face-generation/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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  processed_celeba_small.zip\n",
      "replace processed_celeba_small/.DS_Store? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n"
     ]
    }
   ],
   "source": [
    "# can comment out after executing\n",
    "!unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
    "    \n",
    "    transform = transforms.Compose([transforms.Resize(image_size),\n",
    "                                   transforms.ToTensor()]) \n",
    "    dataset = datasets.ImageFolder(data_dir, transform)\n",
    "    return torch.utils.data.DataLoader(dataset, batch_size = batch_size, shuffle=True)"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 128\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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1736218d68>"
      ]
     },
     "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": 6,
   "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",
    "    return x * (max - min) + min\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.8745)\n",
      "Max:  tensor(0.8745)\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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding = 1, batch_norm= True):\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",
    "    layers.append(conv_layer)\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    layers = []\n",
    "    layers.append(nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=False))\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "# n_classes=1\n",
    "# n_layers=4\n",
    "# in_shape=(32,32,3)\n",
    "# dropout_prob=0.35\n",
    "\n",
    "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",
    "        \n",
    "        super(Discriminator, self).__init__()\n",
    "        self.conv_dim = conv_dim\n",
    "        # 16 x 16, conv_dim\n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False)\n",
    "        # 8 x 8, conv_dim * 2\n",
    "        self.conv2 = conv(conv_dim, conv_dim * 2, 4) \n",
    "        # 4 x 4, conv_dim * 4\n",
    "        self.conv3 = conv(conv_dim * 2, conv_dim * 4, 4)\n",
    "        # 2 x 2, conv_dim * 8\n",
    "        self.conv4 = conv(conv_dim * 4, conv_dim * 8, 4)\n",
    "        \n",
    "        self.classifier = nn.Linear(conv_dim * 8 * 2 * 2, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: Discriminator logits; the output of the neural network\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        out = F.leaky_relu(self.conv1(x), 0.2)\n",
    "        out = F.leaky_relu(self.conv2(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv3(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv4(out), 0.2)\n",
    "        \n",
    "        out = out.view(-1, self.conv_dim * 8 * 2 * 2)\n",
    "        out = self.classifier(out)\n",
    "        return out\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(Discriminator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "The generator should upsample an input and generate a *new* image of the same size as our training data `32x32x3`. This should be mostly transpose convolutional layers with normalization applied to the outputs.\n",
    "\n",
    "#### Exercise: Complete the Generator class\n",
    "* The inputs to the generator are vectors of some length `z_size`\n",
    "* The output should be a image of shape `32x32x3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
    "        self.fc = nn.Linear(z_size, conv_dim*8*2*2)\n",
    "        \n",
    "        self.t_conv1 = deconv(conv_dim * 8, conv_dim * 4, 4)\n",
    "        self.t_conv2 = deconv(conv_dim * 4, conv_dim * 2, 4)\n",
    "        self.t_conv3 = deconv(conv_dim * 2, conv_dim, 4)\n",
    "        self.t_conv4 = deconv(conv_dim, 3, 4, batch_norm=False)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: A 32x32x3 Tensor image as output\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        \n",
    "        out = self.fc(x)\n",
    "        out = out.view(-1, self.conv_dim * 8, 2, 2) # (batch_size, depth, 4, 4)\n",
    "        \n",
    "        out = F.relu(self.t_conv1(out))\n",
    "        out = F.relu(self.t_conv2(out))\n",
    "        out = F.relu(self.t_conv3(out))\n",
    "        \n",
    "        out = self.t_conv4(out)\n",
    "        out = F.tanh(out)\n",
    "        \n",
    "        return out\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(Generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the weights of your networks\n",
    "\n",
    "To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the [original DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf), they say:\n",
    "> All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.\n",
    "\n",
    "So, your next task will be to define a weight initialization function that does just this!\n",
    "\n",
    "You can refer back to the lesson on weight initialization or even consult existing model code, such as that from [the `networks.py` file in CycleGAN Github repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py) to help you complete this function.\n",
    "\n",
