Kannada_MNIST_GAN_PyTorch.ipynb

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      "name": "Kannada MNIST GAN.ipynb",
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        "<a href=\"https://colab.research.google.com/github/kannankumar/jupyter-notebooks/blob/kannada-mnist-gan/Kannada_MNIST_GAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Kannada MNIST GAN\n",
        "### Generating handwritten digits in Kannada (an Indian language)\n",
        "\n",
        "Inspired from [MNIST-GAN notebooks](/~https://github.com/udacity/deep-learning-v2-pytorch/tree/master/gan-mnist) of Udacity Deep Learning Nanodegree course.\n",
        "\n",
        "I try to explore generation of digits in the South Indian language of Kannada. This is an attempt to explore digit generation in a totally different language using the architechture used for standard MNIST digit generation. \n",
        "\n",
        "This is how Digits in the Kannada Language look like. Look at the characters in the top row:\n",
        "![Kannada numbers](https://storage.googleapis.com/kaggle-media/competitions/Kannada-MNIST/kannada.png)\n",
        "If you'd like to take a look at how the generated decimal digits look like, pelase feel free to check out the awesome notebook from Udacity Deep Learning Nanodegree [here](/~https://github.com/udacity/deep-learning-v2-pytorch/blob/master/gan-mnist/MNIST_GAN_Solution.ipynb)\n",
        "```\n",
        "Fun fact: Kannada is the language spoken in the city Bangalore (or Bengaluru as it is called now).\n",
        "```\n",
        "\n",
        "In this notebook, we'll be building a generative adversarial network (GAN) trained on the Kannada MNIST dataset. From this, we'll be able to generate new handwritten digits.\n",
        "\n",
        "GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. \n",
        "\n",
        "\n",
        "The idea behind GANs is that you have two networks, a generator $G$ and a discriminator $D$, competing against each other. The generator makes \"fake\" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. \n",
        "> * The generator is trained to fool the discriminator, it wants to output data that looks _as close as possible_ to real, training data. \n",
        "* The discriminator is a classifier that is trained to figure out which data is real and which is fake. \n",
        "\n",
        "What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator.\n",
        "\n",
        "If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!"
      ]
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    {
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      "metadata": {
        "id": "KTNsjG00Q_J7",
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      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
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    {
      "cell_type": "markdown",
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      "source": [
        "## Dataset\n",
        "The dataset which was released as part of [Kaggle Kannada-MNIST](https://www.kaggle.com/c/Kannada-MNIST/data) competition, remains very similar to MNIST in terms of structure.  \n",
        "I'm using only the `train.csv` file of this dataset which contains multiple other files for the competition.\n",
        "\n",
        "Download the `train.csv` file from the above competition page and upload it here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "krYsSSLPRGKH",
        "colab_type": "code",
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      "source": [
        "%%bash\n",
        "unzip -q train.csv.zip"
      ],
      "execution_count": 0,
      "outputs": []
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
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      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('train.csv')df.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel0  pixel1  pixel2  ...  pixel780  pixel781  pixel782  pixel783\n",
              "0      0       0       0       0  ...         0         0         0         0\n",
              "1      1       0       0       0  ...         0         0         0         0\n",
              "2      2       0       0       0  ...         0         0         0         0\n",
              "3      3       0       0       0  ...         0         0         0         0\n",
              "4      4       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fk6XA6Zre6uw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_KJQh3fmKe7",
        "colab_type": "text"
      },
      "source": [
        "I create a `Custom Dataset` class to simplify the loading of the CSV file as a Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxJBLfGQde6p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Custom dataset class from /~https://github.com/utkuozbulak/pytorch-custom-dataset-examples\n",
        "\n",
        "class CustomDatasetFromCSV(Dataset):\n",
        "    def __init__(self, csv_path, height, width, transforms=None):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            csv_path (string): path to csv file\n",
        "            height (int): image height\n",
        "            width (int): image width\n",
        "            transform: pytorch transforms for transforms and tensor conversion\n",
        "        \"\"\"\n",
        "        self.data = pd.read_csv(csv_path)\n",
        "        self.labels = np.asarray(self.data.iloc[:, 0])\n",
        "        self.height = height\n",
        "        self.width = width\n",
        "        self.transforms = transforms\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        single_image_label = self.labels[index]\n",
        "        # Read each 784 pixels and reshape the 1D array ([784]) to 2D array ([28,28]) \n",
        "        img_as_np = np.asarray(self.data.iloc[index][1:]).reshape(28,28).astype('uint8')\n",
        "\t# Convert image from numpy array to PIL image, mode 'L' is for grayscale\n",
        "        img_as_img = Image.fromarray(img_as_np)\n",
        "        img_as_img = img_as_img.convert('L')\n",
        "        # Transform image to tensor\n",
        "        if self.transforms is not None:\n",
        "            img_as_tensor = self.transforms(img_as_img)\n",
        "        # Return image and the label\n",
