dlnd_face_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "\n",
    "In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate *new* images of faces that look as realistic as possible!\n",
    "\n",
    "The project will be broken down into a series of tasks from **loading in data to defining and training adversarial networks**. At the end of the notebook, you'll be able to visualize the results of your trained Generator to see how it performs; your generated samples should look like fairly realistic faces with small amounts of noise.\n",
    "\n",
    "### Get the Data\n",
    "\n",
    "You'll be using the [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) to train your adversarial networks.\n",
    "\n",
    "This dataset is more complex than the number datasets (like MNIST or SVHN) you've been working with, and so, you should prepare to define deeper networks and train them for a longer time to get good results. It is suggested that you utilize a GPU for training.\n",
    "\n",
    "### Pre-processed Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. Some sample data is show below.\n",
    "\n",
    "<img src='assets/processed_face_data.png' width=60% />\n",
    "\n",
    "> If you are working locally, you can download this data [by clicking here](https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip)\n",
    "\n",
    "This is a zip file that you'll need to extract in the home directory of this notebook for further loading and processing. After extracting the data, you should be left with a directory of data `processed_celeba_small/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "#!unzip processed_celeba_small.zip "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'processed_celeba_small/'\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import problem_unittests as tests\n",
    "#import helper\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the CelebA Data\n",
    "\n",
    "The [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations, you'll only need the images. Note that these are color images with [3 color channels (RGB)](https://en.wikipedia.org/wiki/Channel_(digital_image)#RGB_Images) each.\n",
    "\n",
    "### Pre-process and Load the Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. This *pre-processed* dataset is a smaller subset of the very large CelebA data.\n",
    "\n",
    "> There are a few other steps that you'll need to **transform** this data and create a **DataLoader**.\n",
    "\n",
    "#### Exercise: Complete the following `get_dataloader` function, such that it satisfies these requirements:\n",
    "\n",
    "* Your images should be square, Tensor images of size `image_size x image_size` in the x and y dimension.\n",
    "* Your function should return a DataLoader that shuffles and batches these Tensor images.\n",
    "\n",
    "#### ImageFolder\n",
    "\n",
    "To create a dataset given a directory of images, it's recommended that you use PyTorch's [ImageFolder](https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder) wrapper, with a root directory `processed_celeba_small/` and data transformation passed in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataloader(batch_size, image_size, data_dir='processed_celeba_small/'):\n",
    "    \"\"\"\n",
    "    Batch the neural network data using DataLoader\n",
    "    :param batch_size: The size of each batch; the number of images in a batch\n",
    "    :param img_size: The square size of the image data (x, y)\n",
    "    :param data_dir: Directory where image data is located\n",
    "    :return: DataLoader with batched data\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement function and return a dataloader\n",