    "#### Exercise: Complete the weight initialization function\n",
    "\n",
    "* This should initialize only **convolutional** and **linear** layers\n",
    "* Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.\n",
    "* The bias terms, if they exist, may be left alone or set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
    "\n",
    "    # TODO: Apply initial weights to convolutional and linear layers\n",
    "    isConv = classname.find('Conv') != -1\n",
    "    isLin = classname.find('Linear') != -1\n",
    "    if (hasattr(m, 'weight') and isConv or isLin):\n",
    "        init.normal_(m.weight.data, 0.0, 0.02)"
   ]
  },
  {
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv4): Sequential(\n",
      "    (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (classifier): Linear(in_features=2048, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc): Linear(in_features=100, out_features=2048, bias=True)\n",
      "  (t_conv1): Sequential(\n",
      "    (0): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv2): Sequential(\n",
      "    (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv3): Sequential(\n",
      "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv4): Sequential(\n",
      "    (0): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define model hyperparams\n",
    "d_conv_dim = 64\n",
    "g_conv_dim = 64\n",
    "z_size = 100\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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being real.\n",
    "       param, D_out: discriminator logits\n",
    "       return: real loss'''\n",
    "    \n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.ones(batch_size)\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    \n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being fake.\n",
    "       param, D_out: discriminator logits\n",
    "       return: fake loss'''\n",
    "    \n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.zeros(batch_size)\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "#### Exercise: Define optimizers for your Discriminator (D) and Generator (G)\n",
    "\n",
    "Define optimizers for your models with appropriate hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "lr = 0.0002\n",
    "beta1=0.5\n",
    "beta2=0.999\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "d_optimizer = optim.Adam(D.parameters(), 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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(D, G, n_epochs, print_every=50):\n",
    "    '''Trains adversarial networks for some number of epochs\n",
    "       param, D: the discriminator network\n",
    "       param, G: the generator network\n",
    "       param, n_epochs: number of epochs to train for\n",
    "       param, print_every: when to print and record the models' losses\n",
    "       return: D and G losses'''\n",
    "    \n",
    "    # move models to GPU\n",
    "    if train_on_gpu:\n",
    "        D.cuda()\n",
    "        G.cuda()\n",
    "\n",
    "    # keep track of loss and generated, \"fake\" samples\n",
    "    samples = []\n",
    "    losses = []\n",
    "\n",
    "    # Get some fixed data for sampling. These are images that are held\n",
    "    # constant throughout training, and allow us to inspect the model's performance\n",
    "    sample_size=16\n",
    "    fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
    "    fixed_z = torch.from_numpy(fixed_z).float()\n",
    "    # move z to GPU if available\n",
    "    if train_on_gpu:\n",
    "        fixed_z = fixed_z.cuda()\n",
    "\n",
    "    # epoch training loop\n",
    "    for epoch in range(n_epochs):\n",
    "\n",
    "        # batch training loop\n",
    "        for batch_i, (real_images, _) in enumerate(celeba_train_loader):\n",
    "\n",
    "            batch_size = real_images.size(0)\n",
    "            real_images = scale(real_images)\n",
    "\n",
    "            # ===============================================\n",
    "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
    "            # ===============================================\n",
    "            \n",
    "            # 1. Train the discriminator on real and fake images\n",
    "            d_optimizer.zero_grad()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "                \n",
    "            D_real = D(real_images)\n",
    "            d_real_loss = real_loss(D_real)\n",
    "            \n",
    "            z = np.random.uniform(-1, 1, size = (batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            \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",
    "            \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",
    "            #              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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   20] | d_loss: 1.3387 | g_loss: 1.4542\n",
      "Epoch [    1/   20] | d_loss: 0.0253 | g_loss: 5.9956\n",
      "Epoch [    1/   20] | d_loss: 1.3790 | g_loss: 4.7222\n",
      "Epoch [    1/   20] | d_loss: 0.7460 | g_loss: 6.4396\n",
      "Epoch [    1/   20] | d_loss: 0.4577 | g_loss: 3.2550\n",
