AE_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from tensorflow.keras import regularizers\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "XL=pd.read_csv('9_510_L_resample_95_5.csv')\n",
    "XU_train=pd.read_csv('9_510_U_resample_95_5.csv')# No resampling done in the unlabeled set, It just denotes the pair of L and U\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "import keras.backend as K\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import regularizers\n",
    "from keras.models import load_model\n",
    "import keras\n",
    "from keras.layers import Dropout\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import Adagrad\n",
    "from keras.optimizers import SGD\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "yL_train=XL.iloc[:,-1]\n",
    "XL_train=XL.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "yL_train=pd.get_dummies(yL_train)#One-hot_encoding\n",
    "\n",
    "# Split into train,test and val\n",
    "\n",
    "#randomly split into train(80%) and val(20%)\n",
    "XL_train,XL_val,yL_train,yL_val=train_test_split(XL_train,yL_train,random_state=0,test_size=0.2)\n",
    "\n",
    "#randomly split val into val(10%) and test(10%)\n",
    "XL_val,XL_test,yL_val,yL_test=train_test_split(XL_val,yL_val,random_state=0,test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "#scaling and normalization\n",
    "scaler=StandardScaler()\n",
    "XU_train=scaler.fit_transform(XU_train)\n",
    "XL_train=scaler.fit_transform(XL_train)\n",
    "XL_val=scaler.transform(XL_val)\n",
    "XL_test=scaler.transform(XL_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras_sequential_ascii import keras2ascii\n",
    "from tensorflow.keras import regularizers\n",
    "from keras.models import Sequential\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function AE\n",
    "Inputs: numpy array X, list layers,list activations,int n_epochs\n",
    "\n",
    "layers is a list of no. of hidden nodes in each hidden layer\n",
    "activations is a list of activations used in each encoding layer\n",
    "\n",
    "trains each autoencoder layer and outputs all the encoder-layer models.\n",
    "\n",
    "Output: a list of keras models\n",
    "\n",
    "'''\n",
    "def AE(X,layers,activations,n_epochs):\n",
    "    layers.insert(0,len(X[0]))\n",
    "    \n",
    "    ec=[]\n",
    "    for i in range(len(layers)-1):\n",
    "        encoding_dim = layers[i+1]\n",
    "\n",
    "        # this is our input placeholder\n",
    "        input_ = Input(shape=(layers[i],))\n",
    "\n",
    "        # \"encoded\" is the encoded representation of the input\n",
    "        encoded = Dense(encoding_dim, activation=activations[i],bias_initializer='random_normal',kernel_initializer='he_uniform',name='encoder'+str(i+1))(input_)\n",
    "        \n",
    "        # \"decoded\" is the lossy reconstruction of the input\n",
    "        decoded = Dense(layers[i],activation=None,name='decoder'+str(i+1))(encoded)\n",
    "\n",
    "        # this model maps an input to its reconstruction\n",
    "        autoencoder = Model(input_, decoded)\n",
    "\n",
    "        # this model maps an input to its encoded representation\n",
    "        encoder = Model(input_, encoded)\n",
    "\n",
    "        # create a placeholder for an encoded (32-dimensional) input\n",
    "        encoded_input = Input(shape=(encoding_dim,))\n",
    "\n",
    "        #compile the autoencoder model\n",
    "        autoencoder.compile(optimizer=RMSprop(learning_rate=0.001,clipnorm=0.1), loss='mse')\n",
    "        \n",
    "        print('Training Layer ' + str(i+1)+' : ')\n",
    "        \n",
    "        autoencoder.fit(X,X,\n",
    "                epochs=n_epochs,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(X,X))\n",
    "        ec.append(encoder)\n",
    "        X=encoder.predict(X)\n",
    "    print('All Layers Done!')    \n",
    "    return ec       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Layer 1 : \n",
      "Train on 384366 samples, validate on 384366 samples\n",
      "Epoch 1/10\n",
      "384366/384366 [==============================] - 6s 15us/step - loss: 0.0684 - val_loss: 0.0095\n",
      "Epoch 2/10\n",
      "384366/384366 [==============================] - 6s 15us/step - loss: 0.0069 - val_loss: 0.0045\n",
      "Epoch 3/10\n",
      "384366/384366 [==============================] - 6s 15us/step - loss: 0.0044 - val_loss: 0.0031\n",
      "Epoch 4/10\n",
      "384366/384366 [==============================] - 6s 15us/step - loss: 0.0035 - val_loss: 0.0032\n",
      "Epoch 5/10\n",
      "384366/384366 [==============================] - 6s 15us/step - loss: 0.0031 - val_loss: 0.0023\n",
      "Epoch 6/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0028 - val_loss: 0.0029\n",
      "Epoch 7/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0026 - val_loss: 0.0016\n",
      "Epoch 8/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0024 - val_loss: 0.0020\n",
      "Epoch 9/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0022 - val_loss: 0.0012\n",
      "Epoch 10/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0021 - val_loss: 0.0034\n",
      "Training Layer 2 : \n",
      "Train on 384366 samples, validate on 384366 samples\n",
      "Epoch 1/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0343 - val_loss: 0.0096\n",
      "Epoch 2/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0076 - val_loss: 0.0062\n",
      "Epoch 3/10\n",
      "384366/384366 [==============================] - 5s 13us/step - loss: 0.0058 - val_loss: 0.0046\n",
      "Epoch 4/10\n",
      "384366/384366 [==============================] - 5s 13us/step - loss: 0.0048 - val_loss: 0.0085\n",
      "Epoch 5/10\n",
      "384366/384366 [==============================] - 5s 13us/step - loss: 0.0043 - val_loss: 0.0053\n",
      "Epoch 6/10\n",
      "384366/384366 [==============================] - 5s 13us/step - loss: 0.0038 - val_loss: 0.0019\n",
      "Epoch 7/10\n",
      "384366/384366 [==============================] - 5s 13us/step - loss: 0.0035 - val_loss: 0.0034\n",
      "Epoch 8/10\n",
      "384366/384366 [==============================] - 5s 13us/step - loss: 0.0032 - val_loss: 0.0020\n",
      "Epoch 9/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0029 - val_loss: 0.0014\n",
      "Epoch 10/10\n",
      "384366/384366 [==============================] - 5s 14us/step - loss: 0.0027 - val_loss: 0.0025\n",
      "Training Layer 3 : \n",
      "Train on 384366 samples, validate on 384366 samples\n",
      "Epoch 1/10\n",
      "384366/384366 [==============================] - 11s 29us/step - loss: 0.0112 - val_loss: 0.0043\n",
      "Epoch 2/10\n",
      "384366/384366 [==============================] - 10s 26us/step - loss: 0.0034 - val_loss: 0.0017\n",
      "Epoch 3/10\n",
      "384366/384366 [==============================] - 10s 25us/step - loss: 0.0026 - val_loss: 0.0015\n",
      "Epoch 4/10\n",
      "384366/384366 [==============================] - 10s 26us/step - loss: 0.0021 - val_loss: 0.0029\n",
      "Epoch 5/10\n",
      "384366/384366 [==============================] - 10s 26us/step - loss: 0.0019 - val_loss: 0.0015\n",
      "Epoch 6/10\n",
      "384366/384366 [==============================] - 10s 26us/step - loss: 0.0017 - val_loss: 0.0021\n",
      "Epoch 7/10\n",
      "384366/384366 [==============================] - 10s 26us/step - loss: 0.0015 - val_loss: 9.8204e-04\n",
      "Epoch 8/10\n",
      "384366/384366 [==============================] - 10s 26us/step - loss: 0.0014 - val_loss: 0.0019\n",
      "Epoch 9/10\n",
      "384366/384366 [==============================] - 10s 25us/step - loss: 0.0013 - val_loss: 7.9560e-04\n",
      "Epoch 10/10\n",
      "384366/384366 [==============================] - 10s 26us/step - loss: 0.0012 - val_loss: 0.0014\n",
      "Training Layer 4 : \n",
      "Train on 384366 samples, validate on 384366 samples\n",
      "Epoch 1/10\n",
      "384366/384366 [==============================] - 8s 20us/step - loss: 0.0110 - val_loss: 0.0051\n",
      "Epoch 2/10\n",
      "384366/384366 [==============================] - 7s 19us/step - loss: 0.0042 - val_loss: 0.0051\n",
      "Epoch 3/10\n",
      "384366/384366 [==============================] - 7s 19us/step - loss: 0.0037 - val_loss: 0.0033\n",
      "Epoch 4/10\n",
      "384366/384366 [==============================] - 8s 20us/step - loss: 0.0035 - val_loss: 0.0032\n",
      "Epoch 5/10\n",
      "384366/384366 [==============================] - 8s 21us/step - loss: 0.0034 - val_loss: 0.0032\n",
      "Epoch 6/10\n",
      "384366/384366 [==============================] - 8s 22us/step - loss: 0.0034 - val_loss: 0.0032\n",
      "Epoch 7/10\n",
      "384366/384366 [==============================] - 9s 24us/step - loss: 0.0034 - val_loss: 0.0031\n",
      "Epoch 8/10\n",
      "384366/384366 [==============================] - 9s 24us/step - loss: 0.0034 - val_loss: 0.0032\n",
      "Epoch 9/10\n",
      "384366/384366 [==============================] - 9s 23us/step - loss: 0.0033 - val_loss: 0.0031\n",
      "Epoch 10/10\n",
      "384366/384366 [==============================] - 7s 19us/step - loss: 0.0033 - val_loss: 0.0031\n",
      "All Layers Done!\n"
     ]
    }
   ],
   "source": [
    "#Train The Stacked Autoencoder\n",
    "layers = [100,200,400,50]\n",
    "activations=['relu','relu','relu','relu']\n",
    "traint0=time.time()\n",
    "stacked_layers=AE(XU_train,layers,activations,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add layers to a sequential model\n",
    "model=Sequential()\n",
    "for i in stacked_layers:\n",
    "    model.add(i)\n",
    "\n",
    "#softmax output layer\n",
    "model.add(Dense(len(yL_train.columns),activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=RMSprop(learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 96292 samples, validate on 12036 samples\n",
      "Epoch 1/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 2.6894 - accuracy: 0.2409 - val_loss: 2.2164 - val_accuracy: 0.3676\n",
      "Epoch 2/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 2.0171 - accuracy: 0.4117 - val_loss: 1.8806 - val_accuracy: 0.4521\n",
      "Epoch 3/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.7729 - accuracy: 0.4857 - val_loss: 1.7039 - val_accuracy: 0.5020\n",
      "Epoch 4/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.6203 - accuracy: 0.5282 - val_loss: 1.5878 - val_accuracy: 0.5400\n",
      "Epoch 5/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.5070 - accuracy: 0.5562 - val_loss: 1.4967 - val_accuracy: 0.5543\n",
      "Epoch 6/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.4151 - accuracy: 0.5796 - val_loss: 1.4266 - val_accuracy: 0.5752\n",
      "Epoch 7/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.3381 - accuracy: 0.6028 - val_loss: 1.3401 - val_accuracy: 0.5982\n",
      "Epoch 8/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.2688 - accuracy: 0.6242 - val_loss: 1.2797 - val_accuracy: 0.6073\n",
      "Epoch 9/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.2094 - accuracy: 0.6400 - val_loss: 1.2099 - val_accuracy: 0.6426\n",
      "Epoch 10/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 1.1549 - accuracy: 0.6553 - val_loss: 1.1630 - val_accuracy: 0.6518\n",
      "Epoch 11/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.1060 - accuracy: 0.6697 - val_loss: 1.1176 - val_accuracy: 0.6712\n",
      "Epoch 12/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 1.0616 - accuracy: 0.6830 - val_loss: 1.0785 - val_accuracy: 0.6751\n",
      "Epoch 13/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 1.0216 - accuracy: 0.6936 - val_loss: 1.0382 - val_accuracy: 0.6873\n",
      "Epoch 14/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.9857 - accuracy: 0.7038 - val_loss: 1.0161 - val_accuracy: 0.6951\n",
      "Epoch 15/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.9540 - accuracy: 0.7147 - val_loss: 0.9815 - val_accuracy: 0.7020\n",
      "Epoch 16/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.9240 - accuracy: 0.7238 - val_loss: 0.9595 - val_accuracy: 0.7127\n",
      "Epoch 17/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.8971 - accuracy: 0.7316 - val_loss: 0.9389 - val_accuracy: 0.7105\n",
      "Epoch 18/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.8717 - accuracy: 0.7391 - val_loss: 0.9085 - val_accuracy: 0.7223\n",
      "Epoch 19/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.8480 - accuracy: 0.7460 - val_loss: 0.8802 - val_accuracy: 0.7370\n",
      "Epoch 20/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.8254 - accuracy: 0.7528 - val_loss: 0.8653 - val_accuracy: 0.7342\n",
      "Epoch 21/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.8052 - accuracy: 0.7580 - val_loss: 0.8436 - val_accuracy: 0.7401\n",
      "Epoch 22/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.7852 - accuracy: 0.7634 - val_loss: 0.8327 - val_accuracy: 0.7447\n",
      "Epoch 23/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.7677 - accuracy: 0.7675 - val_loss: 0.8071 - val_accuracy: 0.7584\n",
      "Epoch 24/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.7500 - accuracy: 0.7730 - val_loss: 0.7926 - val_accuracy: 0.7556\n",
      "Epoch 25/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.7340 - accuracy: 0.7765 - val_loss: 0.7732 - val_accuracy: 0.7578\n",
      "Epoch 26/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.7189 - accuracy: 0.7823 - val_loss: 0.7622 - val_accuracy: 0.7614\n",
      "Epoch 27/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.7045 - accuracy: 0.7858 - val_loss: 0.7521 - val_accuracy: 0.7667\n",
      "Epoch 28/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.6905 - accuracy: 0.7898 - val_loss: 0.7320 - val_accuracy: 0.7699\n",
      "Epoch 29/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.6774 - accuracy: 0.7927 - val_loss: 0.7144 - val_accuracy: 0.7786\n",
      "Epoch 30/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.6655 - accuracy: 0.7955 - val_loss: 0.7118 - val_accuracy: 0.7743\n",
      "Epoch 31/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.6537 - accuracy: 0.7994 - val_loss: 0.7011 - val_accuracy: 0.7850\n",
      "Epoch 32/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.6420 - accuracy: 0.8024 - val_loss: 0.7207 - val_accuracy: 0.7722\n",
      "Epoch 33/750\n",
      "96292/96292 [==============================] - 2s 23us/step - loss: 0.6318 - accuracy: 0.8064 - val_loss: 0.6671 - val_accuracy: 0.7877\n",
      "Epoch 34/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.6209 - accuracy: 0.8086 - val_loss: 0.6722 - val_accuracy: 0.7925\n",
      "Epoch 35/750\n",
      "96292/96292 [==============================] - 2s 23us/step - loss: 0.6110 - accuracy: 0.8108 - val_loss: 0.6579 - val_accuracy: 0.7961\n",
      "Epoch 36/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.6018 - accuracy: 0.8146 - val_loss: 0.6635 - val_accuracy: 0.7887\n",
      "Epoch 37/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.5931 - accuracy: 0.8164 - val_loss: 0.6279 - val_accuracy: 0.8002\n",
      "Epoch 38/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.5841 - accuracy: 0.8185 - val_loss: 0.6467 - val_accuracy: 0.8013\n",
      "Epoch 39/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.5755 - accuracy: 0.8217 - val_loss: 0.6294 - val_accuracy: 0.8012\n",
      "Epoch 40/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.5672 - accuracy: 0.8237 - val_loss: 0.6193 - val_accuracy: 0.8048\n",
      "Epoch 41/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.5597 - accuracy: 0.8267 - val_loss: 0.6161 - val_accuracy: 0.8103\n",
      "Epoch 42/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.5519 - accuracy: 0.8295 - val_loss: 0.6062 - val_accuracy: 0.8066\n",
      "Epoch 43/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.5451 - accuracy: 0.8310 - val_loss: 0.6232 - val_accuracy: 0.8122\n",
