Generative Adversarial Networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['KERAS_BACKEND'] = 'theano'\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Dense , Input , BatchNormalization , LeakyReLU , Activation\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Sequential\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0] , cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def latent_layer(n_samples , sample_size):\n",
    "    return np.random.normal(size=(n_samples , sample_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.55987358e-01, -6.63535439e-01, -7.16355526e-01,\n",
       "         1.00168321e+00,  1.17390259e+00, -2.10655769e-01,\n",
       "         9.68888644e-01,  2.11780088e-01,  1.65137137e+00,\n",
       "         1.24327429e-01, -9.97341628e-01,  4.54423730e-01,\n",
       "        -6.50573300e-01,  8.93176190e-01,  5.32724060e-01,\n",
       "         2.24112569e-01, -1.49187634e+00,  1.81342731e+00,\n",
       "        -1.15413174e+00,  1.22559427e+00,  6.25122520e-01,\n",
       "        -1.77778784e-02,  1.60825556e+00,  9.37712195e-01,\n",
       "         1.01555961e+00, -1.35959019e+00,  1.39855363e+00,\n",
       "         1.92708921e-01, -5.94352760e-02,  1.39164812e+00,\n",
       "         7.72892412e-01, -2.31540083e-01,  1.85706410e-02,\n",
       "        -4.16668380e-01, -1.15344942e+00,  2.02034359e+00,\n",
       "         1.31872651e+00,  6.07409222e-01,  9.22944656e-01,\n",
       "        -3.72912564e-04,  2.80570500e-02, -9.33991368e-01,\n",
       "        -1.48368442e+00,  1.21177385e+00, -6.04974156e-01,\n",
       "         6.10294529e-01,  1.02539951e+00, -6.11903872e-01,\n",
       "        -9.47662906e-01,  1.48115450e+00, -2.24961382e+00,\n",
       "         4.94511155e-01, -5.79910106e-01,  3.36985906e-01,\n",
       "         4.40334269e-01,  8.44988738e-01,  3.10555197e-01,\n",
       "        -2.21341774e+00,  1.09762139e+00, -6.04598353e-01,\n",
       "         3.82481283e-01,  4.14151294e-01,  1.80307818e+00,\n",
       "        -3.71001850e-01, -3.43454292e-01,  3.76369816e-01,\n",
       "         1.50065372e-01, -4.81373569e-01,  7.64578846e-01,\n",
       "         4.49960424e-01,  1.50982375e-01,  1.31339241e-01,\n",
       "         7.23334488e-01,  1.78518309e+00,  1.24352770e+00,\n",
       "        -1.65415753e+00,  6.66128115e-01,  1.57595840e+00,\n",
       "         1.16295678e+00,  1.75831294e+00, -4.26243943e-01,\n",
       "         4.76635537e-01,  8.40204073e-01, -7.88166878e-01,\n",
       "        -1.41068112e+00, -4.94338675e-01,  1.48201880e+00,\n",
       "         2.90268762e-01,  1.07859034e+00,  2.04271480e+00,\n",
       "        -1.38674393e-01, -2.37092449e-01,  5.63714443e-02,\n",
       "         1.00610860e+00,  1.03531856e+00,  1.92624055e+00,\n",
       "         8.76499757e-01,  5.28582215e-01,  1.01032140e+00,\n",
       "        -3.17577197e-01]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_layer(1,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 128)               12928     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 784)               101136    \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 784)               0         \n",
      "=================================================================\n",
      "Total params: 114,064\n",
      "Trainable params: 114,064\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator = Sequential([\n",
