notebooks/generative_adversarial_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Quick, Draw!* GAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* code based directly on [Grant Beyleveld's](https://github.com/grantbey/quickdraw-GAN/blob/master/octopus-v1.0.ipynb), which is derived from [Rowel Atienza's](https://towardsdatascience.com/gan-by-example-using-keras-on-tensorflow-backend-1a6d515a60d0) under [MIT License](https://github.com/roatienza/Deep-Learning-Experiments/blob/master/LICENSE)\n",
    "* data provided by [Google](https://github.com/googlecreativelab/quickdraw-dataset) under [Creative Commons Attribution 4.0 license](https://creativecommons.org/licenses/by/4.0/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select processing devices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "# # os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for data input and output:\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# for deep learning: \n",
    "import keras\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Input, Dense, Conv2D, BatchNormalization, Dropout, Flatten\n",
    "from keras.layers import Activation, Reshape, Conv2DTranspose, UpSampling2D # new! \n",
    "from keras.optimizers import RMSprop\n",
    "\n",
    "# for plotting: \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data\n",
    "NumPy bitmap files are [here](https://console.cloud.google.com/storage/browser/quickdraw_dataset/full/numpy_bitmap) -- pick your own drawing category -- you don't have to pick *apples* :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_images = \"../quickdraw_data/apple.npy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.load(input_images) # 28x28 (sound familiar?) grayscale bitmap in numpy .npy format; images are centered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144722, 784)"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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     "execution_count": 6,
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   ],
   "source": [
    "data[4242]"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "data = data/255\n",
    "data = np.reshape(data,(data.shape[0],28,28,1)) # fourth dimension is color\n",
    "img_w,img_h = data.shape[1:3]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
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     "data": {
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sS1LJ3XOfEzazqyXtk7Tc3S8qb3tC0m53f7z8D+eZ7v5gQXp7WNK+vFduLi8oM67vytKS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7141d6db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data[4242,:,:,0], cmap='Greys')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create discriminator network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def discriminator_builder(depth=64,p=0.4):\n",
    "\n",
    "    # Define inputs\n",
    "    inputs = Input((img_w,img_h,1))\n",
    "    \n",
    "    # Convolutional layers\n",
    "    conv1 = Conv2D(depth*1, 5, strides=2, padding='same', activation='relu')(inputs)\n",
    "    conv1 = Dropout(p)(conv1)\n",
    "    \n",