      "Epoch 44/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.5375 - accuracy: 0.8332 - val_loss: 0.5866 - val_accuracy: 0.8141\n",
      "Epoch 45/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.5311 - accuracy: 0.8358 - val_loss: 0.5921 - val_accuracy: 0.8082\n",
      "Epoch 46/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.5244 - accuracy: 0.8367 - val_loss: 0.5819 - val_accuracy: 0.8159\n",
      "Epoch 47/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.5182 - accuracy: 0.8385 - val_loss: 0.5871 - val_accuracy: 0.8124\n",
      "Epoch 48/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.5119 - accuracy: 0.8416 - val_loss: 0.5932 - val_accuracy: 0.8128\n",
      "Epoch 49/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.5062 - accuracy: 0.8423 - val_loss: 0.5857 - val_accuracy: 0.8175\n",
      "Epoch 50/750\n",
      "96292/96292 [==============================] - 2s 23us/step - loss: 0.5014 - accuracy: 0.8435 - val_loss: 0.5467 - val_accuracy: 0.8279\n",
      "Epoch 51/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4951 - accuracy: 0.8455 - val_loss: 0.5956 - val_accuracy: 0.8073\n",
      "Epoch 52/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4901 - accuracy: 0.8474 - val_loss: 0.5758 - val_accuracy: 0.8205\n",
      "Epoch 53/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4848 - accuracy: 0.8485 - val_loss: 0.5430 - val_accuracy: 0.8294\n",
      "Epoch 54/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4797 - accuracy: 0.8503 - val_loss: 0.5704 - val_accuracy: 0.8161\n",
      "Epoch 55/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4748 - accuracy: 0.8526 - val_loss: 0.5463 - val_accuracy: 0.8259\n",
      "Epoch 56/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.4699 - accuracy: 0.8534 - val_loss: 0.5356 - val_accuracy: 0.8333\n",
      "Epoch 57/750\n",
      "96292/96292 [==============================] - ETA: 0s - loss: 0.4655 - accuracy: 0.85 - 2s 20us/step - loss: 0.4652 - accuracy: 0.8548 - val_loss: 0.5218 - val_accuracy: 0.8317\n",
      "Epoch 58/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.4608 - accuracy: 0.8558 - val_loss: 0.5687 - val_accuracy: 0.8165\n",
      "Epoch 59/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.4558 - accuracy: 0.8572 - val_loss: 0.5368 - val_accuracy: 0.8254\n",
      "Epoch 60/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4523 - accuracy: 0.8579 - val_loss: 0.5240 - val_accuracy: 0.8352\n",
      "Epoch 61/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4473 - accuracy: 0.8597 - val_loss: 0.5340 - val_accuracy: 0.8352\n",
      "Epoch 62/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4436 - accuracy: 0.8603 - val_loss: 0.5663 - val_accuracy: 0.8195\n",
      "Epoch 63/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.4397 - accuracy: 0.8618 - val_loss: 0.5149 - val_accuracy: 0.8399\n",
      "Epoch 64/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.4361 - accuracy: 0.8626 - val_loss: 0.5143 - val_accuracy: 0.8364\n",
      "Epoch 65/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4322 - accuracy: 0.8641 - val_loss: 0.4975 - val_accuracy: 0.8403\n",
      "Epoch 66/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4280 - accuracy: 0.8649 - val_loss: 0.5003 - val_accuracy: 0.8402\n",
      "Epoch 67/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4248 - accuracy: 0.8663 - val_loss: 0.5343 - val_accuracy: 0.8244\n",
      "Epoch 68/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4212 - accuracy: 0.8669 - val_loss: 0.4902 - val_accuracy: 0.8458\n",
      "Epoch 69/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4170 - accuracy: 0.8685 - val_loss: 0.4774 - val_accuracy: 0.8495\n",
      "Epoch 70/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4139 - accuracy: 0.8686 - val_loss: 0.5229 - val_accuracy: 0.8336\n",
      "Epoch 71/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4111 - accuracy: 0.8694 - val_loss: 0.5027 - val_accuracy: 0.8382\n",
      "Epoch 72/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4079 - accuracy: 0.8709 - val_loss: 0.5062 - val_accuracy: 0.8406\n",
      "Epoch 73/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4047 - accuracy: 0.8717 - val_loss: 0.4662 - val_accuracy: 0.8532\n",
      "Epoch 74/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.4008 - accuracy: 0.8734 - val_loss: 0.4848 - val_accuracy: 0.8452\n",
      "Epoch 75/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3984 - accuracy: 0.8730 - val_loss: 0.4751 - val_accuracy: 0.8470\n",
      "Epoch 76/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3947 - accuracy: 0.8749 - val_loss: 0.4653 - val_accuracy: 0.8511\n",
      "Epoch 77/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3922 - accuracy: 0.8749 - val_loss: 0.4867 - val_accuracy: 0.8439\n",
      "Epoch 78/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3887 - accuracy: 0.8759 - val_loss: 0.5206 - val_accuracy: 0.8290\n",
      "Epoch 79/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3866 - accuracy: 0.8775 - val_loss: 0.4852 - val_accuracy: 0.8431\n",
      "Epoch 80/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3843 - accuracy: 0.8775 - val_loss: 0.4655 - val_accuracy: 0.8518\n",
      "Epoch 81/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.3812 - accuracy: 0.8792 - val_loss: 0.4625 - val_accuracy: 0.8526\n",
      "Epoch 82/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.3776 - accuracy: 0.8802 - val_loss: 0.4541 - val_accuracy: 0.8552\n",
      "Epoch 83/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3762 - accuracy: 0.8794 - val_loss: 0.4694 - val_accuracy: 0.8493\n",
      "Epoch 84/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3728 - accuracy: 0.8812 - val_loss: 0.4499 - val_accuracy: 0.8512\n",
      "Epoch 85/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3710 - accuracy: 0.8816 - val_loss: 0.4716 - val_accuracy: 0.8523\n",
      "Epoch 86/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3676 - accuracy: 0.8820 - val_loss: 0.4701 - val_accuracy: 0.8496\n",
      "Epoch 87/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3656 - accuracy: 0.8839 - val_loss: 0.4499 - val_accuracy: 0.8539\n",
      "Epoch 88/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3640 - accuracy: 0.8832 - val_loss: 0.4416 - val_accuracy: 0.8588\n",
      "Epoch 89/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3617 - accuracy: 0.8841 - val_loss: 0.4726 - val_accuracy: 0.8521\n",
      "Epoch 90/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3584 - accuracy: 0.8857 - val_loss: 0.4547 - val_accuracy: 0.8586\n",
      "Epoch 91/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3565 - accuracy: 0.8855 - val_loss: 0.4302 - val_accuracy: 0.8617\n",
      "Epoch 92/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3534 - accuracy: 0.8871 - val_loss: 0.4437 - val_accuracy: 0.8586\n",
      "Epoch 93/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3517 - accuracy: 0.8872 - val_loss: 0.4357 - val_accuracy: 0.8659\n",
      "Epoch 94/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3497 - accuracy: 0.8880 - val_loss: 0.4624 - val_accuracy: 0.8455\n",
      "Epoch 95/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3480 - accuracy: 0.8883 - val_loss: 0.4489 - val_accuracy: 0.8556\n",
      "Epoch 96/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3459 - accuracy: 0.8891 - val_loss: 0.4344 - val_accuracy: 0.8622\n",
      "Epoch 97/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3427 - accuracy: 0.8905 - val_loss: 0.4433 - val_accuracy: 0.8598\n",
      "Epoch 98/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3410 - accuracy: 0.8910 - val_loss: 0.4419 - val_accuracy: 0.8625\n",
      "Epoch 99/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3389 - accuracy: 0.8909 - val_loss: 0.4308 - val_accuracy: 0.8617\n",
      "Epoch 100/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3377 - accuracy: 0.8916 - val_loss: 0.4086 - val_accuracy: 0.8659\n",
      "Epoch 101/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3349 - accuracy: 0.8930 - val_loss: 0.4614 - val_accuracy: 0.8464\n",
      "Epoch 102/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3331 - accuracy: 0.8930 - val_loss: 0.4089 - val_accuracy: 0.8759\n",
      "Epoch 103/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3315 - accuracy: 0.8943 - val_loss: 0.4550 - val_accuracy: 0.8464\n",
      "Epoch 104/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.3293 - accuracy: 0.8949 - val_loss: 0.4081 - val_accuracy: 0.8683\n",
      "Epoch 105/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3277 - accuracy: 0.8956 - val_loss: 0.4290 - val_accuracy: 0.8632\n",
      "Epoch 106/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3257 - accuracy: 0.8969 - val_loss: 0.4296 - val_accuracy: 0.8649\n",
      "Epoch 107/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3247 - accuracy: 0.8951 - val_loss: 0.4061 - val_accuracy: 0.8706\n",
      "Epoch 108/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3232 - accuracy: 0.8958 - val_loss: 0.4059 - val_accuracy: 0.8685\n",
      "Epoch 109/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3216 - accuracy: 0.8963 - val_loss: 0.4628 - val_accuracy: 0.8552\n",
      "Epoch 110/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3193 - accuracy: 0.8972 - val_loss: 0.4141 - val_accuracy: 0.8670\n",
      "Epoch 111/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3174 - accuracy: 0.8981 - val_loss: 0.4387 - val_accuracy: 0.8602\n",
      "Epoch 112/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3154 - accuracy: 0.8980 - val_loss: 0.4059 - val_accuracy: 0.8691\n",
      "Epoch 113/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.3143 - accuracy: 0.8996 - val_loss: 0.4135 - val_accuracy: 0.8653\n",
      "Epoch 114/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.3128 - accuracy: 0.8992 - val_loss: 0.3960 - val_accuracy: 0.8728\n",
      "Epoch 115/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3108 - accuracy: 0.9004 - val_loss: 0.4920 - val_accuracy: 0.8464\n",
      "Epoch 116/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3096 - accuracy: 0.9004 - val_loss: 0.3883 - val_accuracy: 0.8809\n",
      "Epoch 117/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3077 - accuracy: 0.9007 - val_loss: 0.3906 - val_accuracy: 0.8756\n",
      "Epoch 118/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.3060 - accuracy: 0.9020 - val_loss: 0.4310 - val_accuracy: 0.8667\n",
      "Epoch 119/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.3045 - accuracy: 0.9022 - val_loss: 0.4164 - val_accuracy: 0.8691\n",
      "Epoch 120/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3035 - accuracy: 0.9019 - val_loss: 0.3836 - val_accuracy: 0.8751\n",
      "Epoch 121/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3019 - accuracy: 0.9026 - val_loss: 0.3764 - val_accuracy: 0.8788\n",
      "Epoch 122/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.3008 - accuracy: 0.9024 - val_loss: 0.4086 - val_accuracy: 0.8652\n",
      "Epoch 123/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2988 - accuracy: 0.9026 - val_loss: 0.4127 - val_accuracy: 0.8691\n",
      "Epoch 124/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2980 - accuracy: 0.9032 - val_loss: 0.4156 - val_accuracy: 0.8627\n",
      "Epoch 125/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2962 - accuracy: 0.9043 - val_loss: 0.4118 - val_accuracy: 0.8701\n",
      "Epoch 126/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2950 - accuracy: 0.9049 - val_loss: 0.4086 - val_accuracy: 0.8723\n",
      "Epoch 127/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2936 - accuracy: 0.9046 - val_loss: 0.3686 - val_accuracy: 0.8862\n",
      "Epoch 128/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2922 - accuracy: 0.9054 - val_loss: 0.4241 - val_accuracy: 0.8617\n",
      "Epoch 129/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2912 - accuracy: 0.9064 - val_loss: 0.3965 - val_accuracy: 0.8735\n",
      "Epoch 130/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2895 - accuracy: 0.9067 - val_loss: 0.3842 - val_accuracy: 0.8774\n",
      "Epoch 131/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2881 - accuracy: 0.9064 - val_loss: 0.3891 - val_accuracy: 0.8738\n",
      "Epoch 132/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2874 - accuracy: 0.9068 - val_loss: 0.3685 - val_accuracy: 0.8829\n",
      "Epoch 133/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2849 - accuracy: 0.9075 - val_loss: 0.3686 - val_accuracy: 0.8859\n",
      "Epoch 134/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2851 - accuracy: 0.9080 - val_loss: 0.4246 - val_accuracy: 0.8622\n",
      "Epoch 135/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2830 - accuracy: 0.9080 - val_loss: 0.3943 - val_accuracy: 0.8760\n",
      "Epoch 136/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2826 - accuracy: 0.9088 - val_loss: 0.3950 - val_accuracy: 0.8718\n",
      "Epoch 137/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2811 - accuracy: 0.9090 - val_loss: 0.3754 - val_accuracy: 0.8810\n",
      "Epoch 138/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2797 - accuracy: 0.9087 - val_loss: 0.3617 - val_accuracy: 0.8838\n",
      "Epoch 139/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2787 - accuracy: 0.9096 - val_loss: 0.3572 - val_accuracy: 0.8883\n",
      "Epoch 140/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2770 - accuracy: 0.9103 - val_loss: 0.3768 - val_accuracy: 0.8766\n",
      "Epoch 141/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2762 - accuracy: 0.9106 - val_loss: 0.3786 - val_accuracy: 0.8779\n",
      "Epoch 142/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2747 - accuracy: 0.9108 - val_loss: 0.3912 - val_accuracy: 0.8729\n",
      "Epoch 143/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2740 - accuracy: 0.9110 - val_loss: 0.3891 - val_accuracy: 0.8726\n",
      "Epoch 144/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2716 - accuracy: 0.9118 - val_loss: 0.3785 - val_accuracy: 0.8781\n",
      "Epoch 145/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2722 - accuracy: 0.9105 - val_loss: 0.3967 - val_accuracy: 0.8734\n",
      "Epoch 146/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2704 - accuracy: 0.9121 - val_loss: 0.3913 - val_accuracy: 0.8721\n",
      "Epoch 147/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2693 - accuracy: 0.9125 - val_loss: 0.3765 - val_accuracy: 0.8795\n",
      "Epoch 148/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2682 - accuracy: 0.9122 - val_loss: 0.3635 - val_accuracy: 0.8836\n",
      "Epoch 149/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2666 - accuracy: 0.9129 - val_loss: 0.3454 - val_accuracy: 0.8901\n",
      "Epoch 150/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2667 - accuracy: 0.9134 - val_loss: 0.3780 - val_accuracy: 0.8783\n",
      "Epoch 151/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2645 - accuracy: 0.9139 - val_loss: 0.3843 - val_accuracy: 0.8770\n",
      "Epoch 152/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2642 - accuracy: 0.9140 - val_loss: 0.3706 - val_accuracy: 0.8804\n",
      "Epoch 153/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2628 - accuracy: 0.9138 - val_loss: 0.3691 - val_accuracy: 0.8821\n",
      "Epoch 154/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2616 - accuracy: 0.9148 - val_loss: 0.3990 - val_accuracy: 0.8792\n",
      "Epoch 155/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2614 - accuracy: 0.9154 - val_loss: 0.3822 - val_accuracy: 0.8801\n",
      "Epoch 156/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2596 - accuracy: 0.9153 - val_loss: 0.3830 - val_accuracy: 0.8815\n",
      "Epoch 157/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2590 - accuracy: 0.9157 - val_loss: 0.3634 - val_accuracy: 0.8848\n",
