Convolutional autoencoder.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "\n",
    "input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
    "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
    "\n",
    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(16, (3, 3), activation='relu')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "\n",
    "(x_train, _), (x_test, _) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format\n",
    "x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 29s 477us/step - loss: 0.2152 - val_loss: 0.1680\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 29s 486us/step - loss: 0.1552 - val_loss: 0.1444\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 29s 483us/step - loss: 0.1429 - val_loss: 0.1356\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 29s 475us/step - loss: 0.1364 - val_loss: 0.1341\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 29s 477us/step - loss: 0.1313 - val_loss: 0.1241\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 29s 488us/step - loss: 0.1274 - val_loss: 0.1264\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 29s 488us/step - loss: 0.1244 - val_loss: 0.1190\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 32s 532us/step - loss: 0.1218 - val_loss: 0.1219\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 30s 505us/step - loss: 0.1197 - val_loss: 0.1161\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 30s 497us/step - loss: 0.1180 - val_loss: 0.1142\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 32s 540us/step - loss: 0.1166 - val_loss: 0.1147\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 33s 558us/step - loss: 0.1155 - val_loss: 0.1111\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 30s 500us/step - loss: 0.1146 - val_loss: 0.1132\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 30s 495us/step - loss: 0.1135 - val_loss: 0.1097\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 29s 481us/step - loss: 0.1126 - val_loss: 0.1108\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 28s 475us/step - loss: 0.1117 - val_loss: 0.1106\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 29s 476us/step - loss: 0.1113 - val_loss: 0.1097\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 29s 487us/step - loss: 0.1106 - val_loss: 0.1141\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 29s 475us/step - loss: 0.1102 - val_loss: 0.1112\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 29s 483us/step - loss: 0.1098 - val_loss: 0.1068\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 29s 480us/step - loss: 0.1091 - val_loss: 0.1051\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 29s 479us/step - loss: 0.1087 - val_loss: 0.1076\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 29s 482us/step - loss: 0.1084 - val_loss: 0.1065\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 30s 494us/step - loss: 0.1080 - val_loss: 0.1069\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 29s 489us/step - loss: 0.1075 - val_loss: 0.1089\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 29s 483us/step - loss: 0.1073 - val_loss: 0.1040\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 29s 486us/step - loss: 0.1070 - val_loss: 0.1043\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 29s 483us/step - loss: 0.1063 - val_loss: 0.1084\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 29s 478us/step - loss: 0.1062 - val_loss: 0.1026\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 29s 477us/step - loss: 0.1061 - val_loss: 0.1051\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 28s 475us/step - loss: 0.1060 - val_loss: 0.1059\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 29s 483us/step - loss: 0.1055 - val_loss: 0.1024\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 29s 479us/step - loss: 0.1050 - val_loss: 0.1039\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 29s 482us/step - loss: 0.1051 - val_loss: 0.1033\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 29s 480us/step - loss: 0.1045 - val_loss: 0.1016\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 30s 492us/step - loss: 0.1044 - val_loss: 0.1042\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 30s 498us/step - loss: 0.1044 - val_loss: 0.1037\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 31s 509us/step - loss: 0.1044 - val_loss: 0.1013\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 30s 496us/step - loss: 0.1042 - val_loss: 0.1041\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 30s 500us/step - loss: 0.1037 - val_loss: 0.1050\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 30s 498us/step - loss: 0.1037 - val_loss: 0.1026\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 29s 487us/step - loss: 0.1034 - val_loss: 0.1064\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 29s 487us/step - loss: 0.1032 - val_loss: 0.1027\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 29s 481us/step - loss: 0.1031 - val_loss: 0.1039\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 31s 510us/step - loss: 0.1029 - val_loss: 0.1022\n",
      "Epoch 46/50\n",
      "29568/60000 [=============>................] - ETA: 14s - loss: 0.1030"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import TensorBoard\n",
    "\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,\n",
    "                batch_size=128,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test),\n",
    "                callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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