    "    Dense(128 , input_shape = (100,)),\n",
    "    LeakyReLU(alpha = 0.01),\n",
    "    Dense(784),\n",
    "    Activation('tanh')\n",
    "] , name = 'generator')\n",
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 129       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 100,609\n",
      "Trainable params: 100,609\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator = Sequential([\n",
    "    Dense(128 , input_shape=(784,)),\n",
    "    LeakyReLU(alpha = 0.01),\n",
    "    Dense(1),\n",
    "    Activation('sigmoid')\n",
    "] , name = 'discriminator')\n",
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "generator (Sequential)       (None, 784)               114064    \n",
      "_________________________________________________________________\n",
      "discriminator (Sequential)   (None, 1)                 100609    \n",
      "=================================================================\n",
      "Total params: 214,673\n",
      "Trainable params: 214,673\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan = Sequential([generator ,\n",
    "                 discriminator])\n",
    "gan.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_trainable(model, trainable):\n",
    "    for layer in model.layers:\n",
    "        layer.trainable = trainable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 129       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 100,609\n",
      "Trainable params: 0\n",
      "Non-trainable params: 100,609\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "make_trainable(discriminator , False)\n",
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makegan(n_input , g_lr , d_lr , alpha , g_hidden , d_hidden):\n",
    "    generator = Sequential([\n",
    "        Dense(units = g_hidden , input_shape = (n_input , )),\n",
    "        LeakyReLU(alpha = alpha),\n",
    "        Dense(784),\n",
    "        Activation('tanh'),\n",
    "    ] , name = 'generator')\n",
    "    \n",
    "    discriminator = Sequential([\n",
    "        Dense(units = d_hidden , input_shape=(784 , )),\n",
    "        LeakyReLU(alpha = alpha),\n",
    "        Dense(1),\n",
    "        Activation('sigmoid')\n",
    "    ],name = 'discriminator')\n",
    "    \n",
    "    gan = Sequential([\n",
    "        generator , \n",
    "        discriminator\n",
    "    ])\n",
    "    \n",
    "    discriminator.compile(optimizer = Adam(lr = d_lr) , loss = 'binary_crossentropy')\n",
    "    gan.compile(optimizer = Adam(lr = g_lr) , loss = 'binary_crossentropy')\n",
    "    \n",
    "    return gan , generator, discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(x):    \n",
    "    x = x.reshape(-1, 784) # 784=28*28\n",
    "    x = np.float64(x)\n",
    "    x = (x / 255 - 0.5) * 2\n",
    "    x = np.clip(x, -1, 1)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reprocess(x):\n",
    "    x = (x / 2 + 1) * 255\n",
    "    x = np.clip(x, 0, 255)\n",
    "    x = np.uint8(x)\n",
    "    x = x.reshape(28, 28)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_real = preprocess(X_train)\n",
    "X_test_real  = preprocess(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_labels(size):\n",