    "    transform = transforms.Compose([transforms.Resize(image_size),# resize \n",
    "                                   transforms.ToTensor()]) #pytorch tensor\n",
    "    img_ds = datasets.ImageFolder(data_dir, transform)\n",
    "    data_loader = torch.utils.data.DataLoader(img_ds, batch_size=batch_size, shuffle=True)\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 = 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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94a982eb8>"
      ]
     },
     "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",
    "    scaled_x = x * (max - min) + min\n",
    "    return scaled_x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-1.)\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": 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": [
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"\n",
    "    Making a function that takes in the args of conv2d layers and returns\n",
    "    a sequential wrapper of all the defined layers.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n",
    "                          kernel_size=kernel_size, stride=stride,\n",
    "                          padding=padding, bias = False)\n",
    "    layers.append(conv_layer)\n",
    "    if batch_norm: # if (batch_norm == True)\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)# making a sequential wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Discriminator Module\n",
    "        :param conv_dim: The depth of the first convolutional layer\n",
    "        \"\"\"\n",
    "        super(Discriminator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "        # All conv layers will decrease the size by a factor of two \n",
    "        # while depth will increase\n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False)# (16,16,conv_dim)\n",
    "        self.conv2 = conv(conv_dim, conv_dim*2, 4)# (8,8,conv_dim*2)\n",
    "        self.conv3 = conv(conv_dim*2, conv_dim*4, 4)# (4,4,conv_dim*4)\n",
    "        self.conv4 = conv(conv_dim*4, conv_dim*8, 4)# (2,2,conv_dim*8)\n",
    "        # a classifier\n",
    "        self.classifier = nn.Linear(conv_dim*8*2*2, 1) # fully connected layer\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",
    "        # using leaky_relu as it allows gradients to flow through the network\n",
    "        out = F.leaky_relu(self.conv1(x), 0.2)# negative slope of 0.2\n",
    "        out = F.leaky_relu(self.conv2(out), 0.2)# negative slope of 0.2\n",
    "        out = F.leaky_relu(self.conv3(out), 0.2)# negative slope of 0.2\n",
    "        out = F.leaky_relu(self.conv4(out), 0.2)# negative slope of 0.2\n",
    "        \n",
    "        #flatten\n",
    "        out = out.view(-1, self.conv_dim*8*2*2) \n",
    "        \n",
    "        out = self.classifier(out) \n",
    "        return out\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",
    "\n",
    "  # Making a transpose conv2d function to increase the size of image. \n",
    "\n",
    "    layers = []\n",
    "    layers.append(nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels,\n",
    "                                kernel_size=kernel_size, stride=stride,\n",
    "                                padding=1, bias=False))\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "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",
    "        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",
    "        out = self.fc(x)\n",
    "        out = out.view(-1, self.conv_dim*8,2,2) \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) #(64x64)\n",
