LandUse_ResNet50_cv_Model_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('always')\n",
    "warnings.filterwarnings('ignore')\n",
    "# import \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import keras\n",
    "from tensorflow.keras.preprocessing.image import  img_to_array, load_img\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from keras.models import load_model\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainB_dir = os.path.join('../Data/trainB')\n",
    "testB_dir = os.path.join('../Data/testB')\n",
    "categories = ['no_tpo', 'tpo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no_tpo 9265 images\n",
      "tpo 10260 images\n"
     ]
    }
   ],
   "source": [
    "for category in categories:\n",
    "    print('{} {} images'.format(category, len(os.listdir(os.path.join(trainB_dir, category)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19525, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainB = []\n",
    "for category_id, category in enumerate(categories):\n",
    "    for file in os.listdir(os.path.join(trainB_dir, category)):\n",
    "        trainB.append(['trainB/{}/{}'.format(category, file), category_id, category])\n",
    "trainB = pd.DataFrame(trainB, columns=['file', 'category_id', 'category'])\n",
    "trainB.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>category_id</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trainB/no_tpo/Lambeth_no (295).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trainB/no_tpo/Lambeth_no (1690).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>trainB/no_tpo/Barnet_no (1941).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>trainB/no_tpo/Barnet_no (130).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>trainB/no_tpo/Lambeth_no (1803).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  file  category_id category\n",
       "0   trainB/no_tpo/Lambeth_no (295).tif            0   no_tpo\n",
       "1  trainB/no_tpo/Lambeth_no (1690).tif            0   no_tpo\n",
       "2   trainB/no_tpo/Barnet_no (1941).tif            0   no_tpo\n",
       "3    trainB/no_tpo/Barnet_no (130).tif            0   no_tpo\n",
       "4  trainB/no_tpo/Lambeth_no (1803).tif            0   no_tpo"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainB.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12797, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testB = []\n",
    "for category_id, category in enumerate(categories):\n",
    "    for file in os.listdir(os.path.join(testB_dir, category)):\n",
    "        testB.append(['testB/{}/{}'.format(category, file), category_id, category])\n",
    "testB = pd.DataFrame(testB,columns = ['filepath', 'category_id','category'])\n",
    "testB.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filepath</th>\n",
       "      <th>category_id</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>testB/no_tpo/non (4845).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>testB/no_tpo/non (3894).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>testB/no_tpo/non (3962).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>testB/no_tpo/non (277).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>testB/no_tpo/non (1891).tif</td>\n",
       "      <td>0</td>\n",
       "      <td>no_tpo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      filepath  category_id category\n",
       "0  testB/no_tpo/non (4845).tif            0   no_tpo\n",
       "1  testB/no_tpo/non (3894).tif            0   no_tpo\n",