    "    return np.ones([size, 1]), np.zeros([size, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nilesh/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  0 generator loss :  2.9762557 discriminator loss :  0.19396114\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nilesh/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  1 generator loss :  2.9309058 discriminator loss :  0.16464868\n",
      "epoch  2 generator loss :  2.3354928 discriminator loss :  0.16485173\n",
      "epoch  3 generator loss :  1.9852487 discriminator loss :  0.29786623\n",
      "epoch  4 generator loss :  6.462553 discriminator loss :  0.826571\n",
      "epoch  5 generator loss :  0.9315147 discriminator loss :  0.6421274\n",
      "epoch  6 generator loss :  3.498249 discriminator loss :  0.2323856\n",
      "epoch  7 generator loss :  2.143071 discriminator loss :  0.3605205\n",
      "epoch  8 generator loss :  1.9532846 discriminator loss :  0.27613446\n",
      "epoch  9 generator loss :  2.1929197 discriminator loss :  0.5496093\n",
      "epoch  10 generator loss :  4.2878985 discriminator loss :  0.29181978\n",
      "epoch  11 generator loss :  1.9981167 discriminator loss :  0.5808022\n",
      "epoch  12 generator loss :  3.5672019 discriminator loss :  0.09813492\n",
      "epoch  13 generator loss :  2.0411863 discriminator loss :  0.34673196\n",
      "epoch  14 generator loss :  2.6201384 discriminator loss :  0.17863299\n",
      "epoch  15 generator loss :  3.1364498 discriminator loss :  0.36921024\n",
      "epoch  16 generator loss :  3.4353383 discriminator loss :  0.23441564\n",
      "epoch  17 generator loss :  4.4730997 discriminator loss :  1.4736066\n",
      "epoch  18 generator loss :  3.8266902 discriminator loss :  0.37743905\n",
      "epoch  19 generator loss :  3.3153408 discriminator loss :  0.31037074\n",
      "epoch  20 generator loss :  2.4929416 discriminator loss :  0.40220797\n",
      "epoch  21 generator loss :  2.4098222 discriminator loss :  0.2894536\n",
      "epoch  22 generator loss :  2.0045033 discriminator loss :  0.36055633\n",
      "epoch  23 generator loss :  2.7881944 discriminator loss :  0.41040066\n",
      "epoch  24 generator loss :  3.8499231 discriminator loss :  0.51485807\n",
      "epoch  25 generator loss :  2.951036 discriminator loss :  0.4037901\n",
      "epoch  26 generator loss :  3.4428878 discriminator loss :  0.40950078\n",
      "epoch  27 generator loss :  2.5147924 discriminator loss :  0.5319851\n",
      "epoch  28 generator loss :  2.3971868 discriminator loss :  0.33722782\n",
      "epoch  29 generator loss :  2.541327 discriminator loss :  0.6152397\n",
      "epoch  30 generator loss :  2.681191 discriminator loss :  0.5743614\n",
      "epoch  31 generator loss :  3.3914616 discriminator loss :  0.8439479\n",
      "epoch  32 generator loss :  2.87123 discriminator loss :  0.5082104\n",
      "epoch  33 generator loss :  3.187413 discriminator loss :  0.69690055\n",
      "epoch  34 generator loss :  2.395429 discriminator loss :  0.5364114\n",
      "epoch  35 generator loss :  2.7258997 discriminator loss :  0.4315912\n",
      "epoch  36 generator loss :  2.6371765 discriminator loss :  0.5692136\n",
      "epoch  37 generator loss :  2.5885851 discriminator loss :  0.62483114\n",
      "epoch  38 generator loss :  3.3849137 discriminator loss :  0.4894985\n",
      "epoch  39 generator loss :  2.6196256 discriminator loss :  0.3286479\n",
      "epoch  40 generator loss :  3.3568609 discriminator loss :  0.84793544\n",
