dlnd_face_generation.ipynb

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    "colab": {
      "name": "dlnd_face_generation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JasperEssien2/face_generator_project/blob/master/dlnd_face_generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWw6PqK8J59m",
        "colab_type": "text"
      },
      "source": [
        "# Face Generation\n",
        "\n",
        "In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate *new* images of faces that look as realistic as possible!\n",
        "\n",
        "The project will be broken down into a series of tasks from **loading in data to defining and training adversarial networks**. At the end of the notebook, you'll be able to visualize the results of your trained Generator to see how it performs; your generated samples should look like fairly realistic faces with small amounts of noise.\n",
        "\n",
        "### Get the Data\n",
        "\n",
        "You'll be using the [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) to train your adversarial networks.\n",
        "\n",
        "This dataset is more complex than the number datasets (like MNIST or SVHN) you've been working with, and so, you should prepare to define deeper networks and train them for a longer time to get good results. It is suggested that you utilize a GPU for training.\n",
        "\n",
        "### Pre-processed Data\n",
        "\n",
        "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. Some sample data is show below.\n",
        "\n",
        "<img src='assets/processed_face_data.png' width=60% />\n",
        "\n",
        "> If you are working locally, you can download this data [by clicking here](https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip)\n",
        "\n",
        "This is a zip file that you'll need to extract in the home directory of this notebook for further loading and processing. After extracting the data, you should be left with a directory of data `processed_celeba_small/`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2SMK6c0Kua3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "763ae02e-71ba-4632-fcc6-dd7af9247673"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        " \n",
        "%cd '/content/gdrive/My Drive/Colab Notebooks/project_face_generation'\n",
        "!pwd\n",
        " \n",
        "import os\n",
        "os.chdir(\"/content/gdrive/My Drive/Colab Notebooks/project_face_generation\")\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive/Colab Notebooks/project_face_generation\n",
            "/content/gdrive/My Drive/Colab Notebooks/project_face_generation\n",
            "__MACOSX\t      processed_celeba_small\t      __pycache__\n",
            "problem_unittests.py  processed_celeba_small_zip.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cT9h_7wSJ59n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# can comment out after executing\n",
        "# !unzip processed_celeba_small_zip.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmQbv3ArJ59q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir = 'processed_celeba_small/'\n",
        "\n",
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL\n",
        "\"\"\"\n",
        "import pickle as pkl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import problem_unittests as tests\n",
        "#import helper\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoWIAlO1J59s",
        "colab_type": "text"
      },
      "source": [
        "## Visualize the CelebA Data\n",
        "\n",
        "The [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations, you'll only need the images. Note that these are color images with [3 color channels (RGB)](https://en.wikipedia.org/wiki/Channel_(digital_image)#RGB_Images) each.\n",
        "\n",
        "### Pre-process and Load the Data\n",
        "\n",
        "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. This *pre-processed* dataset is a smaller subset of the very large CelebA data.\n",
        "\n",
        "> There are a few other steps that you'll need to **transform** this data and create a **DataLoader**.\n",
        "\n",
        "#### Exercise: Complete the following `get_dataloader` function, such that it satisfies these requirements:\n",
        "\n",
        "* Your images should be square, Tensor images of size `image_size x image_size` in the x and y dimension.\n",
        "* Your function should return a DataLoader that shuffles and batches these Tensor images.\n",
        "\n",
        "#### ImageFolder\n",
        "\n",
        "To create a dataset given a directory of images, it's recommended that you use PyTorch's [ImageFolder](https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder) wrapper, with a root directory `processed_celeba_small/` and data transformation passed in."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzITDSNCJ59t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# necessary imports\n",
        "import torch\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLSJEtIwJ59v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_dataloader(batch_size, image_size, data_dir='./processed_celeba_small/'):\n",
        "    \"\"\"\n",
        "    Batch the neural network data using DataLoader\n",
        "    :param batch_size: The size of each batch; the number of images in a batch\n",
        "    :param img_size: The square size of the image data (x, y)\n",
        "    :param data_dir: Directory where image data is located\n",
        "    :return: DataLoader with batched data\n",
        "    \"\"\"\n",
        "    transform = transforms.Compose([transforms.Resize(image_size),\n",
        "                                   transforms.ToTensor()])\n",
        "    # TODO: Implement function and return a dataloader\n",
        "    dataset = datasets.ImageFolder(data_dir, transform=transform)\n",
        "    \n",
        "    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
        "    return dataloader\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfTuNWhlJ59y",
        "colab_type": "text"
      },
      "source": [
        "## Create a DataLoader\n",
        "\n",
        "#### Exercise: Create a DataLoader `celeba_train_loader` with appropriate hyperparameters.\n",
        "\n",
        "Call the above function and create a dataloader to view images. \n",
        "* You can decide on any reasonable `batch_size` parameter\n",
        "* Your `image_size` **must be** `32`. Resizing the data to a smaller size will make for faster training, while still creating convincing images of faces!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QeJFtKS2J59y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define function hyperparameters\n",
        "batch_size = 64\n",
        "img_size = 32\n",
        "\n",
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
        "\"\"\"\n",
        "# Call your function and get a dataloader\n",
        "celeba_train_loader = get_dataloader(batch_size, img_size)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDbA4_q-J591",
        "colab_type": "text"
      },
      "source": [
        "Next, you can view some images! You should seen square images of somewhat-centered faces.\n",
        "\n",
        "Note: You'll need to convert the Tensor images into a NumPy type and transpose the dimensions to correctly display an image, suggested `imshow` code is below, but it may not be perfect."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qAr9SzhJ591",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "45208e2d-20ab-4aa0-de64-23d24e935ba5"
      },
      "source": [
        "# helper display function\n",
        "def imshow(img):\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
        "\"\"\"\n",
        "# obtain one batch of training images\n",
        "dataiter = iter(celeba_train_loader)\n",
        "images, _ = dataiter.next() # _ for no labels\n",
        "\n",
        "# plot the images in the batch, along with the corresponding labels\n",
        "fig = plt.figure(figsize=(20, 4))\n",
        "plot_size=20\n",
        "for idx in np.arange(plot_size):\n",
        "    ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])\n",
        "    imshow(images[idx])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9vKLNKKJ594",
        "colab_type": "text"
      },
      "source": [
        "#### Exercise: Pre-process your image data and scale it to a pixel range of -1 to 1\n",
        "\n",
        "You need to do a bit of pre-processing; you know that the output of a `tanh` activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_x3_0oVSJ594",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# TODO: Complete the scale function\n",
        "def scale(x, feature_range=(-1, 1)):\n",
        "    ''' Scale takes in an image x and returns that image, scaled\n",
        "       with a feature_range of pixel values from -1 to 1. \n",