      "Epoch 158/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2573 - accuracy: 0.9158 - val_loss: 0.3715 - val_accuracy: 0.8794\n",
      "Epoch 159/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2567 - accuracy: 0.9162 - val_loss: 0.3512 - val_accuracy: 0.8886\n",
      "Epoch 160/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2569 - accuracy: 0.9164 - val_loss: 0.3574 - val_accuracy: 0.8863\n",
      "Epoch 161/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2550 - accuracy: 0.9167 - val_loss: 0.4027 - val_accuracy: 0.8782\n",
      "Epoch 162/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2546 - accuracy: 0.9169 - val_loss: 0.3612 - val_accuracy: 0.8870\n",
      "Epoch 163/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2533 - accuracy: 0.9174 - val_loss: 0.3625 - val_accuracy: 0.8895\n",
      "Epoch 164/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2522 - accuracy: 0.9182 - val_loss: 0.3589 - val_accuracy: 0.8843\n",
      "Epoch 165/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2516 - accuracy: 0.9179 - val_loss: 0.3847 - val_accuracy: 0.8759\n",
      "Epoch 166/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2506 - accuracy: 0.9176 - val_loss: 0.3590 - val_accuracy: 0.8902\n",
      "Epoch 167/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2491 - accuracy: 0.9179 - val_loss: 0.3579 - val_accuracy: 0.8886\n",
      "Epoch 168/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2488 - accuracy: 0.9184 - val_loss: 0.3399 - val_accuracy: 0.8951\n",
      "Epoch 169/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2472 - accuracy: 0.9193 - val_loss: 0.3362 - val_accuracy: 0.8951\n",
      "Epoch 170/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2462 - accuracy: 0.9189 - val_loss: 0.3542 - val_accuracy: 0.8879\n",
      "Epoch 171/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2451 - accuracy: 0.9210 - val_loss: 0.3485 - val_accuracy: 0.8895\n",
      "Epoch 172/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2449 - accuracy: 0.9203 - val_loss: 0.3294 - val_accuracy: 0.8983\n",
      "Epoch 173/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2451 - accuracy: 0.9202 - val_loss: 0.3772 - val_accuracy: 0.8791\n",
      "Epoch 174/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2439 - accuracy: 0.9198 - val_loss: 0.3498 - val_accuracy: 0.8912\n",
      "Epoch 175/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2428 - accuracy: 0.9207 - val_loss: 0.3753 - val_accuracy: 0.8809\n",
      "Epoch 176/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2421 - accuracy: 0.9203 - val_loss: 0.3245 - val_accuracy: 0.8962\n",
      "Epoch 177/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2410 - accuracy: 0.9212 - val_loss: 0.3722 - val_accuracy: 0.8798\n",
      "Epoch 178/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2409 - accuracy: 0.9217 - val_loss: 0.3543 - val_accuracy: 0.8885\n",
      "Epoch 179/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2390 - accuracy: 0.9218 - val_loss: 0.3541 - val_accuracy: 0.8872\n",
      "Epoch 180/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2385 - accuracy: 0.9221 - val_loss: 0.3636 - val_accuracy: 0.8888\n",
      "Epoch 181/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2373 - accuracy: 0.9226 - val_loss: 0.3552 - val_accuracy: 0.8921\n",
      "Epoch 182/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2375 - accuracy: 0.9223 - val_loss: 0.3515 - val_accuracy: 0.8943\n",
      "Epoch 183/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2362 - accuracy: 0.9227 - val_loss: 0.4245 - val_accuracy: 0.8649\n",
      "Epoch 184/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2352 - accuracy: 0.9232 - val_loss: 0.4013 - val_accuracy: 0.8720\n",
      "Epoch 185/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2345 - accuracy: 0.9232 - val_loss: 0.3251 - val_accuracy: 0.8959\n",
      "Epoch 186/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.2338 - accuracy: 0.9229 - val_loss: 0.3590 - val_accuracy: 0.8808\n",
      "Epoch 187/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2329 - accuracy: 0.9235 - val_loss: 0.3211 - val_accuracy: 0.8997\n",
      "Epoch 188/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2325 - accuracy: 0.9240 - val_loss: 0.3389 - val_accuracy: 0.8971\n",
      "Epoch 189/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2310 - accuracy: 0.9239 - val_loss: 0.3523 - val_accuracy: 0.8853\n",
      "Epoch 190/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2308 - accuracy: 0.9241 - val_loss: 0.3744 - val_accuracy: 0.8823\n",
      "Epoch 191/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2306 - accuracy: 0.9238 - val_loss: 0.3913 - val_accuracy: 0.8773\n",
      "Epoch 192/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2295 - accuracy: 0.9247 - val_loss: 0.3383 - val_accuracy: 0.8904\n",
      "Epoch 193/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2280 - accuracy: 0.9253 - val_loss: 0.3582 - val_accuracy: 0.8895\n",
      "Epoch 194/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2286 - accuracy: 0.9247 - val_loss: 0.3489 - val_accuracy: 0.8816\n",
      "Epoch 195/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2276 - accuracy: 0.9250 - val_loss: 0.3582 - val_accuracy: 0.8942\n",
      "Epoch 196/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2270 - accuracy: 0.9253 - val_loss: 0.4003 - val_accuracy: 0.8745\n",
      "Epoch 197/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2265 - accuracy: 0.9254 - val_loss: 0.3473 - val_accuracy: 0.8936\n",
      "Epoch 198/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2259 - accuracy: 0.9254 - val_loss: 0.3287 - val_accuracy: 0.8963\n",
      "Epoch 199/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2251 - accuracy: 0.9255 - val_loss: 0.3741 - val_accuracy: 0.8834\n",
      "Epoch 200/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2235 - accuracy: 0.9263 - val_loss: 0.3284 - val_accuracy: 0.8988\n",
      "Epoch 201/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2231 - accuracy: 0.9263 - val_loss: 0.3429 - val_accuracy: 0.8878\n",
      "Epoch 202/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2224 - accuracy: 0.9259 - val_loss: 0.3447 - val_accuracy: 0.8906\n",
      "Epoch 203/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2214 - accuracy: 0.9266 - val_loss: 0.3311 - val_accuracy: 0.8967\n",
      "Epoch 204/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2208 - accuracy: 0.9272 - val_loss: 0.3685 - val_accuracy: 0.8826\n",
      "Epoch 205/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2206 - accuracy: 0.9273 - val_loss: 0.3819 - val_accuracy: 0.8713\n",
      "Epoch 206/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2205 - accuracy: 0.9276 - val_loss: 0.3478 - val_accuracy: 0.8967\n",
      "Epoch 207/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2192 - accuracy: 0.9280 - val_loss: 0.3723 - val_accuracy: 0.8765\n",
      "Epoch 208/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2184 - accuracy: 0.9280 - val_loss: 0.3321 - val_accuracy: 0.8981\n",
      "Epoch 209/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2181 - accuracy: 0.9278 - val_loss: 0.3311 - val_accuracy: 0.9015\n",
      "Epoch 210/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2171 - accuracy: 0.9281 - val_loss: 0.3703 - val_accuracy: 0.8851\n",
      "Epoch 211/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2163 - accuracy: 0.9286 - val_loss: 0.3491 - val_accuracy: 0.8963\n",
      "Epoch 212/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2158 - accuracy: 0.9283 - val_loss: 0.3141 - val_accuracy: 0.9021\n",
      "Epoch 213/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2159 - accuracy: 0.9286 - val_loss: 0.3372 - val_accuracy: 0.8952\n",
      "Epoch 214/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2150 - accuracy: 0.9293 - val_loss: 0.3444 - val_accuracy: 0.8939\n",
      "Epoch 215/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2145 - accuracy: 0.9289 - val_loss: 0.4005 - val_accuracy: 0.8784\n",
      "Epoch 216/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.2130 - accuracy: 0.9296 - val_loss: 0.3645 - val_accuracy: 0.8902\n",
      "Epoch 217/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2126 - accuracy: 0.9294 - val_loss: 0.3860 - val_accuracy: 0.8714\n",
      "Epoch 218/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.2124 - accuracy: 0.9295 - val_loss: 0.3299 - val_accuracy: 0.8964\n",
      "Epoch 219/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2126 - accuracy: 0.9300 - val_loss: 0.4265 - val_accuracy: 0.8809\n",
      "Epoch 220/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2105 - accuracy: 0.9300 - val_loss: 0.3465 - val_accuracy: 0.8869\n",
      "Epoch 221/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2104 - accuracy: 0.9306 - val_loss: 0.3595 - val_accuracy: 0.8853\n",
      "Epoch 222/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.2095 - accuracy: 0.9300 - val_loss: 0.3224 - val_accuracy: 0.9033\n",
      "Epoch 223/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2093 - accuracy: 0.9309 - val_loss: 0.3317 - val_accuracy: 0.8961\n",
      "Epoch 224/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2087 - accuracy: 0.9313 - val_loss: 0.3676 - val_accuracy: 0.8843\n",
      "Epoch 225/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2084 - accuracy: 0.9319 - val_loss: 0.3263 - val_accuracy: 0.9001\n",
      "Epoch 226/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2067 - accuracy: 0.9319 - val_loss: 0.3578 - val_accuracy: 0.8926\n",
      "Epoch 227/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2064 - accuracy: 0.9319 - val_loss: 0.3369 - val_accuracy: 0.9015\n",
      "Epoch 228/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2069 - accuracy: 0.9319 - val_loss: 0.3212 - val_accuracy: 0.9052\n",
      "Epoch 229/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2050 - accuracy: 0.9322 - val_loss: 0.3142 - val_accuracy: 0.9035\n",
      "Epoch 230/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2051 - accuracy: 0.9318 - val_loss: 0.4060 - val_accuracy: 0.8766\n",
      "Epoch 231/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2051 - accuracy: 0.9319 - val_loss: 0.3475 - val_accuracy: 0.8920\n",
      "Epoch 232/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2039 - accuracy: 0.9324 - val_loss: 0.3323 - val_accuracy: 0.8978\n",
      "Epoch 233/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2038 - accuracy: 0.9331 - val_loss: 0.3310 - val_accuracy: 0.9050\n",
      "Epoch 234/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2029 - accuracy: 0.9332 - val_loss: 0.3545 - val_accuracy: 0.8892\n",
      "Epoch 235/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2029 - accuracy: 0.9335 - val_loss: 0.3384 - val_accuracy: 0.8936\n",
      "Epoch 236/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.2028 - accuracy: 0.9335 - val_loss: 0.3436 - val_accuracy: 0.8872\n",
      "Epoch 237/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.2011 - accuracy: 0.9334 - val_loss: 0.3827 - val_accuracy: 0.8839\n",
      "Epoch 238/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2013 - accuracy: 0.9333 - val_loss: 0.3154 - val_accuracy: 0.9019\n",
      "Epoch 239/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2010 - accuracy: 0.9329 - val_loss: 0.3251 - val_accuracy: 0.9015\n",
      "Epoch 240/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.2005 - accuracy: 0.9330 - val_loss: 0.3423 - val_accuracy: 0.8971\n",
      "Epoch 241/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1990 - accuracy: 0.9335 - val_loss: 0.3319 - val_accuracy: 0.9025\n",
      "Epoch 242/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1984 - accuracy: 0.9342 - val_loss: 0.3207 - val_accuracy: 0.8985\n",
      "Epoch 243/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1983 - accuracy: 0.9348 - val_loss: 0.3072 - val_accuracy: 0.9099\n",
      "Epoch 244/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1976 - accuracy: 0.9337 - val_loss: 0.3858 - val_accuracy: 0.8816\n",
      "Epoch 245/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1973 - accuracy: 0.9349 - val_loss: 0.3915 - val_accuracy: 0.8905\n",
      "Epoch 246/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1968 - accuracy: 0.9344 - val_loss: 0.3568 - val_accuracy: 0.8993\n",
      "Epoch 247/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1956 - accuracy: 0.9351 - val_loss: 0.3464 - val_accuracy: 0.8970\n",
      "Epoch 248/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1955 - accuracy: 0.9353 - val_loss: 0.3641 - val_accuracy: 0.8967\n",
      "Epoch 249/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1949 - accuracy: 0.9352 - val_loss: 0.3080 - val_accuracy: 0.9070\n",
      "Epoch 250/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1949 - accuracy: 0.9361 - val_loss: 0.3372 - val_accuracy: 0.8971\n",
      "Epoch 251/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1946 - accuracy: 0.9353 - val_loss: 0.3420 - val_accuracy: 0.9003\n",
      "Epoch 252/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1941 - accuracy: 0.9352 - val_loss: 0.3089 - val_accuracy: 0.9091\n",
      "Epoch 253/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1930 - accuracy: 0.9361 - val_loss: 0.3462 - val_accuracy: 0.8924\n",
      "Epoch 254/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1933 - accuracy: 0.9357 - val_loss: 0.3335 - val_accuracy: 0.9001\n",
      "Epoch 255/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1923 - accuracy: 0.9365 - val_loss: 0.2978 - val_accuracy: 0.9117\n",
      "Epoch 256/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1915 - accuracy: 0.9357 - val_loss: 0.3614 - val_accuracy: 0.8931\n",
      "Epoch 257/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1909 - accuracy: 0.9360 - val_loss: 0.3232 - val_accuracy: 0.9040\n",
      "Epoch 258/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1914 - accuracy: 0.9364 - val_loss: 0.3154 - val_accuracy: 0.9035\n",
      "Epoch 259/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1901 - accuracy: 0.9366 - val_loss: 0.3509 - val_accuracy: 0.8956\n",
      "Epoch 260/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1894 - accuracy: 0.9371 - val_loss: 0.3283 - val_accuracy: 0.8996\n",
      "Epoch 261/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1893 - accuracy: 0.9370 - val_loss: 0.3405 - val_accuracy: 0.9005\n",
      "Epoch 262/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1889 - accuracy: 0.9371 - val_loss: 0.3056 - val_accuracy: 0.9100\n",
      "Epoch 263/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1888 - accuracy: 0.9367 - val_loss: 0.3817 - val_accuracy: 0.8885\n",
      "Epoch 264/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1877 - accuracy: 0.9375 - val_loss: 0.3104 - val_accuracy: 0.9072\n",
      "Epoch 265/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1883 - accuracy: 0.9375 - val_loss: 0.3863 - val_accuracy: 0.8825\n",
      "Epoch 266/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1866 - accuracy: 0.9377 - val_loss: 0.3222 - val_accuracy: 0.9024\n",
      "Epoch 267/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1859 - accuracy: 0.9383 - val_loss: 0.3523 - val_accuracy: 0.8927\n",
      "Epoch 268/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1860 - accuracy: 0.9378 - val_loss: 0.3320 - val_accuracy: 0.9035\n",
      "Epoch 269/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1862 - accuracy: 0.9382 - val_loss: 0.3670 - val_accuracy: 0.8820\n",
      "Epoch 270/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1847 - accuracy: 0.9387 - val_loss: 0.3722 - val_accuracy: 0.8838\n",
      "Epoch 271/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1846 - accuracy: 0.9385 - val_loss: 0.3592 - val_accuracy: 0.9040\n",
      "Epoch 272/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1844 - accuracy: 0.9381 - val_loss: 0.3247 - val_accuracy: 0.9051\n",
