Improving_AutoEncoder_Samples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\NSW00_906882\\AppData\\Local\\Continuum\\anaconda3\\envs\\ssd2\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 128, 128, 1)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 64, 64, 32)        544       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        16416     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        32832     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         131200    \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 8, 8, 64)          73792     \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 8, 8, 32)          18464     \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 1)           2049      \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 8, 8, 32)          320       \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 8, 8, 64)          18496     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         131200    \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 16, 16, 64)        73792     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 16, 16, 64)        65600     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2 (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 32, 32, 32)        18464     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2 (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 32, 32, 32)        16416     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_6 (UpSampling2 (None, 128, 128, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 64, 64, 32)        16416     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_7 (UpSampling2 (None, 128, 128, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 128, 128, 1)       2049      \n",
      "=================================================================\n",
      "Total params: 664,226\n",
      "Trainable params: 664,226\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "from keras import optimizers\n",
    "from keras import backend as K\n",
    "\n",
    "input_img = Input(shape=(128, 128, 1))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "# Encode-----------------------------------------------------------\n",
    "x = Conv2D(32, (4, 4), strides=2 , activation='relu', padding='same')(input_img)\n",
    "x = Conv2D(32, (4, 4), strides=2, activation='relu', padding='same')(x)\n",
    "x = Conv2D(32, (3, 3), strides=1, activation='relu', padding='same')(x)\n",
    "x = Conv2D(64, (4, 4), strides=2, activation='relu', padding='same')(x)\n",
    "x = Conv2D(64, (3, 3), strides=1, activation='relu', padding='same')(x)\n",
    "x = Conv2D(128, (4, 4), strides=2, activation='relu', padding='same')(x)\n",
    "x = Conv2D(64, (3, 3), strides=1, activation='relu', padding='same')(x)\n",
    "x = Conv2D(32, (3, 3), strides=1, activation='relu', padding='same')(x)\n",
    "encoded = Conv2D(1, (8, 8), strides=1, padding='same')(x)\n",
    "\n",
    "# Decode---------------------------------------------------------------------\n",
    "x = Conv2D(32, (3, 3), strides=1, activation='relu', padding='same')(encoded)\n",
    "x = Conv2D(64, (3, 3), strides=1, activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(128, (4, 4), strides=2, activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(64, (3, 3), strides=1, activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(64, (4, 4), strides=2, activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(32, (3, 3), strides=1, activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(32, (4, 4), strides=2, activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((4, 4))(x)\n",
    "x = Conv2D(32, (4, 4), strides=2, activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "decoded = Conv2D(1, (8, 8), activation='sigmoid', padding='same')(x)\n",
    "# ---------------------------------------------------------------------\n",
    "autoencoder = Model(input_img, decoded)\n",
    "adam = optimizers.Adam(lr=0.0002, decay=0.00001)\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \n",
      " [[ 65  85  94 ... 225 231 235]\n",
      " [ 61  76  94 ... 213 222 233]\n",
      " [ 57  63  90 ... 190 213 222]\n",
      " ...\n",
      " [190 188 187 ... 132  89  93]\n",
      " [185 184 182 ... 123 134 156]\n",
      " [180 179 176 ... 117 173 159]]\n",
      "OK 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\NSW00_906882\\AppData\\Local\\Continuum\\anaconda3\\envs\\ssd2\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from PIL import Image\n",
    "\n",
    "# Own Your Image Directory\n",
    "img_dir = (\"./Samples/\")\n",
    "img_files = glob.glob(img_dir + \"*.jpeg\")\n",
    "# Setting Image Propertie\n",
    "width = 128\n",
    "height = 128\n",
    "pixels = width * height * 1 # gray scale \n",
    "\n",
    "# Load Image\n",
    "# AutoEncoder does not have to label data \n",
    "x = []\n",
    "\n",
    "for i, f in enumerate(img_files):\n",
    "    img = Image.open(f)\n",
    "#     img = img.convert(\"RGB\")\n",
    "    img = img.convert(\"L\") # gray sclae\n",
    "    img = img.resize((width,height), 1)\n",
    "    data = np.asarray(img)\n",
    "    x.append(data)\n",
    "    if i % 10 == 0:\n",
    "        print(i, \"\\n\", data)\n",
    "\n",
    "x = np.array(x)\n",
    "(x_train, x_test) = train_test_split(x, shuffle=False, train_size=0.8, random_state=1)\n",
    "\n",
    "img_list = (x_train, x_test)\n",
    "np.save(\"./obj.npy\", img_list)\n",
    "print(\"OK\", len(x)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 128, 128, 1)\n",
      "(2, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "# change to float32\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), 128, 128, 1))  # adapt this if using `channels_first` image data format\n",
    "x_test = np.reshape(x_test, (len(x_test), 128, 128, 1))  # adapt this if using `channels_first` image data format\n",
    "print (x_train.shape)\n",
    "print (x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8 samples, validate on 2 samples\n",
      "Epoch 1/1000\n",
      "8/8 [==============================] - 2s 218ms/step - loss: 0.6933 - val_loss: 0.6946\n",
      "Epoch 2/1000\n",
      "8/8 [==============================] - 1s 184ms/step - loss: 0.6923 - val_loss: 0.6982\n",
      "Epoch 3/1000\n",
      "8/8 [==============================] - 2s 191ms/step - loss: 0.6900 - val_loss: 0.7159\n",
      "Epoch 4/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.6766 - val_loss: 1.0087\n",
      "Epoch 5/1000\n",
      "8/8 [==============================] - 2s 194ms/step - loss: 0.7990 - val_loss: 0.7318\n",
      "Epoch 6/1000\n",
      "8/8 [==============================] - 1s 185ms/step - loss: 0.6676 - val_loss: 0.7086\n",
      "Epoch 7/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.6831 - val_loss: 0.7034\n",
      "Epoch 8/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.6872 - val_loss: 0.7016\n",
      "Epoch 9/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.6884 - val_loss: 0.7010\n",