        "        return (img_as_tensor, single_image_label)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data.index)\n",
        "        \n",
        "\n",
        "# if __name__ == \"__main__\":\n",
        "#     transformations = transforms.Compose([transforms.ToTensor()])\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPtqlFuuQ_J-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# number of subprocesses to use for data loading\n",
        "num_workers = 0\n",
        "# how many samples per batch to load\n",
        "batch_size = 64\n",
        "\n",
        "# convert data to torch.FloatTensor\n",
        "transform = transforms.ToTensor()\n",
        "\n",
        "# get the training datasets\n",
        "kannada_mnist_data = CustomDatasetFromCSV('train.csv', 28, 28, transform)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sHfOXErexIZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# prepare data loader\n",
        "train_loader = torch.utils.data.DataLoader(kannada_mnist_data, batch_size=batch_size,\n",
        "                                           num_workers=num_workers)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKtKFbLqQ_KB",
        "colab_type": "text"
      },
      "source": [
        "## Visualize the data\n",
        "Test the Kannada MNIST dataloader.  \n",
        "Also have a look at how the numbers in the Kannada language look like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxcG7EeTQ_KB",
        "colab_type": "code",
        "outputId": "d6f05d68-d845-4823-d251-751b273749bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        }
      },
      "source": [
        "# obtain one batch of training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "images = images.numpy()\n",
        "\n",
        "# get one image from the batch\n",
        "img = np.squeeze(images[0])\n",
        "\n",
        "fig = plt.figure(figsize = (3,3)) \n",
        "ax = fig.add_subplot(111)\n",
        "ax.imshow(img, cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc650c0e940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G23_taO2Q_KE",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "# Define the Model\n",
        "\n",
        "A GAN is comprised of two adversarial networks, a discriminator and a generator."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8b4mQrCaQ_KF",
        "colab_type": "text"
      },
      "source": [
        "## Discriminator\n",
        "\n",
        "The discriminator network is going to be a pretty typical linear classifier. To make this network a universal function approximator, we'll need at least one hidden layer, and these hidden layers should have one key attribute:\n",
        "> All hidden layers will have a [Leaky ReLu](https://pytorch.org/docs/stable/nn.html#torch.nn.LeakyReLU) activation function applied to their outputs.\n",
        "\n",
        "#### Leaky ReLu\n",
        "\n",
        "We should use a leaky ReLU to allow gradients to flow backwards through the layer unimpeded. A leaky ReLU is like a normal ReLU, except that there is a small non-zero output for negative input values.\n",
        "\n",
        "#### Sigmoid Output\n",
        "\n",
        "We'll also take the approach of using a more numerically stable loss function on the outputs. Recall that we want the discriminator to output a value 0-1 indicating whether an image is _real or fake_. \n",
        "> We will ultimately use [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.html#bcewithlogitsloss), which combines a `sigmoid` activation function **and** and binary cross entropy loss in one function. \n",
        "\n",
        "So, our final output layer should not have any activation function applied to it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVzQawANQ_KF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Discriminator(nn.Module):\n",
        "\n",
        "    def __init__(self, input_size, hidden_dim, output_size):\n",
        "        super(Discriminator, self).__init__()\n",
        "        \n",
        "        # define hidden linear layers\n",
        "        self.fc1 = nn.Linear(input_size, hidden_dim*4)\n",
        "        self.fc2 = nn.Linear(hidden_dim*4, hidden_dim*2)\n",
        "        self.fc3 = nn.Linear(hidden_dim*2, hidden_dim)\n",
        "        \n",
        "        # final fully-connected layer\n",
        "        self.fc4 = nn.Linear(hidden_dim, output_size)\n",
        "        \n",
        "        # dropout layer \n",
        "        self.dropout = nn.Dropout(0.3)\n",
        "        \n",
        "        \n",
        "    def forward(self, x):\n",
        "        # flatten image\n",
        "        x = x.view(-1, 28*28)\n",
        "        # all hidden layers\n",
        "        x = F.leaky_relu(self.fc1(x), 0.2) # (input, negative_slope=0.2)\n",
        "        x = self.dropout(x)\n",
        "        x = F.leaky_relu(self.fc2(x), 0.2)\n",
        "        x = self.dropout(x)\n",
        "        x = F.leaky_relu(self.fc3(x), 0.2)\n",
        "        x = self.dropout(x)\n",
        "        # final layer\n",
        "        out = self.fc4(x)\n",
        "\n",
        "        return out\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8aRgHqZjQ_KI",
        "colab_type": "text"
      },
      "source": [
        "## Generator\n",
        "\n",
        "The generator network will be almost exactly the same as the discriminator network, except that we're applying a [tanh activation function](https://pytorch.org/docs/stable/nn.html#tanh) to our output layer.\n",
        "\n",
        "#### tanh Output\n",
        "The generator has been found to perform the best with $tanh$ for the generator output, which scales the output to be between -1 and 1, instead of 0 and 1. \n",
        "\n",
        "Recall that we also want these outputs to be comparable to the *real* input pixel values, which are read in as normalized values between 0 and 1. \n",
        "> So, we'll also have to **scale our real input images to have pixel values between -1 and 1** when we train the discriminator. \n",
        "\n",
        "I'll do this in the training loop, later on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSwDdneOQ_KI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Generator(nn.Module):\n",
        "\n",
        "    def __init__(self, input_size, hidden_dim, output_size):\n",
        "        super(Generator, self).__init__()\n",
