moody_player.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\envs\\deepl\\lib\\site-packages\\ipykernel_launcher.py:21: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import cv2\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    " \n",
    "dataset_path = 'fer2013.csv'\n",
    "image_size=(48,48)\n",
    " \n",
    "def load_fer2013():\n",
    "    data = pd.read_csv(dataset_path)\n",
    "    pixels = data['pixels'].tolist()\n",
    "    width, height = 48, 48\n",
    "    faces = []\n",
    "    for pixel_sequence in pixels:\n",
    "        face = [int(pixel) for pixel in pixel_sequence.split(' ')]\n",
    "        face = np.asarray(face).reshape(width, height)\n",
    "        face = cv2.resize(face.astype('uint8'),image_size)\n",
    "        faces.append(face.astype('float32'))\n",
    "    faces = np.asarray(faces)\n",
    "    faces = np.expand_dims(faces, -1)\n",
    "    emotions = pd.get_dummies(data['emotion']).as_matrix()\n",
    "    return faces, emotions\n",
    " \n",
    "def preprocess_input(x, v2=True):\n",
    "    x = x.astype('float32')\n",
    "    x = x / 255.0\n",
    "    if v2:\n",
    "        x = x - 0.5\n",
    "        x = x * 2.0\n",
    "    return x\n",
    " \n",
    "faces, emotions = load_fer2013()\n",
    "faces = preprocess_input(faces)\n",
    "xtrain, xtest,ytrain,ytest = train_test_split(faces, emotions,test_size=0.2,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_5 (InputLayer)            (None, 48, 48, 1)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 46, 46, 8)    72          input_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 46, 46, 8)    32          conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 46, 46, 8)    0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 44, 44, 8)    576         activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 44, 44, 8)    32          conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 44, 44, 8)    0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_33 (SeparableC (None, 44, 44, 16)   200         activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 44, 44, 16)   64          separable_conv2d_33[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 44, 44, 16)   0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_34 (SeparableC (None, 44, 44, 16)   400         activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 44, 44, 16)   64          separable_conv2d_34[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 22, 22, 16)   128         activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling2D) (None, 22, 22, 16)   0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 22, 22, 16)   64          conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 22, 22, 16)   0           max_pooling2d_17[0][0]           \n",
      "                                                                 batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_35 (SeparableC (None, 22, 22, 32)   656         add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 22, 22, 32)   128         separable_conv2d_35[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 22, 22, 32)   0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_36 (SeparableC (None, 22, 22, 32)   1312        activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 22, 22, 32)   128         separable_conv2d_36[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 11, 11, 32)   512         add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_18 (MaxPooling2D) (None, 11, 11, 32)   0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 11, 11, 32)   128         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, 11, 11, 32)   0           max_pooling2d_18[0][0]           \n",
      "                                                                 batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_37 (SeparableC (None, 11, 11, 64)   2336        add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 11, 11, 64)   256         separable_conv2d_37[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 11, 11, 64)   0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_38 (SeparableC (None, 11, 11, 64)   4672        activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 11, 11, 64)   256         separable_conv2d_38[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 6, 6, 64)     2048        add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_19 (MaxPooling2D) (None, 6, 6, 64)     0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 6, 6, 64)     256         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, 6, 6, 64)     0           max_pooling2d_19[0][0]           \n",
