Ex10-Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 10: Generative Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use built-in Pytorch function to download MNIST data in usable format \n",
    "# the images are automatically normalised to a mean of zero and sigma of 1 \n",
    "\n",
    "batch_size = 100\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "  torchvision.datasets.MNIST('.', train=True, download=True,\n",
    "                             transform=torchvision.transforms.Compose([\n",
    "                               torchvision.transforms.ToTensor(),\n",
    "                               torchvision.transforms.Normalize(\n",
    "                                 (0.1307,), (0.3081,)) # initial mean and width\n",
    "                             ])),\n",
    "  batch_size=batch_size, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([100, 1, 28, 28])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Helper function to draw MNIST image\n",
    "# expect a 2D tensor as input\n",
    "def draw_image(im):\n",
    "    plt.imshow(im, cmap='gray', interpolation='none')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "examples = enumerate(train_loader)\n",
    "for batch_idx, (example_data, example_targets) in examples:\n",
    "\n",
    "    # First print the shape of the example_data tensor\n",
    "    print(example_data.shape) # 100 examples, 1 color, 28x28 pixel images\n",
    "    \n",
    "    # Show the first 6 images\n",
    "    for i in range(6):\n",
    "        draw_image(example_data[i][0])\n",
    "        \n",
    "\n",
    "    break # Abort the loop over the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A few useful functions\n",
    "\n",
    "# Produce random numbers as input for generator\n",
    "# torch.randn produces random numbers following \n",
    "# a Gaussian with mean=0 and sigma=1\n",
    "def noise(size):\n",
    "    return torch.autograd.Variable(torch.randn(size, 100))\n",
    "\n",
    "# Translate images to flat vectors (784 = 28*28)\n",
    "def images_to_vectors(images):\n",
    "    return images.view(images.size(0), 784)\n",
    "\n",
    "# Translate flat vectors to images in the format (color, vertical, horizontal)\n",
    "def vectors_to_images(vectors):\n",
    "    return vectors.view(vectors.size(0), 1, 28, 28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework\n",
    "\n",
    "\n",
    "Implement two networks, named `discriminator` and `generator` (the exact names are important so the subsequent code picks them up correctly). The Discriminator network should receive an input list with 784 features and have one output node. As it does binary discrimination between real and fake images, using a sigmoid for the last layer is recommended. The generator should receive 100 input features (random numbers) and produce a final output of length 784 (28 times 28). Using `tanh` as activiation function for the final layer might be useful. Using the code below, see what image generation results you can acvieve\n",
    "\n",
    "Produce ten example image after training has completed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class DiscriminatorNet(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(DiscriminatorNet, self).__init__()        \n",
    "\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.lrelu = nn.LeakyReLU(0.2)\n",
    "        self.dropout = nn.Dropout(0.3)\n",
    "        \n",
    "        self.dense1 =  nn.Linear(784, 1024)\n",
    "        self.dense2 =  nn.Linear(1024, 512)\n",
    "        self.dense3 =  nn.Linear(512, 256)\n",
    "        self.dense4 =  nn.Linear(256, 1)\n",
    "        \n",
    "                                \n",
    "    def forward(self, x):\n",
    "\n",
    "        x = self.dropout(self.lrelu(self.dense1(x)))\n",
    "        x = self.dropout(self.lrelu(self.dense2(x)))\n",
    "        x = self.dropout(self.lrelu(self.dense3(x)))\n",
    "        x = self.sigmoid(self.dense4(x))\n",
    "\n",
    "        return x\n",
    "    \n",
    "discriminator = DiscriminatorNet()\n",
    "\n",
    "class GeneratorNet(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    A four hidden-layer generative neural network\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        super(GeneratorNet, self).__init__()        \n",
    "\n",
    "        self.tanh = nn.Tanh()\n",
    "        self.lrelu = nn.LeakyReLU(0.2)\n",
    "\n",