      "Epoch 273/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1835 - accuracy: 0.9385 - val_loss: 0.3761 - val_accuracy: 0.8940\n",
      "Epoch 274/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1837 - accuracy: 0.9392 - val_loss: 0.3231 - val_accuracy: 0.9055\n",
      "Epoch 275/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1828 - accuracy: 0.9386 - val_loss: 0.3184 - val_accuracy: 0.8971\n",
      "Epoch 276/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1823 - accuracy: 0.9395 - val_loss: 0.3388 - val_accuracy: 0.8977\n",
      "Epoch 277/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1820 - accuracy: 0.9397 - val_loss: 0.3322 - val_accuracy: 0.9005\n",
      "Epoch 278/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1815 - accuracy: 0.9396 - val_loss: 0.3019 - val_accuracy: 0.9140\n",
      "Epoch 279/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1818 - accuracy: 0.9390 - val_loss: 0.3309 - val_accuracy: 0.9019\n",
      "Epoch 280/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1808 - accuracy: 0.9400 - val_loss: 0.3279 - val_accuracy: 0.8993\n",
      "Epoch 281/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1806 - accuracy: 0.9402 - val_loss: 0.3109 - val_accuracy: 0.9138\n",
      "Epoch 282/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1801 - accuracy: 0.9397 - val_loss: 0.3343 - val_accuracy: 0.9066\n",
      "Epoch 283/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1798 - accuracy: 0.9407 - val_loss: 0.3496 - val_accuracy: 0.8983\n",
      "Epoch 284/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1790 - accuracy: 0.9407 - val_loss: 0.3414 - val_accuracy: 0.8931\n",
      "Epoch 285/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1783 - accuracy: 0.9405 - val_loss: 0.3017 - val_accuracy: 0.9104\n",
      "Epoch 286/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1781 - accuracy: 0.9406 - val_loss: 0.3294 - val_accuracy: 0.9015\n",
      "Epoch 287/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1782 - accuracy: 0.9400 - val_loss: 0.3230 - val_accuracy: 0.9063\n",
      "Epoch 288/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1775 - accuracy: 0.9413 - val_loss: 0.3514 - val_accuracy: 0.8880\n",
      "Epoch 289/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1775 - accuracy: 0.9402 - val_loss: 0.3120 - val_accuracy: 0.9113\n",
      "Epoch 290/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1764 - accuracy: 0.9406 - val_loss: 0.3351 - val_accuracy: 0.9022\n",
      "Epoch 291/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1768 - accuracy: 0.9410 - val_loss: 0.3755 - val_accuracy: 0.8893\n",
      "Epoch 292/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1763 - accuracy: 0.9409 - val_loss: 0.3071 - val_accuracy: 0.9084\n",
      "Epoch 293/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1765 - accuracy: 0.9410 - val_loss: 0.3121 - val_accuracy: 0.9092\n",
      "Epoch 294/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1751 - accuracy: 0.9410 - val_loss: 0.3064 - val_accuracy: 0.9127\n",
      "Epoch 295/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1750 - accuracy: 0.9417 - val_loss: 0.3263 - val_accuracy: 0.9075\n",
      "Epoch 296/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1744 - accuracy: 0.9419 - val_loss: 0.3309 - val_accuracy: 0.9109\n",
      "Epoch 297/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1737 - accuracy: 0.9419 - val_loss: 0.3360 - val_accuracy: 0.8970\n",
      "Epoch 298/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1734 - accuracy: 0.9426 - val_loss: 0.3412 - val_accuracy: 0.9041\n",
      "Epoch 299/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1733 - accuracy: 0.9421 - val_loss: 0.3152 - val_accuracy: 0.9110\n",
      "Epoch 300/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1731 - accuracy: 0.9423 - val_loss: 0.3452 - val_accuracy: 0.9004\n",
      "Epoch 301/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1732 - accuracy: 0.9421 - val_loss: 0.3729 - val_accuracy: 0.8988\n",
      "Epoch 302/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1722 - accuracy: 0.9431 - val_loss: 0.3526 - val_accuracy: 0.8987\n",
      "Epoch 303/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1721 - accuracy: 0.9427 - val_loss: 0.3619 - val_accuracy: 0.8941\n",
      "Epoch 304/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1720 - accuracy: 0.9424 - val_loss: 0.3050 - val_accuracy: 0.9130\n",
      "Epoch 305/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1706 - accuracy: 0.9439 - val_loss: 0.3643 - val_accuracy: 0.9040\n",
      "Epoch 306/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1706 - accuracy: 0.9427 - val_loss: 0.4133 - val_accuracy: 0.8845\n",
      "Epoch 307/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1704 - accuracy: 0.9426 - val_loss: 0.3395 - val_accuracy: 0.9035\n",
      "Epoch 308/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1700 - accuracy: 0.9434 - val_loss: 0.3448 - val_accuracy: 0.9021\n",
      "Epoch 309/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1696 - accuracy: 0.9432 - val_loss: 0.3252 - val_accuracy: 0.9092\n",
      "Epoch 310/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1701 - accuracy: 0.9432 - val_loss: 0.3270 - val_accuracy: 0.9101\n",
      "Epoch 311/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1690 - accuracy: 0.9433 - val_loss: 0.3028 - val_accuracy: 0.9128\n",
      "Epoch 312/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1687 - accuracy: 0.9433 - val_loss: 0.2991 - val_accuracy: 0.9160\n",
      "Epoch 313/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1679 - accuracy: 0.9439 - val_loss: 0.3297 - val_accuracy: 0.9069\n",
      "Epoch 314/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1681 - accuracy: 0.9443 - val_loss: 0.3444 - val_accuracy: 0.8946\n",
      "Epoch 315/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1678 - accuracy: 0.9434 - val_loss: 0.2897 - val_accuracy: 0.9175\n",
      "Epoch 316/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1673 - accuracy: 0.9437 - val_loss: 0.3725 - val_accuracy: 0.8942\n",
      "Epoch 317/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1675 - accuracy: 0.9439 - val_loss: 0.3369 - val_accuracy: 0.9074\n",
      "Epoch 318/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1666 - accuracy: 0.9448 - val_loss: 0.3046 - val_accuracy: 0.9145\n",
      "Epoch 319/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1663 - accuracy: 0.9440 - val_loss: 0.3391 - val_accuracy: 0.9017\n",
      "Epoch 320/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1660 - accuracy: 0.9443 - val_loss: 0.3330 - val_accuracy: 0.9081\n",
      "Epoch 321/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1657 - accuracy: 0.9446 - val_loss: 0.3176 - val_accuracy: 0.9109\n",
      "Epoch 322/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1654 - accuracy: 0.9453 - val_loss: 0.3122 - val_accuracy: 0.9151\n",
      "Epoch 323/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1647 - accuracy: 0.9454 - val_loss: 0.3716 - val_accuracy: 0.8978\n",
      "Epoch 324/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1640 - accuracy: 0.9452 - val_loss: 0.3645 - val_accuracy: 0.8892\n",
      "Epoch 325/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1645 - accuracy: 0.9448 - val_loss: 0.2918 - val_accuracy: 0.9202\n",
      "Epoch 326/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1644 - accuracy: 0.9452 - val_loss: 0.3398 - val_accuracy: 0.9047\n",
      "Epoch 327/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1630 - accuracy: 0.9453 - val_loss: 0.3451 - val_accuracy: 0.9030\n",
      "Epoch 328/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1632 - accuracy: 0.9453 - val_loss: 0.3622 - val_accuracy: 0.9022\n",
      "Epoch 329/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1630 - accuracy: 0.9448 - val_loss: 0.3725 - val_accuracy: 0.8907\n",
      "Epoch 330/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1625 - accuracy: 0.9451 - val_loss: 0.3524 - val_accuracy: 0.9061\n",
      "Epoch 331/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1625 - accuracy: 0.9456 - val_loss: 0.3190 - val_accuracy: 0.9120\n",
      "Epoch 332/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1613 - accuracy: 0.9460 - val_loss: 0.3221 - val_accuracy: 0.9047\n",
      "Epoch 333/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1624 - accuracy: 0.9456 - val_loss: 0.3770 - val_accuracy: 0.8940\n",
      "Epoch 334/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1617 - accuracy: 0.9455 - val_loss: 0.3573 - val_accuracy: 0.9003\n",
      "Epoch 335/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1611 - accuracy: 0.9457 - val_loss: 0.3176 - val_accuracy: 0.9150\n",
      "Epoch 336/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1610 - accuracy: 0.9460 - val_loss: 0.3264 - val_accuracy: 0.9049\n",
      "Epoch 337/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1609 - accuracy: 0.9459 - val_loss: 0.3426 - val_accuracy: 0.9059\n",
      "Epoch 338/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1602 - accuracy: 0.9460 - val_loss: 0.2994 - val_accuracy: 0.9160\n",
      "Epoch 339/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1601 - accuracy: 0.9456 - val_loss: 0.3460 - val_accuracy: 0.8953\n",
      "Epoch 340/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1595 - accuracy: 0.9460 - val_loss: 0.3185 - val_accuracy: 0.9075\n",
      "Epoch 341/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1593 - accuracy: 0.9470 - val_loss: 0.3079 - val_accuracy: 0.9162\n",
      "Epoch 342/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1596 - accuracy: 0.9460 - val_loss: 0.3430 - val_accuracy: 0.9054\n",
      "Epoch 343/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1588 - accuracy: 0.9469 - val_loss: 0.3291 - val_accuracy: 0.9035\n",
      "Epoch 344/750\n",
      "96292/96292 [==============================] - 2s 23us/step - loss: 0.1585 - accuracy: 0.9463 - val_loss: 0.3157 - val_accuracy: 0.9153\n",
      "Epoch 345/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1580 - accuracy: 0.9476 - val_loss: 0.3389 - val_accuracy: 0.9039\n",
      "Epoch 346/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1578 - accuracy: 0.9475 - val_loss: 0.4132 - val_accuracy: 0.8868\n",
      "Epoch 347/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1576 - accuracy: 0.9474 - val_loss: 0.3579 - val_accuracy: 0.9035\n",
      "Epoch 348/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1568 - accuracy: 0.9477 - val_loss: 0.3024 - val_accuracy: 0.9187\n",
      "Epoch 349/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1568 - accuracy: 0.9478 - val_loss: 0.3044 - val_accuracy: 0.9096\n",
      "Epoch 350/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1563 - accuracy: 0.9468 - val_loss: 0.3507 - val_accuracy: 0.8976\n",
      "Epoch 351/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1561 - accuracy: 0.9477 - val_loss: 0.3306 - val_accuracy: 0.9038\n",
      "Epoch 352/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1558 - accuracy: 0.9472 - val_loss: 0.3266 - val_accuracy: 0.9103\n",
      "Epoch 353/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1562 - accuracy: 0.9476 - val_loss: 0.3200 - val_accuracy: 0.9121\n",
      "Epoch 354/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1567 - accuracy: 0.9475 - val_loss: 0.4073 - val_accuracy: 0.8829\n",
      "Epoch 355/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1551 - accuracy: 0.9480 - val_loss: 0.3565 - val_accuracy: 0.9035\n",
      "Epoch 356/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1559 - accuracy: 0.9483 - val_loss: 0.3171 - val_accuracy: 0.9123\n",
      "Epoch 357/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1543 - accuracy: 0.9483 - val_loss: 0.3650 - val_accuracy: 0.9003\n",
      "Epoch 358/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1554 - accuracy: 0.9479 - val_loss: 0.2971 - val_accuracy: 0.9203\n",
      "Epoch 359/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1548 - accuracy: 0.9478 - val_loss: 0.3262 - val_accuracy: 0.9079\n",
      "Epoch 360/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1534 - accuracy: 0.9481 - val_loss: 0.3285 - val_accuracy: 0.9050\n",
      "Epoch 361/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1540 - accuracy: 0.9485 - val_loss: 0.3220 - val_accuracy: 0.9093\n",
      "Epoch 362/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1533 - accuracy: 0.9488 - val_loss: 0.2918 - val_accuracy: 0.9202\n",
      "Epoch 363/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1538 - accuracy: 0.9483 - val_loss: 0.3048 - val_accuracy: 0.9157\n",
      "Epoch 364/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1527 - accuracy: 0.9489 - val_loss: 0.3732 - val_accuracy: 0.8896\n",
      "Epoch 365/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1529 - accuracy: 0.9480 - val_loss: 0.3426 - val_accuracy: 0.9100\n",
      "Epoch 366/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1524 - accuracy: 0.9492 - val_loss: 0.3177 - val_accuracy: 0.9128\n",
      "Epoch 367/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1528 - accuracy: 0.9491 - val_loss: 0.2939 - val_accuracy: 0.9183\n",
      "Epoch 368/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1523 - accuracy: 0.9485 - val_loss: 0.3168 - val_accuracy: 0.9143\n",
      "Epoch 369/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1513 - accuracy: 0.9493 - val_loss: 0.3123 - val_accuracy: 0.9128\n",
      "Epoch 370/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1516 - accuracy: 0.9491 - val_loss: 0.3160 - val_accuracy: 0.9207\n",
      "Epoch 371/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1519 - accuracy: 0.9492 - val_loss: 0.3403 - val_accuracy: 0.9059\n",
      "Epoch 372/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1514 - accuracy: 0.9492 - val_loss: 0.4110 - val_accuracy: 0.8912\n",
      "Epoch 373/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1509 - accuracy: 0.9490 - val_loss: 0.3118 - val_accuracy: 0.9092\n",
      "Epoch 374/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1509 - accuracy: 0.9498 - val_loss: 0.3559 - val_accuracy: 0.9075\n",
      "Epoch 375/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1499 - accuracy: 0.9494 - val_loss: 0.3131 - val_accuracy: 0.9148\n",
      "Epoch 376/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1502 - accuracy: 0.9489 - val_loss: 0.3452 - val_accuracy: 0.8976\n",
      "Epoch 377/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1501 - accuracy: 0.9495 - val_loss: 0.3447 - val_accuracy: 0.9067\n",
      "Epoch 378/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1496 - accuracy: 0.9497 - val_loss: 0.2951 - val_accuracy: 0.9237\n",
      "Epoch 379/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1487 - accuracy: 0.9497 - val_loss: 0.3494 - val_accuracy: 0.9066\n",
      "Epoch 380/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1493 - accuracy: 0.9500 - val_loss: 0.3457 - val_accuracy: 0.9126\n",
      "Epoch 381/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1486 - accuracy: 0.9501 - val_loss: 0.3451 - val_accuracy: 0.9066\n",
      "Epoch 382/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1484 - accuracy: 0.9500 - val_loss: 0.3287 - val_accuracy: 0.9150\n",
      "Epoch 383/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1484 - accuracy: 0.9501 - val_loss: 0.3203 - val_accuracy: 0.9140\n",
      "Epoch 384/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1481 - accuracy: 0.9498 - val_loss: 0.4097 - val_accuracy: 0.8698\n",
      "Epoch 385/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1476 - accuracy: 0.9503 - val_loss: 0.3502 - val_accuracy: 0.9105\n",
      "Epoch 386/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1481 - accuracy: 0.9503 - val_loss: 0.3403 - val_accuracy: 0.9129\n",