        "       This function assumes that the input x is already scaled from 0-1.'''\n",
        "    # assume x is scaled to (0, 1)\n",
        "    # scale to feature_range and return scaled x\n",
        "    min,max = feature_range[0], feature_range[1]\n",
        "    x = x * (max - min) + min\n",
        "    return x\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvXJZEmyJ596",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "4804a29a-720b-4626-ee9c-5be9ac00d50d"
      },
      "source": [
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
        "\"\"\"\n",
        "# check scaled range\n",
        "# should be close to -1 to 1\n",
        "img = images[0]\n",
        "scaled_img = scale(img)\n",
        "\n",
        "print('Min: ', scaled_img.min())\n",
        "print('Max: ', scaled_img.max())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min:  tensor(-0.9765)\n",
            "Max:  tensor(0.8588)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXld7egzJ599",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "# Define the Model\n",
        "\n",
        "A GAN is comprised of two adversarial networks, a discriminator and a generator.\n",
        "\n",
        "## Discriminator\n",
        "\n",
        "Your first task will be to define the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. To deal with this complex data, it's suggested you use a deep network with **normalization**. You are also allowed to create any helper functions that may be useful.\n",
        "\n",
        "#### Exercise: Complete the Discriminator class\n",
        "* The inputs to the discriminator are 32x32x3 tensor images\n",
        "* The output should be a single value that will indicate whether a given image is real or fake\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4AnwsUf6J599",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JY9gd-Bicrug",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
        "\n",
        "    layers = []\n",
        "    conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=False)\n",
        "    layers.append(conv)\n",
        "    if batch_norm == True:\n",
        "        layers.append(nn.BatchNorm2d(out_channels))\n",
        "\n",
        "    return nn.Sequential(*layers)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kuiHnkLZJ59_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "08ddb87a-efa4-42ec-a8b0-a0cf4498a51f"
      },
      "source": [
        "class Discriminator(nn.Module):\n",
        "\n",
        "    def __init__(self, conv_dim):\n",
        "        \"\"\"\n",
        "        Initialize the Discriminator Module\n",
        "        :param conv_dim: The depth of the first convolutional layer\n",
        "        \"\"\"\n",
        "        super(Discriminator, self).__init__()\n",
        "\n",
        "        # complete init function\n",
        "        self.conv_dim = conv_dim\n",
        "\n",
        "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False) #(16 x 16)\n",
        "        self.conv2 = conv(conv_dim, conv_dim*2, 4) # (8 x 8)\n",
        "        self.conv3 = conv(conv_dim*2, conv_dim*4, 4) #(4 x 4)\n",
        "\n",
        "        self.fc1 = nn.Linear(4*4*(conv_dim*4), 1)\n",
        "        \n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Forward propagation of the neural network\n",
        "        :param x: The input to the neural network     \n",
        "        :return: Discriminator logits; the output of the neural network\n",
        "        \"\"\"\n",
        "        # define feedforward behavior\n",
        "        x = F.leaky_relu(self.conv1(x), 0.2)\n",
        "        x = F.leaky_relu(self.conv2(x), 0.2)\n",
        "        x = F.leaky_relu(self.conv3(x), 0.2)\n",
        "        # x = F.leaky_relu(self.conv4(x), 0.2)\n",
        "\n",
        "        # FLatten input to feed to linear \n",
        "        x = x.view(-1, 4*4*(self.conv_dim*4))\n",
        "        x = self.fc1(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
        "\"\"\"\n",
        "tests.test_discriminator(Discriminator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tests Passed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7U_uf7YJ5-B",
        "colab_type": "text"
      },
      "source": [
        "## Generator\n",
        "\n",
        "The generator should upsample an input and generate a *new* image of the same size as our training data `32x32x3`. This should be mostly transpose convolutional layers with normalization applied to the outputs.\n",
        "\n",
        "#### Exercise: Complete the Generator class\n",
        "* The inputs to the generator are vectors of some length `z_size`\n",
        "* The output should be a image of shape `32x32x3`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnD1ZpnThgLo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def deconv2d(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
        "\n",
        "    layers = []\n",
        "    t_conv = nn.ConvTranspose2d(in_channels, out_channels, 4, stride=stride, padding=padding, bias=False)\n",
        "    layers.append(t_conv)\n",
        "    if batch_norm == True:\n",
        "        layers.append(nn.BatchNorm2d(out_channels))\n",
        "\n",
        "    return nn.Sequential(*layers)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJS61X0iJ5-C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "45cab866-ccc4-493a-e658-4b84b68c6a0e"
      },
      "source": [
        "class Generator(nn.Module):\n",
        "    \n",
        "    def __init__(self, z_size, conv_dim):\n",
        "        \"\"\"\n",
        "        Initialize the Generator Module\n",
        "        :param z_size: The length of the input latent vector, z\n",
        "        :param conv_dim: The depth of the inputs to the *last* transpose convolutional layer\n",
        "        \"\"\"\n",
        "        super(Generator, self).__init__()\n",
        "\n",
        "        # complete init function\n",
        "        self.conv_dim = conv_dim\n",
        "        # fully connected layer\n",
        "        self.fc1 = nn.Linear(z_size, conv_dim*4*4*4)\n",
        "\n",
        "        self.t_conv1 = deconv2d(conv_dim*4, conv_dim*2, 4)\n",
        "        self.t_conv2 = deconv2d(conv_dim*2, conv_dim, 4)\n",
        "        self.t_conv3 = deconv2d(conv_dim, 3, 4, batch_norm=False)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Forward propagation of the neural network\n",
        "        :param x: The input to the neural network     \n",
        "        :return: A 32x32x3 Tensor image as output\n",
        "        \"\"\"\n",
        "        # define feedforward behavior\n",
        "        x = self.fc1(x)\n",
        "\n",
        "        # resize shape to fit conv1 layers\n",
        "        x = x.view(-1, self.conv_dim*4, 4, 4) #(batch_size, depth, height, weight)\n",
        "        x = F.relu(self.t_conv1(x))\n",
        "        x = F.relu(self.t_conv2(x))\n",
        "        x = torch.tanh(self.t_conv3(x))\n",
        "        return x\n",
        "\n",
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
        "\"\"\"\n",
        "tests.test_generator(Generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tests Passed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWiNlSKeJ5-E",
        "colab_type": "text"
      },
      "source": [
        "## Initialize the weights of your networks\n",
        "\n",
        "To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the [original DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf), they say:\n",
        "> All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.\n",
        "\n",
        "So, your next task will be to define a weight initialization function that does just this!\n",
        "\n",
        "You can refer back to the lesson on weight initialization or even consult existing model code, such as that from [the `networks.py` file in CycleGAN Github repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py) to help you complete this function.\n",
        "\n",
        "#### Exercise: Complete the weight initialization function\n",
        "\n",
        "* This should initialize only **convolutional** and **linear** layers\n",
        "* Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.\n",
        "* The bias terms, if they exist, may be left alone or set to 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIW17si0J5-F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def weights_init_normal(m):\n",
        "    \"\"\"\n",
        "    Applies initial weights to certain layers in a model .\n",
        "    The weights are taken from a normal distribution \n",
        "    with mean = 0, std dev = 0.02.\n",
        "    :param m: A module or layer in a network    \n",
        "    \"\"\"\n",
        "    # classname will be something like:\n",
        "    # `Conv`, `BatchNorm2d`, `Linear`, etc.\n",