      "epoch  41 generator loss :  2.829299 discriminator loss :  0.64922017\n",
      "epoch  42 generator loss :  2.6468925 discriminator loss :  0.51328224\n",
      "epoch  43 generator loss :  2.1062448 discriminator loss :  0.7316395\n",
      "epoch  44 generator loss :  2.393556 discriminator loss :  0.48304367\n",
      "epoch  45 generator loss :  2.0988913 discriminator loss :  0.5466627\n",
      "epoch  46 generator loss :  3.3678474 discriminator loss :  0.64429986\n",
      "epoch  47 generator loss :  2.7001913 discriminator loss :  0.7267675\n",
      "epoch  48 generator loss :  3.8341532 discriminator loss :  0.825034\n",
      "epoch  49 generator loss :  3.5586922 discriminator loss :  0.73434544\n",
      "epoch  50 generator loss :  3.8527832 discriminator loss :  0.63878703\n",
      "epoch  51 generator loss :  3.3838468 discriminator loss :  1.1843264\n",
      "epoch  52 generator loss :  3.305821 discriminator loss :  0.6244727\n",
      "epoch  53 generator loss :  2.9342687 discriminator loss :  0.6108793\n",
      "epoch  54 generator loss :  2.6160252 discriminator loss :  0.8038441\n",
      "epoch  55 generator loss :  3.3806863 discriminator loss :  0.67814404\n",
      "epoch  56 generator loss :  3.2639282 discriminator loss :  0.5145045\n",
      "epoch  57 generator loss :  3.4728575 discriminator loss :  0.50751525\n",
      "epoch  58 generator loss :  2.6673207 discriminator loss :  0.9666151\n",
      "epoch  59 generator loss :  2.162832 discriminator loss :  0.8260143\n",
      "epoch  60 generator loss :  2.9201727 discriminator loss :  0.67524457\n",
      "epoch  61 generator loss :  3.4921465 discriminator loss :  0.6752492\n",
      "epoch  62 generator loss :  2.5719588 discriminator loss :  1.0633498\n",
      "epoch  63 generator loss :  3.475363 discriminator loss :  0.9308618\n",
      "epoch  64 generator loss :  3.1308045 discriminator loss :  0.66749\n",
      "epoch  65 generator loss :  2.421985 discriminator loss :  0.66522145\n",
      "epoch  66 generator loss :  3.6961777 discriminator loss :  0.6070822\n",
      "epoch  67 generator loss :  2.9131153 discriminator loss :  0.8817933\n",
      "epoch  68 generator loss :  3.3090825 discriminator loss :  0.98827016\n",
      "epoch  69 generator loss :  2.4757924 discriminator loss :  0.9515116\n",
      "epoch  70 generator loss :  3.0521736 discriminator loss :  0.4153681\n",
      "epoch  71 generator loss :  3.1290777 discriminator loss :  0.49312496\n",
      "epoch  72 generator loss :  2.7142375 discriminator loss :  0.8358338\n",
      "epoch  73 generator loss :  2.7708142 discriminator loss :  0.8058884\n",
      "epoch  74 generator loss :  2.8159404 discriminator loss :  0.6203205\n",
      "epoch  75 generator loss :  2.5687065 discriminator loss :  0.67811006\n",
      "epoch  76 generator loss :  3.2194033 discriminator loss :  0.75632757\n",
      "epoch  77 generator loss :  2.687249 discriminator loss :  0.5191733\n",
      "epoch  78 generator loss :  2.380832 discriminator loss :  1.0591877\n",
      "epoch  79 generator loss :  2.2912343 discriminator loss :  0.5813156\n",
      "epoch  80 generator loss :  2.621216 discriminator loss :  0.7470644\n",
      "epoch  81 generator loss :  2.903464 discriminator loss :  0.69850266\n",
      "epoch  82 generator loss :  2.844067 discriminator loss :  0.5509738\n",
