dlnd_face_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "#!unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "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",
    "    \n",
    "    # TODO: Implement function and return a dataloader\n",
    "    # resize and normalize the images\n",
    "    transform = transforms.Compose([transforms.Resize(image_size), # resize image\n",
    "                                    transforms.ToTensor()])\n",
    "\n",
    "    # get directory\n",
    "    image_path = './' + data_dir\n",
    "    #train_path = os.path.join(image_path, image_type)\n",
    "    #test_path = os.path.join(image_path, 'test_{}'.format(image_type))\n",
    "\n",
    "    # define datasets using ImageFolder\n",
    "    image_dataset = datasets.ImageFolder(image_path, transform)\n",
    "    #test_dataset = datasets.ImageFolder(test_path, transform)\n",
    "\n",
    "    # create and return DataLoaders\n",
    "    image_loader = DataLoader(dataset=image_dataset, batch_size=batch_size, shuffle=True)\n",
    "    #test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "\n",
    "    return image_loader  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 20  # From past CycleGAN example I used 16, but had to push to 20 for the cell below this one\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff7e7fbe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper display function\n",
    "def imshow(img):\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))  # Transposing image of x, y, z to z, x, y (colors=3, width=32, height=32)\n",
    "                                                # where x=width, y=height, and z=number of color layers (RGB)\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])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(1.00000e-02 *\n",
      "       8.6275)\n",
      "Max:  tensor(0.8863)\n"
     ]
    }
   ],
   "source": [
    "# current range\n",
    "img = images[0]\n",
    "\n",
    "print('Min: ', img.min())\n",
    "print('Max: ', img.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "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\n",
    "    x = x * (max - min) + min    \n",
    "    \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.8275)\n",
      "Max:  tensor(0.7725)\n"
     ]
    }
   ],
   "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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper conv function\n",
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, \n",
    "                           kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n",
    "    \n",
    "    layers.append(conv_layer)\n",
    "\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, conv_dim=64):\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",
    "        # Convolutional layers, increasing in depth\n",
    "        \n",
    "        # first layer has *no* batchnorm\n",
    "        # 32 x 32 input\n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False) # x, y = 64, depth 64. No BatchNorm\n",
    "        # 16 x 16 output\n",
    "        \n",
    "        # 16 x 16 input\n",
    "        self.conv2 = conv(conv_dim, (conv_dim * 2), 4) \n",
    "        # 8 x 8 output\n",
    "        \n",
    "        # 8 x 8 input\n",
    "        self.conv3 = conv((conv_dim * 2), (conv_dim * 4), 4) \n",
    "        # 4 x 4 output\n",
    "        \n",
    "        # 4 x 4 input\n",
    "        self.conv4 = conv((conv_dim * 4), (conv_dim * 8), 4)\n",
    "        # 2 x 2 output\n",
    "        \n",
    "        # Classification layer\n",
    "        self.fc = nn.Linear((conv_dim * 8 * 2 * 2), 1)\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",
    "        # leaky_relu applied to all conv layers but last\n",
    "\n",
    "        out = F.leaky_relu(self.conv1(x), 0.2)\n",
    "        out = F.leaky_relu(self.conv2(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv3(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv4(out), 0.2)\n",
    "                \n",
    "        # flatten\n",
    "        out = out.view(-1, self.conv_dim * 8 * 2 * 2)\n",
    "        \n",
    "        # last, classification layer\n",
    "        final_out = self.fc(out)\n",
    "        \n",
    "        return final_out\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(Discriminator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper deconv function\n",
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a transposed-convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    # create a sequence of transpose + optional batch norm layers\n",
    "    layers = []\n",
    "    transpose_conv_layer = nn.ConvTranspose2d(in_channels, out_channels, \n",
    "                                              kernel_size, stride, padding, bias=False)\n",