    "    conv2 = Conv2D(depth*2, 5, strides=2, padding='same', activation='relu')(conv1)\n",
    "    conv2 = Dropout(p)(conv2)\n",
    "    \n",
    "    conv3 = Conv2D(depth*4, 5, strides=2, padding='same', activation='relu')(conv2)\n",
    "    conv3 = Dropout(p)(conv3)\n",
    "    \n",
    "    conv4 = Conv2D(depth*8, 5, strides=1, padding='same', activation='relu')(conv3)\n",
    "    conv4 = Flatten()(Dropout(p)(conv4))\n",
    "    \n",
    "    # Output layer\n",
    "    output = Dense(1, activation='sigmoid')(conv4)\n",
    "    \n",
    "    # Model definition\n",
    "    model = Model(inputs=inputs, outputs=output)\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 64)        1664      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 7, 7, 128)         204928    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 4, 4, 256)         819456    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 4, 4, 512)         3277312   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 8193      \n",
      "=================================================================\n",
      "Total params: 4,311,553\n",
      "Trainable params: 4,311,553\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator = discriminator_builder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "discriminator.compile(loss='binary_crossentropy', \n",
    "                      optimizer=RMSprop(lr=0.0008, decay=6e-8, clipvalue=1.0), \n",
    "                      metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create generator network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator_builder(z_dim=100,depth=64,p=0.4):\n",
    "    \n",
    "    # Define inputs\n",
    "    inputs = Input((z_dim,))\n",
    "    \n",
    "    # First dense layer\n",
    "    dense1 = Dense(7*7*64)(inputs)\n",
    "    dense1 = BatchNormalization(momentum=0.9)(dense1) # default momentum for moving average is 0.99\n",
    "    dense1 = Activation(activation='relu')(dense1)\n",
    "    dense1 = Reshape((7,7,64))(dense1)\n",
    "    dense1 = Dropout(p)(dense1)\n",
    "    \n",
    "    # De-Convolutional layers\n",
    "    conv1 = UpSampling2D()(dense1)\n",
    "    conv1 = Conv2DTranspose(int(depth/2), kernel_size=5, padding='same', activation=None,)(conv1)\n",
    "    conv1 = BatchNormalization(momentum=0.9)(conv1)\n",
    "    conv1 = Activation(activation='relu')(conv1)\n",
    "    \n",
    "    conv2 = UpSampling2D()(conv1)\n",
    "    conv2 = Conv2DTranspose(int(depth/4), kernel_size=5, padding='same', activation=None,)(conv2)\n",
    "    conv2 = BatchNormalization(momentum=0.9)(conv2)\n",
    "    conv2 = Activation(activation='relu')(conv2)\n",
    "    \n",
    "    conv3 = Conv2DTranspose(int(depth/8), kernel_size=5, padding='same', activation=None,)(conv2)\n",
    "    conv3 = BatchNormalization(momentum=0.9)(conv3)\n",
    "    conv3 = Activation(activation='relu')(conv3)\n",
    "\n",
    "    # Output layer\n",
    "    output = Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(conv3)\n",
    "\n",
    "    # Model definition    \n",