    "        self.dense1 =  nn.Linear(100, 256)\n",
    "        self.dense2 =  nn.Linear(256, 512)\n",
    "        self.dense3 =  nn.Linear(512, 1024)\n",
    "        self.dense4 =  nn.Linear(1024, 784)\n",
    "                    \n",
    "    def forward(self, x):\n",
    "\n",
    "        x = self.lrelu(self.dense1(x))\n",
    "        x = self.lrelu(self.dense2(x))\n",
    "        x = self.lrelu(self.dense3(x))\n",
    "        x = self.dense4(x)\n",
    "\n",
    "\n",
    "        return x\n",
    "generator = GeneratorNet()\n",
    "\n",
    "# We need two optimisers, one for the generator, one for the discriminator network\n",
    "d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.0002,betas=(0.5, 0.999))\n",
    "g_optimizer = torch.optim.Adam(generator.parameters(), lr=0.0002,betas=(0.5, 0.999))\n",
    "\n",
    "loss = nn.BCELoss() # Binary cross entropy loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Batch: 0, Error D: 1.3587510585784912, Error G: 0.6789534091949463\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Batch: 500, Error D: 0.2262989580631256, Error G: 4.84857177734375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 0, Error D: 0.21221601963043213, Error G: 6.175994396209717\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 500, Error D: 0.25420767068862915, Error G: 5.9968461990356445\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2, Batch: 0, Error D: 0.35784798860549927, Error G: 6.966495990753174\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2, Batch: 500, Error D: 0.414189875125885, Error G: 7.672125816345215\n"
     ]
    },
    {
     "data": {
      "image/png": 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ANC4QEI0LBETjAgHlNcctLy/Pqd3T2dlZWa9mjt51kt61h2qn1Uyfbexdk+md0evNDD/++GOZqxn0zZs3Za03Y+7v75f5nTt3ZK72Vr2dVm8/29sVVucy19fXy1pvvu3t63rnNqvPHXifOVA9dPDgQRscHGSOC7yMaFwgIBoXCIjGBQKicYGAaFwgIBoXCCivOW5paWlO7Y4+ffpU1q9cuTKZefPGiooKmXszRXWHrbcv680bGxsbZT4xMSHzS5cuJbPdu3fLWm9eee/ePZl71HN29epVWevN5j23b99OZiUlJbL24sWLMvfmtJ5FixYlM+85UfPtY8eO2dDQEHNc4GVE4wIB0bhAQDQuEBCNCwRE4wIB6RnKf8lms/IoUW9solbvtm3bJmuHh4dl7o2irl+/nszU1aFmZuPj4zL31v681Tt1zefvv/8ua71jbb1xX3t7u8zV915XVydrf/rpJ5l7I7w9e/YkM28U5a0UqjVPM/16MTOrqqpKZt7xrGoU5a2nzuEdFwiIxgUConGBgGhcICAaFwiIxgUConGBgPKa405MTMhjUr2rD9W6k1qTMjOrrKyUubr20ExfJ9nV1SVrd+7cKfMLFy68UK6u0pyampK13s/cy73rSYuKipKZt2rp8Y4xPXToUDLr6OiQtd6c9tdff5X5unXrZK4+V+CtearPO8x3zZZ3XCAgGhcIiMYFAqJxgYBoXCAgGhcIiMYFAsrreNZMJvPAzPTgD8CLaM7lcnrR2fJsXAD/H/ivMhAQjQsEROMCAdG4QEA0LhAQjQsEROMCAdG4QEA0LhDQvwDrQjx8+KCEdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3, Batch: 0, Error D: 0.25036299228668213, Error G: 4.350992202758789\n"
     ]
    },
    {
     "data": {
      "image/png": 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+rq4ueVSmOhYxQm9Dev/+ffvaitvWUvXl3AiY2wLV5W7r2eXl5WJ2+vRpudaNp929e1fmbrROjRW640dd793dU9VjdqOWbrTO5eqeRPg+sKL6126L43//dwd+dQB/GwoXSIjCBRKicIGEKFwgIQoXSIjCBRKqNI9bq9XWImLxr3s7wP+9erPZHHH/UaXCBfC/gf9VBhKicIGEKFwgIQoXSIjCBRKicIGEKFwgIQoXSIjCBRL6F+eJByCk5ZCVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3, Batch: 500, Error D: 0.28427040576934814, Error G: 5.332677841186523\n"
     ]
    },
    {
     "data": {
      "image/png": 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Onz9fzN1VmGqcSbWZInzLRo2ARegxMNUqivAtEzee5p4Xdd59b9WWiPBrSt1jVytSnz17Js+ura3J3I2BqveLujY1wo/ldXR0yNy1g9zVq4p6TYaHh2NxcZGxPuDfiMIFEqJwgYQoXCAhChdIiMIFEqJwgYTqGutraGiItra2Yu5WXr4ON4alrv+MiJiamipmrh/p1q+6nqGjrukcHh6WZ2dnZ2XufjZ3Raham+vG11xv3o3m7du3r5i5Pqt6n0b4x+765+pq1vb2dnm2paWlmG23P8wnLpAQhQskROECCVG4QEIULpAQhQskROECCdU1j1upVOYiYvK/93CA/3t9tVqty/2jugoXwD8D/6sMJEThAglRuEBCFC6QEIULJEThAglRuEBCFC6QEIULJPQX/O0hKujK5DgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4, Batch: 0, Error D: 0.2855491638183594, Error G: 6.014540195465088\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4, Batch: 500, Error D: 0.34336233139038086, Error G: 5.533667087554932\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5, Batch: 0, Error D: 0.3859925866127014, Error G: 3.267033338546753\n"
     ]
    },
    {
     "data": {