    "        out = F.tanh(out)\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": 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",
    "    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)\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Build complete network\n",
    "\n",
    "Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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, 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": 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": [
    "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)\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)\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",
    "lr =0.0002\n",
    "beta1= 0.5\n",
    "beta2= 0.999\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": 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",
    "            \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",
    "            #              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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your number of training epochs and train your GAN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   20] | d_loss: 1.5727 | g_loss: 1.3422\n",
      "Epoch [    1/   20] | d_loss: 0.0514 | g_loss: 6.2165\n",
      "Epoch [    1/   20] | d_loss: 0.2253 | g_loss: 3.3273\n",
      "Epoch [    1/   20] | d_loss: 0.4729 | g_loss: 5.2253\n",
      "Epoch [    1/   20] | d_loss: 0.4495 | g_loss: 4.0725\n",
      "Epoch [    1/   20] | d_loss: 0.7023 | g_loss: 3.7874\n",
      "Epoch [    1/   20] | d_loss: 0.9614 | g_loss: 5.6069\n",
      "Epoch [    1/   20] | d_loss: 0.5340 | g_loss: 4.0849\n",
      "Epoch [    1/   20] | d_loss: 1.2672 | g_loss: 2.1741\n",
      "Epoch [    1/   20] | d_loss: 2.5631 | g_loss: 4.5621\n",
      "Epoch [    1/   20] | d_loss: 0.9728 | g_loss: 2.0368\n",
      "Epoch [    1/   20] | d_loss: 0.9553 | g_loss: 2.3677\n",
      "Epoch [    1/   20] | d_loss: 0.7953 | g_loss: 3.1606\n",
      "Epoch [    1/   20] | d_loss: 0.3465 | g_loss: 3.1444\n",
      "Epoch [    1/   20] | d_loss: 0.5230 | g_loss: 3.2902\n",
      "Epoch [    2/   20] | d_loss: 0.9278 | g_loss: 6.0388\n",
      "Epoch [    2/   20] | d_loss: 0.8651 | g_loss: 3.2562\n",
      "Epoch [    2/   20] | d_loss: 0.7928 | g_loss: 3.3645\n",
      "Epoch [    2/   20] | d_loss: 0.6865 | g_loss: 3.8759\n",
      "Epoch [    2/   20] | d_loss: 0.3423 | g_loss: 2.4748\n",
      "Epoch [    2/   20] | d_loss: 0.5953 | g_loss: 2.6587\n",
      "Epoch [    2/   20] | d_loss: 0.5402 | g_loss: 3.3534\n",
      "Epoch [    2/   20] | d_loss: 0.7052 | g_loss: 3.2836\n",
      "Epoch [    2/   20] | d_loss: 0.5436 | g_loss: 3.4874\n",
      "Epoch [    2/   20] | d_loss: 0.5303 | g_loss: 2.5691\n",
      "Epoch [    2/   20] | d_loss: 0.7535 | g_loss: 4.4737\n",
      "Epoch [    2/   20] | d_loss: 0.6368 | g_loss: 2.6078\n",
      "Epoch [    2/   20] | d_loss: 0.6163 | g_loss: 2.1455\n",
      "Epoch [    2/   20] | d_loss: 1.0464 | g_loss: 5.6644\n",
      "Epoch [    2/   20] | d_loss: 0.4538 | g_loss: 2.5678\n",
      "Epoch [    3/   20] | d_loss: 0.4994 | g_loss: 2.3701\n",
      "Epoch [    3/   20] | d_loss: 1.1079 | g_loss: 4.9878\n",
      "Epoch [    3/   20] | d_loss: 0.6005 | g_loss: 2.7344\n",
      "Epoch [    3/   20] | d_loss: 0.7386 | g_loss: 3.7180\n",
      "Epoch [    3/   20] | d_loss: 0.6872 | g_loss: 2.5072\n",
      "Epoch [    3/   20] | d_loss: 0.7393 | g_loss: 3.6834\n",
      "Epoch [    3/   20] | d_loss: 0.5363 | g_loss: 2.8529\n",
      "Epoch [    3/   20] | d_loss: 0.7180 | g_loss: 2.8918\n",
