GAN with MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-alpha0\n",
      "(5923, 28, 28)\n",
      "(60000,)\n",
      "(980, 28, 28)\n",
      "(10000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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F+IGgCD8QFOEHgiL8QFCEHwiK8ANB/R/7QknxGq+fLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "\n",
    "# double check your tf version\n",
    "print(tf.__version__)\n",
    "\n",
    "# get sampling data set\n",
    "fashion_mnist = keras.datasets.mnist\n",
    "(trainx, trainy), (testx, testy) = fashion_mnist.load_data()\n",
    "\n",
    "trainx = trainx[trainy == 0]\n",
    "testx = testx[testy == 0]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "imgplot = plt.imshow(trainx[0], cmap='gray')\n",
    "\n",
    "print(trainx.shape)\n",
    "print(trainy.shape)\n",
    "print(testx.shape)\n",
    "print(testy.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 2 part in GAN : Generator and Discriminator.\n",
    "here is the algorithm:\n",
    "1. fix Generator's parameter and generate randomly sample vector output.\n",
    "2. get real sample from DB\n",
    "3. Train Discriminator with real sample + bad sample\n",
    "4. fix Discriminator's parameter and train Generator.\n",
    "\n",
    "https://ithelp.ithome.com.tw/articles/10208478?sc=iThelpR\n",
    "\n",
    "https://medium.com/@gau820827/教電腦畫畫-初心者的生成式對抗網路-gan-入門筆記-tensorflow-python3-dfad71662952"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class myGAN():\n",
    "    def __init__(self, width=28, height=28, channels=1):\n",
    "        self.width = width\n",
    "        self.height = height\n",
    "        self.channels = channels\n",
    "        self.shape = (self.width, self.height, self.channels)\n",
    "        self.optimizer = tf.keras.optimizers.Adam(lr=0.0002, beta_1=0.5, decay=8e-8)\n",
    "\n",
    "        self.G = self.__generator()\n",
    "        self.G.compile(loss='binary_crossentropy', optimizer=self.optimizer)\n",
    "\n",
    "        self.D = self.__discriminator()\n",
    "        self.D.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy'])\n",
    "\n",
    "        self.GAN = self.__gan()\n",
    "        self.GAN.compile(loss='binary_crossentropy', optimizer=self.optimizer)        \n",
    "      \n",
    "    # use vector as input.\n",
    "    def __generator(self):\n",
    "        model = tf.keras.Sequential(\n",
    "            [\n",
    "                # layer 1\n",
    "                tf.keras.layers.Dense(256, input_shape=(100,)),\n",
    "                tf.keras.layers.LeakyReLU(alpha=0.2),   # 使用 LeakyReLU 激活函數\n",
    "                tf.keras.layers.BatchNormalization(momentum=0.8),  # 使用 BatchNormalization 優化\n",
    "                \n",
    "                # layer 2\n",
    "                tf.keras.layers.Dense(512),\n",
    "                tf.keras.layers.LeakyReLU(alpha=0.2),\n",
    "                tf.keras.layers.BatchNormalization(momentum=0.8),\n",
    "                                \n",
    "                # layer 3\n",
    "                tf.keras.layers.Dense(1024),\n",
    "                tf.keras.layers.LeakyReLU(alpha=0.2),\n",
    "                tf.keras.layers.BatchNormalization(momentum=0.8),\n",
    "                \n",
    "                # convenlution layer\n",
    "                tf.keras.layers.Dense(self.width  * self.height * self.channels, activation='tanh'),\n",
    "                #model.add(tf.keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))\n",
    "                tf.keras.layers.Reshape((self.width, self.height, self.channels))                \n",
    "            ]\n",
    "        )\n",
    "        model.summary()\n",
    "        return model\n",
    "    \n",
    "    def __discriminator(self):\n",
    "        model = tf.keras.Sequential( \n",
    "            [\n",
    "                tf.keras.layers.Flatten(input_shape=self.shape),\n",
    "\n",
    "                tf.keras.layers.Dense((self.width * self.height * self.channels), input_shape=self.shape),\n",
    "                tf.keras.layers.LeakyReLU(alpha=0.2),\n",
    "\n",
    "                tf.keras.layers.Dense(int((self.width * self.height * self.channels)/2)),\n",
    "                tf.keras.layers.LeakyReLU(alpha=0.2),\n",
    "\n",
    "                tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "            ]\n",
    "        )\n",
    "        model.summary()\n",
    "        return model\n",
    "    \n",
    "    def __gan(self):\n",
    "        self.D.trainable = False\n",
    "        self.G.trainable = True\n",
    "\n",
    "        model = tf.keras.Sequential(\n",
    "            [\n",
    "                self.G,\n",
    "                self.D\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        return model\n",
    "    \n",
    "    # 1. fix Generator's parameter and generate randomly sample vector output.\n",
    "    # 2. get real sample from DB\n",
    "    # 3. Train Discriminator with real sample + bad sample\n",
    "    # 4. fix Discriminator's parameter and train Generator.\n",
    "\n",
    "    def train(self, X_train, epochs=10000, batch = 32, save_interval = 100):\n",
    "\n",
    "        for cnt in range(epochs):\n",
    "\n",
    "            ## train discriminator\n",
