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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unzip:  cannot find or open processed_celeba_small.zip, processed_celeba_small.zip.zip or processed_celeba_small.zip.ZIP.\r\n"
     ]
    }
   ],
   "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",
    "    image_transforms = transforms.Compose([transforms.Resize(image_size), \n",
    "                                    transforms.CenterCrop(image_size), \n",
    "                                    transforms.ToTensor()])\n",
    "    data_set = datasets.ImageFolder(data_dir, transform=image_transforms)\n",
    "    data_loader = torch.utils.data.DataLoader(data_set,\n",
    "                                          batch_size=batch_size,\n",
    "                                          shuffle=True)\n",
    "    \n",
    "    return data_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 = 32\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 0x7f8c68fd4cc0>"
      ]
     },
     "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",
    "    \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-1.)\n",
      "Max:  tensor(0.7412)\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": "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, out_channels, \n",
    "                           kernel_size, stride, padding, bias=False)\n",
    "    \n",
    "    # append conv layer\n",
    "    layers.append(conv_layer)\n",
    "\n",
    "    if batch_norm:\n",
    "        # append batchnorm layer\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "     \n",
    "    # using Sequential container\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "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",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "\n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False) # first layer, no batch_norm\n",
    "        self.conv2 = conv(conv_dim, conv_dim*2, 4)\n",
    "        self.conv3 = conv(conv_dim*2, conv_dim*4, 4)\n",
    "        \n",
    "        self.fc = nn.Linear(conv_dim*4*4*4, 1)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: Discriminator logits; the output of the neural network\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        \n",
    "        x = F.leaky_relu(self.conv1(x), 0.2)\n",
    "        x = F.leaky_relu(self.conv2(x), 0.2)\n",
    "        x = F.leaky_relu(self.conv3(x), 0.2)\n",
    "\n",
    "        x = x.view(-1, self.conv_dim*4*4*4)\n",
    "\n",
    "        x = self.fc(x)\n",
    "        \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": [],
   "source": [
    "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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*4*4*4)\n",
    "\n",
    "        # transpose conv layers\n",
    "        self.t_conv1 = deconv(conv_dim*4, conv_dim*2, 4)\n",
    "        self.t_conv2 = deconv(conv_dim*2, conv_dim, 4)\n",
    "        self.t_conv3 = 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",
    "        x = self.fc(x)\n",
    "        x = x.view(-1, self.conv_dim*4, 4, 4) # (batch_size, depth, 4, 4)\n",
    "        x = F.relu(self.t_conv1(x))\n",
    "        x = F.relu(self.t_conv2(x))\n",
    "        \n",
    "        x = self.t_conv3(x)\n",
    "        x = F.tanh(x)\n",
    "        return x\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(Generator)"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import init\n",
    "\n",
    "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",
    "    init_gain = 0.02\n",
    "    # TODO: Apply initial weights to convolutional and linear layers\n",
    "    # Code taken from networks.py file in CycleGAN Github repository \n",
    "    if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n",
