From 06d7d2045ca26a407d4df8583c91f083877a35fc Mon Sep 17 00:00:00 2001
From: maxmatical <max.tian.94@outlook.com>
Date: Thu, 23 Aug 2018 20:08:09 -0400
Subject: [PATCH 1/3] Created using Colaboratory

---
 DCGAN_with_GPU_Google_Colab.ipynb | 559 ++++++++++++++++++++++++++++++
 1 file changed, 559 insertions(+)
 create mode 100644 DCGAN_with_GPU_Google_Colab.ipynb

diff --git a/DCGAN_with_GPU_Google_Colab.ipynb b/DCGAN_with_GPU_Google_Colab.ipynb
new file mode 100644
index 0000000..a05ef53
--- /dev/null
+++ b/DCGAN_with_GPU_Google_Colab.ipynb
@@ -0,0 +1,559 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "DCGAN with GPU - Google Colab.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "[View in Colaboratory](https://colab.research.google.com/github/maxmatical/pytorch-projects/blob/generative-models/DCGAN_with_GPU_Google_Colab.ipynb)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jU1r3xFhO3r5",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# install pytorch 0.4.1 with gpu\n",
+        "\n",
+        "from os import path\n",
+        "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
+        "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
+        "\n",
+        "accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'\n",
+        "\n",
+        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.0-{platform}-linux_x86_64.whl torchvision"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "x1ErFHmrMDkV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "93160f2b-c6f8-44de-ab75-f866f4c2449b"
+      },
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "print(torch.__version__)\n",
+        "print(torch.cuda.is_available())\n",
+        "print('Torch', torch.__version__, 'CUDA', torch.version.cuda)\n",
+        "print('Device:', torch.device('cuda:0'))\n"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "0.4.1\n",
+            "True\n",
+            "Torch 0.4.1 CUDA 8.0.61\n",
+            "Device: cuda:0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FMk_Dd0SMgS1",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "08eedf6d-60c6-49fa-bbda-1577e7e979c8"
+      },
+      "cell_type": "code",
+      "source": [
+        "# install required packagles\n",
+        "!pip install torchsummary"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Collecting torchsummary\n",
+            "  Downloading https://files.pythonhosted.org/packages/2a/61/21b44bb29aedb820fec4716a102e802397f0c21512764a9d98206c17417d/torchsummary-1.4-py3-none-any.whl\n",
+            "Installing collected packages: torchsummary\n",
+            "Successfully installed torchsummary-1.4\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c8qbn6taMF8O",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import torch\n",
+        "import torchvision\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "from torchvision import transforms\n",
+        "from torchvision.utils import save_image\n",
+        "\n",
+        "\n",
+        "from torchsummary import summary"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "OupEpCFeMfS_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# create directory\n",
+        "\n",
+        "sample_dir = 'samples'\n",
+        "if not os.path.exists(sample_dir):\n",
+        "    os.makedirs(sample_dir)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "Qdoaeq-7MHf9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118
+        },
+        "outputId": "06978b55-d020-467f-cdee-fae3851a50c5"
+      },
+      "cell_type": "code",
+      "source": [
+        "# loading and preprocessing MNIST\n",
+        "#batchsize is 128 from paper\n",
+        "bs = 128\n",
+        "img_size = 64 # scale up imgs\n",
+        "\n",
+        "transform = transforms.Compose([\n",
+        "                transforms.Resize(img_size),\n",
+        "                transforms.ToTensor(),\n",
+        "                transforms.Normalize(mean=(0.5, 0.5, 0.5),   # 3 for RGB channels\n",
+        "                                     std=(0.5, 0.5, 0.5))])\n",
+        "\n",
+        "# MNIST dataset\n",
+        "mnist = torchvision.datasets.MNIST(root='../../data/',\n",
+        "                                   train=True,\n",
+        "                                   transform=transform,\n",
+        "                                   download=True)\n",
+        "\n",
+        "# Data loader\n",
+        "data_loader = torch.utils.data.DataLoader(dataset=mnist,\n",
+        "                                          batch_size=bs, \n",
+        "                                          shuffle=True)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+            "Processing...\n",
+            "Done!\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KjgZ9bNpMqHv",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#Discriminator\n",
+        "class discriminator(nn.Module):\n",
+        "    def __init__(self, bs):\n",
+        "        super(discriminator, self).__init__()\n",
+        "        self.bs = bs\n",
+        "        self.conv1 = nn.Conv2d(1, bs, 4, stride=2, padding = 1) # no bn after conv1, input dim is 1\n",
+        "        self.conv2 = nn.Conv2d(bs, bs*2, 4, stride = 2, padding = 1)\n",
+        "        self.bn2 = nn.BatchNorm2d(bs*2) #256 channels\n",
+        "        self.conv3 = nn.Conv2d(bs*2, bs*4, 4, stride = 2, padding = 1)\n",
+        "        self.bn3 = nn.BatchNorm2d(bs*4) #512 channels\n",
+        "        self.conv4 = nn.Conv2d(bs*4, bs*8, 4, stride = 2, padding = 1)\n",
+        "        self.bn4 = nn.BatchNorm2d(bs*8) #1024 channels\n",