      "                                                                 batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_39 (SeparableC (None, 6, 6, 128)    8768        add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 6, 6, 128)    512         separable_conv2d_39[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 6, 6, 128)    0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_40 (SeparableC (None, 6, 6, 128)    17536       activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 6, 6, 128)    512         separable_conv2d_40[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 3, 3, 128)    8192        add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_20 (MaxPooling2D) (None, 3, 3, 128)    0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 3, 3, 128)    512         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, 3, 3, 128)    0           max_pooling2d_20[0][0]           \n",
      "                                                                 batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 3, 3, 7)      8071        add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_5 (Glo (None, 7)            0           conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Activation)        (None, 7)            0           global_average_pooling2d_5[0][0] \n",
      "==================================================================================================\n",
      "Total params: 58,423\n",
      "Trainable params: 56,951\n",
      "Non-trainable params: 1,472\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/110\n",
      "898/897 [==============================] - 139s 155ms/step - loss: 1.7366 - acc: 0.3475 - val_loss: 1.5963 - val_acc: 0.4154\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.59631, saving model to fer2013_mini_XCEPTION.01-0.42.hdf5\n",
      "Epoch 2/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.4893 - acc: 0.4415 - val_loss: 1.5484 - val_acc: 0.4468\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.59631 to 1.54842, saving model to fer2013_mini_XCEPTION.02-0.45.hdf5\n",
      "Epoch 3/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.3846 - acc: 0.4836 - val_loss: 1.3206 - val_acc: 0.5143\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.54842 to 1.32063, saving model to fer2013_mini_XCEPTION.03-0.51.hdf5\n",
      "Epoch 4/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.3195 - acc: 0.5092 - val_loss: 1.4429 - val_acc: 0.4772\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 1.32063\n",
      "Epoch 5/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.2790 - acc: 0.5225 - val_loss: 1.3599 - val_acc: 0.5035\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 1.32063\n",
      "Epoch 6/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.2417 - acc: 0.5323 - val_loss: 1.3085 - val_acc: 0.5210\n",
      "\n",
      "Epoch 00006: val_loss improved from 1.32063 to 1.30853, saving model to fer2013_mini_XCEPTION.06-0.52.hdf5\n",
      "Epoch 7/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.2200 - acc: 0.5396 - val_loss: 1.1861 - val_acc: 0.5641\n",
      "\n",
      "Epoch 00007: val_loss improved from 1.30853 to 1.18608, saving model to fer2013_mini_XCEPTION.07-0.56.hdf5\n",
      "Epoch 8/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.1906 - acc: 0.5540 - val_loss: 1.1788 - val_acc: 0.5575\n",
      "\n",
      "Epoch 00008: val_loss improved from 1.18608 to 1.17885, saving model to fer2013_mini_XCEPTION.08-0.56.hdf5\n",
      "Epoch 9/110\n",
      "898/897 [==============================] - 310s 345ms/step - loss: 1.1747 - acc: 0.5628 - val_loss: 1.1820 - val_acc: 0.5474\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 1.17885\n",
      "Epoch 10/110\n",
      "898/897 [==============================] - 117s 130ms/step - loss: 1.1568 - acc: 0.5636 - val_loss: 1.2900 - val_acc: 0.5350\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 1.17885\n",
      "Epoch 11/110\n",
      "898/897 [==============================] - 119s 133ms/step - loss: 1.1454 - acc: 0.5713 - val_loss: 1.3236 - val_acc: 0.5191\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 1.17885\n",