      "Epoch 10/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6888 - val_loss: 0.7009\n",
      "Epoch 11/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.6889 - val_loss: 0.7012\n",
      "Epoch 12/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6887 - val_loss: 0.7019\n",
      "Epoch 13/1000\n",
      "8/8 [==============================] - 1s 168ms/step - loss: 0.6884 - val_loss: 0.7031\n",
      "Epoch 14/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.6878 - val_loss: 0.7048\n",
      "Epoch 15/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.6870 - val_loss: 0.7073\n",
      "Epoch 16/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.6858 - val_loss: 0.7110\n",
      "Epoch 17/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.6842 - val_loss: 0.7172\n",
      "Epoch 18/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6818 - val_loss: 0.7272\n",
      "Epoch 19/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.6786 - val_loss: 0.7429\n",
      "Epoch 20/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.6748 - val_loss: 0.7720\n",
      "Epoch 21/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.6716 - val_loss: 0.8172\n",
      "Epoch 22/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.6729 - val_loss: 0.8296\n",
      "Epoch 23/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6735 - val_loss: 0.8012\n",
      "Epoch 24/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.6697 - val_loss: 0.7521\n",
      "Epoch 25/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.6698 - val_loss: 0.7392\n",
      "Epoch 26/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.6711 - val_loss: 0.7692\n",
      "Epoch 27/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.6667 - val_loss: 0.8145\n",
      "Epoch 28/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.6666 - val_loss: 0.8323\n",
      "Epoch 29/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.6659 - val_loss: 0.7821\n",
      "Epoch 30/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.6599 - val_loss: 0.7555\n",
      "Epoch 31/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.6587 - val_loss: 0.7841\n",
      "Epoch 32/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6514 - val_loss: 0.8165\n",
      "Epoch 33/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.6599 - val_loss: 0.7007\n",
      "Epoch 34/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.6876 - val_loss: 0.7201\n",
      "Epoch 35/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.6605 - val_loss: 0.7807\n",
      "Epoch 36/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.6501 - val_loss: 0.8097\n",
      "Epoch 37/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6599 - val_loss: 0.7529\n",
      "Epoch 38/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6524 - val_loss: 0.7374\n",
      "Epoch 39/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6580 - val_loss: 0.7370\n",
      "Epoch 40/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6567 - val_loss: 0.7455\n",
      "Epoch 41/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.6497 - val_loss: 0.7683\n",
      "Epoch 42/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.6448 - val_loss: 0.7676\n",
      "Epoch 43/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.6438 - val_loss: 0.7190\n",
      "Epoch 44/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.6511 - val_loss: 0.7553\n",
      "Epoch 45/1000\n",
      "8/8 [==============================] - 2s 190ms/step - loss: 0.6407 - val_loss: 0.7620\n",
      "Epoch 46/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6385 - val_loss: 0.7266\n",
      "Epoch 47/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.6384 - val_loss: 0.7289\n",
      "Epoch 48/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.6336 - val_loss: 0.7547\n",
      "Epoch 49/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.6343 - val_loss: 0.7137\n",
      "Epoch 50/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.6274 - val_loss: 0.7033\n",
      "Epoch 51/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.6221 - val_loss: 0.7185\n",
      "Epoch 52/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.6260 - val_loss: 0.6732\n",
      "Epoch 53/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.6304 - val_loss: 0.6717\n",
      "Epoch 54/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.6136 - val_loss: 0.7128\n",
      "Epoch 55/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.6333 - val_loss: 0.6649\n",
      "Epoch 56/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.6166 - val_loss: 0.6571\n",
      "Epoch 57/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.6289 - val_loss: 0.6562\n",
      "Epoch 58/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.6097 - val_loss: 0.6888\n",
      "Epoch 59/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.6098 - val_loss: 0.6739\n",
      "Epoch 60/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.6045 - val_loss: 0.6333\n",
      "Epoch 61/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.6006 - val_loss: 0.6320\n",
      "Epoch 62/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.6026 - val_loss: 0.6515\n",
      "Epoch 63/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5945 - val_loss: 0.6799\n",
      "Epoch 64/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.6005 - val_loss: 0.6458\n",
      "Epoch 65/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5904 - val_loss: 0.6239\n",
      "Epoch 66/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5966 - val_loss: 0.6296\n",
      "Epoch 67/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5894 - val_loss: 0.6526\n",
      "Epoch 68/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5914 - val_loss: 0.6465\n",
      "Epoch 69/1000\n",
      "8/8 [==============================] - 1s 168ms/step - loss: 0.5882 - val_loss: 0.6311\n",
      "Epoch 70/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5875 - val_loss: 0.6229\n",
      "Epoch 71/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5872 - val_loss: 0.6254\n",
      "Epoch 72/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5838 - val_loss: 0.6312\n",
      "Epoch 73/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5850 - val_loss: 0.6284\n",
      "Epoch 74/1000\n",
      "8/8 [==============================] - 1s 168ms/step - loss: 0.5825 - val_loss: 0.6292\n",
      "Epoch 75/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5829 - val_loss: 0.6291\n",
      "Epoch 76/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5808 - val_loss: 0.6229\n",
      "Epoch 77/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5799 - val_loss: 0.6210\n",
      "Epoch 78/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.5802 - val_loss: 0.6244\n",
      "Epoch 79/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5771 - val_loss: 0.6284\n",
      "Epoch 80/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5782 - val_loss: 0.6234\n",
