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 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",
    "    transform = transforms.Compose([transforms.Resize(image_size),transforms.ToTensor()])\n",
    "    train_dataset = datasets.ImageFolder(data_dir, transform)\n",
    "    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, shuffle=True, batch_size=batch_size)\n",
    "    return train_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\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 0x7f25a5d01dd8>"
      ]
     },
     "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)))\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])"
   ]
  },
  {
   "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": [],
   "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",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.8431)\n",
      "Max:  tensor(0.7490)\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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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)\n",
    "\n",
    "\n",
    "# helper deconv function\n",
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a transpose convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    # append transpose conv layer\n",
    "    layers.append(nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=False))\n",
    "    # optional batch norm layer\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "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",
    "        self.conv1 = nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm1 = nn.BatchNorm2d(conv_dim)\n",
    "        self.conv2 = nn.Conv2d(conv_dim, conv_dim*2,kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm2 = nn.BatchNorm2d(conv_dim*2)\n",
    "        self.conv3 = nn.Conv2d(conv_dim*2, conv_dim*4, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm3 = nn.BatchNorm2d(conv_dim*4)\n",
    "        self.conv4 = nn.Conv2d(conv_dim*4, conv_dim*8, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm4 = nn.BatchNorm2d(conv_dim*8)\n",
    "        self.conv5 = nn.Conv2d(conv_dim*8, conv_dim*16, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.fc = nn.Linear(conv_dim*4*4, 1)\n",
    "\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.batch_norm1(self.conv1(x)), 0.2)\n",
    "        x = F.leaky_relu(self.batch_norm2(self.conv2(x)), 0.2)\n",
    "        x = F.leaky_relu(self.batch_norm3(self.conv3(x)), 0.2)\n",
    "        x = F.leaky_relu(self.batch_norm4(self.conv4(x)), 0.2)\n",
    "        x = self.conv5(x)\n",
    "        # flatten\n",
    "        x = x.view(-1, self.conv_dim*4*4)\n",
    "        # final output layer\n",
    "        x = F.sigmoid(self.fc(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)"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z_size 25\n",
      "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",
    "        self.t_conv1 = nn.ConvTranspose2d(conv_dim, conv_dim*8, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm1 = nn.BatchNorm2d(conv_dim*8)\n",
    "        self.t_conv2 = nn.ConvTranspose2d(conv_dim*8, conv_dim*4, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm2 = nn.BatchNorm2d(conv_dim*4)\n",
    "        self.t_conv3 = nn.ConvTranspose2d(conv_dim*4, conv_dim*2, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.batch_norm3 = nn.BatchNorm2d(conv_dim*2)\n",
    "        self.t_conv4 = nn.ConvTranspose2d(conv_dim*2, 3, kernel_size=4, stride=2, padding=1, bias=False)\n",
    "        self.fc = nn.Linear(z_size, conv_dim*4)\n",
    "        print('z_size', z_size)\n",
    "\n",
    "\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",