      "Epoch 12/110\n",
      "898/897 [==============================] - 121s 134ms/step - loss: 1.1348 - acc: 0.5752 - val_loss: 1.1527 - val_acc: 0.5763\n",
      "\n",
      "Epoch 00012: val_loss improved from 1.17885 to 1.15270, saving model to fer2013_mini_XCEPTION.12-0.58.hdf5\n",
      "Epoch 13/110\n",
      "898/897 [==============================] - 116s 129ms/step - loss: 1.1231 - acc: 0.5806 - val_loss: 1.1411 - val_acc: 0.5840\n",
      "\n",
      "Epoch 00013: val_loss improved from 1.15270 to 1.14108, saving model to fer2013_mini_XCEPTION.13-0.58.hdf5\n",
      "Epoch 14/110\n",
      "898/897 [==============================] - 117s 130ms/step - loss: 1.1189 - acc: 0.5815 - val_loss: 1.1606 - val_acc: 0.5692\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 1.14108\n",
      "Epoch 15/110\n",
      "898/897 [==============================] - 116s 130ms/step - loss: 1.1057 - acc: 0.5863 - val_loss: 1.2091 - val_acc: 0.5548\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 1.14108\n",
      "Epoch 16/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 1.0959 - acc: 0.5884 - val_loss: 1.1110 - val_acc: 0.5886\n",
      "\n",
      "Epoch 00016: val_loss improved from 1.14108 to 1.11103, saving model to fer2013_mini_XCEPTION.16-0.59.hdf5\n",
      "Epoch 17/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0825 - acc: 0.5965 - val_loss: 1.1541 - val_acc: 0.5769\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 1.11103\n",
      "Epoch 18/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0827 - acc: 0.5953 - val_loss: 1.1913 - val_acc: 0.5659\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 1.11103\n",
      "Epoch 19/110\n",
      "898/897 [==============================] - 109s 122ms/step - loss: 1.0705 - acc: 0.5991 - val_loss: 1.1265 - val_acc: 0.5832\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 1.11103\n",
      "Epoch 20/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 1.0632 - acc: 0.6007 - val_loss: 1.1018 - val_acc: 0.5936\n",
      "\n",
      "Epoch 00020: val_loss improved from 1.11103 to 1.10175, saving model to fer2013_mini_XCEPTION.20-0.59.hdf5\n",
      "Epoch 21/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0645 - acc: 0.5989 - val_loss: 1.1286 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 1.10175\n",
      "Epoch 22/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0582 - acc: 0.6044 - val_loss: 1.0924 - val_acc: 0.5989\n",
      "\n",
      "Epoch 00022: val_loss improved from 1.10175 to 1.09242, saving model to fer2013_mini_XCEPTION.22-0.60.hdf5\n",
      "Epoch 23/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0488 - acc: 0.6079 - val_loss: 1.1151 - val_acc: 0.5988\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 1.09242\n",
      "Epoch 24/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0432 - acc: 0.6084 - val_loss: 1.1625 - val_acc: 0.5765\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 1.09242\n",
      "Epoch 25/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0329 - acc: 0.6161 - val_loss: 1.1194 - val_acc: 0.5868\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 1.09242\n",
      "Epoch 26/110\n",
      "898/897 [==============================] - 110s 123ms/step - loss: 1.0236 - acc: 0.6170 - val_loss: 1.0487 - val_acc: 0.6151\n",
      "\n",
      "Epoch 00026: val_loss improved from 1.09242 to 1.04866, saving model to fer2013_mini_XCEPTION.26-0.62.hdf5\n",
      "Epoch 27/110\n",
      "898/897 [==============================] - 108s 121ms/step - loss: 1.0244 - acc: 0.6164 - val_loss: 1.0623 - val_acc: 0.6063\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 1.04866\n",
      "Epoch 28/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0205 - acc: 0.6194 - val_loss: 1.0644 - val_acc: 0.6073\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 1.04866\n",
      "Epoch 29/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0197 - acc: 0.6222 - val_loss: 1.0261 - val_acc: 0.6232\n",
      "\n",
      "Epoch 00029: val_loss improved from 1.04866 to 1.02609, saving model to fer2013_mini_XCEPTION.29-0.62.hdf5\n",
      "Epoch 30/110\n",
      "898/897 [==============================] - 109s 122ms/step - loss: 1.0101 - acc: 0.6218 - val_loss: 1.0642 - val_acc: 0.6028\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 1.02609\n",
