L17_gans/code/gan-cnn-mnist-converge-but-fail.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "STAT 479: Deep Learning (Spring 2019)  \n",
    "Instructor: Sebastian Raschka (sraschka@wisc.edu)  \n",
    "Course website: http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/\n",
    "GitHub repository: https://github.com/rasbt/stat479-deep-learning-ss19"
   ]
  },
  {
   "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 1.0.1.post2\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": [
    "# Convolutional GAN That Fails"
   ]
  },
  {
   "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 torchvision import datasets\n",
    "from torchvision import transforms\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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:2\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Hyperparameters\n",
    "random_seed = 42\n",
    "generator_learning_rate = 0.0001\n",
    "discriminator_learning_rate = 0.0001\n",
    "num_epochs = 100\n",
    "BATCH_SIZE = 128\n",
    "LATENT_DIM = 100\n",
    "IMG_SHAPE = (1, 28, 28)\n",
    "IMG_SIZE = 1\n",
    "for x in IMG_SHAPE:\n",
    "    IMG_SIZE *= x\n",
    "\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",
    "                          num_workers=4,\n",
    "                          shuffle=True)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset, \n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         num_workers=4,\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",
    "class Flatten(nn.Module):\n",
    "    def forward(self, input):\n",
    "        return input.view(input.size(0), -1)\n",
    "    \n",
    "class Reshape1(nn.Module):\n",
    "    def forward(self, input):\n",
    "        return input.view(input.size(0), 64, 7, 7)\n",
    "\n",
    "\n",
    "class GAN(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(GAN, self).__init__()\n",
    "        \n",
    "        \n",
    "        self.generator = nn.Sequential(\n",
    "              \n",
    "            nn.Linear(LATENT_DIM, 3136),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            Reshape1(),\n",
    "            \n",
    "            nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=(2, 2), stride=(2, 2), padding=0),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            nn.Conv2d(in_channels=64, out_channels=32, padding=1, kernel_size=(2, 2)),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.LeakyReLU(inplace=True),    \n",
    "            \n",
    "            nn.ConvTranspose2d(in_channels=32, out_channels=32, kernel_size=(2, 2), stride=(2, 2), padding=0),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            nn.Conv2d(in_channels=32, out_channels=16, padding=1, kernel_size=(2, 2)),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.LeakyReLU(inplace=True),      \n",
    "            \n",
    "            nn.Conv2d(in_channels=16, out_channels=8, padding=1, kernel_size=(3, 3)),\n",
    "            nn.BatchNorm2d(8),\n",
    "            nn.LeakyReLU(inplace=True),    \n",
    "            \n",
    "            nn.Conv2d(in_channels=8, out_channels=4, padding=0, kernel_size=(3, 3)),\n",
    "            nn.BatchNorm2d(4),\n",
    "            nn.LeakyReLU(inplace=True),    \n",
    "            \n",
    "            nn.Conv2d(in_channels=4, out_channels=1, padding=0, kernel_size=(2, 2)),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "        \n",
    "        self.discriminator = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=1, out_channels=8, padding=1, kernel_size=(3, 3)),\n",
    "            nn.BatchNorm2d(8),\n",
    "            nn.LeakyReLU(inplace=True), \n",
    "            nn.Conv2d(in_channels=8, out_channels=8, padding=1, stride=2, kernel_size=(3, 3)),\n",
    "            nn.BatchNorm2d(8),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            \n",
    "            \n",
    "            nn.Conv2d(in_channels=8, out_channels=16, padding=1, kernel_size=(3, 3)),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            nn.Conv2d(in_channels=16, out_channels=16, padding=1, stride=2, kernel_size=(3, 3)),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            \n",
    "            nn.Conv2d(in_channels=16, out_channels=32, padding=1, kernel_size=(3, 3)),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            nn.Conv2d(in_channels=32, out_channels=32, padding=1, stride=2, kernel_size=(3, 3)),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            \n",
    "            nn.AdaptiveAvgPool2d(1),\n",
    "            Flatten(),\n",
    "            nn.Linear(32, 16),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            nn.BatchNorm1d(16),\n",
    "            \n",
