pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.6.8\n",
      "IPython 7.2.0\n",
      "\n",
      "torch 1.0.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Runs on CPU or GPU (if available)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Zoo -- Convolutional Conditional Variational Autoencoder \n",
    "\n",
    "## (with labels in reconstruction loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple convolutional conditional variational autoencoder that compresses 768-pixel MNIST images down to a 50-pixel latent vector representation.\n",
    "\n",
    "This implementation concatenates the inputs with the class labels when computing the reconstruction loss as it is commonly done in non-convolutional conditional variational autoencoders. This leads to substantially poorer results compared to the implementation that does NOT concatenate the labels with the inputs to compute the reconstruction loss. For reference, see the implementation [./autoencoder-cnn-cvae_no-out-concat.ipynb](./autoencoder-cnn-cvae_no-out-concat.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda:0\n",
      "Image batch dimensions: torch.Size([128, 1, 28, 28])\n",
      "Image label dimensions: torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Device\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print('Device:', device)\n",
    "\n",
    "# Hyperparameters\n",
    "random_seed = 0\n",
    "learning_rate = 0.001\n",
    "num_epochs = 50\n",
    "batch_size = 128\n",
    "\n",
    "# Architecture\n",
    "num_classes = 10\n",
    "num_features = 784\n",
    "num_latent = 50\n",
    "\n",
    "\n",
    "##########################\n",
    "### MNIST DATASET\n",
    "##########################\n",
    "\n",
    "# Note transforms.ToTensor() scales input images\n",
    "# to 0-1 range\n",
    "train_dataset = datasets.MNIST(root='data', \n",
    "                               train=True, \n",
    "                               transform=transforms.ToTensor(),\n",
    "                               download=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root='data', \n",
    "                              train=False, \n",
    "                              transform=transforms.ToTensor())\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, \n",
    "                          batch_size=batch_size, \n",
    "                          shuffle=True)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset, \n",
    "                         batch_size=batch_size, \n",
    "                         shuffle=False)\n",
    "\n",
    "# Checking the dataset\n",
    "for images, labels in train_loader:  \n",
    "    print('Image batch dimensions:', images.shape)\n",
    "    print('Image label dimensions:', labels.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### MODEL\n",
    "##########################\n",
    "\n",
    "\n",
    "def to_onehot(labels, num_classes, device):\n",
    "\n",
    "    labels_onehot = torch.zeros(labels.size()[0], num_classes).to(device)\n",
    "    labels_onehot.scatter_(1, labels.view(-1, 1), 1)\n",
    "\n",
    "    return labels_onehot\n",
    "\n",
    "\n",
    "class ConditionalVariationalAutoencoder(torch.nn.Module):\n",
    "\n",
    "    def __init__(self, num_features, num_latent, num_classes):\n",
    "        super(ConditionalVariationalAutoencoder, self).__init__()\n",
    "        \n",
    "        self.num_classes = num_classes\n",
    "        \n",
    "        \n",
    "        ###############\n",
    "        # ENCODER\n",
    "        ##############\n",
    "        \n",
    "        # calculate same padding:\n",
    "        # (w - k + 2*p)/s + 1 = o\n",
    "        # => p = (s(o-1) - w + k)/2\n",
    "\n",
    "        self.enc_conv_1 = torch.nn.Conv2d(in_channels=1+self.num_classes,\n",
    "                                          out_channels=16,\n",
    "                                          kernel_size=(6, 6),\n",
    "                                          stride=(2, 2),\n",
    "                                          padding=0)\n",
    "\n",
    "        self.enc_conv_2 = torch.nn.Conv2d(in_channels=16,\n",
