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": 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\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "    transformations = transforms.Compose([transforms.Resize(image_size),\n",
    "                                          transforms.ToTensor()])\n",
    "    \n",
    "    dataset = datasets.ImageFolder(data_dir, \n",
    "                                   transform=transformations)\n",
    "    \n",
    "    dataloader = DataLoader(dataset,\n",
    "                            batch_size=batch_size,\n",
    "                            shuffle=True)\n",
    "    \n",
    "    return dataloader\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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 64\n",
    "img_size = 32\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# Call your function and get a dataloader\n",
    "celeba_train_loader = get_dataloader(batch_size, img_size)\n"
   ]
  },
  {
   "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": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5594f77cc0>"
      ]
     },
     "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",
    "    x *=(feature_range[1] - feature_range[0]) + feature_range[0]\n",
    "    \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(1.00000e-02 *\n",
      "       2.3529)\n",
      "Max:  tensor(0.9843)\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": [
    "# helper conv function\n",
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, \n",
    "                           kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n",
    "    \n",
    "    layers.append(conv_layer)\n",
    "\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 = conv(3,conv_dim,4,batch_norm=False)\n",
    "        self.conv2 = conv(conv_dim, conv_dim*2,4)\n",
    "        self.conv3 = conv(conv_dim*2, conv_dim*4,4)\n",
    "        \n",
    "        self.fc1 = nn.Linear(conv_dim*64, 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.relu(self.conv1(x))\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.relu(self.conv3(x))\n",
    "        \n",
    "        x = x.view(-1, self.conv_dim*64)\n",
    "        \n",
    "        x = self.fc1(x)\n",
    "\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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper deconv function\n",
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
    "        self.fc1 = nn.Linear(z_size, conv_dim*64)\n",
    "        self.deconv1 = deconv(conv_dim*4, conv_dim*2, 4)\n",
    "        self.deconv2 = deconv(conv_dim*2, conv_dim, 4)\n",
    "        self.deconv3 = deconv(conv_dim, 3, 4, batch_norm=False)\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",
    "        x = self.fc1(x)\n",
    "        \n",
    "        x = x.view(-1, self.conv_dim*4, 4, 4)\n",
    "        \n",
    "        x = F.relu(self.deconv1(x))\n",
    "        x = F.relu(self.deconv2(x))\n",
    "        x = F.tanh(self.deconv3(x))\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": [
    "from torch.nn import init\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",
    "    std_dev = 0.02\n",
    "    mean = 0.\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 all([hasattr(m, 'weight'),any([classname.find('Conv') != -1, classname.find('Linear') != -1])]):\n",
    "        \n",
    "        init.normal_(m.weight.data, mean, std_dev)\n",
    "        \n",
    "        if all([hasattr(m, 'bias'),m.bias is not None]):\n",
    "            \n",
    "                init.constant_(m.bias.data, 0.0)  \n",
    "                \n",
    "    elif classname.find('BatchNorm2d') != -1:  \n",
    "        \n",
    "        init.normal_(m.weight.data, 1.0, std_dev)\n",
    "        \n",
    "        init.constant_(m.bias.data, 0.0)"
   ]
  },
  {
   "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": 31,
   "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",
      "  (fc1): Linear(in_features=4096, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc1): Linear(in_features=100, out_features=4096, bias=True)\n",
      "  (deconv1): 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",
      "  (deconv2): 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",
      "  (deconv3): 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": 32,
   "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": 33,
   "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",
    "\n",
    "    labels = torch.ones(batch_size)\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    \n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being fake.\n",
    "       param, D_out: discriminator logits\n",
    "       return: fake loss'''\n",
    "    batch_size = D_out.size(0)\n",
    "\n",
    "    labels = torch.zeros(batch_size)\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    \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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "lr = 0.0003\n",
    "beta1=0.5\n",