        "    classname = m.__class__.__name__\n",
        "    \n",
        "    # TODO: Apply initial weights to convolutional and linear layers\n",
        "    if classname.find('Conv') != -1:\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "    elif classname.find('Linear') != -1:\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "    elif classname.find('BatchNorm2d') != -1:\n",
        "        nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
        "        nn.init.constant_(m.bias.data, 0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "G3FDTCSAJ5-H",
        "colab_type": "text"
      },
      "source": [
        "## Build complete network\n",
        "\n",
        "Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2AJ8dzNJ5-I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
        "\"\"\"\n",
        "def build_network(d_conv_dim, g_conv_dim, z_size):\n",
        "    # define discriminator and generator\n",
        "    D = Discriminator(d_conv_dim)\n",
        "    G = Generator(z_size=z_size, conv_dim=g_conv_dim)\n",
        "\n",
        "    # initialize model weights\n",
        "    D.apply(weights_init_normal)\n",
        "    G.apply(weights_init_normal)\n",
        "\n",
        "    print(D)\n",
        "    print()\n",
        "    print(G)\n",
        "    \n",
        "    return D, G\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8p5PgwV0J5-K",
        "colab_type": "text"
      },
      "source": [
        "#### Exercise: Define model hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRXF8AXwJ5-K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "eea5cbf1-d443-486d-e038-822a44815f4c"
      },
      "source": [
        "# Define model hyperparams\n",
        "d_conv_dim = 64\n",
        "g_conv_dim = 64\n",
        "z_size = 100\n",
        "\n",
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
        "\"\"\"\n",
        "D, G = build_network(d_conv_dim, g_conv_dim, z_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Discriminator(\n",
            "  (conv1): Sequential(\n",
            "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  )\n",
            "  (conv2): Sequential(\n",
            "    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (conv3): Sequential(\n",
            "    (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (fc1): Linear(in_features=4096, out_features=1, bias=True)\n",
            ")\n",
            "\n",
            "Generator(\n",
            "  (fc1): Linear(in_features=100, out_features=4096, bias=True)\n",
            "  (t_conv1): Sequential(\n",
            "    (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (t_conv2): Sequential(\n",
            "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (t_conv3): Sequential(\n",
            "    (0): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  )\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LYRxhf4qJ5-M",
        "colab_type": "text"
      },
      "source": [
        "### Training on GPU\n",
        "\n",
        "Check if you can train on GPU. Here, we'll set this as a boolean variable `train_on_gpu`. Later, you'll be responsible for making sure that \n",
        ">* Models,\n",
        "* Model inputs, and\n",
        "* Loss function arguments\n",
        "\n",
        "Are moved to GPU, where appropriate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSNg8T-ZJ5-N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cfb2fdc0-e96b-48ce-d486-7d22e96b3382"
      },
      "source": [
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL\n",
        "\"\"\"\n",
        "import torch\n",
        "\n",
        "# Check for a GPU\n",
        "train_on_gpu = torch.cuda.is_available()\n",
        "if not train_on_gpu:\n",
        "    print('No GPU found. Please use a GPU to train your neural network.')\n",
        "else:\n",
        "    print('Training on GPU!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training on GPU!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBOfAo29J5-P",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Discriminator and Generator Losses\n",
        "\n",
        "Now we need to calculate the losses for both types of adversarial networks.\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",
        "\n",
        "### Generator Loss\n",
        "\n",
        "The generator loss will look similar only with flipped labels. The generator's goal is to get the discriminator to *think* its generated images are *real*.\n",
        "\n",
        "#### Exercise: Complete real and fake loss functions\n",
        "\n",
        "**You may choose to use either cross entropy or a least squares error loss to complete the following `real_loss` and `fake_loss` functions.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATvrmEoLJ5-P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def real_loss(D_out):\n",
        "    '''Calculates how close discriminator outputs are to being real.\n",
        "       param, D_out: discriminator logits\n",
        "       return: real loss'''\n",
        "    loss = torch.mean((D_out-1)**2)   \n",
        "    return loss\n",
        "\n",
        "def fake_loss(D_out):\n",
        "    '''Calculates how close discriminator outputs are to being fake.\n",
        "       param, D_out: discriminator logits\n",
        "       return: fake loss'''\n",
        "    loss = torch.mean(D_out**2)   \n",
        "    return loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OB5TGryKJ5-S",
        "colab_type": "text"
      },
      "source": [
        "## Optimizers\n",
        "\n",
        "#### Exercise: Define optimizers for your Discriminator (D) and Generator (G)\n",
        "\n",
        "Define optimizers for your models with appropriate hyperparameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQKA_6w_J5-S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "beta1 = 0.5\n",
        "beta2 = 0.999\n",
        "\n",
        "# Create optimizers for the discriminator D and generator G\n",
        "d_optimizer = optim.Adam(D.parameters(), 0.0002, [beta1, beta2])\n",
        "g_optimizer = optim.Adam(G.parameters(), 0.0002, [beta1, beta2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLV6qzV3J5-U",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Training\n",
        "\n",
        "Training will involve alternating between training the discriminator and the generator. You'll use your functions `real_loss` and `fake_loss` to help you calculate the discriminator losses.\n",
        "\n",
        "* You should train the discriminator by alternating on real and fake images\n",
        "* Then the generator, which tries to trick the discriminator and should have an opposing loss function\n",
        "\n",
        "\n",
        "#### Saving Samples\n",
        "\n",
        "You've been given some code to print out some loss statistics and save some generated \"fake\" samples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypKTPDOfJ5-U",
        "colab_type": "text"
      },
      "source": [
        "#### Exercise: Complete the training function\n",
        "\n",
        "Keep in mind that, if you've moved your models to GPU, you'll also have to move any model inputs to GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cW15WWfTJ5-V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def checkpoint(D, G, checkpoint_dir='./save'):\n",
        "    \"\"\"Saves the parameters of both generators G_YtoX, G_XtoY and discriminators D_X, D_Y.\n",
        "        \"\"\"\n",
        "    \n",
        "    D_path = checkpoint_dir + 'D.pkl' #os.path.join(checkpoint_dir, 'D_X.pkl')\n",
        "    G_path = checkpoint_dir + 'G.pkl' #os.path.join(checkpoint_dir, 'D_Y.pkl')\n",
        "    \n",
        "    torch.save(D.state_dict(), D_path)\n",
        "    torch.save(G.state_dict(), G_path)\n",
        " \n",
        "def train(D, G, n_epochs, print_every=50):\n",
        "    '''Trains adversarial networks for some number of epochs\n",
        "       param, D: the discriminator network\n",
        "       param, G: the generator network\n",
        "       param, n_epochs: number of epochs to train for\n",
        "       param, print_every: when to print and record the models' losses\n",
        "       return: D and G losses'''\n",
        "    \n",
        "    # move models to GPU\n",
        "    if train_on_gpu:\n",
        "        D = D.cuda()\n",
        "        G = G.cuda()\n",
        " \n",
        "    # keep track of loss and generated, \"fake\" samples\n",
        "    samples = []\n",
        "    losses = []\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",
        "    # move z to GPU if available\n",
        "    if train_on_gpu:\n",
        "        fixed_z = fixed_z.cuda()\n",
        " \n",
        "    # epoch training loop\n",
        "    for epoch in range(n_epochs):\n",
        " \n",