    "    model = Model(inputs=inputs, outputs=output)\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3136)              316736    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 3136)              12544     \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 3136)              0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 14, 14, 32)        51232     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 14, 14, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 28, 28, 16)        12816     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 28, 28, 16)        64        \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 8)         3208      \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 28, 28, 8)         32        \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 28, 28, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 28, 28, 1)         201       \n",
      "=================================================================\n",
      "Total params: 396,961\n",
      "Trainable params: 390,577\n",
      "Non-trainable params: 6,384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator = generator_builder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create adversarial network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def adversarial_builder(z_dim=100):\n",
    "    model = Sequential()\n",
    "    model.add(generator)\n",
    "    model.add(discriminator)\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                  optimizer=RMSprop(lr=0.0004, decay=3e-8, clipvalue=1.0), \n",
    "                  metrics=['accuracy'])\n",
    "    model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "model_2 (Model)              (None, 28, 28, 1)         396961    \n",
      "_________________________________________________________________\n",
      "model_1 (Model)              (None, 1)                 4311553   \n",
      "=================================================================\n",
      "Total params: 4,708,514\n",
      "Trainable params: 4,702,130\n",
      "Non-trainable params: 6,384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "adversarial_model = adversarial_builder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_trainable(net, val):\n",
    "    net.trainable = val\n",
    "    for l in net.layers:\n",
    "        l.trainable = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(epochs=2000,batch=128):\n",
    "    \n",
    "    d_metrics = []\n",
    "    a_metrics = []\n",
    "    \n",
    "    running_d_loss = 0\n",
    "    running_d_acc = 0\n",
    "    running_a_loss = 0\n",
    "    running_a_acc = 0\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        \n",
    "        if i%100 == 0:\n",
    "            print(i)\n",
    "        \n",
    "        real_imgs = np.reshape(data[np.random.choice(data.shape[0],batch,replace=False)],(batch,28,28,1))\n",
    "        fake_imgs = generator.predict(np.random.uniform(-1.0, 1.0, size=[batch, 100]))\n",
    "\n",
    "        x = np.concatenate((real_imgs,fake_imgs))\n",
    "        y = np.ones([2*batch,1])\n",
    "        y[batch:,:] = 0\n",
    "        \n",
    "        make_trainable(discriminator, True)\n",
    "        \n",