      "Epoch 387/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1475 - accuracy: 0.9506 - val_loss: 0.3160 - val_accuracy: 0.9132\n",
      "Epoch 388/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1475 - accuracy: 0.9494 - val_loss: 0.3270 - val_accuracy: 0.9105\n",
      "Epoch 389/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1473 - accuracy: 0.9503 - val_loss: 0.3154 - val_accuracy: 0.9182\n",
      "Epoch 390/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1470 - accuracy: 0.9497 - val_loss: 0.3289 - val_accuracy: 0.9040\n",
      "Epoch 391/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1459 - accuracy: 0.9508 - val_loss: 0.2969 - val_accuracy: 0.9187\n",
      "Epoch 392/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1457 - accuracy: 0.9505 - val_loss: 0.3146 - val_accuracy: 0.9176\n",
      "Epoch 393/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1463 - accuracy: 0.9509 - val_loss: 0.3262 - val_accuracy: 0.9133\n",
      "Epoch 394/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1456 - accuracy: 0.9507 - val_loss: 0.3321 - val_accuracy: 0.9043\n",
      "Epoch 395/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1456 - accuracy: 0.9506 - val_loss: 0.3142 - val_accuracy: 0.9164\n",
      "Epoch 396/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1456 - accuracy: 0.9510 - val_loss: 0.3509 - val_accuracy: 0.9073\n",
      "Epoch 397/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1449 - accuracy: 0.9507 - val_loss: 0.3119 - val_accuracy: 0.9192\n",
      "Epoch 398/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1448 - accuracy: 0.9508 - val_loss: 0.3062 - val_accuracy: 0.9158\n",
      "Epoch 399/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1451 - accuracy: 0.9511 - val_loss: 0.3316 - val_accuracy: 0.9133\n",
      "Epoch 400/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1447 - accuracy: 0.9513 - val_loss: 0.3171 - val_accuracy: 0.9163\n",
      "Epoch 401/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1446 - accuracy: 0.9516 - val_loss: 0.3479 - val_accuracy: 0.9069\n",
      "Epoch 402/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1441 - accuracy: 0.9522 - val_loss: 0.3365 - val_accuracy: 0.9142\n",
      "Epoch 403/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1441 - accuracy: 0.9519 - val_loss: 0.3501 - val_accuracy: 0.9091\n",
      "Epoch 404/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1428 - accuracy: 0.9514 - val_loss: 0.3173 - val_accuracy: 0.9170\n",
      "Epoch 405/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1434 - accuracy: 0.9516 - val_loss: 0.3869 - val_accuracy: 0.9019\n",
      "Epoch 406/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1434 - accuracy: 0.9517 - val_loss: 0.3169 - val_accuracy: 0.9185\n",
      "Epoch 407/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1438 - accuracy: 0.9516 - val_loss: 0.3107 - val_accuracy: 0.9170\n",
      "Epoch 408/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1423 - accuracy: 0.9521 - val_loss: 0.3147 - val_accuracy: 0.9170\n",
      "Epoch 409/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1432 - accuracy: 0.9517 - val_loss: 0.3211 - val_accuracy: 0.9191\n",
      "Epoch 410/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1420 - accuracy: 0.9522 - val_loss: 0.3436 - val_accuracy: 0.9095\n",
      "Epoch 411/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1417 - accuracy: 0.9531 - val_loss: 0.4165 - val_accuracy: 0.8900\n",
      "Epoch 412/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1421 - accuracy: 0.9520 - val_loss: 0.3452 - val_accuracy: 0.9179\n",
      "Epoch 413/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1419 - accuracy: 0.9520 - val_loss: 0.3514 - val_accuracy: 0.9123\n",
      "Epoch 414/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1407 - accuracy: 0.9520 - val_loss: 0.3398 - val_accuracy: 0.9104\n",
      "Epoch 415/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1421 - accuracy: 0.9524 - val_loss: 0.3600 - val_accuracy: 0.9030\n",
      "Epoch 416/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1409 - accuracy: 0.9526 - val_loss: 0.3691 - val_accuracy: 0.9085\n",
      "Epoch 417/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1403 - accuracy: 0.9523 - val_loss: 0.3065 - val_accuracy: 0.9239\n",
      "Epoch 418/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1407 - accuracy: 0.9526 - val_loss: 0.3059 - val_accuracy: 0.9229\n",
      "Epoch 419/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1403 - accuracy: 0.9528 - val_loss: 0.3541 - val_accuracy: 0.9139\n",
      "Epoch 420/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1402 - accuracy: 0.9529 - val_loss: 0.3303 - val_accuracy: 0.9133\n",
      "Epoch 421/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1399 - accuracy: 0.9531 - val_loss: 0.3112 - val_accuracy: 0.9226\n",
      "Epoch 422/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1402 - accuracy: 0.9528 - val_loss: 0.3105 - val_accuracy: 0.9192\n",
      "Epoch 423/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1398 - accuracy: 0.9528 - val_loss: 0.3966 - val_accuracy: 0.9013\n",
      "Epoch 424/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1394 - accuracy: 0.9526 - val_loss: 0.3667 - val_accuracy: 0.9104\n",
      "Epoch 425/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1383 - accuracy: 0.9529 - val_loss: 0.3379 - val_accuracy: 0.9166\n",
      "Epoch 426/750\n",
      "96292/96292 [==============================] - 2s 23us/step - loss: 0.1384 - accuracy: 0.9530 - val_loss: 0.3002 - val_accuracy: 0.9231\n",
      "Epoch 427/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1390 - accuracy: 0.9530 - val_loss: 0.3004 - val_accuracy: 0.9203\n",
      "Epoch 428/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1390 - accuracy: 0.9528 - val_loss: 0.3254 - val_accuracy: 0.9154\n",
      "Epoch 429/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1382 - accuracy: 0.9529 - val_loss: 0.3139 - val_accuracy: 0.9233\n",
      "Epoch 430/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1375 - accuracy: 0.9534 - val_loss: 0.4060 - val_accuracy: 0.8866\n",
      "Epoch 431/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1379 - accuracy: 0.9530 - val_loss: 0.3152 - val_accuracy: 0.9202\n",
      "Epoch 432/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1368 - accuracy: 0.9529 - val_loss: 0.2963 - val_accuracy: 0.9266\n",
      "Epoch 433/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1379 - accuracy: 0.9536 - val_loss: 0.3338 - val_accuracy: 0.9095\n",
      "Epoch 434/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1371 - accuracy: 0.9539 - val_loss: 0.4893 - val_accuracy: 0.8762\n",
      "Epoch 435/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1376 - accuracy: 0.9532 - val_loss: 0.3851 - val_accuracy: 0.8964\n",
      "Epoch 436/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1365 - accuracy: 0.9534 - val_loss: 0.3309 - val_accuracy: 0.9134\n",
      "Epoch 437/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1369 - accuracy: 0.9535 - val_loss: 0.3210 - val_accuracy: 0.9165\n",
      "Epoch 438/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1367 - accuracy: 0.9536 - val_loss: 0.3088 - val_accuracy: 0.9178\n",
      "Epoch 439/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1358 - accuracy: 0.9544 - val_loss: 0.3383 - val_accuracy: 0.9167\n",
      "Epoch 440/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1365 - accuracy: 0.9539 - val_loss: 0.3409 - val_accuracy: 0.9133\n",
      "Epoch 441/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1359 - accuracy: 0.9546 - val_loss: 0.3249 - val_accuracy: 0.9143\n",
      "Epoch 442/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1357 - accuracy: 0.9540 - val_loss: 0.4120 - val_accuracy: 0.9016\n",
      "Epoch 443/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1358 - accuracy: 0.9543 - val_loss: 0.3090 - val_accuracy: 0.9228\n",
      "Epoch 444/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1358 - accuracy: 0.9543 - val_loss: 0.3171 - val_accuracy: 0.9194\n",
      "Epoch 445/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1347 - accuracy: 0.9540 - val_loss: 0.3464 - val_accuracy: 0.9064\n",
      "Epoch 446/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1349 - accuracy: 0.9542 - val_loss: 0.3502 - val_accuracy: 0.9172\n",
      "Epoch 447/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1355 - accuracy: 0.9541 - val_loss: 0.3528 - val_accuracy: 0.9095\n",
      "Epoch 448/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1345 - accuracy: 0.9543 - val_loss: 0.3690 - val_accuracy: 0.9052\n",
      "Epoch 449/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1338 - accuracy: 0.9543 - val_loss: 0.3139 - val_accuracy: 0.9144\n",
      "Epoch 450/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1344 - accuracy: 0.9544 - val_loss: 0.3160 - val_accuracy: 0.9222\n",
      "Epoch 451/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1345 - accuracy: 0.9546 - val_loss: 0.3586 - val_accuracy: 0.9007\n",
      "Epoch 452/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1349 - accuracy: 0.9545 - val_loss: 0.3210 - val_accuracy: 0.9245\n",
      "Epoch 453/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1339 - accuracy: 0.9544 - val_loss: 0.3928 - val_accuracy: 0.8949\n",
      "Epoch 454/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1338 - accuracy: 0.9541 - val_loss: 0.3467 - val_accuracy: 0.9164\n",
      "Epoch 455/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1329 - accuracy: 0.9549 - val_loss: 0.3411 - val_accuracy: 0.9136\n",
      "Epoch 456/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1331 - accuracy: 0.9547 - val_loss: 0.3300 - val_accuracy: 0.9198\n",
      "Epoch 457/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1324 - accuracy: 0.9546 - val_loss: 0.3553 - val_accuracy: 0.9028\n",
      "Epoch 458/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1335 - accuracy: 0.9551 - val_loss: 0.3168 - val_accuracy: 0.9247\n",
      "Epoch 459/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1330 - accuracy: 0.9552 - val_loss: 0.3215 - val_accuracy: 0.9173\n",
      "Epoch 460/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1323 - accuracy: 0.9555 - val_loss: 0.3361 - val_accuracy: 0.9128\n",
      "Epoch 461/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1323 - accuracy: 0.9552 - val_loss: 0.3313 - val_accuracy: 0.9212\n",
      "Epoch 462/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1316 - accuracy: 0.9545 - val_loss: 0.3633 - val_accuracy: 0.8966\n",
      "Epoch 463/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1320 - accuracy: 0.9551 - val_loss: 0.3567 - val_accuracy: 0.9059\n",
      "Epoch 464/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1316 - accuracy: 0.9556 - val_loss: 0.3427 - val_accuracy: 0.9099\n",
      "Epoch 465/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1313 - accuracy: 0.9558 - val_loss: 0.3235 - val_accuracy: 0.9170\n",
      "Epoch 466/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1310 - accuracy: 0.9557 - val_loss: 0.3049 - val_accuracy: 0.9217\n",
      "Epoch 467/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1315 - accuracy: 0.9553 - val_loss: 0.3033 - val_accuracy: 0.9242\n",
      "Epoch 468/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1306 - accuracy: 0.9550 - val_loss: 0.3067 - val_accuracy: 0.9273\n",
      "Epoch 469/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1313 - accuracy: 0.9556 - val_loss: 0.3513 - val_accuracy: 0.9077\n",
      "Epoch 470/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1313 - accuracy: 0.9557 - val_loss: 0.3223 - val_accuracy: 0.9198\n",
      "Epoch 471/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1308 - accuracy: 0.9553 - val_loss: 0.4128 - val_accuracy: 0.8963\n",
      "Epoch 472/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1314 - accuracy: 0.9546 - val_loss: 0.3227 - val_accuracy: 0.9192\n",
      "Epoch 473/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1300 - accuracy: 0.9556 - val_loss: 0.3386 - val_accuracy: 0.9182\n",
      "Epoch 474/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1303 - accuracy: 0.9563 - val_loss: 0.3574 - val_accuracy: 0.9088\n",
      "Epoch 475/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1300 - accuracy: 0.9553 - val_loss: 0.3501 - val_accuracy: 0.9146\n",
      "Epoch 476/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1293 - accuracy: 0.9561 - val_loss: 0.3625 - val_accuracy: 0.9081\n",
      "Epoch 477/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1296 - accuracy: 0.9561 - val_loss: 0.3224 - val_accuracy: 0.9233\n",
      "Epoch 478/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1297 - accuracy: 0.9559 - val_loss: 0.3918 - val_accuracy: 0.9013\n",
      "Epoch 479/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1300 - accuracy: 0.9563 - val_loss: 0.3240 - val_accuracy: 0.9250\n",
      "Epoch 480/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1294 - accuracy: 0.9562 - val_loss: 0.3221 - val_accuracy: 0.9221\n",
      "Epoch 481/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1284 - accuracy: 0.9560 - val_loss: 0.3402 - val_accuracy: 0.9161\n",
      "Epoch 482/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1293 - accuracy: 0.9560 - val_loss: 0.3624 - val_accuracy: 0.9181\n",
      "Epoch 483/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1291 - accuracy: 0.9564 - val_loss: 0.3105 - val_accuracy: 0.9261\n",
      "Epoch 484/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1291 - accuracy: 0.9561 - val_loss: 0.3419 - val_accuracy: 0.9229\n",
      "Epoch 485/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1275 - accuracy: 0.9565 - val_loss: 0.3801 - val_accuracy: 0.9091\n",
      "Epoch 486/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1284 - accuracy: 0.9559 - val_loss: 0.3427 - val_accuracy: 0.9113\n",
      "Epoch 487/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1282 - accuracy: 0.9562 - val_loss: 0.3575 - val_accuracy: 0.9176\n",
      "Epoch 488/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1288 - accuracy: 0.9564 - val_loss: 0.3182 - val_accuracy: 0.9246\n",
      "Epoch 489/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1279 - accuracy: 0.9569 - val_loss: 0.3530 - val_accuracy: 0.9186\n",
      "Epoch 490/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1273 - accuracy: 0.9570 - val_loss: 0.3540 - val_accuracy: 0.9180\n",
      "Epoch 491/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1276 - accuracy: 0.9564 - val_loss: 0.3477 - val_accuracy: 0.9167\n",
      "Epoch 492/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1273 - accuracy: 0.9564 - val_loss: 0.3713 - val_accuracy: 0.9100\n",
      "Epoch 493/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1267 - accuracy: 0.9571 - val_loss: 0.3134 - val_accuracy: 0.9266\n",
      "Epoch 494/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1267 - accuracy: 0.9574 - val_loss: 0.3815 - val_accuracy: 0.9093\n",
      "Epoch 495/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1273 - accuracy: 0.9566 - val_loss: 0.3217 - val_accuracy: 0.9219\n",
      "Epoch 496/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1261 - accuracy: 0.9572 - val_loss: 0.3507 - val_accuracy: 0.9110\n",
      "Epoch 497/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1259 - accuracy: 0.9577 - val_loss: 0.3490 - val_accuracy: 0.9187\n",
      "Epoch 498/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1272 - accuracy: 0.9564 - val_loss: 0.3387 - val_accuracy: 0.9183\n",
      "Epoch 499/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1258 - accuracy: 0.9574 - val_loss: 0.4321 - val_accuracy: 0.8751\n",
      "Epoch 500/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1262 - accuracy: 0.9570 - val_loss: 0.3201 - val_accuracy: 0.9256\n",