      "Epoch 31/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0138 - acc: 0.6207 - val_loss: 1.1095 - val_acc: 0.5975\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 1.02609\n",
      "Epoch 32/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 1.0120 - acc: 0.6234 - val_loss: 1.0841 - val_acc: 0.6123\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 1.02609\n",
      "Epoch 33/110\n",
      "898/897 [==============================] - 103s 115ms/step - loss: 1.0044 - acc: 0.6243 - val_loss: 1.0328 - val_acc: 0.6239\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 1.02609\n",
      "Epoch 34/110\n",
      "898/897 [==============================] - 108s 121ms/step - loss: 0.9923 - acc: 0.6329 - val_loss: 1.1172 - val_acc: 0.5864\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 1.02609\n",
      "Epoch 35/110\n",
      "898/897 [==============================] - 103s 115ms/step - loss: 0.9938 - acc: 0.6329 - val_loss: 1.0361 - val_acc: 0.6230\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 1.02609\n",
      "Epoch 36/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9932 - acc: 0.6327 - val_loss: 1.0578 - val_acc: 0.6127\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 1.02609\n",
      "Epoch 37/110\n",
      "898/897 [==============================] - 104s 115ms/step - loss: 0.9916 - acc: 0.6290 - val_loss: 1.0202 - val_acc: 0.6280\n",
      "\n",
      "Epoch 00037: val_loss improved from 1.02609 to 1.02018, saving model to fer2013_mini_XCEPTION.37-0.63.hdf5\n",
      "Epoch 38/110\n",
      "898/897 [==============================] - 103s 115ms/step - loss: 0.9854 - acc: 0.6309 - val_loss: 1.0372 - val_acc: 0.6213\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 1.02018\n",
      "Epoch 39/110\n",
      "898/897 [==============================] - 103s 115ms/step - loss: 0.9782 - acc: 0.6319 - val_loss: 1.0941 - val_acc: 0.6039\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 1.02018\n",
      "Epoch 40/110\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "898/897 [==============================] - 103s 115ms/step - loss: 0.9849 - acc: 0.6327 - val_loss: 1.0643 - val_acc: 0.6169\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 1.02018\n",
      "Epoch 41/110\n",
      "898/897 [==============================] - 103s 115ms/step - loss: 0.9754 - acc: 0.6356 - val_loss: 1.1272 - val_acc: 0.5911\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 1.02018\n",
      "Epoch 42/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9784 - acc: 0.6321 - val_loss: 1.0607 - val_acc: 0.6131\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 1.02018\n",
      "Epoch 43/110\n",
      "898/897 [==============================] - 105s 116ms/step - loss: 0.9683 - acc: 0.6365 - val_loss: 1.0245 - val_acc: 0.6236\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 1.02018\n",
      "Epoch 44/110\n",
      "898/897 [==============================] - 105s 116ms/step - loss: 0.9658 - acc: 0.6415 - val_loss: 1.0402 - val_acc: 0.6181\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 1.02018\n",
      "Epoch 45/110\n",
      "898/897 [==============================] - 111s 123ms/step - loss: 0.9689 - acc: 0.6400 - val_loss: 1.0684 - val_acc: 0.6212\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 1.02018\n",
      "Epoch 46/110\n",
      "898/897 [==============================] - 108s 120ms/step - loss: 0.9609 - acc: 0.6433 - val_loss: 1.0454 - val_acc: 0.6225\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 1.02018\n",
      "Epoch 47/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9618 - acc: 0.6408 - val_loss: 1.0227 - val_acc: 0.6247\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 1.02018\n",
      "Epoch 48/110\n",
      "898/897 [==============================] - 103s 115ms/step - loss: 0.9604 - acc: 0.6401 - val_loss: 1.0191 - val_acc: 0.6329\n",
      "\n",
      "Epoch 00048: val_loss improved from 1.02018 to 1.01913, saving model to fer2013_mini_XCEPTION.48-0.63.hdf5\n",
      "Epoch 49/110\n",
      "898/897 [==============================] - 103s 115ms/step - loss: 0.9560 - acc: 0.6424 - val_loss: 1.0316 - val_acc: 0.6219\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 1.01913\n",
      "Epoch 50/110\n",
      "898/897 [==============================] - 103s 115ms/step - loss: 0.9565 - acc: 0.6411 - val_loss: 1.0276 - val_acc: 0.6241\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 1.01913\n",