       "2  testB/no_tpo/non (3962).tif            0   no_tpo\n",
       "3   testB/no_tpo/non (277).tif            0   no_tpo\n",
       "4  testB/no_tpo/non (1891).tif            0   no_tpo"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testB.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set variables\n",
    "img_size = 224\n",
    "new_input=(img_size,img_size,3)\n",
    "#seed=1\n",
    "#np.random.seed(seed=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Layer Type</th>\n",
       "      <th>Layer Name</th>\n",
       "      <th>Layer Trainable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;tensorflow.python.keras.engine.input_layer.In...</td>\n",
       "      <td>input_1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.convolutional....</td>\n",
       "      <td>conv1_pad</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.convolutional....</td>\n",
       "      <td>conv1_conv</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.normalization_...</td>\n",
       "      <td>conv1_bn</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.core.Activatio...</td>\n",
       "      <td>conv1_relu</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.normalization_...</td>\n",
       "      <td>conv5_block3_3_bn</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.merge.Add obje...</td>\n",
       "      <td>conv5_block3_add</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.core.Activatio...</td>\n",
       "      <td>conv5_block3_out</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.pooling.Global...</td>\n",
       "      <td>avg_pool</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>&lt;tensorflow.python.keras.layers.core.Flatten o...</td>\n",
       "      <td>flatten</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Layer Type         Layer Name  \\\n",
       "0    <tensorflow.python.keras.engine.input_layer.In...            input_1   \n",
       "1    <tensorflow.python.keras.layers.convolutional....          conv1_pad   \n",
       "2    <tensorflow.python.keras.layers.convolutional....         conv1_conv   \n",
       "3    <tensorflow.python.keras.layers.normalization_...           conv1_bn   \n",
       "4    <tensorflow.python.keras.layers.core.Activatio...         conv1_relu   \n",
       "..                                                 ...                ...   \n",
       "172  <tensorflow.python.keras.layers.normalization_...  conv5_block3_3_bn   \n",
       "173  <tensorflow.python.keras.layers.merge.Add obje...   conv5_block3_add   \n",
       "174  <tensorflow.python.keras.layers.core.Activatio...   conv5_block3_out   \n",
       "175  <tensorflow.python.keras.layers.pooling.Global...           avg_pool   \n",
       "176  <tensorflow.python.keras.layers.core.Flatten o...            flatten   \n",
       "\n",
       "     Layer Trainable  \n",
       "0              False  \n",
       "1              False  \n",
       "2              False  \n",
       "3              False  \n",
       "4              False  \n",
       "..               ...  \n",
       "172             True  \n",
       "173             True  \n",
       "174             True  \n",
       "175             True  \n",
       "176             True  \n",
       "\n",
       "[177 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set trainable layers in ResNet50\n",
    "resnet=ResNet50(include_top=False, input_shape=new_input, weights='imagenet', pooling='avg')\n",