        "        # batch training loop\n",
        "        for batch_i, (real_images, _) in enumerate(celeba_train_loader):\n",
        " \n",
        "            batch_size = real_images.size(0)\n",
        "            real_images = scale(real_images)\n",
        " \n",
        "            if train_on_gpu:\n",
        "                real_images = real_images.cuda()\n",
        "            \n",
        "            # ===============================================\n",
        "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
        "            # ===============================================\n",
        " \n",
        "            # clear accumulated gradients\n",
        "            d_optimizer.zero_grad()\n",
        " \n",
        "            # 1. Train the discriminator on real and fake images\n",
        " \n",
        "            # pass real image to the discriminator and calulate the real loss\n",
        "            D_real_out = D(real_images)\n",
        "            D_real_loss = real_loss(D_real_out.cpu())\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",
        "            if train_on_gpu:\n",
        "              z = z.cuda()\n",
        "            G_out = G(z)\n",
        " \n",
        "            # pass fake images to dicriminator and calculate the fake loss\n",
        "            D_fake_out = D(G_out)\n",
        "            D_fake_loss = fake_loss(D_fake_out.cpu())\n",
        " \n",
        "            # The dicriminator loss is a total of real_loss and fake_loss\n",
        "            d_loss = D_real_loss + D_fake_loss\n",
        " \n",
        "            # backward propagate and update weights\n",
        "            d_loss.backward()\n",
        "            d_optimizer.step()\n",
        " \n",
        "            # 2. Train the generator with an adversarial loss\n",
        " \n",
        "            # clear out accumulated gradients from g_optimizer\n",
        "            g_optimizer.zero_grad()\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",
        "            if train_on_gpu:\n",
        "              z = z.cuda()\n",
        "            G_out = G(z)\n",
        "            # Pass it to the dicriminator to decieve discriminator then calculate loss\n",
        "            D_from_G_out = D(G_out)\n",
        "            # pass the generate output to the real_loss to check how well the generator is trying to fool discriminator\n",
        "            g_loss = real_loss(D_from_G_out.cpu())\n",
        " \n",
        "            # back propagate and update weights\n",
        "            g_loss.backward()\n",
        "            g_optimizer.step()          \n",
        "            \n",
        "            # ===============================================\n",
        "            #              END OF YOUR CODE\n",
        "            # ===============================================\n",
        " \n",
        "            # Print some loss stats\n",
        "            if batch_i % print_every == 0:\n",
        "                # append discriminator loss and generator loss\n",
        "                losses.append((d_loss.item(), g_loss.item()))\n",
        "                # print discriminator and generator loss\n",
        "                print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
        "                        epoch+1, n_epochs, d_loss.item(), g_loss.item()))\n",
        " \n",
        "        if epoch % 5 == 0:\n",
        "            print('Saving model ---- epoch: ', epoch+1)\n",
        "            checkpoint(D, G)\n",
        "        ## AFTER EACH EPOCH##    \n",
        "        # this code assumes your generator is named G, feel free to change the name\n",
        "        # generate and save sample, fake images\n",
        "        G.eval() # for generating samples\n",
        "        samples_z = G(fixed_z)\n",
        "        samples.append(samples_z)\n",
        "        G.train() # back to training mode\n",
        " \n",
        "    # Save training generator samples\n",
        "    with open('train_samples.pkl', 'wb') as f:\n",
        "        pkl.dump(samples, f)\n",
        "    \n",
        "    # finally return losses\n",
        "    return losses"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zih6gDfcJ5-X",
        "colab_type": "text"
      },
      "source": [
        "Set your number of training epochs and train your GAN!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxFndMYvJ5-X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9ed8e7ef-5ce4-4a61-a694-235a25288a8b"
      },
      "source": [
        "# set number of epochs \n",
        "n_epochs = 50\n",
        " \n",
        " \n",
        "\"\"\"\n",
        "DON'T MODIFY ANYTHING IN THIS CELL\n",
        "\"\"\"\n",
        "# call training function\n",
        "losses = train(D, G, n_epochs=n_epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [    1/   50] | d_loss: 1.0467 | g_loss: 0.1541\n",
            "Epoch [    1/   50] | d_loss: 0.1902 | g_loss: 4.0184\n",
            "Epoch [    1/   50] | d_loss: 0.3603 | g_loss: 0.0826\n",
            "Epoch [    1/   50] | d_loss: 0.1386 | g_loss: 0.4344\n",
            "Epoch [    1/   50] | d_loss: 0.0521 | g_loss: 1.4073\n",
            "Epoch [    1/   50] | d_loss: 0.0662 | g_loss: 1.3143\n",
            "Epoch [    1/   50] | d_loss: 0.1109 | g_loss: 0.5249\n",
            "Epoch [    1/   50] | d_loss: 0.7068 | g_loss: 0.1045\n",
            "Epoch [    1/   50] | d_loss: 0.0334 | g_loss: 0.9251\n",
            "Epoch [    1/   50] | d_loss: 0.0376 | g_loss: 0.9607\n",
            "Epoch [    1/   50] | d_loss: 0.1571 | g_loss: 0.7238\n",
            "Saving model ---- epoch:  1\n",
            "Epoch [    2/   50] | d_loss: 0.3359 | g_loss: 0.2335\n",
            "Epoch [    2/   50] | d_loss: 0.2498 | g_loss: 0.3549\n",
            "Epoch [    2/   50] | d_loss: 0.0313 | g_loss: 1.2239\n",
            "Epoch [    2/   50] | d_loss: 0.0215 | g_loss: 0.9102\n",
            "Epoch [    2/   50] | d_loss: 0.0461 | g_loss: 0.9628\n",
            "Epoch [    2/   50] | d_loss: 0.0475 | g_loss: 1.2817\n",
            "Epoch [    2/   50] | d_loss: 0.1074 | g_loss: 0.7178\n",
            "Epoch [    2/   50] | d_loss: 0.0330 | g_loss: 0.9030\n",
            "Epoch [    2/   50] | d_loss: 0.0594 | g_loss: 1.1447\n",
            "Epoch [    2/   50] | d_loss: 0.0252 | g_loss: 0.9208\n",
            "Epoch [    2/   50] | d_loss: 0.0227 | g_loss: 1.0132\n",
            "Epoch [    3/   50] | d_loss: 0.0213 | g_loss: 0.9282\n",
            "Epoch [    3/   50] | d_loss: 0.0844 | g_loss: 1.2072\n",
            "Epoch [    3/   50] | d_loss: 0.1924 | g_loss: 0.2865\n",
            "Epoch [    3/   50] | d_loss: 0.1403 | g_loss: 0.5725\n",
            "Epoch [    3/   50] | d_loss: 0.2583 | g_loss: 0.6897\n",
            "Epoch [    3/   50] | d_loss: 0.0914 | g_loss: 0.8973\n",
            "Epoch [    3/   50] | d_loss: 0.1471 | g_loss: 0.8012\n",
            "Epoch [    3/   50] | d_loss: 0.2239 | g_loss: 0.4245\n",
            "Epoch [    3/   50] | d_loss: 0.2026 | g_loss: 0.4711\n",
            "Epoch [    3/   50] | d_loss: 0.2245 | g_loss: 0.4704\n",
            "Epoch [    3/   50] | d_loss: 0.2306 | g_loss: 1.0967\n",
            "Epoch [    4/   50] | d_loss: 0.3791 | g_loss: 0.7634\n",
            "Epoch [    4/   50] | d_loss: 0.2035 | g_loss: 0.6614\n",
            "Epoch [    4/   50] | d_loss: 0.1598 | g_loss: 0.8063\n",
            "Epoch [    4/   50] | d_loss: 0.2239 | g_loss: 0.8744\n",
            "Epoch [    4/   50] | d_loss: 0.3887 | g_loss: 1.1756\n",
            "Epoch [    4/   50] | d_loss: 0.1751 | g_loss: 0.5442\n",
            "Epoch [    4/   50] | d_loss: 0.3137 | g_loss: 0.4648\n",
            "Epoch [    4/   50] | d_loss: 0.4554 | g_loss: 0.4616\n",
            "Epoch [    4/   50] | d_loss: 0.4297 | g_loss: 1.0678\n",
            "Epoch [    4/   50] | d_loss: 0.4610 | g_loss: 0.1814\n",
            "Epoch [    4/   50] | d_loss: 0.2894 | g_loss: 0.8158\n",
            "Epoch [    5/   50] | d_loss: 0.4210 | g_loss: 1.7381\n",
            "Epoch [    5/   50] | d_loss: 0.5226 | g_loss: 0.2210\n",
            "Epoch [    5/   50] | d_loss: 0.2681 | g_loss: 0.4563\n",
            "Epoch [    5/   50] | d_loss: 0.2780 | g_loss: 0.4914\n",
            "Epoch [    5/   50] | d_loss: 0.3951 | g_loss: 1.1147\n",
            "Epoch [    5/   50] | d_loss: 0.2563 | g_loss: 0.8768\n",
            "Epoch [    5/   50] | d_loss: 0.7155 | g_loss: 0.1695\n",
            "Epoch [    5/   50] | d_loss: 0.5918 | g_loss: 0.3017\n",
            "Epoch [    5/   50] | d_loss: 0.4780 | g_loss: 0.5981\n",
            "Epoch [    5/   50] | d_loss: 0.4096 | g_loss: 0.5091\n",
            "Epoch [    5/   50] | d_loss: 0.3079 | g_loss: 1.0191\n",
            "Epoch [    6/   50] | d_loss: 0.7139 | g_loss: 1.0986\n",
            "Epoch [    6/   50] | d_loss: 0.3158 | g_loss: 0.7956\n",