      "Epoch 51/110\n",
      "898/897 [==============================] - 108s 121ms/step - loss: 0.9492 - acc: 0.6448 - val_loss: 1.0756 - val_acc: 0.6092\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 1.01913\n",
      "Epoch 52/110\n",
      "898/897 [==============================] - 107s 119ms/step - loss: 0.9520 - acc: 0.6454 - val_loss: 1.0589 - val_acc: 0.6197\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 1.01913\n",
      "Epoch 53/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.9480 - acc: 0.6456 - val_loss: 1.0662 - val_acc: 0.6134\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 1.01913\n",
      "Epoch 54/110\n",
      "898/897 [==============================] - 107s 119ms/step - loss: 0.9505 - acc: 0.6451 - val_loss: 1.0721 - val_acc: 0.6119\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 1.01913\n",
      "Epoch 55/110\n",
      "898/897 [==============================] - 113s 125ms/step - loss: 0.9442 - acc: 0.6459 - val_loss: 1.0513 - val_acc: 0.6195\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 1.01913\n",
      "Epoch 56/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.9417 - acc: 0.6496 - val_loss: 1.0845 - val_acc: 0.6163\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 1.01913\n",
      "Epoch 57/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9394 - acc: 0.6500 - val_loss: 1.0178 - val_acc: 0.6293\n",
      "\n",
      "Epoch 00057: val_loss improved from 1.01913 to 1.01781, saving model to fer2013_mini_XCEPTION.57-0.63.hdf5\n",
      "Epoch 58/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.9375 - acc: 0.6499 - val_loss: 0.9867 - val_acc: 0.6393\n",
      "\n",
      "Epoch 00058: val_loss improved from 1.01781 to 0.98674, saving model to fer2013_mini_XCEPTION.58-0.64.hdf5\n",
      "Epoch 59/110\n",
      "898/897 [==============================] - 107s 120ms/step - loss: 0.9352 - acc: 0.6523 - val_loss: 1.0291 - val_acc: 0.6293\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.98674\n",
      "Epoch 60/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9371 - acc: 0.6534 - val_loss: 1.0177 - val_acc: 0.6284\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.98674\n",
      "Epoch 61/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9317 - acc: 0.6492 - val_loss: 1.0647 - val_acc: 0.6216\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.98674\n",
      "Epoch 62/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9368 - acc: 0.6525 - val_loss: 0.9872 - val_acc: 0.6404\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.98674\n",
      "Epoch 63/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.9270 - acc: 0.6559 - val_loss: 1.0144 - val_acc: 0.6364\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.98674\n",
      "Epoch 64/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.9320 - acc: 0.6494 - val_loss: 1.0151 - val_acc: 0.6296\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.98674\n",
      "Epoch 65/110\n",
      "898/897 [==============================] - 110s 122ms/step - loss: 0.9216 - acc: 0.6549 - val_loss: 1.0107 - val_acc: 0.6368\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.98674\n",
      "Epoch 66/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.9231 - acc: 0.6560 - val_loss: 1.0632 - val_acc: 0.6149\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.98674\n",
      "Epoch 67/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9225 - acc: 0.6566 - val_loss: 1.0041 - val_acc: 0.6392\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.98674\n",
      "Epoch 68/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.9213 - acc: 0.6583 - val_loss: 1.0041 - val_acc: 0.6353\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.98674\n",
      "Epoch 69/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9173 - acc: 0.6598 - val_loss: 1.0525 - val_acc: 0.6262\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.98674\n",
      "Epoch 70/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.9145 - acc: 0.6590 - val_loss: 1.0354 - val_acc: 0.6266\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.98674\n",
      "\n",
      "Epoch 00070: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 71/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8749 - acc: 0.6769 - val_loss: 0.9722 - val_acc: 0.6453\n",
      "\n",
      "Epoch 00071: val_loss improved from 0.98674 to 0.97218, saving model to fer2013_mini_XCEPTION.71-0.65.hdf5\n",