    "output=resnet.layers[-1].output\n",
    "output=keras.layers.Flatten()(output)\n",
    "resnet=Model(resnet.input, output)\n",
    "resnet.trainable=True\n",
    "for layer in resnet.layers[:144]:\n",
    "    layer.trainable=False\n",
    "layers = [(layer, layer.name, layer.trainable) for layer in resnet.layers]\n",
    "pd.DataFrame(layers, columns=['Layer Type', 'Layer Name', 'Layer Trainable']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
      "                                                                 conv2_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
      "                                                                 conv2_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
      "                                                                 conv2_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
      "                                                                 conv3_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
      "                                                                 conv3_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
      "                                                                 conv3_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
      "                                                                 conv3_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
      "                                                                 conv4_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
      "                                                                 conv4_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
      "                                                                 conv4_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
      "                                                                 conv4_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
      "                                                                 conv4_block5_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
      "                                                                 conv4_block6_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
      "                                                                 conv5_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
      "                                                                 conv5_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
      "                                                                 conv5_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 2048)         0           avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 14,451,200\n",
      "Non-trainable params: 9,136,512\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "resnet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a model with cross validation fold\n",
    "def Resnet50_with_cv(data, cross_validation,\n",
    "                     activation, classes, optimizer, loss, metrics, \n",
    "                     epochs, batch_size):\n",
    "    print('Train Model')\n",
    "    data_generator_valid = ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "                                              validation_split=0.2)\n",
    "\n",
    "    # Data augmentation\n",
    "    data_generator_aug = ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "                                            horizontal_flip = True,\n",
    "                                            vertical_flip = True, \n",