    "            nn.Linear(16, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "            \n",
    "    def generator_forward(self, z):\n",
    "        img = self.generator(z)\n",
    "        return img\n",
    "    \n",
    "    def discriminator_forward(self, img):\n",
    "        pred = model.discriminator(img)\n",
    "        return pred.view(-1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAN(\n",
      "  (generator): Sequential(\n",
      "    (0): Linear(in_features=100, out_features=3136, bias=True)\n",
      "    (1): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (2): Reshape1()\n",
      "    (3): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2))\n",
      "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (6): Conv2d(64, 32, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1))\n",
      "    (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (8): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (9): ConvTranspose2d(32, 32, kernel_size=(2, 2), stride=(2, 2))\n",
      "    (10): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (11): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (12): Conv2d(32, 16, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1))\n",
      "    (13): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (14): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (15): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (16): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (17): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (18): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (19): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (20): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (21): Conv2d(4, 1, kernel_size=(2, 2), stride=(1, 1))\n",
      "    (22): Tanh()\n",
      "  )\n",
      "  (discriminator): Sequential(\n",
      "    (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (3): Conv2d(8, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
      "    (4): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (6): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (8): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (9): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
      "    (10): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (11): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (12): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (14): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (15): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
      "    (16): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (17): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (18): AdaptiveAvgPool2d(output_size=1)\n",
      "    (19): Flatten()\n",
      "    (20): Linear(in_features=32, out_features=16, bias=True)\n",
      "    (21): LeakyReLU(negative_slope=0.01, inplace)\n",
      "    (22): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (23): Linear(in_features=16, out_features=1, bias=True)\n",
      "    (24): Sigmoid()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(random_seed)\n",
    "\n",
    "#del model\n",
    "model = GAN()\n",
    "model = model.to(device)\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\noutputs = []\\ndef hook(module, input, output):\\n    outputs.append(output)\\n\\n#for i, layer in enumerate(model.discriminator):\\n#    if isinstance(layer, torch.nn.modules.conv.Conv2d):\\n#        model.discriminator[i].register_forward_hook(hook)\\n\\nfor i, layer in enumerate(model.generator):\\n    if isinstance(layer, torch.nn.modules.conv.Conv2d):\\n        model.generator[i].register_forward_hook(hook)\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ## FOR DEBUGGING\n",
    "\n",
    "\"\"\"\n",
    "outputs = []\n",
    "def hook(module, input, output):\n",
    "    outputs.append(output)\n",
    "\n",
    "#for i, layer in enumerate(model.discriminator):\n",
    "#    if isinstance(layer, torch.nn.modules.conv.Conv2d):\n",
    "#        model.discriminator[i].register_forward_hook(hook)\n",
    "\n",
    "for i, layer in enumerate(model.generator):\n",
    "    if isinstance(layer, torch.nn.modules.conv.Conv2d):\n",
    "        model.generator[i].register_forward_hook(hook)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "optim_gener = torch.optim.Adam(model.generator.parameters(), lr=generator_learning_rate)\n",
    "optim_discr = torch.optim.Adam(model.discriminator.parameters(), lr=discriminator_learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/100 | Batch 000/469 | Gen/Dis Loss: 0.6598/0.6968\n",
      "Epoch: 001/100 | Batch 100/469 | Gen/Dis Loss: 0.6697/0.6938\n",
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      "Time elapsed: 0.25 min\n",
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      "Time elapsed: 23.37 min\n",