      "Epoch 81/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5757 - val_loss: 0.6210\n",
      "Epoch 82/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5754 - val_loss: 0.6246\n",
      "Epoch 83/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5758 - val_loss: 0.6235\n",
      "Epoch 84/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5731 - val_loss: 0.6229\n",
      "Epoch 85/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5732 - val_loss: 0.6227\n",
      "Epoch 86/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5725 - val_loss: 0.6194\n",
      "Epoch 87/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5709 - val_loss: 0.6230\n",
      "Epoch 88/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5707 - val_loss: 0.6269\n",
      "Epoch 89/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5705 - val_loss: 0.6210\n",
      "Epoch 90/1000\n",
      "8/8 [==============================] - 1s 167ms/step - loss: 0.5690 - val_loss: 0.6203\n",
      "Epoch 91/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5684 - val_loss: 0.6228\n",
      "Epoch 92/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5683 - val_loss: 0.6206\n",
      "Epoch 93/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5685 - val_loss: 0.6202\n",
      "Epoch 94/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5679 - val_loss: 0.6185\n",
      "Epoch 95/1000\n",
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      "Epoch 599/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5337 - val_loss: 0.6990\n",
      "Epoch 600/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5337 - val_loss: 0.6989\n",
      "Epoch 601/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5337 - val_loss: 0.6990\n",
      "Epoch 602/1000\n",
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      "Epoch 616/1000\n",
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      "Epoch 617/1000\n",
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      "Epoch 618/1000\n",
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      "Epoch 619/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5334 - val_loss: 0.6997\n",
      "Epoch 620/1000\n",
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      "Epoch 621/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5333 - val_loss: 0.6998\n",
      "Epoch 622/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5333 - val_loss: 0.6996\n",
      "Epoch 623/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5333 - val_loss: 0.6997\n",
      "Epoch 624/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5333 - val_loss: 0.6997\n",
      "Epoch 625/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.5333 - val_loss: 0.6998\n",
      "Epoch 626/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5333 - val_loss: 0.6997\n",
      "Epoch 627/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5333 - val_loss: 0.6998\n",
      "Epoch 628/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5332 - val_loss: 0.6997\n",
      "Epoch 629/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5332 - val_loss: 0.7000\n",
      "Epoch 630/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5332 - val_loss: 0.6995\n",
      "Epoch 631/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5332 - val_loss: 0.7000\n",
      "Epoch 632/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5332 - val_loss: 0.6998\n",
      "Epoch 633/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.5332 - val_loss: 0.6999\n",
      "Epoch 634/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5332 - val_loss: 0.6999\n",
      "Epoch 635/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5331 - val_loss: 0.6998\n",
      "Epoch 636/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5331 - val_loss: 0.7001\n",
      "Epoch 637/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5331 - val_loss: 0.6995\n",
      "Epoch 638/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5331 - val_loss: 0.7002\n",
      "Epoch 639/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5331 - val_loss: 0.6996\n",
      "Epoch 640/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5331 - val_loss: 0.7000\n",
      "Epoch 641/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5331 - val_loss: 0.6998\n",
      "Epoch 642/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5331 - val_loss: 0.6999\n",
      "Epoch 643/1000\n",
      "8/8 [==============================] - 1s 168ms/step - loss: 0.5330 - val_loss: 0.6999\n",
      "Epoch 644/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5330 - val_loss: 0.6996\n",
      "Epoch 645/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5330 - val_loss: 0.7000\n",
      "Epoch 646/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5330 - val_loss: 0.6995\n",
      "Epoch 647/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5330 - val_loss: 0.7001\n",
      "Epoch 648/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5330 - val_loss: 0.6994\n",
      "Epoch 649/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5330 - val_loss: 0.7000\n",
      "Epoch 650/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5330 - val_loss: 0.6997\n",
      "Epoch 651/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5329 - val_loss: 0.6998\n",
      "Epoch 652/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5329 - val_loss: 0.6999\n",
      "Epoch 653/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5329 - val_loss: 0.6997\n",
      "Epoch 654/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5329 - val_loss: 0.7000\n",
      "Epoch 655/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5329 - val_loss: 0.6995\n",
      "Epoch 656/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5329 - val_loss: 0.7002\n",
      "Epoch 657/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5329 - val_loss: 0.6995\n",
      "Epoch 658/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5329 - val_loss: 0.7004\n",
      "Epoch 659/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5329 - val_loss: 0.6993\n",
      "Epoch 660/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5329 - val_loss: 0.7003\n",
      "Epoch 661/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5328 - val_loss: 0.6996\n",
      "Epoch 662/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5328 - val_loss: 0.7002\n",
      "Epoch 663/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5328 - val_loss: 0.6998\n",
      "Epoch 664/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5328 - val_loss: 0.6998\n",
      "Epoch 665/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5328 - val_loss: 0.7000\n",
      "Epoch 666/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5328 - val_loss: 0.6997\n",
      "Epoch 667/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5328 - val_loss: 0.7002\n",
      "Epoch 668/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5328 - val_loss: 0.6996\n",
      "Epoch 669/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5328 - val_loss: 0.7002\n",