      "Epoch [    3/   20] | d_loss: 0.6428 | g_loss: 1.5826\n",
      "Epoch [    3/   20] | d_loss: 0.5850 | g_loss: 2.0547\n",
      "Epoch [    3/   20] | d_loss: 0.6506 | g_loss: 2.4448\n",
      "Epoch [    3/   20] | d_loss: 0.8726 | g_loss: 3.5117\n",
      "Epoch [    3/   20] | d_loss: 0.7695 | g_loss: 2.6926\n",
      "Epoch [    3/   20] | d_loss: 0.5923 | g_loss: 2.5857\n",
      "Epoch [    3/   20] | d_loss: 0.7781 | g_loss: 3.2275\n",
      "Epoch [    4/   20] | d_loss: 0.5536 | g_loss: 2.9033\n",
      "Epoch [    4/   20] | d_loss: 0.3766 | g_loss: 2.1990\n",
      "Epoch [    4/   20] | d_loss: 0.6896 | g_loss: 3.6070\n",
      "Epoch [    4/   20] | d_loss: 0.6694 | g_loss: 2.4070\n",
      "Epoch [    4/   20] | d_loss: 0.6953 | g_loss: 1.6822\n",
      "Epoch [    4/   20] | d_loss: 0.8815 | g_loss: 3.5416\n",
      "Epoch [    4/   20] | d_loss: 0.6155 | g_loss: 3.0811\n",
      "Epoch [    4/   20] | d_loss: 0.5545 | g_loss: 2.1277\n",
      "Epoch [    4/   20] | d_loss: 0.6235 | g_loss: 3.3984\n",
      "Epoch [    4/   20] | d_loss: 0.7780 | g_loss: 2.9614\n",
      "Epoch [    4/   20] | d_loss: 0.5763 | g_loss: 2.7597\n",
      "Epoch [    4/   20] | d_loss: 0.5399 | g_loss: 1.9942\n",
      "Epoch [    4/   20] | d_loss: 0.6877 | g_loss: 2.2964\n",
      "Epoch [    4/   20] | d_loss: 0.6068 | g_loss: 2.0963\n",
      "Epoch [    4/   20] | d_loss: 0.5577 | g_loss: 1.5431\n",
      "Epoch [    5/   20] | d_loss: 0.8941 | g_loss: 3.7713\n",
      "Epoch [    5/   20] | d_loss: 0.7383 | g_loss: 3.8052\n",
      "Epoch [    5/   20] | d_loss: 1.1076 | g_loss: 4.0592\n",
      "Epoch [    5/   20] | d_loss: 0.7152 | g_loss: 1.6067\n",
      "Epoch [    5/   20] | d_loss: 0.7735 | g_loss: 2.4900\n",
      "Epoch [    5/   20] | d_loss: 0.7478 | g_loss: 3.9061\n",
      "Epoch [    5/   20] | d_loss: 0.7960 | g_loss: 1.3000\n",
      "Epoch [    5/   20] | d_loss: 0.7349 | g_loss: 2.5146\n",
      "Epoch [    5/   20] | d_loss: 0.8598 | g_loss: 2.8385\n",
      "Epoch [    5/   20] | d_loss: 1.0957 | g_loss: 1.4540\n",
      "Epoch [    5/   20] | d_loss: 2.3599 | g_loss: 0.5689\n",
      "Epoch [    5/   20] | d_loss: 0.6364 | g_loss: 2.0799\n",
      "Epoch [    5/   20] | d_loss: 0.7502 | g_loss: 1.9938\n",
      "Epoch [    5/   20] | d_loss: 0.4513 | g_loss: 2.2041\n",
      "Epoch [    5/   20] | d_loss: 0.5074 | g_loss: 2.2440\n",
      "Epoch [    6/   20] | d_loss: 0.6177 | g_loss: 1.5937\n",
      "Epoch [    6/   20] | d_loss: 1.1267 | g_loss: 0.1556\n",
      "Epoch [    6/   20] | d_loss: 1.3521 | g_loss: 1.6880\n",
      "Epoch [    6/   20] | d_loss: 0.6334 | g_loss: 2.3730\n",
      "Epoch [    6/   20] | d_loss: 0.5836 | g_loss: 3.3082\n",
      "Epoch [    6/   20] | d_loss: 0.7028 | g_loss: 1.5867\n",
      "Epoch [    6/   20] | d_loss: 0.7439 | g_loss: 2.9059\n",
      "Epoch [    6/   20] | d_loss: 0.6468 | g_loss: 2.2325\n",
      "Epoch [    6/   20] | d_loss: 0.8604 | g_loss: 1.8092\n",
      "Epoch [    6/   20] | d_loss: 0.5178 | g_loss: 1.6935\n",
      "Epoch [    6/   20] | d_loss: 0.9042 | g_loss: 2.5974\n",
      "Epoch [    6/   20] | d_loss: 0.7931 | g_loss: 2.3039\n",
      "Epoch [    6/   20] | d_loss: 0.6508 | g_loss: 2.3493\n",
      "Epoch [    6/   20] | d_loss: 0.9961 | g_loss: 3.2421\n",
      "Epoch [    6/   20] | d_loss: 0.5782 | g_loss: 2.8983\n",
      "Epoch [    7/   20] | d_loss: 0.7579 | g_loss: 1.8900\n",
      "Epoch [    7/   20] | d_loss: 0.6160 | g_loss: 1.4214\n",
      "Epoch [    7/   20] | d_loss: 0.7304 | g_loss: 1.6928\n",
      "Epoch [    7/   20] | d_loss: 0.9960 | g_loss: 4.1621\n",
