRmb/0qZh99XJBv2NFG/VFTHS3RfCY89iVukA3yaM0i\nZw4lXsP51AgcBhfEliq9m3B416+cyDJiwZBBWgfnR61tzuJW21hez1H1WsJMH+6MWkb3Fl01KiCb\n2cuTul+GC6Mj0NJvjKrC8tHHNxga34qn3mmq5/locr4Bal+swIpaZ+lcU/3g7rIQV6hAWpdGxepZ\nArS785pAywh6e/XSW63O1wO8+Oir88yoegMFCrVrrOZ08qDxnBsstREC8l3WjMN+8zPkkcVHx8CY\nCL6fP4wqKwsi6t6pbq3EyQFz02wMV1bWR3MqMnt5AsLF4rB9LI4aBDw3CQko8/0tIxfTwFD48/9T\n/S96uE7ERAPjlFavxsbX1XGa+Uo1zRS7uRI3WcHBzIrseNaCyRu3saRvHxQ3NB/9p3kfZtq2IdQp\nJLNoczaHpOGZxWQu3kZhwwSKlD4fGt+KB/PqY4rwWkcbN6qFmRc5Xh3SBnkxz2YRZ7MravS5slBk\nZBQxTKWvG2l1jDHMUFBVukdv35PP2p8XYW9gAIhx2e+Hk5pxx6fWCuoHgY89iBlsh11kWIlLj5xO\nHnw/37uIYZaFXoxz6P6/6F7hKX2jP9V5PVSYul8WKCpoug6y/U/Z64WvFrZGZGGO25EnzLK+zsOu\nSpz2qt9+xLS6zL5Yk3rRBeJV8V0qccdnGY0vDMFuzBOmru7DgA3XON+iEops9VO+xI1d6Gt2npBU\nEZIqVsifC/mQ2VNTaf37Fzg+1excFDa0v9/knnqnqQwzn/MXXEFD48w1E2EuNmbYyeE4ob1O7995\nOcQbuzFCnPPmumuLKNQDxA+uTc2ftB85ld5NeP5VNhfdt5AkVxCc6syRMa1wOq9+QsCHI0ZhGpv6\nZqQseS0pcXZkatBGljq6qN2uzmtOSQNnqkqFKVGWrfZrlb+T2ctT47u3Jiiys5E/Teb3g768UmTj\nsixdo89v7hFM1RPFPYZJ8kxsfjFE/jyFWrPg2yoR3F/vpFHbynsx7E13YMKoP+hyJgqJpSVJk33Y\nUH8Dtse0v/05bNfN2VMWaW+WZ/ZblaQN8iJtkBfZXVvo1ObQyIec+HExG+seYWPdIyUes2DkWp2+\nY/+uNVx034ICBSMGB3C0oTmi85pJtxgdvlziFDYfibMjfgcFR5Am6DxyRo60pJVxLiBBbqK/nZnC\no2ZLvzHF7u76xFgk4YmvJdYaLA/FIgWVNhSdntj+J5QR//kAMcIaVHHjHs3mjCP822A6o/46TOxk\nj4vRJY69bkS2woC9d/6kaVB9xtt9gBGaJRoL2yM3aOk3Bsfd78YwAc58Mh8w5diG1UVeT5Zn8nnt\nD9RuR1qjOveXVOWizyqMRVKgeFJ7YdqYpOO32Evtm87Db324OS4IBQp2p1vTZfAYlXRn/u+mTwJj\nIpgT5chSRxcsShlVS0N3a1IWKN1lWpV9IjUhf9QcGt+qyBpJn0hcnZjSZy+vFTKsQzRzmyuU6p26\nKjcE2U1NiJttSGVxNpf7OnPL2xinfeOY9FnJqnZv4/6AFe/0HOZjLRG8yEEv7Wk5ZTxui/2JystW\nva4u95dU5abvujeGCV8/8aRbv5F06zeSZpeGFDn2lxeCUsTirhuKtVMa18ctAWBTmi0burfVWFNX\nEyTOjmpvm5SEXoMQJn25Qy/tFN4MfzBPO5lNdXg+X8kw84d0vj7ynX2HweMX7M7QzFHWsvZ9uu+Y\ngjw6FkV2Nq4/a1e8SVu03b5qd68Xxz5ywmzbBWwWhNI9dLxGn49a0YKLPqsA4Yb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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1569547390651 + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4sAetutZ9t93" + }, + "source": [ + "### Neural Network Architecture\n", + "\n", + "To build our GAN we need two separate networks:\n", + "\n", + "* A generator that takes input noise and outputs generated MNIST digits\n", + "* A discriminator that takes images and outputs a probability of being real or fake\n", + "\n", + "We define functions that build these networks. In the GANEstimator section below we pass the builder functions to the `GANEstimator` constructor. `GANEstimator` handles hooking the generator and discriminator together into the GAN. \n" + ] }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559972047311 + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "oZ9n-jw_MG6C", + "colab": {} + }, + "source": [ + "def _dense(inputs, units, l2_weight=2.5e-5):\n", + " return tf.compat.v1.layers.dense(\n", + " inputs, units, None,\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight)\n", + " )\n", + "def _batch_norm(inputs, is_training):\n", + " return tf.compat.v1.layers.batch_normalization(\n", + " inputs, momentum=0.999, epsilon=0.001, training=is_training)\n", + "\n", + "def _deconv2d(inputs, filters, kernel_size, stride, l2_weight):\n", + " return tf.compat.v1.layers.conv2d_transpose(\n", + " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", + " activation=tf.compat.v1.nn.relu, padding='same',\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", + "\n", + "def _conv2d(inputs, filters, kernel_size, stride, l2_weight):\n", + " return tf.compat.v1.layers.conv2d(\n", + " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", + " activation=None, padding='same',\n", + " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", + " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))" + ], + "execution_count": 0, + "outputs": [] }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559900570952 + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "NHkpn6ks90_R", + "colab": {} + }, + "source": [ + "def unconditional_generator(noise, mode, weight_decay=2.5e-5):\n", + " \"\"\"Generator to produce unconditional MNIST images.\"\"\"\n", + " is_training = (mode == tf.compat.v1.estimator.ModeKeys.TRAIN)\n", + " \n", + " net = _dense(noise, 1024, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = tf.compat.v1.nn.relu(net)\n", + " \n", + " net = _dense(net, 7 * 7 * 256, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = tf.compat.v1.nn.relu(net)\n", + " \n", + " net = tf.reshape(net, [-1, 7, 7, 256])\n", + " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", + " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", + " # Make sure that generator output is in the same range as `inputs`\n", + " # ie [-1, 1].\n", + " net = _conv2d(net, 1, 4, 1, 0.0)\n", + " net = tf.tanh(net)\n", + "\n", + " return net" + ], + "execution_count": 0, + "outputs": [] }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559897391264 + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "w-ZqQ4_thIrP", + "colab": {} + }, + "source": [ + "_leaky_relu = lambda net: tf.nn.leaky_relu(net, alpha=0.01)\n", + "\n", + "def unconditional_discriminator(img, unused_conditioning, mode, weight_decay=2.5e-5):\n", + " del unused_conditioning\n", + " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", + " \n", + " net = _conv2d(img, 64, 4, 2, weight_decay)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = _conv2d(net, 128, 4, 2, weight_decay)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = tf.compat.v1.layers.flatten(net)\n", + " \n", + " net = _dense(net, 1024, weight_decay)\n", + " net = _batch_norm(net, is_training)\n", + " net = _leaky_relu(net)\n", + " \n", + " net = _dense(net, 1, weight_decay)\n", + "\n", + " return net" + ], + "execution_count": 0, + "outputs": [] }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559752800451 + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OhTAjxnyPS5e" + }, + "source": [ + "### Evaluating Generative Models, and evaluating GANs\n", + "\n", + "\n", + "TF-GAN provides some standard methods of evaluating generative models. In this example, we measure:\n", + "\n", + "* Inception Score: called `mnist_score` below.\n", + "* Frechet Inception Distance\n", + "\n", + "We apply a pre-trained classifier to both the real data and the generated data calculate the *Inception Score*. The Inception Score is designed to measure both quality and diversity. See [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) by Salimans et al for more information about the Inception Score.\n", + "\n", + "*Frechet Inception Distance* measures how close the generated image distribution is to the real image distribution. See [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) by Heusel et al for more information about the Frechet Inception distance." + ] }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559719883868 + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "1jF-FW5LPTn6", + "colab": {} + }, + "source": [ + "from tensorflow_gan.examples.mnist import util as eval_util\n", + "import os\n", + "\n", + "def get_eval_metric_ops_fn(gan_model):\n", + " real_data_logits = tf.reduce_mean(gan_model.discriminator_real_outputs)\n", + " gen_data_logits = tf.reduce_mean(gan_model.discriminator_gen_outputs)\n", + " real_mnist_score = eval_util.mnist_score(gan_model.real_data)\n", + " generated_mnist_score = eval_util.mnist_score(gan_model.generated_data)\n", + " frechet_distance = eval_util.mnist_frechet_distance(\n", + " gan_model.real_data, gan_model.generated_data)\n", + " return {\n", + " 'real_data_logits': tf.metrics.mean(real_data_logits),\n", + " 'gen_data_logits': tf.metrics.mean(gen_data_logits),\n", + " 'real_mnist_score': tf.metrics.mean(real_mnist_score),\n", + " 'mnist_score': tf.metrics.mean(generated_mnist_score),\n", + " 'frechet_distance': tf.metrics.mean(frechet_distance),\n", + " }" + ], + "execution_count": 0, + "outputs": [] }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559717312855 + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kxF2-gWHHaej" + }, + "source": [ + "### GANEstimator\n", + "\n", + "The `GANEstimator` assembles and manages the pieces of the whole GAN model. The `GANEstimator` constructor takes the following compoonents for both the generator and discriminator:\n", + "\n", + "* Network builder functions: we defined these in the \"Neural Network Architecture\" section above.\n", + "* Loss functions: here we use the wasserstein loss for both.\n", + "* Optimizers: here we use `tf.train.AdamOptimizer` for both generator and discriminator training." + ] }, { - "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb", - "timestamp": 1559641947244 + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "OBd8Vg7lHit8", + "colab": {} + }, + "source": [ + "train_batch_size = 32 #@param\n", + "noise_dimensions = 64 #@param\n", + "generator_lr = 0.001 #@param\n", + "discriminator_lr = 0.0002 #@param\n", + "\n", + "def gen_opt():\n", + " gstep = tf.compat.v1.train.get_or_create_global_step()\n", + " base_lr = generator_lr\n", + " # Halve the learning rate at 1000 steps.\n", + " lr = tf.cond(gstep < 1000, lambda: base_lr, lambda: base_lr / 2.0)\n", + " return tf.compat.v1.train.AdamOptimizer(lr, 0.5)\n", + "\n", + "gan_estimator = tfgan.estimator.GANEstimator(\n", + " generator_fn=unconditional_generator,\n", + " discriminator_fn=unconditional_discriminator,\n", + " generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n", + " discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,\n", + " params={'batch_size': train_batch_size, 'noise_dims': noise_dimensions},\n", + " generator_optimizer=gen_opt,\n", + " discriminator_optimizer=tf.compat.v1.train.AdamOptimizer(discriminator_lr, 0.5),\n", + " get_eval_metric_ops_fn=get_eval_metric_ops_fn)" + ], + "execution_count": 0, + "outputs": [] }, { - "file_id": "14r58gghjLTBBQVoSFOBdPsvj-I1G6nbd", - "timestamp": 1549819781952 + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "n1uldXfUfstT" + }, + "source": [ + "### Train and eval loop\n", + "\n", + "The `GANEstimator`'s `train()` method initiates GAN training, including the alternating generator and discriminator training phases.\n", + "\n", + "The loop in the code below calls `train()` repeatedly in order to periodically display generator output and evaluation results. But note that the code below does not manage the alternation between discriminator and generator: that's all handled automatically by `train()`." + ] }, { - "file_id": "0Bz8X96EaC_2-ZW9odlhSOEFXdWs", - "timestamp": 1493398103910 + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "AH6gcvcwHvSn", + "outputId": "d209a9a9-6576-43b5-e1ab-1c03bda7b275", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "# Disable noisy output.\n", + "tf.autograph.set_verbosity(0, False)\n", + "\n", + "import time\n", + "steps_per_eval = 500 #@param\n", + "max_train_steps = 5000 #@param\n", + "batches_for_eval_metrics = 100 #@param\n", + "\n", + "# Used to track metrics.