    "                                            width_shift_range = 0.1,\n",
    "                                            height_shift_range = 0.1,\n",
    "                                            brightness_range=[0.8,1.2],\n",
    "                                            rotation_range=90)\n",
    "\n",
    "    print(\"==================================================\")\n",
    "    print('Cross Validation')\n",
    "    kfold = StratifiedKFold(n_splits = cross_validation, shuffle = True)\n",
    "    cvscores = []\n",
    "    iteration = 1\n",
    "    t = data.category_id\n",
    "    for train_index, valid_index in kfold.split(np.zeros(len(t)),t):\n",
    "        print('==================================================')\n",
    "        print(\"Iteration=\", iteration)\n",
    "        iteration+=1\n",
    "        \n",
    "        train=data.loc[train_index]\n",
    "        valid=data.loc[valid_index]\n",
    "        print('==================================================')\n",
    "        # create model\n",
    "        print('Create Model')\n",
    "        layer2=Dense(classes, activation=activation)\n",
    "        model=Sequential([resnet, layer2])\n",
    "        model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "        train_generator = data_generator_aug.flow_from_dataframe(dataframe=data,\n",
    "                                                                 directory= '../Data',\n",
    "                                                                 x_col='file',\n",
    "                                                                 y_col='category',\n",
    "                                                                 #seed=seed,\n",
    "                                                                 target_size=(img_size, img_size),\n",
    "                                                                 batch_size=batch_size, \n",
    "                                                                 class_mode='categorical')\n",
    "        valid_generator = data_generator_valid.flow_from_dataframe(dataframe=data,\n",
    "                                                             directory= '../Data',\n",
    "                                                             x_col='file',\n",
    "                                                             y_col='category',\n",
    "                                                             batch_size=batch_size,\n",
    "                                                             #seed=seed,\n",
    "                                                             target_size=(img_size, img_size), \n",
    "                                                             class_mode='categorical',\n",
    "                                                             subset='validation')\n",
    "        \n",
    "        earlystop = EarlyStopping(monitor='val_loss', patience=7, restore_best_weights=True)\n",
    "        step_train = train_generator.n//train_generator.batch_size\n",
    "        step_valid = valid_generator.n//valid_generator.batch_size\n",
    "        # fit the model\n",
    "        fit_stats = model.fit_generator(train_generator,\n",
    "                                        steps_per_epoch=step_train,\n",
    "                                        validation_data=valid_generator,\n",
    "                                        validation_steps=step_valid,\n",