        "        \n",
        "        # define hidden linear layers\n",
        "        self.fc1 = nn.Linear(input_size, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, hidden_dim*2)\n",
        "        self.fc3 = nn.Linear(hidden_dim*2, hidden_dim*4)\n",
        "        \n",
        "        # final fully-connected layer\n",
        "        self.fc4 = nn.Linear(hidden_dim*4, output_size)\n",
        "        \n",
        "        # dropout layer \n",
        "        self.dropout = nn.Dropout(0.3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # all hidden layers\n",
        "        x = F.leaky_relu(self.fc1(x), 0.2) # (input, negative_slope=0.2)\n",
        "        x = self.dropout(x)\n",
        "        x = F.leaky_relu(self.fc2(x), 0.2)\n",
        "        x = self.dropout(x)\n",
        "        x = F.leaky_relu(self.fc3(x), 0.2)\n",
        "        x = self.dropout(x)\n",
        "        # final layer with tanh applied\n",
        "        out = F.tanh(self.fc4(x))\n",
        "\n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55Xm1zl-Q_KL",
        "colab_type": "text"
      },
      "source": [
        "## Model hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ewa5vhlyQ_KL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Discriminator hyperparams\n",
        "\n",
        "# Size of input image to discriminator (28*28)\n",
        "input_size = 784\n",
        "# Size of discriminator output (real or fake)\n",
        "d_output_size = 1\n",
        "# Size of last hidden layer in the discriminator\n",
        "d_hidden_size = 32\n",
        "\n",
        "# Generator hyperparams\n",
        "\n",
        "# Size of latent vector to give to generator\n",
        "z_size = 100\n",
        "# Size of discriminator output (generated image)\n",
        "g_output_size = 784\n",
        "# Size of first hidden layer in the generator\n",
        "g_hidden_size = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PU8mAanQ_KO",
        "colab_type": "text"
      },
      "source": [
        "## Build complete network\n",
        "\n",
        "Now we're instantiating the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5BfXsoCQ_KP",
        "colab_type": "code",
        "outputId": "743592cd-7d39-4c37-d7aa-4c70b9099fd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# instantiate discriminator and generator\n",
        "D = Discriminator(input_size, d_hidden_size, d_output_size)\n",
        "G = Generator(z_size, g_hidden_size, g_output_size)\n",
        "\n",
        "# check that they are as you expect\n",
        "print(D)\n",
        "print()\n",
        "print(G)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Discriminator(\n",
            "  (fc1): Linear(in_features=784, out_features=128, bias=True)\n",
            "  (fc2): Linear(in_features=128, out_features=64, bias=True)\n",
            "  (fc3): Linear(in_features=64, out_features=32, bias=True)\n",
            "  (fc4): Linear(in_features=32, out_features=1, bias=True)\n",
            "  (dropout): Dropout(p=0.3, inplace=False)\n",
            ")\n",
            "\n",
            "Generator(\n",
            "  (fc1): Linear(in_features=100, out_features=32, bias=True)\n",
            "  (fc2): Linear(in_features=32, out_features=64, bias=True)\n",
            "  (fc3): Linear(in_features=64, out_features=128, bias=True)\n",
            "  (fc4): Linear(in_features=128, out_features=784, bias=True)\n",
            "  (dropout): Dropout(p=0.3, inplace=False)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4WlsQoxNQ_KR",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Discriminator and Generator Losses\n",
        "\n",
        "Now we need to calculate the losses. \n",
        "\n",
        "### Discriminator Losses\n",
        "\n",
        "> * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss`. \n",
        "* Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
        "\n",
        "The losses will by binary cross entropy loss with logits, which we can get with [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.html#bcewithlogitsloss). This combines a `sigmoid` activation function **and** and binary cross entropy loss in one function.\n",
        "\n",
        "For the real images, we want `D(real_images) = 1`. That is, we want the discriminator to classify the the real images with a label = 1, indicating that these are real. To help the discriminator generalize better, the labels are **reduced a bit from 1.0 to 0.9**. For this, we'll use the parameter `smooth`; if True, then we should smooth our labels. In PyTorch, this looks like `labels = torch.ones(size) * 0.9`\n",
        "\n",
        "The discriminator loss for the fake data is similar. We want `D(fake_images) = 0`, where the fake images are the _generator output_, `fake_images = G(z)`. \n",
        "\n",
        "### Generator Loss\n",
        "\n",
        "The generator loss will look similar only with flipped labels. The generator's goal is to get `D(fake_images) = 1`. In this case, the labels are **flipped** to represent that the generator is trying to fool the discriminator into thinking that the images it generates (fakes) are real!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxFKk7oyQ_KR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Calculate losses\n",
        "def real_loss(D_out, smooth=False):\n",
        "    batch_size = D_out.size(0)\n",
        "    # label smoothing\n",
        "    if smooth:\n",
        "        # smooth, real labels = 0.9\n",
        "        labels = torch.ones(batch_size)*0.9\n",
        "    else:\n",
        "        labels = torch.ones(batch_size) # real labels = 1\n",
        "        \n",
        "    # numerically stable loss\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "    # calculate loss\n",
        "    loss = criterion(D_out.squeeze(), labels)\n",
        "    return loss\n",
        "\n",
        "def fake_loss(D_out):\n",
        "    batch_size = D_out.size(0)\n",
        "    labels = torch.zeros(batch_size) # fake labels = 0\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "    # calculate loss\n",
        "    loss = criterion(D_out.squeeze(), labels)\n",
        "    return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vzw-386FQ_KT",
        "colab_type": "text"
      },
      "source": [
        "## Optimizers\n",
        "\n",