    "                                          out_channels=32,\n",
    "                                          kernel_size=(4, 4),\n",
    "                                          stride=(2, 2),\n",
    "                                          padding=0)                 \n",
    "        \n",
    "        self.enc_conv_3 = torch.nn.Conv2d(in_channels=32,\n",
    "                                          out_channels=64,\n",
    "                                          kernel_size=(2, 2),\n",
    "                                          stride=(2, 2),\n",
    "                                          padding=0)                     \n",
    "        \n",
    "        self.z_mean = torch.nn.Linear(64*2*2, num_latent)\n",
    "        # in the original paper (Kingma & Welling 2015, we use\n",
    "        # have a z_mean and z_var, but the problem is that\n",
    "        # the z_var can be negative, which would cause issues\n",
    "        # in the log later. Hence we assume that latent vector\n",
    "        # has a z_mean and z_log_var component, and when we need\n",
    "        # the regular variance or std_dev, we simply use \n",
    "        # an exponential function\n",
    "        self.z_log_var = torch.nn.Linear(64*2*2, num_latent)\n",
    "        \n",
    "        \n",
    "        \n",
    "        ###############\n",
    "        # DECODER\n",
    "        ##############\n",
    "        \n",
    "        self.dec_linear_1 = torch.nn.Linear(num_latent+self.num_classes, 64*2*2)\n",
    "               \n",
    "        self.dec_deconv_1 = torch.nn.ConvTranspose2d(in_channels=64,\n",
    "                                                     out_channels=32,\n",
    "                                                     kernel_size=(2, 2),\n",
    "                                                     stride=(2, 2),\n",
    "                                                     padding=0)\n",
    "                                 \n",
    "        self.dec_deconv_2 = torch.nn.ConvTranspose2d(in_channels=32,\n",
    "                                                     out_channels=16,\n",
    "                                                     kernel_size=(4, 4),\n",
    "                                                     stride=(3, 3),\n",
    "                                                     padding=1)\n",
    "        \n",
    "        self.dec_deconv_3 = torch.nn.ConvTranspose2d(in_channels=16,\n",
    "                                                     out_channels=11,\n",
    "                                                     kernel_size=(6, 6),\n",
    "                                                     stride=(3, 3),\n",
    "                                                     padding=4)        \n",
    "\n",
    "\n",
    "    def reparameterize(self, z_mu, z_log_var):\n",
    "        # Sample epsilon from standard normal distribution\n",
    "        eps = torch.randn(z_mu.size(0), z_mu.size(1)).to(device)\n",
    "        # note that log(x^2) = 2*log(x); hence divide by 2 to get std_dev\n",
    "        # i.e., std_dev = exp(log(std_dev^2)/2) = exp(log(var)/2)\n",
    "        z = z_mu + eps * torch.exp(z_log_var/2.) \n",
    "        return z\n",
    "        \n",
    "    def encoder(self, features, targets):\n",
    "        \n",
    "        ### Add condition\n",
    "        onehot_targets = to_onehot(targets, self.num_classes, device)\n",
    "        onehot_targets = onehot_targets.view(-1, self.num_classes, 1, 1)\n",
    "        \n",
    "        ones = torch.ones(features.size()[0], \n",
    "                          self.num_classes,\n",
    "                          features.size()[2], \n",
    "                          features.size()[3], \n",
    "                          dtype=features.dtype).to(device)\n",
    "        ones = ones * onehot_targets\n",
    "        x = torch.cat((features, ones), dim=1)\n",
    "        \n",
    "        x = self.enc_conv_1(x)\n",
    "        x = F.leaky_relu(x)\n",
    "        #print('conv1 out:', x.size())\n",
    "        \n",
    "        x = self.enc_conv_2(x)\n",
    "        x = F.leaky_relu(x)\n",
    "        #print('conv2 out:', x.size())\n",