    "                                        epochs=epochs,\n",
    "                                        verbose=1,\n",
    "                                        callbacks=earlystop)\n",
    "        scores=model.evaluate_generator(valid_generator, steps=step_valid)\n",
    "        cvscores.append(scores[-1]*100)\n",
    "        print(\"Accuarcy: %.2f%%\" % (scores[-1]*100))\n",
    "\n",
    "        acc=fit_stats.history['accuracy']\n",
    "        los=fit_stats.history['loss']\n",
    "        val_acc=fit_stats.history['val_accuracy']\n",
    "        val_loss=fit_stats.history['val_loss']\n",
    "        epoch=range(len(acc))\n",
    "\n",
    "        plt.plot(epoch, acc, 'r', label='Trainin Accuracy')\n",
    "        plt.plot(epoch, val_acc, 'b', label='Validation Accuracy')\n",
    "        plt.title('Model Accuracy')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "        plt.plot(epoch, los, 'r', label='Training Loss')\n",
    "        plt.plot(epoch, val_loss, 'b', label='Validation Loss')\n",
    "        plt.title('Model Loss')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    accuracy = np.mean(cvscores)\n",
    "    std=np.std(cvscores)\n",
    "    print('CV_Scores:%.2f%%(+/- %.2f%%)'%(accuracy,std))\n",
    "    model.save('LandUse_best_cv_model.h5')\n",
    "    return accuracy, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Model\n",
      "==================================================\n",
      "Cross Validation\n",
      "==================================================\n",
      "Iteration= 1\n",
      "==================================================\n",
      "Create Model\n",
      "Found 19523 validated image filenames belonging to 2 classes.\n",
      "Found 3904 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/25\n",
      "305/305 [==============================] - 262s 837ms/step - loss: 0.7377 - accuracy: 0.5542 - val_loss: 1.1318 - val_accuracy: 0.1934\n",
      "Epoch 2/25\n",
      "305/305 [==============================] - 250s 818ms/step - loss: 0.6577 - accuracy: 0.6152 - val_loss: 0.4767 - val_accuracy: 0.7853\n",
      "Epoch 3/25\n",
      "305/305 [==============================] - 246s 807ms/step - loss: 0.6480 - accuracy: 0.6315 - val_loss: 0.8149 - val_accuracy: 0.4506\n",
      "Epoch 4/25\n",
      "305/305 [==============================] - 253s 829ms/step - loss: 0.6331 - accuracy: 0.6406 - val_loss: 0.6241 - val_accuracy: 0.6460\n",
      "Epoch 5/25\n",
      "305/305 [==============================] - 242s 794ms/step - loss: 0.6266 - accuracy: 0.6568 - val_loss: 0.5999 - val_accuracy: 0.6724\n",
      "Epoch 6/25\n",
      "305/305 [==============================] - 242s 793ms/step - loss: 0.6105 - accuracy: 0.6648 - val_loss: 0.8002 - val_accuracy: 0.5003\n",
      "Epoch 7/25\n",
      "305/305 [==============================] - 241s 791ms/step - loss: 0.6027 - accuracy: 0.6710 - val_loss: 0.8771 - val_accuracy: 0.4270\n",
      "Epoch 8/25\n",
      "305/305 [==============================] - 241s 789ms/step - loss: 0.5955 - accuracy: 0.6740 - val_loss: 0.8905 - val_accuracy: 0.4293\n",
      "Epoch 9/25\n",
      "305/305 [==============================] - 242s 793ms/step - loss: 0.5933 - accuracy: 0.6815 - val_loss: 0.5406 - val_accuracy: 0.7016\n",
      "Accuarcy: 78.53%\n"
     ]
    },
    {
     "data": {