    "    # append transpose convolutional layer\n",
    "    layers.append(transpose_conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        # append batchnorm layer\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "        \n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "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",
    "        \n",
    "        # first, fully-connected layer\n",
    "        self.fc = nn.Linear(z_size, conv_dim * 8 * 2 * 2)\n",
    "\n",
    "        # transpose conv layers\n",
    "        self.t_conv1 = deconv((conv_dim * 8), (conv_dim * 4), 4)\n",
    "        self.t_conv2 = deconv((conv_dim * 4), (conv_dim * 2), 4)\n",
    "        self.t_conv3 = deconv((conv_dim * 2), (conv_dim), 4)\n",
    "        self.t_conv4 = deconv(conv_dim, 3, 4, batch_norm=False)\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: A 32x32x3 Tensor image as output\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        \n",
    "        # fully-connected + reshape \n",
    "        out = self.fc(x)\n",
    "        out = out.view(-1, self.conv_dim * 8, 2, 2) \n",
    "        \n",
    "        # hidden transpose conv layers + relu\n",
    "        out = F.relu(self.t_conv1(out))\n",
    "        out = F.relu(self.t_conv2(out))\n",
    "        out = F.relu(self.t_conv3(out))\n",
    "        \n",
    "        # last layer + tanh activation\n",
    "        out = self.t_conv4(out)\n",
    "        out = F.tanh(out) \n",
    "        \n",
    "        return out\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(Generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "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",
    "    if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n",
    "        m.weight.data.normal_(0.0, 0.2)\n",
    "\n",
    "        if hasattr(m, 'bias') and m.bias is not None:\n",
    "            m.bias.data.zero_() # set bias terms to zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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",
      "  (conv4): Sequential(\n",
      "    (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (fc): Linear(in_features=2048, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc): Linear(in_features=100, out_features=2048, bias=True)\n",
      "  (t_conv1): Sequential(\n",
      "    (0): ConvTranspose2d(512, 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",
      "  (t_conv2): 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_conv3): 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_conv4): Sequential(\n",
      "    (0): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define model hyperparams\n",
    "d_conv_dim = 64\n",
    "g_conv_dim = 64\n",
    "z_size = 100  # to start with\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on GPU!\n"
     ]
    }
   ],
   "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!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "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",
    "    batch_size = D_out.size(0)\n",
    "    '''\n",
    "    if smooth:\n",
    "        labels = torch.ones(batch_size) * 0.9\n",
    "    else:\n",
    "        labels = torch.ones(batch_size)\n",
    "    '''\n",
    "    labels = torch.ones(batch_size) * 0.9\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "        \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    \n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    \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",
    "    batch_size = D_out.size(0)\n",
    "    \n",
    "    labels = torch.zeros(batch_size)\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    \n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "d_optimizer = optim.Adam(D.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
    "g_optimizer = optim.Adam(G.parameters(), lr=0.0002, betas=(0.5, 0.999))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "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.cuda()\n",
    "        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",
    "            # ===============================================\n",
    "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
    "            # ===============================================\n",
    "            \n",
    "            # 1. Train the discriminator on real and fake images\n",
    "            d_optimizer.zero_grad()\n",
    "        \n",
    "            # 1. Train with real images\n",
    "\n",
    "            # Compute the discriminator losses on real images \n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "        \n",
    "            D_real = D(real_images)\n",
    "            d_real_loss = real_loss(D_real)\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",