      "epoch  83 generator loss :  2.5146387 discriminator loss :  0.7254691\n",
      "epoch  84 generator loss :  2.943741 discriminator loss :  0.9865317\n",
      "epoch  85 generator loss :  2.4617653 discriminator loss :  0.6367273\n",
      "epoch  86 generator loss :  2.6654587 discriminator loss :  0.8087566\n",
      "epoch  87 generator loss :  2.561267 discriminator loss :  0.85518676\n",
      "epoch  88 generator loss :  2.9373164 discriminator loss :  0.6843586\n",
      "epoch  89 generator loss :  3.2150402 discriminator loss :  0.68540907\n",
      "epoch  90 generator loss :  2.9549155 discriminator loss :  1.2551764\n",
      "epoch  91 generator loss :  3.1771781 discriminator loss :  0.5463974\n",
      "epoch  92 generator loss :  3.1887841 discriminator loss :  0.769708\n",
      "epoch  93 generator loss :  3.5442338 discriminator loss :  0.803779\n",
      "epoch  94 generator loss :  2.4292502 discriminator loss :  0.71541524\n",
      "epoch  95 generator loss :  2.2340565 discriminator loss :  1.0503321\n",
      "epoch  96 generator loss :  2.2534144 discriminator loss :  1.2504528\n",
      "epoch  97 generator loss :  2.7501788 discriminator loss :  0.71221983\n",
      "epoch  98 generator loss :  2.443533 discriminator loss :  0.7001031\n",
      "epoch  99 generator loss :  2.4910188 discriminator loss :  1.0781727\n"
     ]
    }
   ],
   "source": [
    "n_input     = 100     \n",
    "g_hidden_size   = 128\n",
    "d_hidden_size   = 128\n",
    "leaky_alpha     = 0.01\n",
    "g_learning_rate = 0.0001  \n",
    "d_learning_rate = 0.001   \n",
    "epochs          = 100\n",
    "batch_size      = 64     \n",
    "eval_size       = 16     \n",
    "smooth          = 0.1\n",
    "\n",
    "y_train_real, y_train_fake = make_labels(batch_size)\n",
    "y_eval_real,  y_eval_fake  = make_labels(eval_size)\n",
    "\n",
    "gan , generator , discriminator = makegan(n_input , g_learning_rate , d_learning_rate,\n",
    "                                         leaky_alpha , g_hidden_size , d_hidden_size)\n",
    "losses = []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for i in range(len(X_train_real)//batch_size):\n",
    "        x_train = X_train_real[i*batch_size : (i+1)*batch_size]\n",
    "        latent_samples = latent_layer(batch_size , n_input)\n",
    "        X_batch_fake = generator.predict_on_batch(latent_samples)\n",
    "        \n",
    "        make_trainable(discriminator , True)\n",
    "        discriminator.train_on_batch(x_train, y_train_real * (1 - smooth))\n",
    "        discriminator.train_on_batch(X_batch_fake, y_train_fake)\n",
    "        \n",
    "        make_trainable(discriminator , False)\n",
    "        gan.train_on_batch(latent_samples, y_train_real)\n",
    "        \n",
    "    X_eval_real = X_test_real[np.random.choice(len(X_test_real), eval_size, replace=False)]\n",
    "    \n",
    "    latent_samples = latent_layer(eval_size, n_input)\n",
    "    X_eval_fake = generator.predict_on_batch(latent_samples)\n",
    "\n",
    "    d_loss  = discriminator.test_on_batch(X_eval_real, y_eval_real) + discriminator.test_on_batch(X_eval_fake, y_eval_fake)\n",
    "    g_loss  = gan.test_on_batch(latent_samples, y_eval_real)\n",
    "    losses.append((d_loss , g_loss))\n",
    "    \n",
    "    print('epoch ',epoch , end=' ')\n",
    "    print('generator loss : ' , g_loss , end = ' ')\n",