        "We want to update the generator and discriminator variables separately. So, we'll define two separate Adam optimizers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "LhpAC_hpQ_KT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "# Optimizers\n",
        "lr = 0.002\n",
        "\n",
        "# Create optimizers for the discriminator and generator\n",
        "d_optimizer = optim.Adam(D.parameters(), lr)\n",
        "g_optimizer = optim.Adam(G.parameters(), lr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1B3pJexeQ_KW",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Training\n",
        "\n",
        "Training will involve alternating between training the discriminator and the generator. We'll use our functions `real_loss` and `fake_loss` to help us calculate the discriminator losses in all of the following cases.\n",
        "\n",
        "### Discriminator training\n",
        "1. Compute the discriminator loss on real, training images        \n",
        "2. Generate fake images\n",
        "3. Compute the discriminator loss on fake, generated images     \n",
        "4. Add up real and fake loss\n",
        "5. Perform backpropagation + an optimization step to update the discriminator's weights\n",
        "\n",
        "### Generator training\n",
        "1. Generate fake images\n",
        "2. Compute the discriminator loss on fake images, using **flipped** labels!\n",
        "3. Perform backpropagation + an optimization step to update the generator's weights\n",
        "\n",
        "#### Saving Samples\n",
        "\n",
        "As we train, we'll also print out some loss statistics and save some generated \"fake\" samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "TI3KZPzKQ_KW",
        "colab_type": "code",
        "outputId": "916c884c-3e7d-4ab2-8a5d-cfd9ef80e140",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import pickle as pkl\n",
        "\n",
        "# training hyperparams\n",
        "num_epochs = 100\n",
        "\n",
        "# keep track of loss and generated, \"fake\" samples\n",
        "samples = []\n",
        "losses = []\n",
        "\n",
        "print_every = 400\n",
        "\n",
        "# Get some fixed data for sampling. These are images that are held\n",
        "# constant throughout training, and allow us to inspect the model's performance\n",
        "sample_size=16\n",
        "fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
        "fixed_z = torch.from_numpy(fixed_z).float()\n",
        "\n",
        "# train the network\n",
        "D.train()\n",
        "G.train()\n",
        "for epoch in range(num_epochs):\n",
        "    \n",
        "    for batch_i, (real_images, _) in enumerate(train_loader):\n",
        "                \n",
        "        batch_size = real_images.size(0)\n",
        "        \n",
        "        ## Important rescaling step ## \n",
        "        real_images = real_images*2 - 1  # rescale input images from [0,1) to [-1, 1)\n",
        "        \n",
        "        # ============================================\n",
        "        #            TRAIN THE DISCRIMINATOR\n",
        "        # ============================================\n",
        "        \n",
        "        d_optimizer.zero_grad()\n",
        "        \n",
        "        # 1. Train with real images\n",
        "\n",
        "        # Compute the discriminator losses on real images \n",
        "        # smooth the real labels\n",
        "        D_real = D(real_images)\n",
        "        d_real_loss = real_loss(D_real, smooth=True)\n",
        "        \n",
        "        # 2. Train with fake images\n",
        "        \n",
        "        # Generate fake images\n",
        "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
        "        z = torch.from_numpy(z).float()\n",
        "        fake_images = G(z)\n",
        "        \n",
        "        # Compute the discriminator losses on fake images        \n",
        "        D_fake = D(fake_images)\n",
        "        d_fake_loss = fake_loss(D_fake)\n",
        "        \n",
        "        # add up loss and perform backprop\n",
        "        d_loss = d_real_loss + d_fake_loss\n",
        "        d_loss.backward()\n",
        "        d_optimizer.step()\n",
        "        \n",
        "        \n",
        "        # =========================================\n",
        "        #            TRAIN THE GENERATOR\n",
        "        # =========================================\n",
        "        g_optimizer.zero_grad()\n",
        "        \n",
        "        # 1. Train with fake images and flipped labels\n",
        "        \n",
        "        # Generate fake images\n",
        "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
        "        z = torch.from_numpy(z).float()\n",
        "        fake_images = G(z)\n",
        "        \n",
        "        # Compute the discriminator losses on fake images \n",
        "        # using flipped labels!\n",
        "        D_fake = D(fake_images)\n",
        "        g_loss = real_loss(D_fake) # use real loss to flip labels\n",
        "        \n",
        "        # perform backprop\n",
        "        g_loss.backward()\n",
        "        g_optimizer.step()\n",
        "\n",
        "        # Print some loss stats\n",
        "        if batch_i % print_every == 0:\n",
        "            # print discriminator and generator loss\n",
        "            print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
        "                    epoch+1, num_epochs, d_loss.item(), g_loss.item()))\n",
        "\n",
        "    \n",
        "    ## AFTER EACH EPOCH##\n",
        "    # append discriminator loss and generator loss\n",
        "    losses.append((d_loss.item(), g_loss.item()))\n",
        "    \n",
        "    # generate and save sample, fake images\n",
        "    G.eval() # eval mode for generating samples\n",
        "    samples_z = G(fixed_z)\n",
        "    samples.append(samples_z)\n",
        "    G.train() # back to train mode\n",
        "\n",
        "\n",
        "# Save training generator samples\n",
        "with open('train_samples.pkl', 'wb') as f:\n",
        "    pkl.dump(samples, f)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1340: 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/  100] | d_loss: 1.4083 | g_loss: 0.6714\n",