    "        d_metrics.append(discriminator.train_on_batch(x,y))\n",
    "        running_d_loss += d_metrics[-1][0]\n",
    "        running_d_acc += d_metrics[-1][1]\n",
    "        \n",
    "        make_trainable(discriminator, False)\n",
    "        \n",
    "        noise = np.random.uniform(-1.0, 1.0, size=[batch, 100])\n",
    "        y = np.ones([batch,1])\n",
    "\n",
    "        a_metrics.append(adversarial_model.train_on_batch(noise,y)) \n",
    "        running_a_loss += a_metrics[-1][0]\n",
    "        running_a_acc += a_metrics[-1][1]\n",
    "        \n",
    "        if (i+1)%500 == 0:\n",
    "\n",
    "            print('Epoch #{}'.format(i+1))\n",
    "            log_mesg = \"%d: [D loss: %f, acc: %f]\" % (i, running_d_loss/i, running_d_acc/i)\n",
    "            log_mesg = \"%s  [A loss: %f, acc: %f]\" % (log_mesg, running_a_loss/i, running_a_acc/i)\n",
    "            print(log_mesg)\n",
    "\n",
    "            noise = np.random.uniform(-1.0, 1.0, size=[16, 100])\n",
    "            gen_imgs = generator.predict(noise)\n",
    "\n",
    "            plt.figure(figsize=(5,5))\n",
    "\n",
    "            for k in range(gen_imgs.shape[0]):\n",
    "                plt.subplot(4, 4, k+1)\n",
    "                plt.imshow(gen_imgs[k, :, :, 0], cmap='gray')\n",
    "                plt.axis('off')\n",
    "                \n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "    \n",
    "    return a_metrics, d_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "Epoch #500\n",
      "499: [D loss: 0.402295, acc: 0.850717]  [A loss: 2.249226, acc: 0.180987]\n"
     ]
    },
    {
     "data": {
      "image/png": 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WpFUUzZo1A1wOQ74zjlwxRWwYhhGYIIpYSkd+xxNPPBGAjTfeGMhdCUtZX375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\nu+qCyPdZM4sPwtsWoHHjxilwbU2x/v7aUB3fB1x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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70cc3c4e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n",
      "600\n",
      "700\n",
      "800\n",
      "900\n",
      "Epoch #1000\n",
      "999: [D loss: 0.512194, acc: 0.757249]  [A loss: 1.765992, acc: 0.151480]\n"
     ]
    },
    {
     "data": {
      "image/png": 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UfVaVv1UPYZGev9WO9CWXXAKEnPmHH36YypVqZSLPpfLp6vUqv23SkQJTnl3e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\nYyLjidgYYyLz/wAfRjuwLAL+AAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70cc093588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n",
      "1100\n",
      "1200\n",
      "1300\n",
      "1400\n",
      "Epoch #1500\n",
      "1499: [D loss: 0.556011, acc: 0.726159]  [A loss: 1.677926, acc: 0.146556]\n"
     ]
    },
    {
     "data": {
