03.03.02.MNIST_featuring_ConvAutoEncoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering with Convolutional AutoEncoder\n",
    "\n",
    "이번 실습은 MNIST를 복원하는 AutoEncoder를 만드는 것은 이전 실습과 동일하지만, Encoder와 Decoder에 Conv layer를 활용함으로써 보다 정교한 ConvAutoEncoder를 제작해 보겠습니다.  \n",
    "\n",
    "(참고)  \n",
    "https://blog.keras.io/building-autoencoders-in-keras.html  \n",
    "https://www.datacamp.com/community/tutorials/autoencoder-keras-tutorial  \n",
    "https://medium.com/datadriveninvestor/deep-autoencoder-using-keras-b77cd3e8be95  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "\n",
    "from keras.datasets import mnist\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt #for plotting\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to reproduce same training result\n",
    "from numpy.random import seed\n",
    "seed(1)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prepare dataset for ConvAutoEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "x_train = np.expand_dims(x_train, axis=3)\n",
    "x_test = np.expand_dims(x_test, axis=3)\n",
    "\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoEncoder with Deep Conv Layers  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = x_train.shape[1:]\n",
    "\n",
    "# this is our input placeholder\n",
    "input_img = Input(shape=input_shape)\n",
    "\n",
    "input_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\uhmpp\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "encode_conv_layer_1 = Conv2D(16, (3, 3), activation='relu', padding='same')\n",
    "encode_pool_layer_1 = MaxPooling2D((2, 2), padding='same')\n",
    "encode_conv_layer_2 = Conv2D(8, (3, 3), activation='relu', padding='same')\n",
    "encode_pool_layer_2 = MaxPooling2D((2, 2), padding='same')\n",
    "encode_conv_layer_3 = Conv2D(4, (3, 3), activation='relu', padding='same')\n",
    "encode_pool_layer_3 = MaxPooling2D((2, 2), padding='same')\n",
    "\n",
    "decode_conv_layer_1 = Conv2D(4, (3, 3), activation='relu', padding='same')\n",
    "decode_upsample_layer_1 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_2 = Conv2D(8, (3, 3), activation='relu', padding='same')\n",
    "decode_upsample_layer_2 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_3 = Conv2D(16, (3, 3), activation='relu')\n",
    "decode_upsample_layer_3 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_4 = Conv2D(1, (3, 3), activation='sigmoid', padding='same')\n",
    "\n",
    "encoded = encode_conv_layer_1(input_img)\n",
    "encoded = encode_pool_layer_1(encoded)\n",
    "encoded = encode_conv_layer_2(encoded)\n",
    "encoded = encode_pool_layer_2(encoded)\n",
    "encoded = encode_conv_layer_3(encoded)\n",
    "encoded = encode_pool_layer_3(encoded)\n",
    "\n",
    "decoded = decode_conv_layer_1(encoded)\n",
    "decoded = decode_upsample_layer_1(decoded)\n",
    "decoded = decode_conv_layer_2(decoded)\n",
    "decoded = decode_upsample_layer_2(decoded)\n",
    "decoded = decode_conv_layer_3(decoded)\n",
    "decoded = decode_upsample_layer_3(decoded)\n",
    "decoded = decode_conv_layer_4(decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 16)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 14, 14, 8)         1160      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 8)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 7, 7, 4)           292       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 4)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 4, 4, 4)           148       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 8, 8, 4)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 8, 8)           296       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 14, 14, 16)        1168      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 28, 28, 1)         145       \n",
      "=================================================================\n",
      "Total params: 3,369\n",
      "Trainable params: 3,369\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder=Model(input_img, decoded)\n",
    "\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 16)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 14, 14, 8)         1160      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 8)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 7, 7, 4)           292       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 4)           0         \n",
      "=================================================================\n",
      "Total params: 1,612\n",
      "Trainable params: 1,612\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# this model maps an input to its encoded representation\n",
    "encoder = Model(input_img, encoded)\n",
    "\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_input_shape = tuple(encoded.get_shape().as_list()[1:])\n",
    "\n",
    "decoder_input_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 4, 4, 4)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 4, 4, 4)           148       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 8, 8, 4)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 8, 8)           296       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 14, 14, 16)        1168      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 28, 28, 1)         145       \n",
      "=================================================================\n",
      "Total params: 1,757\n",
      "Trainable params: 1,757\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# input placeholder for decoder\n",
    "input_decoder = Input(shape=decoder_input_shape)\n",
    "\n",
    "output_decoder = decode_conv_layer_1(input_decoder)\n",
    "output_decoder = decode_upsample_layer_1(output_decoder)\n",
    "output_decoder = decode_conv_layer_2(output_decoder)\n",
    "output_decoder = decode_upsample_layer_2(output_decoder)\n",