    "        batch_s = x.shape[0]\n",
    "        x = self.fc(x)\n",
    "        x = x.view(batch_s, self.conv_dim, 2, 2)        \n",
    "        x = F.relu(self.batch_norm1(self.t_conv1(x)))\n",
    "        x = F.relu(self.batch_norm2(self.t_conv2(x)))\n",
    "        x = F.relu(self.batch_norm3(self.t_conv3(x)))\n",
    "        x = self.t_conv4(x)\n",
    "        x = F.tanh(x)\n",
    "\n",
    "        \n",
    "        return x\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": 13,
   "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",
    "    \n",
    "    # TODO: Apply initial weights to convolutional and linear layers\n",
    "    if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n",
    "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
    "        if  hasattr(m.bias, 'data'):\n",
    "            nn.init.constant_(m.bias.data, 0.0) \n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z_size 100\n",
      "Discriminator(\n",
      "  (conv1): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv3): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv4): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv5): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (fc): Linear(in_features=512, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (t_conv1): ConvTranspose2d(32, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (t_conv2): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (t_conv3): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (batch_norm3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (t_conv4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (fc): Linear(in_features=100, out_features=128, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define model hyperparams\n",
    "d_conv_dim = 32\n",
    "g_conv_dim = 32\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)"
   ]
  },
  {
   "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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import random\n",
    "def real_loss(D_out, smooth=False):\n",
    "    batch_size = D_out.size(0)\n",
    "    # label smoothing\n",
    "    if smooth:\n",
    "        # smooth, real labels = 0.9\n",
    "        labels = torch.ones(batch_size)*0.9\n",
    "    else:\n",
    "        labels = torch.ones(batch_size) # real labels = 1\n",
    "    # move labels to GPU if available     \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    # binary cross entropy with logits loss\n",
    "    criterion = nn.BCELoss()\n",
    "    # calculate loss\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.zeros(batch_size)\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCELoss()\n",
    "    # calculate loss\n",
    "    loss = criterion(D_out.squeeze(), labels)\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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "lr=0.0005\n",
    "beta1 = 0.1\n",
    "beta2 = 0.999\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])"
   ]
  },
  {
   "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": 19,
   "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",
    "            # 1. Train the discriminator on real and fake images\n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "            d_optimizer.zero_grad()\n",
    "            D_real = D(real_images)\n",
    "            d_real_loss = real_loss(D_real)\n",
    "            z_flex = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z_flex = torch.from_numpy(z_flex).float()\n",
    "            if train_on_gpu:\n",
    "                z_flex = z_flex.cuda()\n",
    "\n",
    "\n",
    "            fake_images = G(z_flex)\n",
    "            D_fake = D(fake_images)\n",