+        "        self.conv5 =  nn.Conv2d(bs*8, 1, 4, stride = 1, padding = 0)\n",
+        "        self.leaky_relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
+        "        self.sigmoid = nn.Sigmoid()\n",
+        "    \n",
+        "    def forward(self, input):\n",
+        "        out = self.leaky_relu(self.conv1(input))\n",
+        "        out = self.leaky_relu(self.bn2(self.conv2(out)))\n",
+        "        out = self.leaky_relu(self.bn3(self.conv3(out)))\n",
+        "        out = self.leaky_relu(self.bn4(self.conv4(out)))\n",
+        "        out = self.sigmoid(self.conv5(out))\n",
+        "    \n",
+        "        return out\n",
+        "    \n",
+        "D = discriminator(bs)\n",
+        "        \n",
+        "# Generator\n",
+        "class generator(nn.Module):\n",
+        "    def __init__(self, bs):\n",
+        "        super(generator, self).__init__()\n",
+        "        self.bs = bs\n",
+        "        self.deconv1 = nn.ConvTranspose2d(100, bs*8, 4, stride = 1, padding = 0) # 100 in, 1024 outchannels\n",
+        "        self.bn1 = nn.BatchNorm2d(bs*8) #1024 channels\n",
+        "        self.deconv2 = nn.ConvTranspose2d(bs*8, bs*4, 4, stride = 2, padding = 1)\n",
+        "        self.bn2 = nn.BatchNorm2d(bs*4) \n",
+        "        self.deconv3 = nn.ConvTranspose2d(bs*4, bs*2, 4, stride = 2, padding = 1)\n",
+        "        self.bn3 = nn.BatchNorm2d(bs*2) \n",
+        "        self.deconv4 = nn.ConvTranspose2d(bs*2, bs, 4, stride = 2, padding = 1)\n",
+        "        self.bn4 = nn.BatchNorm2d(bs) \n",
+        "        self.deconv5 = nn.ConvTranspose2d(bs, 1, 4, stride=2, padding = 1)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.tanh = nn.Tanh()\n",
+        "    def forward(self, input):\n",
+        "        out = self.relu(self.bn1(self.deconv1(input)))\n",
+        "        out = self.relu(self.bn2(self.deconv2(out)))\n",
+        "        out = self.relu(self.bn3(self.deconv3(out)))\n",
+        "        out = self.relu(self.bn4(self.deconv4(out)))\n",
+        "        out = self.tanh(self.deconv5(out))\n",
+        "        return out\n",
+        "G = generator(bs)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "qQvIR6BsNExy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# enable GPU\n",
+        "use_cuda = True"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "A0Y6hRz6NNdc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "if use_cuda and torch.cuda.is_available():\n",
+        "    G.cuda()\n",
+        "    D.cuda()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "fFHPRUn3NQ0g",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# initialization for the networks\n",
+        "def init_weight(m):\n",
+        "    if isinstance(m, nn.Conv2d):\n",
+        "        nn.init.normal_(m.weight, mean=0, std=0.02)\n",
+        "    elif isinstance(m, nn.ConvTranspose2d):\n",
+        "        nn.init.normal_(m.weight, mean=0, std=0.02)\n",
+        "    elif type(m) == nn.BatchNorm2d:\n",
+        "        torch.nn.init.constant_(m.weight, 1)\n",
+        "        torch.nn.init.constant_(m.bias, 1)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ilF7vZ63NT9H",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 218
+        },
+        "outputId": "69625263-1f2e-442b-83d5-934737a31301"
+      },
+      "cell_type": "code",
+      "source": [
+        "# apply initializer\n",
+        "\n",
+        "G.apply(init_weight)\n",
+        "D.apply(init_weight)"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "discriminator(\n",
+              "  (conv1): Conv2d(1, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+              "  (conv2): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+              "  (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "  (conv3): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+              "  (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "  (conv4): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
+              "  (bn4): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "  (conv5): Conv2d(1024, 1, kernel_size=(4, 4), stride=(1, 1))\n",
+              "  (leaky_relu): LeakyReLU(negative_slope=0.2, inplace)\n",
+              "  (sigmoid): Sigmoid()\n",
+              ")"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jeNFTU6xNVSp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "outputId": "d65fe0fb-da6d-4e66-8d66-c635b9f61b6b"
+      },
+      "cell_type": "code",
+      "source": [
+        "#summary of networks\n",
+        "#summary(G, (100))\n",
+        "summary(D, (1, 64, 64))"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "----------------------------------------------------------------\n",
+            "        Layer (type)               Output Shape         Param #\n",
+            "================================================================\n",
+            "            Conv2d-1          [-1, 128, 32, 32]           2,176\n",
+            "         LeakyReLU-2          [-1, 128, 32, 32]               0\n",
+            "            Conv2d-3          [-1, 256, 16, 16]         524,544\n",
+            "       BatchNorm2d-4          [-1, 256, 16, 16]             512\n",
+            "         LeakyReLU-5          [-1, 256, 16, 16]               0\n",
+            "            Conv2d-6            [-1, 512, 8, 8]       2,097,664\n",
+            "       BatchNorm2d-7            [-1, 512, 8, 8]           1,024\n",
+            "         LeakyReLU-8            [-1, 512, 8, 8]               0\n",
+            "            Conv2d-9           [-1, 1024, 4, 4]       8,389,632\n",