      "image/png": 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txUy12CIibty4IXN1vWhExMLCQjFz12DeunVL5q6V5Vo6w8PDxcy9X9xjfxx84gIJUbhAQhQukBCFCyRE4QIJUbhAQhQukFDF9Uf/7vjx49XR0dFifurUKXleXSHoRufUFZ0Rfrytp6enmLlrNN2Il1uB6r6+up7U9ZhdP3JqakrmbuxPvT/Uqt4I/7y4/rZaqeuu8HTPi1vv6l4z1Yt1PWBVB2NjY3H//n29czf4xAVSonCBhChcICEKF0iIwgUSonCBhChcIKGa+riVSuXPiLj373s4wP97fdVqtdP9oZoKF8B/B/6pDCRE4QIJUbhAQhQukBCFCyRE4QIJUbhAQhQukBCFCyT0PxB8RDw+bTGtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5, Batch: 500, Error D: 0.3107297122478485, Error G: 3.2567853927612305\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6, Batch: 0, Error D: 0.33265256881713867, Error G: 4.326235771179199\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6, Batch: 500, Error D: 0.49624893069267273, Error G: 4.70477294921875\n"
     ]
    },
    {
     "data": {
      "image/png": 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ldBm1G6dUOa/aVraR+s7OjqyrTNONUrp76nJ/l0Gr666y7wh9zV2+XMcTF0iIxgUSonGBhGhcICEaF0iIxgUSonGBhCqNHusXEVGpVKoRoQMwAM0Yq9VqQ+6LSjUugG8D/1UGEqJxgYRoXCAhGhdIiMYFEqJxgYRoXCAhGhdIiMYFEvoHh2xArBWamUEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 7, Batch: 0, Error D: 0.5209382176399231, Error G: 4.617907524108887\n"
     ]
    },
    {
     "data": {
      "image/png": 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e/f395b/M5JUzMzPHPuuubHQ5sBq96+7ulmeXl5dl3T12V1dc1umet7vis9FoyLrKmF1W6sYZFxcXZV2txVX5coQftXTcc7t161ax5p5XT09PscZYH/A/jMYFEqJxgYRoXCAhGhdIiMYFEqJxgYQqXbNZq9WeRYTeWwmgE9fa7fZ594cqNS6Afwf+VxlIiMYFEqJxgYRoXCAhGhdIiMYFEqJxgYRoXCAhGhdI6P8ABKsAdpgaYyMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 7, Batch: 500, Error D: 0.43661415576934814, Error G: 4.578525543212891\n"
     ]
    },
    {
     "data": {
      "image/png": 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tA7edpmpduGMR3TakbnzNbVOq2gdu69jx8XGZu+MmXatLHTHqWk1uRM21stSz5rbMbWlpkbnbBtW1ZY4fP17MfvjhB7m2q6tL5s+Dd1wgIQoXSIjCBRKicIGEKFwgIQoXSIjCBRKqqo/b3NwcY2Njxdz1xtQo1ujoqFy7vr4uczf2NzExUcympqbkWjeeprasjQg5Chmhe4au5zc7Oyvzd999V+auB63ui/u9XW/ere/u7i5mbqzPPYvunrjrrraH3bVLvx+q/rT7m4F/fsZzfRWAfxUKF0iIwgUSonCBhChcICEKF0iIwgUSqjzvdpAREZVK5U5ElM+rBFCrg9vb23pAPKosXAD/DvxXGUiIwgUSonCBhChcICEKF0iIwgUSonCBhChcICEKF0jofwB7cBHGFhTHiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8, Batch: 0, Error D: 0.3835148215293884, Error G: 3.5000948905944824\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8, Batch: 500, Error D: 0.49797195196151733, Error G: 3.0147266387939453\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 9, Batch: 0, Error D: 0.5862435698509216, Error G: 2.514057159423828\n"
     ]
    },
    {
     "data": {
      "image/png": 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BhChcICEKF0iIwgUSonCBhKpqB717907+RO9aC83NzcXMjda5sT712BF69E4dHRrht0B1W4W6ETPFjYipMcsIf/Tp0tKSzFX7z21r29/fL3N3NOrp06eLmWpTRUT8/fffMlfbDEdEPHnyROa9vb3FzG3Hq6457SDgPxiFCyRE4QIJUbhAQhQukBCFCyRE4QIJVdXHra2tlT2qlpYWuV6NSrkRL3csouvrudemuD6u64UODg7KfHR0tJi5Pq0bs5yenpb5/Py8zJW+vj6ZT0xM7Gm96vO674vrh46Pj8vc9eafP39ezFx/W2396r5rFdxxgYQoXCAhChdIiMIFEqJwgYQoXCAhChdIqGan838RETU1NbMRofcaBbAXJ7e3t4+6f1RV4QL4/4H/VQYSonCBhChcICEKF0iIwgUSonCBhChcICEKF0iIwgUS+h+hLw0g2JqC3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 9, Batch: 500, Error D: 0.6014596223831177, Error G: 2.8160898685455322\n"
     ]
    },
    {
     "data": {
      "image/png": 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bde1eU9cfV3cmuxlpd8+su1fYPS+qT+y+tpq5dbO8a/jEBRKicIGEKFwgIQoXSIjCBRKicIGEKmoHra6uynWf7tf/6lf0rm1x+/ZtmbsxLvW93SiVa3uoFlmEHxGbnp4uZm71q1sj6tbeuud9aGiomLmfW61XjfCrZ1XuvrcbKayurn6pXD22gYEBefbkyZPFzK3TXcMnLpAQhQskROECCVG4QEIULpAQhQskROECCVXUx3Vcv1KNiLkRL7fy0l3xqc5v2LBBnt2xY4fMx8fHZe5+NjUG5vqJ7ipLt7a2qalJ5mqNqfu53GrY5uZmmavnZefOnfKsG4ccHByUubuGU+VqdWtExIEDB4rZ7OysPLuGT1wgIQoXSIjCBRKicIGEKFwgIQoXSIjCBRKqcjO0//EfV1XdiQi9qxTAy+hfXV3VzfeosHAB/P/A/yoDCVG4QEIULpAQhQskROECCVG4QEIULpAQhQskROECCf0LI6UgE4DIjCMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10, Batch: 0, Error D: 0.5227433443069458, Error G: 2.785640001296997\n"
     ]
    },
    {
     "data": {
      "image/png": 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LJEThAglRuEBCFC6QEIULJEThAgmV1Q4qlUqy9TE6OirPq5aMu/bQrTitrNT/BqnrQaenp+XZI0eOyNyNr6nVsBG6neRWfbqWi7tG886dOzJXI2odHR3yrGuTuVHNycnJwsy16A4cOCBz1daM8OOU6ufi1s6q15u7mnQb77hAQhQukBCFCyRE4QIJUbhAQhQukBCFCyRUVh+3oqIiamtrC/ONjQ15Xo0zuZ5fe3u7zN11lGoFqrr2MML3Ql1P0F3peP78+cKsqqpKnnWrY11/3I1DqlWiQ0ND8qxbuTsxMSFz1aN2P7OdjhzOzs7KXPXu3bii+tyuB7yNd1wgIQoXSIjCBRKicIGEKFwgIQoXSIjCBRKqcNcs/q8Prqh4GBG6+QZgJw6WSqXi4fH/UVbhAvh74L/KQEIULpAQhQskROECCVG4QEIULpAQhQskROECCVG4QEL/Au1KDvQucssSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10, Batch: 500, Error D: 0.6596181392669678, Error G: 2.085066556930542\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 11, Batch: 0, Error D: 0.5430829524993896, Error G: 3.095409631729126\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 11, Batch: 500, Error D: 0.875658392906189, Error G: 2.7878503799438477\n"