\n", + "steps = []\n", + "real_logits, fake_logits = [], []\n", + "real_mnist_scores, mnist_scores, frechet_distances = [], [], []\n", + "\n", + "cur_step = 0\n", + "start_time = time.time()\n", + "while cur_step < max_train_steps:\n", + " next_step = min(cur_step + steps_per_eval, max_train_steps)\n", + "\n", + " start = time.time()\n", + " gan_estimator.train(input_fn, max_steps=next_step)\n", + " steps_taken = next_step - cur_step\n", + " time_taken = time.time() - start\n", + " print('Time since start: %.2f min' % ((time.time() - start_time) / 60.0))\n", + " print('Trained from step %i to %i in %.2f steps / sec' % (\n", + " cur_step, next_step, steps_taken / time_taken))\n", + " cur_step = next_step\n", + " \n", + " # Calculate some metrics.\n", + " metrics = gan_estimator.evaluate(input_fn, steps=batches_for_eval_metrics)\n", + " steps.append(cur_step)\n", + " real_logits.append(metrics['real_data_logits'])\n", + " fake_logits.append(metrics['gen_data_logits'])\n", + " real_mnist_scores.append(metrics['real_mnist_score'])\n", + " mnist_scores.append(metrics['mnist_score'])\n", + " frechet_distances.append(metrics['frechet_distance'])\n", + " print('Average discriminator output on Real: %.2f Fake: %.2f' % (\n", + " real_logits[-1], fake_logits[-1]))\n", + " print('Inception Score: %.2f / %.2f Frechet Distance: %.2f' % (\n", + " mnist_scores[-1], real_mnist_scores[-1], frechet_distances[-1]))\n", + " \n", + " # Vizualize some images.\n", + " iterator = gan_estimator.predict(\n", + " input_fn, hooks=[tf.train.StopAtStepHook(num_steps=21)])\n", + " try:\n", + " imgs = np.array([next(iterator) for _ in range(20)])\n", + " except StopIteration:\n", + " pass\n", + " tiled = tfgan.eval.python_image_grid(imgs, grid_shape=(2, 10))\n", + " plt.axis('off')\n", + " plt.imshow(np.squeeze(tiled))\n", + " plt.show()\n", + " \n", + " \n", + "# Plot the metrics vs step.\n", + "plt.title('MNIST Frechet distance per step')\n", + "plt.plot(steps, frechet_distances)\n", + "plt.figure()\n", + "plt.title('MNIST Score per step')\n", + "plt.plot(steps, mnist_scores)\n", + "plt.plot(steps, real_mnist_scores)\n", + "plt.show()" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Time since start: 0.45 min\n", + "Trained from step 0 to 500 in 18.49 steps / sec\n", + "Average discriminator output on Real: -9.86 Fake: -8.81\n", + "Inception Score: 6.09 / 8.35 Frechet Distance: 79.25\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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qnKuKgKmy1x/HN81p6NVyAnkqFOwtJq3ViZx97iecGiGdiiVBD2m2Rj7dMl0u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\nxn2EW2vEEfr5a5eQ/2Q3UpIbvas7+t0ZGdwJ4pi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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 0.99 min\n", + "Trained from step 500 to 1000 in 19.99 steps / sec\n", + "Average discriminator output on Real: -7.35 Fake: -7.74\n", + "Inception Score: 6.86 / 8.35 Frechet Distance: 64.99\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 1.52 min\n", + "Trained from step 1000 to 1500 in 19.95 steps / sec\n", + "Average discriminator output on Real: -11.58 Fake: -12.06\n", + "Inception Score: 6.93 / 8.35 Frechet Distance: 64.94\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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UB/UIkncLbZuY/H/GFLomJiYmFYgpdP8HUOOrsHdcgijO4gmuU/t0E5P/D5hC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\nuDnRP6f9OU7US2hRoQRKMmd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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 2.07 min\n", + "Trained from step 1500 to 2000 in 18.49 steps / sec\n", + "Average discriminator output on Real: -10.50 Fake: -11.63\n", + "Inception Score: 7.27 / 8.35 Frechet Distance: 55.55\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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o1XsjBz+xea7QjyThvzycGUkLAbBKFlY5ZPrNHoJfrVx6Jp3vwNE/dCM2SeHo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\nAgft6U21YevRXS6vLTTHjbW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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 2.66 min\n", + "Trained from step 2000 to 2500 in 17.79 steps / sec\n", + "Average discriminator output on Real: 5.02 Fake: 0.77\n", + "Inception Score: 7.28 / 8.35 Frechet Distance: 54.92\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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XpqPpfbqyApomErHrcX79fxVH3y6888EE7Lo48z14zDOEH/4b5e02E5LRxKGX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\n1K2xpW4SS/pyv3QUSSZYMXO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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 3.24 min\n", + "Trained from step 2500 to 3000 in 17.90 steps / sec\n", + "Average discriminator output on Real: 17.78 Fake: 16.35\n", + "Inception Score: 7.29 / 8.35 Frechet Distance: 56.14\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 3.82 min\n", + "Trained from step 3000 to 3500 in 17.81 steps / sec\n", + "Average discriminator output on Real: 73.79 Fake: 66.71\n", + "Inception Score: 7.61 / 8.35 Frechet Distance: 52.60\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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WY3u12JT7ZYjY2dzr/s8I3SY/SpOH6yEPi4vaIxz2ytKPfhqYlRtp+/sg1p/3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\nNx8hOt13jTJbjFrB5R3+xXe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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 4.40 min\n", + "Trained from step 3500 to 4000 in 18.09 steps / sec\n", + "Average discriminator output on Real: 37.14 Fake: 19.75\n", + "Inception Score: 7.36 / 8.35 Frechet Distance: 59.37\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Sp3krQLleTbf1IwFw7wmi2Wt7ZUF1H1caK7itJ4uensSTOZcDkHuRy6uFoYTF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\nDEDOfTYCFRdWDKJVW21ixtY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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 5.00 min\n", + "Trained from step 4000 to 4500 in 17.20 steps / sec\n", + "Average discriminator output on Real: 129.79 Fake: 126.22\n", + "Inception Score: 7.42 / 8.35 Frechet Distance: 58.30\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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03ReXbe58hyNM84KJiYlJFeJfTfcSaxvCbkfYZPB+bme5NQmZvTpgfRIWC+/s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\ndMf+5fOBxnA6CBZeft4rw6f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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Time since start: 5.61 min\n", + "Trained from step 4500 to 5000 in 17.14 steps / sec\n", + "Average discriminator output on Real: 34.10 Fake: 17.50\n", + "Inception Score: 7.31 / 8.35 Frechet Distance: 60.51\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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"_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } }, "cells": [ { @@ -105,11 +342,11 @@ "base_uri": "https://localhost:8080/", "height": 270 }, - "outputId": "c25b57da-0b4f-49d1-8004-36599a96d291" + "outputId": "bcd3973e-0605-4c05-c8c6-354c53b498ac" }, "source": [ "# Check that imports for the rest of the file work.\n", - "import tensorflow as tf\n", + "import tensorflow.compat.v1 as tf\n", "!pip install tensorflow-gan\n", "import tensorflow_gan as tfgan\n", "import tensorflow_datasets as tfds\n", @@ -117,9 +354,9 @@ "import numpy as np\n", "# Allow matplotlib images to render immediately.\n", "%matplotlib inline\n", - "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # Disable noisy outputs." + "tf.logging.set_verbosity(tf.logging.ERROR) # Disable noisy outputs." ], - "execution_count": 2, + "execution_count": 1, "outputs": [ { "output_type": "display_data", @@ -143,13 +380,13 @@ "output_type": "stream", "text": [ "Requirement already satisfied: tensorflow-gan in /usr/local/lib/python3.6/dist-packages (2.0.0)\n", - "Requirement already satisfied: tensorflow-hub>=0.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gan) (0.7.0)\n", "Requirement already satisfied: tensorflow-probability>=0.7 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gan) (0.7.0)\n", - "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.17.5)\n", - "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (1.12.0)\n", - "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (3.10.0)\n", - "Requirement already satisfied: cloudpickle>=0.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.2.2)\n", + "Requirement already satisfied: tensorflow-hub>=0.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gan) (0.7.0)\n", "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (4.4.1)\n", + "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.17.5)\n", + "Requirement already satisfied: cloudpickle>=0.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.2.2)\n", + "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.12.0)\n", + "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (3.10.0)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.4.0->tensorflow-hub>=0.2->tensorflow-gan) (45.1.0)\n", "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n", "\n" @@ -240,7 +477,7 @@ " just_noise = (mode == tf.estimator.ModeKeys.PREDICT)\n", " \n", " noise_ds = (tf.data.Dataset.from_tensors(0).repeat()\n", - " .map(lambda _: tf.compat.v1.random_normal([bs, nd])))\n", + " .map(lambda _: tf.random_normal([bs, nd])))\n", " \n", " if just_noise:\n", " return noise_ds\n", @@ -280,10 +517,20 @@ "metadata": { "colab_type": "code", "id": "zEhgLuGo8OGc", - "outputId": "7e87aa29-411f-46ce-d0d0-95320511fa18", + "outputId": "11c7bbb1-9447-448e-add4-6bc43aa97ecc", "colab": { "base_uri": "https://localhost:8080/", - "height": 285 + "height": 470, + "referenced_widgets": [ + "eb3ed95bb74a48deb9a702227b744dae", + "dc4c15628e8445ce9df79da5c7f0a231", + "46f93549528a49b991d7d62c08ea12d3", + "078e0cf205654497933e46a05b75f143", + "9526ef5130034feea363d52aabd54246", + "6ba669ac584c488190bdecc31a7e96b8", + "61b0985f208f43288dc96f8d1d642c6e", + "7ec4f1b83fd34210a6714068606ba4f3" + ] } }, "source": [ @@ -294,26 +541,63 @@ "\n", "params = {'batch_size': 100, 'noise_dims':64}\n", "with tf.Graph().as_default():\n", - " ds = input_fn(tf.compat.v1.estimator.ModeKeys.TRAIN, params)\n", + " ds = input_fn(tf.estimator.ModeKeys.TRAIN, params)\n", " numpy_imgs = next(tfds.as_numpy(ds))[1]\n", "img_grid = tfgan.eval.python_image_grid(numpy_imgs, grid_shape=(10, 10))\n", "plt.axis('off')\n", "plt.imshow(np.squeeze(img_grid))\n", "plt.show()" ], - "execution_count": 4, + "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ - "WARNING: Entity . at 0x7f4eb3decc80> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: expected exactly one node node, found []\n" + "WARNING: Entity . at 0x7f556fbd9b70> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: expected exactly one node node, found []\n", + "\u001b[1mDownloading and preparing dataset mnist (11.06 MiB) to /root/tensorflow_datasets/mnist/3.0.0...\u001b[0m\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n", + "local data directory. If you'd instead prefer to read directly from our public\n", + "GCS bucket (recommended if you're running on GCP), you can instead set\n", + "data_dir=gs://tfds-data/datasets.\n", + "\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb3ed95bb74a48deb9a702227b744dae", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, description='Dl Completed...', max=4, style=ProgressStyle(description_widt…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.0. Subsequent calls will reuse this data.\u001b[0m\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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NXeHXlBnxMWy89xcFnVu80d+V16Ml+1MiVGqRR1OjMP3LjGeD9HtjPZoapbpx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EJrZi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\nG2c55bynlBtnOeW8p5QbZznlvKf8H2IY7pwsIywUAAAAAElFTkSuQmCC\n", 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cGWYyY8obZZLyluvKQnvnlMlQOlfk5gI7rjX++dXBo0U+8vC3ith30t05lZVc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"text/plain": [ "