      "Epoch 501/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1265 - accuracy: 0.9570 - val_loss: 0.3071 - val_accuracy: 0.9276\n",
      "Epoch 502/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1257 - accuracy: 0.9571 - val_loss: 0.4375 - val_accuracy: 0.8902\n",
      "Epoch 503/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1256 - accuracy: 0.9572 - val_loss: 0.3623 - val_accuracy: 0.9161\n",
      "Epoch 504/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1251 - accuracy: 0.9573 - val_loss: 0.3368 - val_accuracy: 0.9178\n",
      "Epoch 505/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1253 - accuracy: 0.9577 - val_loss: 0.3349 - val_accuracy: 0.9212\n",
      "Epoch 506/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1261 - accuracy: 0.9573 - val_loss: 0.3616 - val_accuracy: 0.9151\n",
      "Epoch 507/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1260 - accuracy: 0.9566 - val_loss: 0.3194 - val_accuracy: 0.9209\n",
      "Epoch 508/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1244 - accuracy: 0.9578 - val_loss: 0.3134 - val_accuracy: 0.9254\n",
      "Epoch 509/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1252 - accuracy: 0.9575 - val_loss: 0.3386 - val_accuracy: 0.9177\n",
      "Epoch 510/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1250 - accuracy: 0.9576 - val_loss: 0.4017 - val_accuracy: 0.9044\n",
      "Epoch 511/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1234 - accuracy: 0.9588 - val_loss: 0.3898 - val_accuracy: 0.8995\n",
      "Epoch 512/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1244 - accuracy: 0.9575 - val_loss: 0.3844 - val_accuracy: 0.9066\n",
      "Epoch 513/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1241 - accuracy: 0.9579 - val_loss: 0.3246 - val_accuracy: 0.9243\n",
      "Epoch 514/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1234 - accuracy: 0.9576 - val_loss: 0.3658 - val_accuracy: 0.9118\n",
      "Epoch 515/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1236 - accuracy: 0.9575 - val_loss: 0.3680 - val_accuracy: 0.9167\n",
      "Epoch 516/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1239 - accuracy: 0.9580 - val_loss: 0.3340 - val_accuracy: 0.9158\n",
      "Epoch 517/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1237 - accuracy: 0.9575 - val_loss: 0.3402 - val_accuracy: 0.9120\n",
      "Epoch 518/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1239 - accuracy: 0.9579 - val_loss: 0.3371 - val_accuracy: 0.9242\n",
      "Epoch 519/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1226 - accuracy: 0.9586 - val_loss: 0.3254 - val_accuracy: 0.9236\n",
      "Epoch 520/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1216 - accuracy: 0.9584 - val_loss: 0.3491 - val_accuracy: 0.9176\n",
      "Epoch 521/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1226 - accuracy: 0.9580 - val_loss: 0.3165 - val_accuracy: 0.9266\n",
      "Epoch 522/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1234 - accuracy: 0.9578 - val_loss: 0.4228 - val_accuracy: 0.9107\n",
      "Epoch 523/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1227 - accuracy: 0.9584 - val_loss: 0.3367 - val_accuracy: 0.9132\n",
      "Epoch 524/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1219 - accuracy: 0.9580 - val_loss: 0.3341 - val_accuracy: 0.9230\n",
      "Epoch 525/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1221 - accuracy: 0.9584 - val_loss: 0.3172 - val_accuracy: 0.9289\n",
      "Epoch 526/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1226 - accuracy: 0.9588 - val_loss: 0.3029 - val_accuracy: 0.9303\n",
      "Epoch 527/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1232 - accuracy: 0.9575 - val_loss: 0.3282 - val_accuracy: 0.9254\n",
      "Epoch 528/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1214 - accuracy: 0.9585 - val_loss: 0.3102 - val_accuracy: 0.9280\n",
      "Epoch 529/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1221 - accuracy: 0.9582 - val_loss: 0.3522 - val_accuracy: 0.9139\n",
      "Epoch 530/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1223 - accuracy: 0.9585 - val_loss: 0.3474 - val_accuracy: 0.9197\n",
      "Epoch 531/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1200 - accuracy: 0.9589 - val_loss: 0.3898 - val_accuracy: 0.9143\n",
      "Epoch 532/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1210 - accuracy: 0.9586 - val_loss: 0.3056 - val_accuracy: 0.9292\n",
      "Epoch 533/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1213 - accuracy: 0.9585 - val_loss: 0.3686 - val_accuracy: 0.9170\n",
      "Epoch 534/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1214 - accuracy: 0.9585 - val_loss: 0.3132 - val_accuracy: 0.9262\n",
      "Epoch 535/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1207 - accuracy: 0.9591 - val_loss: 0.3705 - val_accuracy: 0.9198\n",
      "Epoch 536/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1209 - accuracy: 0.9593 - val_loss: 0.3059 - val_accuracy: 0.9320\n",
      "Epoch 537/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1208 - accuracy: 0.9590 - val_loss: 0.3207 - val_accuracy: 0.9182\n",
      "Epoch 538/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1202 - accuracy: 0.9592 - val_loss: 0.3561 - val_accuracy: 0.9178\n",
      "Epoch 539/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1206 - accuracy: 0.9589 - val_loss: 0.4133 - val_accuracy: 0.9044\n",
      "Epoch 540/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1200 - accuracy: 0.9589 - val_loss: 0.3252 - val_accuracy: 0.9183\n",
      "Epoch 541/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1196 - accuracy: 0.9586 - val_loss: 0.4159 - val_accuracy: 0.8922\n",
      "Epoch 542/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1205 - accuracy: 0.9583 - val_loss: 0.3633 - val_accuracy: 0.9166\n",
      "Epoch 543/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1201 - accuracy: 0.9591 - val_loss: 0.3694 - val_accuracy: 0.9110\n",
      "Epoch 544/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1200 - accuracy: 0.9589 - val_loss: 0.3915 - val_accuracy: 0.9153\n",
      "Epoch 545/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1192 - accuracy: 0.9594 - val_loss: 0.3332 - val_accuracy: 0.9190\n",
      "Epoch 546/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1188 - accuracy: 0.9594 - val_loss: 0.3331 - val_accuracy: 0.9207\n",
      "Epoch 547/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1192 - accuracy: 0.9593 - val_loss: 0.3350 - val_accuracy: 0.9229\n",
      "Epoch 548/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1197 - accuracy: 0.9583 - val_loss: 0.3945 - val_accuracy: 0.9102\n",
      "Epoch 549/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1183 - accuracy: 0.9594 - val_loss: 0.3307 - val_accuracy: 0.9217\n",
      "Epoch 550/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1187 - accuracy: 0.9595 - val_loss: 0.4411 - val_accuracy: 0.8811\n",
      "Epoch 551/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1188 - accuracy: 0.9597 - val_loss: 0.4198 - val_accuracy: 0.9038\n",
      "Epoch 552/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1184 - accuracy: 0.9598 - val_loss: 0.3226 - val_accuracy: 0.9195\n",
      "Epoch 553/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1184 - accuracy: 0.9594 - val_loss: 0.3304 - val_accuracy: 0.9207\n",
      "Epoch 554/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1180 - accuracy: 0.9595 - val_loss: 0.3153 - val_accuracy: 0.9306\n",
      "Epoch 555/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1183 - accuracy: 0.9594 - val_loss: 0.3576 - val_accuracy: 0.9188\n",
      "Epoch 556/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1186 - accuracy: 0.9598 - val_loss: 0.3228 - val_accuracy: 0.9248\n",
      "Epoch 557/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1176 - accuracy: 0.9597 - val_loss: 0.3396 - val_accuracy: 0.9215\n",
      "Epoch 558/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1175 - accuracy: 0.9603 - val_loss: 0.3277 - val_accuracy: 0.9227\n",
      "Epoch 559/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1175 - accuracy: 0.9593 - val_loss: 0.3368 - val_accuracy: 0.9256\n",
      "Epoch 560/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1173 - accuracy: 0.9596 - val_loss: 0.3405 - val_accuracy: 0.9252\n",
      "Epoch 561/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1163 - accuracy: 0.9600 - val_loss: 0.3449 - val_accuracy: 0.9205\n",
      "Epoch 562/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1173 - accuracy: 0.9599 - val_loss: 0.3157 - val_accuracy: 0.9202\n",
      "Epoch 563/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1164 - accuracy: 0.9598 - val_loss: 0.4649 - val_accuracy: 0.8990\n",
      "Epoch 564/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1168 - accuracy: 0.9603 - val_loss: 0.3541 - val_accuracy: 0.9142\n",
      "Epoch 565/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1167 - accuracy: 0.9602 - val_loss: 0.3583 - val_accuracy: 0.9124\n",
      "Epoch 566/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1170 - accuracy: 0.9598 - val_loss: 0.3890 - val_accuracy: 0.9104\n",
      "Epoch 567/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1157 - accuracy: 0.9600 - val_loss: 0.3294 - val_accuracy: 0.9261\n",
      "Epoch 568/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1167 - accuracy: 0.9601 - val_loss: 0.3241 - val_accuracy: 0.9293\n",
      "Epoch 569/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1162 - accuracy: 0.9604 - val_loss: 0.3172 - val_accuracy: 0.9325\n",
      "Epoch 570/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1158 - accuracy: 0.9603 - val_loss: 0.3334 - val_accuracy: 0.9158\n",
      "Epoch 571/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1152 - accuracy: 0.9602 - val_loss: 0.3708 - val_accuracy: 0.9161\n",
      "Epoch 572/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1163 - accuracy: 0.9604 - val_loss: 0.4403 - val_accuracy: 0.9054\n",
      "Epoch 573/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1156 - accuracy: 0.9604 - val_loss: 0.3305 - val_accuracy: 0.9226\n",
      "Epoch 574/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1163 - accuracy: 0.9599 - val_loss: 0.3603 - val_accuracy: 0.9226\n",
      "Epoch 575/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1158 - accuracy: 0.9605 - val_loss: 0.3378 - val_accuracy: 0.9230\n",
      "Epoch 576/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1145 - accuracy: 0.9608 - val_loss: 0.3401 - val_accuracy: 0.9195\n",
      "Epoch 577/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1154 - accuracy: 0.9602 - val_loss: 0.3366 - val_accuracy: 0.9222\n",
      "Epoch 578/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1155 - accuracy: 0.9599 - val_loss: 0.3386 - val_accuracy: 0.9276\n",
      "Epoch 579/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1147 - accuracy: 0.9606 - val_loss: 0.3524 - val_accuracy: 0.9178\n",
      "Epoch 580/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1141 - accuracy: 0.9606 - val_loss: 0.3727 - val_accuracy: 0.9175\n",
      "Epoch 581/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1154 - accuracy: 0.9610 - val_loss: 0.3681 - val_accuracy: 0.9195\n",
      "Epoch 582/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1144 - accuracy: 0.9607 - val_loss: 0.3366 - val_accuracy: 0.9177\n",
      "Epoch 583/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1145 - accuracy: 0.9610 - val_loss: 0.3606 - val_accuracy: 0.9139\n",
      "Epoch 584/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1149 - accuracy: 0.9599 - val_loss: 0.3309 - val_accuracy: 0.9220\n",
      "Epoch 585/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1138 - accuracy: 0.9610 - val_loss: 0.3805 - val_accuracy: 0.9059\n",
      "Epoch 586/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1146 - accuracy: 0.9610 - val_loss: 0.3712 - val_accuracy: 0.9060\n",
      "Epoch 587/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1138 - accuracy: 0.9607 - val_loss: 0.3543 - val_accuracy: 0.9143\n",
      "Epoch 588/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1139 - accuracy: 0.9607 - val_loss: 0.3692 - val_accuracy: 0.9049\n",
      "Epoch 589/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1138 - accuracy: 0.9612 - val_loss: 0.3231 - val_accuracy: 0.9237\n",
      "Epoch 590/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1141 - accuracy: 0.9604 - val_loss: 0.3463 - val_accuracy: 0.9230\n",
      "Epoch 591/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1141 - accuracy: 0.9607 - val_loss: 0.4851 - val_accuracy: 0.8816\n",
      "Epoch 592/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1134 - accuracy: 0.9611 - val_loss: 0.4326 - val_accuracy: 0.9022\n",
      "Epoch 593/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1142 - accuracy: 0.9607 - val_loss: 0.3272 - val_accuracy: 0.9216\n",
      "Epoch 594/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1130 - accuracy: 0.9614 - val_loss: 0.4190 - val_accuracy: 0.9129\n",
      "Epoch 595/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1132 - accuracy: 0.9616 - val_loss: 0.4350 - val_accuracy: 0.9097\n",
      "Epoch 596/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1129 - accuracy: 0.9611 - val_loss: 0.3505 - val_accuracy: 0.9187\n",
      "Epoch 597/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1128 - accuracy: 0.9613 - val_loss: 0.4093 - val_accuracy: 0.8977\n",
      "Epoch 598/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1118 - accuracy: 0.9619 - val_loss: 0.3521 - val_accuracy: 0.9217\n",
      "Epoch 599/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1130 - accuracy: 0.9605 - val_loss: 0.3205 - val_accuracy: 0.9312\n",
      "Epoch 600/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1127 - accuracy: 0.9609 - val_loss: 0.3259 - val_accuracy: 0.9272\n",
      "Epoch 601/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1126 - accuracy: 0.9615 - val_loss: 0.3704 - val_accuracy: 0.9108\n",
      "Epoch 602/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1117 - accuracy: 0.9617 - val_loss: 0.3523 - val_accuracy: 0.9235\n",
      "Epoch 603/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1117 - accuracy: 0.9617 - val_loss: 0.3613 - val_accuracy: 0.9261\n",
      "Epoch 604/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1124 - accuracy: 0.9615 - val_loss: 0.3514 - val_accuracy: 0.9243\n",
      "Epoch 605/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1127 - accuracy: 0.9613 - val_loss: 0.3856 - val_accuracy: 0.9009\n",
      "Epoch 606/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1121 - accuracy: 0.9618 - val_loss: 0.3564 - val_accuracy: 0.9198\n",
      "Epoch 607/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1121 - accuracy: 0.9614 - val_loss: 0.3121 - val_accuracy: 0.9309\n",
      "Epoch 608/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1117 - accuracy: 0.9619 - val_loss: 0.4775 - val_accuracy: 0.8869\n",
      "Epoch 609/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1119 - accuracy: 0.9613 - val_loss: 0.3682 - val_accuracy: 0.9232\n",
      "Epoch 610/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1116 - accuracy: 0.9617 - val_loss: 0.3385 - val_accuracy: 0.9267\n",
      "Epoch 611/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1123 - accuracy: 0.9614 - val_loss: 0.3496 - val_accuracy: 0.9208\n",
      "Epoch 612/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1107 - accuracy: 0.9620 - val_loss: 0.3311 - val_accuracy: 0.9303\n",
      "Epoch 613/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1108 - accuracy: 0.9621 - val_loss: 0.3890 - val_accuracy: 0.9105\n",
      "Epoch 614/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1117 - accuracy: 0.9615 - val_loss: 0.3538 - val_accuracy: 0.9256\n",
      "Epoch 615/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1116 - accuracy: 0.9617 - val_loss: 0.3926 - val_accuracy: 0.9104\n",