    "            d_fake_loss = fake_loss(D_fake)\n",
    "\n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "            # d_loss = \n",
    "\n",
    "            # 2. Train the generator with an adversarial loss\n",
    "            g_optimizer.zero_grad()\n",
    "            z_flex = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z_flex = torch.from_numpy(z_flex).float()\n",
    "            if train_on_gpu:\n",
    "                z_flex = z_flex.cuda()\n",
    "            fake_images = G(z_flex)\n",
    "            D_fake = D(fake_images)\n",
    "            g_loss = real_loss(D_fake, True) # use real loss to flip labels\n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()\n",
    "\n",
    "            # g_loss = \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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   12] | d_loss: 1.3014 | g_loss: 0.7601\n",
      "Epoch [    1/   12] | d_loss: 1.7272 | g_loss: 0.5632\n",
      "Epoch [    1/   12] | d_loss: 1.1988 | g_loss: 0.9849\n",
      "Epoch [    1/   12] | d_loss: 1.2730 | g_loss: 1.1918\n",
      "Epoch [    1/   12] | d_loss: 1.4052 | g_loss: 1.9485\n",
      "Epoch [    1/   12] | d_loss: 1.2401 | g_loss: 0.5440\n",
      "Epoch [    1/   12] | d_loss: 1.1874 | g_loss: 1.0290\n",
      "Epoch [    1/   12] | d_loss: 1.3801 | g_loss: 0.8863\n",
      "Epoch [    1/   12] | d_loss: 1.1183 | g_loss: 0.8665\n",
      "Epoch [    1/   12] | d_loss: 1.5323 | g_loss: 0.6741\n",
      "Epoch [    1/   12] | d_loss: 1.2877 | g_loss: 1.0486\n",
      "Epoch [    1/   12] | d_loss: 1.3972 | g_loss: 1.2037\n",
      "Epoch [    1/   12] | d_loss: 1.2458 | g_loss: 1.2214\n",
      "Epoch [    1/   12] | d_loss: 1.2906 | g_loss: 1.3999\n",
      "Epoch [    1/   12] | d_loss: 1.0524 | g_loss: 0.9815\n",
      "Epoch [    1/   12] | d_loss: 1.3685 | g_loss: 1.3479\n",
      "Epoch [    1/   12] | d_loss: 1.1458 | g_loss: 1.1338\n",
      "Epoch [    1/   12] | d_loss: 1.3620 | g_loss: 1.3187\n",
      "Epoch [    1/   12] | d_loss: 1.0221 | g_loss: 1.1891\n",
      "Epoch [    1/   12] | d_loss: 1.5375 | g_loss: 1.6816\n",
      "Epoch [    1/   12] | d_loss: 1.3039 | g_loss: 0.8487\n",
      "Epoch [    1/   12] | d_loss: 1.2133 | g_loss: 1.2407\n",
      "Epoch [    1/   12] | d_loss: 1.2740 | g_loss: 1.2291\n",
      "Epoch [    1/   12] | d_loss: 1.2876 | g_loss: 1.1231\n",
      "Epoch [    1/   12] | d_loss: 1.1575 | g_loss: 1.7622\n",
      "Epoch [    1/   12] | d_loss: 1.2643 | g_loss: 1.4470\n",
      "Epoch [    1/   12] | d_loss: 1.1331 | g_loss: 0.8744\n",
      "Epoch [    1/   12] | d_loss: 1.2863 | g_loss: 1.4728\n",
      "Epoch [    1/   12] | d_loss: 1.1055 | g_loss: 0.9379\n",
      "Epoch [    1/   12] | d_loss: 0.9114 | g_loss: 1.7053\n",
      "Epoch [    1/   12] | d_loss: 1.1133 | g_loss: 1.0675\n",
      "Epoch [    1/   12] | d_loss: 1.2828 | g_loss: 0.8821\n",
      "Epoch [    1/   12] | d_loss: 1.0612 | g_loss: 1.0629\n",
      "Epoch [    1/   12] | d_loss: 0.9641 | g_loss: 0.9689\n",
      "Epoch [    1/   12] | d_loss: 1.2976 | g_loss: 1.0019\n",
      "Epoch [    1/   12] | d_loss: 1.2679 | g_loss: 1.1547\n",
      "Epoch [    1/   12] | d_loss: 1.2069 | g_loss: 1.1501\n",
      "Epoch [    1/   12] | d_loss: 1.1362 | g_loss: 1.1015\n",
      "Epoch [    1/   12] | d_loss: 1.1328 | g_loss: 0.7936\n",
      "Epoch [    1/   12] | d_loss: 1.0540 | g_loss: 1.0553\n",
      "Epoch [    1/   12] | d_loss: 1.3898 | g_loss: 1.0357\n",
      "Epoch [    1/   12] | d_loss: 1.1881 | g_loss: 1.3347\n",
      "Epoch [    1/   12] | d_loss: 1.0682 | g_loss: 0.7769\n",