" ] @@ -349,27 +633,27 @@ "colab": {} }, "source": [ - "def _dense(inputs, units, l2_weight=2.5e-5):\n", - " return tf.compat.v1.layers.dense(\n", + "def _dense(inputs, units, l2_weight):\n", + " return tf.layers.dense(\n", " inputs, units, None,\n", " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", - " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight)\n", - " )\n", + " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", + "\n", "def _batch_norm(inputs, is_training):\n", - " return tf.compat.v1.layers.batch_normalization(\n", + " return tf.layers.batch_normalization(\n", " inputs, momentum=0.999, epsilon=0.001, training=is_training)\n", "\n", "def _deconv2d(inputs, filters, kernel_size, stride, l2_weight):\n", - " return tf.compat.v1.layers.conv2d_transpose(\n", + " return tf.layers.conv2d_transpose(\n", " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", - " activation=tf.compat.v1.nn.relu, padding='same',\n", + " activation=tf.nn.relu, padding='same',\n", " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", " kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n", " bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n", "\n", "def _conv2d(inputs, filters, kernel_size, stride, l2_weight):\n", - " return tf.compat.v1.layers.conv2d(\n", + " return tf.layers.conv2d(\n", " inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n", " activation=None, padding='same',\n", " kernel_initializer=tf.keras.initializers.glorot_uniform,\n", @@ -389,15 +673,15 @@ "source": [ "def unconditional_generator(noise, mode, weight_decay=2.5e-5):\n", " \"\"\"Generator to produce unconditional MNIST images.\"\"\"\n", - " is_training = (mode == tf.compat.v1.estimator.ModeKeys.TRAIN)\n", + " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", " \n", " net = _dense(noise, 1024, weight_decay)\n", " net = _batch_norm(net, is_training)\n", - " net = tf.compat.v1.nn.relu(net)\n", + " net = tf.nn.relu(net)\n", " \n", " net = _dense(net, 7 * 7 * 256, weight_decay)\n", " net = _batch_norm(net, is_training)\n", - " net = tf.compat.v1.nn.relu(net)\n", + " net = tf.nn.relu(net)\n", " \n", " net = tf.reshape(net, [-1, 7, 7, 256])\n", " net = _deconv2d(net, 64, 4, 2, weight_decay)\n", @@ -432,7 +716,7 @@ " net = _conv2d(net, 128, 4, 2, weight_decay)\n", " net = _leaky_relu(net)\n", " \n", - " net = tf.compat.v1.layers.flatten(net)\n", + " net = tf.layers.flatten(net)\n", " \n", " net = _dense(net, 1024, weight_decay)\n", " net = _batch_norm(net, is_training)\n", @@ -524,11 +808,11 @@ "discriminator_lr = 0.0002 #@param\n", "\n", "def gen_opt():\n", - " gstep = tf.compat.v1.train.get_or_create_global_step()\n", + " gstep = tf.train.get_or_create_global_step()\n", " base_lr = generator_lr\n", " # Halve the learning rate at 1000 steps.\n", " lr = tf.cond(gstep < 1000, lambda: base_lr, lambda: base_lr / 2.0)\n", - " return tf.compat.v1.train.AdamOptimizer(lr, 0.5)\n", + " return tf.train.AdamOptimizer(lr, 0.5)\n", "\n", "gan_estimator = tfgan.estimator.GANEstimator(\n", " generator_fn=unconditional_generator,\n", @@ -537,7 +821,7 @@ " discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,\n", " params={'batch_size': train_batch_size, 'noise_dims': noise_dimensions},\n", " generator_optimizer=gen_opt,\n", - " discriminator_optimizer=tf.compat.v1.train.AdamOptimizer(discriminator_lr, 0.5),\n", + " discriminator_optimizer=tf.train.AdamOptimizer(discriminator_lr, 0.5),\n", " get_eval_metric_ops_fn=get_eval_metric_ops_fn)" ], "execution_count": 0, @@ -562,7 +846,7 @@ "metadata": { "colab_type": "code", "id": "AH6gcvcwHvSn", - "outputId": "d209a9a9-6576-43b5-e1ab-1c03bda7b275", + "outputId": "0f9e15fb-4846-4089-e2b4-711fd4b746ac", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 @@ -631,22 +915,22 @@ "plt.plot(steps, real_mnist_scores)\n", "plt.show()" ], - "execution_count": 10, + "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ - "Time since start: 0.45 min\n", - "Trained from step 0 to 500 in 18.49 steps / sec\n", - "Average discriminator output on Real: -9.86 Fake: -8.81\n", - "Inception Score: 6.09 / 8.35 Frechet Distance: 79.25\n" + "Time since start: 0.68 min\n", + "Trained from step 0 to 500 in 12.19 steps / sec\n", + "Average discriminator output on Real: -15.10 Fake: -13.89\n", + "Inception Score: 6.26 / 8.35 Frechet Distance: 67.16\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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qnKuKgKmy1x/HN81p6NVyAnkqFOwtJq3ViZx97iecGiGdiiVBD2m2Rj7dMl0u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\nkyO3gaGjSrLYcMxktuJ0giJj5GchyTJGRKDKGF2tO+Gwp1Q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5H+CIC0+TVQfL5110v8pdQz8nQmnlj/+5hLybgvdrqHGxTD5ZzheXYid/dlnX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\naXMuANYve897r6QkUTrejhhXw5A46TM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" ] @@ -658,17 +942,17 @@ { "output_type": "stream", "text": [ - "Time since start: 0.99 min\n", - "Trained from step 500 to 1000 in 19.99 steps / sec\n", - "Average discriminator output on Real: -7.35 Fake: -7.74\n", - "Inception Score: 6.86 / 8.35 Frechet Distance: 64.99\n" + "Time since start: 1.32 min\n", + "Trained from step 500 to 1000 in 17.63 steps / sec\n", + "Average discriminator output on Real: -4.28 Fake: -4.68\n", + "Inception Score: 6.90 / 8.35 Frechet Distance: 63.44\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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AxrLc+iy6vx3K0TSMeG/+eISd8TWtaH1OcvxgFBGbpdmh+s+HybyiOuktwoja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\n5NZgVbY0tex/tC6115bdrGB63OXqSK2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kXBHZx4fs6XFUVgil4a6mq5HRWfBSJ3y/2wCSsARqf1Uq9H8c2c8PPTUOeX/W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\nEvDrAV7e3Z2BiWsZN7YPqZ+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" ] @@ -680,17 +964,17 @@ { "output_type": "stream", "text": [ - "Time since start: 1.52 min\n", - "Trained from step 1000 to 1500 in 19.95 steps / sec\n", - "Average discriminator output on Real: -11.58 Fake: -12.06\n", - "Inception Score: 6.93 / 8.35 Frechet Distance: 64.94\n" + "Time since start: 1.89 min\n", + "Trained from step 1000 to 1500 in 17.37 steps / sec\n", + "Average discriminator output on Real: -17.18 Fake: -17.50\n", + "Inception Score: 7.39 / 8.35 Frechet Distance: 57.32\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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UB/UIkncLbZuY/H/GFLomJiYmFYgpdP8HUOOrsHdcgijO4gmuU/t0E5P/D5hC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\n", 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" ] @@ -702,17 +986,17 @@ { "output_type": "stream", "text": [ - "Time since start: 2.07 min\n", - "Trained from step 1500 to 2000 in 18.49 steps / sec\n", - "Average discriminator output on Real: -10.50 Fake: -11.63\n", - "Inception Score: 7.27 / 8.35 Frechet Distance: 55.55\n" + "Time since start: 2.46 min\n", + "Trained from step 1500 to 2000 in 17.47 steps / sec\n", + "Average discriminator output on Real: -13.30 Fake: -14.13\n", + "Inception Score: 7.27 / 8.35 Frechet Distance: 63.34\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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o1XsjBz+xea7QjyThvzycGUkLAbBKFlY5ZPrNHoJfrVx6Jp3vwNE/dCM2SeHo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\nAgft6U21YevRXS6vLTTHjbW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" ] @@ -724,17 +1008,17 @@ { "output_type": "stream", "text": [ - "Time since start: 2.66 min\n", - "Trained from step 2000 to 2500 in 17.79 steps / sec\n", - "Average discriminator output on Real: 5.02 Fake: 0.77\n", - "Inception Score: 7.28 / 8.35 Frechet Distance: 54.92\n" + "Time since start: 3.04 min\n", + "Trained from step 2000 to 2500 in 17.45 steps / sec\n", + "Average discriminator output on Real: -23.74 Fake: -26.40\n", + "Inception Score: 7.19 / 8.35 Frechet Distance: 59.34\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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XpqPpfbqyApomErHrcX79fxVH3y6888EE7Lo48z14zDOEH/4b5e02E5LRxKGX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\nH1li7HfXk5S37eQTM+77ojsb5jc9FTU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rrsJ99JhX5yXb7ex+vxHj24j6IK/93pvke9aDYSCpKnJinDjwvSImJHzN70X1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\nXb497+DohjzaYC79V4+k/l1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" ] @@ -746,17 +1030,17 @@ { "output_type": "stream", "text": [ - "Time since start: 3.24 min\n", - "Trained from step 2500 to 3000 in 17.90 steps / sec\n", - "Average discriminator output on Real: 17.78 Fake: 16.35\n", - "Inception Score: 7.29 / 8.35 Frechet Distance: 56.14\n" + "Time since start: 3.62 min\n", + "Trained from step 2500 to 3000 in 17.08 steps / sec\n", + "Average discriminator output on Real: 62.13 Fake: 63.63\n", + "Inception Score: 7.40 / 8.35 Frechet Distance: 