            "Epoch [    1/  100] | d_loss: 1.4085 | g_loss: 1.5117\n",
            "Epoch [    1/  100] | d_loss: 1.3538 | g_loss: 0.9138\n",
            "Epoch [    2/  100] | d_loss: 1.1176 | g_loss: 1.5799\n",
            "Epoch [    2/  100] | d_loss: 1.5974 | g_loss: 1.3136\n",
            "Epoch [    2/  100] | d_loss: 0.9556 | g_loss: 4.1226\n",
            "Epoch [    3/  100] | d_loss: 1.1283 | g_loss: 1.2818\n",
            "Epoch [    3/  100] | d_loss: 1.2695 | g_loss: 0.9823\n",
            "Epoch [    3/  100] | d_loss: 1.1157 | g_loss: 2.9314\n",
            "Epoch [    4/  100] | d_loss: 1.2667 | g_loss: 1.4945\n",
            "Epoch [    4/  100] | d_loss: 1.1934 | g_loss: 1.3431\n",
            "Epoch [    4/  100] | d_loss: 1.0725 | g_loss: 1.7790\n",
            "Epoch [    5/  100] | d_loss: 1.3573 | g_loss: 1.4805\n",
            "Epoch [    5/  100] | d_loss: 1.0576 | g_loss: 1.2531\n",
            "Epoch [    5/  100] | d_loss: 1.7553 | g_loss: 1.1310\n",
            "Epoch [    6/  100] | d_loss: 1.1320 | g_loss: 1.7974\n",
            "Epoch [    6/  100] | d_loss: 0.9521 | g_loss: 1.6721\n",
            "Epoch [    6/  100] | d_loss: 1.5614 | g_loss: 0.7516\n",
            "Epoch [    7/  100] | d_loss: 1.1119 | g_loss: 1.0449\n",
            "Epoch [    7/  100] | d_loss: 1.1142 | g_loss: 1.3197\n",
            "Epoch [    7/  100] | d_loss: 1.3009 | g_loss: 1.2138\n",
            "Epoch [    8/  100] | d_loss: 1.1804 | g_loss: 1.5390\n",
            "Epoch [    8/  100] | d_loss: 1.3174 | g_loss: 1.1675\n",
            "Epoch [    8/  100] | d_loss: 1.5769 | g_loss: 1.8083\n",
            "Epoch [    9/  100] | d_loss: 1.2207 | g_loss: 1.3664\n",
            "Epoch [    9/  100] | d_loss: 1.2295 | g_loss: 1.6092\n",
            "Epoch [    9/  100] | d_loss: 1.9341 | g_loss: 1.5993\n",
            "Epoch [   10/  100] | d_loss: 1.0851 | g_loss: 1.7439\n",
            "Epoch [   10/  100] | d_loss: 0.9726 | g_loss: 2.4545\n",
            "Epoch [   10/  100] | d_loss: 1.6651 | g_loss: 1.6231\n",
            "Epoch [   11/  100] | d_loss: 1.0537 | g_loss: 2.5847\n",
            "Epoch [   11/  100] | d_loss: 1.4521 | g_loss: 1.6321\n",
            "Epoch [   11/  100] | d_loss: 1.2356 | g_loss: 3.5565\n",
            "Epoch [   12/  100] | d_loss: 0.7584 | g_loss: 3.5177\n",
            "Epoch [   12/  100] | d_loss: 1.0326 | g_loss: 3.5798\n",
            "Epoch [   12/  100] | d_loss: 1.0486 | g_loss: 2.9805\n",
            "Epoch [   13/  100] | d_loss: 0.9667 | g_loss: 4.5164\n",
            "Epoch [   13/  100] | d_loss: 1.1809 | g_loss: 1.7735\n",
            "Epoch [   13/  100] | d_loss: 1.1857 | g_loss: 2.2606\n",
            "Epoch [   14/  100] | d_loss: 0.9622 | g_loss: 2.2622\n",
            "Epoch [   14/  100] | d_loss: 0.9434 | g_loss: 1.9157\n",
            "Epoch [   14/  100] | d_loss: 1.3226 | g_loss: 2.0192\n",
            "Epoch [   15/  100] | d_loss: 1.0621 | g_loss: 2.4403\n",
            "Epoch [   15/  100] | d_loss: 1.0429 | g_loss: 2.1500\n",
            "Epoch [   15/  100] | d_loss: 1.5082 | g_loss: 1.4903\n",
            "Epoch [   16/  100] | d_loss: 1.2376 | g_loss: 1.4839\n",
            "Epoch [   16/  100] | d_loss: 1.3130 | g_loss: 1.5559\n",
            "Epoch [   16/  100] | d_loss: 1.4549 | g_loss: 1.2387\n",
            "Epoch [   17/  100] | d_loss: 1.1860 | g_loss: 1.5520\n",
            "Epoch [   17/  100] | d_loss: 1.4762 | g_loss: 1.2348\n",
            "Epoch [   17/  100] | d_loss: 1.1174 | g_loss: 1.5573\n",
            "Epoch [   18/  100] | d_loss: 1.2821 | g_loss: 1.1888\n",
            "Epoch [   18/  100] | d_loss: 1.5320 | g_loss: 1.5615\n",
            "Epoch [   18/  100] | d_loss: 1.8323 | g_loss: 0.8413\n",
            "Epoch [   19/  100] | d_loss: 1.3015 | g_loss: 1.1261\n",
            "Epoch [   19/  100] | d_loss: 1.3852 | g_loss: 1.4910\n",
            "Epoch [   19/  100] | d_loss: 1.2582 | g_loss: 1.1231\n",
            "Epoch [   20/  100] | d_loss: 1.2179 | g_loss: 1.6318\n",
            "Epoch [   20/  100] | d_loss: 1.4333 | g_loss: 1.2604\n",
            "Epoch [   20/  100] | d_loss: 1.5804 | g_loss: 1.0134\n",
            "Epoch [   21/  100] | d_loss: 1.2220 | g_loss: 0.9937\n",
            "Epoch [   21/  100] | d_loss: 1.3533 | g_loss: 1.1989\n",
            "Epoch [   21/  100] | d_loss: 1.1495 | g_loss: 1.4637\n",
            "Epoch [   22/  100] | d_loss: 1.3295 | g_loss: 1.2650\n",
            "Epoch [   22/  100] | d_loss: 1.5079 | g_loss: 1.4028\n",
            "Epoch [   22/  100] | d_loss: 1.3263 | g_loss: 1.3868\n",
            "Epoch [   23/  100] | d_loss: 1.3493 | g_loss: 1.2995\n",
            "Epoch [   23/  100] | d_loss: 1.5287 | g_loss: 1.5749\n",
            "Epoch [   23/  100] | d_loss: 1.3800 | g_loss: 1.0326\n",
            "Epoch [   24/  100] | d_loss: 1.0546 | g_loss: 1.8582\n",
            "Epoch [   24/  100] | d_loss: 1.5172 | g_loss: 1.7673\n",
            "Epoch [   24/  100] | d_loss: 1.0098 | g_loss: 2.4950\n",
            "Epoch [   25/  100] | d_loss: 1.2866 | g_loss: 1.0204\n",
            "Epoch [   25/  100] | d_loss: 1.4364 | g_loss: 1.4736\n",
            "Epoch [   25/  100] | d_loss: 1.3661 | g_loss: 1.3878\n",
            "Epoch [   26/  100] | d_loss: 1.2369 | g_loss: 1.4057\n",
            "Epoch [   26/  100] | d_loss: 1.0954 | g_loss: 1.6668\n",
            "Epoch [   26/  100] | d_loss: 1.3559 | g_loss: 1.6362\n",
            "Epoch [   27/  100] | d_loss: 1.3352 | g_loss: 1.3186\n",
            "Epoch [   27/  100] | d_loss: 1.2937 | g_loss: 1.0696\n",
            "Epoch [   27/  100] | d_loss: 1.5380 | g_loss: 1.4997\n",
            "Epoch [   28/  100] | d_loss: 1.4025 | g_loss: 1.4533\n",
            "Epoch [   28/  100] | d_loss: 1.3489 | g_loss: 1.1710\n",
            "Epoch [   28/  100] | d_loss: 1.5899 | g_loss: 1.2036\n",
            "Epoch [   29/  100] | d_loss: 1.3449 | g_loss: 1.6307\n",
            "Epoch [   29/  100] | d_loss: 1.4383 | g_loss: 1.1801\n",
            "Epoch [   29/  100] | d_loss: 1.3138 | g_loss: 1.4615\n",
            "Epoch [   30/  100] | d_loss: 1.4114 | g_loss: 1.4395\n",
            "Epoch [   30/  100] | d_loss: 1.3882 | g_loss: 1.1652\n",
            "Epoch [   30/  100] | d_loss: 1.8064 | g_loss: 1.1415\n",
            "Epoch [   31/  100] | d_loss: 1.3996 | g_loss: 1.0625\n",
            "Epoch [   31/  100] | d_loss: 1.2849 | g_loss: 1.2610\n",