      "Epoch [    1/   12] | d_loss: 1.1488 | g_loss: 0.9490\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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      "Epoch [   11/   12] | d_loss: 0.6524 | g_loss: 1.3392\n",
      "Epoch [   12/   12] | d_loss: 1.2752 | g_loss: 3.3585\n",
      "Epoch [   12/   12] | d_loss: 0.5245 | g_loss: 2.0399\n",
      "Epoch [   12/   12] | d_loss: 0.5120 | g_loss: 2.9751\n",
      "Epoch [   12/   12] | d_loss: 1.1332 | g_loss: 0.5895\n",
      "Epoch [   12/   12] | d_loss: 0.6041 | g_loss: 1.4857\n",
      "Epoch [   12/   12] | d_loss: 0.4672 | g_loss: 4.1325\n",
      "Epoch [   12/   12] | d_loss: 0.7316 | g_loss: 1.0816\n",
      "Epoch [   12/   12] | d_loss: 1.3909 | g_loss: 4.3426\n",
      "Epoch [   12/   12] | d_loss: 0.7227 | g_loss: 2.4428\n",
      "Epoch [   12/   12] | d_loss: 0.5148 | g_loss: 3.5013\n",
      "Epoch [   12/   12] | d_loss: 0.5908 | g_loss: 2.9490\n",
      "Epoch [   12/   12] | d_loss: 0.5481 | g_loss: 2.2859\n",
      "Epoch [   12/   12] | d_loss: 0.2881 | g_loss: 3.3989\n",
      "Epoch [   12/   12] | d_loss: 0.2095 | g_loss: 1.5491\n",
      "Epoch [   12/   12] | d_loss: 0.5380 | g_loss: 2.1495\n",
      "Epoch [   12/   12] | d_loss: 1.1115 | g_loss: 3.7586\n",
      "Epoch [   12/   12] | d_loss: 0.5925 | g_loss: 1.6219\n",
      "Epoch [   12/   12] | d_loss: 0.2163 | g_loss: 1.6854\n",
      "Epoch [   12/   12] | d_loss: 0.3551 | g_loss: 2.7795\n",
      "Epoch [   12/   12] | d_loss: 1.2962 | g_loss: 1.5511\n",
      "Epoch [   12/   12] | d_loss: 0.4537 | g_loss: 1.2145\n",
      "Epoch [   12/   12] | d_loss: 0.3981 | g_loss: 4.1875\n",
      "Epoch [   12/   12] | d_loss: 0.6060 | g_loss: 1.1984\n",
      "Epoch [   12/   12] | d_loss: 0.7866 | g_loss: 2.5643\n",
      "Epoch [   12/   12] | d_loss: 0.5367 | g_loss: 2.0363\n",
      "Epoch [   12/   12] | d_loss: 0.6757 | g_loss: 2.9970\n",
      "Epoch [   12/   12] | d_loss: 0.9425 | g_loss: 2.5962\n",
      "Epoch [   12/   12] | d_loss: 1.0113 | g_loss: 0.7810\n",
      "Epoch [   12/   12] | d_loss: 0.2816 | g_loss: 3.8443\n",
      "Epoch [   12/   12] | d_loss: 0.5870 | g_loss: 2.3653\n",
      "Epoch [   12/   12] | d_loss: 1.0525 | g_loss: 2.1725\n",
      "Epoch [   12/   12] | d_loss: 0.5252 | g_loss: 1.9395\n",
      "Epoch [   12/   12] | d_loss: 0.8537 | g_loss: 1.1933\n",
      "Epoch [   12/   12] | d_loss: 0.6617 | g_loss: 3.2174\n",
      "Epoch [   12/   12] | d_loss: 0.7806 | g_loss: 0.8256\n",
      "Epoch [   12/   12] | d_loss: 0.8006 | g_loss: 3.0894\n",
      "Epoch [   12/   12] | d_loss: 0.4169 | g_loss: 1.8765\n",
      "Epoch [   12/   12] | d_loss: 0.4558 | g_loss: 2.6620\n",
      "Epoch [   12/   12] | d_loss: 0.7425 | g_loss: 4.6887\n",
      "Epoch [   12/   12] | d_loss: 0.2672 | g_loss: 2.0002\n",
      "Epoch [   12/   12] | d_loss: 1.9516 | g_loss: 0.8901\n",
      "Epoch [   12/   12] | d_loss: 0.2937 | g_loss: 2.5175\n",
      "Epoch [   12/   12] | d_loss: 0.4802 | g_loss: 2.0856\n",
      "Epoch [   12/   12] | d_loss: 0.6099 | g_loss: 2.5838\n",
      "Epoch [   12/   12] | d_loss: 0.5313 | g_loss: 3.3076\n",
      "Epoch [   12/   12] | d_loss: 0.8340 | g_loss: 1.8611\n",
      "Epoch [   12/   12] | d_loss: 0.4655 | g_loss: 2.8254\n",
      "Epoch [   12/   12] | d_loss: 0.9276 | g_loss: 2.7410\n",
      "Epoch [   12/   12] | d_loss: 0.5768 | g_loss: 0.5745\n",
      "Epoch [   12/   12] | d_loss: 0.6628 | g_loss: 3.7007\n",
      "Epoch [   12/   12] | d_loss: 0.1643 | g_loss: 3.4068\n",
      "Epoch [   12/   12] | d_loss: 0.3599 | g_loss: 2.6169\n",
      "Epoch [   12/   12] | d_loss: 0.9275 | g_loss: 3.0282\n",
      "Epoch [   12/   12] | d_loss: 0.7004 | g_loss: 2.5152\n",