57.84\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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24HvTT9wU8QgAtYce8XpthH2vN2NI8DTuOdgLyUvlFZWoSMauXUpjaxG9H3oc\nAMviMwq4ywplvURNhiOdZfp0Xs+8hW2p9a+NldKM902J4o3mHxIolxE1rojfHKIF+xu7uxEx3o68\nced/7a7qspAk5MZ1GfbNt8QovgBo6Cwr9SVCKeThoN20XCraGfX/4lFqPVO563IuMjuLRq3VrflE\nLj500WL2SoSoERH7yl6ylns/0UYJC+XA9Or08V/P6Kz2AKR1s1+RlgtXKSNNUlUOfNyAr9vOoM+6\nwST2F4VZjCiyIaliHdk7sQVju/1AJ999BMsaww/fAsC2jOr4rvUjZlU+6beGUOstMbah9WQlCUlR\nKL2hGSMnfQZAPWsOhZqFMl1l8Lb7KC0Rlep9U33wy9b4+JWJ1FLt/FwiCsC/Ofhu1OXe2TqpCfEA\nPL/sa1IsLnrf9yjKCuMLz0g2G3GrVN6t/iuKJFdUXFvYJBLdWY7jhpYMmfINcZ5aty1sblQU8rVS\nOm4YTM37xcN+JaaBgn5tKImSiV2ez9EOIZxqJSqpfdZ+FnWt5Wi6zvM5ndjfSxR/ceccM+AbXxly\nQIDXuxWcDyUpgYlLPiHBYuGUJhahZ7O7kvFAdXI6hCPffJy1TT8HYEWpnTfbX4fraI5xE5AVihbU\nBCA7J/iSCiAdHdEOgIcH/sRPTaK8uniq8TWo/lUuE2NX8GVhPHNvaAVw0YqBF8pIuyqabtbjrVh4\nzXim53Uk5YljuAysaHTyLmEfev/GmcSqhXRfNhw1z0LQPvG5lgw+xzU6zPmNSYFbGflKR8PGBlHK\n78B9kZSHuWlY/zCxqhDmt858grCdbgLXpVPt2NlFXtToKKJek7FICt/kiflb1uwwpLvEX5EDArj9\nJ1EZP0l10uyXR0n0gsAFKLmhMW9UexOw8e7JaizpWgcA3Z0LkkTn19YQrJQQpwqhquKLIsmEK34s\na/UuD4fcCVyZ0A38bD2BgAZEpkKkJyrjxdDr2TsumeE9FzA8YgVzl4kGjD+PuBbb2l3G235Pxz+f\nR7mRVJXMQQ2JmbSuyjP1JJsN++xCEi02lpX6MrF/PwAsqQdAPkbEB4dQFkfz8c+iQt/t/mnsfiOW\nxHuNE7p5A1txbYTwY+y6J5iL6dCy3c7jg+cCsL6gNrqz2LC5nDWOn5+Y04vhzK82j2y3i/efuwX/\n9Mr7XLyi6Z6e8O4p9Uge+GdnBjUmGoAuS/aSYs9i6t13oG8yrjK91LQ+Q74Slfrfz2qP88bic2oQ\nksXKC3vWEaqUMayWR+hWcstU3qMlAD3fWM6G/FoceTcRe74b60mxCsubdqG73eccp3hhAisafs2q\nMisT23UBvKR5SRIHX23Db8bnkPcAABOiSURBVPeKfmED0ntxqsMJwx92OUBo67dtPED/wAy2lmu8\n0LMf7l37zjquoF8bygMkPDKXp56bw61+RQDkuYu5ZdQoAPy/Nta5KNvtnLq1CR3GrWdIqGjot9MZ\nzjOTBxA53Vjhdzqk6EKFfPbOaEnCV1rVOYU8KHWT+HDxhxRqOsMa9zpndS85IIDdU1LEPHvMoEQv\np89djxhSa1eJiuTJdYt5fuhAgIt3cZEV6m6ScGliAT1wrSoKJhmMWi2W7HfEPfx9k9kUajLD7xmK\ntObSv3OVarpyQADS92LCtlWWivcli5VrFok+XH0Cf+e61Y9Re3OqYeNKqkqXOev5pUBoU87uJ89b\nnFtvmkKsupybfhtItLbrnMdc1thN65PwgjjP9FVdSBmZSpDj7D5Lf3uMPRqQu1NT5tefyhGXzoQ+\n96PneK+WaMHdrfnuronscYrLXtQ/CPTjho+ze4Lo5tovYAl5bhcD3xxL9K6/F4wP/Ozs3+hl+310\nfX4yPpKVINlOVjcRvpb8jbF2O62sjMBvNrPG2ZoRk4XQ7WQ/SfjtGcgf+Biq7V5K1bTkwZuI3+jD\nrv+0wmf+RsPGPh+SRZi24j/OQAaGNbkJ98lzm9e0oiLqvZwLwK4uThJUifShkLC68vM43D+RQOkH\n7L+K9jgXC1bMnZ/I9PDZDGsiaiNrxca3mJIsVna+HMMfzUQbnx1OG2NGPorPGuOuixm9YGJiYlKF\nGKvpShIhC1WS/EV1fu2lYxUa3oGXmvNdmOgt337rABL77+JCpo3LRU6oyQNBK7n/9iEA6GXnNltI\nNhuvffke0463J6bPvkrbTSWbjeznNQaGCu9/1jgJ7Tw2atnPD620DDUynF1P1QJgR++3UCQrd987\nCHnr1krO5nyTlKBlAxa8Pok9ThuDpw4DIHqfF9oVtWnEHze+DYAiKcw+2YTq32VeUnv1qGXZpD5l\npY1NVPJ/qsNPAMwPSDS8zq5ktZJ1k4sAWTwCMjLXR+1kmTPMuDEuo3j62q+bMmfyZB6KEp2dw2d4\np2WNEhHBnskioubLmOk0X/UotfMvsG3WdSgT9/MLh2/iy9oLsaX6Vnoesp8fN/ddTf8JI4ksvsh9\nKCuc6teSTxtN4r5xYwjI91633oLezVh63RvscordwLN3P4TPBmOb1xoqdMt6tWRajSnc20kY5HVX\nOiCcSyv6TSDXY86MeOgkboPbgWR3jeKkBvy+94LHpT/dnFqWX9hxYyy6K+uCx14KusOB5cdg6jQR\nzoWCryMI7u+HK/voOY8/+nhr1oyZhI8kLqqGQofUvgSt9ILA9Zgwjo5oy6zH30SWJO5e+Ah1ZqZ6\nxjYYWaH3B0vxlcV3W1aq8PNLnfA/eGk2WVdaOkN/v4d1LT7CiZtrfYUNeNpDtxI92dgFIveexrzb\n/j1OakIoKkCKLZuFHa4VTkwjImms1opOtxfzsFebvJHQYU4mjxPb2ldmNKn0+Ofi4NAkNncSbeH3\nO1WSh2de1HmlnRILnlWxU6KXI1cyslNuUIf88S6WZsUS+1PGeRfk05FIef1bMvfZCQxNu4PAb3/z\nioMZhAI167XJlOsyA8cPByBivfGLn6FCN6eVQr8bBqCl7a54TwkJ4dbv1hEqW7npfhGDquYY7zCI\n2ljA5ydbopef5+b2CKAP75tKt2dGEZJp3I8Z9t467ur1EADLWswifa2V1zJ6UnaHhDtX2EzV2GiO\n9KnJ5iemImNlk0PcOmPGDiVo7ubznrsySE3qAbBw5HhyNZVmX48k5YnNaGdoX7KvL3q9BPbd64/u\nMf0njdhwRTZUNS6W2/1X4dRF6ujANQNI+SH1soR75GQ7JZ848ZUsVFeEw+SjxyczbnLry57POZEk\n9LaNKIqDMt3CfqdwdO12xOArO5g4ezqHXSEMX3EPAClDt15xNwOtpKTCfir7+l7QVqy7XHReOYzt\nnWcAoNaqaXg/N9nPj9fv/RCLZyG4e/1AauVdPD5bqh4DwDs15nBK04j7+vAl7VzOPonE3ndFZM47\nXT7mxWf6E/TlBlznus8kCSUygiP3JgLw2MPzyXD5UjA5Dh9n9uWOfMlkPdacAHkZ/R8cTsQK72nT\nhgld2W7Ht8kJ3It8UDzRC/m3NeSJ5z6jtT2Ljs8/QdhyL3b53LabrSfjQBLdPdHPXr8L7hIP7dtH\ngwj5yPgfNPY24Qzo1X8MxbESSBDawU1ZkOgG+8yTc7jBNx+Q6bH7FtRewjnhV+altF9ZIeYdEUsY\nqtho99OjJI/agK7rFcHl2Xck8fBjP1DbuoVY9RTBsniUHpnc74o6Fx++I44A2Uq+JhyYdcdm4rrM\nTsO6Z3FUJIkyXcxnXNodwJHLns+5kBQF5WQptV7fz/SXGkDjZDHekVx0RzkvPtOHBX0msqj7FAB6\nzB5WqeaZukuohemfpuC/yJ+w2ed/Bmx7fLBc5yk4pBlfA6O8TR26+qxgh1OMkTj4IO6LLK6y3U7y\n50L4hyi+TDzWEPd5dnEXQq1Vk6U9RNRM16UjSP7i/M+g1KwezWb/zrQQERrmJ0ssKK6F/4rdF9XK\nrxQ1OoqZj06l05IRJC/3jhJUMZZRJ5Jq12R+k9m4P4O+O/oDUFpegCJp7HcGEv7RJq9tCwB0t5sD\nJ8JwvJwAQK2n/7y5ZT8/bnpqBQALXrwWP9179Q1CP1hHKGKrIteuyawFHwJQ1+qLW1fIdpdw6uPq\nhAeJ7avi42NsYoYH7ZpGvF5N2FZ3laskP7oFdJ3yHi0Z9/bHALSwzccuKWx2+HLXrFFUW10KgHzo\nCsOBrjlJiebk8cOezruXGfYm2+3c/e6PBMl2NDQWlYgQQ7lXnmGmEN3lwr3zDBPU5h1irp6Xtcds\noE/WE/w6ciIAczq8xyvBna+8WaJHqNW8aydZo1tTcntrfOee+/6L6pRJiS52aq7Dxiwyp5EDAuj7\n9s/YJJXPTogAf3fRhcOtJJuNA8815cfoaeJ4HT7f3oJE1+WbwpwxwQR5isPUe+Ho+U0KNhut399G\nZ/9dOBHHT8lry/onWmIpMH6HLPsK+3TbxYfYVlaD5Ie8K3DBjF4wMTExqVIM03S1/emccIs014Ji\nYdOLDi6knT2H1kuGk+zy8gqi61S/J51x28W25fDtoXyW1Rrt2QhOxdkJVX8AIGDBduMdSOeajsNB\nbuswanqcAX+Ul9L/j/txLI0goNjNrhdF6mP8XB3r4i3GJihIEimTduIviTjpx/f2xdeSxZERrfnl\nsQkVNr1Tms6oI9dzYHI94r6qvKPK/kMQxc01NvwhTCrJ+qV5fU9rG3teb8g9AWuQkXDqOk9tuB2A\nJLf3Ypf/hq4T91kaWcPE9YhVStCSakBlk3g0N7ET1iI3qIMcVx0tTNiSM3oEU3NOOtistIw4wNis\n6zzzKK3ceGciK8Qu0Xkg8BDHtXL+GCUK1sv6BXY0ssLhMc3Z/sCbKJ77aH6xPykjD1/RFl/dnsYp\nTfymaZNCqNnv2J8tzz3Zgoe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Ta5mq0VEAeL6K4rHa35DjSeHVAx1ZlSeXTu/Wm0xjh8q2uyHjV9OGPSHC6WTf\nwOZUJMqL1ZkvSF5SQSvXXroHong4ED8HqqmhqELB5XeveZJ8QVVuwmanZKD8TaupIRzQKvh6cnui\nr9hP74Y7ALgraiOzm99l6jLyrFFUct5s7H/wEz02dcf2x1rT60GrMTHyD02DGsl4Y0MRukFFslR6\nu6/WES4No8JG+DYbyQsrAKhIchC6z4O6cjN6hfusaxccCVjWHbGeBnYHqpAL7SP/a4aODw234SNc\nOI++DiSqYSy+5D3av9Ofaj39nbQNc2snqJlpbLo3ESPZTdfl9wAwqckEHMXm/gKmW7rP1vieF3bK\ntjw1hi2pqlak/LGSRLW06rjEFUGsxyBEVXFzNTaGvT3TyLw5h45xGwDZEqSJK5dk1YMCtJvzKFl3\nn3/leiUsjPDv5Ff8Ve2v+NkdyYyBHVAXrCNCkf7DG59/lOl9hjPlknH8W7QMnDJSVLZPrMvvbYfy\nwv4rAfhzcjP2PVxJjjeKxGcU80r5+VvUaO0b82zKGI602Lb7gxrO2Ap5TJAaLyqx0Uxt9JH8m1CW\neZKpOacQ5XMvy+bLdi2XuLz8e+pE/nNxR1lxLZgIgVBVfJc24pfrhwOwwhOD/kQchtfcOgH6pU2o\nPULWn7g5bjENHSV4DYNQ5ag9rRsGbkMnQpHXrvaQvCZLdI1VlfGUaCFM2H0JdPT7wM/wmj3SXv6F\n5NGoQlY08xhe8jW5Mn5h/1Us+qwpuhPuv306vSNkh5lwxYmCwClsJISXIVQpq2Hm9aOoUFhC+tQw\nlA07ONRLBs4imnmJW1Nu3jgEQunmXo/3wWgADN8x1ZyEIFb1ArDNGxkU94LicmFkp5P3spfh9acC\nsir+kR8RwOfvH+A1NBQcFOmVZHziPf+xQ0OpnB7H+6ly6f7EvitYNK4Zcb/+vU5t5uhtjLmqA7H2\nsoBaf/m3t+SXNkMp1AXbBmYCkFKRT+vbV1OohSE851/Kz92tJXcM/ZZWrh0AJKi/EeHvFuExvCzz\nyF5Y9ZP3c+j6FoRvKUJfvfG8xz0dngY1SVLlpe5D49k13amxNgefz8edf9wOQE7H8bRwauRPiiW6\nR5nppf7+ia26XOnkXV6LqClLMXw+dnRzEOG37h748i7qLDc34KwmJND/o2l0CJGKPF+HcQXNmL2v\nPpcmbmXObhk80ubGU/27PezqWZ05D79JoionJq+oIMedwqfbLib5ln1oZ3G9KmFhNBsm3QhHeuUV\n6RW8V9CIn+9oJY/JPUDyIbn6nT66Jl9ecg0An04YRTVbOPlaJa7bfPgCVUArOoK8xuEkFiTw3JBP\nAIhSBLZDJaZ2GTFV6QqbnRaR7qgAABPhSURBVO35sXhvlEvq1LVHrRnhcOA2pKK7Z+kt1GG1mUMf\nI4Qcw1arBtuGRTOv1bskqSFVL5cbgnKjEt0wCFXsuA2pbH4sT+Kl9V1Jft2Osmjlecuw85EmLKg3\nnO/KagKwZUAG8auWHrdU1KrHc13MLwwZ1Z8kE/1Gx6KEhTFkyGfYheCeAY/gXOq34ps3oH34Ru6a\ndg/pm8/fsnce9qCgk2GX6T1H+qJpho4NlStDpMVwRfoPeEdrvHiwDeuuikXLL5BvEKBJpyjdUSXL\nXp+H1IGH8flXYNnPyKVqp8n3cseob3kpazpP97uLuPEByq5QVDzXNKPJf+T3PS0nnuipKiIslCe6\nzGC//+7OfH+P6a1jtoypxuUhe7l65R0AxL/sRN2YS1jlPpa7FeI54l7ahA+oNjyXDuFP8MYtHwPw\nzNpbqNFvFwklOWethAp6XMSTCcP9j8LQDJ3ppTX57dp6GLnSpXPse+puN67tMsbg9l8WHgOMADWo\nRNcQpeWEHtTYfnMynUNLAFjoCTvvjtj/xMpesLCwsAgiplq6hrcSx6woGt8uc04Lxsbj2y8tieIe\nTfEavwCQ+VQRgahLL5xODvdrBsDkZ4eRbgtBFeGU65Vs8srpckphG3KKk7g+6S+uDdvOA37/c9HA\nJJJNypVVQkJ4st9UDmg64wbfAIDrH0tFW51UAP712Twuc5VgBCJE7bf6t09Io33IXFp9+RgZsxYf\nfVkzuP+DAdT91JyGfMqyDXzR5VJWfbkTgO4xK2jmcGMXKjbUYzoCqziFnYfjf+fOeg+gLJbtkQPV\nrcBWblQ1v9zsjam6JoGq3FzH7j08/0tPlnYdyX2DpjF1fLL5gghBXv+WTH52GPmaDFqtukXegtuG\nNOSasFlc/vNDAGTtNnclaKtdi9EtvuCTokYk9pZxDL28/LQWa51R6xi5uC8AKbOXnpPfX9hsDHj2\nmyq3AkCFUcnnPTug5Z48eOvbtgOQmSZPxv1Fvw39CM8LUCcYRcVwe9jV1eCLq0ZhF7Kl1bLyNNNX\nYKb7dJPn7GbwU3MAiF3i5ZYHByF0ePU/4/ilPAsA346dZg8rW8+kp3LLI7MAyLLLJYxm6IzIb8SC\nm2QCNgcOkzMqhdF1vqbcgF2jpEzhq8zr36ZERtAuZAcrPCmEzJJ+rGN/NluN6vT8QSq/vhH7KNC9\nVP/07Jdsp5WjkfTRLWzzPgs8cSQvNDBaN2L/k9InNq3pOB7P7YH7PXOUneGtxLdtB+taSz/uWq05\nu55uxS29fuKxuLWo/1hYVVNDiX1zJyV9pX8zINcFED11Bdfc2geAlPAi4MSpcZmTKinvYhCtmhs4\nOYK768V8+++hRCs2Huki5TF8OShN6vN7v2Ec0lQy3tP8z5s7AXmrxTAtvxlzl19EXe8Zus+EoPCa\nbKK+lx2hzznQelFduoT9ChxVuoc0H+zef/yxiooS4kKoKqVXyOvXLn7Di8bIul/wfON+R+MAJivD\n4g5ZrOg8knDFWTVJf/TpNVQ32e1nutL15e7iqW63ATDsuwnsudFLWISbJLWUFx+9GgCHEYAgmq4h\nKjwU+OQPe1grI1+Hh7b2wrhyLxhyRt39VFsmtB1Lma7QbcFA0r86f1/mcYS4iFAEQ7d0IkrbWvW0\nsNlQoiIZ+edU0m3Sz+wxNFr98AhZh82VQ9gdbHpcWlP5uk6OO4Xrn/+RmyNXEeYP1rgNuL3anwx6\nph8ZT60wrcPHse9T85UF/D4shu+ve5CHX5kCQPewwziFHVUoPFjtR/6dcTcA9gApXcNbSehLEQCU\nHgLEifvB2Xce5puShoz7vAs1Tb7RlLAwur/+Iw4haPbpo9RZd9RnfNfU74hSHHRd1Ze4pVLBme3d\nthWWU1AZSq/WS5g5pC0AzTqvZ9WBFGw/RlNt7n60rTIv9kijyJBfErmn2mTez+0BgFi46pzGLqsd\nTozi+ttzdgGHbmxA3KTlVePdvX4TnUMPo6PjNjS8xg8AhCoq4cJJcwd8+8NEWi+7FQDlhxji3zfJ\n965rRP6ymf0aZKsqpbr0HUfuML89Z0Aqfehr5Uw0uN4ViI8Mns6eTddfHyDrJznDBipG79uey6Km\n8sddVvMmDJcTduxCcTopur4JALPuexM78GDu9dR9Ki8gfe6NvAJ+KEulvNJOjN/aLH7Dw0uZ06lt\nKyLdHo7HkBkSTSc8TN0Xjw+wnS/bn2/Oz5fJBG+XENiFRtuQbUwvzWbawKsAOHCxi8/uH0HNxvtM\nHv3v6G434VMXMWFGNgBT54XzRdpcVKFQ01bOniukZVz7x8DJIBbK5fqpIu5GeTmjfu1EvbfWmN4J\nV2+QRovQP8nTBN6kStRsmUFSMEKnY+hCwIb4Kg5D21x1Tum/WhG9dJ8pKwBj+y7W/dCM1n1mc1uv\neQDcHPUX3urgbqqiPG7g9a9Efi/P5PeCTFpH5zDk276kLT6/LIo9HUBHB44GVgFCD2kIVUFEyWyn\np7/sy6uN8ogPLWfzriSeaDkbgNYh28iwV+IUdpzCzpxmHwIwOLEzBz+0mbYq0PLyuX7xANZeOoG9\n/n6OMUv2m+4KDWh5Jd3tZmbbsaTYBBPfKEULRodRvxL15cp238JmY9PQFvxxwzAAohQHvbZ0R+tS\nhF5+OCAiaMXFvDLtXzzfcyrtZkrroZYtXL5mhLLbV0q/ux4BoPZcc1qdH4utWjKT+o4m1O/T/a6s\nDuM+7sp3v5YgVmxA9UmXR/WFTrbdE0+px4nDd/5pcqdD90eeSx7LxDdNQ0UhVrGdx7r1zBE26aPD\n0E96k2r5BWQ/sxmtpOQM3/TMd2gpa7dy7/Jb6ZGxip6N/qJwgkzDeixhISqCfM1Dws+7q25wW2pN\n0gdt4ECb4jOT5TTobjc1Xl/Mj2NrQkIcAF9f0pHiNPCFGiQtgeLaUunOuP9NqtsLeK9Xd9JWLjrv\nZXy9t/OY1imRzmEyVU1FoAP2Uh96pRf8tT5qP3NQVoCLDKde9TCmhXUA4ING15HXQuPtjhPpGFJC\nnCJXiYOS5/FUwzswVq4/L/mOJXJOGMqlggRF3jtG8RleC2eBlb1gYWFhEUQCXkg0WYUiXUPbuPX0\nB5uNEHgva8ybXScTq8rc0VWVUPFSCrbywC6pk5bqdOibS5Ty95KB+7Ryrhn7BNXnBa7KlnY4nx3e\neG5bdh0AaY/kkbJ3IRjG36xqNT6OS10H8M2Or/J5BwPbwSKK9EoSVTtuQ8NXJ0C5l36E3UHOO3I3\n1Kcd3+eVnrfIqm7/tOAMA62g4Pg3UFRsKcns6JfKa3d8DMCIh/sSMn9NlfV+OvTycmr1Wstf4VEo\n4WGgSHtnzOSOtEqfzgJ3Cr6du6tk8uXu4kBbk8tg6prc4u7f5h6zeRsxx7xc9pD09SaoNlzCi7Lz\n7DZAnAxt83bezOlEh6ZHdgVClKLiWL8b7Z/uvX/ICBC3BOI+FLztbMKM3wRjq8tOow0cNjb1iyTj\nPNPqj6XNwGWoQkHxrxJFSMh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" ] @@ -768,17 +1052,17 @@ { "output_type": "stream", "text": [ - "Time since start: 3.82 min\n", - "Trained from step 3000 to 3500 in 17.81 steps / sec\n", - "Average discriminator output on Real: 73.79 Fake: 66.71\n", - "Inception Score: 7.61 / 8.35 Frechet Distance: 52.60\n" + "Time since start: 4.20 min\n", + "Trained from step 3000 to 3500 in 17.43 steps / sec\n", + "Average discriminator output on Real: 88.08 Fake: 79.57\n", + "Inception Score: 7.47 / 8.35 Frechet Distance: 58.19\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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WY3u12JT7ZYjY2dzr/s8I3SY/SpOH6yEPi4vaIxz2ytKPfhqYlRtp+/sg1p/3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\nNx8hOt13jTJbjFrB5R3+xXe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Fe1alRook8Ys26+JHL8gOx/kvMjExqRymwPUp5fHRhlH1Knfn+TszZMzExMSk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\nYWNXER88LGILH7zWjZQHhdC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" ] @@ -790,17 +1074,17 @@ { "output_type": "stream", "text": [ - "Time since start: 4.40 min\n", - "Trained from step 3500 to 4000 in 18.09 steps / sec\n", - "Average discriminator output on Real: 37.14 Fake: 19.75\n", - "Inception Score: 7.36 / 8.35 Frechet Distance: 59.37\n" + "Time since start: 4.78 min\n", + "Trained