      "Epoch 616/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1112 - accuracy: 0.9618 - val_loss: 0.4164 - val_accuracy: 0.9039\n",
      "Epoch 617/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1106 - accuracy: 0.9616 - val_loss: 0.3441 - val_accuracy: 0.9307\n",
      "Epoch 618/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1108 - accuracy: 0.9618 - val_loss: 0.3765 - val_accuracy: 0.9163\n",
      "Epoch 619/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1103 - accuracy: 0.9622 - val_loss: 0.3300 - val_accuracy: 0.9203\n",
      "Epoch 620/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1102 - accuracy: 0.9616 - val_loss: 0.3794 - val_accuracy: 0.9166\n",
      "Epoch 621/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1100 - accuracy: 0.9619 - val_loss: 0.3239 - val_accuracy: 0.9260\n",
      "Epoch 622/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1109 - accuracy: 0.9624 - val_loss: 0.3317 - val_accuracy: 0.9231\n",
      "Epoch 623/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1094 - accuracy: 0.9621 - val_loss: 0.3809 - val_accuracy: 0.9138\n",
      "Epoch 624/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1107 - accuracy: 0.9620 - val_loss: 0.3262 - val_accuracy: 0.9287\n",
      "Epoch 625/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1088 - accuracy: 0.9631 - val_loss: 0.3303 - val_accuracy: 0.9295\n",
      "Epoch 626/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1099 - accuracy: 0.9623 - val_loss: 0.3652 - val_accuracy: 0.9185\n",
      "Epoch 627/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1101 - accuracy: 0.9622 - val_loss: 0.3736 - val_accuracy: 0.9079\n",
      "Epoch 628/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1097 - accuracy: 0.9619 - val_loss: 0.3440 - val_accuracy: 0.9262\n",
      "Epoch 629/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1094 - accuracy: 0.9620 - val_loss: 0.3492 - val_accuracy: 0.9289\n",
      "Epoch 630/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1099 - accuracy: 0.9620 - val_loss: 0.3626 - val_accuracy: 0.9252\n",
      "Epoch 631/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1089 - accuracy: 0.9623 - val_loss: 0.3658 - val_accuracy: 0.9206\n",
      "Epoch 632/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1090 - accuracy: 0.9632 - val_loss: 0.3278 - val_accuracy: 0.9311\n",
      "Epoch 633/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1076 - accuracy: 0.9631 - val_loss: 0.3665 - val_accuracy: 0.9262\n",
      "Epoch 634/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1083 - accuracy: 0.9626 - val_loss: 0.3455 - val_accuracy: 0.9176\n",
      "Epoch 635/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1092 - accuracy: 0.9624 - val_loss: 0.3517 - val_accuracy: 0.9276\n",
      "Epoch 636/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1085 - accuracy: 0.9628 - val_loss: 0.3370 - val_accuracy: 0.9224\n",
      "Epoch 637/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1082 - accuracy: 0.9624 - val_loss: 0.3433 - val_accuracy: 0.9261\n",
      "Epoch 638/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1081 - accuracy: 0.9626 - val_loss: 0.3769 - val_accuracy: 0.9241\n",
      "Epoch 639/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1083 - accuracy: 0.9626 - val_loss: 0.3556 - val_accuracy: 0.9273\n",
      "Epoch 640/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1077 - accuracy: 0.9632 - val_loss: 0.3308 - val_accuracy: 0.9301\n",
      "Epoch 641/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1077 - accuracy: 0.9632 - val_loss: 0.3807 - val_accuracy: 0.9113\n",
      "Epoch 642/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1081 - accuracy: 0.9629 - val_loss: 0.3911 - val_accuracy: 0.9128\n",
      "Epoch 643/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1070 - accuracy: 0.9627 - val_loss: 0.5149 - val_accuracy: 0.8880\n",
      "Epoch 644/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1079 - accuracy: 0.9628 - val_loss: 0.3488 - val_accuracy: 0.9236\n",
      "Epoch 645/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1085 - accuracy: 0.9627 - val_loss: 0.3827 - val_accuracy: 0.9095\n",
      "Epoch 646/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1072 - accuracy: 0.9633 - val_loss: 0.3816 - val_accuracy: 0.9172\n",
      "Epoch 647/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1081 - accuracy: 0.9626 - val_loss: 0.3609 - val_accuracy: 0.9275\n",
      "Epoch 648/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1070 - accuracy: 0.9630 - val_loss: 0.3901 - val_accuracy: 0.9192\n",
      "Epoch 649/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1074 - accuracy: 0.9625 - val_loss: 0.3253 - val_accuracy: 0.9326\n",
      "Epoch 650/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1073 - accuracy: 0.9630 - val_loss: 0.3955 - val_accuracy: 0.9223\n",
      "Epoch 651/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1074 - accuracy: 0.9631 - val_loss: 0.3939 - val_accuracy: 0.9223\n",
      "Epoch 652/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1073 - accuracy: 0.9627 - val_loss: 0.3528 - val_accuracy: 0.9271\n",
      "Epoch 653/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1075 - accuracy: 0.9629 - val_loss: 0.4126 - val_accuracy: 0.9066\n",
      "Epoch 654/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1074 - accuracy: 0.9630 - val_loss: 0.3133 - val_accuracy: 0.9369\n",
      "Epoch 655/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1068 - accuracy: 0.9632 - val_loss: 0.3808 - val_accuracy: 0.9227\n",
      "Epoch 656/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1073 - accuracy: 0.9627 - val_loss: 0.3479 - val_accuracy: 0.9259\n",
      "Epoch 657/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1057 - accuracy: 0.9637 - val_loss: 0.4000 - val_accuracy: 0.9177\n",
      "Epoch 658/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1070 - accuracy: 0.9630 - val_loss: 0.4263 - val_accuracy: 0.9105\n",
      "Epoch 659/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1064 - accuracy: 0.9629 - val_loss: 0.3499 - val_accuracy: 0.9164\n",
      "Epoch 660/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1062 - accuracy: 0.9637 - val_loss: 0.4255 - val_accuracy: 0.9056\n",
      "Epoch 661/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1069 - accuracy: 0.9631 - val_loss: 0.3608 - val_accuracy: 0.9204\n",
      "Epoch 662/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1061 - accuracy: 0.9633 - val_loss: 0.3423 - val_accuracy: 0.9252\n",
      "Epoch 663/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1082 - accuracy: 0.9641 - val_loss: 0.3703 - val_accuracy: 0.9259\n",
      "Epoch 664/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1050 - accuracy: 0.9639 - val_loss: 0.3556 - val_accuracy: 0.9248\n",
      "Epoch 665/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1061 - accuracy: 0.9634 - val_loss: 0.4327 - val_accuracy: 0.9102\n",
      "Epoch 666/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1053 - accuracy: 0.9636 - val_loss: 0.3991 - val_accuracy: 0.9208\n",
      "Epoch 667/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1057 - accuracy: 0.9639 - val_loss: 0.3586 - val_accuracy: 0.9211\n",
      "Epoch 668/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1053 - accuracy: 0.9643 - val_loss: 0.3518 - val_accuracy: 0.9254\n",
      "Epoch 669/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1048 - accuracy: 0.9640 - val_loss: 0.3519 - val_accuracy: 0.9296\n",
      "Epoch 670/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1048 - accuracy: 0.9641 - val_loss: 0.3230 - val_accuracy: 0.9326\n",
      "Epoch 671/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1050 - accuracy: 0.9637 - val_loss: 0.3917 - val_accuracy: 0.9087\n",
      "Epoch 672/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1066 - accuracy: 0.9633 - val_loss: 0.3654 - val_accuracy: 0.9199\n",
      "Epoch 673/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1055 - accuracy: 0.9637 - val_loss: 0.4579 - val_accuracy: 0.9031\n",
      "Epoch 674/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1045 - accuracy: 0.9635 - val_loss: 0.3215 - val_accuracy: 0.9326\n",
      "Epoch 675/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1036 - accuracy: 0.9642 - val_loss: 0.4211 - val_accuracy: 0.8957\n",
      "Epoch 676/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1046 - accuracy: 0.9639 - val_loss: 0.4210 - val_accuracy: 0.9069\n",
      "Epoch 677/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1048 - accuracy: 0.9637 - val_loss: 0.3860 - val_accuracy: 0.9217\n",
      "Epoch 678/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1044 - accuracy: 0.9638 - val_loss: 0.3312 - val_accuracy: 0.9276\n",
      "Epoch 679/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1043 - accuracy: 0.9641 - val_loss: 0.4218 - val_accuracy: 0.9036\n",
      "Epoch 680/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1048 - accuracy: 0.9637 - val_loss: 0.3631 - val_accuracy: 0.9222\n",
      "Epoch 681/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1038 - accuracy: 0.9644 - val_loss: 0.3901 - val_accuracy: 0.9158\n",
      "Epoch 682/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1044 - accuracy: 0.9644 - val_loss: 0.3861 - val_accuracy: 0.9183\n",
      "Epoch 683/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1041 - accuracy: 0.9641 - val_loss: 0.3818 - val_accuracy: 0.9287\n",
      "Epoch 684/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1033 - accuracy: 0.9645 - val_loss: 0.3372 - val_accuracy: 0.9315\n",
      "Epoch 685/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1039 - accuracy: 0.9641 - val_loss: 0.3331 - val_accuracy: 0.9348\n",
      "Epoch 686/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1037 - accuracy: 0.9638 - val_loss: 0.3623 - val_accuracy: 0.9258\n",
      "Epoch 687/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1040 - accuracy: 0.9641 - val_loss: 0.3544 - val_accuracy: 0.9231\n",
      "Epoch 688/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1035 - accuracy: 0.9644 - val_loss: 0.3525 - val_accuracy: 0.9237\n",
      "Epoch 689/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1046 - accuracy: 0.9640 - val_loss: 0.3619 - val_accuracy: 0.9298\n",
      "Epoch 690/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1035 - accuracy: 0.9647 - val_loss: 0.3417 - val_accuracy: 0.9249\n",
      "Epoch 691/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1032 - accuracy: 0.9645 - val_loss: 0.3274 - val_accuracy: 0.9377\n",
      "Epoch 692/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1021 - accuracy: 0.9646 - val_loss: 0.3319 - val_accuracy: 0.9296\n",
      "Epoch 693/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1026 - accuracy: 0.9643 - val_loss: 0.3649 - val_accuracy: 0.9282\n",
      "Epoch 694/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1031 - accuracy: 0.9641 - val_loss: 0.4609 - val_accuracy: 0.8981\n",
      "Epoch 695/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1029 - accuracy: 0.9649 - val_loss: 0.3402 - val_accuracy: 0.9315\n",
      "Epoch 696/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1028 - accuracy: 0.9640 - val_loss: 0.3593 - val_accuracy: 0.9284\n",
      "Epoch 697/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1027 - accuracy: 0.9645 - val_loss: 0.3869 - val_accuracy: 0.9261\n",
      "Epoch 698/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1029 - accuracy: 0.9647 - val_loss: 0.3357 - val_accuracy: 0.9265\n",
      "Epoch 699/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1026 - accuracy: 0.9644 - val_loss: 0.3642 - val_accuracy: 0.9316\n",
      "Epoch 700/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1023 - accuracy: 0.9649 - val_loss: 0.4689 - val_accuracy: 0.8902\n",
      "Epoch 701/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1021 - accuracy: 0.9650 - val_loss: 0.3495 - val_accuracy: 0.9232\n",
      "Epoch 702/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1025 - accuracy: 0.9642 - val_loss: 0.3825 - val_accuracy: 0.9245\n",
      "Epoch 703/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1018 - accuracy: 0.9646 - val_loss: 0.3967 - val_accuracy: 0.9143\n",
      "Epoch 704/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1034 - accuracy: 0.9638 - val_loss: 0.3389 - val_accuracy: 0.9366\n",
      "Epoch 705/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1020 - accuracy: 0.9646 - val_loss: 0.3729 - val_accuracy: 0.9213\n",
      "Epoch 706/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1011 - accuracy: 0.9656 - val_loss: 0.3105 - val_accuracy: 0.9363\n",
      "Epoch 707/750\n",
      "96292/96292 [==============================] - 2s 25us/step - loss: 0.1019 - accuracy: 0.9651 - val_loss: 0.3542 - val_accuracy: 0.9240\n",
      "Epoch 708/750\n",
      "96292/96292 [==============================] - 2s 25us/step - loss: 0.1024 - accuracy: 0.9650 - val_loss: 0.3638 - val_accuracy: 0.9276\n",
      "Epoch 709/750\n",
      "96292/96292 [==============================] - 2s 22us/step - loss: 0.1024 - accuracy: 0.9644 - val_loss: 0.3823 - val_accuracy: 0.9212\n",
      "Epoch 710/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1013 - accuracy: 0.9648 - val_loss: 0.3325 - val_accuracy: 0.9328\n",
      "Epoch 711/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1021 - accuracy: 0.9646 - val_loss: 0.3618 - val_accuracy: 0.9286\n",
      "Epoch 712/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1012 - accuracy: 0.9647 - val_loss: 0.3386 - val_accuracy: 0.9339\n",
      "Epoch 713/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1016 - accuracy: 0.9651 - val_loss: 0.3884 - val_accuracy: 0.9032\n",
      "Epoch 714/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1006 - accuracy: 0.9657 - val_loss: 0.3542 - val_accuracy: 0.9280\n",
      "Epoch 715/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1005 - accuracy: 0.9649 - val_loss: 0.3690 - val_accuracy: 0.9190\n",
      "Epoch 716/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1010 - accuracy: 0.9656 - val_loss: 0.3782 - val_accuracy: 0.9193\n",
      "Epoch 717/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1022 - accuracy: 0.9638 - val_loss: 0.3467 - val_accuracy: 0.9304\n",
      "Epoch 718/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1009 - accuracy: 0.9652 - val_loss: 0.3591 - val_accuracy: 0.9265\n",
      "Epoch 719/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1009 - accuracy: 0.9645 - val_loss: 0.3284 - val_accuracy: 0.9369\n",
      "Epoch 720/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1006 - accuracy: 0.9651 - val_loss: 0.5344 - val_accuracy: 0.8714\n",
      "Epoch 721/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1010 - accuracy: 0.9646 - val_loss: 0.3889 - val_accuracy: 0.9268\n",
      "Epoch 722/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1002 - accuracy: 0.9652 - val_loss: 0.3457 - val_accuracy: 0.9299\n",
      "Epoch 723/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1006 - accuracy: 0.9651 - val_loss: 0.3805 - val_accuracy: 0.9264\n",
      "Epoch 724/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1001 - accuracy: 0.9650 - val_loss: 0.3551 - val_accuracy: 0.9270\n",
      "Epoch 725/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1014 - accuracy: 0.9650 - val_loss: 0.3854 - val_accuracy: 0.9172\n",
      "Epoch 726/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1009 - accuracy: 0.9653 - val_loss: 0.3502 - val_accuracy: 0.9280\n",
      "Epoch 727/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0998 - accuracy: 0.9655 - val_loss: 0.4303 - val_accuracy: 0.9117\n",
      "Epoch 728/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0996 - accuracy: 0.9654 - val_loss: 0.3433 - val_accuracy: 0.9282\n",
      "Epoch 729/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1000 - accuracy: 0.9649 - val_loss: 0.3398 - val_accuracy: 0.9316\n",