    "        \n",
    "        x = self.enc_conv_3(x)\n",
    "        x = F.leaky_relu(x)\n",
    "        #print('conv3 out:', x.size())\n",
    "        \n",
    "        z_mean = self.z_mean(x.view(-1, 64*2*2))\n",
    "        z_log_var = self.z_log_var(x.view(-1, 64*2*2))\n",
    "        encoded = self.reparameterize(z_mean, z_log_var)\n",
    "        return z_mean, z_log_var, encoded\n",
    "    \n",
    "    def decoder(self, encoded, targets):\n",
    "        ### Add condition\n",
    "        onehot_targets = to_onehot(targets, self.num_classes, device)\n",
    "        encoded = torch.cat((encoded, onehot_targets), dim=1)        \n",
    "        \n",
    "        x = self.dec_linear_1(encoded)\n",
    "        x = x.view(-1, 64, 2, 2)\n",
    "        \n",
    "        x = self.dec_deconv_1(x)\n",
    "        x = F.leaky_relu(x)\n",
    "        #print('deconv1 out:', x.size())\n",
    "        \n",
    "        x = self.dec_deconv_2(x)\n",
    "        x = F.leaky_relu(x)\n",
    "        #print('deconv2 out:', x.size())\n",
    "        \n",
    "        x = self.dec_deconv_3(x)\n",
    "        x = F.leaky_relu(x)\n",
    "        #print('deconv1 out:', x.size())\n",
    "        \n",
    "        decoded = torch.sigmoid(x)\n",
    "        return decoded\n",
    "\n",
    "    def forward(self, features, targets):\n",
    "        \n",
    "        z_mean, z_log_var, encoded = self.encoder(features, targets)\n",
    "        decoded = self.decoder(encoded, targets)\n",
    "        \n",
    "        return z_mean, z_log_var, encoded, decoded\n",
    "\n",
    "    \n",
    "torch.manual_seed(random_seed)\n",
    "model = ConditionalVariationalAutoencoder(num_features,\n",
    "                                          num_latent,\n",
    "                                          num_classes)\n",
    "model = model.to(device)\n",
    "    \n",
    "\n",
    "##########################\n",
    "### COST AND OPTIMIZER\n",
    "##########################\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch: 040/050 | Batch 450/469 | Cost: 24029.9238\n",
      "Time elapsed: 6.28 min\n",
      "Epoch: 041/050 | Batch 000/469 | Cost: 24140.9375\n",
      "Epoch: 041/050 | Batch 050/469 | Cost: 24123.9629\n",
      "Epoch: 041/050 | Batch 100/469 | Cost: 24918.5645\n",
      "Epoch: 041/050 | Batch 150/469 | Cost: 24718.9492\n",
      "Epoch: 041/050 | Batch 200/469 | Cost: 24464.7383\n",
      "Epoch: 041/050 | Batch 250/469 | Cost: 23528.3867\n",
      "Epoch: 041/050 | Batch 300/469 | Cost: 24874.0156\n",
      "Epoch: 041/050 | Batch 350/469 | Cost: 24976.7266\n",
      "Epoch: 041/050 | Batch 400/469 | Cost: 24297.3750\n",
      "Epoch: 041/050 | Batch 450/469 | Cost: 24892.3906\n",
      "Time elapsed: 6.44 min\n",
      "Epoch: 042/050 | Batch 000/469 | Cost: 23911.4434\n",
      "Epoch: 042/050 | Batch 050/469 | Cost: 24480.0840\n",
      "Epoch: 042/050 | Batch 100/469 | Cost: 24132.7617\n",
      "Epoch: 042/050 | Batch 150/469 | Cost: 26151.5430\n",
      "Epoch: 042/050 | Batch 200/469 | Cost: 24691.4297\n",
      "Epoch: 042/050 | Batch 250/469 | Cost: 32332.8027\n",
      "Epoch: 042/050 | Batch 300/469 | Cost: 26727.7930\n",
      "Epoch: 042/050 | Batch 350/469 | Cost: 24738.1543\n",
      "Epoch: 042/050 | Batch 400/469 | Cost: 25356.9297\n",
      "Epoch: 042/050 | Batch 450/469 | Cost: 25567.9824\n",
      "Time elapsed: 6.60 min\n",
      "Epoch: 043/050 | Batch 000/469 | Cost: 24626.9219\n",
      "Epoch: 043/050 | Batch 050/469 | Cost: 24583.6211\n",
      "Epoch: 043/050 | Batch 100/469 | Cost: 23771.1250\n",
      "Epoch: 043/050 | Batch 150/469 | Cost: 23740.8223\n",
      "Epoch: 043/050 | Batch 200/469 | Cost: 25974.3535\n",
      "Epoch: 043/050 | Batch 250/469 | Cost: 24506.7715\n",
      "Epoch: 043/050 | Batch 300/469 | Cost: 24850.9629\n",
      "Epoch: 043/050 | Batch 350/469 | Cost: 23420.8887\n",
      "Epoch: 043/050 | Batch 400/469 | Cost: 23890.6582\n",
      "Epoch: 043/050 | Batch 450/469 | Cost: 24406.9375\n",
      "Time elapsed: 6.76 min\n",
      "Epoch: 044/050 | Batch 000/469 | Cost: 24515.1172\n",
      "Epoch: 044/050 | Batch 050/469 | Cost: 24865.0742\n",