from step 3500 to 4000 in 17.25 steps / sec\n", + "Average discriminator output on Real: 136.10 Fake: 130.25\n", + "Inception Score: 7.53 / 8.35 Frechet Distance: 56.25\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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Sp3krQLleTbf1IwFw7wmi2Wt7ZUF1H1caK7itJ4uensSTOZcDkHuRy6uFoYTF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\nDEDOfTYCFRdWDKJVW21ixtY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LNwR1l1s1IZ5NE2SmyOVtlzAo+meiFR+ttf37vzVqvz5TZ9S+9ry5sjcAuaP9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\nF3hz3yn4TRUqE4M/7iGosbE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" ] @@ -812,17 +1096,17 @@ { "output_type": "stream", "text": [ - "Time since start: 5.00 min\n", - "Trained from step 4000 to 4500 in 17.20 steps / sec\n", - "Average discriminator output on Real: 129.79 Fake: 126.22\n", - "Inception Score: 7.42 / 8.35 Frechet Distance: 58.30\n" + "Time since start: 5.37 min\n", + "Trained from step 4000 to 4500 in 17.17 steps / sec\n", + "Average discriminator output on Real: 73.92 Fake: 57.28\n", + "Inception Score: 7.45 / 8.35 Frechet Distance: 57.75\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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03ReXbe58hyNM84KJiYlJFeJfTfcSaxvCbkfYZPB+bme5NQmZvTpgfRIWC+/s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\ndMf+5fOBxnA6CBZeft4rw6f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GuX4TK71pXBG+ri6s1B/2Jx9qAJaFjOnrNh72dcMhaOtwML0sidSB2+VrXq9Vw/6RQNzl\nu6XtGBy9HodQcYmD2m4ztRrnhp34QyhwAXac46GJqvFbdTjNftgfvEWlqKwf14HI6i1suUgK16wi\n6aHPOSmb6G9+rntrj+fvJnlJw9Icj5Um01fSq88wlp8zgbaBDTFTK+OcMC+OQCTJXl0Kl2tn3U+b\nz3bgt9iOKJol8drDUpvu6FQpN3x8XJ7KR7u7MKHlBwC4BSytyiTt6+qgODaLLzmJaGUORXolGTML\n/xC5YXq9VJn+urW6qjIVfNZFLhyLD6U2UqLJqwt4/410nn33Qn7u/SpPnhUHQPIqy6YDyFNHty+3\ncV2kjA9eVRPGroooosIrMSos2nCOB6HQ2bMVOGjTPdDBoInFwwS14I0SHs64F8ZioPD+GV0wyvYG\nczjg4ML59pQ0xk04iyVnTsClHmpi8O8NrmmjPrWVijqctwGHULnt68FkrQ2iE8/QOenZQkry9v9B\nW6xOjiRWCeNrrzy2Jr8YXIELcoPNGriMGzL6kzh9PwDjUr7DIZzopsF3lWE8+cjtAGR9vAy/1aFL\nvTrSdsIa2julg2SPXsnpP9xN2+fLYedeJs+V8dsPxi/ndM9m3mriIOJo12sglU0CjhnAFx/+B2eK\nCHOzX9fxCCmO87zx6FZpug3A9NVQU+HEgaD61ECc6svWjrHhn60pyEtm5gfSYZj2ZQljP36bghUx\nvHpy55ALXuF0BGqleFECIZ1mmPUbcFCF7o47O9HO+RND8s9E3xvaKlZ6aSmth25m9yqIUg7WXjAA\nJdxjecDzkRApTQF4POVdfKYgbpkSdI+wf8vWw76+42ypaY4ZeL2cG9ZXUDoSen4BTzWbC0CEIsXa\nL9UKo4fcSOTcxQCW23CFy0XVk6WMSl5SF0nT59P7aTNyNbrXC0Iwdf5pADzYdzlJqsLe7goRMy2d\nBgBN5xVTPKIKtxDsus9Hix0y+cK/NXD6Kyvnw7JODI+VR/hfNrUiE4tVy+NBCBISS6kyDZy/RQZl\niMx7Dw0FNIH3i3syLOFn1o85ibYPytNzKIrdACgx0Zwetg2IqJMXnVrlU3mkyoENHceyK9nY2NjY\n/ClB0XRrj9RD/vYFCoKNL5xEhBnaaAEAkRBHS4fjkApj0YpK4XXtiX8jNDnuu89OBCBDU1leo5H4\n0TprqpkdL4rKI/1mUm5WoyySZRxD5RVWo6LY9FA7mqqHmlUGfXQHreYuClpyiN69LbPavoIqPMyt\nlCamNo/nSi0XQCi0nCmrve2/rIYkNQx/gi8oKdHGqvWc+doIKpv7SfxVxb/9UKeUWV3NpC/O5/rr\nZdU9U1fq/BMnAjU+jj5NNzGxuCcpb64FDp9BaDVrz4zksltH0vfGRXz6ckcA2gyT0ROG1xvcUqi6\njkccmmzcKWYHC01rGx8EReh6z5YhILfH/Eyl6SOs0BeMYQ6LliLLB657pDnLLn0Jlzi0+rtbaHS+\nbRX5ky1M7zwCQtO4eZhMZXQIlRvnDKF1eeiO9PXR0lMZELmYpTWhrVvvvbIne66r5Oku0+rSelVk\nzYHMR5cFNQ175+kePIoDn6lz7yuyPmrTffXs2IaOViHXplMIfKaOqFCDY/4xTVKfProNXfFDeEBB\n6Jixg0qhgGnB93Ocx2MlPJy8oa0ZGTOREf/+O3EHQleERy8tJeX9LUT/rZLXT5sCwKhZFxHuqGbD\nV+0bXN/2WDASYnHXK4YF0raOqLDmPgSwvp6upqHfJR1VBgYdvhpG619WBlWr0lqmU9wjmepowYBh\n3wAwO/ZTHPUEri/wpeXUGKya0JEYI/gZacLlonuYjMf8odJD24e3o5+IouXAxtub4kdn6JPDifOF\n5iFSY6LJfnAtDyUs4Z6PbiHtmvEAdHb5GLDlIsyaxhc6OhpGQEEpNqpI/fh3WZLI8MK93aR92SNU\niowaMmadmPsDkDG7nP03Su32lLgtzFFjG7Up1Sog4l2DwjfTiZu5HOMo8cfCJR2ssd86eSt1FHfk\nXU3CzDUhDd8SDiebhmbwWuyHRAa6VFT65SmlxRu5QdW2ha7XVRmrjV64NH4lbxoZlo5judBVWqXz\nbftpAJQYPiJyHUEtpiE6t+Phj6aS5agkWnHWCw872C2iSK/grCW3AZDyL5OEvXn4g1zoBqCyz0l0\ncsoiP0O2nYNZVh70MQ+H4naz4PrnKTMgJrcy6OPVxkpvfSOV95pP4ktvKmnf1HCzfhcAj1/9AfmT\nsogJcoJMxof7KBlcQ4RwsH64dGhmP1qEEhWJaRh4u7Vg3IgJAOiYnDP/LlrOWRbUOR0Nf4SDOEU+\n7NdHL2de+s3ouVsafsHAUXlyqw+p+rfJ6w/05KeRpxC2aLP8vWlgVFRi6jqiS1vOnyy1yGujVvF8\nYR+qzj8Q0lh24XCy+amuvH7Na+w3NM5+914AWj29EsPrDb55o3A/P1c159qIg467KsOBcDgtlWGW\nCl0tOYmwNw7gEvKyTxT2oPmPpUHVctWSCgr1KDo7/YfE49byUXkUD318By0flpqtYegh2bkVj4cb\nxnxRF2T925Z0stkQgpHrEXjoiq7vTLTyC+evuwr38tyg//2+02UthXGdJ5OvKzw652oYYPDh2TLm\nSEdQenk5MVODOw9jYx7TSttxR0wuy66R1bty+jrZZ4RzkqOIGEWpKx85tawFWY+XHtJxJNQo1Xrd\ns5PrF4iaxpnl/PkyAeemrHPYfm8X3hnyEiPeXMoeXWrzDgH5fg9Zjkp2+n/lveJeAPzt5uFo81dh\n+oMvcNXYWGpOlprkBePmMcg5jSEz/k6rf68io0KeyEKlaRsHSngz/3Subft5XcjY9poElIhw9GLr\nhK4dvWBjY2MTQiztHJF/Qys+SRvF1oBZbMkDXdGWLLVsiMPh37KV1668lE5fvEGGctDLuLamkiun\n3UfmlEJabV4c9Lq5v8fomEkP9xz0QNWisLVhwc3EOwzCKb+PsP67KTaq0J6IxajYFtxBFZWSVgfv\nw+elnejabguJ7nKSAjVKywwFc11wYj/rY/pq+HLImRS/Es6jCTLmtbtLoIoqCKRA5PmkyWfi01cR\nnWthCckGYLgOmsQK9UiMov3WXLeqipRnf+WxCeeSd097kk7bCcCorJm00Lxs8YUx9Lm7SJyyHAC1\nejlHa1jbYGo7CXc7iW0PwNgu79PBWYwaOJFt8bkZPGkYGaN+wwjx8woysUodHs7mL8uJDDSOfeeL\ns8hUrD2hWiZ0teQkRt35JnGqSq9Jsg5q2g/Bz3gCMNasJ1+PoJnm4xuv7O478bTzydiz4MSEZwlB\n7k1hZDpMfqqKAiDt7U0hn4saGwPAXRk/csGzI0icH/z7oYS5yfybXKR93DWcE7YBX/w6fKZOju+g\nMI7eZB63V71B81m0jk9fP4PPzpcRNf3SV3B99FLy9QjuGXUHTb+SR/DobSdW4AK4tu6jxJAbU6E/\nCiys5QsyMiDtyYNr4DG61/07gQVBPcYLTaPkmm4APPHUm/R2l7GmxkHvb+8h6y1pRqmOc5H644oT\nInBr0ddu4M4Wp9X9nIH1MsSyFuyVfXvw5YSxlBl+BmWfC3BUT+n/d/YM681797/AbQ9KZ0DkjNA/\n1LXe6KpzOuL6cnFoxnQ4KfggE4Dfuk/GIWRrpNvzz2Xt2+0AiMupQluc8z+9Pg6H1rwZUxbKdLhC\nXXBv67NC6siysY6jtWC3TOjSqyObrvWQNbUMc2Ug8PsE7lh/GWqPVPZ3YfNnCEHFV9KpVJCXQPYd\noS20b2MdoRG6NjY2NjbA0YWuHb1gY2NjE0JsoWtjY2MTQmyha2NjYxNCbKFrY/O/jhB1kS42wSe0\nJadsbP6HUMLDqZqdAECNrhLdf1/ICnIfE0JQ+XU6X7abzn7Dz+3nyB6Gjar3YPOn/E8IXaFpmPUD\nzU9gfr3N/whCoJ+cxYuZrwGy3sTAv99Di/e24d+5+y8RQqif0ZlpbccSJjzcmHM9YcfQR82m8QRV\n6ApN44pVu7gxajNd5g8h4/rV8hchFHr7Bp9Ch8FruDJepiNvqk6mmaOYZK2EvXokv5ZlAbD4P12J\n+HCRZXPbN/gUnn7oLcb2vQKQmS4nHCEQqlrvZ0VWTwpC0e7/euoXs27Id2OaKFU+pu4/BYC7En7i\nx7tGs3BwE+6eM4CmP8r74N7vx11QhuHUUPcWo++RfQRNw6y7V5ZX6atNu/2bkIXb0Sn7rClh5okV\nukpkZMjaaJ1IbJuujY2NTQgJanKE1rwZby+cSaIaTrlRxfkP3ANA1EfLMP2+4GpXQrDv1l688shY\nVExaO2RmuR4oNOk1dBJVD5Wm1CK8ps7lqwcSd3MxelHji2trTZPZcV1Lms09AICxMueEapPC4WTr\no93whxvoUfJoq0b4+PCU19itR/HAqn60uF8Wf/Fv3d6oudbW01WTE/EX7PpLHKWFJg91x9ItREtO\nwjFD/jvW5WXP+QK9tLRB46qZMsNs27VNub7/j3h1J3fG/0p14Os9YDi55ufbcbj8qKqBoshfVFU5\ncDh0Hmj/HU20Uu7+/kYAS7LUtBapADw2Zza93Cp79QoGXnIrxqr1f/JJa1HCwyns3xHvRVK7faXz\nNG5fOoBW9xfj31Fw8H0uF0Z19X/VaeyEZaSpSYmM/u0T2jnDAMieNxCAzOE70QuD2x3Ye2VPJr/0\nAi4BW/wRPJV3GQCGKTg5dgePJs5ntc/Do7lXAvBu26nEKRpj93fipy6R1rXyCRzl1MhIzJYpiK0F\nGPVaawtNwzRNhKZhVFYFTUCZp57Mw++8Q6pWSnSgglKJYaIj+LUyg+7ubXxX0RaQ7esbKmQA9gzv\nDcD8kWPqNrk8n8I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" ] @@ -834,17 +1118,17 @@ { "output_type": "stream", "text": [ - "Time since start: 5.61 min\n", - "Trained from step 4500 to 5000 in 17.14 steps / sec\n", - "Average discriminator output on Real: 34.10 Fake: 17.50\n", - "Inception Score: 7.31 / 8.35 Frechet Distance: 60.51\n" + "Time since start: 5.95 min\n", + "Trained from step 4500 to 5000 in 17.12 steps / sec\n", + "Average discriminator output on Real: 153.41 Fake: 148.77\n", + "Inception Score: 7.48 / 8.35 Frechet Distance: 57.44\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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o0Q5bWmP5L67hmZ7S/2vMb9fTpDFapNRqP5rwDJGKCx2dmPPyUKbKTgZnMuPl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\nBc9/2YWHrm8HwNcPP0O8TWq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YEIo5bF3FPmb8fgG3TBvCLdOGel2OCRMHGluYce98YkX4z7cnkZTQ9f1C/3H0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\nh7fZnb8XyCRC2sL1e+D7gM9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C9wH/2tXCInKbiJSLSHltbe2Z1Bo0nr7wQXy4cS/l26wVbowJT74E+C5gl6oucZ6/jCfQ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\npY2fvLaae15dzXlFPXn9Oxc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"flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - } - } + "display_name": "Python 3", + "name": "python3" } }, "cells": [ @@ -338,11 +88,7 @@ "metadata": { "colab_type": "code", "id": "83-azWpoYsDg", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 270 - }, - "outputId": "bcd3973e-0605-4c05-c8c6-354c53b498ac" + "colab": {} }, "source": [ "# Check that imports for the rest of the file work.\n", @@ -356,44 +102,8 @@ "%matplotlib inline\n", "tf.logging.set_verbosity(tf.logging.ERROR) # Disable noisy outputs." ], - "execution_count": 1, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "

\n", - "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.
\n", - "We recommend you upgrade now \n", - "or ensure your notebook will continue to use TensorFlow 1.x via the %tensorflow_version 1.x magic:\n", - "more info.

\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "Requirement already satisfied: tensorflow-gan in /usr/local/lib/python3.6/dist-packages (2.0.0)\n", - "Requirement already satisfied: tensorflow-probability>=0.7 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gan) (0.7.0)\n", - "Requirement already satisfied: tensorflow-hub>=0.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gan) (0.7.0)\n", - "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (4.4.1)\n", - "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.17.5)\n", - "Requirement already satisfied: cloudpickle>=0.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.2.2)\n", - "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability>=0.7->tensorflow-gan) (1.12.0)\n", - "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub>=0.2->tensorflow-gan) (3.10.0)\n", - "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.4.0->tensorflow-hub>=0.2->tensorflow-gan) (45.1.0)\n", - "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n", - "\n" - ], - "name": "stdout" - } - ] + "execution_count": 0, + "outputs": [] }, { "cell_type": "markdown", @@ -465,7 +175,7 @@ }, "source": [ "import tensorflow_datasets as tfds\n", - "import tensorflow as tf\n", + "import tensorflow.compat.v1 as tf\n", "\n", "def input_fn(mode, params):\n", " assert 'batch_size' in params\n", @@ -517,20 +227,9 @@ "metadata": { "colab_type": "code", "id": "zEhgLuGo8OGc", - "outputId": "11c7bbb1-9447-448e-add4-6bc43aa97ecc", + "outputId": "efd62ab6-6d5c-4ee3-f6ed-85447922b54e", "colab": { - "base_uri": "https://localhost:8080/", - "height": 470, - "referenced_widgets": [ - "eb3ed95bb74a48deb9a702227b744dae", - "dc4c15628e8445ce9df79da5c7f0a231", - "46f93549528a49b991d7d62c08ea12d3", - "078e0cf205654497933e46a05b75f143", - "9526ef5130034feea363d52aabd54246", - "6ba669ac584c488190bdecc31a7e96b8", - "61b0985f208f43288dc96f8d1d642c6e", - "7ec4f1b83fd34210a6714068606ba4f3" - ] + "height": 279 } }, "source": [ @@ -548,58 +247,14 @@ "plt.imshow(np.squeeze(img_grid))\n", "plt.show()" ], - "execution_count": 3, + "execution_count": 0, "outputs": [ - { - "output_type": "stream", - "text": [ - "WARNING: Entity . at 0x7f556fbd9b70> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: expected exactly one node node, found []\n", - "\u001b[1mDownloading and preparing dataset mnist (11.06 MiB) to /root/tensorflow_datasets/mnist/3.0.0...