    "            random_index = np.random.randint(0, len(X_train))  # len(X_train) - batch/2\n",
    "            legit_images = X_train[random_index : random_index + int(batch/2)].reshape(int(batch/2), self.width, self.height, self.channels)\n",
    "\n",
    "            gen_noise = np.random.normal(0, 1, (int(batch/2), 100)) \n",
    "            syntetic_images = self.G.predict(gen_noise)\n",
    "\n",
    "            x_combined_batch = np.concatenate((legit_images, syntetic_images))\n",
    "            y_combined_batch = np.concatenate((np.ones((int(batch/2), 1)), np.zeros((int(batch/2), 1))))\n",
    "\n",
    "            d_loss = self.D.train_on_batch(x_combined_batch, y_combined_batch)\n",
    "\n",
    "\n",
    "            # train generator\n",
    "            noise = np.random.normal(0, 1, (batch, 100))  # 添加高斯噪聲\n",
    "            y_mislabled = np.ones((batch, 1))\n",
    "\n",
    "            g_loss = self.GAN.train_on_batch(noise, y_mislabled)\n",
    "\n",
    "            print('epoch: %d, [Discriminator :: d_loss: %f], [ Generator :: loss: %f]' % (cnt, d_loss[0], g_loss))\n",
    "\n",
    "            if cnt % save_interval == 0:\n",
    "                self.plot_images(save2file=True, step=cnt)\n",
    "                \n",
    "    def plot_images(self, save2file=False, samples=16, step=0):\n",
    "        ''' Plot and generated images '''\n",
    "        filename = \"./Result/gan_%d.png\" % step\n",
    "        noise = np.random.normal(0, 1, (samples, 100))\n",
    "\n",
    "        images = self.G.predict(noise)\n",
    "\n",
    "        plt.figure(figsize=(10, 10))\n",
    "\n",
    "        for i in range(images.shape[0]):\n",
    "            plt.subplot(4, 4, i+1)\n",
    "            image = images[i, :, :, :]\n",
    "            image = np.reshape(image, [self.height, self.width])\n",
    "            plt.imshow(image, cmap='gray')\n",
    "            plt.axis('off')\n",
    "        plt.tight_layout()\n",
    "\n",
    "        if save2file:\n",
    "            plt.savefig(filename)\n",
    "            plt.close('all')\n",
    "        else:\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_20\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_53 (Dense)             (None, 256)               25856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_38 (LeakyReLU)   (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_24 (B (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dense_54 (Dense)             (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_39 (LeakyReLU)   (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_25 (B (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dense_55 (Dense)             (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_40 (LeakyReLU)   (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_26 (B (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dense_56 (Dense)             (None, 784)               803600    \n",
      "_________________________________________________________________\n",
      "reshape_8 (Reshape)          (None, 28, 28, 1)         0         \n",
      "=================================================================\n",
      "Total params: 1,493,520\n",
      "Trainable params: 1,489,936\n",
      "Non-trainable params: 3,584\n",
      "_________________________________________________________________\n",
      "Model: \"sequential_21\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_7 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_57 (Dense)             (None, 784)               615440    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_41 (LeakyReLU)   (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_58 (Dense)             (None, 392)               307720    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_42 (LeakyReLU)   (None, 392)               0         \n",
      "_________________________________________________________________\n",
      "dense_59 (Dense)             (None, 1)                 393       \n",
      "=================================================================\n",
      "Total params: 923,553\n",
      "Trainable params: 923,553\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0808 23:27:38.852127  5400 training.py:2131] Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, [Discriminator :: d_loss: 51.442188], [ Generator :: loss: 0.754501]\n",
      "epoch: 1, [Discriminator :: d_loss: 4.056028], [ Generator :: loss: 0.689249]\n",
      "epoch: 2, [Discriminator :: d_loss: 0.343324], [ Generator :: loss: 0.627051]\n",
      "epoch: 3, [Discriminator :: d_loss: 0.359982], [ Generator :: loss: 0.541141]\n",
      "epoch: 4, [Discriminator :: d_loss: 0.397553], [ Generator :: loss: 0.516803]\n",
      "epoch: 5, [Discriminator :: d_loss: 0.388021], [ Generator :: loss: 0.538756]\n",
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      "epoch: 9870, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.413612]\n",
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      "epoch: 9872, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.448679]\n",
      "epoch: 9873, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.424143]\n",
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      "epoch: 9875, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.490400]\n",
      "epoch: 9876, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.336705]\n",