      "Epoch 670/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5327 - val_loss: 0.6994\n",
      "Epoch 671/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5327 - val_loss: 0.7007\n",
      "Epoch 672/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5327 - val_loss: 0.6991\n",
      "Epoch 673/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5327 - val_loss: 0.7008\n",
      "Epoch 674/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5327 - val_loss: 0.6993\n",
      "Epoch 675/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5327 - val_loss: 0.7004\n",
      "Epoch 676/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5327 - val_loss: 0.6998\n",
      "Epoch 677/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5327 - val_loss: 0.6999\n",
      "Epoch 678/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5327 - val_loss: 0.7003\n",
      "Epoch 679/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5327 - val_loss: 0.6995\n",
      "Epoch 680/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5326 - val_loss: 0.7008\n",
      "Epoch 681/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5326 - val_loss: 0.6991\n",
      "Epoch 682/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5326 - val_loss: 0.7008\n",
      "Epoch 683/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5326 - val_loss: 0.6993\n",
      "Epoch 684/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5326 - val_loss: 0.7007\n",
      "Epoch 685/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5326 - val_loss: 0.6996\n",
      "Epoch 686/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5326 - val_loss: 0.7002\n",
      "Epoch 687/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5326 - val_loss: 0.7002\n",
      "Epoch 688/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5326 - val_loss: 0.7000\n",
      "Epoch 689/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5326 - val_loss: 0.7004\n",
      "Epoch 690/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.5326 - val_loss: 0.6995\n",
      "Epoch 691/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5325 - val_loss: 0.7010\n",
      "Epoch 692/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5325 - val_loss: 0.6988\n",
      "Epoch 693/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5325 - val_loss: 0.7018\n",
      "Epoch 694/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5325 - val_loss: 0.6986\n",
      "Epoch 695/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5325 - val_loss: 0.7017\n",
      "Epoch 696/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5325 - val_loss: 0.6992\n",
      "Epoch 697/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5325 - val_loss: 0.7006\n",
      "Epoch 698/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5325 - val_loss: 0.7002\n",
      "Epoch 699/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5325 - val_loss: 0.6997\n",
      "Epoch 700/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5325 - val_loss: 0.7013\n",
      "Epoch 701/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5325 - val_loss: 0.6987\n",
      "Epoch 702/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5325 - val_loss: 0.7019\n",
      "Epoch 703/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5325 - val_loss: 0.6985\n",
      "Epoch 704/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5325 - val_loss: 0.7016\n",
      "Epoch 705/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5324 - val_loss: 0.6992\n",
      "Epoch 706/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5324 - val_loss: 0.7007\n",
      "Epoch 707/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5324 - val_loss: 0.7003\n",
      "Epoch 708/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5324 - val_loss: 0.6997\n",
      "Epoch 709/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5324 - val_loss: 0.7011\n",
      "Epoch 710/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5324 - val_loss: 0.6991\n",
      "Epoch 711/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5324 - val_loss: 0.7018\n",
      "Epoch 712/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5324 - val_loss: 0.6987\n",
      "Epoch 713/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5324 - val_loss: 0.7020\n",
      "Epoch 714/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5324 - val_loss: 0.6986\n",
      "Epoch 715/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5324 - val_loss: 0.7019\n",
      "Epoch 716/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5324 - val_loss: 0.6992\n",
      "Epoch 717/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5323 - val_loss: 0.7008\n",
      "Epoch 718/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5323 - val_loss: 0.7004\n",
      "Epoch 719/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5323 - val_loss: 0.6999\n",
      "Epoch 720/1000\n",
      "8/8 [==============================] - 1s 185ms/step - loss: 0.5323 - val_loss: 0.7012\n",
      "Epoch 721/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5323 - val_loss: 0.6992\n",
      "Epoch 722/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5323 - val_loss: 0.7020\n",
      "Epoch 723/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5323 - val_loss: 0.6985\n",
      "Epoch 724/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5323 - val_loss: 0.7026\n",
      "Epoch 725/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5323 - val_loss: 0.6981\n",
      "Epoch 726/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5323 - val_loss: 0.7025\n",
      "Epoch 727/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5323 - val_loss: 0.6986\n",
      "Epoch 728/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5323 - val_loss: 0.7016\n",
      "Epoch 729/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5322 - val_loss: 0.6997\n",
      "Epoch 730/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5322 - val_loss: 0.7003\n",
      "Epoch 731/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5322 - val_loss: 0.7011\n",
      "Epoch 732/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5322 - val_loss: 0.6991\n",
      "Epoch 733/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5322 - val_loss: 0.7019\n",
      "Epoch 734/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5322 - val_loss: 0.6986\n",
      "Epoch 735/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5322 - val_loss: 0.7024\n",
      "Epoch 736/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.5322 - val_loss: 0.6984\n",
      "Epoch 737/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5322 - val_loss: 0.7023\n",
      "Epoch 738/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5322 - val_loss: 0.6989\n",
      "Epoch 739/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.5322 - val_loss: 0.7015\n",
      "Epoch 740/1000\n",
      "8/8 [==============================] - 1s 185ms/step - loss: 0.5322 - val_loss: 0.6997\n",
      "Epoch 741/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5321 - val_loss: 0.7004\n",