    "            # move x to GPU, if available\n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\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",
    "            # 2. Train the generator with an adversarial loss\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",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\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",
    "            \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",
    "            \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",
    "\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your number of training epochs and train your GAN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   50] | d_loss: 1.5100 | g_loss: 1.3077\n",
      "Epoch [    1/   50] | d_loss: 0.6134 | g_loss: 5.6967\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    2/   50] | d_loss: 0.6896 | g_loss: 2.6585\n",
      "Epoch [    2/   50] | d_loss: 0.8217 | g_loss: 0.9949\n",
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     ]
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     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch [   48/   50] | d_loss: 0.3538 | g_loss: 3.5265\n",
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      "Epoch [   48/   50] | d_loss: 0.3536 | g_loss: 4.1986\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [   48/   50] | d_loss: 0.4118 | g_loss: 3.6804\n",
      "Epoch [   48/   50] | d_loss: 0.6233 | g_loss: 3.8648\n",
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      "Epoch [   48/   50] | d_loss: 0.3617 | g_loss: 4.4650\n",
      "Epoch [   48/   50] | d_loss: 0.5549 | g_loss: 3.9053\n",
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      "Epoch [   49/   50] | d_loss: 0.3745 | g_loss: 3.1212\n",
      "Epoch [   49/   50] | d_loss: 0.3801 | g_loss: 3.6451\n",
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      "Epoch [   49/   50] | d_loss: 0.4137 | g_loss: 2.5325\n",
      "Epoch [   49/   50] | d_loss: 0.4269 | g_loss: 4.0218\n",
      "Epoch [   49/   50] | d_loss: 0.3843 | g_loss: 3.6285\n",
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      "Epoch [   49/   50] | d_loss: 0.5992 | g_loss: 2.8926\n",
      "Epoch [   49/   50] | d_loss: 0.4669 | g_loss: 3.8701\n",
      "Epoch [   49/   50] | d_loss: 0.3603 | g_loss: 4.8438\n",
      "Epoch [   50/   50] | d_loss: 0.8751 | g_loss: 2.6358\n",
      "Epoch [   50/   50] | d_loss: 0.5900 | g_loss: 4.5998\n",
      "Epoch [   50/   50] | d_loss: 0.4316 | g_loss: 2.4390\n",
      "Epoch [   50/   50] | d_loss: 0.3725 | g_loss: 4.0817\n",
      "Epoch [   50/   50] | d_loss: 0.3723 | g_loss: 4.8016\n",
      "Epoch [   50/   50] | d_loss: 0.4615 | g_loss: 3.3215\n",
      "Epoch [   50/   50] | d_loss: 0.4067 | g_loss: 4.0171\n",
      "Epoch [   50/   50] | d_loss: 0.3769 | g_loss: 3.6444\n",
      "Epoch [   50/   50] | d_loss: 0.6428 | g_loss: 2.8983\n",
      "Epoch [   50/   50] | d_loss: 0.3971 | g_loss: 4.5818\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [   50/   50] | d_loss: 0.3811 | g_loss: 4.0050\n",
      "Epoch [   50/   50] | d_loss: 0.6836 | g_loss: 2.6592\n",
      "Epoch [   50/   50] | d_loss: 0.4329 | g_loss: 4.0630\n",
      "Epoch [   50/   50] | d_loss: 0.3503 | g_loss: 4.6209\n",
      "Epoch [   50/   50] | d_loss: 0.5699 | g_loss: 3.7212\n",
      "Epoch [   50/   50] | d_loss: 0.4400 | g_loss: 3.5508\n",
      "Epoch [   50/   50] | d_loss: 0.4603 | g_loss: 4.5349\n",
      "Epoch [   50/   50] | d_loss: 0.7955 | g_loss: 3.2774\n",
      "Epoch [   50/   50] | d_loss: 0.3850 | g_loss: 2.4690\n",
      "Epoch [   50/   50] | d_loss: 0.4228 | g_loss: 2.5721\n",
      "Epoch [   50/   50] | d_loss: 0.3799 | g_loss: 4.6287\n",
      "Epoch [   50/   50] | d_loss: 0.4073 | g_loss: 3.7549\n",
      "Epoch [   50/   50] | d_loss: 0.4407 | g_loss: 3.7784\n",
      "Epoch [   50/   50] | d_loss: 0.4660 | g_loss: 2.1902\n",
      "Epoch [   50/   50] | d_loss: 0.7811 | g_loss: 2.7103\n",
      "Epoch [   50/   50] | d_loss: 0.4708 | g_loss: 3.4852\n",
      "Epoch [   50/   50] | d_loss: 0.3582 | g_loss: 3.0926\n",
      "Epoch [   50/   50] | d_loss: 0.3853 | g_loss: 4.5519\n",
      "Epoch [   50/   50] | d_loss: 0.7139 | g_loss: 3.1226\n",
      "Epoch [   50/   50] | d_loss: 0.3928 | g_loss: 4.8423\n",
      "Epoch [   50/   50] | d_loss: 0.3995 | g_loss: 4.6826\n",
      "Epoch [   50/   50] | d_loss: 0.3634 | g_loss: 3.5397\n",
      "Epoch [   50/   50] | d_loss: 0.4132 | g_loss: 5.5808\n",
      "Epoch [   50/   50] | d_loss: 0.3825 | g_loss: 3.2679\n",
      "Epoch [   50/   50] | d_loss: 0.3848 | g_loss: 2.9845\n",
      "Epoch [   50/   50] | d_loss: 0.3995 | g_loss: 4.8555\n",
      "Epoch [   50/   50] | d_loss: 0.5657 | g_loss: 3.7125\n",
      "Epoch [   50/   50] | d_loss: 0.3905 | g_loss: 4.8116\n",