    "beta2=0.99\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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(D, G, n_epochs, print_every=500):\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_out = D(real_images)\n",
    "            \n",
    "            d_real_loss = real_loss(d_out)\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",
    "            \n",
    "            d_out = D(fake_images)\n",
    "            d_fake_loss = fake_loss(d_out)\n",
    "            \n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            \n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "            \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",
    "            \n",
    "            d_out = D(fake_images)\n",
    "            \n",
    "            \n",
    "            g_loss = real_loss(d_out)\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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   15] | d_loss: 1.7376 | g_loss: 1.1154\n",
      "Epoch [    1/   15] | d_loss: 0.8470 | g_loss: 1.3932\n",
      "Epoch [    1/   15] | d_loss: 0.9636 | g_loss: 1.1522\n",
      "Epoch [    2/   15] | d_loss: 0.7311 | g_loss: 1.3634\n",
      "Epoch [    2/   15] | d_loss: 1.1198 | g_loss: 1.5373\n",
      "Epoch [    2/   15] | d_loss: 0.7989 | g_loss: 1.4835\n",
      "Epoch [    3/   15] | d_loss: 0.7877 | g_loss: 1.1361\n",
      "Epoch [    3/   15] | d_loss: 0.7068 | g_loss: 1.2110\n",
      "Epoch [    3/   15] | d_loss: 0.4834 | g_loss: 2.6525\n",
      "Epoch [    4/   15] | d_loss: 1.1411 | g_loss: 3.6160\n",
      "Epoch [    4/   15] | d_loss: 0.5913 | g_loss: 1.5158\n",
      "Epoch [    4/   15] | d_loss: 0.4945 | g_loss: 2.7061\n",
      "Epoch [    5/   15] | d_loss: 0.7757 | g_loss: 3.3665\n",
      "Epoch [    5/   15] | d_loss: 0.8799 | g_loss: 2.4491\n",
      "Epoch [    5/   15] | d_loss: 0.4398 | g_loss: 2.2132\n",
      "Epoch [    6/   15] | d_loss: 0.5832 | g_loss: 1.8211\n",
      "Epoch [    6/   15] | d_loss: 0.4618 | g_loss: 1.6500\n",
      "Epoch [    6/   15] | d_loss: 0.4171 | g_loss: 2.0081\n",
      "Epoch [    7/   15] | d_loss: 1.4375 | g_loss: 4.8596\n",
      "Epoch [    7/   15] | d_loss: 0.6187 | g_loss: 2.1693\n",
      "Epoch [    7/   15] | d_loss: 0.3883 | g_loss: 2.7768\n",
      "Epoch [    8/   15] | d_loss: 0.5226 | g_loss: 3.5557\n",
      "Epoch [    8/   15] | d_loss: 1.2601 | g_loss: 0.5657\n",
      "Epoch [    8/   15] | d_loss: 0.3457 | g_loss: 2.9631\n",
      "Epoch [    9/   15] | d_loss: 0.4999 | g_loss: 1.4532\n",
      "Epoch [    9/   15] | d_loss: 0.2877 | g_loss: 2.0995\n",
      "Epoch [    9/   15] | d_loss: 0.4380 | g_loss: 2.9878\n",
      "Epoch [   10/   15] | d_loss: 0.7459 | g_loss: 3.3810\n",
      "Epoch [   10/   15] | d_loss: 0.2765 | g_loss: 2.7201\n",
      "Epoch [   10/   15] | d_loss: 0.2871 | g_loss: 3.7216\n",
      "Epoch [   11/   15] | d_loss: 1.5499 | g_loss: 2.9348\n",
      "Epoch [   11/   15] | d_loss: 0.4381 | g_loss: 2.3913\n",
      "Epoch [   11/   15] | d_loss: 0.4833 | g_loss: 3.2294\n",
      "Epoch [   12/   15] | d_loss: 0.5368 | g_loss: 1.9358\n",
      "Epoch [   12/   15] | d_loss: 0.3338 | g_loss: 3.7653\n",
      "Epoch [   12/   15] | d_loss: 0.5296 | g_loss: 2.9071\n",
      "Epoch [   13/   15] | d_loss: 1.6074 | g_loss: 4.0596\n",
      "Epoch [   13/   15] | d_loss: 0.4718 | g_loss: 4.4559\n",
      "Epoch [   13/   15] | d_loss: 0.5164 | g_loss: 3.9079\n",
      "Epoch [   14/   15] | d_loss: 0.2935 | g_loss: 3.3163\n",
      "Epoch [   14/   15] | d_loss: 0.4959 | g_loss: 2.0176\n",
      "Epoch [   14/   15] | d_loss: 0.3451 | g_loss: 3.0393\n",
      "Epoch [   15/   15] | d_loss: 0.4196 | g_loss: 2.7333\n",
      "Epoch [   15/   15] | d_loss: 0.2172 | g_loss: 3.6526\n",
      "Epoch [   15/   15] | d_loss: 0.5828 | g_loss: 2.0799\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 15\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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f55900b2278>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55902bc860>"
      ]
     },
     "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": 38,
   "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": 39,
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55900d7780>"
      ]
     },
     "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:** The generated samples are low-res and look hazy , increasing the number of layers to learn more complex features and increasing the output dimensions for generator may have improved the performance , \n",
    "\n",
    "yes the model is sort of biased , it shows mostly images of people with light skin tones , and all the ones with darker tones are either not sensible or too distorted to be called a face.  \n",
    "\n",
    "I initially trained for about 50 epochs with a higher lr but then I reduced the number of epochs as the error of generator was increasing  after 15-20 epochs ,and I also tuned the lr with it "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"problem_unittests.py\" files in your submission."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}