MNIST_GAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "3hqFBSY0C3lH",
    "outputId": "32a33f89-c5dc-4526-bb93-7fe6e2397636"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras.layers import Dense, Activation, Conv2D, Flatten, Dense, Dropout, BatchNormalization\n",
    "from keras.layers import LeakyReLU, Input, Reshape, Conv2DTranspose, UpSampling2D, MaxPooling2D\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZcMTnYDuDvyf"
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XOfSPEawmkIV"
   },
   "outputs": [],
   "source": [
    "adam = Adam(lr=0.0002, beta_1=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 734
    },
    "colab_type": "code",
    "id": "eqFOMGaeGXZ3",
    "outputId": "833c3223-5b7e-4f34-e193-bd017dd75f60"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0704 18:05:35.582295 140575307949952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0704 18:05:35.584968 140575307949952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0704 18:05:35.588042 140575307949952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0704 18:05:35.615741 140575307949952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W0704 18:05:35.675508 140575307949952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W0704 18:05:35.682057 140575307949952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "W0704 18:05:35.687842 140575307949952 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 64)        640       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 24, 24, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 10, 10, 64)        36928     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 2, 2, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 148,609\n",
      "Trainable params: 148,609\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    discriminator = load_model('discriminator.h5')\n",
    "except:\n",
    "    discriminator = Sequential()\n",
    "    \n",
    "    discriminator.add(Conv2D(64,(3,3), activation='relu',input_shape=(28,28,1)))\n",
    "    discriminator.add(Conv2D(64,(3,3), activation='relu'))\n",
    "    discriminator.add(MaxPooling2D((2,2)))\n",
    "    \n",
    "    discriminator.add(Conv2D(64,(3,3), activation='relu'))\n",
    "    discriminator.add(Conv2D(64,(3,3), activation='relu'))\n",
    "    discriminator.add(MaxPooling2D((2,2)))\n",
    "    \n",
    "    discriminator.add(Conv2D(64,(3,3), activation='relu'))\n",
    "    discriminator.add(Flatten())\n",
    "    \n",
    "    discriminator.add(Dense(1, activation='sigmoid'))\n",
    "discriminator.compile(loss='binary_crossentropy', optimizer=adam)\n",
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XUbG_mBd2axU"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "colab_type": "code",
    "id": "3IMlD2EGNPwh",
    "outputId": "8c5b38d1-6c5e-48bb-f19b-edc0bc3dab7c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0704 18:05:35.757119 140575307949952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 800)               80800     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 5, 5, 32)          0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 10, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 12, 12, 256)       73984     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 24, 24, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 26, 26, 128)       295040    \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         1153      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 28, 28, 1)         0         \n",
      "=================================================================\n",
      "Total params: 450,977\n",
      "Trainable params: 450,977\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    generator = load_model('generator.h5')\n",
    "except:\n",
    "    generator = Sequential()\n",
    "    generator.add(Dense(800, input_dim=100))\n",
    "    generator.add(LeakyReLU(0.2))\n",
    "    \n",
    "    generator.add(Reshape((5,5,32)))\n",
    "    generator.add(UpSampling2D((2,2)))\n",
    "    generator.add(Conv2DTranspose(256,(3,3),activation='relu'))\n",
    "    \n",
    "    generator.add(UpSampling2D((2,2)))\n",
    "    generator.add(Conv2DTranspose(128,(3,3),activation='relu'))\n",
    "    generator.add(Conv2DTranspose(1,(3,3),activation='relu'))\n",
    "    generator.add(Activation('tanh'))\n",
    "    \n",
    "generator.compile(loss='binary_crossentropy', optimizer=adam)\n",
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fYH2iAO0l9Ta"
   },
   "outputs": [],
   "source": [