            "Epoch [   31/  100] | d_loss: 1.4191 | g_loss: 1.0814\n",
            "Epoch [   32/  100] | d_loss: 1.3699 | g_loss: 1.5626\n",
            "Epoch [   32/  100] | d_loss: 1.2476 | g_loss: 1.0621\n",
            "Epoch [   32/  100] | d_loss: 1.4490 | g_loss: 1.4799\n",
            "Epoch [   33/  100] | d_loss: 1.2397 | g_loss: 1.5461\n",
            "Epoch [   33/  100] | d_loss: 1.2129 | g_loss: 1.4828\n",
            "Epoch [   33/  100] | d_loss: 1.5061 | g_loss: 1.2140\n",
            "Epoch [   34/  100] | d_loss: 1.2560 | g_loss: 1.4624\n",
            "Epoch [   34/  100] | d_loss: 1.3045 | g_loss: 1.1079\n",
            "Epoch [   34/  100] | d_loss: 1.3468 | g_loss: 1.7541\n",
            "Epoch [   35/  100] | d_loss: 1.4158 | g_loss: 2.0925\n",
            "Epoch [   35/  100] | d_loss: 1.4213 | g_loss: 1.3304\n",
            "Epoch [   35/  100] | d_loss: 1.2117 | g_loss: 1.1204\n",
            "Epoch [   36/  100] | d_loss: 1.3932 | g_loss: 2.0142\n",
            "Epoch [   36/  100] | d_loss: 1.3287 | g_loss: 1.5498\n",
            "Epoch [   36/  100] | d_loss: 1.3940 | g_loss: 1.8130\n",
            "Epoch [   37/  100] | d_loss: 1.2679 | g_loss: 1.1641\n",
            "Epoch [   37/  100] | d_loss: 1.5887 | g_loss: 1.5759\n",
            "Epoch [   37/  100] | d_loss: 1.5613 | g_loss: 1.1440\n",
            "Epoch [   38/  100] | d_loss: 1.2825 | g_loss: 0.9649\n",
            "Epoch [   38/  100] | d_loss: 1.3650 | g_loss: 1.4864\n",
            "Epoch [   38/  100] | d_loss: 1.3649 | g_loss: 1.0746\n",
            "Epoch [   39/  100] | d_loss: 1.2230 | g_loss: 1.4342\n",
            "Epoch [   39/  100] | d_loss: 1.1086 | g_loss: 1.9060\n",
            "Epoch [   39/  100] | d_loss: 1.1963 | g_loss: 0.9933\n",
            "Epoch [   40/  100] | d_loss: 1.2838 | g_loss: 1.1732\n",
            "Epoch [   40/  100] | d_loss: 1.3593 | g_loss: 1.4707\n",
            "Epoch [   40/  100] | d_loss: 1.2806 | g_loss: 1.1178\n",
            "Epoch [   41/  100] | d_loss: 1.1408 | g_loss: 1.3782\n",
            "Epoch [   41/  100] | d_loss: 1.4356 | g_loss: 1.3716\n",
            "Epoch [   41/  100] | d_loss: 1.4858 | g_loss: 1.2544\n",
            "Epoch [   42/  100] | d_loss: 1.2586 | g_loss: 1.4896\n",
            "Epoch [   42/  100] | d_loss: 1.2500 | g_loss: 1.6016\n",
            "Epoch [   42/  100] | d_loss: 1.4833 | g_loss: 1.2285\n",
            "Epoch [   43/  100] | d_loss: 1.2868 | g_loss: 1.3497\n",
            "Epoch [   43/  100] | d_loss: 1.3218 | g_loss: 1.6790\n",
            "Epoch [   43/  100] | d_loss: 1.4667 | g_loss: 1.0930\n",
            "Epoch [   44/  100] | d_loss: 1.4780 | g_loss: 1.4229\n",
            "Epoch [   44/  100] | d_loss: 1.5086 | g_loss: 1.2914\n",
            "Epoch [   44/  100] | d_loss: 1.4629 | g_loss: 1.2462\n",
            "Epoch [   45/  100] | d_loss: 1.1247 | g_loss: 1.3326\n",
            "Epoch [   45/  100] | d_loss: 1.1717 | g_loss: 1.1776\n",
            "Epoch [   45/  100] | d_loss: 1.3344 | g_loss: 1.6446\n",
            "Epoch [   46/  100] | d_loss: 1.2131 | g_loss: 1.5792\n",
            "Epoch [   46/  100] | d_loss: 1.2413 | g_loss: 1.9468\n",
            "Epoch [   46/  100] | d_loss: 1.3605 | g_loss: 1.0911\n",
            "Epoch [   47/  100] | d_loss: 1.1843 | g_loss: 1.0417\n",
            "Epoch [   47/  100] | d_loss: 1.3438 | g_loss: 1.3769\n",
            "Epoch [   47/  100] | d_loss: 1.2360 | g_loss: 1.1814\n",
            "Epoch [   48/  100] | d_loss: 1.2313 | g_loss: 1.5061\n",
            "Epoch [   48/  100] | d_loss: 1.2245 | g_loss: 1.4118\n",
            "Epoch [   48/  100] | d_loss: 1.6683 | g_loss: 1.2975\n",
            "Epoch [   49/  100] | d_loss: 1.1752 | g_loss: 1.3546\n",
            "Epoch [   49/  100] | d_loss: 1.3178 | g_loss: 1.1031\n",
            "Epoch [   49/  100] | d_loss: 1.4392 | g_loss: 1.0157\n",
            "Epoch [   50/  100] | d_loss: 1.2132 | g_loss: 1.2201\n",
            "Epoch [   50/  100] | d_loss: 1.2941 | g_loss: 1.7519\n",
            "Epoch [   50/  100] | d_loss: 1.3572 | g_loss: 0.9350\n",
            "Epoch [   51/  100] | d_loss: 1.2304 | g_loss: 1.1900\n",
            "Epoch [   51/  100] | d_loss: 1.3876 | g_loss: 1.1363\n",
            "Epoch [   51/  100] | d_loss: 1.3770 | g_loss: 1.2229\n",
            "Epoch [   52/  100] | d_loss: 1.1723 | g_loss: 1.2131\n",
            "Epoch [   52/  100] | d_loss: 1.2287 | g_loss: 1.5002\n",
            "Epoch [   52/  100] | d_loss: 1.2573 | g_loss: 1.2633\n",
            "Epoch [   53/  100] | d_loss: 1.2147 | g_loss: 1.4567\n",
            "Epoch [   53/  100] | d_loss: 1.3306 | g_loss: 1.8946\n",
            "Epoch [   53/  100] | d_loss: 1.3938 | g_loss: 1.4061\n",
            "Epoch [   54/  100] | d_loss: 1.0742 | g_loss: 1.2578\n",
            "Epoch [   54/  100] | d_loss: 1.1947 | g_loss: 1.4666\n",
            "Epoch [   54/  100] | d_loss: 1.4334 | g_loss: 0.9247\n",
            "Epoch [   55/  100] | d_loss: 1.2436 | g_loss: 1.6776\n",
            "Epoch [   55/  100] | d_loss: 1.3992 | g_loss: 1.8024\n",
            "Epoch [   55/  100] | d_loss: 1.1780 | g_loss: 1.5784\n",
            "Epoch [   56/  100] | d_loss: 1.3647 | g_loss: 1.2672\n",
            "Epoch [   56/  100] | d_loss: 1.3594 | g_loss: 1.1939\n",
            "Epoch [   56/  100] | d_loss: 1.3045 | g_loss: 1.1997\n",
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            "Epoch [   58/  100] | d_loss: 1.2739 | g_loss: 1.1025\n",
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            "Epoch [   59/  100] | d_loss: 1.4239 | g_loss: 1.3513\n",
            "Epoch [   59/  100] | d_loss: 1.3265 | g_loss: 1.3036\n",
            "Epoch [   59/  100] | d_loss: 1.4305 | g_loss: 1.4027\n",
            "Epoch [   60/  100] | d_loss: 1.2237 | g_loss: 1.6080\n",
            "Epoch [   60/  100] | d_loss: 1.3358 | g_loss: 1.4978\n",
            "Epoch [   60/  100] | d_loss: 1.2935 | g_loss: 1.1736\n",
            "Epoch [   61/  100] | d_loss: 1.3100 | g_loss: 1.6308\n",
            "Epoch [   61/  100] | d_loss: 1.1645 | g_loss: 1.7365\n",
            "Epoch [   61/  100] | d_loss: 1.4765 | g_loss: 1.4394\n",
            "Epoch [   62/  100] | d_loss: 1.2771 | g_loss: 1.1468\n",
            "Epoch [   62/  100] | d_loss: 1.1970 | g_loss: 1.4954\n",
            "Epoch [   62/  100] | d_loss: 1.4950 | g_loss: 1.1878\n",