      "image/png": 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0Sr3FaAKk2N91111AaKbz/vvvL/D3yzW2OkT1wQcfBEKjeK268m2tmuG1AcFz\nK9eE8qhyT9RE7NhC7fOCnE/PPfccEHLz2v8pZR5c8VaLBNUhqCmQcsfCJc7GGJMQyipHLJTXvf/+\n+4FQvaKd4XxQRYzyTFLX5dAeshhILaii6PXXXwdg4MCBQLizlwKpCbVm/Oabb4Bw3HzS0JHraoX4\nwAMPADBx4kQgNDLXYZfFqNKUGtdKRyqxNiWcJNSIS3UFav6j11pKdKyV3nPFP10JZ4sVsTHGRKYs\nc8RCR7irxl9+Ynl9c0Et9LTL/PTTTwPMd4RKtsTOtWUaWzUBUnP2lVZaCSjNoaLpjYcuvfRSAM48\n80ygfJuX1xbb9ObhOmxAvlflNlXFqcY8+XDkkUcCcMUVVwDhYM0uXboAmedNY8cWMo+vKu3USF95\n8FKsZuVG0UGxmpu011LTvpVzxMYYkxDKWhHLqycVp6PhTz31VCDkZzK5I+ouqgNKpRi0y5xvdVds\nZZFpbPV61fFMzgX5p4uRx5QLZurUqUCo/mrXrh0As2bNWujfJyW2Qk3alQuX8leuM338ZhJzuV/U\nkFzHgqn/hVY42To1YscWMo+vOiKqslGtJ5WDLwbKS+voKR2woD4j2ieoCStiY4xJCGWtiIUqsaSM\nd9hhBz0eEA5d1JHx8+Y7pSQmTJgAhDyeKunUpzdfYiuLbGMrr6uOp7r44ouBcFxRukpTrHMZL1Is\n6i+90UYbAZn3ZkhabIXG3uqrrw6EMad+H6rIU7533nGrHigDBgwAwi69VnaTJ08GYOONNwZyd0nE\nji1kHl+tkJUrVoy095BPRWOTJk0AOPzww4GQz2/VqlW1ryeccAIQKlZrw4rYGGMSQiIUcTrKc0pJ\n6EA/HZQ4c+bMVM5T1VqjR4+u9jtS2bl26k8ntrLINrbpR0atscYaAAwbNgwInlipNI0T9TnOBHmX\ntZK55pprADjllFOqPWZtJC22NSEFp54JGr/yzY8ZMyblqe7RowcQ+mNL8Q4dOhSAIUOGVPvbXIkd\nW8g+vur9q540U6ZMAULOWJ7e9FVdvXr1UvsTXbt2BUIcVb2n/L7iPWbMGAAeffRRAG677Tag8K4U\nK2JjjIlMIhXxPI8HBD+hTp/YeeedUzv1UnTybqp3bKGruWIri1xjq9XFddddB4SqJe3wy58q7+/Y\nsWNTFY7qDy3VtssuuwBV8QdYYYUVgFDN161bNyB7h0pSY7uQxwOCB1V9t1dbbbXUuJTak29VK5RC\nreBE7Ni4bJ1/AAAgAElEQVRC7vHdYIMNgFCBqzzuu+++C4QaAeWWe/bsmaqMk/LVCk/eb3V00/5F\nqdxUVsTGGBOZRCviBTw+UHW3kxpT79hnnnkGCFVOhSa2ssg3tlII2i2WB1Ynm6g7XcOGDWs8Tl55\nM60+zjvvPCD0Ps7Vo5z02NaGFFvz5s1TOV85AIr9+YwdW8g/vksuuSQQenuou588wBqXv//+O5Mm\nTQLCCvDJJ58Eavey54oVsTHGJIQ6pYhreE6g7iuLQsdWrgop5c6dOwNVeV/5gHWGmzyt6h+tc7uk\n6vLtF1vXYltOxI4tFC6++qxr30OrYq0ypk+fntr7KEYF6YKwIjbGmIRQ5xVxqYitLBzb4uHYFhfH\n14rYGGOiU1JFbIwxZn6siI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKe\niI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0x\nJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKe\niI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjKeiI0xJjINSvlkFRUVlcV8/IYNGwLw119/8fff\nfxfzqeajsrKyoqRPmEaxYluvXtW9umnTpgD88ssvjm0dInZswfGFOqKI69evT/369enXrx/9+vWj\nSZMmsS+pztCgQQMaNGjAhAkTmDBhAl27do19SXWGRo0a0ahRI0aPHs3o0aNp1apV7Euqk7Rs2ZKW\nLVuy3XbbpcZzbWhOKRV1YiI2xpgkU1FZWbpVQbGWIIsssggA33zzDQDrr78+n376aTGeqkZiL/GK\nFdtFF10UgNmzZwPw0Ucf0bFjx2I8VY3UtdhKaX333XcAKSW80047MWrUqEI+Va3Eji0UPzWx5ppr\nAvD6669z0kknAXDjjTcC8OeffwKgeVCpOK2qf/nll7ye+x+VmjDGmCRT0s26YqHNo59++gmA1q1b\nl1wR11X++OMPAKZMmQIEFWdyR+M1fS9j9OjRMS6nzjNz5kygajX38ccfA2EVLbTR37JlSwC+/fbb\nEl6hFbExxkSnTihi7YLOmTMHgPbt2/PGG2/EvKQ6wxprrAHA999/D1QphpVWWgmATz75JNp1JRnl\nIxs1ajTfz5o1awYExTZ9+vTSXVgdQ/PCYYcdBkDz5s25++67Afjss88AqKioSuFqTJ911lkAXH75\n5SW9VitiY4yJTJ1wTTRu3BiAr7/+GqjK9yy22GJAUB/FJvbuc6FjKzXx+eefA7DEEksA8MMPP6RU\n27PPPgsE1TZw4EAApk2bVshLqbOxnTt3bvrzcPLJJwOw7777ArDBBhsU8qnnI3ZsofDx7dSpEwB3\n3XUXAGuvvTZQ5YiQK0J7H8oFt27dGoBll10WgJ9//rkg12LXhDHGJIRE54jlx9xkk00AeO655wDo\n3r07Q4cOBeC0004D5lcfZuEsvfTSAPz+++8A3HfffQCceOKJKa/rbrvtBlSVlAM88cQTAIwcObKk\n15o0DjrooGrfL2jVpty81LP8rmZ+FKPnn38eCPOBVK+8wIssskgqLy9Xlcb1KqusAhROCWeLFbEx\nxkQmUYpY+R2ptXHjxgGhMqlLly5AVYMa7ZTuv//+1X72wQcfAKXLHScV+YV33313AA444ACgSn1I\nTay77rpAqFJ67LHHSnyVyULjV2NSY1DKDWDxxRcHgq9VX62I50dKeMSIEUBQwj/++CMQxq7mhzlz\n5qT+ZrPNNgNgww03BGDTTTcFYMcddwSCp7tU84QVsTHGRKasXRPKAesuNXjwYCCoBu3e77fffgCM\nGTMGqKpYWm211QB4+umngeDLfPnll4FwF73//vuBUJWXa4vH2LvPxXKkNG/eHIAvv/wSgFGjRrHk\nkksCwbe96667AsVTD3UlttrNf/TRR4GwslOL0QYNGqSU2HbbbQcENSfV/OuvvxbiUlLEji1kH195\nfwcNGgTA6aefDsCECROAsPrN5LOsVcqxxx4LwIABAwA4//zzAbjmmmuA3Me2XRPGGJMQylIRy7P6\n0ksvAaHqZdasWQBccsklAKkqGak13QErKipSdzCp5j322AMId0vt+EuNSN3JZXHzzTdXe8zaiK0s\niqWI27VrB5Cq0f/2229THm2tWI4//nggqAt1apO6U84uV+pKbDVet99+ewDeffddALbaaiugatUm\nRXbCCScAsOqqqwJhFaiVm7zb+X5+Y8cWso/v+uuvD8AzzzxT7evee+8N5L6qBejfvz8ARx11FBBy\nyLlWOFoRG2NMQigrRawKmCeffBIIalX138OHDwfgt99+y/kapOL02NrBVp5J+U95EuW+UIVZTcRW\nFtmqClUjajdeXuB05LvUyRzjx4/nvffeA2CppZaq9rtSIvqqsSUf5y677AKElU6mJC226WjMKb+r\nmP/nP/8B4OyzzwaqxqQcKD179gSC51gxPOKII4Dg2b7uuuuqPWa2xI4tZB5f7fNIAbdv3x4IHuB8\n5gWhVZ1yw+qxojkoW6yIjTEmIZSFItbO8Ntvvw0E9am8bjF74MpXqJMo+vTpA4Q7oHrGyoWhfHQ6\nsZVFbapCO83Kr2kH/5577gHm75GrFYPyvVLQTZo0SXWuknqQr1iqecaMGUCI6auvvgqEen5VjWXa\nM7rcY1sTUsJabZ177rkAfPXVVwD06NEDCE6e9u3bM378eADatGkDhNz8sGHDANhzzz0BWHHFFYHQ\nAW+HHXYAqnuSMyF2bCHz+O6zzz4AXHXVVUBwUxWj06L2qZ566ikgOIN0ClCmWBEbY0xCiKqI27Zt\nC4QuXnI4dOjQASi8ZzITpBxXWGEFoOqcKwh33Z133hmYf7c6trLIVBEPGTIEgHXWWQcIeVu9Hinm\nBx54AAh5OXle9TgQKuoOP/zwao+Rjh5D6m7SpEkA/Otf/8rkpZV9bGtCXvVu3boBYTWlikT18bjw\nwgsBeOGFF3jwwQeBsNp46KGHgKoeHxA+E0ceeSQQfK/ywx944IFZXWPs2ELt8dUqbfLkyUBYva28\n8srVvi/wNQEhri+++CIAl112WVaPY0VsjDEJIYoi1h3u/fffZ97vlYfN13daCHRH3HrrrYHQ21Q5\nOuX1RGxlkalqu/3224FQl6+YyzWh1y23hHzXjz/+OFCV99SYUX49U0WiHKl67up8MCnDmkhKbIVW\nAMqVq7pTToh0h4P2SLp3707v3r0BUl9rOjtNivnhhx8GYNtttwWYr+qxNmLHFmqPr5TvW2+9BYRV\n3QUXXFDkKwsrRu2pXH311UDtY1ZkGt8oTX+UelBqQg1lymECFppstCTRsvLOO+8EgmUmac1Y0ifa\nBaRYgGAFkpVQzWdGjBiRWh5nuyTUJqwmKpVPZzqok4I2dhQzpW5qGisa97fddhu33nprRs+h2B99\n9NFAEDXawFJqqS4gm5o2P3XzKQX63CuNpmtRvAuFUxPGGBOZKIpYyyctr2TQLkfUUF72IW1QyT6k\nDaikoNcjNSqVUZu6lVLu1atXysqWLSpKEPNu/NUFNJ5VJjt16lQgFAXURi5pQpU8a5Wicum6pIi1\nelN89ZpLgVJDKkt/5JFHACtiY4ypc0RRxNrkEbKtFbNwI1+0UaC78iGHHAKEVnxJQa0Ce/XqBYSN\n0kyPkqqsrMzZVrjmmmumHgNCE6e6glYK2j/QKqqYKO+scam8dF1i4sSJQFi1qbRehwUXk7XWWgsI\n9s1MVzfZYkVsjDGRiaKIZesRKp4o53zrDz/8AAQ1V87XujBee+01IChgWciKeWiicsE77bQTEJRM\nXXNLpKtT5TaLiVaXUsJ1bZUBoaxYKzE1SVKJeDFQXG+44QYguIh01FqhsSI2xpjIRFHEU6ZMAYK6\nVPPrsWPHVvv/ckL+TKkeFTiU47UuDDWJ0R1eBSuZ+ldzQQ1s1OC/X79+QPJiVxsaG8oj6kDKYqLG\nSlLEhd7NLwfUyEiruS222AII+xvFaIVwzDHHALDccssBYU+lEK02F4QVsTHGRCZKibPyL/LoKWes\nJuxqBSiFoa8xFJQcHXJNKL+pklKVBscuFc20DFfK6c033wSCJ1M+yULmbZUb1upBronOnTsDmR8/\nk5TYCjXxUdtGuSiybVGZCYqlWo3qmHi5Y2ojdmwh8/jKuy8XhRoh3XTTTQW7FuX3p02bBgQFrCrg\nbOcgN/0xxpiEELUNpiqQzjzzTCDsMks56KvuTspvqr4+08biuaDKM7W/VJ5TX9N3p2Mri2xV2+ab\nbw6E3hmHHnooUNgqR3kv9T7p0Nczzjgjq8dJWmxbtGgBBOWmcX7vvfcW+MrCIaNqqCTllqkLJnZs\nIfP4Sq1qdaqDBtQUqBC5YlUm6nOw3nrrAeGg12yxIjbGmIRQFkclLbvssgCcf/75QDhyR8e2606o\nXVJ9r11UtabM9hiTeZEaV9NuNfXWXVdHdavWPJ3YyiJb1ab87fXXXw8EJaBOeIptPuPjyiuvBMIB\nrVIu6T7y2khabMU555wDQN++fYGw21/bQbTZcPfddwNh3OoYqpoOg00ndmwh+/jq8IIXXngBCAeq\n3nHHHTlfg/atVN2rNrfdu3fP+THBitgYYxJDWSjiDP4OCDv+OiZGCloKWQf99e7dO9UYWz+TI+Oo\no44Cgj9QSlh3RLkG5NQ4+OCDAXj00UeBmhVibGWRa2x1SKiOq1Kv6FtuuQUIvTSy2fHXSuaLL74A\ngkdZ+cxi7TwXi1xjq94T48aNA8KK7thjjwWCosvlqB/1dNYq8IorrgBCrjhTYscWcl/NSbWqOnSj\njTYCYObMmVlfg1a6qhfQykI1D7liRWyMMQkhEYq4JqS8lBvS3ayioiKVh9ROqvLQQr0jRo4cWe3r\nO++8AwRXRKY7sbGVRb6xlXqTojr++OOBECfFVvFZEKry0o7z4osvDoTcaLa5YZH02EqxDR8+HAin\nPciTvsceewBVfQwy/TxqZTd06FAg9PhO7/lcG7FjC7nHV75ieaY1vqRmM/HEa2zqJBodFqyK03yx\nIjbGmISQaEWczvLLLw/Addddl+proLuklIPOu8q0/26mxFYWhYqtco/qpatDLMXEiRM55ZRTgHCe\nlw4glZpWnllOk1GjRuV1TXUlttqHkG9elWH6/3feeSflAFCXL+