    "y_train = np_utils.to_categorical(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "COAxB1-fmKbi",
    "outputId": "16fc4cef-504b-4aef-a2d7-d2e138605d74"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 10), (60000, 28, 28))"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape,X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lzj6wWCa0AMr"
   },
   "outputs": [],
   "source": [
    "X_train = X_train.astype('float64')\n",
    "X_train /= 255\n",
    "X_test = X_test.astype('float64')\n",
    "X_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E3sCZ244m8U0"
   },
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(len(X_train),28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "nxPNgdUTpsrC",
    "outputId": "c1e54291-0542-4119-d2e3-5f838e8ed058"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 10), (60000, 28, 28, 1))"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape,X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2LXDSmVJmAHX"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "PKE6FsWN1--J",
    "outputId": "a8187356-2d28-4190-8bb8-d108dace49e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "sequential_2 (Sequential)    (None, 28, 28, 1)         450977    \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 1)                 148609    \n",
      "=================================================================\n",
      "Total params: 599,586\n",
      "Trainable params: 450,977\n",
      "Non-trainable params: 148,609\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    gan = load_model( 'gan.h5')\n",
    "except:\n",
    "    discriminator.trainable = False\n",
    "    # gan input (noise) will be 100-dimensional vectors\n",
    "    gan_input = Input(shape=(100,))\n",
    "    # the output of the generator (an image)\n",
    "    x = generator(gan_input)\n",
    "    # get the output of the discriminator (probability if the image is real or not)\n",
    "    gan_output = discriminator(x)\n",
    "    gan = Model(inputs=gan_input, outputs=gan_output)\n",
    "gan.compile(loss='binary_crossentropy', optimizer=adam)\n",
    "gan.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "99dKSbX83f7r"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OkcTTn3UAjYN"
   },
   "outputs": [],
   "source": [
    "def plot_loss(losses):\n",
    "    d_loss = losses['D']\n",
    "    g_loss = losses['G']\n",
    "    \n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.plot(d_loss, label=\"Discriminator loss\")\n",
    "    plt.plot(g_loss, label=\"Generator loss\")\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i6_0zhFJWDni"
   },
   "outputs": [],
   "source": [
    "def plot_generated(figsize=(12, 2)):\n",
    "    noise = (np.random.random_sample((10,100))*2 - 1)\n",
    "    generated_images = generator.predict(noise)\n",
    "    generated_images = generated_images.reshape(10, 28, 28)\n",
    "#     generated_images -= 1\n",
    "#     generated_images *=255\n",
    "    plt.figure(figsize=figsize)\n",
    "    for i in range(generated_images.shape[0]):\n",
    "        plt.subplot(1, 10, i+1)\n",
    "        plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')\n",
    "        plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 159
    },
    "colab_type": "code",
    "id": "aNEI3reBYWWS",
    "outputId": "c3b753b1-24b7-45d9-cf91-74adc355812a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0704 18:05:36.416211 140575307949952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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pfQZ97jVr1gBB1Er7fMABBxSNYGnOqfLf9B777LMPEDzJax2N7du3A0FkJ0oV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\nCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_generated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xIF5WQuhZCOM"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gcbIIUKUmcgT"
   },
   "outputs": [],
   "source": [
    "def train(epoch=1, ploting_ep = 20 , batch_size=128):\n",
    "    losses = {'D':[],'G':[]}\n",
    "    batch_count = X_train.shape[0] // batch_size\n",
    "    for i in range(1,epoch+1):\n",
    "        for j in range(batch_count):\n",
    "            # Input for the generator\n",
    "            noise_input = (np.random.random_sample((batch_size,100))*2 - 1)\n",
    "\n",
    "            # getting images from X_train of size=batch_size \n",
    "            # these are the real images that will be fed to the discriminator\n",
    "            image_batch = X_train[j*batch_size:(j+1)*batch_size]\n",
    "\n",
    "            # these are the predicted images from the generator\n",