     ]
    },
    {
     "data": {
      "image/png": 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QEIULJEThAglRuEBCVY/1qV+Du7G+zs7OwsyNxrk1pI66vtCthnWtgY6ODpm7tog6766idG0w1+5xPzM1vuZG65aWlmTuxgJVm214eFievX//vswPHjy4p1w9dve8VE47CHiBUbhAQhQukBCFCyRE4QIJUbhAQhQukFBVfdydnR3Z03S9VtWXa29vl2fdGJcbzVtcXCzM3HWParVrhB9vcytU1ZhXY2OjPOt6pe4K0WPHjslcjd65793T0yNztyJVPTb3856ZmZG5+5m0tLTIXPVb3d8FTE9PF2Y7OzvybAWfuEBCFC6QEIULJEThAglRuEBCFC6QEIULJFSz2/m/iIiamprliCge0ASwVz3lcvmI+6KqChfA84H/VQYSonCBhChcICEKF0iIwgUSonCBhChcICEKF0iIwgUS+g+0iS8uDbRUVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 12, Batch: 0, Error D: 0.6133257746696472, Error G: 2.6500988006591797\n"
     ]
    },
    {
     "data": {
      "image/png": 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EqprHbWhomFH9LdfXGx0dLWaqtxXh+41unlftB3Yzq26e1j129/1PnDhRzFxf786dOzJftmyZzN3cqppLnZ6elmfd86J6xBG6R63uaY6IOHXqlMzVezEioqmpSeanT58uZlu2bJFn1fuht7c3Hjx4wDwu8DyicIGEKFwgIQoXSIjCBRKicIGEqhrrm5yclO2HlpYWeV6Nebm2lBvrc2Navb29c/7e7kpGt4ZUraV1uVthunr1aplfunRJ5m1tbTL/O9xj37Ztm8zV1ai//PKLPKvafxG+zeZe866urmLmxvrcGOls8IkLJEThAglRuEBCFC6QEIULJEThAglRuEBCVfVxa2pqZK/2u+++k+cPHjxYzCYnJ+XZoaEhmbvemBq1ciOD7t9W/cYIP0Km+p1u9WtdXZ3M3WrZ8fFxmavn1Z11q2NPnjwp85qammK2a9cuedb1kNX1oRER9+7dm3PueuOqx+xe72f4xAUSonCBhChcICEKF0iIwgUSonCBhChcIKGq1rNWKpW7ETHw33s4wP+9tpmZmRfcF1VVuAD+GfhfZSAhChdIiMIFEqJwgYQoXCAhChdIiMIFEqJwgYQoXCChfwH3Gy3JON/PtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 12, Batch: 500, Error D: 0.5731082558631897, Error G: 2.233398675918579\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 13, Batch: 0, Error D: 0.5222264528274536, Error G: 2.2846386432647705\n"
     ]
    },
    {
     "data": {
      "image/png": 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9PT0y915PGzduTGZ3796VZ9Vj+v3339vNmzfp4wKPIwoXCIjCBQKicIGAKFwgIAoXCIjCBQLKqY9bWFiYrampSeZeP3NwcDCZef1Kb4/uUvp6mYxum3n7g707aKuqqmSuZpG9u3srKipk7vXHvXtmL126lMyeeeYZeXZsbEzm3gy2+tu837uhoUHm3u82NDQkc7X/uL6+Xp5VM9L9/f02OztLHxd4HFG4QEAULhAQhQsEROECAVG4QEA5tYNKS0uzaiTJ+17q6kL1fc3Mzpw5I3NvPE2tb/XG9rzf7bfffpP55s2bZa7aA62trfKs59SpUzL31rOqNabedZLec+JdZameF6/l4rUXvb/bW2tbXl6ezEZHR+VZ1Zo8fPiwTUxM0A4CHkcULhAQhQsEROECAVG4QEAULhAQhQsEpGe+/kM2m5V9vcnJSXl+ZmYmlx/3b7yen7catq6uLpl5Y329vb0yb25ulrm3/rWpqSmZqZW2Zv4Vod5VlrW1tTIfHx9PZmrE08zvj6srW8302lpvBWpRUZHMvfPe46LGSL3XorrC07sudhHvuEBAFC4QEIULBEThAgFRuEBAFC4QEIULBJTTPG4mkxkzM32vI4ClWJ/NZtd4X5RT4QL4Z+B/lYGAKFwgIAoXCIjCBQKicIGAKFwgIAoXCIjCBQKicIGA/gcVhefOqEtxcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 13, Batch: 500, Error D: 0.7804341912269592, Error G: 2.0891671180725098\n"
     ]
    },
    {
     "data": {
      "image/png": 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qO9wI3TKJ8O0Ft8WqGrdsaGiQa9WWuO6xI3Sryh2N6j5TN9bnWlmqHaWOB43QnxljfcBrjMIFEqJwgYQoXCAhChdIiMIFEqJwgYTK6uNWV1dHa2trYf7gwQO5XvUUW1pa5Fq33eb4+LjM1eidG31z25C6rULdMZ41NTWFmbreERFzc3Myd9fN9RzVOOXU1JRc29fXJ/M///xT5qpX674vt27dkrkbC2xvb5f56dOnC7OZmRm5dnJysjBz/eMd3HGBhChcICEKF0iIwgUSonCBhChcICEKF0iotNv5v4iIUqn0JCJ0wxTAfnRvb2/r/X6jzMIF8M/Af5WBhChcICEKF0iIwgUSonCBhChcICEKF0iIwgUSonCBhP4Fx7r1ajYfECsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 14, Batch: 0, Error D: 0.5480718612670898, Error G: 2.6996874809265137\n"
     ]
    },
    {
     "data": {
      "image/png": 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6VsWtClbX0V67di2Wl5cZ6wNeRgQXSIjgAgkRXCAhggskRHCBhAgukFBVY307Ozvyysn/ZF+ur69P1u/duyfrat2mG21zK0zdiJnrUas1parnF+H7je6xqR5yRMTg4GCx5kbnFhcXa/reqk9cy3Ma4df9ulFNdX50dFSeVauInz17Js/u4RMXSIjgAgkRXCAhggskRHCBhAgukBDBBRKqah63rq7uz4jQuycB1OLU7u5up/uPqgougP8N/K8ykBDBBRIiuEBCBBdIiOACCRFcICGCCyREcIGECC6Q0D8A5VLwcQkzhGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 14, Batch: 500, Error D: 0.6016818881034851, Error G: 2.4452385902404785\n"
     ]
    },
    {
     "data": {