+            "      BatchNorm2d-10           [-1, 1024, 4, 4]           2,048\n",
+            "        LeakyReLU-11           [-1, 1024, 4, 4]               0\n",
+            "           Conv2d-12              [-1, 1, 1, 1]          16,385\n",
+            "          Sigmoid-13              [-1, 1, 1, 1]               0\n",
+            "================================================================\n",
+            "Total params: 11,033,985\n",
+            "Trainable params: 11,033,985\n",
+            "Non-trainable params: 0\n",
+            "----------------------------------------------------------------\n",
+            "Input size (MB): 0.02\n",
+            "Forward/backward pass size (MB): 4.63\n",
+            "Params size (MB): 42.09\n",
+            "Estimated Total Size (MB): 46.73\n",
+            "----------------------------------------------------------------\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bLhYpLNINW5m",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# define loss and optimizers\n",
+        "criterion = nn.BCELoss()\n",
+        "d_opt = torch.optim.Adam(D.parameters(), lr = 0.0002, betas=(0.5, 0.999))\n",
+        "g_opt = torch.optim.Adam(G.parameters(), lr = 0.0002, betas=(0.5, 0.999))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "w0EDBZyhNZWX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# denorm for plotting\n",
+        "def denorm(x):\n",
+        "    out = (x + 1) / 2\n",
+        "    return out.clamp(0, 1)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "cuGP2bxkNaiU",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 699
+        },
+        "outputId": "93c54872-2d7f-4bb0-e940-086479b5895a"
+      },
+      "cell_type": "code",
+      "source": [
+        "# training\n",
+        "n_epochs = 20\n",
+        "total_step = len(data_loader)\n",
+        "for epoch in range(n_epochs):\n",
+        "    for i, (images, _) in enumerate(data_loader):\n",
+        "        \n",
+        "        #create labels (1 for real img, 0 for fake img)\n",
+        "        \n",
+        "        real_labels = torch.ones(bs).view(-1,1,1,1) \n",
+        "        fake_labels = torch.zeros(bs).view(-1,1,1,1)\n",
+        "        \n",
+        "        #enabling cuda\n",
+        "        if use_cuda and torch.cuda.is_available():\n",
+        "          images = images.cuda()\n",
+        "          real_labels = real_labels.cuda()\n",
+        "          fake_labels = fake_labels.cuda()\n",
+        "        \n",
+        "\n",
+        "        \n",
+        "        \n",
+        "        ########################\n",
+        "        # training discrimiator\n",
+        "        ########################\n",
+        "        \n",
+        "        # zeroing gradientsy\n",
+        "        d_opt.zero_grad()\n",
+        "        \n",
+        "        # loss for real images\n",
+        "        outputs = D(images).view(-1,1,1,1)\n",
+        "        d_loss_real = criterion(outputs, real_labels)\n",
+        "        real_score = outputs\n",
+        "        \n",
+        "        # loss for fake images\n",
+        "        z = torch.randn(bs, 100).view(-1, 100, 1,1) # 100 is input channels for G\n",
+        "        fake_img = G(z)\n",
+        "        outputs = D(fake_img).view(-1,1,1,1)\n",
+        "        d_loss_fake = criterion(outputs, fake_labels)\n",
+        "        fake_score = outputs\n",
+        "        \n",
+        "        # total loss\n",
+        "        total_loss = d_loss_real + d_loss_fake\n",
+        "\n",
+        "        total_loss.backward()\n",
+        "        d_opt.step()\n",
+        "        \n",
+        "        ########################\n",
+        "        # training generator\n",
+        "        ########################\n",
+        "        # zeroing gradients\n",
+        "        g_opt.zero_grad()\n",
+        "        \n",
+        "        # generating fake imgs \n",
+        "        z = torch.randn(bs, 100).view(-1, 100, 1, 1)\n",
+        "        if use_cuda and torch.cuda.is_available():\n",
+        "          z = z.cuda()\n",
+        "        fake_img = G(z)\n",
+        "        outputs = D(fake_img).view(-1,1,1,1)\n",
+        "        \n",
+        "        # loss for generator\n",
+        "        g_loss = criterion(outputs, real_labels)\n",
+        "        g_loss.backward()\n",
+        "        g_opt.step()\n",
+        "        \n",
+        "        if (i+5) % 100 == 0:\n",
+        "            print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}' \n",
+        "                  .format(epoch+1, n_epochs, i+1, total_step, total_loss.item(), g_loss.item(), \n",
+        "                          real_score.mean().item(), fake_score.mean().item()))\n",
+        "\n",
+        "\n",
+        "# # Save the model checkpoints \n",
+        "# #torch.save(G.state_dict(), 'G.ckpt')\n",
+        "# #torch.save(D.state_dict(), 'D.ckpt')\n"
+      ],