            "Epoch [   63/  100] | d_loss: 1.3762 | g_loss: 1.0438\n",
            "Epoch [   63/  100] | d_loss: 1.2123 | g_loss: 1.5164\n",
            "Epoch [   63/  100] | d_loss: 1.3520 | g_loss: 1.6397\n",
            "Epoch [   64/  100] | d_loss: 1.2980 | g_loss: 1.3536\n",
            "Epoch [   64/  100] | d_loss: 1.2258 | g_loss: 1.1652\n",
            "Epoch [   64/  100] | d_loss: 1.2774 | g_loss: 1.1900\n",
            "Epoch [   65/  100] | d_loss: 1.2740 | g_loss: 1.2269\n",
            "Epoch [   65/  100] | d_loss: 1.3481 | g_loss: 1.3808\n",
            "Epoch [   65/  100] | d_loss: 1.2500 | g_loss: 1.9662\n",
            "Epoch [   66/  100] | d_loss: 1.2973 | g_loss: 1.0905\n",
            "Epoch [   66/  100] | d_loss: 1.3857 | g_loss: 1.3862\n",
            "Epoch [   66/  100] | d_loss: 1.3799 | g_loss: 1.1701\n",
            "Epoch [   67/  100] | d_loss: 1.2581 | g_loss: 1.1602\n",
            "Epoch [   67/  100] | d_loss: 1.2588 | g_loss: 1.2738\n",
            "Epoch [   67/  100] | d_loss: 1.4983 | g_loss: 1.3309\n",
            "Epoch [   68/  100] | d_loss: 1.2206 | g_loss: 1.0745\n",
            "Epoch [   68/  100] | d_loss: 1.3632 | g_loss: 1.1167\n",
            "Epoch [   68/  100] | d_loss: 1.2500 | g_loss: 1.3383\n",
            "Epoch [   69/  100] | d_loss: 1.4172 | g_loss: 1.0385\n",
            "Epoch [   69/  100] | d_loss: 1.2601 | g_loss: 1.3928\n",
            "Epoch [   69/  100] | d_loss: 1.2939 | g_loss: 1.3444\n",
            "Epoch [   70/  100] | d_loss: 1.3797 | g_loss: 0.9774\n",
            "Epoch [   70/  100] | d_loss: 1.2988 | g_loss: 1.5934\n",
            "Epoch [   70/  100] | d_loss: 1.3627 | g_loss: 1.0073\n",
            "Epoch [   71/  100] | d_loss: 1.3888 | g_loss: 1.3946\n",
            "Epoch [   71/  100] | d_loss: 1.3226 | g_loss: 2.0219\n",
            "Epoch [   71/  100] | d_loss: 1.4127 | g_loss: 1.4328\n",
            "Epoch [   72/  100] | d_loss: 1.3010 | g_loss: 0.9676\n",
            "Epoch [   72/  100] | d_loss: 1.2349 | g_loss: 2.2384\n",
            "Epoch [   72/  100] | d_loss: 1.4103 | g_loss: 1.2500\n",
            "Epoch [   73/  100] | d_loss: 1.2460 | g_loss: 1.7723\n",
            "Epoch [   73/  100] | d_loss: 1.3699 | g_loss: 1.4234\n",
            "Epoch [   73/  100] | d_loss: 1.2624 | g_loss: 1.2169\n",
            "Epoch [   74/  100] | d_loss: 1.2728 | g_loss: 1.2000\n",
            "Epoch [   74/  100] | d_loss: 1.2386 | g_loss: 1.5929\n",
            "Epoch [   74/  100] | d_loss: 1.2632 | g_loss: 1.3343\n",
            "Epoch [   75/  100] | d_loss: 1.1978 | g_loss: 1.3998\n",
            "Epoch [   75/  100] | d_loss: 1.3778 | g_loss: 1.1288\n",
            "Epoch [   75/  100] | d_loss: 1.2243 | g_loss: 1.3699\n",
            "Epoch [   76/  100] | d_loss: 1.2913 | g_loss: 1.5743\n",
            "Epoch [   76/  100] | d_loss: 1.2021 | g_loss: 1.2814\n",
            "Epoch [   76/  100] | d_loss: 1.4932 | g_loss: 1.8648\n",
            "Epoch [   77/  100] | d_loss: 1.3069 | g_loss: 1.5082\n",
            "Epoch [   77/  100] | d_loss: 1.2137 | g_loss: 1.6100\n",
            "Epoch [   77/  100] | d_loss: 1.3479 | g_loss: 1.4498\n",
            "Epoch [   78/  100] | d_loss: 1.4055 | g_loss: 1.3478\n",
            "Epoch [   78/  100] | d_loss: 1.4074 | g_loss: 1.2370\n",
            "Epoch [   78/  100] | d_loss: 1.2782 | g_loss: 1.1387\n",
            "Epoch [   79/  100] | d_loss: 1.2691 | g_loss: 0.9800\n",
            "Epoch [   79/  100] | d_loss: 1.3458 | g_loss: 1.4691\n",
            "Epoch [   79/  100] | d_loss: 1.2384 | g_loss: 1.2399\n",
            "Epoch [   80/  100] | d_loss: 1.2271 | g_loss: 1.0185\n",
            "Epoch [   80/  100] | d_loss: 1.2751 | g_loss: 1.5156\n",
            "Epoch [   80/  100] | d_loss: 1.3676 | g_loss: 1.0046\n",
            "Epoch [   81/  100] | d_loss: 1.2310 | g_loss: 1.2077\n",
            "Epoch [   81/  100] | d_loss: 1.3049 | g_loss: 1.3777\n",
            "Epoch [   81/  100] | d_loss: 1.0949 | g_loss: 1.0824\n",
            "Epoch [   82/  100] | d_loss: 1.1342 | g_loss: 1.2694\n",
            "Epoch [   82/  100] | d_loss: 1.4348 | g_loss: 1.4014\n",
            "Epoch [   82/  100] | d_loss: 1.7297 | g_loss: 1.2201\n",
            "Epoch [   83/  100] | d_loss: 1.1582 | g_loss: 1.0298\n",
            "Epoch [   83/  100] | d_loss: 1.1932 | g_loss: 1.2743\n",
            "Epoch [   83/  100] | d_loss: 1.3520 | g_loss: 1.1249\n",
            "Epoch [   84/  100] | d_loss: 1.1651 | g_loss: 1.4376\n",
            "Epoch [   84/  100] | d_loss: 1.3375 | g_loss: 1.1041\n",
            "Epoch [   84/  100] | d_loss: 1.4971 | g_loss: 1.0579\n",
            "Epoch [   85/  100] | d_loss: 1.3455 | g_loss: 1.1102\n",
            "Epoch [   85/  100] | d_loss: 1.3016 | g_loss: 1.4407\n",
            "Epoch [   85/  100] | d_loss: 1.6973 | g_loss: 2.2994\n",
            "Epoch [   86/  100] | d_loss: 1.0401 | g_loss: 1.8245\n",
            "Epoch [   86/  100] | d_loss: 1.2772 | g_loss: 1.1009\n",
            "Epoch [   86/  100] | d_loss: 1.4298 | g_loss: 1.3106\n",
            "Epoch [   87/  100] | d_loss: 1.2682 | g_loss: 1.3659\n",
            "Epoch [   87/  100] | d_loss: 1.3460 | g_loss: 1.5190\n",
            "Epoch [   87/  100] | d_loss: 1.3212 | g_loss: 1.2289\n",
            "Epoch [   88/  100] | d_loss: 1.3105 | g_loss: 1.3210\n",
            "Epoch [   88/  100] | d_loss: 1.1798 | g_loss: 1.7379\n",
            "Epoch [   88/  100] | d_loss: 1.6625 | g_loss: 1.5789\n",
            "Epoch [   89/  100] | d_loss: 1.2589 | g_loss: 1.1415\n",
            "Epoch [   89/  100] | d_loss: 1.4104 | g_loss: 1.8009\n",
            "Epoch [   89/  100] | d_loss: 1.7930 | g_loss: 1.6613\n",
            "Epoch [   90/  100] | d_loss: 1.3840 | g_loss: 1.1730\n",
            "Epoch [   90/  100] | d_loss: 1.3204 | g_loss: 1.5186\n",
            "Epoch [   90/  100] | d_loss: 1.5127 | g_loss: 1.4479\n",
            "Epoch [   91/  100] | d_loss: 1.3634 | g_loss: 1.2433\n",
            "Epoch [   91/  100] | d_loss: 1.4526 | g_loss: 1.6156\n",
            "Epoch [   91/  100] | d_loss: 1.4455 | g_loss: 1.3021\n",
            "Epoch [   92/  100] | d_loss: 1.1998 | g_loss: 1.6254\n",
            "Epoch [   92/  100] | d_loss: 1.3230 | g_loss: 2.0020\n",
            "Epoch [   92/  100] | d_loss: 1.3974 | g_loss: 1.4070\n",