      "Epoch 72/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.8566 - acc: 0.6839 - val_loss: 0.9635 - val_acc: 0.6510\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.97218 to 0.96351, saving model to fer2013_mini_XCEPTION.72-0.65.hdf5\n",
      "Epoch 73/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.8528 - acc: 0.6835 - val_loss: 0.9633 - val_acc: 0.6520\n",
      "\n",
      "Epoch 00073: val_loss improved from 0.96351 to 0.96334, saving model to fer2013_mini_XCEPTION.73-0.65.hdf5\n",
      "Epoch 74/110\n",
      "898/897 [==============================] - 104s 116ms/step - loss: 0.8433 - acc: 0.6885 - val_loss: 0.9696 - val_acc: 0.6493\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.96334\n",
      "Epoch 75/110\n",
      "898/897 [==============================] - 28366s 32s/step - loss: 0.8443 - acc: 0.6899 - val_loss: 0.9630 - val_acc: 0.6542\n",
      "\n",
      "Epoch 00075: val_loss improved from 0.96334 to 0.96298, saving model to fer2013_mini_XCEPTION.75-0.65.hdf5\n",
      "Epoch 76/110\n",
      "898/897 [==============================] - 142s 158ms/step - loss: 0.8381 - acc: 0.6850 - val_loss: 0.9632 - val_acc: 0.6542\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.96298\n",
      "Epoch 77/110\n",
      "898/897 [==============================] - 124s 138ms/step - loss: 0.8405 - acc: 0.6886 - val_loss: 0.9682 - val_acc: 0.6535\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.96298\n",
      "Epoch 78/110\n",
      "898/897 [==============================] - 117s 131ms/step - loss: 0.8361 - acc: 0.6911 - val_loss: 0.9666 - val_acc: 0.6546\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.96298\n",
      "Epoch 79/110\n",
      "898/897 [==============================] - 115s 128ms/step - loss: 0.8344 - acc: 0.6892 - val_loss: 0.9772 - val_acc: 0.6500\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.96298\n",
      "Epoch 80/110\n",
      "898/897 [==============================] - 110s 122ms/step - loss: 0.8345 - acc: 0.6929 - val_loss: 0.9683 - val_acc: 0.6528\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00080: val_loss did not improve from 0.96298\n",
      "Epoch 81/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8335 - acc: 0.6904 - val_loss: 0.9681 - val_acc: 0.6499\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.96298\n",
      "Epoch 82/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8338 - acc: 0.6917 - val_loss: 0.9653 - val_acc: 0.6520\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.96298\n",
      "Epoch 83/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.8361 - acc: 0.6892 - val_loss: 0.9662 - val_acc: 0.6530\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.96298\n",
      "Epoch 84/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8338 - acc: 0.6887 - val_loss: 0.9656 - val_acc: 0.6532\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 0.96298\n",
      "Epoch 85/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8293 - acc: 0.6918 - val_loss: 0.9676 - val_acc: 0.6530\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 0.96298\n",
      "Epoch 86/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8268 - acc: 0.6947 - val_loss: 0.9714 - val_acc: 0.6507\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.96298\n",
      "Epoch 87/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8262 - acc: 0.6931 - val_loss: 0.9632 - val_acc: 0.6520\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.96298\n",
      "\n",
      "Epoch 00087: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 88/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8168 - acc: 0.6955 - val_loss: 0.9640 - val_acc: 0.6532\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.96298\n",
      "Epoch 89/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8209 - acc: 0.6953 - val_loss: 0.9651 - val_acc: 0.6544\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.96298\n",
      "Epoch 90/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8178 - acc: 0.6990 - val_loss: 0.9637 - val_acc: 0.6532\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.96298\n",
      "Epoch 91/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8144 - acc: 0.7002 - val_loss: 0.9646 - val_acc: 0.6534\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.96298\n",