      "image/png": 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tm3nd6Gvmnhc1H67uv43Qrzd7lYHXGIULJEThAglRuEBCFC6QEIULJFRXO6ipqUmOOx04cECeV79iv3//vjx74sQJmc/MzMhcrSnt6OiQZ90aUTc652zbtq2YuRWorn3gcjfWqdpsbrSutbV1Q4+tWjpu7eydO3dk7kYxXTtIjVu6UUu3tnYt+MQFEqJwgYQoXCAhChdIiMIFEqJwgYQoXCChuvq4tVpN9gVdf0qtIXXctYqNjY3r/tpuHNGNiLn1q8PDwzJXfVzXG3erY1taWmSu1tZG6PE110t1r4m7vrRSKW8pde8l93O7a10bGvRnmhqXdD/31q1b1312FZ+4QEIULpAQhQskROECCVG4QEIULpAQhQskVNc1m5VK5WFE6MFZABvRW6vV9B2fUWfhAvh34H+VgYQoXCAhChdIiMIFEqJwgYQoXCAhChdIiMIFEqJwgYT+C77pNJLJFSBHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 15, Batch: 0, Error D: 0.7169704437255859, Error G: 2.410141944885254\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 15, Batch: 500, Error D: 0.7178243398666382, Error G: 2.4082283973693848\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 16, Batch: 0, Error D: 0.726662278175354, Error G: 1.782008171081543\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 16, Batch: 500, Error D: 0.6913634538650513, Error G: 2.0374948978424072\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vc3Njd27rV3dUpduGVG0VevHiRbnWtYvc0afutW1tbRWzg447uueu/rZ73mr71AjffnRjfaoVplpFERFra2vFjO1Zgb8wChdIiMIFEqJwgYQoXCAhChdIiMIFEqq5j6t6d0tLS3K9Gm+bnZ2t5an8C9fnVX1c1VeL8MdkuvE01zNU19SNM7rROrUtbYQ/hnNqaqqY1dXVybVudM69Z2oLVddndT1md13V6F2EPmJUHScbobfUbWzcX0lyxwUSonCBhChcICEKF0iIwgUSonCBhChcIKG6/R7rFxFRV1e3EBHlxh6Agzqzt7enB5mjxsIF8P+B/1UGEqJwgYQoXCAhChdIiMIFEqJwgYQoXCAhChdIiMIFEvobjWkdcUVpFOYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 17, Batch: 0, Error D: 0.6620819568634033, Error G: 2.419494152069092\n"
     ]
    },
    {
     "data": {
      "image/png": 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gIQoXSIjCBRKicIGEKmoHVVVVyV+zq6sHI/SVkGNjY/Jsa2urzN21iWr8zV2TeeXKFZlfvnxZ5jdu3JC5aoW5dtDw8LDM3fiau65StT1c68K9Zvfu3ZO5apu4UUvVtozw61vdmKkab3XXbKoaco/rz69b01cB+FuhcIGEKFwgIQoXSIjCBRKicIGEKFwgoYrXsy4sLBRzlUXoXqpbv7pz506ZP3jwQOaqX/ny5Ut51vVC3YiZy9XVitu2bdvQn+3OuxWp6rxbDetWpLa3t8tcjda5kUH3mrn328TEhMwXFxeLmbtmU/Xm3eNaxScukBCFCyRE4QIJUbhAQhQukBCFCyRE4QIJVa2srKz9i6uqnkbEH/+9hwP83zu0srKiG8FRYeEC+Hvgn8pAQhQukBCFCyRE4QIJUbhAQhQukBCFCyRE4QIJUbhAQv8C+yj4wr1oIgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 17, Batch: 500, Error D: 0.6368703842163086, Error G: 2.3120081424713135\n"
     ]
    },
    {
     "data": {
      "image/png": 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6W8+q3jM3xqlWu0b49avu50m1Fzc3N+VZdUXn4uKiPLuHT1wgIYILJERwgYQILpAQwQUSIrhAQgQXSKju9ayqf+VWhaq6W3Hqvvb9+/dlXfWQXb9R9d0ifJ/WUaNzbo2oGxFzo3fudVdjgW4kUL3m+3lstQLVnb18+bKsu7HAhYUFWVe9Wvd9/xn4xAUSIrhAQgQXSIjgAgkRXCAhggskRHCBhCr7vdYvIqJSqTyJiAd/3dMB/u+dqtVq3e4f1RVcAH8P/K8ykBDBBRIiuEBCBBdIiOACCRFcICGCCyREcIGECC6Q0L8A5sz0MdArfuIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18, Batch: 0, Error D: 1.0073965787887573, Error G: 2.208848476409912\n"
     ]
    },
    {
     "data": {
      "image/png": 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hChdIiMIFEqJwgYQoXCAhChdIqObG6f6pvb19Z2RkpJi7MS41puX6sG67TNeLVX3c9vZ2udZtr+p6pW4r0a6urmI2Pj4u146Ojsrc9QXdMZ6qF+u2IXWfqRutU1yfVm3tGhFx9uxZmbvXpv42dwRoo9EoZhMTE7G8vFyTDxBccYGUKFwgIQoXSIjCBRKicIGEKFwgIQoXSKhSH7dWq92PiNv/uZcD/N8b2NnZ0UPYUbFwAfxv4H+VgYQoXCAhChdIiMIFEqJwgYQoXCAhChdIiMIFEqJwgYT+BWbTAonLW1v0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18, Batch: 500, Error D: 0.5986905097961426, Error G: 2.86362361907959\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 19, Batch: 0, Error D: 0.6297651529312134, Error G: 2.546051502227783\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 19, Batch: 500, Error D: 0.7222439050674438, Error G: 2.419201135635376\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20, Batch: 0, Error D: 0.6298494338989258, Error G: 2.7913379669189453\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20, Batch: 500, Error D: 0.5750642418861389, Error G: 2.5908501148223877\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 21, Batch: 0, Error D: 0.6981134414672852, Error G: 3.0459933280944824\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 21, Batch: 500, Error D: 0.6584916114807129, Error G: 2.247340202331543\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 22, Batch: 0, Error D: 0.6920044422149658, Error G: 2.4458510875701904\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 22, Batch: 500, Error D: 0.5805072784423828, Error G: 2.55666184425354\n"
     ]
    },
    {
     "data": {
      "image/png": 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YqbG7iIipqSmZu0fwqi0yPDws17a1tcncHbPp3pu6Lq6t4UbrXJuto6ND5ouLi8XMtXPc96Gzs1Pm6ojPrq4uudZ9JmosL8K3gxS3ne9mrukG7rhAQhQukBCFCyRE4QIJUbhAQhQukBCFCyRUUR+3pqYmWltbi7kbIVM9qpGREbnW9cbcz56fny9m7e3tcq3b6nP37t0yX1hYkLm6pm6k0PVxXb/TbaG6c+fOYqbG7iL8iNro6KjM1VGXrjfuvi+ud9/b2ytzNWbqtmft7u4uZu59b+COCyRE4QIJUbhAQhQukBCFCyRE4QIJUbhAQhUds1lVVTUREeWBXACb9eb6+nq5ef4fFRUugH8G/lcZSIjCBRKicIGEKFwgIQoXSIjCBRKicIGEKFwgIQoXSOhf1ebajJ+3C+wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 23, Batch: 0, Error D: 0.5016701221466064, Error G: 2.2422397136688232\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 23, Batch: 500, Error D: 0.639472484588623, Error G: 2.6261255741119385\n"