      "Epoch [    7/   20] | d_loss: 0.7860 | g_loss: 1.5047\n",
      "Epoch [    7/   20] | d_loss: 0.7585 | g_loss: 1.2867\n",
      "Epoch [    7/   20] | d_loss: 1.0504 | g_loss: 3.9643\n",
      "Epoch [    7/   20] | d_loss: 0.7860 | g_loss: 2.3865\n",
      "Epoch [    7/   20] | d_loss: 1.1996 | g_loss: 4.7686\n",
      "Epoch [    7/   20] | d_loss: 0.6527 | g_loss: 3.0295\n",
      "Epoch [    7/   20] | d_loss: 0.9123 | g_loss: 3.3848\n",
      "Epoch [    7/   20] | d_loss: 0.6671 | g_loss: 2.8874\n",
      "Epoch [    7/   20] | d_loss: 0.7827 | g_loss: 2.7146\n",
      "Epoch [    7/   20] | d_loss: 0.8416 | g_loss: 3.0288\n",
      "Epoch [    7/   20] | d_loss: 0.5988 | g_loss: 2.6882\n",
      "Epoch [    8/   20] | d_loss: 1.2049 | g_loss: 1.1284\n",
      "Epoch [    8/   20] | d_loss: 0.7517 | g_loss: 2.3363\n",
      "Epoch [    8/   20] | d_loss: 0.8813 | g_loss: 3.3543\n",
      "Epoch [    8/   20] | d_loss: 0.8867 | g_loss: 1.1044\n",
      "Epoch [    8/   20] | d_loss: 0.7607 | g_loss: 1.6494\n",
      "Epoch [    8/   20] | d_loss: 0.7231 | g_loss: 2.6614\n",
      "Epoch [    8/   20] | d_loss: 0.7016 | g_loss: 2.5504\n",
      "Epoch [    8/   20] | d_loss: 0.6038 | g_loss: 1.6635\n",
      "Epoch [    8/   20] | d_loss: 0.6923 | g_loss: 3.1568\n",
      "Epoch [    8/   20] | d_loss: 0.6866 | g_loss: 2.7133\n",
      "Epoch [    8/   20] | d_loss: 0.5603 | g_loss: 1.7393\n",
      "Epoch [    8/   20] | d_loss: 0.7448 | g_loss: 2.0866\n",
      "Epoch [    8/   20] | d_loss: 0.8376 | g_loss: 1.5593\n",
      "Epoch [    8/   20] | d_loss: 0.8408 | g_loss: 1.9603\n",
      "Epoch [    8/   20] | d_loss: 0.9015 | g_loss: 2.4666\n",
      "Epoch [    9/   20] | d_loss: 0.6774 | g_loss: 2.8366\n",
      "Epoch [    9/   20] | d_loss: 0.6419 | g_loss: 1.9287\n",
      "Epoch [    9/   20] | d_loss: 0.7100 | g_loss: 2.1836\n",
      "Epoch [    9/   20] | d_loss: 0.8760 | g_loss: 3.5751\n",
      "Epoch [    9/   20] | d_loss: 0.7594 | g_loss: 1.1645\n",
      "Epoch [    9/   20] | d_loss: 0.9558 | g_loss: 3.8733\n",
      "Epoch [    9/   20] | d_loss: 0.6225 | g_loss: 1.8506\n",
      "Epoch [    9/   20] | d_loss: 0.7071 | g_loss: 2.4016\n",
      "Epoch [    9/   20] | d_loss: 0.6854 | g_loss: 3.2986\n",
      "Epoch [    9/   20] | d_loss: 0.5380 | g_loss: 2.6178\n",
      "Epoch [    9/   20] | d_loss: 0.6940 | g_loss: 2.6045\n",
      "Epoch [    9/   20] | d_loss: 0.5837 | g_loss: 2.9120\n",
      "Epoch [    9/   20] | d_loss: 0.7508 | g_loss: 2.0414\n",
      "Epoch [    9/   20] | d_loss: 0.6523 | g_loss: 2.1129\n",
      "Epoch [    9/   20] | d_loss: 0.6884 | g_loss: 2.4330\n",
      "Epoch [   10/   20] | d_loss: 0.7660 | g_loss: 1.8293\n",
      "Epoch [   10/   20] | d_loss: 0.5839 | g_loss: 1.1558\n",
      "Epoch [   10/   20] | d_loss: 0.6815 | g_loss: 2.2854\n",
      "Epoch [   10/   20] | d_loss: 0.9139 | g_loss: 1.1721\n",
      "Epoch [   10/   20] | d_loss: 0.7753 | g_loss: 0.8774\n",
      "Epoch [   10/   20] | d_loss: 0.6427 | g_loss: 2.9642\n",
      "Epoch [   10/   20] | d_loss: 0.7674 | g_loss: 2.0728\n",
      "Epoch [   10/   20] | d_loss: 0.6580 | g_loss: 2.5610\n",
      "Epoch [   10/   20] | d_loss: 0.7603 | g_loss: 1.9255\n",
      "Epoch [   10/   20] | d_loss: 0.6374 | g_loss: 2.2598\n",
      "Epoch [   10/   20] | d_loss: 0.6975 | g_loss: 1.3776\n",
      "Epoch [   10/   20] | d_loss: 0.6326 | g_loss: 1.5343\n",
      "Epoch [   10/   20] | d_loss: 0.6332 | g_loss: 2.1022\n",
      "Epoch [   10/   20] | d_loss: 0.5656 | g_loss: 1.2993\n",
      "Epoch [   10/   20] | d_loss: 0.7286 | g_loss: 1.1445\n",
      "Epoch [   11/   20] | d_loss: 0.6027 | g_loss: 1.3860\n",