    "            predictions = generator.predict(noise_input, batch_size=batch_size)\n",
    "            \n",
    "            # the discriminator takes in the real images and the generated images\n",
    "            X = np.concatenate([ image_batch,predictions])\n",
    "            # labels for the discriminator\n",
    "            y_discriminator = np.zeros(2*batch_size)\n",
    "            y_discriminator[:batch_size] = 1\n",
    "            # Let's train the discriminator\n",
    "            discriminator.trainable = True\n",
    "            d_loss = discriminator.train_on_batch(X, y_discriminator)\n",
    "\n",
    "            # Let's train the generator\n",
    "            y_generator = np.ones(batch_size)\n",
    "            discriminator.trainable = False\n",
    "            \n",
    "            g_loss = gan.train_on_batch(noise_input, y_generator)\n",
    "        losses['D'].append(d_loss)\n",
    "        losses['G'].append(g_loss)\n",
    "        print(\"Epoch :  {0}/{1}  Dicriminator Loss : {2:.2f}  Generator Loss : {3:.2f}\".format(i,epoch,d_loss,g_loss))\n",
    "        if i==1 or i%ploting_ep==0:\n",
    "            plot_generated()\n",
    "    plot_loss(losses)\n",
    "    return losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "CtA3pQ3gacpL",
    "outputId": "8b8336c6-8142-4f75-82d8-bff84e897217"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch :  1/20  Dicriminator Loss : 0.41  Generator Loss : 1.92\n"
     ]
    },
    {
     "data": {
      "image/png": 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vvRUoUYTYfvvtASPbC/Pyyy8DJeKS19bcI+Xx5GapffK+nn6CqRPQmqFDhwKw9957A+Wa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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch :  2/20  Dicriminator Loss : 0.37  Generator Loss : 2.10\n",
      "Epoch :  3/20  Dicriminator Loss : 0.43  Generator Loss : 2.01\n",
      "Epoch :  4/20  Dicriminator Loss : 0.35  Generator Loss : 1.81\n",
      "Epoch :  5/20  Dicriminator Loss : 0.39  Generator Loss : 2.09\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch :  6/20  Dicriminator Loss : 0.36  Generator Loss : 2.27\n",
      "Epoch :  7/20  Dicriminator Loss : 0.32  Generator Loss : 1.86\n",
      "Epoch :  8/20  Dicriminator Loss : 0.37  Generator Loss : 1.92\n",
      "Epoch :  9/20  Dicriminator Loss : 0.35  Generator Loss : 1.99\n",
      "Epoch :  10/20  Dicriminator Loss : 0.37  Generator Loss : 1.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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Y8+8Pf/hDm49nBlvtu+OOO1r8O9GD9qKAqYJVO9Of6mTp66KWUtkuUZh5Xc9POeUUoPEi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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch :  11/20  Dicriminator Loss : 0.35  Generator Loss : 2.00\n",
      "Epoch :  12/20  Dicriminator Loss : 0.39  Generator Loss : 2.52\n",
      "Epoch :  13/20  Dicriminator Loss : 0.39  Generator Loss : 2.40\n",
      "Epoch :  14/20  Dicriminator Loss : 0.39  Generator Loss : 2.76\n",
      "Epoch :  15/20  Dicriminator Loss : 0.33  Generator Loss : 2.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch :  16/20  Dicriminator Loss : 0.34  Generator Loss : 2.20\n",
      "Epoch :  17/20  Dicriminator Loss : 0.37  Generator Loss : 2.51\n",
      "Epoch :  18/20  Dicriminator Loss : 0.35  Generator Loss : 2.55\n",
      "Epoch :  19/20  Dicriminator Loss : 0.35  Generator Loss : 2.76\n",
      "Epoch :  20/20  Dicriminator Loss : 0.29  Generator Loss : 2.42\n"
     ]
    },
    {
     "data": {
      "image/png": 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VVVcF4Pbbb6/4+Y477liP4TSVnB2+4447Kn7+rne9C4Dtt9++5mNqJHfeeSdQ9L2oPqe+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\nJEkl+n+u2HyqpZfUUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1REYHX1f+UzEG4i+DjV/bGyNC1eF9MPc/0Pg8aNzddTUSYApkIiKu5WTZzuHN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\nwArjdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 32s, sys: 2min 44s, total: 8min 16s\n",
      "Wall time: 9min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "losses = train(20,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hidZCTmTYlXt"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TWKHYH6QUAJn"
   },
   "outputs": [],
   "source": [
    "gan.save('gan.h5')\n",
    "generator.save('generator.h5')\n",
    "discriminator.save('discriminator.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bZlihSSh_SDX"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "MNIST.ipynb",
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