     ]
    },
    {
     "data": {
      "image/png": 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iuEBClcb6dnZ25BiZG4VS/dJjx47Js260zo153b17t1hz/UjXp3X962vXrsl6X19fseb6ja5/7Xqp7jpK1Ut1a2ld/3txcVHWW1tbi7WzZ8/Ks27168DAgKy752V9fb1Yc8+LGrXc3t6WZw/wiQskRHCBhAgukBDBBRIiuEBCBBdIiOACCVWax63VaosRUd5LCaBR5/b390+5f6hScAH8f+B/lYGECC6QEMEFEiK4QEIEF0iI4AIJEVwgIYILJERwgYT+BY0HEbt85s16AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 24, Batch: 0, Error D: 0.6227951645851135, Error G: 1.8855681419372559\n"
     ]
    },
    {
     "data": {
      "image/png": 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ECC6QEMEFEiK4QEINt3Xon3640ViMiMdv7ukA//dGu92uPlM2KgYXwD8D/1UGEiK4QEIEF0iI4AIJEVwgIYILJERwgYQILpAQwQUS+hfRgb9fXWJFCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 24, Batch: 500, Error D: 0.587397575378418, Error G: 2.5783801078796387\n"
     ]
    },
    {
     "data": {
      "image/png": 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K1rGxMXn+7NmzhZlrLbiP2F27SF0X6doWfX19Mn/27JnMXevhjTfeKMxmZ2flWce1RVwbTj0vvb298uz09LTMm5ubZa5+tsnJSXm2sbFR5mpkMCJie3tb5rW1tYWZey2qr33YMVvecYGEKFwgIQoXSIjCBRKicIGEKFwgIQoXSKis9awnT57cV9dZuusq1RpUN+LlxvZc7+zRo0eFmev5ubE8Na4YodevRuhVn6+88oo869avOu5xU9dVut67Mzc3J/OhoaHCTI1CRvjfy/WBL1y4IHM13ur6+upq1Dt37sT6+jrrWYEXEYULJEThAglRuEBCFC6QEIULJEThAgmV1cetqKh4GBF6yBLAUZzb39/X+4KjzMIF8N/A/yoDCVG4QEIULpAQhQskROECCVG4QEIULpAQhQskROECCf0fWrkYMRx1JLEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The code below handles the training of the GAN\n",
    "\n",
    "# Total number of epochs to train\n",
    "n_epochs = 25 # start with one epoch (you will want to increase this number)\n",
    "\n",
    "# Loop over epochs\n",
    "for epoch in range(n_epochs):\n",
    "    \n",
    "    # Loop over training examples\n",
    "    examples = enumerate(train_loader)\n",
    "    for i_batch, (real_batch, _) in examples:\n",
    "\n",
    "        # Batch size\n",
    "        N = real_batch.size(0)\n",
    "        \n",
    "        # First Train Discriminator\n",
    "        real_data = torch.autograd.Variable(images_to_vectors(real_batch))\n",
    "\n",
    "        # Generate fake data\n",
    "        # (detach avoid calculating gradients for generator when training discriminator)\n",
    "        fake_data = generator(noise(N)).detach()\n",
    "\n",
    "        # Reset gradient\n",
    "        d_optimizer.zero_grad()\n",
    "    \n",
    "        # Train discriminator on real data\n",
    "        prediction_real = discriminator(real_data)\n",
    "\n",
    "        # Calculate error and backpropagate\n",
    "        # We want the discriminator to report \"1\" for real images\n",
    "        ones = torch.autograd.Variable(torch.ones(N, 1))\n",
    "        error_real = loss(prediction_real, ones)\n",
    "        error_real.backward()\n",
    "\n",
    "        # Train discriminator on fake data\n",
    "        prediction_fake = discriminator(fake_data)\n",
    "        \n",
    "        # Calculate error and backpropagate\n",
    "        # We want the discriminator to report \"0\" for fake images\n",
    "        zeros =  torch.autograd.Variable(torch.zeros(N, 1))\n",
    "        error_fake = loss(prediction_fake, zeros)\n",
    "        error_fake.backward()\n",
    "\n",
    "        # Update weights with gradients\n",
    "        d_optimizer.step()\n",
    "\n",
    "        # Total discriminator error\n",
    "        d_error =  error_real + error_fake\n",
    "\n",
    "        # Train Generator\n",
    "        # Generate fake data\n",
    "        fake_data = generator(noise(N))\n",
    "\n",
    "        # Reset gradients\n",
    "        g_optimizer.zero_grad()\n",
    "\n",
    "        # Sample noise and generate fake data\n",
    "        prediction = discriminator(fake_data)\n",
    "\n",
    "        # Calculate error and backpropagate\n",
    "        # instead of maximising the loss for zeros we minimise the loss for ones\n",
    "        ones = torch.autograd.Variable(torch.ones(N, 1))\n",
    "        g_error = loss(prediction, ones)\n",
    "        g_error.backward()\n",
    "\n",
    "        # Update weights with gradients\n",
    "        g_optimizer.step()\n",
    "\n",
    "        # Show status every few batches\n",
    "        if (i_batch) % 500 == 0: \n",
    "                        \n",