      "Epoch [   11/   20] | d_loss: 0.6030 | g_loss: 2.6291\n",
      "Epoch [   11/   20] | d_loss: 0.6275 | g_loss: 2.4790\n",
      "Epoch [   11/   20] | d_loss: 1.2401 | g_loss: 1.2663\n",
      "Epoch [   11/   20] | d_loss: 0.5249 | g_loss: 1.7499\n",
      "Epoch [   11/   20] | d_loss: 0.5689 | g_loss: 1.3671\n",
      "Epoch [   11/   20] | d_loss: 0.5836 | g_loss: 1.5041\n",
      "Epoch [   11/   20] | d_loss: 0.6945 | g_loss: 0.9678\n",
      "Epoch [   11/   20] | d_loss: 0.3803 | g_loss: 1.5661\n",
      "Epoch [   11/   20] | d_loss: 0.5016 | g_loss: 2.4891\n",
      "Epoch [   11/   20] | d_loss: 1.6042 | g_loss: 1.0056\n",
      "Epoch [   11/   20] | d_loss: 0.6840 | g_loss: 2.0393\n",
      "Epoch [   11/   20] | d_loss: 0.9378 | g_loss: 1.6990\n",
      "Epoch [   11/   20] | d_loss: 0.9984 | g_loss: 1.6771\n",
      "Epoch [   11/   20] | d_loss: 0.6379 | g_loss: 3.6631\n",
      "Epoch [   12/   20] | d_loss: 0.3435 | g_loss: 2.6258\n",
      "Epoch [   12/   20] | d_loss: 0.5440 | g_loss: 2.7778\n",
      "Epoch [   12/   20] | d_loss: 0.6001 | g_loss: 2.6061\n",
      "Epoch [   12/   20] | d_loss: 0.5635 | g_loss: 2.4277\n",
      "Epoch [   12/   20] | d_loss: 0.4597 | g_loss: 2.2251\n",
      "Epoch [   12/   20] | d_loss: 0.9295 | g_loss: 1.4393\n",
      "Epoch [   12/   20] | d_loss: 0.4973 | g_loss: 2.4579\n",
      "Epoch [   12/   20] | d_loss: 1.0080 | g_loss: 3.0519\n",
      "Epoch [   12/   20] | d_loss: 0.5599 | g_loss: 2.3184\n",
      "Epoch [   12/   20] | d_loss: 0.5094 | g_loss: 2.1690\n",
      "Epoch [   12/   20] | d_loss: 0.5542 | g_loss: 2.1257\n",
      "Epoch [   12/   20] | d_loss: 0.9714 | g_loss: 1.6287\n",
      "Epoch [   12/   20] | d_loss: 0.6176 | g_loss: 2.0445\n",
      "Epoch [   12/   20] | d_loss: 0.5000 | g_loss: 2.2564\n",
      "Epoch [   12/   20] | d_loss: 1.0113 | g_loss: 1.3444\n",
      "Epoch [   13/   20] | d_loss: 0.6162 | g_loss: 1.6406\n",
      "Epoch [   13/   20] | d_loss: 1.0840 | g_loss: 3.4216\n",
      "Epoch [   13/   20] | d_loss: 0.8837 | g_loss: 4.0754\n",
      "Epoch [   13/   20] | d_loss: 0.3475 | g_loss: 2.9301\n",
      "Epoch [   13/   20] | d_loss: 0.6974 | g_loss: 3.4378\n",
      "Epoch [   13/   20] | d_loss: 1.4529 | g_loss: 0.7764\n",
      "Epoch [   13/   20] | d_loss: 0.7233 | g_loss: 1.7197\n",
      "Epoch [   13/   20] | d_loss: 0.5857 | g_loss: 1.4645\n",
      "Epoch [   13/   20] | d_loss: 0.5436 | g_loss: 2.6523\n",
      "Epoch [   13/   20] | d_loss: 0.7389 | g_loss: 2.9465\n",
      "Epoch [   13/   20] | d_loss: 0.6671 | g_loss: 5.1228\n",
      "Epoch [   13/   20] | d_loss: 0.6935 | g_loss: 3.2987\n",
      "Epoch [   13/   20] | d_loss: 0.5145 | g_loss: 2.5704\n",
      "Epoch [   13/   20] | d_loss: 0.7557 | g_loss: 1.7221\n",
      "Epoch [   13/   20] | d_loss: 0.8166 | g_loss: 3.5837\n",
      "Epoch [   14/   20] | d_loss: 1.1917 | g_loss: 3.9758\n",
      "Epoch [   14/   20] | d_loss: 0.5713 | g_loss: 2.2412\n",
      "Epoch [   14/   20] | d_loss: 0.7864 | g_loss: 3.3294\n",
      "Epoch [   14/   20] | d_loss: 0.7560 | g_loss: 2.9544\n",
      "Epoch [   14/   20] | d_loss: 0.9978 | g_loss: 5.1374\n",
      "Epoch [   14/   20] | d_loss: 0.5348 | g_loss: 2.4722\n",
      "Epoch [   14/   20] | d_loss: 0.7528 | g_loss: 2.4960\n",
      "Epoch [   14/   20] | d_loss: 0.5737 | g_loss: 2.4956\n",
      "Epoch [   14/   20] | d_loss: 0.4316 | g_loss: 2.8146\n",
      "Epoch [   14/   20] | d_loss: 0.5054 | g_loss: 1.7994\n",
      "Epoch [   14/   20] | d_loss: 0.4841 | g_loss: 1.8661\n",
      "Epoch [   14/   20] | d_loss: 0.5823 | g_loss: 2.4456\n",