      "image/png": 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wvL+FF2DBBFyzpn5t2PDI20IdV/C2KlXCP8+hiRk7duhAbcAAfW8tL5bQw+Tz6f/JY4+N7PNWrgx9+sC772qtN8HmhMps2zZN2oEJzMxMTeoAzZvDxRdr6aRbNy3ziqAHbNgA32QfmbADl82bj1yPXL06pKbq7rHmzbmkYw5//OobHvn+fi67pSHtT9prCdiE5Y03dEFPeU2GBthO0TC1awdNmsD//hf55/7kE50UnThRuzEmK+dg9epDte+5c3U1CkClSjri7tblIN1O2kLXY77nmL1rQyfrUK2Za9fWZJ2aCo0bH7oefKlT54hkvG0btG6tb+bffKNxGFOUvDxtD3L00dHp12Q7Rcto3z5Yvlx7sERDjx662mLs2ORK6AcO6NJALZ845s5x/LxFa751qv9Gt8Y/cG3npXSr8BWn7JlFtVVrYN72I5+oXr1DSblLlyMTdePGOoIuhXr1dML68st1kvTBB8vyE5tk8OmnOo/z5pvl/9qW0MOwdCnk5kZvo0nFitrbZfRoXbRQo0Z0XqfcOac/0JYtR1yWrajAme/czC+/1QTgeFnL+W4OpzOHbszl5L0rqPCd0xpXaio0T4UefwidrMvSKS0MffroiUlGjNA33LJ02jSJLyNDB2hXXFH+r20JPQyR3PJfmPR0eOEFLelceWX0XqdMnNP1myESdKGXEJtQHHC7zMRJDu82v59uJ22hYfOa/iTdE1Kv0+sNG8bMWs7nntOR16BBMG+e7ew1of34I3z0EdxzjzcrP+3PMgxZWYfaQkTLGWfoYHTcuHJM6Hl5upY53OS8dWvhy+0CDYsaNNAfpG3bQ98XuEz4siEzr6nG889Bv1v+WU4/bNnUr6+nD7viCl1XfN99XkdkYtEL/mbiQ4Z48/o2KRqGU0/VBRCzZkX3dW6/HV56CX7+udQlX7VzJ6xfrxOE69frio+ffz4yQW/bVvhuwdq1C03IIS9hlj327dMJo6OO0jfKeBvpXnEFTJqkn9patfI6GhNLfvtNF0507QoffBC917FJ0TLIyYHFi3VreLSlp+tH+0mT4I9/LOSg3bs1SQcn7ODr2dl6TEFHH30o+bZooWv9GjSA3/3uyORcv37USh1PPAE//KD9a+ItmYPWR2fN0tLL3Lnx+TOY6Hj3XR0nlfdSxWD251iMVau0DBzN+nnAaW33kHpsFcY9v4M/HpgcOnHv2nXkA489VocGLVvqTqXUVP2+SZND66pjYL3d+vXa079fP13ZE49+9zt4/nndMPLUU/CXv3gdkYkVGRlw0knQs6d3MVhCL0agZW6ZV7js3Rt6RB10vcKOHaTzOM9uvp3t8+6kLjs0gzRpok1HevQ4lKQDCbtRo5iZOCzO3XfrvOoTT3gdSdmkp+tcx4MPagvkli29jsh4bcECnSx/+mlvuy1YQi+Gz6e7N1u0KObAgwd1F8G6dUeWQNav18nHgho00OTcrBmceSakppJ+oDVPPlSZD/69ikF31E6YJkmff67r7P/2N21FEs9EdG16q1bap+OLL+w0gskuI0PXBVx3nbdx2KRoMXr00Im8efMKOWDlSnj1VV1E/tNPh26vV+/I0XTgemANdYh9/s7pYLx5c5g6NTo/U3nLzdVdnr/8or+u6tW9jigy3noLrrkGnnwS7rzT62iMV7Zt0//O1113aJVLNNmkaCk5pyP0AQMK3LFnD4wfD6+8oo1GKlbUpiLXXgtt2uhml1JmLRFdtvj447pKMNzugLHs5Zdh0SItUyRKMgeduB4/Hv76Vy29NG/udUTGC6+9pvNsXk6GBlhvzSKsXasrADt0QLP7V1/BDTfohpfrr9e35sce07LKxIm6pbB58zJnrfR0HdVOmBCZn8NL27drwjvrLG92zkWTiI7IAi1SizpfhElMeXlafjvjjNjYQWwJvQj5LXOXvaUdmrp21ULwlVfqmrUVK3SZwzHHRPR109J0tnzcuIg+rSceekiT+jPPJGYTwkaNdCJszhxd/WKSy9SpsGZNbIzOwRJ6aDk58L//4fvru6SQQ9tnb9Ctoi+/rD20X3lFk3uUMpSIjtJnzz68LB9vli7V3ZU336xvUonq2mv1JMD33Xfo3KQmOWRk6KrhPn28jkSFldBFpJeIrBKR1SJybyHHdBeRhSKyTEQ+i2yY5WT1aq0P/P73cPHFZK2tS6v6W6i63Ke18uuvL+MWzvClp+vHuffeK5eXizjnYNgw3RE6YoTX0USXiJ7ZvWJF/ROx0ktyWLMGPv4YbropdlYOF5vQRSQFyAAuAFoBA0SkVYFj6gD/BS51zrUG4qdaunev9rns3l3r348+qkXzCRPwHd2TDhc21L3q5ax1a51fjdeyywcfaDOrhx/WBT+JLjUV/vMf+Oyz8lnpYLw3cqSuOb/pJq8jOSScEXoXYLVzbo1z7gAwFuhd4JirgAnOuR8BnHM/RzbMCHNOT30zdKhOcF57rU5sPvKItkubPJnNp/Vh82aJWsvccKSn6xrn7GzvYiiNfft0GV+bNt41KfLC4MF6opK779YJdZO49u7VymufPrqoLVaEk9AbA+uDvs/23xbsJKCuiMwWkQUicm2oJxKRm0QkU0Qyt4Q6q0y0bdums3Pt2+vZg994A3r31mL1d9/B/ffn/+uUR8vc4qSn69d33/UuhtJ48kndX/XMM8nV60REm6tVqKCLoTza4mHKwdixOtl/661eR3K4cBJ6qJm/gn+qFYFOwEXA+cCDInLSEQ9ybpRzrrNzrnODBg1KHGyp5OXp6dvT03VJwrBhWvAaOVInOEeP1jV1BSY4A1v+27cvnzBDad5cqz/xVHbJztZ+LX37wtlnex1N+TvuON1DMHOm1tVN4nFOJ0Nbt9YN3rEknISeDTQJ+j4V2BjimKnOuV+dc1uBzwFv1zX88AMMH67b6s8/H2bM0BLLokUwf77WAmrXLvThPh+ccIJO6nkpPR2+/lpHvPHg7rv1PTTe+7WUxU036ZvZXXfpn6FJLF9/rQO+W26JvaW44ST0+UBzEWkmIpWB/sCkAsd8CJwhIhVFpDpwKrAisqGGYf9+/Sx07rmayB9+WDsnjRsHGzfqguF27cJ6Kp/P23JLQOBkF+PHextHOL74AsaM0aX50TwZSKwT0fqqc3DjjVZ6STQZGbrY7eqrvY7kSMUmdOdcDnArMA1N0uOdc8tEZIiIDPEfswKYCiwGvgFeds4tjV7YBSxapGeHaNRI9+l/953uaFm7FqZN06xYgiZXO3bokiQvJ0QDmjXT8x7HetklN1f/CZo0gXtDLmxNLk2b6ibiTz7RVj8mMfz8sw6urruu3FYwl4xzzpNLp06dXJls3+5cRoZznTo5B85Vruxc//7OffKJc7m5ZXrqWbP0KT/+uGwhRsqTT2o8333ndSSFe/FFjXHsWK8jiR25uc517+7cUUc5t36919GYSPjnP/XvfPly72IAMl0heTX+doouXqyfdRo21CJWTg48+6xOcI4ZA+ecU+aGxPlb/mNghA6Hyi6xOkoP9Gs588wYPsG1BypU0M3FOTlaV7fSS3zLzdU9