    "            init.normal_(m.weight.data, 0.0, init_gain)\n",
    "            if hasattr(m, 'bias') and m.bias is not None:\n",
    "                init.constant_(m.bias.data, 0.0)\n",
    "    elif classname.find('BatchNorm2d') != -1:  # BatchNorm Layer's weight is not a matrix; only normal distribution applies.\n",
    "            init.normal_(m.weight.data, 1.0, init_gain)\n",
    "            init.constant_(m.bias.data, 0.0) \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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(32, 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",
      "  (conv3): 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",
      "  (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(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_conv2): Sequential(\n",
      "    (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv3): Sequential(\n",
      "    (0): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from random import uniform\n",
    "\n",
    "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",
    "    labels = torch.ones(batch_size) *  np.random.uniform(0.7,1.05) # label smoothing \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\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",
    "    labels = torch.zeros(batch_size) * np.random.uniform(0.0,0.35)\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "# parameters taken directly from DCGAN paper in the assignment \n",
    "lr = 0.0002\n",
    "beta1=0.5\n",
    "beta2=0.999 # default value\n",
    "\n",
    "# Create optimizers for the discriminator and generator\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": 20,
   "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",
    "            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",
    "            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",
    "            \n",
    "            fake_images = G(z)\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",
    "            # 2. Train the generator with an adversarial loss\n",
    "            g_optimizer.zero_grad()\n",
    "            \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",
    "            \n",
    "            fake_images = G(z)\n",
    "            D_fake = D(fake_images)\n",
    "            g_loss = real_loss(D_fake)\n",
    "            \n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()\n",
    "            \n",
    "            \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/   10] | d_loss: 1.2678 | g_loss: 0.9246\n",
      "Epoch [    1/   10] | d_loss: 0.4066 | g_loss: 3.0303\n",
      "Epoch [    1/   10] | d_loss: 0.3463 | g_loss: 3.6230\n",
      "Epoch [    1/   10] | d_loss: 0.7510 | g_loss: 2.2199\n",
      "Epoch [    1/   10] | d_loss: 1.0820 | g_loss: 2.6453\n",
      "Epoch [    1/   10] | d_loss: 0.8641 | g_loss: 1.7200\n",
      "Epoch [    1/   10] | d_loss: 0.4731 | g_loss: 2.4129\n",
      "Epoch [    1/   10] | d_loss: 0.8035 | g_loss: 2.5367\n",
      "Epoch [    1/   10] | d_loss: 0.8018 | g_loss: 2.1384\n",
      "Epoch [    1/   10] | d_loss: 1.1427 | g_loss: 2.5015\n",
      "Epoch [    1/   10] | d_loss: 1.3028 | g_loss: 1.9352\n",
      "Epoch [    1/   10] | d_loss: 0.9036 | g_loss: 2.4863\n",
      "Epoch [    1/   10] | d_loss: 0.8076 | g_loss: 0.9899\n",
      "Epoch [    1/   10] | d_loss: 0.7579 | g_loss: 1.4733\n",
      "Epoch [    1/   10] | d_loss: 1.1025 | g_loss: 2.2748\n",
      "Epoch [    1/   10] | d_loss: 1.0385 | g_loss: 1.5898\n",
      "Epoch [    1/   10] | d_loss: 1.0108 | g_loss: 1.0029\n",
      "Epoch [    1/   10] | d_loss: 0.9887 | g_loss: 2.1093\n",
      "Epoch [    1/   10] | d_loss: 1.0585 | g_loss: 1.3705\n",