            "Epoch [    6/   50] | d_loss: 0.4464 | g_loss: 0.4055\n",
            "Epoch [    6/   50] | d_loss: 0.3017 | g_loss: 0.6152\n",
            "Epoch [    6/   50] | d_loss: 0.2639 | g_loss: 0.4628\n",
            "Epoch [    6/   50] | d_loss: 0.3730 | g_loss: 0.4040\n",
            "Epoch [    6/   50] | d_loss: 0.7636 | g_loss: 0.0977\n",
            "Epoch [    6/   50] | d_loss: 0.6958 | g_loss: 0.1094\n",
            "Epoch [    6/   50] | d_loss: 0.3126 | g_loss: 0.7040\n",
            "Epoch [    6/   50] | d_loss: 0.2993 | g_loss: 0.7190\n",
            "Epoch [    6/   50] | d_loss: 0.2824 | g_loss: 0.7740\n",
            "Saving model ---- epoch:  6\n",
            "Epoch [    7/   50] | d_loss: 0.4244 | g_loss: 0.4234\n",
            "Epoch [    7/   50] | d_loss: 0.6397 | g_loss: 0.1478\n",
            "Epoch [    7/   50] | d_loss: 0.4083 | g_loss: 0.9250\n",
            "Epoch [    7/   50] | d_loss: 0.3628 | g_loss: 0.6451\n",
            "Epoch [    7/   50] | d_loss: 0.3996 | g_loss: 0.7213\n",
            "Epoch [    7/   50] | d_loss: 0.3916 | g_loss: 0.5937\n",
            "Epoch [    7/   50] | d_loss: 0.3593 | g_loss: 0.4178\n",
            "Epoch [    7/   50] | d_loss: 0.3958 | g_loss: 0.5182\n",
            "Epoch [    7/   50] | d_loss: 0.3948 | g_loss: 0.4563\n",
            "Epoch [    7/   50] | d_loss: 0.3948 | g_loss: 0.9032\n",
            "Epoch [    7/   50] | d_loss: 0.4411 | g_loss: 0.3428\n",
            "Epoch [    8/   50] | d_loss: 0.4120 | g_loss: 0.7195\n",
            "Epoch [    8/   50] | d_loss: 0.3275 | g_loss: 0.7552\n",
            "Epoch [    8/   50] | d_loss: 0.5349 | g_loss: 0.2140\n",
            "Epoch [    8/   50] | d_loss: 0.4139 | g_loss: 0.5633\n",
            "Epoch [    8/   50] | d_loss: 1.1316 | g_loss: 1.1828\n",
            "Epoch [    8/   50] | d_loss: 0.4623 | g_loss: 0.3060\n",
            "Epoch [    8/   50] | d_loss: 0.3016 | g_loss: 0.8121\n",
            "Epoch [    8/   50] | d_loss: 0.3494 | g_loss: 0.4139\n",
            "Epoch [    8/   50] | d_loss: 0.4525 | g_loss: 0.4435\n",
            "Epoch [    8/   50] | d_loss: 0.3983 | g_loss: 0.7973\n",
            "Epoch [    8/   50] | d_loss: 0.4679 | g_loss: 0.7267\n",
            "Epoch [    9/   50] | d_loss: 0.3895 | g_loss: 0.6134\n",
            "Epoch [    9/   50] | d_loss: 0.2621 | g_loss: 1.0334\n",
            "Epoch [    9/   50] | d_loss: 0.2770 | g_loss: 0.8248\n",
            "Epoch [    9/   50] | d_loss: 0.2853 | g_loss: 0.6957\n",
            "Epoch [    9/   50] | d_loss: 0.6190 | g_loss: 0.2296\n",
            "Epoch [    9/   50] | d_loss: 0.4607 | g_loss: 0.8036\n",
            "Epoch [    9/   50] | d_loss: 0.4210 | g_loss: 0.4952\n",
            "Epoch [    9/   50] | d_loss: 0.4078 | g_loss: 0.3011\n",
            "Epoch [    9/   50] | d_loss: 0.3309 | g_loss: 0.4800\n",
            "Epoch [    9/   50] | d_loss: 0.4227 | g_loss: 0.7605\n",
            "Epoch [    9/   50] | d_loss: 0.3943 | g_loss: 0.4650\n",
            "Epoch [   10/   50] | d_loss: 0.3024 | g_loss: 1.3560\n",
            "Epoch [   10/   50] | d_loss: 0.4131 | g_loss: 0.3185\n",
            "Epoch [   10/   50] | d_loss: 0.3883 | g_loss: 0.6361\n",
            "Epoch [   10/   50] | d_loss: 0.4070 | g_loss: 0.6272\n",
            "Epoch [   10/   50] | d_loss: 0.5022 | g_loss: 0.7878\n",
            "Epoch [   10/   50] | d_loss: 0.3669 | g_loss: 0.5492\n",
            "Epoch [   10/   50] | d_loss: 0.4983 | g_loss: 0.3181\n",
            "Epoch [   10/   50] | d_loss: 0.2869 | g_loss: 0.5452\n",
            "Epoch [   10/   50] | d_loss: 0.5002 | g_loss: 0.1486\n",
            "Epoch [   10/   50] | d_loss: 0.4108 | g_loss: 0.3604\n",
            "Epoch [   10/   50] | d_loss: 0.3660 | g_loss: 0.4053\n",
            "Epoch [   11/   50] | d_loss: 0.5591 | g_loss: 0.5838\n",
            "Epoch [   11/   50] | d_loss: 0.3491 | g_loss: 0.4633\n",
            "Epoch [   11/   50] | d_loss: 0.4188 | g_loss: 0.4137\n",
            "Epoch [   11/   50] | d_loss: 0.4175 | g_loss: 0.3623\n",
            "Epoch [   11/   50] | d_loss: 0.3748 | g_loss: 0.3062\n",
            "Epoch [   11/   50] | d_loss: 0.3500 | g_loss: 0.8015\n",
            "Epoch [   11/   50] | d_loss: 0.4968 | g_loss: 0.8242\n",
            "Epoch [   11/   50] | d_loss: 0.3931 | g_loss: 0.6772\n",
            "Epoch [   11/   50] | d_loss: 0.3676 | g_loss: 0.2301\n",
            "Epoch [   11/   50] | d_loss: 0.3762 | g_loss: 0.5065\n",
            "Epoch [   11/   50] | d_loss: 0.3448 | g_loss: 0.7302\n",
            "Saving model ---- epoch:  11\n",
            "Epoch [   12/   50] | d_loss: 0.4277 | g_loss: 0.9508\n",
            "Epoch [   12/   50] | d_loss: 0.4602 | g_loss: 0.2439\n",
            "Epoch [   12/   50] | d_loss: 0.3111 | g_loss: 0.6993\n",
            "Epoch [   12/   50] | d_loss: 0.3735 | g_loss: 0.5891\n",
            "Epoch [   12/   50] | d_loss: 0.3898 | g_loss: 0.2667\n",
            "Epoch [   12/   50] | d_loss: 0.3621 | g_loss: 0.6145\n",
            "Epoch [   12/   50] | d_loss: 0.3393 | g_loss: 0.5018\n",
            "Epoch [   12/   50] | d_loss: 0.3626 | g_loss: 0.5198\n",
            "Epoch [   12/   50] | d_loss: 0.3051 | g_loss: 0.5329\n",
            "Epoch [   12/   50] | d_loss: 0.4817 | g_loss: 0.6370\n",
            "Epoch [   12/   50] | d_loss: 0.3762 | g_loss: 0.4385\n",
            "Epoch [   13/   50] | d_loss: 0.3606 | g_loss: 1.0073\n",
            "Epoch [   13/   50] | d_loss: 0.6300 | g_loss: 0.2109\n",
            "Epoch [   13/   50] | d_loss: 0.4442 | g_loss: 0.3695\n",
            "Epoch [   13/   50] | d_loss: 0.5165 | g_loss: 0.2850\n",
            "Epoch [   13/   50] | d_loss: 0.2870 | g_loss: 0.4043\n",
            "Epoch [   13/   50] | d_loss: 0.3459 | g_loss: 0.6385\n",
            "Epoch [   13/   50] | d_loss: 0.2561 | g_loss: 0.8385\n",
            "Epoch [   13/   50] | d_loss: 0.3375 | g_loss: 0.7793\n",
            "Epoch [   13/   50] | d_loss: 0.5043 | g_loss: 0.7857\n",
            "Epoch [   13/   50] | d_loss: 0.4873 | g_loss: 0.3315\n",
            "Epoch [   13/   50] | d_loss: 0.3957 | g_loss: 0.4202\n",
            "Epoch [   14/   50] | d_loss: 0.4135 | g_loss: 0.5381\n",
            "Epoch [   14/   50] | d_loss: 0.3462 | g_loss: 0.7006\n",
            "Epoch [   14/   50] | d_loss: 0.4214 | g_loss: 0.6595\n",
            "Epoch [   14/   50] | d_loss: 0.4599 | g_loss: 0.7747\n",
            "Epoch [   14/   50] | d_loss: 0.4131 | g_loss: 0.2919\n",
            "Epoch [   14/   50] | d_loss: 0.3167 | g_loss: 0.4953\n",
            "Epoch [   14/   50] | d_loss: 0.6438 | g_loss: 0.1543\n",
            "Epoch [   14/   50] | d_loss: 0.3944 | g_loss: 1.1110\n",
            "Epoch [   14/   50] | d_loss: 0.4788 | g_loss: 0.5945\n",
            "Epoch [   14/   50] | d_loss: 0.4894 | g_loss: 1.0324\n",
            "Epoch [   14/   50] | d_loss: 0.3045 | g_loss: 0.6021\n",
            "Epoch [   15/   50] | d_loss: 0.4451 | g_loss: 0.5064\n",
            "Epoch [   15/   50] | d_loss: 0.3025 | g_loss: 0.4158\n",
            "Epoch [   15/   50] | d_loss: 0.4039 | g_loss: 0.2605\n",
            "Epoch [   15/   50] | d_loss: 0.3946 | g_loss: 1.2848\n",
            "Epoch [   15/   50] | d_loss: 0.5934 | g_loss: 0.2535\n",
            "Epoch [   15/   50] | d_loss: 0.2360 | g_loss: 0.5712\n",
            "Epoch [   15/   50] | d_loss: 0.3431 | g_loss: 0.6710\n",
            "Epoch [   15/   50] | d_loss: 0.4736 | g_loss: 0.8616\n",
            "Epoch [   15/   50] | d_loss: 0.3340 | g_loss: 0.2805\n",
            "Epoch [   15/   50] | d_loss: 0.2773 | g_loss: 0.7120\n",
            "Epoch [   15/   50] | d_loss: 0.2881 | g_loss: 0.6542\n",
            "Epoch [   16/   50] | d_loss: 0.3523 | g_loss: 0.7352\n",
            "Epoch [   16/   50] | d_loss: 0.4213 | g_loss: 0.3249\n",
            "Epoch [   16/   50] | d_loss: 0.6205 | g_loss: 0.0779\n",
            "Epoch [   16/   50] | d_loss: 0.2819 | g_loss: 0.5661\n",
            "Epoch [   16/   50] | d_loss: 0.3251 | g_loss: 0.4987\n",
            "Epoch [   16/   50] | d_loss: 0.4677 | g_loss: 0.3110\n",
            "Epoch [   16/   50] | d_loss: 0.4075 | g_loss: 0.3876\n",
            "Epoch [   16/   50] | d_loss: 0.4690 | g_loss: 0.2911\n",
            "Epoch [   16/   50] | d_loss: 0.2752 | g_loss: 0.4602\n",
            "Epoch [   16/   50] | d_loss: 0.3546 | g_loss: 0.8751\n",
            "Epoch [   16/   50] | d_loss: 0.2640 | g_loss: 0.9695\n",
            "Saving model ---- epoch:  16\n",
            "Epoch [   17/   50] | d_loss: 0.5017 | g_loss: 1.1171\n",
            "Epoch [   17/   50] | d_loss: 0.2728 | g_loss: 0.7042\n",
            "Epoch [   17/   50] | d_loss: 0.3119 | g_loss: 0.3513\n",
            "Epoch [   17/   50] | d_loss: 0.3700 | g_loss: 1.5892\n",
            "Epoch [   17/   50] | d_loss: 0.4056 | g_loss: 0.6093\n",