    "output_decoder = decode_conv_layer_3(output_decoder)\n",
    "output_decoder = decode_upsample_layer_3(output_decoder)\n",
    "output_decoder = decode_conv_layer_4(output_decoder)\n",
    "\n",
    "# this model maps an input to its encoded representation\n",
    "decoder = Model(input_decoder, output_decoder)\n",
    "\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\uhmpp\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 6s 99us/step - loss: 0.2499 - val_loss: 0.2054\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.1892 - val_loss: 0.1791\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1748 - val_loss: 0.1693\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 2s 41us/step - loss: 0.1660 - val_loss: 0.1632\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.1596 - val_loss: 0.1580\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.1540 - val_loss: 0.1585\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1495 - val_loss: 0.1449\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 2s 40us/step - loss: 0.1455 - val_loss: 0.1435\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1425 - val_loss: 0.1406\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.1398 - val_loss: 0.1368\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.1373 - val_loss: 0.1333\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1361 - val_loss: 0.1317\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.1347 - val_loss: 0.1346\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1333 - val_loss: 0.1340\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.1320 - val_loss: 0.1297\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.1307 - val_loss: 0.1277\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1298 - val_loss: 0.1290\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.1290 - val_loss: 0.1303\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1278 - val_loss: 0.1237\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1272 - val_loss: 0.1243\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1266 - val_loss: 0.1264\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1258 - val_loss: 0.1270\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1255 - val_loss: 0.1272\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1249 - val_loss: 0.1225\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1244 - val_loss: 0.1211\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1238 - val_loss: 0.1230\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1232 - val_loss: 0.1203\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1228 - val_loss: 0.1195\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1222 - val_loss: 0.1199\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1215 - val_loss: 0.1197\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1214 - val_loss: 0.1210\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1210 - val_loss: 0.1220\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1205 - val_loss: 0.1245\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1200 - val_loss: 0.1196\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1198 - val_loss: 0.1197\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1198 - val_loss: 0.1178\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1195 - val_loss: 0.1169\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1190 - val_loss: 0.1204\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1191 - val_loss: 0.1196\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1188 - val_loss: 0.1187\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1181 - val_loss: 0.1141\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1178 - val_loss: 0.1151\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1174 - val_loss: 0.1181\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1174 - val_loss: 0.1200\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1173 - val_loss: 0.1160\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1170 - val_loss: 0.1170\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1166 - val_loss: 0.1159\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1162 - val_loss: 0.1149\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1160 - val_loss: 0.1138\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.1159 - val_loss: 0.1130\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1fd1b580fd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
    "\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reconstruction Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_10 = x_test[:10]\n",
    "x_test_hat = autoencoder.predict(x_test_10)\n",
    "z_test = encoder.predict(x_test_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 8640x3600 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test_imgs = x_test_10.reshape(-1, 28, 28)\n",
    "x_test_hat_imgs = x_test_hat.reshape(-1, 28, 28)\n",
    "z_test = z_test.reshape(-1, 8, 8)\n",
    "\n",
    "plt.figure(figsize=(120,50))\n",
    "for i in range(10):  \n",
    "    # 원본이미지 출력\n",
    "    plt.subplot(3, 10, i+1)\n",
    "    plt.imshow(x_test_imgs[i])\n",
    "    # hidden feature 출력\n",
    "    plt.subplot(3, 10, i+11)\n",
    "    plt.imshow(z_test[i])\n",
    "    # 생성된 이미지 출력\n",
    "    plt.subplot(3, 10, i+21)\n",
    "    plt.imshow(x_test_hat_imgs[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save trained encoder model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json = encoder.to_json()\n",
    "with open(\"checkpoints/conv_ae_encoder.json\", \"w\") as json_file : \n",
    "    json.dump(model_json, json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder.save_weights(\"checkpoints/conv_ae_encoder.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py36",
   "language": "python",
   "name": "py36"
  },
  "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": 2
}