      "Epoch 730/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0997 - accuracy: 0.9659 - val_loss: 0.3913 - val_accuracy: 0.9255\n",
      "Epoch 731/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0994 - accuracy: 0.9653 - val_loss: 0.4115 - val_accuracy: 0.9062\n",
      "Epoch 732/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1000 - accuracy: 0.9659 - val_loss: 0.5219 - val_accuracy: 0.8789\n",
      "Epoch 733/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1005 - accuracy: 0.9653 - val_loss: 0.3532 - val_accuracy: 0.9362\n",
      "Epoch 734/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0993 - accuracy: 0.9655 - val_loss: 0.3782 - val_accuracy: 0.9287\n",
      "Epoch 735/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.1000 - accuracy: 0.9653 - val_loss: 0.3842 - val_accuracy: 0.9191\n",
      "Epoch 736/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.0990 - accuracy: 0.9655 - val_loss: 0.3616 - val_accuracy: 0.9302\n",
      "Epoch 737/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0989 - accuracy: 0.9654 - val_loss: 0.3627 - val_accuracy: 0.9307\n",
      "Epoch 738/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0991 - accuracy: 0.9650 - val_loss: 0.3608 - val_accuracy: 0.9331\n",
      "Epoch 739/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.1010 - accuracy: 0.9649 - val_loss: 0.3480 - val_accuracy: 0.9285\n",
      "Epoch 740/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0983 - accuracy: 0.9657 - val_loss: 0.3417 - val_accuracy: 0.9332\n",
      "Epoch 741/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0994 - accuracy: 0.9657 - val_loss: 0.3828 - val_accuracy: 0.9223\n",
      "Epoch 742/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0999 - accuracy: 0.9655 - val_loss: 0.3717 - val_accuracy: 0.9197\n",
      "Epoch 743/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0992 - accuracy: 0.9658 - val_loss: 0.3706 - val_accuracy: 0.9321\n",
      "Epoch 744/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0991 - accuracy: 0.9653 - val_loss: 0.4210 - val_accuracy: 0.9137\n",
      "Epoch 745/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0993 - accuracy: 0.9657 - val_loss: 0.3730 - val_accuracy: 0.9296\n",
      "Epoch 746/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0996 - accuracy: 0.9654 - val_loss: 0.4061 - val_accuracy: 0.9186\n",
      "Epoch 747/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.0986 - accuracy: 0.9664 - val_loss: 0.3582 - val_accuracy: 0.9328\n",
      "Epoch 748/750\n",
      "96292/96292 [==============================] - 2s 21us/step - loss: 0.0983 - accuracy: 0.9658 - val_loss: 0.4047 - val_accuracy: 0.9202\n",
      "Epoch 749/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0988 - accuracy: 0.9654 - val_loss: 0.4387 - val_accuracy: 0.9050\n",
      "Epoch 750/750\n",
      "96292/96292 [==============================] - 2s 20us/step - loss: 0.0992 - accuracy: 0.9660 - val_loss: 0.3561 - val_accuracy: 0.9296\n"
     ]
    }
   ],
   "source": [
    "#fine tune model runs for no. of epochs set below\n",
    "history=model.fit(XL_train,yL_train,\n",
    "                epochs=750,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(XL_val, yL_val))\n",
    "traint1=time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_time: 1776.4297544956207\n"
     ]
    }
   ],
   "source": [
    "print(\"Train_time: \"+str(traint1-traint0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12037/12037 [==============================] - 0s 23us/step\n",
      "[0.36441210355568104, 0.9306305646896362]\n",
      "Test_time: 0.28174805641174316\n"
     ]
    }
   ],
   "source": [
    "test0=time.time()\n",
    "print(model.evaluate(XL_test,yL_test))\n",
    "test1=time.time()\n",
    "print(\"Test_time: \"+str(test1-test0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d508f62d68>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualise convergence with no. of epochs\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(XL_test)\n",
    "y_pred1=y_pred.argmax(axis=1)\n",
    "y_pred1=np.append(y_pred1,np.arange(24))\n",
    "y_pred1=pd.get_dummies(y_pred1,columns=yL_train.columns)\n",
    "y_pred1.columns=yL_test.columns\n",
    "y_pred1=y_pred1.iloc[:-24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMAZON            500\n",
       "APPLE             474\n",
       "APPLE_ITUNES      515\n",
       "CLOUDFLARE        461\n",
       "CONTENT_FLASH     494\n",
       "DROPBOX           487\n",
       "EBAY              508\n",
       "FACEBOOK          473\n",
       "GMAIL             501\n",
       "GOOGLE            550\n",
       "HTTP              491\n",
       "HTTP_CONNECT      490\n",
       "HTTP_PROXY        533\n",
       "MICROSOFT         497\n",
       "MSN               497\n",
       "OFFICE_365        522\n",
       "SKYPE             491\n",
       "SSL               517\n",
       "TWITTER           509\n",
       "WHATSAPP          506\n",
       "WIKIPEDIA         511\n",
       "WINDOWS_UPDATE    502\n",
       "YAHOO             515\n",
       "YOUTUBE           493\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yL_test.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test1 = yL_test.columns[np.where(yL_test!=0)[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=y_pred1.columns[np.where(y_pred1!=0)[1]]\n",
    "cm=confusion_matrix(y_test1,y_pred1,labels=yL_test.columns)\n",
    "cm_pc=pd.DataFrame((cm.T/np.sum(cm,axis=1)).T,columns=yL_test.columns,index=yL_test.columns)\n",
    "cm_pc=cm_pc.replace(np.nan,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2520x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(35,25))\n",
    "df_cm=pd.DataFrame(cm,columns=yL_test.columns,index=yL_test.columns)\n",
    "sns.set(font_scale=1.0)\n",
    "sns.heatmap(cm_pc, annot=True,fmt='.1%',cmap=sns.color_palette(\"Reds\",50)) # font size\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy : 0.9258951566004818\n"
     ]
    }
   ],
   "source": [
    "print('Test Accuracy : '+str(accuracy_score(y_test1, y_pred1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weighted F1 Score : 0.9273424425245091\n",
      "Macro F1 Score : 0.9265045586908415\n",
      "Micro F1 Score : 0.9258951566004818\n",
      "F1 Scores : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.946446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.996845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.999030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.991398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.995968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.995910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.962080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.963475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.623729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.720992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.828108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.660959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.977778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.979021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.997135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.980789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.776678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.985366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.993131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.997073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.976517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.993250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.894434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                F1-score\n",
       "AMAZON          0.946446\n",
       "APPLE           0.996845\n",
       "APPLE_ITUNES    0.999030\n",
       "CLOUDFLARE      0.991398\n",
       "CONTENT_FLASH   0.995968\n",
       "DROPBOX         0.995910\n",
       "EBAY            1.000000\n",
       "FACEBOOK        0.962080\n",
       "GMAIL           0.963475\n",
       "GOOGLE          0.623729\n",
       "HTTP            0.720992\n",
       "HTTP_CONNECT    0.828108\n",
       "HTTP_PROXY      0.660959\n",
       "MICROSOFT       0.977778\n",
       "MSN             0.979021\n",
       "OFFICE_365      0.997135\n",
       "SKYPE           0.980789\n",
       "SSL             0.776678\n",
       "TWITTER         0.985366\n",
       "WHATSAPP        0.993131\n",
       "WIKIPEDIA       0.997073\n",
       "WINDOWS_UPDATE  0.976517\n",
       "YAHOO           0.993250\n",
       "YOUTUBE         0.894434"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('weighted F1 Score : ' + str(f1_score(y_pred1,y_test1,average = 'weighted')))\n",
    "print('Macro F1 Score : ' + str(f1_score(y_pred1,y_test1,average = 'macro')))\n",
    "print('Micro F1 Score : ' + str(f1_score(y_pred1,y_test1,average = 'micro')))\n",
    "print('F1 Scores : ' )\n",
    "pd.DataFrame(f1_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['F1-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro Presicion : 0.9311481066645017\n",
      "Micro Presicion : 0.9258951566004818\n",
      "Weighted Presicion : 0.9299448808565974\n",
      "Precision Scores : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.972000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.938689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.974052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.501818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.828921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.781633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.724203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.973843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.987780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.682785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.992141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.994024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.945233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Precision-score\n",
       "AMAZON                 0.972000\n",
       "APPLE                  1.000000\n",
       "APPLE_ITUNES           1.000000\n",
       "CLOUDFLARE             1.000000\n",
       "CONTENT_FLASH          1.000000\n",
       "DROPBOX                1.000000\n",
       "EBAY                   1.000000\n",
       "FACEBOOK               0.938689\n",
       "GMAIL                  0.974052\n",
       "GOOGLE                 0.501818\n",
       "HTTP                   0.828921\n",
       "HTTP_CONNECT           0.781633\n",
       "HTTP_PROXY             0.724203\n",
       "MICROSOFT              0.973843\n",
       "MSN                    0.985915\n",
       "OFFICE_365             1.000000\n",
       "SKYPE                  0.987780\n",
       "SSL                    0.682785\n",
       "TWITTER                0.992141\n",
       "WHATSAPP               1.000000\n",
       "WIKIPEDIA              1.000000\n",
       "WINDOWS_UPDATE         0.994024\n",
       "YAHOO                  1.000000\n",
       "YOUTUBE                0.945233"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Macro Presicion : '+str(precision_score(y_test1, y_pred1,average=\"macro\")))\n",
    "print('Micro Presicion : '+str(precision_score(y_test1, y_pred1,average=\"micro\")))\n",
    "print('Weighted Presicion : '+str(precision_score(y_test1, y_pred1,average=\"weighted\")))\n",
    "print('Precision Scores : ' )\n",
    "pd.DataFrame(precision_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['Precision-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro Recall : 0.9284599025639003\n",
      "Micro Recall : 0.9258951566004818\n",
      "Weighted Recall : 0.9258951566004818\n",
      "Recall Scores : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recall-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.922201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.993711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.998062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.982942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.991968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.991853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.986667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.953125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.823881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.637931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.880460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.607874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.981744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.972222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.994286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.973896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.900510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.978682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.986355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.994163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.959615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.986590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.848816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                recall-score\n",
       "AMAZON              0.922201\n",
       "APPLE               0.993711\n",
       "APPLE_ITUNES        0.998062\n",
       "CLOUDFLARE          0.982942\n",
       "CONTENT_FLASH       0.991968\n",
       "DROPBOX             0.991853\n",
       "EBAY                1.000000\n",
       "FACEBOOK            0.986667\n",
       "GMAIL               0.953125\n",
       "GOOGLE              0.823881\n",
       "HTTP                0.637931\n",
       "HTTP_CONNECT        0.880460\n",
       "HTTP_PROXY          0.607874\n",
       "MICROSOFT           0.981744\n",
       "MSN                 0.972222\n",
       "OFFICE_365          0.994286\n",
       "SKYPE               0.973896\n",
       "SSL                 0.900510\n",
       "TWITTER             0.978682\n",
       "WHATSAPP            0.986355\n",
       "WIKIPEDIA           0.994163\n",
       "WINDOWS_UPDATE      0.959615\n",
       "YAHOO               0.986590\n",
       "YOUTUBE             0.848816"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Macro Recall : '+str(recall_score(y_test1, y_pred1,average=\"macro\")))\n",
    "print('Micro Recall : '+str(recall_score(y_test1, y_pred1,average=\"micro\")))\n",
    "print('Weighted Recall : '+str(recall_score(y_test1, y_pred1,average=\"weighted\")))\n",
    "print('Recall Scores : ' )\n",
    "pd.DataFrame(recall_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['recall-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion Matrix with 10 chosen well-known classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classes : AMAZON, FACEBOOK, GMAIL, GOOGLE, HTTP, OFFICE_365, SKYPE, TWITTER, WHATSAPP, YOUTUBE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "apps=['AMAZON','FACEBOOK','GMAIL','GOOGLE','HTTP','OFFICE_365','SKYPE','TWITTER','WHATSAPP','YOUTUBE']#reporting for these well-known Apps\n",
    "y_test1[y_test1.isin(apps)]\n",
    "cm=confusion_matrix(y_test1[y_test1.isin(apps)],y_pred1[y_test1.isin(apps)],labels=y_test1[y_test1.isin(apps)].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_pc=pd.DataFrame((cm.T/np.sum(cm,axis=1)).T,columns=y_test1[y_test1.isin(apps)].unique(),index=y_test1[y_test1.isin(apps)].unique())\n",
    "cm_pc=cm_pc.replace(np.nan,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "df_cm=pd.DataFrame(cm,columns=y_test1[y_test1.isin(apps)].unique(),index=y_test1[y_test1.isin(apps)].unique())\n",
    "sns.set(font_scale=1.1)\n",
    "sns.heatmap(cm_pc, annot=True,fmt='.1%',cmap=sns.color_palette(\"Reds\",150)) # font size\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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}