      "Epoch [    1/   20] | d_loss: 0.2864 | g_loss: 2.6791\n",
      "Epoch [    1/   20] | d_loss: 0.5415 | g_loss: 3.2310\n",
      "Epoch [    1/   20] | d_loss: 0.6709 | g_loss: 2.2707\n",
      "Epoch [    1/   20] | d_loss: 0.3854 | g_loss: 2.2642\n",
      "Epoch [    1/   20] | d_loss: 0.4460 | g_loss: 3.9075\n",
      "Epoch [    1/   20] | d_loss: 0.6673 | g_loss: 2.8495\n",
      "Epoch [    1/   20] | d_loss: 0.7938 | g_loss: 3.2533\n",
      "Epoch [    1/   20] | d_loss: 0.6143 | g_loss: 3.1454\n",
      "Epoch [    1/   20] | d_loss: 0.6393 | g_loss: 4.9985\n",
      "Epoch [    1/   20] | d_loss: 0.5029 | g_loss: 2.6677\n",
      "Epoch [    2/   20] | d_loss: 1.0179 | g_loss: 6.1260\n",
      "Epoch [    2/   20] | d_loss: 1.2126 | g_loss: 2.9480\n",
      "Epoch [    2/   20] | d_loss: 0.4119 | g_loss: 3.2113\n",
      "Epoch [    2/   20] | d_loss: 0.7115 | g_loss: 2.7207\n",
      "Epoch [    2/   20] | d_loss: 0.4507 | g_loss: 1.9588\n",
      "Epoch [    2/   20] | d_loss: 0.7287 | g_loss: 3.1836\n",
      "Epoch [    2/   20] | d_loss: 0.5877 | g_loss: 3.3743\n",
      "Epoch [    2/   20] | d_loss: 0.4948 | g_loss: 1.8990\n",
      "Epoch [    2/   20] | d_loss: 0.4347 | g_loss: 3.2167\n",
      "Epoch [    2/   20] | d_loss: 0.5111 | g_loss: 3.6132\n",
      "Epoch [    2/   20] | d_loss: 0.4789 | g_loss: 3.6218\n",
      "Epoch [    2/   20] | d_loss: 0.7149 | g_loss: 3.6259\n",
      "Epoch [    2/   20] | d_loss: 0.5367 | g_loss: 2.9105\n",
      "Epoch [    2/   20] | d_loss: 0.6455 | g_loss: 2.7682\n",
      "Epoch [    2/   20] | d_loss: 0.5226 | g_loss: 2.4548\n",
      "Epoch [    3/   20] | d_loss: 0.5862 | g_loss: 2.5114\n",
      "Epoch [    3/   20] | d_loss: 1.1974 | g_loss: 1.6394\n",
      "Epoch [    3/   20] | d_loss: 0.6508 | g_loss: 2.3182\n",
      "Epoch [    3/   20] | d_loss: 0.7635 | g_loss: 1.8928\n",
      "Epoch [    3/   20] | d_loss: 0.4864 | g_loss: 2.5755\n",
      "Epoch [    3/   20] | d_loss: 0.8115 | g_loss: 1.1150\n",
      "Epoch [    3/   20] | d_loss: 1.1047 | g_loss: 4.3957\n",
      "Epoch [    3/   20] | d_loss: 0.6621 | g_loss: 2.4360\n",
      "Epoch [    3/   20] | d_loss: 0.6424 | g_loss: 2.6774\n",
      "Epoch [    3/   20] | d_loss: 0.6100 | g_loss: 1.8860\n",
      "Epoch [    3/   20] | d_loss: 0.6073 | g_loss: 2.8077\n",
      "Epoch [    3/   20] | d_loss: 0.7676 | g_loss: 2.8357\n",
      "Epoch [    3/   20] | d_loss: 0.7746 | g_loss: 3.2273\n",
      "Epoch [    3/   20] | d_loss: 0.7069 | g_loss: 3.0287\n",
      "Epoch [    3/   20] | d_loss: 0.8357 | g_loss: 1.4874\n",
      "Epoch [    4/   20] | d_loss: 0.9295 | g_loss: 2.9790\n",
      "Epoch [    4/   20] | d_loss: 0.5510 | g_loss: 2.9047\n",
      "Epoch [    4/   20] | d_loss: 0.5479 | g_loss: 2.6926\n",
      "Epoch [    4/   20] | d_loss: 0.6079 | g_loss: 2.1983\n",
      "Epoch [    4/   20] | d_loss: 0.6903 | g_loss: 2.1621\n",
      "Epoch [    4/   20] | d_loss: 0.7163 | g_loss: 1.6793\n",
      "Epoch [    4/   20] | d_loss: 0.5902 | g_loss: 2.1292\n",
      "Epoch [    4/   20] | d_loss: 0.4680 | g_loss: 2.6619\n",
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      "Epoch [   20/   20] | d_loss: 0.3763 | g_loss: 1.3717\n",
      "Epoch [   20/   20] | d_loss: 0.4307 | g_loss: 2.9822\n",
      "Epoch [   20/   20] | d_loss: 0.2306 | g_loss: 3.8555\n",
      "Epoch [   20/   20] | d_loss: 0.5153 | g_loss: 4.6667\n",
      "Epoch [   20/   20] | d_loss: 0.3709 | g_loss: 4.1289\n",
      "Epoch [   20/   20] | d_loss: 0.3823 | g_loss: 2.3639\n",
      "Epoch [   20/   20] | d_loss: 0.4503 | g_loss: 3.0640\n",
      "Epoch [   20/   20] | d_loss: 0.1313 | g_loss: 4.2366\n",
      "Epoch [   20/   20] | d_loss: 0.3971 | g_loss: 3.0481\n",
      "Epoch [   20/   20] | d_loss: 0.3771 | g_loss: 3.5309\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 20\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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f17346e8668>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17346e8f60>"
      ]
     },
     "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": 21,
   "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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1734652198>"
      ]
     },
     "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:** (Write your answer in this cell)\n",
    "- The input images are only celebrities which means the network will only generate faces with features that are similar to the input. In order to make more variety of faces that represent (you know, humanity?) we should make a wider range of images as input.\n",
    "- It seems that the angles of faces are not very accurate, maybe becaue there not enough angles to train with? or because we should stick with one angle as an input, and limit our generated faces set to that angle.\n",
    "- I haven't used the label smoothing and that could be some extra step to enhance the quality of the images.\n",
    "- The resolution of images are very low, maybe I can try higher resolution images."
   ]
  },
  {
   "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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