      "epoch: 9877, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.411846]\n",
      "epoch: 9878, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.267054]\n",
      "epoch: 9879, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.273716]\n",
      "epoch: 9880, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.510244]\n",
      "epoch: 9881, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.336371]\n",
      "epoch: 9882, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.434181]\n",
      "epoch: 9883, [Discriminator :: d_loss: 0.000005], [ Generator :: loss: 11.460108]\n",
      "epoch: 9884, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.427607]\n",
      "epoch: 9885, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.369836]\n",
      "epoch: 9886, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.303222]\n",
      "epoch: 9887, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.410155]\n",
      "epoch: 9888, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.429386]\n",
      "epoch: 9889, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.359766]\n",
      "epoch: 9890, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.373716]\n",
      "epoch: 9891, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.306170]\n",
      "epoch: 9892, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.347335]\n",
      "epoch: 9893, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.492487]\n",
      "epoch: 9894, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.416258]\n",
      "epoch: 9895, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.440262]\n",
      "epoch: 9896, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.367489]\n",
      "epoch: 9897, [Discriminator :: d_loss: 0.000005], [ Generator :: loss: 11.429186]\n",
      "epoch: 9898, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.347870]\n",
      "epoch: 9899, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.625655]\n",
      "epoch: 9900, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.394876]\n",
      "epoch: 9901, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.324146]\n",
      "epoch: 9902, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.324930]\n",
      "epoch: 9903, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.299656]\n",
      "epoch: 9904, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.369225]\n",
      "epoch: 9905, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.546214]\n",
      "epoch: 9906, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.424898]\n",
      "epoch: 9907, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.344727]\n",
      "epoch: 9908, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.397472]\n",
      "epoch: 9909, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.371355]\n",
      "epoch: 9910, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.559311]\n",
      "epoch: 9911, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.438424]\n",
      "epoch: 9912, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.499485]\n",
      "epoch: 9913, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.321774]\n",
      "epoch: 9914, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.369196]\n",
      "epoch: 9915, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.449623]\n",
      "epoch: 9916, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.492102]\n",
      "epoch: 9917, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.368687]\n",
      "epoch: 9918, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.583616]\n",
      "epoch: 9919, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.370588]\n",
      "epoch: 9920, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.430067]\n",
      "epoch: 9921, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.365523]\n",
      "epoch: 9922, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.577729]\n",
      "epoch: 9923, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.545671]\n",
      "epoch: 9924, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.398233]\n",
      "epoch: 9925, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.390345]\n",
      "epoch: 9926, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.396609]\n",
      "epoch: 9927, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.426979]\n",
      "epoch: 9928, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.603896]\n",
      "epoch: 9929, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.463292]\n",
      "epoch: 9930, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.546957]\n",
      "epoch: 9931, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.468115]\n",
      "epoch: 9932, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.484623]\n",
      "epoch: 9933, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.456038]\n",
      "epoch: 9934, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.467009]\n",
      "epoch: 9935, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.530304]\n",
      "epoch: 9936, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.464310]\n",
      "epoch: 9937, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.673812]\n",
      "epoch: 9938, [Discriminator :: d_loss: 0.000005], [ Generator :: loss: 11.641035]\n",
      "epoch: 9939, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.546901]\n",