\u001b[0m\n" - ], - "name": "stdout" - }, - { - "output_type": "stream", - "text": [ - "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n", - "local data directory. If you'd instead prefer to read directly from our public\n", - "GCS bucket (recommended if you're running on GCP), you can instead set\n", - "data_dir=gs://tfds-data/datasets.\n", - "\n" - ], - "name": "stderr" - }, { "output_type": "display_data", "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "eb3ed95bb74a48deb9a702227b744dae", - "version_minor": 0, - "version_major": 2 - }, + "image/png": 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WPtqfzL5pNg/1OYEyd0KqNyd9+npuJ6Uw//JJSn1yhxk/fIRrExP92k8kKce+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\nW8/FkZlvXxSzO1vPezkZeGQ+Q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"text/plain": [ - "HBox(children=(IntProgress(value=0, description='Dl Completed...', max=4, style=ProgressStyle(description_widt…" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.0. Subsequent calls will reuse this data.\u001b[0m\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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cGWYyY8obZZLyluvKQnvnlMlQOlfk5gI7rjX++dXBo0U+8vC3ith30t05lZVc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" + "" ] }, "metadata": { @@ -846,10 +501,9 @@ "metadata": { "colab_type": "code", "id": "AH6gcvcwHvSn", - "outputId": "0f9e15fb-4846-4089-e2b4-711fd4b746ac", + "outputId": "a72e2218-95a8-4585-8a5c-7c4ec896ac0c", "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "height": 2281 } }, "source": [ @@ -915,24 +569,24 @@ "plt.plot(steps, real_mnist_scores)\n", "plt.show()" ], - "execution_count": 9, + "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ - "Time since start: 0.68 min\n", - "Trained from step 0 to 500 in 12.19 steps / sec\n", - "Average discriminator output on Real: -15.10 Fake: -13.89\n", - "Inception Score: 6.26 / 8.35 Frechet Distance: 67.16\n" + "Time since start: 0.32 min\n", + "Trained from step 0 to 500 in 25.67 steps / sec\n", + "Average discriminator output on Real: -10.51 Fake: -10.17\n", + "Inception Score: 5.97 / 8.38 Frechet Distance: 98.58\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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5H+CIC0+TVQfL5110v8pdQz8nQmnlj/+5hLybgvdrqHGxTD5ZzheXYid/dlnX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\naXMuANYve897r6QkUTrejhhXw5A46TM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QwZBPlyAIIkZo0yUIgogR2nQJgiBihDZdgiCI\nGKFNlyAIIkZo0yUIgoiR/3puyNBFk+38AAAAAElFTkSuQmCC\n", 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hQJpURqocfekk+1szbyKVXZEOFNFoHOJxQKE8GzQd5TQJihWC0ydQZoA/XcM2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" + "" ] }, "metadata": { @@ -942,19 +596,19 @@ { "output_type": "stream", "text": [ - "Time since start: 1.32 min\n", - "Trained from step 500 to 1000 in 17.63 steps / sec\n", - "Average discriminator output on Real: -4.28 Fake: -4.68\n", - "Inception Score: 6.90 / 8.35 Frechet Distance: 63.44\n" + "Time since start: 0.69 min\n", + "Trained from step 500 to 1000 in 33.82 steps / sec\n", + "Average discriminator output on Real: -6.00 Fake: -5.53\n", + "Inception Score: 6.61 / 8.38 Frechet Distance: 69.39\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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kXBHZx4fs6XFUVgil4a6mq5HRWfBSJ3y/2wCSsARqf1Uq9H8c2c8PPTUOeX/W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\n/wMuINSPrlQ5JgAAAABJRU5ErkJggg==\n", 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ZFHTQ8ehBtjy5ndbo1J6Vy1bUVVlBIBACsCBn6m/wCb6ZK/9gCdUBi637j3OwuzAxdGrA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\nif9zQzEbt96PuLSalhPN/NMLzQx6A1O+6BoY+GM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" + "" ] }, "metadata": { @@ -964,19 +618,19 @@ { "output_type": "stream", "text": [ - "Time since start: 1.89 min\n", - "Trained from step 1000 to 1500 in 17.37 steps / sec\n", - "Average discriminator output on Real: -17.18 Fake: -17.50\n", - "Inception Score: 7.39 / 8.35 Frechet Distance: 57.32\n" + "Time since start: 1.05 min\n", + "Trained from step 1000 to 1500 in 33.28 steps / sec\n", + "Average discriminator output on Real: -20.26 Fake: -20.98\n", + "Inception Score: 7.15 / 8.38 Frechet Distance: 58.26\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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dW0moHMAht2iP3r96u3I9B0pMNPV+FmO8Hb25tElrvlZUWnA/SLKSoTpps2AI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\nVk2QbLvobwO8YVSJJguvRa/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+dzaVIVyFaL0dnHlgH31n+oi9hgfCpaIbBl27ujnqOk59QQFlBVaEOxcUM3Lj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\n5edPDvDo7ACyrr/fpw9glc0I2YQ+NZ4aZJQmK3J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" + "" ] }, "metadata": { @@ -986,19 +640,19 @@ { "output_type": "stream", "text": [ - "Time since start: 2.46 min\n", - "Trained from step 1500 to 2000 in 17.47 steps / sec\n", - "Average discriminator output on Real: -13.30 Fake: -14.13\n", - "Inception Score: 7.27 / 8.35 Frechet Distance: 63.34\n" + "Time since start: 1.43 min\n", + "Trained from step 1500 to 2000 in 29.00 steps / sec\n", + "Average discriminator output on Real: -28.74 Fake: -29.66\n", + "Inception Score: 7.35 / 8.38 Frechet Distance: 54.52\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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64z3C5QAU6UzlL+EiTC/RSbbofHSyNovuqIeWsccX07gwkkSTOzZhkcA4XXD5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\n6JqYmJj4kf8DDWQqEXg8KR8AAAAASUVORK5CYII=\n", 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iClmw+2csg9bRIC3H2qmpURENzahu/7msXkYHsUf6c9Y4ZWjMy9CIm4ZEhMxg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\n38OvBvxpOTzmr9l1nQ+uWUzJVesQeQV07ulnT+P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" + "" ] }, "metadata": { @@ -1008,19 +662,19 @@ { "output_type": "stream", "text": [ - "Time since start: 3.04 min\n", - "Trained from step 2000 to 2500 in 17.45 steps / sec\n", - "Average discriminator output on Real: -23.74 Fake: -26.40\n", - "Inception Score: 7.19 / 8.35 Frechet Distance: 59.34\n" + "Time since start: 1.79 min\n", + "Trained from step 2000 to 2500 in 33.04 steps / sec\n", + "Average discriminator output on Real: -27.47 Fake: -28.54\n", + "Inception Score: 7.17 / 8.38 Frechet Distance: 62.29\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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rrsJ99JhX5yXb7ex+vxHj24j6IK/93pvke9aDYSCpKnJinDjwvSImJHzN70X1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\nAABJRU5ErkJggg==\n", 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jIZrqamncbab8aiPClc6GJXaG4nOgLYe+kV6UYBCnsLDaPZNr17iZs3YmomTW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\n4B3B/ex+PNG3Rw6ouoaaIrv2H6NIBmZ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aRLOs6fgKnJINv+jAjEYAE6PpAk2d1bTErz5d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\nLRaLZQaxgrrFYrHMIFZQt1gslhnECuoWi8Uyg1hB3WKxWGY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" + "" ] }, "metadata": { @@ -1030,19 +684,19 @@ { "output_type": "stream", "text": [ - "Time since start: 3.62 min\n", - "Trained from step 2500 to 3000 in 17.08 steps / sec\n", - "Average discriminator output on Real: 62.13 Fake: 63.63\n", - "Inception Score: 7.40 / 8.35 Frechet Distance: 57.84\n" + "Time since start: 2.14 min\n", + "Trained from step 2500 to 3000 in 33.26 steps / sec\n", + "Average discriminator output on Real: 1.75 Fake: 2.23\n", + "Inception Score: 7.38 / 8.38 Frechet Distance: 55.90\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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Ta5mq0VEAeL6K4rHa35DjSeHVAx1ZlSeXTu/Wm0xjh8q2uyHjV9OGPSHC6WTf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\nQmCC\n", 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x2IU/FpqSBfZ0aEgOJwP8x6sHWZPIYulVgsbmLGavmUvp/gCR1Mikiv29vlip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\nnznt/hiKlmQVEkWWkPOsxT8DLJ6DmW/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" + "" ] }, "metadata": { @@ -1052,19 +706,19 @@ { "output_type": "stream", "text": [ - "Time since start: 4.20 min\n", - "Trained from step 3000 to 3500 in 17.43 steps / sec\n", - "Average discriminator output on Real: 88.08 Fake: 79.57\n", - "Inception Score: 7.47 / 8.35 Frechet Distance: 58.19\n" + "Time since start: 2.51 min\n", + "Trained from step 3000 to 3500 in 31.49 steps / sec\n", + "Average discriminator output on Real: -2.78 Fake: -13.50\n", + "Inception Score: 7.38 / 8.38 Frechet Distance: 58.81\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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Fe1alRook8Ys26+JHL8gOx/kvMjExqRymwPUp5fHRhlH1Knfn+TszZMzExMSk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\nYWNXER88LGILH7zWjZQHhdC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B7HgIafZCbm50cavTg1AdY45CLYHw5XHVTZdw5YIyrGNNaCd6ad3soMkSJBWF\nuoBG41wfzruuRegmR9s7GErjY+BXXBT7ChAFJWCZbFvTQ8uRkbcVmLNlJBUh1tuKPdQNbxB1LAN7\nsJsCM0pjbSl6Zz+4nFTn1+B1eMiXXcwvz+Xmm4uQPW7sZIy9OwbYt3fiOvE4JQVnoACpsAJbNxjd\n1czw4OC4nXHpQCDwKi6kuAXGKeW1zHGZovZs7WXP5E6uuS8Hz8IK2N4K+pkX8amFPiqvW4Ck+Qkc\nOcmNVRKeqbNACH7XHeLpgYnNJ4gZSYZSYTAN7Mgw4cQoyTQFM7Rv20Z7TS6TbrqJqz+1giW9R9jT\n1MpQJIJmGchCwqc4KREwr66O2qVLGSZO88svv0X481WFyQ6FeDTOTw7b9JxD4uBFK+o3uTTq3QpS\noJCs/CqqPXkMxUex3uE4IxCUOgPM9Jcj3D4QMnaWF5znl/QhELjcCpOvtXEXuhCFNdz6gQqurp5C\n01dWc0fvQUJnCN2SENQKg3wJ8p0BarOKxi3qUnkxyrx6hKwgAkVgmWNhfAhs08I2LbR4HKGoCGSk\n4lqk4sk4F0ao/zDUJ6IItw/LmYWZTKD1dWC8+hJ/8+MdbOlIj8AJoNqZzZKC6YhJszFHQ/w62c4+\nKz2OajQdtDcJo2kgTZ7JgqopzL+mDXO9hKirRZraOFYqQEgIjx/Jl4uNwOhsZvNwM9v0iXNgV7hz\nqcypQPgLSIV1tvxogFB3egt5wdj1ViUBsowqK5iGgW4YZ9xovIZDVpiXU0nO1Tci1U4FLYUVD48r\n1HKz1s/MUDNXSzNQbvwA4qGtED/zAvad22ZxyWQ/j+3qY60yyBVFFUjZhejBHqKhARITvPA5ZZUs\n2TEW+jrUhZVKps238p19A0TzdvLglBKyZyzi8f/+NF/94pOs3r6LrvgIfsXNitxqPu+ymfSpK5Cl\nQX741Ga+uPattW6u8rv5YqGPeDzCuuAxgudQ5OyiFfWOiJdg0oEPm6nZEv86X3DnBoWhhPW2x+aq\nrALuXDmX+z+2EJGVDbbFoafD9B08vxfKxmYgluDGXx3kq+slLvv4VeRcVY9rRiWVn2uk5Jt9xIeG\nTsvKcwmF2/JmsfAL1xCY6aLnhf0cDHed1/hvRKjOsWYOkjy2szI07FgI62gT8Q17ad1l80/xXjwO\nPxWKj4WKoFHSMBUnZZ9dhOg6DpEIa1uS/HRXkGgqQs9gF+2hNNUOALwOD4urFG6ancRMmBz77FN0\nNZ14k1cqckFCAAAgAElEQVR/HOg6tv6mXZXqQqhOhG3BZB9ScTXC5QSHG6SxhBuENHaSiQzx8g/2\nsfuVE4S19H3vN3PnLAd3zHGDrBA3IjxsdtA9Tp/Om1ElhcVFPr59aQXKbXchef0Ef/UqTz63hYdH\nB88Yd+6QFar9fv5plsmU8gKEy0ty23Zij46v2opm6mPvgCQjOVzkuQOER+On1VKRhCDfHaD3hQSP\nbNnCcc1iaVUjjg+uBIeD7/1gG5u3TqzpBSAguylz5SEsCf25DZhDby04Nh5e2tND4Icb+eoDGtL0\nRXzyq/fwkeRdGJaOZINHVsiVZZQswUOPrOan294adVTmyWPS8oUMLpjO97/0LKHRc6s4e9GK+kkh\nERSCSiHhKilm2ofu5vP7n+ExqZceNBKGRliLIxC4VQfzXaXcfP1crr5jEfl1UwGw+lp5oreVpvj5\nxyMblk3LSJxvRQ7z+58luWJzE9fXlpF/YyP/9pU8Rne8SvJID7ZuoOarqH4f1YuvpKqhmFe3tLDm\n5WbixvheaKei0nE0yi+/u5NUVjOSbdOhBxmIDRMd6iHZPUB40GafmUCWVLIklY0CCoSFLav4vnES\nORHFTCXpDGkcG9IwbJO4nkqbXVkgmOXIY0H9InIvv4RYIsZPujvoSMTSlkU5tC/J0O4ohbMTSE73\n2LivCbctYbsUhMt9ajacOsmAbZoYw0FaH3yRX27YxP6R3gnL7BRCkDdnNjmzZ4NtYSTCNMcHiacx\nX2GSO59rFk3nrlvnMGNmHWJSDUJxkLovC8nhQf3tFl6wkxRIHobNOE5JYbYqsaTMQcnVdcxYuABX\nRSV2KsbWzl4eOTG+U4tl21haClJx/IUlPPh3txHt76J1Qy8kYlQvLUSetQSXojJD9hG3LRbZFvk5\nPsjOIfTfT7Bp01Y6gsPpuUDvwAwFbnBJmLZKzy5IhdP7HPSNRnhpTxtVrhw+/Jlq8vLyyHNLgBOE\nBHoC4Q1gx4KsqHLhratgZ9LP84Txyy6ulxUarp3DtNoAvZu28WzzUZLnEPkCF7Go79aHuTrYzuzR\nGkROMVkLFnH7n0vkD7cxHOwh0dVNsH2YyIiTsgYP0+vm03jVfMrmTMY2TOLb1vP4K4d5saOVHnP8\ndrq9epjmA0doO9ZOc2UptULnztkBHNPyiAVThHsM4kk3uuxmqCvKz05sYuP24+w81jFu2bRtm0Nd\n/Yz0byWFhQQMmDFCepyEob3JCZsiCLx2NhAIxNYRJCGwbGvCxEwSgvlVbuY3lGN7cgk9s5514S6C\naXQObu4bpf7AYW45VoyY3giyMibfYuzPmIS/IYPS1LGH+4gcPcHBrSM89tx6Xg11MGKl3xTyGqok\nIxdXIhWWYQ2PkHp5HdFYOK1JR2UOP5dMqmbJ0jlIxbUgyQghcE6vof4OmaziYqYZcQImjGphVCRq\nXS6mlwfwzK9EKpsKlsnWDbt5bP1eNo9GxzUfG5ue1hAHXm5h9nUerrx0HrZeT19tGJFKUTwrG6l6\nxtgvi7GTJraJPTLC6PoN/M8zG2nq7SV+ARzJAUui1FTQkHlhxGY4zbmBmmXQNjLCz7ccIZSrYDhl\nTFMHBJKkkOOQuO+KGbgDPqbNqqXclUf9vDAzHBYeycFiWaJ8WoDDra08vvMY3YngOW+8LlpRP5Ts\nZ92eXUyamsvMpV5kv4+yjyzn7vA07L4OrLZ24seDBHvclK5wocyahcgvJTWYpGfbPja/upr/2NhH\nx2goba2r4kaK7UaKfceiVP5wAGllKVmyTbwnznC7zUjSJClidO7YyJZYJ4N6NC32Os006DBH6Eie\n+1HRPhVqOBHlZt9IqTNAQ2Ml1Q1FDA+MsvaJdXQFB9HS2DZshx4icHgfPJlAnR/D5/JTJdwUlThx\nF3sRHi+GAc37uohqGhEtQqLzGEM7DrB1U5wnIm0T3mKw2BHA7xqrax/sH+HVp19FS6VZrGwbcyhC\n6nAHTqEgFVZhywpCUfE01DBjViXTtSRoCWwtPlYd0+FGysoGRcUe7mXvwXZ+/tg6nn91P1F9fAXw\nLNvm4PF+fvbkdi7T+phUNZ/JC+sou7p2zARmA7Y59m8JsAyCPSFOvLqP/U8+zfe6hwhpF6ZL11h9\nIJuUqfEyCUbe0QNxfsSMFPuGOmh9dJCwFn9df2QhUez3Uh4boXFSDXnTJuGtzWV2scJsl4/UoMXe\nZIIjXd2s39XCb/rPHDnzx7hoRT1l6Pzv2oPowsUXfX5yS6bidJgInw9pyjzkuvn4r7bwmzrIKsSi\njPSPcOKpXbz40Bq+GW+dsD6EKVPn+Mggn3hi4mqov5eQhcSVFVOoW3wlUuVUWjft5eu9YWJpztZM\nGBqrm/tY3dyH+uPNVHuLuJMClq/Mo+TySYjSScTjFj/8hxdoD49yIjnIQCqMZhhvqS8+UUxxF1Cg\nesHUORGK8S8tGvE0D91pRtmz6wCTh9sou206hcuuRcnLHSs5LCtjNV/cPnD7ENhjiSu2ia1ZaF09\ndO94ngd/uof1zR2MpsG3YNs2x5PDHD80zE+P7Of23BPc+/9uZNbMSSiKg7htoRg6km2jqyrJeJg9\nLzbx+M838+zoAOYF6hcK4HIa+DxxrFSciDkxrexsQLcMRpKnB0eYtkV/OMY//7aNjzt6mLNoP1kl\nQDiOUBwMbE7w5YE+9sYj46rtftGKOozF0z694QAje0I8oBbSMGMA7/3Xo0yvHwvtsy1IhEF1of/6\nUX744jF+enSIvugIxoRWzs7wGgJBjtvHBz86ndmLSjEONBF57Bf0xYITegd00+D4aA8P0sO3nhaI\n30tjZhgbdH0sRM+esEj0t6dZG2RAC2EF+0h0HaUvnv4iXp2xQX4QH+I3IS8rei3+Ze0+8m+9BKVh\nHiKv9JTz2DWWWm4aWMNd2MkoqdYQzV//HQ8MdXAiEj6rvI9zJWHpPDa0n6e/eJRP+yopceWwSYJK\nyYPbFhyzY+yPddMRGSSRSk1oH4IzkUwqRMIu/JJyKinrwmLYJk3Bk3wOkJ4VSJIYi8hxuBiOhklZ\n1juF+J8VF7WoA4wkoqxLtXJAtJMVMpCO/xzhdp/qmM6p0D4Je3iI3tEkg0kjI+gXCIEgz6ny8OwA\nc+pnI/tyeKkrxr8fjl2QO2BjowOYNpgTWyjrbBlORuh/7Hl++fwGvtPWTtxIpb9SJ2DYNgOpGC91\nH+fIsIXSPIjwrAVFRZJk5FNZk6ZpYOmpsS5QSYNkd5AThnZWxabOFxOLWDLBj7WTqFInEQFOZASQ\nxCRu6mgTUMH0bJCEjSyZOA2Dq9QSOkQXcSbOx3ImbGw0ANNGmALTMpEli6SVnk3IRS/qpm0RMpOE\nACJAZGL6gGY4d2RJItsXoOGua/FVVWIdO0zHrm3sHZm46ocXO0lD59fNrdjAkXhiQs8Kpm0R1BME\ndSCSAv4QxSIJ8br9+N3ABnqt1Dtmlr4baJZM3HRS4vdy05XZPP0bhc709m45J2xsTNs6lZyXnnsl\npeVTMvzJIlQZeVo1+oHDrPvtS6zdfmDcxdPey9jY7I7H2ROPvwvGnz9g2fa7JugXMwMImpFQHBJT\nl5WSV+BDkdJXROx8MC2LpJG+k8tFv1PPcG6kq2zp2WBaFqF4jE07DqC+2sTD+07y0sj4wuMyZJhI\nTtopnhvpxr9pN46ohWY7xrJxrbdPapxobMb6EKQLYb+Ly7mslr5bQ2fIkOFPFIFAliRKs/IYSUbQ\nLQPTtiYsWm6iMPWeM/48s1PPkCHDnxQ2NoZl0h0dwrLT2U344iAj6hkyZPiTZKJbC75bZBylGTJk\nyPA+IrNTz5DhTxxJCBRJwXhTXfWJdBx6FRfVAS+zK1SEorDvZJKTwQhR8083cipdZEQ9Q4Y/EV7r\nDWpaFpIQZEtO8r0OAjkO3C43iWQSyzIQbh8DkSTtPekJ4JaEQJUUvJKDomwZp1uhNLuUq+pqeWBF\nFrid/OjFEZ7ZepimnnaGJrDg2sWKIslICFySimYZJK3zj4bJRL+kCcFYcwwBSAIseyzv4mJww6hi\nrCSscQEKe8GYTU85lfyCfaoCriQQkoQ49XNLNzFt+6K4Pu93JAQOWcbjcOBWnYS1JC5F5SZPNfcs\nKmfhtV7w+SEaAU1DzFjEQ5ta+dw/PjT+sYUgy+Gmyp/PIncFn7zNR820XKTJM5Emzxmr22SZkIhy\n+D828NDja3g4cvxdjfG/EIy1txHI0ti98akenIqTaa5iTsYHaYm/c00cIQSGdubex5md+jgRQuBW\nnNS481ngKKTRlphRMcyG4QA7EjqHEwN0xobelYdUIMhRZH48uZSiWhffPTbMk63jr1opEEiShHQq\nNMypqGCPZZhmKU4a/W7+rlwiOuBAT8l48nS8C0pwLJoJLi+2w8Xxb23nyeMn2KgNM5yMXLCCW+8F\nnIrj9Rfasu1x1Y7JUl1c6qng5jI3jdMN1GmVMHMhwuHE7yskkJONnCWPVVO0zLFFV0jAibR8lypX\nHrc2zuNjn7scjyeH3OJcVLdzrC5PeAi7rw1yChHePGpuqWR6ahKBX3cTSk1cE5N3E0kISrJyUSWF\n+XIe187OZ1q1zOHfDfOVxAm2xI+RMs9cgE6VFYQQyEhku7Ledoz3nKgXe3K4s9bLdFR6Tio0C4ln\nwsdJXUBRkMRYR3Kfw81fLcyjrnEW/tIaCpwBCrJyyfbEKY5JrEwmGNy3l20vvsK3OkYmNMNPFjKl\nWblMV3KYCVQXmwRmZuGuq2PJ1Jl48v18ZiSC++lX+MmTa89rDIes8LnabGasmI8yuRpkBcntQ5YV\nsCyEqaEImXyng5lqEj2lYMtuFDmFkutAyvNhBwcxdjbxy0gXTakgYT2e9oJXf4ws1YkkJBKG9q7H\nJnsdbsBmmuTn2qIC6pY4UBetRN+5ice3tbK2Y/Sce5sKwC07WOmr5tp75jFr5gyq8nLIy7aQAl7I\nLUBIMigqSG+VALu1CbvzWFq+39zFU1j1lzdSPaMCLAOr7Sjbtg2y9kgvR0InseNhcLr50qqrmD5t\nMivra+kr3M83O89P1F9roH0x7vQFkK0ofGVSFoV33UxhQRGlQ2107tnJ/8a7CBqJM9bkkYVMjsuL\nbhnE9BQWBuHU25dLfk+JuktRuXthJR9eXMUkyclQe4rmlMkrz3UyOMFp2QJBgdPPHI+TOeV+vMvn\n4pFVbpnsoMiGZHCURFzHcGocFjLCNGlLDXKiZ4DOpD6h5eAqnQ4+UFFA+Y3XUeXJY5JlUuwJ43JG\n0IZMPKUelOqpzJFlGlp7UX+7Af0MvVXfiSyHzF8sruLelUuoWrIAqaQIOxjEPHwYyeMg3AGSkWBU\nyLQmXTylRwibSWzFQTwVQzc1hGWRHO7FPNLC6t4obVrqgomqdKoL0mJnMdlOL0gSim7gsA1K/TFU\nj4Xskdkf1lnXGT3n63O2c3DIKmWyi8vdAXKX5OAtrwaHiyrVz6LCQspnqIips4i0tPCK3X5eGwG3\n7KQ+t4xVtyzmklsvIae2ClTXWBtE04BUDKvjOCTiYzt0txtyC5Byx8yh8f2dxJvG34IRoLAkm6mz\ny9EMwbbf7GX/gVfY0dTL7o4g7cngqesi8YmV02BGHUWuPKbZb78L/WPcc/lMKiuK2NvSz5qth9/1\nRfuNOGSVqtxirrz9UgrnFfNySz/P7zlC674ONmtv7eMrEKiyjEd1Yds2umlgWuapejFv71C+6EVd\nEgKX7CBfyaJS8TAlL4uTtkwHUDTVybzSbHJf9RFMpibkRXQIiUbFT25NFjWVk7m0sJAVNX7cl03H\n6BihaXiU3Vv2ENzbTWTEiWZJdMgOJBt260McTA2fsV9kushW3FxSXMwXrp5B1l0LSPYkOR4cZXM4\nReJ4D+aGo1yfZ+ItKEf4c5CFhENWzvlaKZLE9EIvmreSpk6NwbZm4l0n0de+hPCpjByRUITOIAp7\nIy467QRDRhQbCGtxNMtAEoK4fmGdYAKBV3FS7clm0mQ3t5bPJ8uTjSUk/Mj4sZicN4rTbyH7JH63\nv4cTg4c4Hh9fi7fXshbdkkyD7MY/NRdHXjYuT4A6l58PenIpucZHKuEjojnRFRcOh5N4yMDe0sKr\ne4c4Ppg4rf/t2eCXXdQXl3PHFYu57L6FZFVUgpAZONnDiWM9hFKjGLERjGMHELEokqqSl5vN/JlT\nUJdfS+JIDzvXH2P/sfT07kx0hwhuPgrOJD/78Qu82NPMsP6HXbhTllhZk0N+fj7C4aIzBbvD535y\ncwjBMl8Wn7rjMuoXzOLlV/Zj9Q9zqCuJZpukbJ24kRpXnfLxIBCU5Aa4+fL5+G+4kl0bN/KjX23g\n+f3tZ7Sd58ouShx+LFli2EoSSkZPexbeabG/qEU9IFTys/2Ul5Qw111GbbfGo2tPsH/1HmQhcf3C\nmfz7F+YwyZlNjxRMq6gLBFmKzDSvh69XzGfmB4pR62qIeAvpT0YwN20m8swhvtkRY1MoRFgbf8u8\n86HcEaC+bAq9jZOx9+2g738P8ZNjXbyYDBHXUxRlOVjemk1WIgmOKCRjY0fUcySc1Pn06mbuX2PS\nI3S2pQYYSIROOx1dyLozf4zXIj2yZAczAsXcWz+XD3ykFE/DYoQvB2QVoThe/30hJCwtQZm0hfqX\nBs5b1AUCh6SQk+WkKNtLkS+PrzrzmbJqOu5ZNZiufBKGk5ip0dl3nM4fb6alJUrIVglgMEmNY1mC\nB2N97EuefecsgSBPVplXWMEdVyzn3v9zNZK/AIREuKuLDU9u4ic/38jhRB8JUyNlaKiygiopzMt2\n8v3FgxQV13H0u8/y/e27+H10/KIuEHRsPcHGk09QMGOUF/u7GNH/YEpyIDEly8/3715A/rwFxCwP\nrwz286h29r1KBeCWZOo8br7bWE/V9EbksmpWzk9RdfsCfrQmTNjWGTZitPf20hMMEsE858VyvDhl\nlVm1FXz+M1cjnDL/8fQBXj7QfZqgCwT5kkpWwE1jXjmLHeUcHg7x2OCec5rvRSvqAsFd7koeuO0a\n6j5zBYmozncf+AntI6HXqwD2mim8ZdP5C7mKdnoJp7Euslt1cFlRgB8sKcH/t/cjWVGaf7CLnz/3\nFI9qneiGRjKVQrMszHexGl67Mcr3m3bxb3/zCrppYBsWhmVjYiMLCcnhQFnciAj4sIN9aENdxLRz\njwW2gZiW5GHtEDacsbnBxSLoMGar9ihOZjoLuGv+fD704B0objfC4RozOWDDa/fttUXONDiW6Gdt\ntO28x/WoTiqzCrh3UTl/fmc98rxLUaIj0N2CdXAH3dsivLoxxfNKlKZoByOxCCldH3uGbBshxjo3\n6NjnVLXW63Dxl/4i7rtpGcWfvA4pu2DsLwyNZx/bxcO/WMv24ROnRRtppoFAcCyk8IPdDj729w/z\nYM8wG1PRtNxLG5uNWj/bOodw9CmEU9ppnzpZ8fGVgsUE7vgEwu9kw3fX8PwvN76lY9A74ZBVFvsC\n/NeUaiq+9nHUyjJAIFVOoeYDXr52WRe2JEBWaX2kiWdfOsIT5iCHRzvH/f3OhSKnn2m5NUh55Zhb\nf09wqJOEcbpeuVUnX86q4ZqPLyN/ZTVHDvbw9DfWYJyjz+miFXUbm5eMQaqjrcwariYSM1g91MSA\nPlYFUJFkXKoL1akyozSEd0CHNC2++S4/d66YyV/efQnZtVMQssbPfriNZ9bvYG+og2EzicXFUdo0\nrqfO6PDzKE6WFGfz1UtLyZmzHOH288LjO3hhbee4Xti3a0DiVFSyFBembRHREhfc+SkQOBSFfEcW\nf6ZWccmnlpI7qwqPJ4e8QA6K1wvJKLaRxB7swmw5AqNRpLkNSKVTwenBatpBcv824sa5bw7GOkB5\n+ZdbpjFvxXIK6qbhIUbk+ad4eIPOpr5jDESHiY1qRCIWIWERN1MYtnX6c3SOt0YgCLg8fOOzt3PV\n/HkUlJeh+nMAGzsZw9y1gaYDGzgQ6jpj+OgCZz6rpk5nxa3laI8epy8SI3qOjtl3wgISlkkydfro\nla5crlmwmOVfvgc128vvH3yWnz67hm2hk+f0+deXe/j7K2dQdusdKOWVoDrGFmnViZxbguzNHlvE\nhUztX5dz/91LueZYK13f+x0doz5aZEGrSDJgJUhYOklLoy8eRH9TItZ4mS3D7XaC4MnjfPZ7mznU\nGXr97wSCXEXl3wvKWP7JG8jLifHs48/yb+tPcCI4gnGOQSAXragD9BgxfrV9H82jgyRNm7bRIfRT\nXzCgeihxBtAMg81JJyErfRUPrvS4ubu2htoF80gmDX738AYeW7edfX2dhI34RbQfPVW/4g0TEgjy\nXD6umFzAqssm03DVCpS8IvRXXmbHhnXs6TpzbOv5kCU7qfcVcUOWTv6MbNw1FVi6weGDx/jB3kES\nuvb/27vzOCnOet/jn6eqepvu2feNYVhmhi3seyBACAlgYjazvY4xJuq5etWrnvM6eo7LUY9XPV49\nXu91uxpNNDEmMbuGLEBYAoSBYR2YYZt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" + "" ] }, "metadata": { @@ -1074,19 +728,19 @@ { "output_type": "stream", "text": [ - "Time since start: 4.78 min\n", - "Trained from step 3500 to 4000 in 17.25 steps / sec\n", - "Average discriminator output on Real: 136.10 Fake: 130.25\n", - "Inception Score: 7.53 / 8.35 Frechet Distance: 56.25\n" + "Time since start: 2.86 min\n", + "Trained from step 3500 to 4000 in 32.85 steps / sec\n", + "Average discriminator output on Real: 84.46 Fake: 79.25\n", + "Inception Score: 7.63 / 8.38 Frechet Distance: 56.05\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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LNwR1l1s1IZ5NE2SmyOVtlzAo+meiFR+ttf37vzVqvz5TZ9S+9ry5sjcAuaP9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\n", 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CCAlLCIqdMYqdMZjk+9IZkocZeTWIsqlYwSh+LTyhJ8mz4ZQUVLsTVAf6GWMt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\nHl7c1cj9o0EiqTevLzMQD9DS5GWgLUE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" + "" ] }, "metadata": { @@ -1096,19 +750,19 @@ { "output_type": "stream", "text": [ - "Time since start: 5.37 min\n", - "Trained from step 4000 to 4500 in 17.17 steps / sec\n", - "Average discriminator output on Real: 73.92 Fake: 57.28\n", - "Inception Score: 7.45 / 8.35 Frechet Distance: 57.75\n" + "Time since start: 3.20 min\n", + "Trained from step 4000 to 4500 in 33.85 steps / sec\n", + "Average discriminator output on Real: 139.49 Fake: 130.57\n", + "Inception Score: 7.48 / 8.38 Frechet Distance: 59.30\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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npZ0B+PW2rrB4TWBiv1tiiopQhKWtlLTmzdg+Xhb/SbunDP+2/CO+V42NpeDt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\nhBBb6NrY2NiEEFvo2tjY2ISQ/wPCcULDuonSFAAAAABJRU5ErkJggg==\n", 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2RkUmRePmufl8ZtMyZq9bCTYnIuYmb4YVTBZEMoHRWEP0XBtvN1moGemm7+gZ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\n4WvS4X2RmKrQPTDGc88dY+0MFzUzc5BmFeAqkClKGqVMgCx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" + "" ] }, "metadata": { @@ -1118,19 +772,19 @@ { "output_type": "stream", "text": [ - "Time since start: 5.95 min\n", - "Trained from step 4500 to 5000 in 17.12 steps / sec\n", - "Average discriminator output on Real: 153.41 Fake: 148.77\n", - "Inception Score: 7.48 / 8.35 Frechet Distance: 57.44\n" + "Time since start: 3.58 min\n", + "Trained from step 4500 to 5000 in 32.00 steps / sec\n", + "Average discriminator output on Real: 197.43 Fake: 191.78\n", + "Inception Score: 7.44 / 8.38 Frechet Distance: 