      "Epoch [   14/   20] | d_loss: 0.3450 | g_loss: 2.9833\n",
      "Epoch [   14/   20] | d_loss: 0.7972 | g_loss: 2.4107\n",
      "Epoch [   14/   20] | d_loss: 0.5046 | g_loss: 2.4696\n",
      "Epoch [   15/   20] | d_loss: 0.5148 | g_loss: 3.0150\n",
      "Epoch [   15/   20] | d_loss: 0.9153 | g_loss: 1.7805\n",
      "Epoch [   15/   20] | d_loss: 0.3386 | g_loss: 3.6095\n",
      "Epoch [   15/   20] | d_loss: 0.5752 | g_loss: 1.8818\n",
      "Epoch [   15/   20] | d_loss: 0.3146 | g_loss: 3.0563\n",
      "Epoch [   15/   20] | d_loss: 1.0054 | g_loss: 1.3939\n",
      "Epoch [   15/   20] | d_loss: 0.8179 | g_loss: 2.7937\n",
      "Epoch [   15/   20] | d_loss: 0.5241 | g_loss: 3.6446\n",
      "Epoch [   15/   20] | d_loss: 0.4656 | g_loss: 3.0117\n",
      "Epoch [   15/   20] | d_loss: 0.5519 | g_loss: 1.0415\n",
      "Epoch [   15/   20] | d_loss: 0.6850 | g_loss: 1.5934\n",
      "Epoch [   15/   20] | d_loss: 0.3777 | g_loss: 2.7377\n",
      "Epoch [   15/   20] | d_loss: 0.5619 | g_loss: 3.2413\n",
      "Epoch [   15/   20] | d_loss: 0.3603 | g_loss: 1.6733\n",
      "Epoch [   15/   20] | d_loss: 1.0929 | g_loss: 2.1694\n",
      "Epoch [   16/   20] | d_loss: 0.3578 | g_loss: 2.4308\n",
      "Epoch [   16/   20] | d_loss: 0.5528 | g_loss: 1.2329\n",
      "Epoch [   16/   20] | d_loss: 0.6907 | g_loss: 3.6424\n",
      "Epoch [   16/   20] | d_loss: 0.5061 | g_loss: 3.0905\n",
      "Epoch [   16/   20] | d_loss: 0.4123 | g_loss: 3.1904\n",
      "Epoch [   16/   20] | d_loss: 0.6935 | g_loss: 3.6366\n",
      "Epoch [   16/   20] | d_loss: 0.4729 | g_loss: 2.2319\n",
      "Epoch [   16/   20] | d_loss: 0.7996 | g_loss: 4.3467\n",
      "Epoch [   16/   20] | d_loss: 0.7706 | g_loss: 1.7222\n",
      "Epoch [   16/   20] | d_loss: 0.3055 | g_loss: 2.7082\n",
      "Epoch [   16/   20] | d_loss: 0.5611 | g_loss: 1.6343\n",
      "Epoch [   16/   20] | d_loss: 0.5549 | g_loss: 1.3045\n",
      "Epoch [   16/   20] | d_loss: 0.4717 | g_loss: 2.9806\n",
      "Epoch [   16/   20] | d_loss: 0.3381 | g_loss: 3.1632\n",
      "Epoch [   16/   20] | d_loss: 0.3704 | g_loss: 3.0181\n",
      "Epoch [   17/   20] | d_loss: 0.3093 | g_loss: 3.1456\n",
      "Epoch [   17/   20] | d_loss: 1.3194 | g_loss: 5.1809\n",
      "Epoch [   17/   20] | d_loss: 0.4894 | g_loss: 2.7030\n",
      "Epoch [   17/   20] | d_loss: 0.4460 | g_loss: 1.7847\n",
      "Epoch [   17/   20] | d_loss: 0.6715 | g_loss: 3.8470\n",
      "Epoch [   17/   20] | d_loss: 0.4422 | g_loss: 2.7221\n",
      "Epoch [   17/   20] | d_loss: 0.4538 | g_loss: 3.9042\n",
      "Epoch [   17/   20] | d_loss: 0.5539 | g_loss: 1.2802\n",
      "Epoch [   17/   20] | d_loss: 0.2708 | g_loss: 3.1173\n",
      "Epoch [   17/   20] | d_loss: 0.4971 | g_loss: 1.5849\n",
      "Epoch [   17/   20] | d_loss: 0.2631 | g_loss: 2.7631\n",
      "Epoch [   17/   20] | d_loss: 1.4123 | g_loss: 5.3078\n",
      "Epoch [   17/   20] | d_loss: 0.4937 | g_loss: 2.4815\n",
      "Epoch [   17/   20] | d_loss: 0.8923 | g_loss: 4.9992\n",
      "Epoch [   17/   20] | d_loss: 0.6070 | g_loss: 2.8890\n",
      "Epoch [   18/   20] | d_loss: 0.3406 | g_loss: 2.3799\n",
      "Epoch [   18/   20] | d_loss: 0.3121 | g_loss: 2.3301\n",
      "Epoch [   18/   20] | d_loss: 0.4233 | g_loss: 2.3630\n",
      "Epoch [   18/   20] | d_loss: 0.4746 | g_loss: 2.2105\n",
      "Epoch [   18/   20] | d_loss: 0.3618 | g_loss: 2.9287\n",
      "Epoch [   18/   20] | d_loss: 0.8876 | g_loss: 2.6596\n",
      "Epoch [   18/   20] | d_loss: 0.3447 | g_loss: 2.9117\n",
      "Epoch [   18/   20] | d_loss: 0.6091 | g_loss: 2.1998\n",