      "Epoch [   50/   50] | d_loss: 0.3965 | g_loss: 3.2283\n",
      "Epoch [   50/   50] | d_loss: 0.3573 | g_loss: 3.0492\n",
      "Epoch [   50/   50] | d_loss: 0.4805 | g_loss: 3.6823\n",
      "Epoch [   50/   50] | d_loss: 0.3576 | g_loss: 3.6165\n",
      "Epoch [   50/   50] | d_loss: 0.4358 | g_loss: 2.8658\n",
      "Epoch [   50/   50] | d_loss: 0.3695 | g_loss: 3.5630\n",
      "Epoch [   50/   50] | d_loss: 0.5985 | g_loss: 2.7304\n",
      "Epoch [   50/   50] | d_loss: 0.3649 | g_loss: 3.5190\n",
      "Epoch [   50/   50] | d_loss: 0.4152 | g_loss: 3.4616\n",
      "Epoch [   50/   50] | d_loss: 0.3795 | g_loss: 3.7392\n",
      "Epoch [   50/   50] | d_loss: 0.4491 | g_loss: 2.7256\n",
      "Epoch [   50/   50] | d_loss: 0.3912 | g_loss: 4.4979\n",
      "Epoch [   50/   50] | d_loss: 1.0344 | g_loss: 2.4872\n",
      "Epoch [   50/   50] | d_loss: 0.3961 | g_loss: 2.8710\n",
      "Epoch [   50/   50] | d_loss: 0.4139 | g_loss: 5.1688\n",
      "Epoch [   50/   50] | d_loss: 0.4228 | g_loss: 3.5541\n",
      "Epoch [   50/   50] | d_loss: 0.4372 | g_loss: 2.9539\n",
      "Epoch [   50/   50] | d_loss: 0.4797 | g_loss: 3.4015\n",
      "Epoch [   50/   50] | d_loss: 0.4863 | g_loss: 3.1610\n",
      "Epoch [   50/   50] | d_loss: 0.4119 | g_loss: 4.2623\n",
      "Epoch [   50/   50] | d_loss: 0.4033 | g_loss: 4.4782\n",
      "Epoch [   50/   50] | d_loss: 0.5906 | g_loss: 2.3790\n",
      "Epoch [   50/   50] | d_loss: 0.3629 | g_loss: 3.6540\n",
      "Epoch [   50/   50] | d_loss: 0.5497 | g_loss: 3.4515\n",
      "Epoch [   50/   50] | d_loss: 0.4645 | g_loss: 3.8971\n",
      "Epoch [   50/   50] | d_loss: 0.4056 | g_loss: 4.2988\n",
      "Epoch [   50/   50] | d_loss: 0.3919 | g_loss: 5.1841\n",
      "Epoch [   50/   50] | d_loss: 0.3748 | g_loss: 3.7855\n",
      "Epoch [   50/   50] | d_loss: 0.7041 | g_loss: 3.9243\n",
      "Epoch [   50/   50] | d_loss: 0.3603 | g_loss: 4.5467\n",
      "Epoch [   50/   50] | d_loss: 0.7785 | g_loss: 3.4010\n",
      "Epoch [   50/   50] | d_loss: 0.4259 | g_loss: 4.0953\n",
      "Epoch [   50/   50] | d_loss: 0.4589 | g_loss: 5.7141\n",
      "Epoch [   50/   50] | d_loss: 0.3715 | g_loss: 3.9884\n",
      "Epoch [   50/   50] | d_loss: 0.4236 | g_loss: 4.7391\n",
      "Epoch [   50/   50] | d_loss: 0.3961 | g_loss: 2.9893\n",
      "Epoch [   50/   50] | d_loss: 0.3724 | g_loss: 5.9547\n",
      "Epoch [   50/   50] | d_loss: 0.3797 | g_loss: 5.0078\n",
      "Epoch [   50/   50] | d_loss: 0.4516 | g_loss: 4.0768\n",
      "Epoch [   50/   50] | d_loss: 0.4213 | g_loss: 4.1120\n",
      "Epoch [   50/   50] | d_loss: 0.4738 | g_loss: 2.3855\n",
      "Epoch [   50/   50] | d_loss: 0.4173 | g_loss: 2.6163\n",
      "Epoch [   50/   50] | d_loss: 0.4583 | g_loss: 2.9302\n",
      "Epoch [   50/   50] | d_loss: 0.4170 | g_loss: 4.4945\n",
      "Epoch [   50/   50] | d_loss: 0.4886 | g_loss: 4.4333\n",
      "Epoch [   50/   50] | d_loss: 0.3401 | g_loss: 5.4062\n",
      "Epoch [   50/   50] | d_loss: 0.4372 | g_loss: 3.5996\n",
      "Epoch [   50/   50] | d_loss: 0.7727 | g_loss: 3.2607\n",
      "Epoch [   50/   50] | d_loss: 0.3526 | g_loss: 4.1240\n",
      "Epoch [   50/   50] | d_loss: 0.4995 | g_loss: 3.1288\n",
      "Epoch [   50/   50] | d_loss: 0.3711 | g_loss: 3.5555\n",
      "Epoch [   50/   50] | d_loss: 0.3998 | g_loss: 2.8857\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Training loss\n",
    "\n",
    "Plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7eff5f6c1198>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff7ac84ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "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)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load samples from generator, taken while training\n",
    "with open('train_samples.pkl', 'rb') as f:\n",
    "    samples = pkl.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff72e57c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "**Answer:** \n",
    "Looking closely, the left and right side of the generated samples are from different people.  I wonder if I cleaned up my sample/training data and only allowed evenly, front facing images if this phenomenon would go away.  Probably the most important thing I would do next is increase the layer count in both my Generator and my Discriminator.  That way I could pick up more features that would possibly bring a more realistic output from the Generator.  I also think that if I used a higher pixel count (instead of 64 x 64), the model would be able to identify finer features that would help the output from the Generator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"problem_unittests.py\" files in your submission."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}