      "Epoch: 099/100 | Batch 000/469 | Gen/Dis Loss: 0.6931/0.6931\n",
      "Epoch: 099/100 | Batch 100/469 | Gen/Dis Loss: 0.6932/0.6931\n",
      "Epoch: 099/100 | Batch 200/469 | Gen/Dis Loss: 0.6931/0.6931\n",
      "Epoch: 099/100 | Batch 300/469 | Gen/Dis Loss: 0.6932/0.6931\n",
      "Epoch: 099/100 | Batch 400/469 | Gen/Dis Loss: 0.6931/0.6931\n",
      "Time elapsed: 23.59 min\n",
      "Epoch: 100/100 | Batch 000/469 | Gen/Dis Loss: 0.6932/0.6931\n",
      "Epoch: 100/100 | Batch 100/469 | Gen/Dis Loss: 0.6931/0.6931\n",
      "Epoch: 100/100 | Batch 200/469 | Gen/Dis Loss: 0.6931/0.6931\n",
      "Epoch: 100/100 | Batch 300/469 | Gen/Dis Loss: 0.6931/0.6931\n",
      "Epoch: 100/100 | Batch 400/469 | Gen/Dis Loss: 0.6932/0.6931\n",
      "Time elapsed: 23.82 min\n",
      "Total Training Time: 23.82 min\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()    \n",
    "\n",
    "discr_costs = []\n",
    "gener_costs = []\n",
    "for epoch in range(num_epochs):\n",
    "    model = model.train()\n",
    "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "\n",
    "        \n",
    "        # Normalize images to [-1, 1] range\n",
    "        features = (features - 0.5)*2.\n",
    "        features = features.view(-1, IMG_SIZE).to(device) \n",
    "\n",
    "        targets = targets.to(device)\n",
    "\n",
    "        valid = torch.ones(targets.size(0)).float().to(device)\n",
    "        fake = torch.zeros(targets.size(0)).float().to(device)\n",
    "        \n",
    "\n",
    "        ### FORWARD AND BACK PROP\n",
    "        \n",
    "        \n",
    "        # --------------------------\n",
    "        # Train Generator\n",
    "        # --------------------------\n",
    "        \n",
    "        # Make new images\n",
    "        z = torch.zeros((targets.size(0), LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
    "        generated_features = model.generator_forward(z)\n",
    "        \n",
    "        # Loss for fooling the discriminator\n",
    "        discr_pred = model.discriminator_forward(generated_features.view(targets.size(0), 1, 28, 28))\n",
    "        \n",
    "        gener_loss = F.binary_cross_entropy(discr_pred, valid)\n",
    "        \n",
    "        optim_gener.zero_grad()\n",
    "        gener_loss.backward()\n",
    "        optim_gener.step()\n",
    "        \n",
    "        # --------------------------\n",
    "        # Train Discriminator\n",
    "        # --------------------------        \n",
    "        \n",
    "        discr_pred_real = model.discriminator_forward(features.view(targets.size(0), 1, 28, 28))\n",
    "        real_loss = F.binary_cross_entropy(discr_pred_real, valid)\n",
    "        \n",
    "        discr_pred_fake = model.discriminator_forward(generated_features.view(targets.size(0), 1, 28, 28).detach())\n",
    "        fake_loss = F.binary_cross_entropy(discr_pred_fake, fake)\n",
    "        \n",
    "        discr_loss = 0.5*(real_loss + fake_loss)\n",
    "\n",
    "        optim_discr.zero_grad()\n",
    "        discr_loss.backward()\n",
    "        optim_discr.step()        \n",
    "        \n",
    "        discr_costs.append(discr_loss.item())\n",
    "        gener_costs.append(gener_loss.item())\n",
    "        \n",
    "        \n",
    "        ### LOGGING\n",
    "        if not batch_idx % 100:\n",
    "            print ('Epoch: %03d/%03d | Batch %03d/%03d | Gen/Dis Loss: %.4f/%.4f' \n",
    "                   %(epoch+1, num_epochs, batch_idx, \n",
    "                     len(train_loader), gener_loss, discr_loss))\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": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor i in outputs:\\n    print(i.size())\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### For Debugging\n",
    "\n",
    "\"\"\"\n",
    "for i in outputs:\n",
    "    print(i.size())\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(gener_costs)), gener_costs, label='generator loss')\n",
    "plt.plot(range(len(discr_costs)), discr_costs, label='discriminator loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################\n",
    "### VISUALIZATION\n",
    "##########################\n",
    "\n",
    "\n",
    "model.eval()\n",
    "# Make new images\n",
    "z = torch.zeros((5, LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
    "generated_features = model.generator_forward(z)\n",
    "imgs = generated_features.view(-1, 28, 28)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(20, 2.5))\n",
    "\n",
    "\n",
    "for i, ax in enumerate(axes):\n",
    "    axes[i].imshow(imgs[i].to(torch.device('cpu')).detach(), cmap='binary')"
   ]
  }
 ],
 "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": "3.6.8"
  },
  "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
}