      "Epoch 742/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5321 - val_loss: 0.7006\n",
      "Epoch 743/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5321 - val_loss: 0.6996\n",
      "Epoch 744/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5321 - val_loss: 0.7015\n",
      "Epoch 745/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5321 - val_loss: 0.6990\n",
      "Epoch 746/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5321 - val_loss: 0.7025\n",
      "Epoch 747/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5321 - val_loss: 0.6981\n",
      "Epoch 748/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5322 - val_loss: 0.7034\n",
      "Epoch 749/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5322 - val_loss: 0.6971\n",
      "Epoch 750/1000\n",
      "8/8 [==============================] - 1s 184ms/step - loss: 0.5322 - val_loss: 0.7042\n",
      "Epoch 751/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5322 - val_loss: 0.6969\n",
      "Epoch 752/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.5322 - val_loss: 0.7033\n",
      "Epoch 753/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5322 - val_loss: 0.6988\n",
      "Epoch 754/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5321 - val_loss: 0.7006\n",
      "Epoch 755/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5320 - val_loss: 0.7015\n",
      "Epoch 756/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5320 - val_loss: 0.6984\n",
      "Epoch 757/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5321 - val_loss: 0.7028\n",
      "Epoch 758/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5321 - val_loss: 0.6983\n",
      "Epoch 759/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5321 - val_loss: 0.7022\n",
      "Epoch 760/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5321 - val_loss: 0.6991\n",
      "Epoch 761/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5320 - val_loss: 0.7011\n",
      "Epoch 762/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5320 - val_loss: 0.7004\n",
      "Epoch 763/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5320 - val_loss: 0.7000\n",
      "Epoch 764/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5320 - val_loss: 0.7016\n",
      "Epoch 765/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5320 - val_loss: 0.6991\n",
      "Epoch 766/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5320 - val_loss: 0.7022\n",
      "Epoch 767/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5320 - val_loss: 0.6988\n",
      "Epoch 768/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5320 - val_loss: 0.7028\n",
      "Epoch 769/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5320 - val_loss: 0.6981\n",
      "Epoch 770/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5320 - val_loss: 0.7032\n",
      "Epoch 771/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5320 - val_loss: 0.6983\n",
      "Epoch 772/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5320 - val_loss: 0.7028\n",
      "Epoch 773/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5320 - val_loss: 0.6988\n",
      "Epoch 774/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5320 - val_loss: 0.7020\n",
      "Epoch 775/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5320 - val_loss: 0.6997\n",
      "Epoch 776/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5319 - val_loss: 0.7011\n",
      "Epoch 777/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5319 - val_loss: 0.7004\n",
      "Epoch 778/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5319 - val_loss: 0.7006\n",
      "Epoch 779/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5319 - val_loss: 0.7009\n",
      "Epoch 780/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5319 - val_loss: 0.7001\n",
      "Epoch 781/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5319 - val_loss: 0.7017\n",
      "Epoch 782/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5319 - val_loss: 0.6994\n",
      "Epoch 783/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5319 - val_loss: 0.7027\n",
      "Epoch 784/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5319 - val_loss: 0.6981\n",
      "Epoch 785/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.5319 - val_loss: 0.7049\n",
      "Epoch 786/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5320 - val_loss: 0.6959\n",
      "Epoch 787/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5322 - val_loss: 0.7084\n",
      "Epoch 788/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5325 - val_loss: 0.6922\n",
      "Epoch 789/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5327 - val_loss: 0.7103\n",
      "Epoch 790/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5329 - val_loss: 0.6929\n",
      "Epoch 791/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5325 - val_loss: 0.7034\n",
      "Epoch 792/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5320 - val_loss: 0.7022\n",
      "Epoch 793/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5319 - val_loss: 0.6950\n",
      "Epoch 794/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5321 - val_loss: 0.7062\n",
      "Epoch 795/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5323 - val_loss: 0.6963\n",
      "Epoch 796/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5321 - val_loss: 0.7007\n",
      "Epoch 797/1000\n",
      "8/8 [==============================] - 1s 187ms/step - loss: 0.5318 - val_loss: 0.7027\n",
      "Epoch 798/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5319 - val_loss: 0.6965\n",
      "Epoch 799/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5320 - val_loss: 0.7034\n",
      "Epoch 800/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5320 - val_loss: 0.6983\n",
      "Epoch 801/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5319 - val_loss: 0.6997\n",
      "Epoch 802/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5318 - val_loss: 0.7023\n",
      "Epoch 803/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5318 - val_loss: 0.6972\n",
      "Epoch 804/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5319 - val_loss: 0.7023\n",
      "Epoch 805/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5318 - val_loss: 0.6992\n",
      "Epoch 806/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5317 - val_loss: 0.6993\n",
      "Epoch 807/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5317 - val_loss: 0.7019\n",
      "Epoch 808/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5318 - val_loss: 0.6978\n",
      "Epoch 809/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5318 - val_loss: 0.7016\n",
      "Epoch 810/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5317 - val_loss: 0.6996\n",
      "Epoch 811/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.5317 - val_loss: 0.6996\n",
      "Epoch 812/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.5317 - val_loss: 0.7016\n",