            "Epoch [   17/   50] | d_loss: 0.3839 | g_loss: 0.4238\n",
            "Epoch [   17/   50] | d_loss: 0.5172 | g_loss: 0.8319\n",
            "Epoch [   17/   50] | d_loss: 0.3617 | g_loss: 0.6535\n",
            "Epoch [   17/   50] | d_loss: 0.3543 | g_loss: 0.3897\n",
            "Epoch [   17/   50] | d_loss: 0.3515 | g_loss: 0.4210\n",
            "Epoch [   17/   50] | d_loss: 0.3817 | g_loss: 0.7098\n",
            "Epoch [   18/   50] | d_loss: 0.3468 | g_loss: 0.7355\n",
            "Epoch [   18/   50] | d_loss: 0.2865 | g_loss: 0.7908\n",
            "Epoch [   18/   50] | d_loss: 0.2449 | g_loss: 0.8342\n",
            "Epoch [   18/   50] | d_loss: 0.2861 | g_loss: 0.5538\n",
            "Epoch [   18/   50] | d_loss: 0.2525 | g_loss: 0.5295\n",
            "Epoch [   18/   50] | d_loss: 0.3930 | g_loss: 1.0606\n",
            "Epoch [   18/   50] | d_loss: 0.2667 | g_loss: 0.6961\n",
            "Epoch [   18/   50] | d_loss: 0.3441 | g_loss: 0.2915\n",
            "Epoch [   18/   50] | d_loss: 0.2001 | g_loss: 1.2040\n",
            "Epoch [   18/   50] | d_loss: 0.3169 | g_loss: 0.5360\n",
            "Epoch [   18/   50] | d_loss: 0.3067 | g_loss: 0.4576\n",
            "Epoch [   19/   50] | d_loss: 0.5989 | g_loss: 1.0644\n",
            "Epoch [   19/   50] | d_loss: 0.3589 | g_loss: 0.4536\n",
            "Epoch [   19/   50] | d_loss: 0.3978 | g_loss: 0.3003\n",
            "Epoch [   19/   50] | d_loss: 0.4074 | g_loss: 0.5475\n",
            "Epoch [   19/   50] | d_loss: 0.4041 | g_loss: 0.3665\n",
            "Epoch [   19/   50] | d_loss: 0.4109 | g_loss: 0.8636\n",
            "Epoch [   19/   50] | d_loss: 0.5256 | g_loss: 0.3105\n",
            "Epoch [   19/   50] | d_loss: 0.4361 | g_loss: 0.8267\n",
            "Epoch [   19/   50] | d_loss: 0.4237 | g_loss: 0.4081\n",
            "Epoch [   19/   50] | d_loss: 0.3986 | g_loss: 0.8526\n",
            "Epoch [   19/   50] | d_loss: 0.4082 | g_loss: 0.7934\n",
            "Epoch [   20/   50] | d_loss: 0.4606 | g_loss: 0.6295\n",
            "Epoch [   20/   50] | d_loss: 0.4592 | g_loss: 0.7694\n",
            "Epoch [   20/   50] | d_loss: 0.3360 | g_loss: 0.6131\n",
            "Epoch [   20/   50] | d_loss: 0.3257 | g_loss: 0.4438\n",
            "Epoch [   20/   50] | d_loss: 0.3076 | g_loss: 0.5286\n",
            "Epoch [   20/   50] | d_loss: 0.3212 | g_loss: 0.4692\n",
            "Epoch [   20/   50] | d_loss: 0.4016 | g_loss: 0.5022\n",
            "Epoch [   20/   50] | d_loss: 0.4141 | g_loss: 0.4624\n",
            "Epoch [   20/   50] | d_loss: 0.3687 | g_loss: 0.7467\n",
            "Epoch [   20/   50] | d_loss: 0.2866 | g_loss: 0.6840\n",
            "Epoch [   20/   50] | d_loss: 0.5125 | g_loss: 0.7325\n",
            "Epoch [   21/   50] | d_loss: 0.4489 | g_loss: 0.8976\n",
            "Epoch [   21/   50] | d_loss: 0.3737 | g_loss: 0.5993\n",
            "Epoch [   21/   50] | d_loss: 0.4561 | g_loss: 0.6717\n",
            "Epoch [   21/   50] | d_loss: 0.3490 | g_loss: 0.4943\n",
            "Epoch [   21/   50] | d_loss: 0.3316 | g_loss: 0.4164\n",
            "Epoch [   21/   50] | d_loss: 0.3006 | g_loss: 0.4221\n",
            "Epoch [   21/   50] | d_loss: 0.2676 | g_loss: 0.4654\n",
            "Epoch [   21/   50] | d_loss: 0.3308 | g_loss: 0.6034\n",
            "Epoch [   21/   50] | d_loss: 0.3485 | g_loss: 0.4299\n",
            "Epoch [   21/   50] | d_loss: 0.3460 | g_loss: 0.6583\n",
            "Epoch [   21/   50] | d_loss: 0.3647 | g_loss: 0.5825\n",
            "Saving model ---- epoch:  21\n",
            "Epoch [   22/   50] | d_loss: 0.7007 | g_loss: 0.5763\n",
            "Epoch [   22/   50] | d_loss: 0.8222 | g_loss: 0.0597\n",
            "Epoch [   22/   50] | d_loss: 0.3195 | g_loss: 0.5118\n",
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            "Epoch [   22/   50] | d_loss: 0.3305 | g_loss: 0.4675\n",
            "Epoch [   22/   50] | d_loss: 0.2397 | g_loss: 0.4708\n",
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            "Epoch [   22/   50] | d_loss: 0.3239 | g_loss: 1.0687\n",
            "Epoch [   22/   50] | d_loss: 0.3409 | g_loss: 0.6040\n",
            "Epoch [   22/   50] | d_loss: 0.3916 | g_loss: 0.4890\n",
            "Epoch [   23/   50] | d_loss: 0.3159 | g_loss: 0.3185\n",
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            "Epoch [   23/   50] | d_loss: 0.2997 | g_loss: 1.0255\n",
            "Epoch [   23/   50] | d_loss: 0.3097 | g_loss: 1.1454\n",
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            "Epoch [   23/   50] | d_loss: 0.3587 | g_loss: 0.6484\n",
            "Epoch [   23/   50] | d_loss: 0.2600 | g_loss: 0.9289\n",
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            "Epoch [   23/   50] | d_loss: 0.3678 | g_loss: 0.3774\n",
            "Epoch [   24/   50] | d_loss: 0.7511 | g_loss: 2.1509\n",
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            "Epoch [   24/   50] | d_loss: 0.3428 | g_loss: 0.3787\n",
            "Epoch [   25/   50] | d_loss: 0.4382 | g_loss: 1.6613\n",
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            "Epoch [   25/   50] | d_loss: 0.2216 | g_loss: 0.6891\n",
            "Epoch [   25/   50] | d_loss: 0.2333 | g_loss: 0.7798\n",
            "Epoch [   25/   50] | d_loss: 0.3825 | g_loss: 0.9773\n",
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            "Epoch [   25/   50] | d_loss: 0.2485 | g_loss: 0.2588\n",
            "Epoch [   25/   50] | d_loss: 0.2680 | g_loss: 0.6801\n",
            "Epoch [   26/   50] | d_loss: 0.2321 | g_loss: 1.0195\n",
            "Epoch [   26/   50] | d_loss: 0.3594 | g_loss: 0.2665\n",
            "Epoch [   26/   50] | d_loss: 0.3640 | g_loss: 0.2570\n",
            "Epoch [   26/   50] | d_loss: 0.3202 | g_loss: 0.9521\n",
            "Epoch [   26/   50] | d_loss: 0.3699 | g_loss: 0.5883\n",
            "Epoch [   26/   50] | d_loss: 0.2672 | g_loss: 0.6798\n",
            "Epoch [   26/   50] | d_loss: 0.3896 | g_loss: 0.4378\n",
            "Epoch [   26/   50] | d_loss: 0.2773 | g_loss: 0.6443\n",
            "Epoch [   26/   50] | d_loss: 0.3817 | g_loss: 0.8184\n",
            "Epoch [   26/   50] | d_loss: 0.3114 | g_loss: 0.4920\n",
            "Epoch [   26/   50] | d_loss: 0.3558 | g_loss: 0.2897\n",
            "Saving model ---- epoch:  26\n",
            "Epoch [   27/   50] | d_loss: 0.2605 | g_loss: 0.5500\n",
            "Epoch [   27/   50] | d_loss: 0.2395 | g_loss: 0.5706\n",
            "Epoch [   27/   50] | d_loss: 0.2424 | g_loss: 0.4222\n",
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            "Epoch [   27/   50] | d_loss: 0.2413 | g_loss: 0.5832\n",
            "Epoch [   27/   50] | d_loss: 0.2739 | g_loss: 0.5580\n",
            "Epoch [   27/   50] | d_loss: 0.2366 | g_loss: 0.5759\n",
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            "Epoch [   27/   50] | d_loss: 0.2623 | g_loss: 0.5621\n",
            "Epoch [   27/   50] | d_loss: 0.3528 | g_loss: 0.9734\n",
            "Epoch [   27/   50] | d_loss: 0.3068 | g_loss: 0.6956\n",
            "Epoch [   28/   50] | d_loss: 0.3213 | g_loss: 0.5114\n",
            "Epoch [   28/   50] | d_loss: 0.6427 | g_loss: 0.4683\n",
            "Epoch [   28/   50] | d_loss: 0.2632 | g_loss: 0.7464\n",
            "Epoch [   28/   50] | d_loss: 0.3761 | g_loss: 0.9919\n",
            "Epoch [   28/   50] | d_loss: 0.2709 | g_loss: 0.7805\n",
            "Epoch [   28/   50] | d_loss: 0.3504 | g_loss: 0.5121\n",
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            "Epoch [   28/   50] | d_loss: 0.2524 | g_loss: 0.8968\n",
            "Epoch [   28/   50] | d_loss: 0.2209 | g_loss: 0.9681\n",
            "Epoch [   29/   50] | d_loss: 0.3202 | g_loss: 0.9377\n",
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            "Epoch [   29/   50] | d_loss: 0.2373 | g_loss: 0.5279\n",
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            "Epoch [   30/   50] | d_loss: 0.2713 | g_loss: 0.5678\n",
            "Epoch [   30/   50] | d_loss: 0.3737 | g_loss: 0.1972\n",
            "Epoch [   30/   50] | d_loss: 0.2237 | g_loss: 1.0001\n",
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            "Epoch [   30/   50] | d_loss: 0.5236 | g_loss: 1.0289\n",
            "Epoch [   30/   50] | d_loss: 0.3738 | g_loss: 1.0387\n",
            "Epoch [   30/   50] | d_loss: 0.3624 | g_loss: 0.7094\n",
            "Epoch [   30/   50] | d_loss: 0.3198 | g_loss: 0.6737\n",
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            "Epoch [   30/   50] | d_loss: 0.3795 | g_loss: 0.7109\n",
            "Epoch [   30/   50] | d_loss: 0.4287 | g_loss: 0.2315\n",
            "Epoch [   31/   50] | d_loss: 0.3133 | g_loss: 0.9162\n",
            "Epoch [   31/   50] | d_loss: 0.2210 | g_loss: 0.7529\n",
            "Epoch [   31/   50] | d_loss: 0.3845 | g_loss: 0.2905\n",