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "RuntimeError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-28-3334654f75a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0;31m# loss for fake images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 100 is input channels for G\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m         \u001b[0mfake_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfake_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0md_loss_fake\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfake_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-10-ff06956ca9a4>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtanh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTanh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\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[0m\u001b[1;32m     45\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\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     46\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeconv3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\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/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, output_size)\u001b[0m\n\u001b[1;32m    689\u001b[0m         return F.conv_transpose2d(\n\u001b[1;32m    690\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 691\u001b[0;31m             output_padding, self.groups, self.dilation)\n\u001b[0m\u001b[1;32m    692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mRuntimeError\u001b[0m: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'weight'"
+          ]
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "G1IRJYYMNcj-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 9dfaa6bd386514d5fe2013093251ae7075131dd2 Mon Sep 17 00:00:00 2001
From: maxmatical <max.tian.94@outlook.com>
Date: Thu, 23 Aug 2018 21:28:01 -0400
Subject: [PATCH 2/3] Created using Colaboratory

---
 DCGAN_with_GPU_Google_Colab.ipynb | 166 ++++++++++++++++++------------
 1 file changed, 103 insertions(+), 63 deletions(-)

diff --git a/DCGAN_with_GPU_Google_Colab.ipynb b/DCGAN_with_GPU_Google_Colab.ipynb
index a05ef53..35757d7 100644
--- a/DCGAN_with_GPU_Google_Colab.ipynb
+++ b/DCGAN_with_GPU_Google_Colab.ipynb
@@ -6,6 +6,7 @@
       "name": "DCGAN with GPU - Google Colab.ipynb",
       "version": "0.3.2",
       "provenance": [],
+      "collapsed_sections": [],
       "include_colab_link": true
     },
     "kernelspec": {
@@ -29,7 +30,11 @@
       "metadata": {
         "id": "jU1r3xFhO3r5",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "19d47883-09d6-4ac8-d2fc-69e93cd8a715"
       },
       "cell_type": "code",
       "source": [
@@ -41,10 +46,18 @@
         "\n",
         "accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'\n",
         "\n",
-        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.0-{platform}-linux_x86_64.whl torchvision"
+        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.1-{platform}-linux_x86_64.whl torchvision"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "^C\r\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "metadata": {
@@ -54,7 +67,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 84
         },
-        "outputId": "93160f2b-c6f8-44de-ab75-f866f4c2449b"
+        "outputId": "1f9e284a-943c-43cb-d6b3-8e2d43fe79e1"
       },
       "cell_type": "code",
       "source": [
@@ -64,7 +77,7 @@
         "print('Torch', torch.__version__, 'CUDA', torch.version.cuda)\n",
         "print('Device:', torch.device('cuda:0'))\n"
       ],
-      "execution_count": 8,
+      "execution_count": 30,
       "outputs": [
         {
           "output_type": "stream",
@@ -84,24 +97,21 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 84
+          "height": 34
         },
-        "outputId": "08eedf6d-60c6-49fa-bbda-1577e7e979c8"
+        "outputId": "8fef025f-6197-4288-d30d-6becd55567c6"
       },
       "cell_type": "code",
       "source": [
         "# install required packagles\n",
         "!pip install torchsummary"
       ],
-      "execution_count": 3,
+      "execution_count": 31,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "Collecting torchsummary\n",
-            "  Downloading https://files.pythonhosted.org/packages/2a/61/21b44bb29aedb820fec4716a102e802397f0c21512764a9d98206c17417d/torchsummary-1.4-py3-none-any.whl\n",
-            "Installing collected packages: torchsummary\n",
-            "Successfully installed torchsummary-1.4\n"
+            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.4)\r\n"
           ],
           "name": "stdout"
         }
@@ -150,11 +160,7 @@
       "metadata": {
         "id": "Qdoaeq-7MHf9",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 118
-        },
-        "outputId": "06978b55-d020-467f-cdee-fae3851a50c5"
+        "colab": {}
       },
       "cell_type": "code",
       "source": [
@@ -180,21 +186,8 @@
         "                                          batch_size=bs, \n",
         "                                          shuffle=True)"
       ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
-            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
-            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
-            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
-            "Processing...\n",
-            "Done!\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "execution_count": 0,
+      "outputs": []
     },
     {
       "metadata": {
@@ -274,6 +267,27 @@
       "execution_count": 0,
       "outputs": []
     },
+    {
+      "metadata": {
+        "id": "fFHPRUn3NQ0g",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# initialization for the networks\n",
+        "def init_weight(m):\n",
+        "    if isinstance(m, nn.Conv2d):\n",
+        "        nn.init.normal_(m.weight, mean=0, std=0.02)\n",
+        "    elif isinstance(m, nn.ConvTranspose2d):\n",
+        "        nn.init.normal_(m.weight, mean=0, std=0.02)\n",
+        "    elif type(m) == nn.BatchNorm2d:\n",
+        "        torch.nn.init.constant_(m.weight, 1)\n",
+        "        torch.nn.init.constant_(m.bias, 1)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
       "metadata": {
         "id": "A0Y6hRz6NNdc",