      "epoch: 9940, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.470343]\n",
      "epoch: 9941, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.472912]\n",
      "epoch: 9942, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.499407]\n",
      "epoch: 9943, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.483692]\n",
      "epoch: 9944, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.452896]\n",
      "epoch: 9945, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.597212]\n",
      "epoch: 9946, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.511034]\n",
      "epoch: 9947, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.495622]\n",
      "epoch: 9948, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.539024]\n",
      "epoch: 9949, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.448277]\n",
      "epoch: 9950, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.443632]\n",
      "epoch: 9951, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.429343]\n",
      "epoch: 9952, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.406010]\n",
      "epoch: 9953, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.636904]\n",
      "epoch: 9954, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.389891]\n",
      "epoch: 9955, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.382166]\n",
      "epoch: 9956, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.327682]\n",
      "epoch: 9957, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.380169]\n",
      "epoch: 9958, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.398603]\n",
      "epoch: 9959, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.468038]\n",
      "epoch: 9960, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.672044]\n",
      "epoch: 9961, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.462964]\n",
      "epoch: 9962, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.368133]\n",
      "epoch: 9963, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.443968]\n",
      "epoch: 9964, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.374956]\n",
      "epoch: 9965, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.684755]\n",
      "epoch: 9966, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.460428]\n",
      "epoch: 9967, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.358871]\n",
      "epoch: 9968, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.345221]\n",
      "epoch: 9969, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.381987]\n",
      "epoch: 9970, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.540470]\n",
      "epoch: 9971, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.442493]\n",
      "epoch: 9972, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.451056]\n",
      "epoch: 9973, [Discriminator :: d_loss: 0.000005], [ Generator :: loss: 11.426934]\n",
      "epoch: 9974, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.512170]\n",
      "epoch: 9975, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.523314]\n",
      "epoch: 9976, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.521479]\n",
      "epoch: 9977, [Discriminator :: d_loss: 0.000005], [ Generator :: loss: 11.458693]\n",
      "epoch: 9978, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.441378]\n",
      "epoch: 9979, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.520351]\n",
      "epoch: 9980, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.514841]\n",
      "epoch: 9981, [Discriminator :: d_loss: 0.000007], [ Generator :: loss: 11.776639]\n",
      "epoch: 9982, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.463432]\n",
      "epoch: 9983, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.487437]\n",
      "epoch: 9984, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.415123]\n",
      "epoch: 9985, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.451246]\n",
      "epoch: 9986, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.665254]\n",
      "epoch: 9987, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.804495]\n",
      "epoch: 9988, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.607696]\n",
      "epoch: 9989, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.499155]\n",
      "epoch: 9990, [Discriminator :: d_loss: 0.000005], [ Generator :: loss: 11.469805]\n",
      "epoch: 9991, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.477894]\n",
      "epoch: 9992, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.456532]\n",
      "epoch: 9993, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.512909]\n",
      "epoch: 9994, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.384566]\n",
      "epoch: 9995, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.492665]\n",
      "epoch: 9996, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.606159]\n",
      "epoch: 9997, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.504323]\n",
      "epoch: 9998, [Discriminator :: d_loss: 0.000006], [ Generator :: loss: 11.457991]\n",
      "epoch: 9999, [Discriminator :: d_loss: 0.000005], [ Generator :: loss: 11.575754]\n"
     ]
    }
   ],
   "source": [
    "go = myGAN()\n",
    "go.train(trainx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian\n",
    "jovian.commit()"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}