      "Epoch 813/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5317 - val_loss: 0.6986\n",
      "Epoch 814/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5317 - val_loss: 0.7016\n",
      "Epoch 815/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5317 - val_loss: 0.6992\n",
      "Epoch 816/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5317 - val_loss: 0.7005\n",
      "Epoch 817/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5317 - val_loss: 0.7007\n",
      "Epoch 818/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5317 - val_loss: 0.6996\n",
      "Epoch 819/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5317 - val_loss: 0.7010\n",
      "Epoch 820/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5316 - val_loss: 0.6993\n",
      "Epoch 821/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5316 - val_loss: 0.7011\n",
      "Epoch 822/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5316 - val_loss: 0.6996\n",
      "Epoch 823/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5316 - val_loss: 0.7012\n",
      "Epoch 824/1000\n",
      "8/8 [==============================] - 1s 166ms/step - loss: 0.5316 - val_loss: 0.6997\n",
      "Epoch 825/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5316 - val_loss: 0.7009\n",
      "Epoch 826/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5316 - val_loss: 0.7000\n",
      "Epoch 827/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5316 - val_loss: 0.7012\n",
      "Epoch 828/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5316 - val_loss: 0.6995\n",
      "Epoch 829/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5316 - val_loss: 0.7018\n",
      "Epoch 830/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5316 - val_loss: 0.6987\n",
      "Epoch 831/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5316 - val_loss: 0.7033\n",
      "Epoch 832/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5316 - val_loss: 0.6973\n",
      "Epoch 833/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5317 - val_loss: 0.7049\n",
      "Epoch 834/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5317 - val_loss: 0.6960\n",
      "Epoch 835/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5318 - val_loss: 0.7059\n",
      "Epoch 836/1000\n",
      "8/8 [==============================] - 2s 189ms/step - loss: 0.5318 - val_loss: 0.6955\n",
      "Epoch 837/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5319 - val_loss: 0.7054\n",
      "Epoch 838/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5319 - val_loss: 0.6971\n",
      "Epoch 839/1000\n",
      "8/8 [==============================] - 2s 222ms/step - loss: 0.5317 - val_loss: 0.7021\n",
      "Epoch 840/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5316 - val_loss: 0.7004\n",
      "Epoch 841/1000\n",
      "8/8 [==============================] - 2s 188ms/step - loss: 0.5315 - val_loss: 0.6988\n",
      "Epoch 842/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5316 - val_loss: 0.7028\n",
      "Epoch 843/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5316 - val_loss: 0.6981\n",
      "Epoch 844/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5316 - val_loss: 0.7025\n",
      "Epoch 845/1000\n",
      "8/8 [==============================] - 1s 187ms/step - loss: 0.5316 - val_loss: 0.6990\n",
      "Epoch 846/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5316 - val_loss: 0.7009\n",
      "Epoch 847/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5316 - val_loss: 0.7000\n",
      "Epoch 848/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5315 - val_loss: 0.7003\n",
      "Epoch 849/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5315 - val_loss: 0.7005\n",
      "Epoch 850/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5315 - val_loss: 0.7000\n",
      "Epoch 851/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5315 - val_loss: 0.7009\n",
      "Epoch 852/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5315 - val_loss: 0.6995\n",
      "Epoch 853/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5315 - val_loss: 0.7017\n",
      "Epoch 854/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5315 - val_loss: 0.6983\n",
      "Epoch 855/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5315 - val_loss: 0.7027\n",
      "Epoch 856/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5315 - val_loss: 0.6976\n",
      "Epoch 857/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5315 - val_loss: 0.7036\n",
      "Epoch 858/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5315 - val_loss: 0.6971\n",
      "Epoch 859/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5315 - val_loss: 0.7038\n",
      "Epoch 860/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5315 - val_loss: 0.6972\n",
      "Epoch 861/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5315 - val_loss: 0.7033\n",
      "Epoch 862/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5316 - val_loss: 0.6978\n",
      "Epoch 863/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5315 - val_loss: 0.7033\n",
      "Epoch 864/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5315 - val_loss: 0.6975\n",
      "Epoch 865/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5315 - val_loss: 0.7028\n",
      "Epoch 866/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5315 - val_loss: 0.6986\n",
      "Epoch 867/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5314 - val_loss: 0.7016\n",
      "Epoch 868/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5314 - val_loss: 0.6995\n",
      "Epoch 869/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5314 - val_loss: 0.7007\n",
      "Epoch 870/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5314 - val_loss: 0.7001\n",
      "Epoch 871/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5314 - val_loss: 0.7006\n",
      "Epoch 872/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5314 - val_loss: 0.7004\n",
      "Epoch 873/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5314 - val_loss: 0.7003\n",
      "Epoch 874/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5314 - val_loss: 0.7010\n",
      "Epoch 875/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5314 - val_loss: 0.6991\n",
      "Epoch 876/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5313 - val_loss: 0.7022\n",
      "Epoch 877/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5314 - val_loss: 0.6977\n",
      "Epoch 878/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5314 - val_loss: 0.7045\n",
      "Epoch 879/1000\n",
      "8/8 [==============================] - 1s 168ms/step - loss: 0.5315 - val_loss: 0.6946\n",
      "Epoch 880/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5317 - val_loss: 0.7092\n",
      "Epoch 881/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5321 - val_loss: 0.6885\n",
      "Epoch 882/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5330 - val_loss: 0.7170\n",
      "Epoch 883/1000\n",
      "8/8 [==============================] - 1s 167ms/step - loss: 0.5342 - val_loss: 0.6842\n",