      "Epoch 92/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8152 - acc: 0.6973 - val_loss: 0.9641 - val_acc: 0.6535\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.96298\n",
      "Epoch 93/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8148 - acc: 0.6947 - val_loss: 0.9661 - val_acc: 0.6541\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.96298\n",
      "Epoch 94/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8165 - acc: 0.6973 - val_loss: 0.9655 - val_acc: 0.6551\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.96298\n",
      "Epoch 95/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.8155 - acc: 0.6990 - val_loss: 0.9649 - val_acc: 0.6539\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 0.96298\n",
      "Epoch 96/110\n",
      "898/897 [==============================] - 108s 120ms/step - loss: 0.8180 - acc: 0.6972 - val_loss: 0.9644 - val_acc: 0.6541\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.96298\n",
      "Epoch 97/110\n",
      "898/897 [==============================] - 110s 123ms/step - loss: 0.8162 - acc: 0.7011 - val_loss: 0.9630 - val_acc: 0.6544\n",
      "\n",
      "Epoch 00097: val_loss improved from 0.96298 to 0.96298, saving model to fer2013_mini_XCEPTION.97-0.65.hdf5\n",
      "Epoch 98/110\n",
      "898/897 [==============================] - 106s 119ms/step - loss: 0.8166 - acc: 0.6959 - val_loss: 0.9628 - val_acc: 0.6559\n",
      "\n",
      "Epoch 00098: val_loss improved from 0.96298 to 0.96281, saving model to fer2013_mini_XCEPTION.98-0.66.hdf5\n",
      "Epoch 99/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8162 - acc: 0.6985 - val_loss: 0.9632 - val_acc: 0.6549\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.96281\n",
      "Epoch 100/110\n",
      "898/897 [==============================] - 105s 116ms/step - loss: 0.8180 - acc: 0.6973 - val_loss: 0.9634 - val_acc: 0.6555\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.96281\n",
      "Epoch 101/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.8110 - acc: 0.6998 - val_loss: 0.9630 - val_acc: 0.6544\n",
      "\n",
      "Epoch 00101: val_loss did not improve from 0.96281\n",
      "Epoch 102/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8119 - acc: 0.6977 - val_loss: 0.9634 - val_acc: 0.6552\n",
      "\n",
      "Epoch 00102: val_loss did not improve from 0.96281\n",
      "Epoch 103/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8161 - acc: 0.6963 - val_loss: 0.9645 - val_acc: 0.6538\n",
      "\n",
      "Epoch 00103: val_loss did not improve from 0.96281\n",
      "Epoch 104/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.8136 - acc: 0.6986 - val_loss: 0.9634 - val_acc: 0.6538\n",
      "\n",
      "Epoch 00104: val_loss did not improve from 0.96281\n",
      "Epoch 105/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.8157 - acc: 0.6988 - val_loss: 0.9640 - val_acc: 0.6548\n",
      "\n",
      "Epoch 00105: val_loss did not improve from 0.96281\n",
      "Epoch 106/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.8178 - acc: 0.6954 - val_loss: 0.9634 - val_acc: 0.6530\n",
      "\n",
      "Epoch 00106: val_loss did not improve from 0.96281\n",
      "Epoch 107/110\n",
      "898/897 [==============================] - 105s 117ms/step - loss: 0.8155 - acc: 0.6957 - val_loss: 0.9630 - val_acc: 0.6534\n",
      "\n",
      "Epoch 00107: val_loss did not improve from 0.96281\n",
      "Epoch 108/110\n",
      "898/897 [==============================] - 107s 119ms/step - loss: 0.8190 - acc: 0.6959 - val_loss: 0.9633 - val_acc: 0.6542\n",
      "\n",
      "Epoch 00108: val_loss did not improve from 0.96281\n",
      "Epoch 109/110\n",
      "898/897 [==============================] - 106s 118ms/step - loss: 0.8098 - acc: 0.7006 - val_loss: 0.9643 - val_acc: 0.6542\n",
      "\n",
      "Epoch 00109: val_loss did not improve from 0.96281\n",
      "Epoch 110/110\n",
      "898/897 [==============================] - 108s 120ms/step - loss: 0.8137 - acc: 0.6961 - val_loss: 0.9623 - val_acc: 0.6542\n",
      "\n",
      "Epoch 00110: val_loss improved from 0.96281 to 0.96235, saving model to fer2013_mini_XCEPTION.110-0.65.hdf5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x25abacaad68>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Activation, Convolution2D, Dropout, Conv2D\n",
    "from keras.layers import AveragePooling2D, BatchNormalization\n",