            "Epoch [   31/   50] | d_loss: 0.2989 | g_loss: 0.6880\n",
            "Epoch [   31/   50] | d_loss: 0.3662 | g_loss: 0.7884\n",
            "Epoch [   31/   50] | d_loss: 0.4308 | g_loss: 0.4995\n",
            "Epoch [   31/   50] | d_loss: 0.3952 | g_loss: 0.6556\n",
            "Epoch [   31/   50] | d_loss: 0.4130 | g_loss: 0.3011\n",
            "Epoch [   31/   50] | d_loss: 0.2896 | g_loss: 0.3483\n",
            "Epoch [   31/   50] | d_loss: 0.2167 | g_loss: 0.7199\n",
            "Epoch [   31/   50] | d_loss: 0.2876 | g_loss: 0.6543\n",
            "Saving model ---- epoch:  31\n",
            "Epoch [   32/   50] | d_loss: 0.3218 | g_loss: 0.7726\n",
            "Epoch [   32/   50] | d_loss: 0.2372 | g_loss: 0.4936\n",
            "Epoch [   32/   50] | d_loss: 0.3376 | g_loss: 0.5777\n",
            "Epoch [   32/   50] | d_loss: 0.2931 | g_loss: 0.4553\n",
            "Epoch [   32/   50] | d_loss: 0.5542 | g_loss: 0.9734\n",
            "Epoch [   32/   50] | d_loss: 0.2830 | g_loss: 1.0032\n",
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            "Epoch [   32/   50] | d_loss: 0.2982 | g_loss: 0.4627\n",
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            "Epoch [   32/   50] | d_loss: 0.1978 | g_loss: 0.6519\n",
            "Epoch [   33/   50] | d_loss: 0.3557 | g_loss: 0.3786\n",
            "Epoch [   33/   50] | d_loss: 0.3023 | g_loss: 0.5687\n",
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            "Epoch [   34/   50] | d_loss: 0.3568 | g_loss: 0.4741\n",
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            "Epoch [   34/   50] | d_loss: 0.3366 | g_loss: 0.4207\n",
            "Epoch [   35/   50] | d_loss: 0.6998 | g_loss: 0.1075\n",
            "Epoch [   35/   50] | d_loss: 0.2556 | g_loss: 0.5985\n",
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            "Epoch [   35/   50] | d_loss: 0.3198 | g_loss: 0.9617\n",
            "Epoch [   35/   50] | d_loss: 0.3848 | g_loss: 0.5517\n",
            "Epoch [   35/   50] | d_loss: 0.3198 | g_loss: 0.8166\n",
            "Epoch [   35/   50] | d_loss: 0.1873 | g_loss: 0.5539\n",
            "Epoch [   35/   50] | d_loss: 0.2804 | g_loss: 0.8195\n",
            "Epoch [   35/   50] | d_loss: 0.3373 | g_loss: 0.5985\n",
            "Epoch [   35/   50] | d_loss: 0.2236 | g_loss: 0.5521\n",
            "Epoch [   36/   50] | d_loss: 0.2691 | g_loss: 1.0575\n",
            "Epoch [   36/   50] | d_loss: 0.1764 | g_loss: 0.6478\n",
            "Epoch [   36/   50] | d_loss: 0.1719 | g_loss: 0.5908\n",
            "Epoch [   36/   50] | d_loss: 0.3820 | g_loss: 1.0130\n",
            "Epoch [   36/   50] | d_loss: 0.3843 | g_loss: 0.6601\n",
            "Epoch [   36/   50] | d_loss: 0.1936 | g_loss: 0.6247\n",
            "Epoch [   36/   50] | d_loss: 0.2487 | g_loss: 0.4352\n",
            "Epoch [   36/   50] | d_loss: 0.3150 | g_loss: 0.4698\n",
            "Epoch [   36/   50] | d_loss: 0.3024 | g_loss: 0.2392\n",
            "Epoch [   36/   50] | d_loss: 0.7559 | g_loss: 1.2862\n",
            "Epoch [   36/   50] | d_loss: 0.3000 | g_loss: 0.6152\n",
            "Saving model ---- epoch:  36\n",
            "Epoch [   37/   50] | d_loss: 0.5876 | g_loss: 0.7957\n",
            "Epoch [   37/   50] | d_loss: 0.2690 | g_loss: 0.4941\n",
            "Epoch [   37/   50] | d_loss: 0.3073 | g_loss: 0.6776\n",
            "Epoch [   37/   50] | d_loss: 0.1798 | g_loss: 0.8712\n",
            "Epoch [   37/   50] | d_loss: 0.3571 | g_loss: 0.5274\n",
            "Epoch [   37/   50] | d_loss: 0.2522 | g_loss: 0.4645\n",
            "Epoch [   37/   50] | d_loss: 0.3170 | g_loss: 0.9093\n",
            "Epoch [   37/   50] | d_loss: 0.2235 | g_loss: 0.5121\n",
            "Epoch [   37/   50] | d_loss: 0.6501 | g_loss: 1.0079\n",
            "Epoch [   37/   50] | d_loss: 0.3341 | g_loss: 0.4821\n",
            "Epoch [   37/   50] | d_loss: 0.2743 | g_loss: 0.6361\n",
            "Epoch [   38/   50] | d_loss: 0.3785 | g_loss: 0.3187\n",
            "Epoch [   38/   50] | d_loss: 0.2491 | g_loss: 0.6592\n",
            "Epoch [   38/   50] | d_loss: 0.2053 | g_loss: 0.5260\n",
            "Epoch [   38/   50] | d_loss: 0.1697 | g_loss: 0.2465\n",
            "Epoch [   38/   50] | d_loss: 0.2423 | g_loss: 0.7800\n",
            "Epoch [   38/   50] | d_loss: 0.3075 | g_loss: 0.2692\n",
            "Epoch [   38/   50] | d_loss: 0.2916 | g_loss: 0.7199\n",
            "Epoch [   38/   50] | d_loss: 0.2717 | g_loss: 0.9788\n",
            "Epoch [   38/   50] | d_loss: 0.1721 | g_loss: 0.5947\n",
            "Epoch [   38/   50] | d_loss: 0.1948 | g_loss: 0.5353\n",
            "Epoch [   38/   50] | d_loss: 0.2719 | g_loss: 0.9866\n",
            "Epoch [   39/   50] | d_loss: 0.2185 | g_loss: 0.5016\n",
            "Epoch [   39/   50] | d_loss: 0.4891 | g_loss: 0.3410\n",
            "Epoch [   39/   50] | d_loss: 0.2001 | g_loss: 0.5396\n",
            "Epoch [   39/   50] | d_loss: 0.2947 | g_loss: 0.3465\n",
            "Epoch [   39/   50] | d_loss: 0.2781 | g_loss: 0.3204\n",
            "Epoch [   39/   50] | d_loss: 0.1532 | g_loss: 0.9785\n",
            "Epoch [   39/   50] | d_loss: 0.4407 | g_loss: 0.8937\n",
            "Epoch [   39/   50] | d_loss: 0.3551 | g_loss: 0.3602\n",
            "Epoch [   39/   50] | d_loss: 0.1809 | g_loss: 0.6694\n",
            "Epoch [   39/   50] | d_loss: 0.2034 | g_loss: 0.6704\n",
            "Epoch [   39/   50] | d_loss: 0.2578 | g_loss: 0.6245\n",
            "Epoch [   40/   50] | d_loss: 0.3348 | g_loss: 1.3699\n",
            "Epoch [   40/   50] | d_loss: 0.2585 | g_loss: 0.7019\n",
            "Epoch [   40/   50] | d_loss: 0.6221 | g_loss: 0.2282\n",
            "Epoch [   40/   50] | d_loss: 0.2309 | g_loss: 0.8074\n",
            "Epoch [   40/   50] | d_loss: 0.2260 | g_loss: 0.4776\n",
            "Epoch [   40/   50] | d_loss: 0.4362 | g_loss: 0.4778\n",
            "Epoch [   40/   50] | d_loss: 0.2270 | g_loss: 0.8306\n",
            "Epoch [   40/   50] | d_loss: 0.1999 | g_loss: 0.5027\n",
            "Epoch [   40/   50] | d_loss: 0.1836 | g_loss: 0.9641\n",
            "Epoch [   40/   50] | d_loss: 0.2429 | g_loss: 0.6602\n",
            "Epoch [   40/   50] | d_loss: 0.2542 | g_loss: 0.5999\n",
            "Epoch [   41/   50] | d_loss: 0.3103 | g_loss: 1.0680\n",
            "Epoch [   41/   50] | d_loss: 0.2016 | g_loss: 0.6025\n",
            "Epoch [   41/   50] | d_loss: 0.1222 | g_loss: 0.6036\n",
            "Epoch [   41/   50] | d_loss: 0.2544 | g_loss: 1.1320\n",
            "Epoch [   41/   50] | d_loss: 0.4407 | g_loss: 0.3684\n",
            "Epoch [   41/   50] | d_loss: 0.1964 | g_loss: 0.6182\n",
            "Epoch [   41/   50] | d_loss: 0.2560 | g_loss: 0.6443\n",
            "Epoch [   41/   50] | d_loss: 0.2819 | g_loss: 0.5078\n",
            "Epoch [   41/   50] | d_loss: 0.2772 | g_loss: 0.6345\n",
            "Epoch [   41/   50] | d_loss: 0.3692 | g_loss: 0.9852\n",
            "Epoch [   41/   50] | d_loss: 0.1977 | g_loss: 0.6036\n",
            "Saving model ---- epoch:  41\n",
            "Epoch [   42/   50] | d_loss: 0.4754 | g_loss: 1.0538\n",
            "Epoch [   42/   50] | d_loss: 0.2227 | g_loss: 0.7227\n",
            "Epoch [   42/   50] | d_loss: 0.2244 | g_loss: 0.6936\n",
            "Epoch [   42/   50] | d_loss: 0.2797 | g_loss: 0.8281\n",
            "Epoch [   42/   50] | d_loss: 0.2509 | g_loss: 1.0461\n",
            "Epoch [   42/   50] | d_loss: 0.2482 | g_loss: 0.6892\n",
            "Epoch [   42/   50] | d_loss: 0.2303 | g_loss: 0.6767\n",
            "Epoch [   42/   50] | d_loss: 0.2672 | g_loss: 0.7482\n",
            "Epoch [   42/   50] | d_loss: 0.2745 | g_loss: 0.2797\n",
            "Epoch [   42/   50] | d_loss: 0.3192 | g_loss: 0.3584\n",
            "Epoch [   42/   50] | d_loss: 0.2880 | g_loss: 1.2568\n",
            "Epoch [   43/   50] | d_loss: 0.5174 | g_loss: 0.1668\n",
            "Epoch [   43/   50] | d_loss: 0.2081 | g_loss: 0.5615\n",
            "Epoch [   43/   50] | d_loss: 0.2211 | g_loss: 0.6832\n",
            "Epoch [   43/   50] | d_loss: 0.1637 | g_loss: 0.9973\n",
            "Epoch [   43/   50] | d_loss: 0.2699 | g_loss: 0.3652\n",
            "Epoch [   43/   50] | d_loss: 0.1411 | g_loss: 0.7999\n",
            "Epoch [   43/   50] | d_loss: 0.2069 | g_loss: 1.0523\n",
            "Epoch [   43/   50] | d_loss: 0.2739 | g_loss: 1.1951\n",
            "Epoch [   43/   50] | d_loss: 0.1943 | g_loss: 0.7813\n",
            "Epoch [   43/   50] | d_loss: 0.3370 | g_loss: 1.0934\n",
            "Epoch [   43/   50] | d_loss: 0.1829 | g_loss: 0.8956\n",
            "Epoch [   44/   50] | d_loss: 0.2557 | g_loss: 0.7784\n",
            "Epoch [   44/   50] | d_loss: 0.1404 | g_loss: 0.9063\n",
            "Epoch [   44/   50] | d_loss: 0.1567 | g_loss: 0.7993\n",
            "Epoch [   44/   50] | d_loss: 0.3104 | g_loss: 0.4491\n",