@@ -291,24 +305,31 @@
     },
     {
       "metadata": {
-        "id": "fFHPRUn3NQ0g",
+        "id": "O2dkp7DEfBu0",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        },
+        "outputId": "2aeca40e-4391-4d4e-c9bb-6026a9ed7ea5"
       },
       "cell_type": "code",
       "source": [
-        "# initialization for the networks\n",
-        "def init_weight(m):\n",
-        "    if isinstance(m, nn.Conv2d):\n",
-        "        nn.init.normal_(m.weight, mean=0, std=0.02)\n",
-        "    elif isinstance(m, nn.ConvTranspose2d):\n",
-        "        nn.init.normal_(m.weight, mean=0, std=0.02)\n",
-        "    elif type(m) == nn.BatchNorm2d:\n",
-        "        torch.nn.init.constant_(m.weight, 1)\n",
-        "        torch.nn.init.constant_(m.bias, 1)\n"
+        "# check if models are on cuda\n",
+        "print(next(G.parameters()).is_cuda)\n",
+        "print(next(D.parameters()).is_cuda)\n"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "True\n",
+            "True\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "metadata": {
@@ -318,7 +339,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 218
         },
-        "outputId": "69625263-1f2e-442b-83d5-934737a31301"
+        "outputId": "e56e7de1-e45d-4bae-d233-becd33404917"
       },
       "cell_type": "code",
       "source": [
@@ -327,7 +348,7 @@
         "G.apply(init_weight)\n",
         "D.apply(init_weight)"
       ],
-      "execution_count": 24,
+      "execution_count": 42,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -350,7 +371,7 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 24
+          "execution_count": 42
         }
       ]
     },
@@ -362,7 +383,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 454
         },
-        "outputId": "d65fe0fb-da6d-4e66-8d66-c635b9f61b6b"
+        "outputId": "344ae573-a725-414d-fb24-e81bb52ee483"
       },
       "cell_type": "code",
       "source": [
@@ -370,7 +391,7 @@
         "#summary(G, (100))\n",
         "summary(D, (1, 64, 64))"
       ],
-      "execution_count": 25,
+      "execution_count": 43,
       "outputs": [
         {
           "output_type": "stream",
@@ -444,9 +465,9 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 699
+          "height": 702
         },
-        "outputId": "93c54872-2d7f-4bb0-e940-086479b5895a"
+        "outputId": "dddb7e4a-2c2c-4acd-b74d-2dfc54057a77"
       },
       "cell_type": "code",
       "source": [
@@ -467,7 +488,7 @@
         "          real_labels = real_labels.cuda()\n",
         "          fake_labels = fake_labels.cuda()\n",
         "        \n",
-        "\n",
+        "        \n",
         "        \n",
         "        \n",
         "        ########################\n",
@@ -484,6 +505,8 @@
         "        \n",
         "        # loss for fake images\n",
         "        z = torch.randn(bs, 100).view(-1, 100, 1,1) # 100 is input channels for G\n",
+        "        if use_cuda and torch.cuda.is_available():\n",
+        "          z = z.cuda()\n",
         "        fake_img = G(z)\n",
         "        outputs = D(fake_img).view(-1,1,1,1)\n",
         "        d_loss_fake = criterion(outputs, fake_labels)\n",
@@ -523,21 +546,38 @@
         "# #torch.save(G.state_dict(), 'G.ckpt')\n",
         "# #torch.save(D.state_dict(), 'D.ckpt')\n"
       ],
-      "execution_count": 28,
+      "execution_count": 49,
       "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch [1/20], Step [96/469], d_loss: 27.6310, g_loss: 0.0000, D(x): 1.00, D(G(z)): 1.00\n",
+            "Epoch [1/20], Step [196/469], d_loss: 27.6310, g_loss: 0.0000, D(x): 1.00, D(G(z)): 1.00\n",
+            "Epoch [1/20], Step [296/469], d_loss: 27.6310, g_loss: 0.0000, D(x): 1.00, D(G(z)): 1.00\n",
+            "Epoch [1/20], Step [396/469], d_loss: 27.6310, g_loss: 0.0000, D(x): 1.00, D(G(z)): 1.00\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1594: UserWarning: Using a target size (torch.Size([128, 1, 1, 1])) that is different to the input size (torch.Size([96, 1, 1, 1])) is deprecated. Please ensure they have the same size.\n",
+            "  See :class:`~torch.nn.MultiLabelMarginLoss` for details.\n"
+          ],
+          "name": "stderr"
+        },
         {
           "output_type": "error",
-          "ename": "RuntimeError",
+          "ename": "ValueError",
           "evalue": "ignored",
           "traceback": [
             "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-28-3334654f75a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0;31m# loss for fake images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 100 is input channels for G\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m         \u001b[0mfake_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfake_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0md_loss_fake\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfake_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m<ipython-input-10-ff06956ca9a4>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtanh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTanh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\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[0m\u001b[1;32m     45\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\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     46\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeconv3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\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/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, output_size)\u001b[0m\n\u001b[1;32m    689\u001b[0m         return F.conv_transpose2d(\n\u001b[1;32m    690\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 691\u001b[0;31m             output_padding, self.groups, self.dilation)\n\u001b[0m\u001b[1;32m    692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mRuntimeError\u001b[0m: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'weight'"