      "Epoch: 044/050 | Batch 100/469 | Cost: 24439.4609\n",
      "Epoch: 044/050 | Batch 150/469 | Cost: 24490.3047\n",
      "Epoch: 044/050 | Batch 200/469 | Cost: 23753.9219\n",
      "Epoch: 044/050 | Batch 250/469 | Cost: 23811.8926\n",
      "Epoch: 044/050 | Batch 300/469 | Cost: 24070.1172\n",
      "Epoch: 044/050 | Batch 350/469 | Cost: 24404.0664\n",
      "Epoch: 044/050 | Batch 400/469 | Cost: 25219.6699\n",
      "Epoch: 044/050 | Batch 450/469 | Cost: 23585.7500\n",
      "Time elapsed: 6.91 min\n",
      "Epoch: 045/050 | Batch 000/469 | Cost: 23822.3262\n",
      "Epoch: 045/050 | Batch 050/469 | Cost: 23653.2695\n",
      "Epoch: 045/050 | Batch 100/469 | Cost: 25814.4062\n",
      "Epoch: 045/050 | Batch 150/469 | Cost: 23872.3867\n",
      "Epoch: 045/050 | Batch 200/469 | Cost: 25231.3008\n",
      "Epoch: 045/050 | Batch 250/469 | Cost: 24211.3652\n",
      "Epoch: 045/050 | Batch 300/469 | Cost: 24564.8242\n",
      "Epoch: 045/050 | Batch 350/469 | Cost: 23450.6211\n",
      "Epoch: 045/050 | Batch 400/469 | Cost: 24501.6504\n",
      "Epoch: 045/050 | Batch 450/469 | Cost: 26215.8633\n",
      "Time elapsed: 7.07 min\n",
      "Epoch: 046/050 | Batch 000/469 | Cost: 24400.6562\n",
      "Epoch: 046/050 | Batch 050/469 | Cost: 24448.3691\n",
      "Epoch: 046/050 | Batch 100/469 | Cost: 24466.0859\n",
      "Epoch: 046/050 | Batch 150/469 | Cost: 24153.8711\n",
      "Epoch: 046/050 | Batch 200/469 | Cost: 24351.0098\n",
      "Epoch: 046/050 | Batch 250/469 | Cost: 23123.2500\n",
      "Epoch: 046/050 | Batch 300/469 | Cost: 24734.2773\n",
      "Epoch: 046/050 | Batch 350/469 | Cost: 23785.1875\n",
      "Epoch: 046/050 | Batch 400/469 | Cost: 24901.5039\n",
      "Epoch: 046/050 | Batch 450/469 | Cost: 23700.1133\n",
      "Time elapsed: 7.22 min\n",
      "Epoch: 047/050 | Batch 000/469 | Cost: 25294.2520\n",
      "Epoch: 047/050 | Batch 050/469 | Cost: 24074.6992\n",
      "Epoch: 047/050 | Batch 100/469 | Cost: 24112.8848\n",
      "Epoch: 047/050 | Batch 150/469 | Cost: 24861.8926\n",
      "Epoch: 047/050 | Batch 200/469 | Cost: 22852.8594\n",
      "Epoch: 047/050 | Batch 250/469 | Cost: 23799.0703\n",
      "Epoch: 047/050 | Batch 300/469 | Cost: 23758.0039\n",
      "Epoch: 047/050 | Batch 350/469 | Cost: 23628.5391\n",
      "Epoch: 047/050 | Batch 400/469 | Cost: 23933.1504\n",
      "Epoch: 047/050 | Batch 450/469 | Cost: 22900.7715\n",
      "Time elapsed: 7.38 min\n",
      "Epoch: 048/050 | Batch 000/469 | Cost: 23949.8223\n",
      "Epoch: 048/050 | Batch 050/469 | Cost: 24267.9609\n",
      "Epoch: 048/050 | Batch 100/469 | Cost: 22838.5234\n",
      "Epoch: 048/050 | Batch 150/469 | Cost: 24212.3223\n",
      "Epoch: 048/050 | Batch 200/469 | Cost: 23809.5449\n",
      "Epoch: 048/050 | Batch 250/469 | Cost: 23827.1680\n",
      "Epoch: 048/050 | Batch 300/469 | Cost: 25127.4844\n",
      "Epoch: 048/050 | Batch 350/469 | Cost: 23184.9473\n",
      "Epoch: 048/050 | Batch 400/469 | Cost: 24065.0840\n",
      "Epoch: 048/050 | Batch 450/469 | Cost: 23201.5645\n",
      "Time elapsed: 7.53 min\n",
      "Epoch: 049/050 | Batch 000/469 | Cost: 23682.0781\n",
      "Epoch: 049/050 | Batch 050/469 | Cost: 23740.3887\n",
      "Epoch: 049/050 | Batch 100/469 | Cost: 23290.7441\n",
      "Epoch: 049/050 | Batch 150/469 | Cost: 23001.3262\n",
      "Epoch: 049/050 | Batch 200/469 | Cost: 23265.8105\n",
      "Epoch: 049/050 | Batch 250/469 | Cost: 22163.1328\n",
      "Epoch: 049/050 | Batch 300/469 | Cost: 24283.0508\n",
      "Epoch: 049/050 | Batch 350/469 | Cost: 23822.0098\n",
      "Epoch: 049/050 | Batch 400/469 | Cost: 22784.8594\n",
      "Epoch: 049/050 | Batch 450/469 | Cost: 24202.4961\n",
      "Time elapsed: 7.69 min\n",
      "Epoch: 050/050 | Batch 000/469 | Cost: 23966.5840\n",
      "Epoch: 050/050 | Batch 050/469 | Cost: 24665.5449\n",
      "Epoch: 050/050 | Batch 100/469 | Cost: 23895.6406\n",
      "Epoch: 050/050 | Batch 150/469 | Cost: 24318.3926\n",
      "Epoch: 050/050 | Batch 200/469 | Cost: 23685.9727\n",
      "Epoch: 050/050 | Batch 250/469 | Cost: 23648.9336\n",
      "Epoch: 050/050 | Batch 300/469 | Cost: 23634.2500\n",