      "Epoch 884/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5344 - val_loss: 0.7113\n",
      "Epoch 885/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5342 - val_loss: 0.6953\n",
      "Epoch 886/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5324 - val_loss: 0.6943\n",
      "Epoch 887/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5320 - val_loss: 0.7092\n",
      "Epoch 888/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5332 - val_loss: 0.6907\n",
      "Epoch 889/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5327 - val_loss: 0.7005\n",
      "Epoch 890/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5319 - val_loss: 0.7044\n",
      "Epoch 891/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5322 - val_loss: 0.6891\n",
      "Epoch 892/1000\n",
      "8/8 [==============================] - 2s 188ms/step - loss: 0.5325 - val_loss: 0.7036\n",
      "Epoch 893/1000\n",
      "8/8 [==============================] - 2s 188ms/step - loss: 0.5321 - val_loss: 0.7002\n",
      "Epoch 894/1000\n",
      "8/8 [==============================] - 2s 189ms/step - loss: 0.5318 - val_loss: 0.6895\n",
      "Epoch 895/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5322 - val_loss: 0.7029\n",
      "Epoch 896/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5319 - val_loss: 0.6987\n",
      "Epoch 897/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5318 - val_loss: 0.6933\n",
      "Epoch 898/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5317 - val_loss: 0.7013\n",
      "Epoch 899/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5318 - val_loss: 0.6971\n",
      "Epoch 900/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5316 - val_loss: 0.6966\n",
      "Epoch 901/1000\n",
      "8/8 [==============================] - 2s 190ms/step - loss: 0.5316 - val_loss: 0.7007\n",
      "Epoch 902/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5316 - val_loss: 0.6955\n",
      "Epoch 903/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5316 - val_loss: 0.6967\n",
      "Epoch 904/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5315 - val_loss: 0.6975\n",
      "Epoch 905/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5315 - val_loss: 0.6964\n",
      "Epoch 906/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5314 - val_loss: 0.6992\n",
      "Epoch 907/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5315 - val_loss: 0.6964\n",
      "Epoch 908/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5314 - val_loss: 0.6954\n",
      "Epoch 909/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5314 - val_loss: 0.6985\n",
      "Epoch 910/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5313 - val_loss: 0.6964\n",
      "Epoch 911/1000\n",
      "8/8 [==============================] - 2s 188ms/step - loss: 0.5314 - val_loss: 0.6969\n",
      "Epoch 912/1000\n",
      "8/8 [==============================] - 1s 185ms/step - loss: 0.5313 - val_loss: 0.6975\n",
      "Epoch 913/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5313 - val_loss: 0.6963\n",
      "Epoch 914/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5313 - val_loss: 0.6975\n",
      "Epoch 915/1000\n",
      "8/8 [==============================] - 1s 170ms/step - loss: 0.5313 - val_loss: 0.6974\n",
      "Epoch 916/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5313 - val_loss: 0.6971\n",
      "Epoch 917/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5312 - val_loss: 0.6977\n",
      "Epoch 918/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5312 - val_loss: 0.6972\n",
      "Epoch 919/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5312 - val_loss: 0.6973\n",
      "Epoch 920/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5312 - val_loss: 0.6977\n",
      "Epoch 921/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5312 - val_loss: 0.6979\n",
      "Epoch 922/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5312 - val_loss: 0.6972\n",
      "Epoch 923/1000\n",
      "8/8 [==============================] - 1s 185ms/step - loss: 0.5312 - val_loss: 0.6973\n",
      "Epoch 924/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5312 - val_loss: 0.6982\n",
      "Epoch 925/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5311 - val_loss: 0.6974\n",
      "Epoch 926/1000\n",
      "8/8 [==============================] - 1s 171ms/step - loss: 0.5311 - val_loss: 0.6976\n",
      "Epoch 927/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5311 - val_loss: 0.6970\n",
      "Epoch 928/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5311 - val_loss: 0.6976\n",
      "Epoch 929/1000\n",
      "8/8 [==============================] - 1s 184ms/step - loss: 0.5311 - val_loss: 0.6983\n",
      "Epoch 930/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5311 - val_loss: 0.6967\n",
      "Epoch 931/1000\n",
      "8/8 [==============================] - 2s 190ms/step - loss: 0.5311 - val_loss: 0.6979\n",
      "Epoch 932/1000\n",
      "8/8 [==============================] - 2s 192ms/step - loss: 0.5311 - val_loss: 0.6973\n",
      "Epoch 933/1000\n",
      "8/8 [==============================] - 2s 191ms/step - loss: 0.5311 - val_loss: 0.6978\n",
      "Epoch 934/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5311 - val_loss: 0.6978\n",
      "Epoch 935/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5311 - val_loss: 0.6971\n",
      "Epoch 936/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.5311 - val_loss: 0.6985\n",
      "Epoch 937/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5311 - val_loss: 0.6969\n",
      "Epoch 938/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5311 - val_loss: 0.6984\n",
      "Epoch 939/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5310 - val_loss: 0.6972\n",
      "Epoch 940/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5310 - val_loss: 0.6981\n",
      "Epoch 941/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5310 - val_loss: 0.6971\n",
      "Epoch 942/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5310 - val_loss: 0.6987\n",
      "Epoch 943/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5310 - val_loss: 0.6965\n",
      "Epoch 944/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5310 - val_loss: 0.6991\n",
      "Epoch 945/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5311 - val_loss: 0.6960\n",
      "Epoch 946/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.5311 - val_loss: 0.7000\n",
      "Epoch 947/1000\n",
      "8/8 [==============================] - 2s 198ms/step - loss: 0.5311 - val_loss: 0.6947\n",
      "Epoch 948/1000\n",
      "8/8 [==============================] - 2s 199ms/step - loss: 0.5311 - val_loss: 0.7007\n",
      "Epoch 949/1000\n",
      "8/8 [==============================] - 2s 212ms/step - loss: 0.5311 - val_loss: 0.6949\n",
      "Epoch 950/1000\n",
      "8/8 [==============================] - 2s 210ms/step - loss: 0.5311 - val_loss: 0.7006\n",
      "Epoch 951/1000\n",