    "from keras.layers import GlobalAveragePooling2D\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Flatten\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import SeparableConv2D\n",
    "from keras import layers\n",
    "from keras.regularizers import l2\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import numpy as np\n",
    " \n",
    "# parameters\n",
    "batch_size = 32\n",
    "num_epochs = 110\n",
    "input_shape = (48, 48, 1)\n",
    "verbose = 1\n",
    "num_classes = 7\n",
    "patience = 50\n",
    "base_path = 'models/'\n",
    "l2_regularization=0.01\n",
    " \n",
    "# data generator\n",
    "data_generator = ImageDataGenerator(\n",
    "                        featurewise_center=False,\n",
    "                        featurewise_std_normalization=False,\n",
    "                        rotation_range=10,\n",
    "                        width_shift_range=0.1,\n",
    "                        height_shift_range=0.1,\n",
    "                        zoom_range=.1,\n",
    "                        horizontal_flip=True)\n",
    " \n",
    "# model parameters\n",
    "regularization = l2(l2_regularization)\n",
    " \n",
    "# base\n",
    "img_input = Input(input_shape)\n",
    "x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization, use_bias=False)(img_input)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation('relu')(x)\n",
    "x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization, use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation('relu')(x)\n",
    " \n",
    "# module 1\n",
    "residual = Conv2D(16, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)\n",
    "residual = BatchNormalization()(residual)\n",
    "x = SeparableConv2D(16, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation('relu')(x)\n",
    "x = SeparableConv2D(16, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
    "x = layers.add([x, residual])\n",
    " \n",
    "# module 2\n",
    "residual = Conv2D(32, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)\n",
    "residual = BatchNormalization()(residual)\n",
    "x = SeparableConv2D(32, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation('relu')(x)\n",
    "x = SeparableConv2D(32, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
    "x = layers.add([x, residual])\n",
    " \n",
    "# module 3\n",
    "residual = Conv2D(64, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)\n",
    "residual = BatchNormalization()(residual)\n",
    "x = SeparableConv2D(64, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation('relu')(x)\n",
    "x = SeparableConv2D(64, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
    "x = layers.add([x, residual])\n",
    " \n",
    "# module 4\n",
    "residual = Conv2D(128, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)\n",
    "residual = BatchNormalization()(residual)\n",
    "x = SeparableConv2D(128, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation('relu')(x)\n",
    "x = SeparableConv2D(128, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
    "x = layers.add([x, residual])\n",
    "x = Conv2D(num_classes, (3, 3), padding='same')(x)\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "output = Activation('softmax',name='predictions')(x)\n",
    " \n",
    "model = Model(img_input, output)\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "model.summary()\n",
    " \n",
    "# callbacks\n",
    "base_path=r'fer2013'\n",
    "log_file_path = base_path + '_emotion_training.log'\n",
    "csv_logger = CSVLogger(log_file_path, append=False)\n",
    "early_stop = EarlyStopping('val_loss', patience=patience)\n",
    "reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1, patience=int(patience/4), verbose=1)\n",
    "trained_models_path = base_path + '_mini_XCEPTION'\n",
    "model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'\n",
    "model_checkpoint = ModelCheckpoint(model_names, 'val_loss', verbose=1,save_best_only=True)\n",
    "callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]\n",
    " \n",
    "model.fit_generator(data_generator.flow(xtrain, ytrain,batch_size),\n",
    "                        steps_per_epoch=len(xtrain) / batch_size,\n",
    "                        epochs=num_epochs, verbose=1, callbacks=callbacks,\n",
    "                        validation_data=(xtest,ytest))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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