XwtfqTj13Viuproeq9bIkdW8h/\n70hnBN5zzz1A8FCrZ7j6b3z99depvR9VeepMSq38OnbsCBSup4QVsTHGJIQ6pYjTnqva98V+nbGV\nRaFjK4eKco/XXnstAN26dUspOO32y6Gi3iE6gjzfHWdR12KrsbnMMssAoarx6KOPTsVbPmDFWuNX\n/y8FpwrJXKtMY8cW8o+v4rnOOusAId+rGoB55wKthJU/Vl/xLbfcEoDZs2fncynzYUVsjDEJoc4q\n4lITW1kUO7byY7du3TrV4atVq1ZAyGemO04KNbbqemyl2Jo2bZryGMvFopOgdYqNqvKUI04/OSZb\nYscWCh9f7XPovDmt0OrXr58as+odcfvttwPZu00yxYrYGGMSghVxgYitLBzb4hFjb6NUn8vYsQWP\nXbAiNsaY6JRUERtjjJkfK2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImM\nJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2Jj\njImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImM\nJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjImMJ2JjjIlMg1I+WUVFRWUpn6+UVFZWVsR8fse2\neDi2xaVevXqV/38txX4eKiqqXu7ff/9NKZ4z0/haERtjTGRKqogLje5uoth3N7Ng9D40bNgQgFVW\nWQWA7777DoAffvghzoXVQRTrdGWXZEr1uW3bti2DBg0C4LjjjgPg999/L8lz14YVsTHGRKailCqy\n0Lk2qYLmzZsD8PPPPxfy4bMidq6tHPKYSyyxBAAXXnghAF999RUA5557LpC78vknxbZevSptJKXb\npEkTADp27AjAEUccAUD79u0BOOCAAwCYNm1aTs8XO7ZQuvj++9//5rzzzgNg+eWXB+DLL78s6nM6\nR2yMMQkh0TliKazOnTsDId8zfvz4aNf0T6RBg6ph1KlTJwBWXnllIChj5+4zZ7HFFgNCzO677z4g\nxPjFF18E4Omnnwbgr7/+KvUlJg6tnH/88cfUyuHHH3+MeUnzYUVsjDGRSXSOWLRp0waAl19+GYC1\n11675Pni2Lm2mDnioUOHAmEnun79+gAsueSSQP7q458S2yZNmvDggw8CYUyvsMIKQMhlNmrUCIAv\nvvgCgMceewyAK6+8EsheIceOLRQ/vsoHP/XUU9xyyy0AXHLJJUDxXSfOERtjTEJIdI5YSHF99NFH\nAPTp04cbbrgBgD/++CPaddV1VlppJQBOOOEEIOz4T58+vdpXs3CaNWsGVKnexRdfHAgOICne1Vdf\nHYA11lgDgA4dOgCw2WabAUEh33///SW66uSwxRZbAFV59kUXXRSY350SGytiY4yJTJ1QxHJLvPHG\nGwCcffbZtGvXDoDTTz8dgD///DPKtSUF5R7nzp0LQKtWrQD49ddfAZg9e3bqd7ULfdhhhwFBXUjF\nbbzxxoDdErXRuHFjIKjepk2bpn522mmnAaRWdltttRUADzzwABDeA6lp5T4feeQR4J+9EpTDZPvt\ntweq/MMA33//Peuuuy4Q4tizZ08gjPtYWBEbY0xk6oQiljo45JBDAGjdujU77bQTAE8++SQAH374\nIQCfffZZhCssf6644goADjroICAoZK02lLucVzlIjYnRo0cDYYe/3PJwpUI9NxTD9ddfH4DLL78c\ngGWXXRYIinjChAlAVX537733BkLlnPj8888B2GGHHao9hnzGyn0OHjwYgFNPPbWgrykJSAk/9dRT\nAGy44YYAfPDBBwAMGjQo5aC4+uqrAfjmm2+AkEd+//33S3fB82BFbIwxkakTinje3BpU5Sq33XZb\nAHr06AGEjmC33XYbAHPmzCnhFZYvUm+77747EBSCcsLanf/vf/8LwDnnnJNaXUiFpVeB6fuNNtoI\nCAr522+/Bepuvl6KTM4FjUHla+VsUFc6VSCuuOKKQJUK1upuxx13BMIex8cff1ztufR76j1xzTXX\nAEEp/5NWI3qtN910EwCbb745EGJyxhlnAPDbb7+l4jZx4kQAHn30UQD+97//AbDnnnsCIW9fKqyI\njTEmMomurNPd7aeffgKCQps9ezbPP/88QCpXrNepu6f+RorjhRdeAODTTz8Fqu6e2RC7QinX2GqF\noNxk165dgdCv4/HHHwdg6623BqoUllSzKue0Ivnkk0+AUFmn6jD1I5bKltrT79dGUmKr16WVgXzt\nu+66KwBff/01EMZi3759ARg2bBhQtTrRmNYqQpV1NX1OtaJR7wmtNpRLrk0Rx44t5D8v9OrVCwir\ntltvvRWAY445Blj4CmyRRRYB4N577wXCfKE8sz4Xua6gXVlnjDEJIdGKWPnfkSNHAsEb+NFHH6Uq\nkOQllNKVWmvdujUQdq6llFWlJweA8kvpaiad2Moi29jqdUt5TZo0CaiufCEoLvlaBw4cmHIDzJo1\nCwj+7ddeew0IO9BSd3qutdZaq9r/L7fcckDtJ3iUe2z1ep599lkguCSWXnppIHix02nbti0A77zz\nDlDlQlG81b+jf//+GV2jcp6qdpTLpbZ8fOzYQu7zgj7Deu3ysa+99tpAdqdv6D3UnHL77bcDMHXq\nVAC22247oMqLnA1WxMYYkxDKQhGnVwkttdRSQLibK/cmX6AcEJtssgkAv/zyCxCqwerVq5dSuMob\nKyecjv7m4IMPrvbYu+22GxAUoXLJ119/PTC/Mo6tLDJVFXo92hXWyQ9SsTpVoybq16+fer8ydT/o\nvdD7JeWoHPGmm24KwMyZMxf49+UeW7kltLpQB7Wjjz56oY+r/OR7770HVCk8PVZ6X+LauPPOOwHY\nZ599gDDua1OFsWMLuStiveadd94ZCM6f5557Lu9rUm8PuSp0Uor83ZmqbStiY4xJCCVVxPXq1auE\ncJdP97BeddVVQFC4b775JhDUgRSVlLPymFLKb7/9dt7XKLWnPOioUaMAWG+99QBYZ511gJBzFrGV\nRaaq4qWXXgJCHk0KSu6IUrDMMssAoX+0csx77bXXAn+/3GOrXO+UKVOAkGdXzrw2unXrBsCIESNS\nY1o53kyRu0LjUtekvY2aiB1byH1/Q1Wyes2Ke005+VyQL/utt94Cwqkzl112WUZ/b0VsjDEJoaSV\ndcoV6tTlcePGASFHdumllwLB/aBKJPU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70b14022b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500\n",