      "Epoch [    1/   10] | d_loss: 1.0139 | g_loss: 1.4829\n",
      "Epoch [    1/   10] | d_loss: 1.2857 | g_loss: 0.8230\n",
      "Epoch [    1/   10] | d_loss: 1.0279 | g_loss: 1.5098\n",
      "Epoch [    1/   10] | d_loss: 1.1163 | g_loss: 1.5503\n",
      "Epoch [    1/   10] | d_loss: 1.1857 | g_loss: 1.4569\n",
      "Epoch [    1/   10] | d_loss: 1.3020 | g_loss: 0.9233\n",
      "Epoch [    1/   10] | d_loss: 1.2003 | g_loss: 1.0737\n",
      "Epoch [    1/   10] | d_loss: 1.0013 | g_loss: 1.5149\n",
      "Epoch [    1/   10] | d_loss: 1.1473 | g_loss: 0.7903\n",
      "Epoch [    1/   10] | d_loss: 1.1901 | g_loss: 1.0956\n",
      "Epoch [    1/   10] | d_loss: 1.1829 | g_loss: 0.9849\n",
      "Epoch [    1/   10] | d_loss: 1.1705 | g_loss: 1.1290\n",
      "Epoch [    1/   10] | d_loss: 1.0306 | g_loss: 1.4243\n",
      "Epoch [    1/   10] | d_loss: 1.4603 | g_loss: 0.6889\n",
      "Epoch [    1/   10] | d_loss: 1.4630 | g_loss: 2.3193\n",
      "Epoch [    1/   10] | d_loss: 1.2888 | g_loss: 1.2635\n",
      "Epoch [    1/   10] | d_loss: 1.2287 | g_loss: 1.1767\n",
      "Epoch [    1/   10] | d_loss: 0.9460 | g_loss: 1.8262\n",
      "Epoch [    1/   10] | d_loss: 0.9701 | g_loss: 1.1467\n",
      "Epoch [    1/   10] | d_loss: 1.1270 | g_loss: 1.6041\n",
      "Epoch [    1/   10] | d_loss: 1.2799 | g_loss: 0.8857\n",
      "Epoch [    1/   10] | d_loss: 1.1419 | g_loss: 0.7270\n",
      "Epoch [    1/   10] | d_loss: 1.3838 | g_loss: 1.0005\n",
      "Epoch [    1/   10] | d_loss: 0.9639 | g_loss: 1.3356\n",
      "Epoch [    1/   10] | d_loss: 1.3735 | g_loss: 0.7654\n",
      "Epoch [    1/   10] | d_loss: 1.0290 | g_loss: 1.5347\n",
      "Epoch [    1/   10] | d_loss: 1.0874 | g_loss: 1.4622\n",
      "Epoch [    1/   10] | d_loss: 1.2760 | g_loss: 1.1929\n",
      "Epoch [    1/   10] | d_loss: 0.9778 | g_loss: 1.1624\n",
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      "Epoch [   10/   10] | d_loss: 0.6339 | g_loss: 2.2795\n",
      "Epoch [   10/   10] | d_loss: 0.8208 | g_loss: 1.4850\n",
      "Epoch [   10/   10] | d_loss: 0.9465 | g_loss: 2.1939\n",
      "Epoch [   10/   10] | d_loss: 0.5797 | g_loss: 2.6370\n",
      "Epoch [   10/   10] | d_loss: 1.2278 | g_loss: 2.4698\n",
      "Epoch [   10/   10] | d_loss: 0.6971 | g_loss: 2.2442\n",
      "Epoch [   10/   10] | d_loss: 0.8793 | g_loss: 1.4431\n",
      "Epoch [   10/   10] | d_loss: 0.7058 | g_loss: 1.8931\n",
      "Epoch [   10/   10] | d_loss: 0.6888 | g_loss: 1.9359\n",
      "Epoch [   10/   10] | d_loss: 0.6826 | g_loss: 1.9103\n",
      "Epoch [   10/   10] | d_loss: 0.3129 | g_loss: 3.3168\n",
      "Epoch [   10/   10] | d_loss: 0.9475 | g_loss: 1.6362\n",
      "Epoch [   10/   10] | d_loss: 0.8743 | g_loss: 2.2536\n",
      "Epoch [   10/   10] | d_loss: 0.7464 | g_loss: 2.6127\n",
      "Epoch [   10/   10] | d_loss: 0.6762 | g_loss: 2.0413\n",
      "Epoch [   10/   10] | d_loss: 0.4293 | g_loss: 2.3299\n",
      "Epoch [   10/   10] | d_loss: 0.9826 | g_loss: 1.9518\n",
      "Epoch [   10/   10] | d_loss: 0.8323 | g_loss: 2.4976\n",
      "Epoch [   10/   10] | d_loss: 0.5863 | g_loss: 2.6466\n",
      "Epoch [   10/   10] | d_loss: 0.7563 | g_loss: 1.9813\n",
      "Epoch [   10/   10] | d_loss: 0.6710 | g_loss: 1.6453\n",
      "Epoch [   10/   10] | d_loss: 0.7930 | g_loss: 2.3644\n",
      "Epoch [   10/   10] | d_loss: 0.8080 | g_loss: 1.8537\n",
      "Epoch [   10/   10] | d_loss: 0.8347 | g_loss: 3.4878\n",
      "Epoch [   10/   10] | d_loss: 1.2580 | g_loss: 1.5513\n",
      "Epoch [   10/   10] | d_loss: 0.7526 | g_loss: 1.4803\n",
      "Epoch [   10/   10] | d_loss: 0.6357 | g_loss: 1.8403\n",
      "Epoch [   10/   10] | d_loss: 0.7628 | g_loss: 2.2734\n",
      "Epoch [   10/   10] | d_loss: 1.1632 | g_loss: 1.3058\n",