            "Epoch [   44/   50] | d_loss: 0.1782 | g_loss: 0.6252\n",
            "Epoch [   44/   50] | d_loss: 0.2429 | g_loss: 0.6529\n",
            "Epoch [   44/   50] | d_loss: 0.2332 | g_loss: 0.8663\n",
            "Epoch [   44/   50] | d_loss: 0.4406 | g_loss: 0.2310\n",
            "Epoch [   44/   50] | d_loss: 0.1608 | g_loss: 0.4365\n",
            "Epoch [   44/   50] | d_loss: 0.1596 | g_loss: 0.2807\n",
            "Epoch [   44/   50] | d_loss: 0.1847 | g_loss: 0.6563\n",
            "Epoch [   45/   50] | d_loss: 0.2567 | g_loss: 0.7363\n",
            "Epoch [   45/   50] | d_loss: 0.1630 | g_loss: 0.6723\n",
            "Epoch [   45/   50] | d_loss: 0.2395 | g_loss: 0.3777\n",
            "Epoch [   45/   50] | d_loss: 0.3610 | g_loss: 0.9383\n",
            "Epoch [   45/   50] | d_loss: 0.1658 | g_loss: 0.8130\n",
            "Epoch [   45/   50] | d_loss: 0.1839 | g_loss: 0.3765\n",
            "Epoch [   45/   50] | d_loss: 0.2077 | g_loss: 0.7095\n",
            "Epoch [   45/   50] | d_loss: 0.2422 | g_loss: 0.7334\n",
            "Epoch [   45/   50] | d_loss: 0.1717 | g_loss: 0.8839\n",
            "Epoch [   45/   50] | d_loss: 0.2244 | g_loss: 1.0138\n",
            "Epoch [   45/   50] | d_loss: 0.2737 | g_loss: 0.3634\n",
            "Epoch [   46/   50] | d_loss: 0.4775 | g_loss: 1.1285\n",
            "Epoch [   46/   50] | d_loss: 0.1298 | g_loss: 0.9641\n",
            "Epoch [   46/   50] | d_loss: 0.4012 | g_loss: 0.3515\n",
            "Epoch [   46/   50] | d_loss: 0.1661 | g_loss: 0.8654\n",
            "Epoch [   46/   50] | d_loss: 0.1390 | g_loss: 0.8572\n",
            "Epoch [   46/   50] | d_loss: 0.1719 | g_loss: 1.0150\n",
            "Epoch [   46/   50] | d_loss: 0.2818 | g_loss: 0.9650\n",
            "Epoch [   46/   50] | d_loss: 0.2372 | g_loss: 0.6396\n",
            "Epoch [   46/   50] | d_loss: 0.2310 | g_loss: 0.5504\n",
            "Epoch [   46/   50] | d_loss: 0.1502 | g_loss: 0.9389\n",
            "Saving model ---- epoch:  46\n",
            "Epoch [   47/   50] | d_loss: 0.1560 | g_loss: 0.4831\n",
            "Epoch [   47/   50] | d_loss: 0.1855 | g_loss: 0.7188\n",
            "Epoch [   47/   50] | d_loss: 0.1258 | g_loss: 0.7535\n",
            "Epoch [   47/   50] | d_loss: 0.2466 | g_loss: 0.8094\n",
            "Epoch [   47/   50] | d_loss: 0.2875 | g_loss: 0.8582\n",
            "Epoch [   47/   50] | d_loss: 0.2511 | g_loss: 1.4635\n",
            "Epoch [   47/   50] | d_loss: 0.2117 | g_loss: 1.4895\n",
            "Epoch [   47/   50] | d_loss: 0.1432 | g_loss: 0.6239\n",
            "Epoch [   47/   50] | d_loss: 0.1499 | g_loss: 0.9779\n",
            "Epoch [   47/   50] | d_loss: 0.2545 | g_loss: 0.6538\n",
            "Epoch [   47/   50] | d_loss: 0.1453 | g_loss: 0.7137\n",
            "Epoch [   48/   50] | d_loss: 0.2552 | g_loss: 1.1877\n",
            "Epoch [   48/   50] | d_loss: 0.2091 | g_loss: 0.7294\n",
            "Epoch [   48/   50] | d_loss: 0.2069 | g_loss: 0.9918\n",
            "Epoch [   48/   50] | d_loss: 0.1684 | g_loss: 0.9106\n",
            "Epoch [   48/   50] | d_loss: 0.1909 | g_loss: 0.3017\n",
            "Epoch [   48/   50] | d_loss: 0.2057 | g_loss: 0.6526\n",
            "Epoch [   48/   50] | d_loss: 0.1660 | g_loss: 0.8178\n",
            "Epoch [   48/   50] | d_loss: 0.1294 | g_loss: 1.1170\n",
            "Epoch [   48/   50] | d_loss: 0.2167 | g_loss: 0.3241\n",
            "Epoch [   48/   50] | d_loss: 0.2377 | g_loss: 0.6168\n",
            "Epoch [   48/   50] | d_loss: 0.2591 | g_loss: 0.6031\n",
            "Epoch [   49/   50] | d_loss: 0.2275 | g_loss: 0.6233\n",
            "Epoch [   49/   50] | d_loss: 0.0941 | g_loss: 0.6884\n",
            "Epoch [   49/   50] | d_loss: 0.1994 | g_loss: 0.6057\n",
            "Epoch [   49/   50] | d_loss: 0.1846 | g_loss: 0.8358\n",
            "Epoch [   49/   50] | d_loss: 0.1673 | g_loss: 0.7665\n",
            "Epoch [   49/   50] | d_loss: 0.1714 | g_loss: 0.6925\n",
            "Epoch [   49/   50] | d_loss: 0.2621 | g_loss: 0.3486\n",
            "Epoch [   49/   50] | d_loss: 0.1996 | g_loss: 0.5755\n",
            "Epoch [   49/   50] | d_loss: 0.2568 | g_loss: 0.6072\n",
            "Epoch [   49/   50] | d_loss: 0.1894 | g_loss: 1.0980\n",
            "Epoch [   49/   50] | d_loss: 0.2803 | g_loss: 0.4573\n",
            "Epoch [   50/   50] | d_loss: 0.1716 | g_loss: 1.3156\n",
            "Epoch [   50/   50] | d_loss: 0.1500 | g_loss: 0.7353\n",
            "Epoch [   50/   50] | d_loss: 0.2446 | g_loss: 0.4990\n",
            "Epoch [   50/   50] | d_loss: 0.2342 | g_loss: 1.0227\n",
            "Epoch [   50/   50] | d_loss: 0.1527 | g_loss: 0.4053\n",
            "Epoch [   50/   50] | d_loss: 0.3408 | g_loss: 0.2085\n",
            "Epoch [   50/   50] | d_loss: 0.3326 | g_loss: 0.4069\n",
            "Epoch [   50/   50] | d_loss: 0.1582 | g_loss: 0.8551\n",
            "Epoch [   50/   50] | d_loss: 0.1855 | g_loss: 0.5399\n",
            "Epoch [   50/   50] | d_loss: 0.2377 | g_loss: 1.0204\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/storage.py:34: FutureWarning: pickle support for Storage will be removed in 1.5. Use `torch.save` instead\n",
            "  warnings.warn(\"pickle support for Storage will be removed in 1.5. Use `torch.save` instead\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "SmsHVgJiJ5-a",
        "colab_type": "text"
      },
      "source": [
        "## Training loss\n",
        "\n",
        "Plot the training losses for the generator and discriminator, recorded after each epoch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "623VMbXlJ5-b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "b804ea40-6793-41b7-87d2-b28e095efde1"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "losses = np.array(losses)\n",
        "plt.plot(losses.T[0], label='Discriminator', alpha=0.5)\n",
        "plt.plot(losses.T[1], label='Generator', alpha=0.5)\n",
        "plt.title(\"Training Losses\")\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0ffa9fcc88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "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": {
        "collapsed": true,
        "id": "0NjeAJVbJ5-e",
        "colab_type": "text"
      },
      "source": [
        "## Generator samples from training\n",
        "\n",
        "View samples of images from the generator, and answer a question about the strengths and weaknesses of your trained models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvq_I2VuJ5-f",
        "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=(16,4), nrows=2, ncols=8, sharey=True, sharex=True)\n",
        "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
        "        img = img.detach().cpu().numpy()\n",
        "        img = np.transpose(img, (1, 2, 0))\n",
        "        img = ((img + 1)*255 / (2)).astype(np.uint8)\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)\n",
        "        im = ax.imshow(img.reshape((32,32,3)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjxNm1MsJ5-h",
        "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rawuViV9J5-j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "153cc56c-56eb-4c27-bf8a-0bb9e60a885a"
      },
      "source": [
        "_ = view_samples(-1, samples)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TDx8irCJ5-l",
        "colab_type": "text"
      },
      "source": [
        "### Question: What do you notice about your generated samples and how might you improve this model?\n",
        "When you answer this question, consider the following factors:\n",
        "* The dataset is biased; it is made of \"celebrity\" faces that are mostly white\n",
        "* Model size; larger models have the opportunity to learn more features in a data feature space\n",
        "* Optimization strategy; optimizers and number of epochs affect your final result\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ThAhsowJ5-m",
        "colab_type": "text"
      },
      "source": [
        "**Answer:** (Write your answer in this cell)\n",
        "- I noticed the generated images aren't clear enough, weird eye positions in some images.\n",
        "\n",
        "          **How i can improve model**\n",
        "\n",
        "- I can improve this model by making the datasets to contain images of different race, of different skin colors\n",
        "- Increasing the input image size, and using more deeper models and adding more depth.\n",
        "- Training for more epochs, training for 50 epochs made impressive results, training for more i believe will make result more impressive\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EE5zBFAJ5-m",
        "colab_type": "text"
      },
      "source": [
        "### Submitting This Project\n",
        "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"problem_unittests.py\" files in your submission."
      ]
    }
  ]
}