      "Epoch [   18/   20] | d_loss: 0.2623 | g_loss: 2.3193\n",
      "Epoch [   18/   20] | d_loss: 0.3330 | g_loss: 1.9740\n",
      "Epoch [   18/   20] | d_loss: 0.5179 | g_loss: 2.8025\n",
      "Epoch [   18/   20] | d_loss: 0.4375 | g_loss: 2.9266\n",
      "Epoch [   18/   20] | d_loss: 0.4698 | g_loss: 2.4564\n",
      "Epoch [   18/   20] | d_loss: 0.3549 | g_loss: 2.4285\n",
      "Epoch [   18/   20] | d_loss: 0.6418 | g_loss: 3.4808\n",
      "Epoch [   19/   20] | d_loss: 0.9091 | g_loss: 2.8798\n",
      "Epoch [   19/   20] | d_loss: 0.4295 | g_loss: 2.2632\n",
      "Epoch [   19/   20] | d_loss: 0.2720 | g_loss: 2.0350\n",
      "Epoch [   19/   20] | d_loss: 1.0100 | g_loss: 1.5963\n",
      "Epoch [   19/   20] | d_loss: 0.5830 | g_loss: 3.1061\n",
      "Epoch [   19/   20] | d_loss: 0.3397 | g_loss: 3.3348\n",
      "Epoch [   19/   20] | d_loss: 0.3746 | g_loss: 3.3444\n",
      "Epoch [   19/   20] | d_loss: 1.0607 | g_loss: 4.8293\n",
      "Epoch [   19/   20] | d_loss: 0.3254 | g_loss: 2.6985\n",
      "Epoch [   19/   20] | d_loss: 0.7383 | g_loss: 2.4258\n",
      "Epoch [   19/   20] | d_loss: 0.6002 | g_loss: 3.7173\n",
      "Epoch [   19/   20] | d_loss: 0.2136 | g_loss: 3.0334\n",
      "Epoch [   19/   20] | d_loss: 0.6911 | g_loss: 1.1938\n",
      "Epoch [   19/   20] | d_loss: 0.3172 | g_loss: 2.9622\n",
      "Epoch [   19/   20] | d_loss: 0.2457 | g_loss: 3.1010\n",
      "Epoch [   20/   20] | d_loss: 0.3626 | g_loss: 1.6476\n",
      "Epoch [   20/   20] | d_loss: 0.3483 | g_loss: 4.7733\n",
      "Epoch [   20/   20] | d_loss: 0.3931 | g_loss: 2.8599\n",
      "Epoch [   20/   20] | d_loss: 0.2812 | g_loss: 3.4690\n",
      "Epoch [   20/   20] | d_loss: 0.2508 | g_loss: 2.3906\n",
      "Epoch [   20/   20] | d_loss: 0.5203 | g_loss: 2.9885\n",
      "Epoch [   20/   20] | d_loss: 0.4307 | g_loss: 3.1960\n",
      "Epoch [   20/   20] | d_loss: 0.4378 | g_loss: 1.9535\n",
      "Epoch [   20/   20] | d_loss: 0.5612 | g_loss: 1.7332\n",
      "Epoch [   20/   20] | d_loss: 0.4673 | g_loss: 3.0699\n",
      "Epoch [   20/   20] | d_loss: 0.4742 | g_loss: 2.6681\n",
      "Epoch [   20/   20] | d_loss: 0.2812 | g_loss: 3.0315\n",
      "Epoch [   20/   20] | d_loss: 0.3548 | g_loss: 3.0158\n",
      "Epoch [   20/   20] | d_loss: 0.6343 | g_loss: 4.9251\n",
      "Epoch [   20/   20] | d_loss: 0.4057 | g_loss: 2.7165\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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa92f229eb8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa948ddeef0>"
      ]
     },
     "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 0x7fa949046710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What do you notice about your generated samples and how might you improve this model?\n",
    "When you answer this question, consider the following factors:\n",
    "* The dataset is biased; it is made of \"celebrity\" faces that are mostly white\n",
    "* Model size; larger models have the opportunity to learn more features in a data feature space\n",
    "* Optimization strategy; optimizers and number of epochs affect your final result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** \n",
    "Yes as we know that most of these faces are of white people. So its not much diverse but there are as well some darker complexions as well. \n",
    "We can definitely improve this model by adding label smoothing, residual blocks and training it for more epochs. \n",
    "And finally getting images of higher resolution will also improve the results."
   ]
  },
  {
   "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
}