      "Epoch: 050/050 | Batch 350/469 | Cost: 27888.9062\n",
      "Epoch: 050/050 | Batch 400/469 | Cost: 23649.8242\n",
      "Epoch: 050/050 | Batch 450/469 | Cost: 22728.7891\n",
      "Time elapsed: 7.85 min\n",
      "Total Training Time: 7.85 min\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "        \n",
    "        features = features.to(device)\n",
    "        targets = targets.to(device)\n",
    "\n",
    "        ### FORWARD AND BACK PROP\n",
    "        z_mean, z_log_var, encoded, decoded = model(features, targets)\n",
    "\n",
    "        # cost = reconstruction loss + Kullback-Leibler divergence\n",
    "        kl_divergence = (0.5 * (z_mean**2 + \n",
    "                                torch.exp(z_log_var) - z_log_var - 1)).sum()\n",
    "        \n",
    "        \n",
    "        ### Add condition\n",
    "        onehot_targets = to_onehot(targets, num_classes, device)\n",
    "        onehot_targets = onehot_targets.view(-1, num_classes, 1, 1)\n",
    "        \n",
    "        ones = torch.ones(features.size()[0], \n",
    "                          num_classes,\n",
    "                          features.size()[2], \n",
    "                          features.size()[3], \n",
    "                          dtype=features.dtype).to(device)\n",
    "        ones = ones * onehot_targets\n",
    "        x_con = torch.cat((features, ones), dim=1)\n",
    "        \n",
    "        \n",
    "        ### Compute loss\n",
    "        pixelwise_bce = F.binary_cross_entropy(decoded, x_con, reduction='sum')\n",
    "        cost = kl_divergence + pixelwise_bce\n",
    "        \n",
    "        ### UPDATE MODEL PARAMETERS\n",
    "        optimizer.zero_grad()\n",
    "        cost.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        ### LOGGING\n",
    "        if not batch_idx % 50:\n",
    "            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \n",
    "                   %(epoch+1, num_epochs, batch_idx, \n",
    "                     len(train_loader), cost))\n",
    "            \n",
    "    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
    "    \n",
    "print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x180 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "##########################\n",
    "### VISUALIZATION\n",
    "##########################\n",
    "\n",
    "n_images = 15\n",
    "image_width = 28\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=n_images, \n",
    "                         sharex=True, sharey=True, figsize=(20, 2.5))\n",
    "orig_images = features[:n_images]\n",
    "decoded_images = decoded[:n_images, 0]\n",
    "\n",
    "for i in range(n_images):\n",
    "    for ax, img in zip(axes, [orig_images, decoded_images]):\n",
    "        ax[i].imshow(img[i].detach().to(torch.device('cpu')).reshape((image_width, image_width)), cmap='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New random-conditional images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "\n",
    "    ##########################\n",
    "    ### RANDOM SAMPLE\n",
    "    ##########################    \n",
    "    \n",
    "    labels = torch.tensor([i]*10).to(device)\n",
    "    n_images = labels.size()[0]\n",
    "    rand_features = torch.randn(n_images, num_latent).to(device)\n",
    "    new_images = model.decoder(rand_features, labels)\n",
    "\n",
    "    ##########################\n",
    "    ### VISUALIZATION\n",
    "    ##########################\n",
    "\n",
    "    image_width = 28\n",
    "\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=n_images, figsize=(10, 2.5), sharey=True)\n",
    "    decoded_images = new_images[:n_images, 0]\n",
    "\n",
    "    print('Class Label %d' % i)\n",
    "\n",
    "    for ax, img in zip(axes, decoded_images):\n",
    "        ax.imshow(img.detach().to(torch.device('cpu')).reshape((image_width, image_width)), cmap='binary')\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy       1.15.4\n",
      "torch       1.0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -iv"
   ]
  }
 ],
 "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": "3.7.1"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}