    "            test_images = vectors_to_images(generator(noise(1)))\n",
    "            test_images = test_images.data\n",
    "            print(\"Epoch: {0}, Batch: {1}, Error D: {2}, Error G: {3}\".format(epoch,i_batch,d_error,g_error))\n",
    "            draw_image(test_images[0][0])\n",
    "                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6sO1e2ejGBmdnZ4s1N/rmHpu76tJdw7m9vV2sufWs6myE78XOzMwUa26NabvrfN143fr6erHm1tKqMVHXX97DJy6QEMEFEiK4QEIEF0iI4AIJEVwgIYILJFTb7zrIiIharbYYEXp4FUA7Rlqtlh5Oj4rBBfD3wD+VgYQILpAQwQUSIrhAQgQXSIjgAgkRXCAhggskRHCBhP4B89v0BNCFWP0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QEIULJEThAglRuEBCFC6QEIULJFR3O0hdGek+BlfUmFSEb8m41oQaX1Mfz0dEtLS0yPzhw4cyd+OOqm0yPDwsz7qxPdcWcWN9Sltbm8xdO8hd8Tk3N1fM3DWZ7ne2sLAgc3WNZoQezXMjh2rM07VUa3jHBRKicIGEKFwgIQoXSIjCBRKicIGEKFwgobr6uFtbW/LqRNdT3L27/O1cL3VxcfGZv3ZExMbGRjFz61ldr9OtuHU9anXdpOuVuu/tVsMuLS3JXPXPXS/UXVV59epVmavfqVuJq3rAEf4KUHX1aYTu3avXWoS+AtS9Vmp4xwUSonCBhChcICEKF0iIwgUSonCBhChcIKG6rtmsVCr3ImL6f+/hAP/vHd/e3tZD1FFn4QL4Z+B/lYGEKFwgIQoXSIjCBRKicIGEKFwgIQoXSIjCBRKicIGE/gvrXRctfxp2IAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1ftPHBd5jBBdIiOACCRFcICGCCyREcIGECC6QUK3dvlFERK1WW4iI6X/ucoD/e2OtVmvA/VCl4AJ4N/BfZSAhggskRHCBhAgukBDBBRIiuEBCBBdIiOACCRFcIKH/AEBL/AzBlUt3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hclnn8PCwrLsRM7WG1F2z6XI/t4bU5cDq9a7X6/LslStXZN2dV8+by6cXFxdl3V2N6rJYdf1ob2+vPKvei+7a00N84gIJ0bhAQjQukBCNCyRE4wIJ0bhAQjQukFClazZrtVo9Iub/vocD/OeNN5tNfadsVGxcAP8O/K8ykBCNCyRE4wIJ0bhAQjQukBCNCyRE4wIJ0bhAQjQukND/AN61WtYBlL3zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GxmLmxstca8C1k9xxkqq14bZPdeNprr3gRu/USKIbOVxYWJB5d3e3zNXn7kbn1NGlERF37tyReUdHx57Xu/eljtkcHx+P9fV1xvqApxGFCyRE4QIJUbhAQhQukBCFCyRE4QIJ1TTWt729LbfzPHLkiFyvxuO6urrk2v7+fpm7Pu/vv/9ezIaGhuTa/fZS3fatantY14dVx4dG7K9X6h7fjeW59+3+hkDl7nNx45Cqrx/he9Tqvbstc9UoJcdsAk8xChdIiMIFEqJwgYQoXCAhChdIiMIFEqppHrdSqSxGhN6DFcB+DFSrVd18jxoLF8A/A/+rDCRE4QIJUbhAQhQukBCFCyRE4QIJUbhAQhQukBCFCyT0L51LJCBJWnI5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO4AAADuCAYAAAA+7jsiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADvFJREFUeJzt3ctSlOcWxvEF2A1CQBQQIcpJjMYQTRjkUKmkMktlFDPKPeQqciG5gWScicMMrJQVqUqMIUYNCgielYNgkG56jzqDXfU9D5/sXdvl/v+myxcaup90qhdrvS2NRiMA5NL6v34AAMojuEBCBBdIiOACCRFcICGCCyREcIGECC6QEMEFEtpX6h/v29eoVCqqLs/v7OwU1tra2uTZWq0m6y0tLS9cr9fr8uxeucemfnb3l23qdxoR0dqq/9vsHpviHpv7vbrnXJ1Xr8Pd2N7e3tN59Xtvb2+XZ9XPtbW1FbVazT4ppYJbqVRicnKysH7o0CF5/u+//y6sdXd3y7OPHj2Sdfci6OjoeOGv7bjv7eq9vb2Fta2tLXlW/U4jIrq6umS9Wq3KunqBPn/+XJ59+vSprB84cEDWV1dXC2sDAwPyrPsP1tLSkqy7NyH1s504cUKeffLkSWFtdnZWnm3if5WBhAgukBDBBRIiuEBCBBdIqNSnys79+/dlXX3S5z4FdO0g9ymj+iRPfeIc4T+53b9/v6y7T5XVp7OupeJaD6+99pqsu09X1af97ud2dfeJufpEfH19XZ51rSrVHYmIWFlZkXX1qfPjx4/lWfU7dTn459/t6l8BeKkQXCAhggskRHCBhAgukBDBBRIiuEBCpfq4bW1tsi/oRqXU2Y2NDXnWja+5fqSawHF9XNdDdj3F/v5+WVf9TPe93XTP5uamrLvxONVLvXTpkjzrpmQc9Zw9e/ZMnu3s7JT1tbU1WXeTTQcPHiysuT6u6r3TxwVeYQQXSIjgAgkRXCAhggskRHCBhEq1g3Z2duSImxulunfvXmHNtUzcYrLDhw/Luho53MvCtAjfUnn48KGsq/E31XbYDdfqcgvbbt68WVhzI4ULCwuyPj09Les9PT2FNdfOce0i93txCwTVIju3OXN+fr6w5l7nTbzjAgkRXCAhggskRHCBhAgukBDBBRIiuEBCL81Yn1th2tfXJ+vuwjG1nvXOnTvy7PHjx2XdjRSOjIzI+pEjRwprY2Nj8uyHH34o66oPGxFx/fp1WVc97L2OHLperKq7r+1GLd0YqRopdN//xo0b8qx6re729kTecYGECC6QEMEFEiK4QEIEF0iI4AIJEVwgoVJ93FqtJmdLXS9V9UvHx8flWTX/GOFngVXPcWpqSp51qzqHhoZk3fUcJyYmCmuffvqpPKvWp0bo6yAj/Mysur7UzUCrudOIiNnZWVlXV2G6K13d9aLuWlZ3zabqEw8ODsqz6ppN93w18Y4LJERwgYQILpAQwQUSIrhAQgQXSIjgAgmVnsdVc4quj6v6oeqqyQg9sxoRsbi4KOvq6//555/yrOvLucfudiPX6/XC2l9//SXPumsZZ2ZmZN31Ys+cOVNYc3uV3Ryz232s5lrd3Kp7ztw8rtuVrfrE7vWkesDsVQZeYQQXSIjgAgkRXCAhggskRHCBhP6j12y66yTdR+zKtWvXZN21RVQbyz1uNyLmWgvuvGoXudaCGyl0vxfV7omIuHv3rqwrbkWqu+rStRcVd02ma4O5Uc7Hjx8X1lwryj223eAdF0iI4AIJEVwgIYILJERwgYQILpAQwQUSKt3HVf0tt/Jyc3OzsLZ//355tqenR9bVNZoRulfqrvh0Y3v9/f2y7kYSVZ+3s7NTnnVXhLrv/e2338r66dOnC2tqzWiEXzXqXi/qvBt3dL1195y6/rj6ewbXp1X96eXlZXm2iXdcICGCCyREcIGECC6QEMEFEiK4QEIEF0joPzqP63pnqi/34MEDeXZkZETWT5w4IesXL14srL311lvyrFsj6tavqms0IyK2t7cLa5cvX5ZnHddLdTPSqifp1rO63vzw8LCsqxlq13t361uXlpZk3c1oq9+r+7nUClb3uJt4xwUSIrhAQgQXSIjgAgkRXCAhggskRHCBhEr1cavVquynuhlHNb+pZnUjIubm5mTdzXaqnqK7orNWq8m6m0t1j119f9XjjYi4evWqrE9OTsq668UuLCwU1qampuRZtxfZ9c9VD9s939evX5d1t9N5dHRU1lV/W/2tQ4T+u4CdnR15tol3XCAhggskRHCBhAgukBDBBRIiuEBCBBdIqFQft62tTe43dnepqhlId4es26tcrVZlXe0+np+fl2fHx8dl3d21+tlnn8n6+fPnC2vuXmDXh1X3uEb4vc2q7vrfbj77l19+kfU33nijsOb6sDdv3pR193r57bffZF3NYLu9yqo3X6/X5dkm3nGBhAgukBDBBRIiuEBCBBdIiOACCZVqB9Xr9VhbWyusu5EkVe/q6rLfW3ErVFdXVwtrb775pjzrrqr88ssvZd21i1Tb5NKlS/Ls66+/LutuJNGNDaq1t250zq1nPXv2rKy71bLK0aNHZf2nn36S9VOnTsm6Wifsnm/VLmI9K/AKI7hAQgQXSIjgAgkRXCAhggskRHCBhEo3ylQ/1V2Fqca43EpLN1rnzqvxN9cL/eKLL2TdPbbvv/9e1tfX1wtr7rG5lbjnzp2Tdbda9ocffiisjY2NybNqtWtExJUrV2R9enq6sHby5El5dmVlRdbdGKlbe6v6xI1GQ55Vfxfgrv9s4h0XSIjgAgkRXCAhggskRHCBhAgukBDBBRIq1cdtaWmJSqVSWHc9RTVz6+Yn3eynu9JR9fXcrK/rT7vzBw4ckPWHDx8W1tx61Q8++EDW3cpct2JV9b9v3bolz6r+dEREb2+vrKvVsO57u9ei6/MeP35c1p8+fVpYa23V74fqCk+3Fvaf77GrfwXgpUJwgYQILpAQwQUSIrhAQgQXSIjgAgmV6uPWajXZc3S7kScmJgprT548kWfVNZkRfgevmh1955135Fm3H9j1FFXPLyJic3OzsOZ2Pru9ye4KUfW9IyJ+//33wprbNz0wMCDrbg+3emyTk5PyrJv1VX+PEBExNzcn66oH7XZVq16tez6aeMcFEiK4QEIEF0iI4AIJEVwgIYILJERwgYRK9XGr1aqcTW1ra5Pn1R21jptxvH//vqyrvtvg4KA86/q4Q0NDsq7uUo3QfT03y+v2C7sesps1Vve1XrhwQZ49duyYrKu7dyMiPvroo8Ka29nsfu6Ojo49nVdc/1rd5exe5//8u1KPCMBLgeACCRFcICGCCyREcIGECC6QUOlrNlV7YGNjQ55VY1wHDx6UZ11Lpa+vT9ZVq+rXX3+VZ7/66itZd2N9jx49kvXTp08X1p4/fy7PuutF3XWS165dk3W1pvTMmTPyrFst61pRqiXjxvbc+lXXwnNjpqql48b6enp6CmuupdrEOy6QEMEFEiK4QEIEF0iI4AIJEVwgIYILJFSqj7u9vR337t0rrLt+pRpBc1cyun6kO6+uwnRnv/vuO1l3Y1zd3d2yrnq1a2tr8my1WpV1dwWoW/+qxiHdSlzX5/38889lXfWB93rNplozHKH/XiFC94Fdb131eRuNhjzbxDsukBDBBRIiuEBCBBdIiOACCRFcICGCCyRUqo9br9dlX7Gzs1OeVytUXd/M9ULVfGSEnhW+ffu2POt6qW7+0vU71QpWt67T1ZeXl2Xd9TvV7KjrAX/yySey7nqWly9fLqy5eVlH/Vy7oZ5zt85X/S2Euza1iXdcICGCCyREcIGECC6QEMEFEiK4QEIEF0ioVB+3tbVV9mrdbKjqGbp5W3fdpJuBVLPCi4uL8qzb/+u0t7fL+vvvv19Ym5+fl2f/+OMPWR8eHpb1Q4cOyfrExERh7ezZs/Ks+739+OOPsj47O1tY29zclGddb93NULtrW9UMtevjqufEvRabeMcFEiK4QEIEF0iI4AIJEVwgIYILJFT6mk01iuWuylxdXS2suY/n3RWeg4ODsq7WcboRsfPnz8v6e++9J+sff/yxrN+9e7ew5n4vbrXs2NiYrLtrHd9+++3Cmnu+v/nmG1lX12hG6NaIurI1wreLKpXKns6r9qVb/apG91wbq4l3XCAhggskRHCBhAgukBDBBRIiuEBCBBdIqGW31/pFRHR1dTWmpqYK666vt7S0tOvv9e9UrzMi4tSpU7Ku1piqEa0IP57W0dEh6+fOnZN1dZ2k6+O69axuZe5erpucmZmRZy9cuCDrrleqxkTdylu3rtdx/XG1LtitGlY96KtXr8bGxob+AsE7LpASwQUSIrhAQgQXSIjgAgkRXCAhggskVKqP293d3Zieni6sr6ysyPNqbaW6ejDCr2d187pHjhwprLlrNF3P8PDhw7LueqVff/11Yc1dL+rmaX/++WdZdz/b3NxcYc31cd3KXUc9L+6azL0+p0ePHpV11at1OVB9/5mZmVhfX6ePC7yKCC6QEMEFEiK4QEIEF0iI4AIJEVwgoVJ93M7OzsbJkycL6+6qS7VPtqurS551X7uvr0/W1d7lixcvyrNDQ0Oy7nbhuplYtV/Y9ULdLLG7+lTNAkfofqe6ujTC/972MkvsZnldL9X1cfv7+2V9YWHhhb+2ek5v3LgRz549o48LvIoILpAQwQUSIrhAQgQXSIjgAgkRXCChUvfj7tu3T+5OfvDggTzvZigVN4+7tbUl66rvttc+7ejoqKzfuXNH1sfHx1/4rJvXdbuuXZ/4ypUrhbV3331XnnX3Dqu+fkREb29vYa29vV2edT1i12t19++q+W53VuXg1q1b8mwT77hAQgQXSIjgAgkRXCAhggskRHCBhEq1g+r1urx+0H1Erz6Cd2N7bnytUqnIulqn6VoD7mu7x+baHouLi4U1t/p1eXlZ1uv1uqy79a5jY2Mv/L3daJ1rVd2+fbuw5sb63Cile2yq3ROhH9vw8PALf2/3fDXxjgskRHCBhAgukBDBBRIiuEBCBBdIiOACCZXq4zYaDdmzdCNmqld77Ngxedb13dwImVpDOjExIc+qaxEj/DWa7rwaQXPrc10Peq+PTY08qmtTI/x4m+shq9+L69O6sT73eltdXZX1kZGRwprrMavXKn1c4BVGcIGECC6QEMEFEiK4QEIEF0iI4AIJlbpms6Wl5UFEzP/3Hg7wf2+00WgMuH9UKrgAXg78rzKQEMEFEiK4QEIEF0iI4AIJEVwgIYILJERwgYQILpDQvwCmqfRcrldRfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8dZOuTeaokcTx8XF51rXBXCtLvdfu378vzzpdXV0ydy3Czs7OYuYem2qbzszMxPb2tp45DD5xgZQoXCAhChdIiMIFEqJwgYQoXCAhChdIqOGxPnW9oRtBU6NQJ0+elGd3dnZkrvpqEboX69azumsT3epY14tVj315eVmedVyvdH19XebqZ+rG9tzYn3tdJyYmipn7+4OjXp3qxgLVmKnr26sVx26V7yE+cYGEKFwgIQoXSIjCBRKicIGEKFwgIQoXSKihedxKpbIcEdP/uYcD/L83UKvV9CBzNFi4AP5v4H+VgYQoXCAhChdIiMIFEqJwgYQoXCAhChdIiMIFEqJwgYT+B+sfOAAcCsn2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gIQoXSIjCBRKicIGEKFwgoYr6uEuXLo0NGzYU85GREXle7WRet26dPKvu5Y3wO3rVfadullfdXxsRMTw8LHO3X1j1My9duiTPtre3y9zNW6v9wBG69+76sM9L9dfdvKzrw7r9xe65jY6OFjPVf47Qe5Xd7PYCPnGBhChcICEKF0iIwgUSonCBhChcIKGK2kFzc3NyJEmtGY2IGBsbK2a1tbXyrFun6dpJavzNtRbcCtOaGv0yujaZupbRfW/Ximpubpb5b7/99sznV69eLc+6lbuu5aLec9Xei/CjlC5376m65vPChQvyrBrdox0EvMAoXCAhChdIiMIFEqJwgYQoXCAhChdIqKJrNhsaGuZ7e3uL+fOMeXV2dj7z2YiIK1euyFytZ+3q6pJn3cpM12ttbW2V+cTERDFzY3tqRGwx3GPv7u4uZq7n6K7RdCt5VW/ePW93DadbubtlyxaZq+fmvrYaQR0dHY2pqSmu2QReRBQukBCFCyRE4QIJUbhAQhQukBCFCyRUUR+3qqpqLCJu/P89HOC/Xvf8/Pwq948qKlwA/xn4X2UgIQoXSIjCBRKicIGEKFwgIQoXSIjCBRKicIGEKFwgof8BuY4yP/xVd8QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WbUb4VZ9utezS0pKsq9Wzrg/r+plu3afrvaueoxs5dH3ctbU1WVcjh26cUf283deO8H3cH3/8sVhzfxfwV+AdF0iI4AIJEVwgIYILJERwgYQILpAQwQUSqjWP22g0liNi7u97OsA/XquqKr3vN2oGF8CLgf9VBhIiuEBCBBdIiOACCRFcICGCCyREcIGECC6QEMEFEvoXYq/6oeItcdAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce 20 examples\n",
    "\n",
    "test_images = vectors_to_images(generator(noise(20)))\n",
    "test_images = test_images.data\n",
    "for i in range(20):\n",
    "    draw_image(test_images[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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