            "Epoch [   93/  100] | d_loss: 1.2657 | g_loss: 1.1808\n",
            "Epoch [   93/  100] | d_loss: 1.3950 | g_loss: 1.3241\n",
            "Epoch [   93/  100] | d_loss: 1.4714 | g_loss: 0.9867\n",
            "Epoch [   94/  100] | d_loss: 1.2418 | g_loss: 1.3529\n",
            "Epoch [   94/  100] | d_loss: 1.4752 | g_loss: 1.5436\n",
            "Epoch [   94/  100] | d_loss: 1.2516 | g_loss: 1.1992\n",
            "Epoch [   95/  100] | d_loss: 1.2104 | g_loss: 1.1440\n",
            "Epoch [   95/  100] | d_loss: 1.2204 | g_loss: 2.0741\n",
            "Epoch [   95/  100] | d_loss: 1.2338 | g_loss: 1.5336\n",
            "Epoch [   96/  100] | d_loss: 1.1581 | g_loss: 1.0768\n",
            "Epoch [   96/  100] | d_loss: 1.0945 | g_loss: 1.7526\n",
            "Epoch [   96/  100] | d_loss: 1.2092 | g_loss: 1.2115\n",
            "Epoch [   97/  100] | d_loss: 1.2011 | g_loss: 1.2412\n",
            "Epoch [   97/  100] | d_loss: 1.2235 | g_loss: 1.7923\n",
            "Epoch [   97/  100] | d_loss: 1.6248 | g_loss: 1.2296\n",
            "Epoch [   98/  100] | d_loss: 1.1853 | g_loss: 1.4408\n",
            "Epoch [   98/  100] | d_loss: 1.3106 | g_loss: 1.5801\n",
            "Epoch [   98/  100] | d_loss: 1.6361 | g_loss: 1.8007\n",
            "Epoch [   99/  100] | d_loss: 1.2092 | g_loss: 1.0475\n",
            "Epoch [   99/  100] | d_loss: 1.2519 | g_loss: 1.8282\n",
            "Epoch [   99/  100] | d_loss: 1.4451 | g_loss: 1.4302\n",
            "Epoch [  100/  100] | d_loss: 1.2603 | g_loss: 0.9857\n",
            "Epoch [  100/  100] | d_loss: 1.3051 | g_loss: 1.4614\n",
            "Epoch [  100/  100] | d_loss: 1.4098 | g_loss: 1.5620\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3B2C_u1Q_KY",
        "colab_type": "text"
      },
      "source": [
        "## Training loss\n",
        "\n",
        "Here we'll plot the training losses for the generator and discriminator, recorded after each epoch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDmyF96WQ_KZ",
        "colab_type": "code",
        "outputId": "2562e82e-f0f5-422d-ad52-3ccbdd734c12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "losses = np.array(losses)\n",
        "plt.plot(losses.T[0], label='Discriminator')\n",
        "plt.plot(losses.T[1], label='Generator')\n",
        "plt.title(\"Training Losses\")\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc650ae31d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpnwyFx9Q_Kd",
        "colab_type": "text"
      },
      "source": [
        "## Generator samples from training\n",
        "\n",
        "Here we can view samples of images from the generator. First we'll look at the images we saved during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haObR2iiQ_Ke",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# helper function for viewing a list of passed in sample images\n",
        "def view_samples(epoch, samples):\n",
        "    fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n",
        "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
        "        img = img.detach()\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)\n",
        "        im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSG9z-XtQ_Kf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load samples from generator, taken while training\n",
        "with open('train_samples.pkl', 'rb') as f:\n",
        "    samples = pkl.load(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roq6-BY4Q_Kh",
        "colab_type": "text"
      },
      "source": [
        "These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 1, 7, 3, 2. Since this is just a sample, it isn't representative of the full range of images this generator can make."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4coVeaJQ_Ki",
        "colab_type": "code",
        "outputId": "70666961-d441-4dee-b6ca-b56469017c02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "source": [
        "# -1 indicates final epoch's samples (the last in the list)\n",
        "view_samples(-1, samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soLPjyBpQ_Kj",
        "colab_type": "text"
      },
      "source": [
        "Below I'm showing the generated images as the network was training, every 10 epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoU49asnQ_Kk",
        "colab_type": "code",
        "outputId": "edb9723a-3942-4f9b-f29a-084521fb4af3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        }
      },
      "source": [
        "rows = 10 # split epochs into 10, so 100/10 = every 10 epochs\n",
        "cols = 6\n",
        "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n",
        "\n",
        "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n",
        "    for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n",
        "        img = img.detach()\n",
        "        ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x864 with 60 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcnNiStLQ_Km",
        "colab_type": "text"
      },
      "source": [
        "It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise like 1s and 9s."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GMG0m0XQ_Kn",
        "colab_type": "text"
      },
      "source": [
        "## Sampling from the generator\n",
        "\n",
        "We can also get completely new images from the generator by using the checkpoint we saved after training. **We just need to pass in a new latent vector $z$ and we'll get new samples**!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlU4-KV6Q_Kn",
        "colab_type": "code",
        "outputId": "39f70536-74da-406d-cebf-6f56581a32b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "# randomly generated, new latent vectors\n",
        "sample_size=16\n",
        "rand_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
        "rand_z = torch.from_numpy(rand_z).float()\n",
        "\n",
        "G.eval() # eval mode\n",
        "# generated samples\n",
        "rand_images = G(rand_z)\n",
        "\n",
        "# 0 indicates the first set of samples in the passed in list\n",
        "# and we only have one batch of samples, here\n",
        "view_samples(0, [rand_images])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1340: 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": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}