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-49-38a5a48d3f53>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;31m# loss for real images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         \u001b[0md_loss_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m         \u001b[0mreal_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mtracing_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m         \u001b[0mtracing_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_traced_module_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m    484\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 486\u001b[0;31m     \"\"\"\n\u001b[0m\u001b[1;32m    487\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBCEWithLogitsLoss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbinary_cross_entropy\u001b[0;34m(input, target, weight, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m   1595\u001b[0m \"\"\")\n\u001b[1;32m   1596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1597\u001b[0;31m soft_margin_loss = _add_docstr(torch._C._nn.soft_margin_loss, r\"\"\"\n\u001b[0m\u001b[1;32m   1598\u001b[0m \u001b[0msoft_margin_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1599\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Target and input must have the same number of elements. target nelement (128) != input nelement (96)"
           ]
         }
       ]

From 405b2e55bcc64c2848640b42b8f8463064857e7e Mon Sep 17 00:00:00 2001
From: maxmatical <max.tian.94@outlook.com>
Date: Sat, 25 Aug 2018 14:17:21 -0400
Subject: [PATCH 3/3] Created using Colaboratory

---
 DCGAN_with_GPU_Google_Colab.ipynb | 154 ++++++++++++++++++++++--------
 1 file changed, 116 insertions(+), 38 deletions(-)

diff --git a/DCGAN_with_GPU_Google_Colab.ipynb b/DCGAN_with_GPU_Google_Colab.ipynb
index 35757d7..eb6f4f5 100644
--- a/DCGAN_with_GPU_Google_Colab.ipynb
+++ b/DCGAN_with_GPU_Google_Colab.ipynb
@@ -32,9 +32,9 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 34
+          "height": 54
         },
-        "outputId": "19d47883-09d6-4ac8-d2fc-69e93cd8a715"
+        "outputId": "fa9282d5-f322-4e97-bbc5-2dddccdb111a"
       },
       "cell_type": "code",
       "source": [
@@ -48,12 +48,12 @@
         "\n",
         "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.1-{platform}-linux_x86_64.whl torchvision"
       ],
-      "execution_count": 29,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "^C\r\n"
+            "tcmalloc: large alloc 1073750016 bytes == 0x5635c000 @  0x7f22ad7181c4 0x46d6a4 0x5fcbcc 0x4c494d 0x54f3c4 0x553aaf 0x54e4c8 0x54f4f6 0x553aaf 0x54efc1 0x54f24d 0x553aaf 0x54efc1 0x54f24d 0x553aaf 0x54efc1 0x54f24d 0x551ee0 0x54e4c8 0x54f4f6 0x553aaf 0x54efc1 0x54f24d 0x551ee0 0x54efc1 0x54f24d 0x551ee0 0x54e4c8 0x54f4f6 0x553aaf 0x54e4c8\r\n"
           ],
           "name": "stdout"
         }
@@ -67,7 +67,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 84
         },
-        "outputId": "1f9e284a-943c-43cb-d6b3-8e2d43fe79e1"
+        "outputId": "c09afed0-cc1e-4eff-ccb0-42c9493264b0"
       },
       "cell_type": "code",
       "source": [
@@ -77,7 +77,7 @@
         "print('Torch', torch.__version__, 'CUDA', torch.version.cuda)\n",
         "print('Device:', torch.device('cuda:0'))\n"
       ],
-      "execution_count": 30,
+      "execution_count": 2,
       "outputs": [
         {
           "output_type": "stream",
@@ -97,21 +97,24 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 34
+          "height": 84
         },
-        "outputId": "8fef025f-6197-4288-d30d-6becd55567c6"
+        "outputId": "493ade85-2b6f-49d2-9f0a-9491555624f7"
       },
       "cell_type": "code",
       "source": [
         "# install required packagles\n",
         "!pip install torchsummary"
       ],
-      "execution_count": 31,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.4)\r\n"
+            "Collecting torchsummary\n",
+            "  Downloading https://files.pythonhosted.org/packages/2a/61/21b44bb29aedb820fec4716a102e802397f0c21512764a9d98206c17417d/torchsummary-1.4-py3-none-any.whl\n",
+            "Installing collected packages: torchsummary\n",
+            "Successfully installed torchsummary-1.4\n"
           ],
           "name": "stdout"
         }
@@ -184,7 +187,8 @@
         "# Data loader\n",
         "data_loader = torch.utils.data.DataLoader(dataset=mnist,\n",
         "                                          batch_size=bs, \n",
-        "                                          shuffle=True)"
+        "                                          shuffle=True,\n",
+        "                                          num_workers=2)"
       ],
       "execution_count": 0,
       "outputs": []
@@ -212,14 +216,14 @@
         "        self.bn4 = nn.BatchNorm2d(bs*8) #1024 channels\n",
         "        self.conv5 =  nn.Conv2d(bs*8, 1, 4, stride = 1, padding = 0)\n",
         "        self.leaky_relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
-        "        self.sigmoid = nn.Sigmoid()\n",
+        "        #self.sigmoid = nn.Sigmoid()\n",
         "    \n",
         "    def forward(self, input):\n",
         "        out = self.leaky_relu(self.conv1(input))\n",
         "        out = self.leaky_relu(self.bn2(self.conv2(out)))\n",