      "Epoch [   10/   10] | d_loss: 1.2032 | g_loss: 2.4284\n",
      "Epoch [   10/   10] | d_loss: 0.4442 | g_loss: 2.0361\n",
      "Epoch [   10/   10] | d_loss: 0.7586 | g_loss: 1.5800\n",
      "Epoch [   10/   10] | d_loss: 1.0770 | g_loss: 2.1147\n",
      "Epoch [   10/   10] | d_loss: 0.5621 | g_loss: 2.3573\n",
      "Epoch [   10/   10] | d_loss: 0.6883 | g_loss: 2.2031\n",
      "Epoch [   10/   10] | d_loss: 0.6806 | g_loss: 1.2756\n",
      "Epoch [   10/   10] | d_loss: 0.8227 | g_loss: 1.6562\n",
      "Epoch [   10/   10] | d_loss: 1.0561 | g_loss: 2.3412\n",
      "Epoch [   10/   10] | d_loss: 0.6667 | g_loss: 2.1279\n",
      "Epoch [   10/   10] | d_loss: 0.7123 | g_loss: 2.4858\n",
      "Epoch [   10/   10] | d_loss: 0.9022 | g_loss: 2.6269\n",
      "Epoch [   10/   10] | d_loss: 0.7500 | g_loss: 2.1928\n",
      "Epoch [   10/   10] | d_loss: 0.7724 | g_loss: 1.8066\n",
      "Epoch [   10/   10] | d_loss: 1.0985 | g_loss: 2.2414\n",
      "Epoch [   10/   10] | d_loss: 0.7816 | g_loss: 2.3334\n",
      "Epoch [   10/   10] | d_loss: 0.6751 | g_loss: 1.8605\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 10\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 0x7f8c654567f0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c675d1a20>"
      ]
     },
     "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 0x7f8c675d1240>"
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   ],
   "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",
    "\n",
    "My network for Generator and Discriminator is the same network which I used for finishing my DCGAN assignment. However I got to play with real and fake loss functions with label smoothing. The dataset is indeed biased and hence most of the generated faces are white.\n",
    "\n",
    "The dataset can be more diverse and large, which can improve the model to give more realistic and real-world images. \n",
    "\n",
    "I got to play little with hyper-parameters because of the time-constraint.\n",
    "\n",
    "However the first samples generated by my model were very very bad and looked quite fake. That is why I had to experiment with hyperparameters and label smoothing.\n",
    "\n",
    "At first, I didn't use label smoothing and the loss took quite time to decrease even after 15 epochs of training.\n",
    "Then I used a label smoothing constant 0.9 for real loss and 0.1 for fake loss. Though it gave quite good result, I wanted that label smoothing factor to be uniformly random.\n",
    "\n",
    "Hence I went through some previous Github repositories and used random.uniform function to smooth the labels in a uniformly random fashion\n",
    "\n",
    "For real loss - I used a range of (0.7,1.05)\n",
    "For fake loss - I used a range of (0.0,0.35)\n",
    "\n",
    "My d_conv_dim and g_conv_dim were assignned to 32 considering my network model.\n",
    "batch-size and image-size = 32\n",
    "\n",
    "I used Adam Optimizers for the network optimization.\n",
    "The other hyperparameters I used are -\n",
    "lr = 0.0002\n",
    "beta1=0.5\n",
    "beta2=0.999\n",
    "\n",
    "And these are taken from DCGAN paper and these gave a pretty good result with generation faces.\n",
    "\n",
    "However some of the present generated samples are still distorted and needs some more training. The no of epochs I used were 10 for the current hyperparameters. I could have trained for another 5 epochs to see improved faces. I also observed that some faces which were realistic are of white people. May be because of that bias, white people faces are quite realistic when generated. \n",
    "\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."
   ]
  }
 ],
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