      "8/8 [==============================] - 2s 189ms/step - loss: 0.5311 - val_loss: 0.6950\n",
      "Epoch 952/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.5311 - val_loss: 0.6995\n",
      "Epoch 953/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5310 - val_loss: 0.6966\n",
      "Epoch 954/1000\n",
      "8/8 [==============================] - 2s 188ms/step - loss: 0.5310 - val_loss: 0.6977\n",
      "Epoch 955/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5310 - val_loss: 0.6981\n",
      "Epoch 956/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.5310 - val_loss: 0.6967\n",
      "Epoch 957/1000\n",
      "8/8 [==============================] - 1s 186ms/step - loss: 0.5310 - val_loss: 0.6990\n",
      "Epoch 958/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5310 - val_loss: 0.6962\n",
      "Epoch 959/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5310 - val_loss: 0.6989\n",
      "Epoch 960/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5310 - val_loss: 0.6965\n",
      "Epoch 961/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5310 - val_loss: 0.6986\n",
      "Epoch 962/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5310 - val_loss: 0.6965\n",
      "Epoch 963/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5310 - val_loss: 0.6993\n",
      "Epoch 964/1000\n",
      "8/8 [==============================] - 1s 185ms/step - loss: 0.5311 - val_loss: 0.6954\n",
      "Epoch 965/1000\n",
      "8/8 [==============================] - 1s 187ms/step - loss: 0.5311 - val_loss: 0.7006\n",
      "Epoch 966/1000\n",
      "8/8 [==============================] - 1s 184ms/step - loss: 0.5312 - val_loss: 0.6936\n",
      "Epoch 967/1000\n",
      "8/8 [==============================] - 2s 189ms/step - loss: 0.5312 - val_loss: 0.7024\n",
      "Epoch 968/1000\n",
      "8/8 [==============================] - 1s 185ms/step - loss: 0.5313 - val_loss: 0.6914\n",
      "Epoch 969/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5313 - val_loss: 0.7046\n",
      "Epoch 970/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5315 - val_loss: 0.6901\n",
      "Epoch 971/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5315 - val_loss: 0.7038\n",
      "Epoch 972/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5314 - val_loss: 0.6932\n",
      "Epoch 973/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5311 - val_loss: 0.6987\n",
      "Epoch 974/1000\n",
      "8/8 [==============================] - 1s 178ms/step - loss: 0.5310 - val_loss: 0.6988\n",
      "Epoch 975/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5310 - val_loss: 0.6937\n",
      "Epoch 976/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5311 - val_loss: 0.7022\n",
      "Epoch 977/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5311 - val_loss: 0.6933\n",
      "Epoch 978/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5311 - val_loss: 0.6998\n",
      "Epoch 979/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5310 - val_loss: 0.6963\n",
      "Epoch 980/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5310 - val_loss: 0.6966\n",
      "Epoch 981/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5310 - val_loss: 0.6987\n",
      "Epoch 982/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5310 - val_loss: 0.6957\n",
      "Epoch 983/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5310 - val_loss: 0.6981\n",
      "Epoch 984/1000\n",
      "8/8 [==============================] - 1s 180ms/step - loss: 0.5309 - val_loss: 0.6973\n",
      "Epoch 985/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5309 - val_loss: 0.6968\n",
      "Epoch 986/1000\n",
      "8/8 [==============================] - 1s 169ms/step - loss: 0.5309 - val_loss: 0.6983\n",
      "Epoch 987/1000\n",
      "8/8 [==============================] - 1s 181ms/step - loss: 0.5309 - val_loss: 0.6958\n",
      "Epoch 988/1000\n",
      "8/8 [==============================] - 1s 183ms/step - loss: 0.5309 - val_loss: 0.6987\n",
      "Epoch 989/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5309 - val_loss: 0.6963\n",
      "Epoch 990/1000\n",
      "8/8 [==============================] - 1s 179ms/step - loss: 0.5309 - val_loss: 0.6976\n",
      "Epoch 991/1000\n",
      "8/8 [==============================] - 1s 172ms/step - loss: 0.5308 - val_loss: 0.6979\n",
      "Epoch 992/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5308 - val_loss: 0.6964\n",
      "Epoch 993/1000\n",
      "8/8 [==============================] - 1s 173ms/step - loss: 0.5308 - val_loss: 0.6984\n",
      "Epoch 994/1000\n",
      "8/8 [==============================] - 1s 182ms/step - loss: 0.5308 - val_loss: 0.6961\n",
      "Epoch 995/1000\n",
      "8/8 [==============================] - 1s 185ms/step - loss: 0.5308 - val_loss: 0.6986\n",
      "Epoch 996/1000\n",
      "8/8 [==============================] - 1s 177ms/step - loss: 0.5308 - val_loss: 0.6963\n",
      "Epoch 997/1000\n",
      "8/8 [==============================] - 1s 175ms/step - loss: 0.5308 - val_loss: 0.6982\n",
      "Epoch 998/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5308 - val_loss: 0.6964\n",
      "Epoch 999/1000\n",
      "8/8 [==============================] - 1s 174ms/step - loss: 0.5308 - val_loss: 0.6982\n",
      "Epoch 1000/1000\n",
      "8/8 [==============================] - 1s 176ms/step - loss: 0.5308 - val_loss: 0.6963\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x137b8860>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import TensorBoard\n",
    "\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=1000,\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "decoded_imgs = autoencoder.predict(x_train)\n",
    "\n",
    "n = 8  # how many digits we will display\n",
    "plt.figure(figsize=(20, 5), dpi=100)\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_train[i].reshape(128, 128))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(True)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "    # SSIM Encode\n",
    "    ax.set_title(\"Encode_Image\")\n",
    "    \n",
    "    npImg = x_train[i]\n",
    "    npImg = npImg.reshape((128,128))\n",
    "    formatted = (npImg * 255 / np.max(npImg)).astype('uint8')\n",
    "    img = Image.fromarray(formatted)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(128, 128))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(True)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "    # SSIM Decoded    \n",
    "    npDecoded = decoded_imgs[i]\n",
    "    npDecoded = npDecoded.reshape((128,128))\n",
    "    formatted2 = (npDecoded * 255 / np.max(npDecoded)).astype('uint8')\n",
    "    decoded = Image.fromarray(formatted2)\n",
    "    \n",
    "    from SSIM_PIL import compare_ssim as ssim\n",
    "    value = ssim(img, decoded)\n",
    "\n",
    "    label = 'SSIM: {:.3f}'\n",
    "    \n",
    "    ax.set_title(\"Decoded_Image\")\n",
    "    ax.set_xlabel(label.format(value))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}