      "Epoch [   12/   12] | d_loss: 0.6006 | g_loss: 2.8004\n",
      "Epoch [   12/   12] | d_loss: 1.2380 | g_loss: 3.5189\n",
      "Epoch [   12/   12] | d_loss: 0.2573 | g_loss: 1.4686\n",
      "Epoch [   12/   12] | d_loss: 0.7081 | g_loss: 1.2660\n",
      "Epoch [   12/   12] | d_loss: 0.3007 | g_loss: 2.2960\n",
      "Epoch [   12/   12] | d_loss: 0.2960 | g_loss: 2.1708\n",
      "Epoch [   12/   12] | d_loss: 0.2207 | g_loss: 2.6752\n",
      "Epoch [   12/   12] | d_loss: 1.3051 | g_loss: 1.5401\n",
      "Epoch [   12/   12] | d_loss: 0.4247 | g_loss: 1.7934\n",
      "Epoch [   12/   12] | d_loss: 0.5860 | g_loss: 3.3299\n",
      "Epoch [   12/   12] | d_loss: 1.4118 | g_loss: 4.2426\n",
      "Epoch [   12/   12] | d_loss: 0.6650 | g_loss: 2.2660\n",
      "Epoch [   12/   12] | d_loss: 0.2361 | g_loss: 3.1265\n",
      "Epoch [   12/   12] | d_loss: 0.4376 | g_loss: 3.0495\n",
      "Epoch [   12/   12] | d_loss: 0.6095 | g_loss: 3.1245\n",
      "Epoch [   12/   12] | d_loss: 0.2044 | g_loss: 1.9236\n",
      "Epoch [   12/   12] | d_loss: 0.8187 | g_loss: 2.4071\n",
      "Epoch [   12/   12] | d_loss: 0.9133 | g_loss: 1.9782\n",
      "Epoch [   12/   12] | d_loss: 1.1837 | g_loss: 1.8353\n",
      "Epoch [   12/   12] | d_loss: 0.2768 | g_loss: 3.6734\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [   12/   12] | d_loss: 1.1235 | g_loss: 3.0052\n",
      "Epoch [   12/   12] | d_loss: 2.0993 | g_loss: 3.0530\n",
      "Epoch [   12/   12] | d_loss: 0.8687 | g_loss: 4.4923\n",
      "Epoch [   12/   12] | d_loss: 0.7188 | g_loss: 3.2488\n",
      "Epoch [   12/   12] | d_loss: 0.6142 | g_loss: 2.2549\n",
      "Epoch [   12/   12] | d_loss: 0.2900 | g_loss: 3.0532\n",
      "Epoch [   12/   12] | d_loss: 0.9370 | g_loss: 2.9220\n",
      "Epoch [   12/   12] | d_loss: 0.2270 | g_loss: 0.8875\n",
      "Epoch [   12/   12] | d_loss: 0.5455 | g_loss: 0.8016\n",
      "Epoch [   12/   12] | d_loss: 0.1925 | g_loss: 2.2055\n",
      "Epoch [   12/   12] | d_loss: 0.6066 | g_loss: 2.5636\n",
      "Epoch [   12/   12] | d_loss: 0.6086 | g_loss: 3.5049\n",
      "Epoch [   12/   12] | d_loss: 1.0450 | g_loss: 3.4200\n",
      "Epoch [   12/   12] | d_loss: 0.4465 | g_loss: 1.2046\n",
      "Epoch [   12/   12] | d_loss: 0.2926 | g_loss: 0.6434\n",
      "Epoch [   12/   12] | d_loss: 1.0969 | g_loss: 1.4189\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 12\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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f25a42e6748>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25a42e6390>"
      ]
     },
     "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": 23,
   "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": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f259b7fa9b0>"
      ]
     },
     "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",
    "- more variety of faces might help to train neural network better and generate a new type of faces.\n",
    "-Model size matters we have to ensure that our models reconginze and generate faces correctly. Deep models allow to catch some more characteristrics of the faces.\n",
    "-I used suggested beta1 0.5 and it generated more types of faces than beta1 0.1 which was suggested by the paper.\n",
    "-Adam is the best choice for GAN's as well as other architectures. \n",
    "-Number of epochs is a critical component of GAN's. Especially spread betwenn batch size of a generator and a descriminator.\n"
   ]
  },
  {
   "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"
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  "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
}