         "        out = self.leaky_relu(self.bn3(self.conv3(out)))\n",
         "        out = self.leaky_relu(self.bn4(self.conv4(out)))\n",
-        "        out = self.sigmoid(self.conv5(out))\n",
+        "        out = F.sigmoid((self.conv5(out)))\n",
         "    \n",
         "        return out\n",
         "    \n",
@@ -240,13 +244,13 @@
         "        self.bn4 = nn.BatchNorm2d(bs) \n",
         "        self.deconv5 = nn.ConvTranspose2d(bs, 1, 4, stride=2, padding = 1)\n",
         "        self.relu = nn.ReLU(inplace=True)\n",
-        "        self.tanh = nn.Tanh()\n",
+        "        #self.tanh = nn.Tanh()\n",
         "    def forward(self, input):\n",
         "        out = self.relu(self.bn1(self.deconv1(input)))\n",
         "        out = self.relu(self.bn2(self.deconv2(out)))\n",
         "        out = self.relu(self.bn3(self.deconv3(out)))\n",
         "        out = self.relu(self.bn4(self.deconv4(out)))\n",
-        "        out = self.tanh(self.deconv5(out))\n",
+        "        out = F.tanh((self.deconv5(out)))\n",
         "        return out\n",
         "G = generator(bs)\n"
       ],
@@ -311,7 +315,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 50
         },
-        "outputId": "2aeca40e-4391-4d4e-c9bb-6026a9ed7ea5"
+        "outputId": "e9f56552-721b-49a3-8f72-008d27dc64cd"
       },
       "cell_type": "code",
       "source": [
@@ -319,7 +323,7 @@
         "print(next(G.parameters()).is_cuda)\n",
         "print(next(D.parameters()).is_cuda)\n"
       ],
-      "execution_count": 41,
+      "execution_count": 26,
       "outputs": [
         {
           "output_type": "stream",
@@ -337,9 +341,9 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 218
+          "height": 202
         },
-        "outputId": "e56e7de1-e45d-4bae-d233-becd33404917"
+        "outputId": "63109b4d-3659-4d1f-b09b-e95490d0d49a"
       },
       "cell_type": "code",
       "source": [
@@ -348,7 +352,7 @@
         "G.apply(init_weight)\n",
         "D.apply(init_weight)"
       ],
-      "execution_count": 42,
+      "execution_count": 27,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -364,14 +368,13 @@
               "  (bn4): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
               "  (conv5): Conv2d(1024, 1, kernel_size=(4, 4), stride=(1, 1))\n",
               "  (leaky_relu): LeakyReLU(negative_slope=0.2, inplace)\n",
-              "  (sigmoid): Sigmoid()\n",
               ")"
             ]
           },
           "metadata": {
             "tags": []
           },
-          "execution_count": 42
+          "execution_count": 27
         }
       ]
     },
@@ -381,9 +384,9 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 454
+          "height": 470
         },
-        "outputId": "344ae573-a725-414d-fb24-e81bb52ee483"
+        "outputId": "118d5eaa-1a8e-4c0f-ab40-bfa4b5d6a125"
       },
       "cell_type": "code",
       "source": [
@@ -391,7 +394,7 @@
         "#summary(G, (100))\n",
         "summary(D, (1, 64, 64))"
       ],
-      "execution_count": 43,
+      "execution_count": 28,
       "outputs": [
         {
           "output_type": "stream",
@@ -411,7 +414,6 @@
             "      BatchNorm2d-10           [-1, 1024, 4, 4]           2,048\n",
             "        LeakyReLU-11           [-1, 1024, 4, 4]               0\n",
             "           Conv2d-12              [-1, 1, 1, 1]          16,385\n",
-            "          Sigmoid-13              [-1, 1, 1, 1]               0\n",
             "================================================================\n",
             "Total params: 11,033,985\n",
             "Trainable params: 11,033,985\n",
@@ -424,6 +426,72 @@
             "----------------------------------------------------------------\n"
           ],
           "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
+            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bgjuSe0mt7cO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "outputId": "37bcc7ce-0841-4550-8eb4-8d68dc028c24"
+      },
+      "cell_type": "code",
+      "source": [
+        "summary(G, (100,1,1))"
+      ],
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "----------------------------------------------------------------\n",
+            "        Layer (type)               Output Shape         Param #\n",
+            "================================================================\n",
+            "   ConvTranspose2d-1           [-1, 1024, 4, 4]       1,639,424\n",
+            "       BatchNorm2d-2           [-1, 1024, 4, 4]           2,048\n",
+            "              ReLU-3           [-1, 1024, 4, 4]               0\n",
+            "   ConvTranspose2d-4            [-1, 512, 8, 8]       8,389,120\n",
+            "       BatchNorm2d-5            [-1, 512, 8, 8]           1,024\n",
+            "              ReLU-6            [-1, 512, 8, 8]               0\n",
+            "   ConvTranspose2d-7          [-1, 256, 16, 16]       2,097,408\n",
+            "       BatchNorm2d-8          [-1, 256, 16, 16]             512\n",
+            "              ReLU-9          [-1, 256, 16, 16]               0\n",
+            "  ConvTranspose2d-10          [-1, 128, 32, 32]         524,416\n",
+            "      BatchNorm2d-11          [-1, 128, 32, 32]             256\n",
+            "             ReLU-12          [-1, 128, 32, 32]               0\n",
+            "  ConvTranspose2d-13            [-1, 1, 64, 64]           2,049\n",
+            "================================================================\n",
+            "Total params: 12,656,257\n",