      "1600\n",
      "1700\n",
      "1800\n",
      "1900\n",
      "Epoch #2000\n",
      "1999: [D loss: 0.577811, acc: 0.710590]  [A loss: 1.667581, acc: 0.148051]\n"
     ]
    },
    {
     "data": {
      "image/png": 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oRSzkG5IPSE8tHWui7viKBNekzltPRh0jr2Ob+vXrB1Q+ZrsqYiuLQhWxkNp4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\nyot1rE8p0JNM5ao6xicNiuLv0NE7Qlk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70c43f7c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "2100\n",
      "2200\n",
      "2300\n",
      "2400\n",
      "Epoch #2500\n",
      "2499: [D loss: 0.592545, acc: 0.699589]  [A loss: 1.649355, acc: 0.149444]\n"
     ]
    },
    {
     "data": {
      "image/png": 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EursoP/vZZ58V/BrkG9bdN5/VTzHR36ljeqSEb7vtNiA0iZGSkt9Yx9Vcf/31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\nLo4YMQKARx55BIAnn3wSWLSSVhV46mu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70b0c70978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2500\n",
      "2600\n",
      "2700\n",
      "2800\n",
      "2900\n",
      "Epoch #3000\n",
      "2999: [D loss: 0.606397, acc: 0.687436]  [A loss: 1.603370, acc: 0.153140]\n"
     ]
    },
    {
     "data": {
      "image/png": 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uqKOOAkIEspiox+nJJ58MhGquUaNGAUFpqCeDOmmpa1m3bt2A3FHR2CNnco2b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70c42b1208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_metrics_complete, d_metrics_complete = train(epochs=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f70b1261748>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XtazPe0t/47Y3FlM7J5NbT+pIl2Z1mTpvLbVyMnl3yXrGX9ibM/8VeuF9/6aj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70b1086a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame(\n",
    "    {\n",
    "        'Generator': [metric[0] for metric in a_metrics_complete],\n",
    "        'Discriminator': [metric[0] for metric in d_metrics_complete],\n",
    "    }\n",
    ").plot(title='Training Loss', logy=True)\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "ax.set_ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f70c4096fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/DlzhvnNvMU3r1gg4t2uf0Su4b/hcCotKeK9v56gt/2KzNp+zZmelAW/lt1fw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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70c42e2c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame(\n",
    "    {\n",
    "        'Generator': [metric[1] for metric in a_metrics_complete],\n",
    "        'Discriminator': [metric[1] for metric in d_metrics_complete],\n",
    "    }\n",
    ").plot(title='Training Accuracy')\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "ax.set_ylabel(\"Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}