    "    print('discriminator loss : ' , d_loss)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZwBpV7QCwGcBUAHPR+5PBL/s7TlUbVbVOVe1F8ogoVyWFX0QGozf4v1PVHQCgqmdU9Zqq9gD4DYD52Q2TiCotGH7p3f72twCOqOqmPo+P7/O0HwH4uvLDI6KslNLqWwBgL4BD6G31AcA6AE+j90d+BdAMYFXyj4PWufKbPzxAMa3CDz74wDy2vr7erMe0GW9lRbZnq/k1jx1bxVp9qvp3AP2dzOzpE1F14x1+RE4x/EROMfxETjH8RE4x/EROMfxETuW6dPegQYN0xIgRqfWLFy+Wfe7buW8bIzTltrOzM7NrA3YvP9THL9LMmTPNelNTk1mP+d4WLlxo1tva2lJrzc3NuHr1KpfuJqJ0DD+RUww/kVMMP5FTDD+RUww/kVMMP5FTeW/RfRbAiT4PjQFwLrcBDEy1jq1axwVwbOWq5NjuU9V/KuWJuYb/OxcX2Veta/tV69iqdVwAx1auosbGH/uJnGL4iZwqOvyNBV/fUq1jq9ZxARxbuQoZW6G/8xNRcYp+5yeighQSfhGpF5H/FZEmEVlbxBjSiEiziBwSkQNFbzGWbIPWJiJf93msVkQ+FJFjycd+t0kraGyvisj/Ja/dARFZVNDYJonIpyJyRET+R0T+LXm80NfOGFchr1vuP/aLyJ0AjgJ4AkALgC8BPK2qh3MdSAoRaQZQp6qF94RFZCGAywDeU9XZyWP/AaBdVTcmf3GOVtV/r5KxvQrgctE7Nycbyozvu7M0gCcBPIcCXztjXMtQwOtWxDv/fABNqnpcVbsA/AHA0gLGUfVU9TMA7Tc9vBTAtuTzbej9w5O7lLFVBVU9par7k88vAbi+s3Shr50xrkIUEf57Afyjz9ctqK4tvxXA30TkKxFpKHow/bjn+s5IycexBY/nZsGdm/N0087SVfPalbPjdaUVEf7+lhiqppbD91X1IQA/BPDT5MdbKk1JOzfnpZ+dpatCuTteV1oR4W8BMKnP1xMBtBYwjn6pamvysQ3ATlTf7sNnrm+SmnxMX9AtZ9W0c3N/O0ujCl67atrxuojwfwlguoh8T0SGAFgOYFcB4/gOERmW/EMMRGQYgB+g+nYf3gVgZfL5SgDvFziWG1TLzs1pO0uj4Neu2na8LuQmn6SV8WsAdwLYoqq/yH0Q/RCR+9H7bg/0bmL6+yLHJiLbATyK3llfZwD8HMBfAPwJwGQAJwH8WFVz/4e3lLE9igHu3JzR2NJ2lv4cBb52ldzxuiLj4R1+RD7xDj8ipxh+IqcYfiKnGH4ipxh+IqcYfiKnGH4ipxh+Iqf+H6n1Jfvmm32uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RIzMbkPRZtW/14b2StmWXt0l6usFaPqQtKzd3WllaDT93bVvxupGTfLKujH+T1CdpzN3/pfYi5mFmQ5o92kuzIx5/0mRtZvakpOs0O+rrqKTvSPoPST+TdJmkP0n6orvX/sVbh9qu0wJXbq6otk4rS7+kBp+7Mle8LqUezvADYuIMPyAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQf0fXJzi1X6QbpcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "latent_samples = latent_layer(20 , n_input)\n",
    "images = generator.predict(latent_samples)\n",
    "plt.imshow(reprocess(images[0]) , cmap = 'gray')\n",
    "plt.show()\n",
    "plt.imshow(reprocess(images[1]) , cmap = 'gray')\n",
    "plt.show()\n",
    "plt.imshow(reprocess(images[2]) , cmap = 'gray')\n",
    "plt.show()\n",
    "plt.imshow(reprocess(images[3]) , cmap = 'gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p6dMvbbie0+XO3Fyn02aWbs1z18+M12VrIvwzXWKoTS2Ha9393ZI+KOn27OUtetPTzM11mWFm6Vbod8brsjUR/n2Slkz7/gpJ+xuoY0buvj/7ekjSw2rf7MMHT02Smn091HA9b2jTzM0zzSytFjx3bZrxuonwPyFpuZldaWZzJd0maUsDdZzBzEayD2JkZiOSblD7Zh/eImltdnutpM0N1vIr2jJzc7eZpdXwc9e2Ga8bOcgna2V8QdKgpI3u/pnai5iBmf26pvb20tQkpl9tsjYze0jSdZo66+ugpE9L+rakb0h6i6QXJN3i7rV/8Naltus09dL1jZmbT73Hrrm235b0r5J2Supki9dp6v11Y89doq41auB54wg/ICiO8AOCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/ENT/A1vjk6qKXUZUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kd85bfIekjdnrjZr8x1O7DrX1BXcfd/cd2et3JZ2bWbrRc5eoqxFNhH+JpDenvN+v/pry2yU9Z2bbzWxt08VM49Js2vRz06cvarie8+XO3Fyn82aW7ptz18uM12VrIvzTzf7TT10ON7j7pyXdJumr2cdbdKermZvrMs3M0n2h1xmvy9ZE+PdLWjbl/VJJBxqoY1rufiD7eUjSM+q/2YcPnpskNfuZM5Ffffpp5ubpZpZWH5y7fprxuonwb5O0wsyWm9mQpC9K2tRAHe9jZsPZH2JkZsOSPqv+m314k6Q12es1kp5tsJb36JeZmzvNLK2Gz12/zXjdyE0+WVfGP0hqSdrg7n9bexHTMLOrNHm1lyZHNv5ek7WZ2ROSbtLkU18HJT0g6Z8lPSnpckm/kvQFd6/9D28dartJkx9d/3/m5nPfsWuu7UZJ/yHpJUntbPE6TX6/buzcJeparQbOG3f4AUFxhx8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaD+DwqUo8qL1W14AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for image in images:\n",
    "    plt.imshow(reprocess(image))\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}