+            "Trainable params: 12,656,257\n",
+            "Non-trainable params: 0\n",
+            "----------------------------------------------------------------\n",
+            "Input size (MB): 0.00\n",
+            "Forward/backward pass size (MB): 5.66\n",
+            "Params size (MB): 48.28\n",
+            "Estimated Total Size (MB): 53.94\n",
+            "----------------------------------------------------------------\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:995: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
+            "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
+          ],
+          "name": "stderr"
         }
       ]
     },
@@ -465,9 +533,9 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 702
+          "height": 770
         },
-        "outputId": "dddb7e4a-2c2c-4acd-b74d-2dfc54057a77"
+        "outputId": "a9cace4b-6295-4674-9e67-21efc1455b9a"
       },
       "cell_type": "code",
       "source": [
@@ -546,15 +614,25 @@
         "# #torch.save(G.state_dict(), 'G.ckpt')\n",
         "# #torch.save(D.state_dict(), 'D.ckpt')\n"
       ],
-      "execution_count": 49,
+      "execution_count": 32,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "Epoch [1/20], Step [96/469], d_loss: 27.6310, g_loss: 0.0000, D(x): 1.00, D(G(z)): 1.00\n",
-            "Epoch [1/20], Step [196/469], d_loss: 27.6310, g_loss: 0.0000, D(x): 1.00, D(G(z)): 1.00\n",
-            "Epoch [1/20], Step [296/469], d_loss: 27.6310, g_loss: 0.0000, D(x): 1.00, D(G(z)): 1.00\n",
-            "Epoch [1/20], Step [396/469], d_loss: 27.6310, g_loss: 0.0000, D(x): 1.00, D(G(z)): 1.00\n"
+            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
+            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n",
+            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:995: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
+            "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch [1/20], Step [96/469], d_loss: 0.5494, g_loss: 5.2465, D(x): 0.95, D(G(z)): 0.23\n",
+            "Epoch [1/20], Step [196/469], d_loss: 1.2054, g_loss: 5.1388, D(x): 0.86, D(G(z)): 0.54\n",
+            "Epoch [1/20], Step [296/469], d_loss: 1.2517, g_loss: 4.7196, D(x): 0.91, D(G(z)): 0.61\n",
+            "Epoch [1/20], Step [396/469], d_loss: 1.4117, g_loss: 0.7615, D(x): 0.39, D(G(z)): 0.06\n"
           ],
           "name": "stdout"
         },
@@ -562,7 +640,7 @@
           "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1594: UserWarning: Using a target size (torch.Size([128, 1, 1, 1])) that is different to the input size (torch.Size([96, 1, 1, 1])) is deprecated. Please ensure they have the same size.\n",
-            "  See :class:`~torch.nn.MultiLabelMarginLoss` for details.\n"
+            "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
           ],
           "name": "stderr"
         },
@@ -573,10 +651,10 @@
           "traceback": [
             "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
             "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-49-38a5a48d3f53>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;31m# loss for real images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         \u001b[0md_loss_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m         \u001b[0mreal_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mtracing_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m         \u001b[0mtracing_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_traced_module_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m    484\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 486\u001b[0;31m     \"\"\"\n\u001b[0m\u001b[1;32m    487\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBCEWithLogitsLoss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbinary_cross_entropy\u001b[0;34m(input, target, weight, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m   1595\u001b[0m \"\"\")\n\u001b[1;32m   1596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1597\u001b[0;31m soft_margin_loss = _add_docstr(torch._C._nn.soft_margin_loss, r\"\"\"\n\u001b[0m\u001b[1;32m   1598\u001b[0m \u001b[0msoft_margin_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1599\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-32-38a5a48d3f53>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;31m# loss for real images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         \u001b[0md_loss_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m         \u001b[0mreal_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 486\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_cross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    487\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbinary_cross_entropy\u001b[0;34m(input, target, weight, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m   1595\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnelement\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnelement\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   1596\u001b[0m         raise ValueError(\"Target and input must have the same number of elements. target nelement ({}) \"\n\u001b[0;32m-> 1597\u001b[0;31m                          \"!= input nelement ({})\".format(target.nelement(), input.nelement()))\n\u001b[0m\u001b[1;32m   1598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1599\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
             "\u001b[0;31mValueError\u001b[0m: Target and input must have the same number of elements. target nelement (128) != input nelement (96)"
           ]
         }