55.80\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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mYwAkzBInnGNz4tjSfg6Our4vdSmZTMgB/qj57qJc7nKhSo1w1DM5v7zxL3xn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\ncm6qSqCplHy3Bp6XpODv5TE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63nWUzDKix3iq7RAv/MkJvuVu4JY/v5Wbbt2AT9G56TuDk77DDTi9VBSpFJW/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\naT5KaSWGZ5Ajvi6OJScyd9GPkWdxcse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MyL5ZAe8Hb2xp5b11NUwvycflCq+xv0/HlaP7eIaafd8GuTKxzQI8xEqLC1ix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\nH5WcZJjf7y6YAAAAAElFTkSuQmCC\n", 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GQkICrl27htWrV0OlUsHR0RHx8fEYOHAgAOhc1hElJZUWVSjc3fujqOimuWO0YIm5mEk/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\nVzvrmKGeJvuG3JFM+tJoBK7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VFYmmzZ07F0lJSQaN12IxeHt7o7i4GHl5eQgICEBubi7KysrQvXt30XLjxo3D999/jzFj\nxqCiogK7d+/Ghg0bdI45ffp0JCQkiKbJZDIAQFlZNTQawaAX0xY8PBxx5UqVqWOIMJN+zDETYJ65\nmEk/5pZJKpXAzc0BGzdu1LnHYKgWi8Hd3R1LlizBiy++qH3zfu+99yCXy5GYmIh58+YhJCQEcXFx\nOHnyJMaOHQuJRII5c+bA399f55h3s4tDRERixj4MLxEEwXz+PAf3GPTBTPoxx0yAeeZiJv2YW6ab\newxGH9foIxIRUYfGYiAiIhEWAxERibAYiIhIhMVAREQiLAYiIhJhMRARkQiLgYiIRFgMREQkwmIg\nIiIRFgMREYmwGIiISITFQEREIiwGIiISYTEQEZEIi4GIiERYDEREJMJiICIiERYDERGJsBiIiEiE\nxUBERCIsBiIiErEwdQAioltpBAGnc8tgXahCN3db2NtYmjpSl8NiICKzIAgC0s+VYtvBPBRcrgYA\nSCUS9OnmhLBe7hjY2x1eLnYmTtk1sBiIyKQEQUDG+VJsPZCH/MvV8HKxxfMx/dC7hxv2Hc9HxvlS\nfJeWg+/ScuDjZnejJHq5o5efE6RSianjd0osBiIyCUEQkJHzZyGUVMPTxRYzYoIR0c8LMqkUHh6O\ncHewxMQHA3G5sg4nc0qRcb4Uv/xegNSj+XCwtcSAQDeE9XJHSIArbK35dmYsXJNE1K4EQcDJ3DJs\nPZCHS8VV8HS2xXMPB2NoyI1C0MXT2RZjBnfDmMHdUHutEZl5ZcjIKcXJnFIcyiyGTCpBUHdnhPX2\nwMBebnB3sm3nV9W5sBiIqF0IgoDTF24UQp6yCu5ONnhmQhDuD/VuthB0sbOxwH3BXrgv2AtqjQY5\nhVeRkVOKjJwybNx1Dht3Af4e9gjrfeOQU4CPHFIJDzm1BouBiNrUjUIo/7MQVDcKYXwQhoV6w0J2\nd5+Yl0ml6NvdBX27u+DxqN4oLq9FxvlSZOSU4qfDl/DvQ5cgt7fCgEA33NvLHf16uMLaSmakV9Z5\ntVgMRUVFmDNnDiR/Nu7Vq1dRU1ODo0ePipZLTk7GP//5T3h5eQEAwsPD8eabb7ZBZCLqCARBQFZe\nOVIO5OGCQgU3uQ2eHn9jD+FuC6E53q52GBfRHeMiuqO6rgGnL5Qh43wpjp+9jAOnlLCQSdGvh4v2\nBLaLo3Wb5OjoWiwGPz8/pKSkaH9fvnw5NBqNzmXj4+PxyiuvGC8dEXU4giAg6+KNPYTcIhXc5NZ4\nalxfjOjv02aFoIuDrSWGhXhjWIg3GtUanCuovHHI6XwpTuWWATvP4h4vRwzs5YZ7e3ugu5eD9g/g\nrq5Vh5IaGhqwfft2fP311zrnC4JglFBE1PEIgoA/LlVg64E85BRehavcGk9F98WIAe1bCLrc2FNw\nRb8erpg6qjcUpTV/npcoxfaDF7Ht4EW4OFpjYKAbBvZyR/A9LrCy7LqHnFpVDLt374a3tzeCg4N1\nzk9NTcWhQ4fg7u6OpKQkhIWF6VxOpVJBpVKJpslkMvj4+LQmDhGZAUEQcOZSBVIO5OF84VW4OFpj\n2tg+GDHAF5YW5nfXHYlEAj8PB/h5OODhYT2gqqnHqdwynMwpxeGsEuzNUMDKUoqQHq4Y+OchJyd7\nK1PHviOlUgm1Wi2aJpfLIZfLDRpPIrTiz/zExEQ88MADePLJJ5vMKysrg7OzM2QyGQ4dOoSFCxci\nNTUVTk5OTZZdvXo1kpOTRdP8/PyQlpZmwEsgIlM5lXMF/9x5FlkXyuAqt8HkUb0xdug9sLTomH9t\n1zeocTq3FMeyinEsqxilV69BIgH6dHPBkBAv9A90h4ujDZwcrGBrbWE2h56ioqJQVFQkmjZ37lwk\nJSUZNJ7exXD58mVER0dj7969Ot/sb/foo49i8eLFGDx4cJN5d9pjKCurhkZjPoekPDwcceVKlalj\niDCTfswxE2CeuVqb6Wx+BVL25+FsQSWcHKzw8NB78GCYr1ELwdTrSRAEFFyu1l4vkacUZ7GQSeBo\nZwUHW0s42lnC0c4Kjrf+fNv/29lYGP1js1KpBG5uDkbfY9D7UNKPP/6IyMjIZkuhpKRE+4mk7Oxs\nKBQKBAQE6Fz2bgITkemcK6hEyv4LOJNfCSd7K0wd3RuRRi4EcyGRSNDdyxHdvRwROzwAFVXXcfVa\nIwqVKlTV1aOqtgFVtfWorm1AVV0DrlReRVVtA67Vq5sZD3+WyB0KxPa/P9vbWup9bsbYh+H1LoaU\nlJQmHz9NTEzEvHnzEBISglWrViErKwtSqRRWVlZYuXIl3NzcjBqWiEzjXEElth7IQ/alCsjtrTBl\n1I1C6EonaF0crdGnpzt6eNjfcbmGRvWfpdGAqro/i+PPn7XTa+tReKUGVbUVqLnW2OxYdtYWtxXI\nrcViBQ9nWwxzczD2S9W/GHbs2NFk2rp167Q/v//++8ZJRERmI6fwKlIOXMAfFysgt7PE41G9EHmv\nH6y7UCG0lqWFDK5yGVzlNnotr9ZoUFPXiKraP4ujruG/P9fWo7ruRplcqazDBYUK1XUNUP95uN3T\nxRbD7vU3+mvglc9E1ERO0VVsPZCHrLxyONpZYvJDvfBQOAuhLcikUsjtrSDX85NPgiCg9nojqmob\nUN+o+7DV3WIxEJFWruIqtu7PQ2ZeORxsLTHpoUBE3evP20iYEYlEAnsbS9jbWLbZbcdZDESEc/kV\n+GZ7Fk5fKIODrSUeiwxEVLgfbKz4FtEV8V+dqB3UXW/EocxiHM0ugUYAGv88BCDBn3/x3fKH361/\nA/73040SHdNumSqa1nQB0Zi3DVTfoMbF4irY21hg4oM9MWqQPwuhi+O/PlEbUpTWIC29EAczi3G9\nXo3ung7wdndAfX2j6BYyuq7cuTlbuHWurh+bGUfXFUq6LluysZJh2vhgDA3y4JfdEAAWA90ljSDg\n7KUKnL5UgQBPBzjY8ovb1RoNMs6XIS29ENmXKmAhk+C+YC9Ehfujp6/c5Bdu6WKOmch0WAxkkKra\nehw8XYx9GUUoqagDAMikEgwIdMOwEG8M7OXWKS96uhNVTT1+O6nA3owilKuuw1VujYkP9sTIgb6Q\n25n3vXaIbsViIL0JgoBzBZXYm6HA8bOX0agW0NvfCbHDAxAU6I6dh/JwNLsEJ86XwtZahkF9PTEs\nxBt9uzt32m/QEgQBF5QqpB0vxO9nbqyT4Htc8JfRfTCwl1urvpmMyFywGKhF1XUNOJR5Y+9AWVYL\nW2sLRIb54cEwX/h53Ljq0sPDES62Fpj8UC9k51fgSGYx/nPmxpejuDhaI6KfF4aFeKObp/Gv0jSF\n+gY1jmVfRlp6IS4WV8HGSoYHB/rhoXA/+Lrf+cpYInPHYiCdBEFATtFV7D2hwH/OXkZDowaBvnI8\nOyEYQ4I9m73QSSqVIKSHK0J6uOLJBjVO5pTiSFYJdv1egB1H8+HnYY9hId4Y2s9L7ytDzUlpZR32\nZBRh/0klqusa4ONmhyfH9sGwEG+euKVOg1syidRea/jznvRFKLpSAxsrGUYM8MGDA33R3cuxVWNZ\nW8q0X9peVVuP389cxpGsEmzem4vNe3PRt5szhoV6Y3BfD9jZmO9Ja40gIPtiBXYfL8TJ3FJIIMG9\nvd0RNcgfQd2dzebWy0TGwmIg7XHyfScUOJZdgvpGDXp4O+Lp8UG4L9jTKJ9pd7SzQlS4P6LC/XG5\nohZH/ijBkawSfJN6Bht+OYuBge4YGuKFAYHuZvPlLrXXGnEwU4m09CKUlNfC0c4SDw+7B5Fhfh1y\nb4dIXyyGLqzueiOOZBVjb4YCBZerYW0pw7BQb0SG+eEe79btHbSGp4sdYocH4JH7e+BicRWOZJXg\naHYJjp+7AjtrCwwO8sSwEC/07maak9aFV6qRll6Ew5nFuN6gRqCvHLEx/TA4yNNsSouoLbEYuqCL\nxSrsPaHA0T9KcL3hxkVX06L7Ymg/r3Y9Ti6RSBDgI0eAjxyTowKRfbECh7NKcPSPEvx2UgFX+X9P\nWvt7tO1J60a1BhnnS5GWXogz+ZWwkEkR0c8TUeH+CPDhd4dQ18Ji6CKu1TfiWPZl7DlRhEvFVbCy\nkOK+fl6IDPNDgI+jyY+Ty6RShPZ0Q2hPN1yvV+NEzhUcySrBzqMFSD2SD38PBwwL9UJEsHFPWl+t\nqcdvGUXYm6FARdV1uDvZYFJkIEYM8IEjrz2gLorF0Mnll1RhX4YCh7OKca1eDT8Pezwxpg+GhXiZ\n7QlfaysZhvbzxtB+3lDV3DhpfTirGD/sycXmPbno290Zw0K8MaivJ+xsWr8JC4KAXMV/rz1QawSE\nBLhi2ti+GBDo1mZ3rCTqKFgMndD1BjWOZZdgX4YCFxQqWFpIMSTIE5Fhfgj0k5t876A15PZWGDXI\nH6MG+aOk/MZJ68NZxfhH6hl8+8s5hPW6caV1/0C3Fr8Gsb5BjaN/lGB3eiHyS6phay3DQ+F+iAr3\nh7erXTu9IiLzx2LoRIquVGNvhgKHMotRd70RPm52mDKqN+4P9e4U9zDycrVD3IgAxA7vgTxlFQ5n\nFeNYdgn+c/YK7G0sMCTIE0NDvNHL30l00vpyZR32phdh/ykFaq41ws/DHtOi+2JYiBfvIkqkA/+r\n6ODqG9T4z9nL2JuhQE7hVVjIJBjc1xMPhvmiT7fO+Rl7iUSCnr5y9PSV4/GoXvjjYgWOZBXj0J+f\nsHKT22BoiBf69/ZE6sELOJVbBolEgvC+HhgV7tdp1wuRsbAYOihlWQ32ZShw8LQSNdca4eVii8kP\n9cLw/t5d6qSphUyKAYFuGBDohmv1jThxrhSH/yjGz0cu4afDlyC3t0LM/T0Qea8fXBytTR2XqENg\nMXQwWRfLsfOHk8jMLYNMKkF4Hw9Ehvki6B6XLv9XsI2VBYaFemNYqDeu1tSjpl4DT7lVi+ceiEiM\nxdCBnMotxer/Ow03Z1s8FhmIEf199P4C8a7Gyd4KvXrwOwaIDMFi6CDOFVTi0y2Z8PdwwIqkkait\nvmbqSETUSXEfuwO4VFyFTzafhJvcBvMfHwj7TvAJIyIyXywGM1dcXouPvs+ArbUFFk4J4zeBEVGb\nYzGYsXLVNXz43QkAwMuPh/GOnkTULlgMZkpVW4+/fZeB2uuNWDA5DD5u/FYwImofLAYzVHe9Eav+\ndRJlqmuY99jANr0FNhHR7Vr8VFJRURHmzJmj/Yz81atXUVNTg6NHj4qW02g0WLZsGQ4cOACpVIoZ\nM2Zg0qRJbZO6E6tvUOPvm0+h8Eo1kib2R59uzqaORERdTIvF4Ofnh5SUFO3vy5cvh0ajabLctm3b\nUFBQgF27dqG8vBwJCQkYPnw4fH19jZu4E2tUa7A2JRPnCirxfGw/DAh0N3UkIuqCWnUoqaGhAdu3\nb8fEiRObzEtNTcXkyZMBAK6urhg9ejR27NhhnJRdgEYQ8PXP2TiZW4Ynx/bB0H7epo5ERF1Uqy5w\n2717N7y9vREcHNxknkKhEO0d+Pj4QKlU6hxHpVJBpVKJpslkMvj4+LQmTqchCAI27TqPI1klePSB\nnngo3N/UkYioA1EqlVCr1aJpcrkccrlh3z7YqmL48ccfde4ttNb69euRnJwsmubn54e0tDS4ubXt\nVzgawsOjbU/+btiRjd3phUiI7IWnY/rpdc+jts5kCGbSnznmYib9mGOmJ554AkVFRaJpc+fORVJS\nkkHj6V0Mly9fxu+//46VK1fqnO/r6wuFQoHQ0FAANxrMz89P57LTp09HQkKCaJpMJgMAlJVVQ6MR\n9I3V5jw82vZ+O78cy8e/0nIwcoAPYiK6obS02uSZDMFM+jPHXMykH3PLJJVK4ObmgI0bN+rcYzCU\n3sXw448/IjIyEk5OTjrnjxs3Dt9//z3GjBmDiooK7N69Gxs2bNC57N3s4nQm+08p8F1aDgb19cD0\ncUFd/u6oRGQYYx+G1/vkc0pKCh577DHRtMTERGRlZQEA4uLi4O/vj7Fjx2LKlCmYM2cO/P15rLw5\nx89ewTepZxDSwwWJj4Twe4aJyGzovceg6xNG69at0/4slUqxZMkSo4Tq7P64WI7Pt2Wip48ccx7t\nD0sLXmdIROaD70jtLFdxFav/7zS8Xe3w0uSB/M5hIjI7LIZ2VHilGh9/fxJye0sseDwM9ja8fTYR\nmR8WQztUF4QtAAAUFUlEQVS5UlmHD/+VAQsLKRZOuRfODvz+YSIyTyyGdlBZfR1/++4EGhs1WPh4\nGDycbU0diYioWSyGNlZd14AP/5UBVU0DXpo8EH4e5ncBHxHRrVgMbehafSM++eEkSsprkTSxPwJ9\ndV8DQkRkTlgMbaShUYNPfzyNC0oVZsaGol8PV1NHIiLSC4uhDWg0AtZtz0LWxQo8Mz4Yg/p6mDoS\nEZHeWAxGJggC1u84g+Nnr2DKqN4YMaBr3jGWiDouFoMRCYKA7/fkYP8pJR65vwfGDulm6khERK3G\nYjCin49cws5jBRgV7o/4kQGmjkNEZBAWg5HsSS/E/+27gKEhXpg6pjfvlEpEHRaLwQiO/FGMDb+c\nQ1gvdzw7IRhSlgIRdWAshrt0MqcUX/07G326OeOFuBBYyLhKiahj47vYXThXUIk1KZnw93DAi48N\ngJWlzNSRiIjuGovBQJeKq/DJ5pNwd7LB/McHwtaat88mos6BxWAAZVkNPvo+A3bWFnj58TDI7axM\nHYmIyGhYDK1UrrqGD/+VAQmAl6fcC1e5jakjEREZFYuhFVQ19fjbdxmou96IBY+HwdvVztSRiIiM\njsWgp9prjfjo+wyUq65h3mMD0d3L0dSRiIjaBItBD/UNavz9/06h6EoNZieEok83Z1NHIiJqMyyG\nFjSqNViTkonzBZWYEdMPAwLdTR2JiKhNsRjuQCMI+HjTCZzKLcO06L6I6Odl6khERG2OxdAMQRCw\nadd57DtRiIkP9kTkvX6mjkRE1C54VVYz9p9SYnd6IeIfDMSEod1NHYeIqN1wj0GHi8UqbPjlHEIC\nXPF0TAjvlEpEXQqL4TbVdQ349MdMONlbIvGRfpBJWQpE1LWwGG6hEW58V/PVmuuYndAfjrzVBRF1\nQXqdY6ivr8fy5ctx+PBhWFtbIywsDEuXLhUtk5ycjH/+85/w8rrxyZ3w8HC8+eabxk/chrYfvIjM\nC+V4KrovAnzkpo5DRGQSehXDBx98ABsbG+zcuRMAUF5ernO5+Ph4vPLKK8ZL145OXyjDtgN5uD/U\nGw+G+Zo6DhGRybRYDLW1tdi6dSt+++037TRXV1edywqCYLxk7ai0sg7rtmXBz8MB06L78mQzEXVp\nLRZDfn4+nJ2dsXr1ahw9ehT29vaYN28eBg0a1GTZ1NRUHDp0CO7u7khKSkJYWJjOMVUqFVQqlWia\nTCaDj4+PgS/DcA2NanyakgmNAMx5NBTW/LIdIupglEol1Gq1aJpcLodcbtghcYnQwp/5WVlZmDhx\nIj766CNMmDABp06dwgsvvIBdu3bB3t5eu1xZWRmcnZ0hk8lw6NAhLFy4EKmpqXBycmoy5urVq5Gc\nnCya5ufnh7S0NINexN1I/iEDO49cwhvP3IeI0PYvJiKiuxUVFYWioiLRtLlz5yIpKcmg8VrcY/D1\n9YWFhQUmTJgAABgwYABcXFxw8eJFhISEaJdzc3PT/nz//ffD29sb58+fx+DBg5uMOX36dCQkJIim\nyWQ3/lIvK6uGRtM+h6T2n1Jg55FLeHjYPejp5YArV6qaLOPh4ahzuikxk37MMRNgnrmYST/mlkkq\nlcDNzQEbN27UucdgqBaLwcXFBRERETh48CCGDx+OvLw8lJeX45577hEtV1JSov1EUnZ2NhQKBQIC\nAnSOeTe7OMaSX1KFDb+cQ/A9LogfqTsnEVFHYOzD8Hp9KmnJkiVYvHgx3n//fVhaWmLlypVwcHBA\nYmIi5s2bh5CQEKxatQpZWVmQSqWwsrLCypUrRXsR5qTmWgM+3XIaDraWmBkbApmUl3MQEd2kVzF0\n69YN3377bZPp69at0/78/vvvGy9VG9IIAr7c/gfKVdex6IlwyO15ERsR0a263J/KPx2+hJO5ZZgy\nqjcC/ZqeGCci6uq6VDFk5ZUj5bcLGNrPC1HhvI02EZEuXaYYylXX8Pm2LPi622P6uCBexEZE1Iwu\nUQwNjRp8uiUTjWoN5jzaH9ZWvIiNiKg5XaIYvks7jzylCs89HAxvVztTxyEiMmudvhgOZxZjT3oR\nxt3XHYP6epo6DhGR2evUxVBwuRrrd5xB327OmBjZ09RxiIg6hE5bDLXXGvHpltOwtbHAC3G8iI2I\nSF+d8t1SEAR89dMfKLt6DbPiQuHkYG3qSEREHUanLIYdR/Nx4nwpJj3UC326OZs6DhFRh9LpiiH7\nUgU278vFkCBPjBnsb+o4REQdTqcqhoqq6/h8aya8Xe3w9HhexEZEZIhOUwyNag3WpJzG9QYN5iT0\nh621XvcHJCKi23SaYvg+LQe5RSo8MyEIvu72LT+AiIh06hTFcOSPYvx6vBBjBnfDfcFepo5DRNSh\ndfhiKLpSjW9Sz6CXvxMmPRRo6jhERB1ehy6GuuuN+HRLJmysLDArLhQWsg79coiIzEKHfScVBAFf\n/5yNyxV1mBUXAhdHXsRGRGQMHbYYfvm9AMfPXsFjkYHo293F1HGIiDqNDlkMZ/Mr8MOeXAzq44Ho\n+7qZOg4RUafS4Yqhsvo6PtuaBQ8XWzz7cDAvYiMiMrIOVQyNag0+S8lEXX0j5iSE8iI2IqI20KGK\nYfPeXJwrvIqnxwXB38PB1HGIiDqlDlMM/zlzGb/8XoBR4f4YGuJt6jhERJ1WhygGZVkNvvo5G4G+\ncjw+qpep4xARdWpmXwzX6huR/ONpWFlIMSueF7EREbU1s36XFQQB36SeQXF5LWbGhsBVbmPqSERE\nnZ5ZF8OvxwtxLPsyHn2gJ/r1cDV1HCKiLkGvz3vW19dj+fLlOHz4MKytrREWFoalS5eKltFoNFi2\nbBkOHDgAqVSKGTNmYNKkSQYHO19Yie/TchDWyx3jh95j8DhERNQ6ehXDBx98ABsbG+zcuRMAUF5e\n3mSZbdu2oaCgALt27UJ5eTkSEhIwfPhw+Pr6tjrU1Zp6rE3JhJvcBjNigiHlRWxERO2mxUNJtbW1\n2Lp1K+bNm6ed5ura9LBOamoqJk+erJ0/evRo7Nixo9WBNBoNPt+aidprjZidEAo7G8tWj0FERIZr\nsRjy8/Ph7OyM1atXY+LEiXjqqadw/PjxJsspFArR3oGPjw+USmWrA+04VoAz+ZWYFt0X3b0cW/14\nIiK6Oy0eSlKr1SgoKEBoaCheeeUVnDp1Ci+88AJ27doFe3vDvkJTpVJBpVKJpslkMvj4+GDviSJE\nhvlieH8fg8YmIupqlEol1Gq1aJpcLodcLjdovBaLwdfXFxYWFpgwYQIAYMCAAXBxccHFixcREhIi\nWk6hUCA0NFQb1M/PT+eY69evR3Jysmian58f0tLS0MNXjhenhsPSQmbQC2oLHh7mt+fCTPoxx0yA\neeZiJv2YY6YnnngCRUVFomlz585FUlKSQeO1WAwuLi6IiIjAwYMHMXz4cOTl5aG8vBz33CP+pNC4\ncePw/fffY8yYMaioqMDu3buxYcMGnWNOnz4dCQkJomky2Y0i+EtUb1RW1Br0YtqCh4cjrlypMnUM\nEWbSjzlmAswzFzPpx9wySaUSuLk5YOPGjTr3GAyl16eSlixZgsWLF+P999+HpaUlVq5cCQcHByQm\nJmLevHkICQlBXFwcTp48ibF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