Bmef7cnWlLDEX0LfvBkmT9ZFv5mZmn1vu01PsRYhPp9WbyLcoqXUUlP1jHGxmtCHD9fWuInar6UsTjhBV/18/LEuqDLxa/Jk3aYSK31bQom/fuh5eXo21jBPSlwabdpoDXTy5Ki9RIk9/7y+by1bFlsnJ162TPu03HCD7ZAsTF6eroxdulR/X40aeR2RKY1zz9V+/mvXeru/oqh+6PE3Qq9QIarJfN8+/UeLlXJLQL9++qPH0ig90K+lVi34xz+8jiZ2VaigE6P792ujMiu9xJ9Vq3Tl85Ahsb1ZLv4SepQtWaK1slhL6Mceq6O8ceNiJyF8+KH+kT/8cGKciCOamjfXzhKTJ8Pbb3sdjSmp//5Xz7N+ww1eR1I0S+gFxMKW/8Kkp+tIYfFiryPR0eadd+puuaFDvY4mPtxxB5x2mi7v3LzZ62hMuPbsgddf1xO0xMq8WmEsoReQlQV168bmiYz79tWTEcdC2eXJJ7WWmGz9WsoiJUVLL3v36ptgrHzSMkV7+23YtSu2J0MDLKEX4PNp/5ZYXK1Rvz707Ol92SU7G/75T7j8co3HhK9lS+0P/8EHsfHGbIoW6NvSvr1+uop1ltCD5ORoDT0Wyy0B/fvrLtYFC7yL4Z57dJ4hmfu1lMWdd+ru31tv1Z2HJnbNmaM5IRb7toRiCT3IypVaG461CdFgl12mkzNeje7mzoV33tF+Lc2aeRNDvEtJ0TPF794dHx/jk1lGhp598qqrvI4kPJbQgwRa5sZyQq9bV5tHjhtX/qc6y83VtfCpqdavpaxatdINWe+9F3/97pPFpk3w/vswaBBUr+51NOGxhB7E59Ml7i1aeB1J0dLTYf16mDevfF/31Vf1d/T441CjRvm+diL6y1+gUycdpXtxvhdTtFGjtAwbT6u4LKEH8fl012NKiteRFO3SS7V5ZHmWXXbs0BM6nXHGoTMpmbKpWFFLLzt26FJGEzsOHoQXX4RevXQPQbywhO6Xl6cJPZbLLQFHHQUXXqgf1XNzy+c1//73Q2fwi4fJoXjRti08+KC28Z840etoTMAHH2jJJd7mOCyh+61dq2tN4yGhg46SN23SWfhoW74cnntOT9YQL7+feHLvvbosbuhQfdM03svI0H5OF1zgdSQlYwndL9Za5hbn4ot1oibaZRfr1xJ9lSrpTsRt2/R3bby1dCl89pm+wcZ6+bUgS+h+Pp/WNNu08TqS8NSoAZdcoqskcnKi9zqTJulZd/7+dyiv83ono7Q0naN46y346COvo0lu//2vzlENHux1JCVnCd0vK0uXklWt6nUk4UtP19URs2dH5/kD/VpatYqvmf549de/ak395pv1pCGm/O3aBW++qRv44rHhnCV0v3iZEA12wQVaChk7NjrP/5//6K7UZ57RsoCJrsqVtfTy88/w5z97HU1yGj1am3HF22RogCV0dHLxp59ie8t/KFWrQu/eMGECHDgQ2efesEH7tVx2mZ7Vz5SPjh11kvSNN2DKFK+jSS6Bvi2nnKKXeGQJnfibEA2Wnq4fz2fMiOzz3nOP1uaffDKyz2uK9+CD2pb4pptg506vo0keM2dq+494HZ2DJXTg0Jb/tDRv4yiN887TXhORXO0yd662DL3rLjj++Mg9rwlPlSq64WjTJvi///M6muSRkQH16sX3xjlL6OgI/cQTdcNOvKlcGfr00Y0Q+/eX/flyc3XXYuPGcN99ZX8+UzqnnKKtAV55BaZN8zqaxLd+vZ6B64Yb4mthREGW0NGEHm/182Dp6To7H4n/+K+9pp9YHnvM+rV4bfhw7Z9+443672ui58UXtYY+ZIjXkZRNWAldRHqJyCoRWS0iR/TZE5HuIrJTRBb6L3+LfKjRsX277hKNx/p5wNln6xKrspZdAv1aunWDAQMiEpopg6pV9Q12wwa4+26vo0lcv/0GL72km/WaNvU6mrIpNqGLSAqQAVwAtAIGiEirEId+4Zxr7788HOE4o2bhQv0azwm9UiU9Pd2kSXp6s9J6+GHYulW3+Vu/ltjwhz/oEsYXX4RPP/U6msT0/vu6VDSeJ0MDwhmhdwFWO+fWOOcOAGOB3tENq/zE8wqXYOnp8OuvpV/qtmKFJvIbboj/30WiGTECTjoJrr/eSi/RkJGhc2jnnut1JGUXTkJvDKwP+j7bf1tBp4nIIhH5WERaRyS6cuDz6QTg737ndSRlc+aZcOyxpdtkFOjXUqMGPPJIxEMzZVStmvaiX79e32xtpB45CxfCl1/Cn/4EFRJgRjGcHyHUh++CpyjOAn7vnEsDngM+CPlEIjeJSKaIZG6JkY7+WVmJMSJNSYF+/eB//9NTm5XERx/B9OnWryWWdeum66QrVNCNXtdfb+0BIiEjQ98wBw70OpLICCehZwNNgr5PBTYGH+Cc2+Wc2+O/PgWoJCJHdEJwzo1yznV2znVuEAOZY+9e3UiQCAkdtOyyf3/Jmjv99tuhfi1/+lP0YjNld9ZZsHixbvp64w04+WRtzuYKDq9MWLZv1/0WV1+tp3ZMBOEk9PlAcxFpJiKVgf7ApOADRORYEZ1GE5Eu/ueN+c7OS5boiS3ieclisK5dtXxUktUuTz0F338PTz9t/VriQbVq8OijMH++/ltfcQVcfjls3Fj8Y83hXnsN9u1LjMnQgGITunMuB7gVmAasAMY755aJyBARCaza7AcsFZFFwLNAf+dif9yQKBOiARUqwJVXwtSpugSxOBs2aI/z3r0TY0IomXToAF9/Df/+t/57n3yyngOzvE8cHq/y8rRNbrdu8blDvDBhTQM456Y4505yzp3gnHvEf9sLzrkX/Nefd861ds6lOef+4Jz7MppBR0pWln7UOu44ryOJnP79tVHXhx8Wf+y992q/lv/8J/pxmcirWFHXpy9Zoiebvvlm3ZPw7bdeRxb7pk/XT6aJNDqHJN8pGmiZm0hrrk85BZo1K77s8tVXejKF//s/69cS7048UVe+vPyyrtpo107LMgcPeh1Z7MrIgGOO0f0biSRpE/rBgzqySZT6eYCIll0++aTw81Pm5cFtt0GjRtavJVGI6MqXFSt0x+N990GXLrBggdeRxZ61a3U12I03ai+kRJK0CX3lSl3hkSj182Dp6VpKmTAh9P2vvab/0R97DGrWLN/YTHQ1bKgrXyZM0B7/XbpoWaYsO4gTzQsv6HzTzTd7HUnkJW1CD7TMTcSE3r49NG8euuyyc6f2a+naFa66qtxDM+WkTx9YvlxH7Y8/rmWYmTO9jsp7+/drB8vevSE11etoIi9pE7rPB9Wr65bqRCOio/RZs3SUFuzhh/U8pM8+m1hzB+ZIderoypeZM/XfumdPbe2QzBuSxo3TUmSiTYYGJHVCT0vTHZaJKD1da+Xvv3/otpUrNZFff72uijDJoUePQxuSXn9dN5EF/10kk+ef1yWePXp4HUl0JGVCz8uLz5NCl0SbNvofN1B2sX4tyS2wIembb7TO3q9f8m1I+uYbyMzU0XmifjpNyoS+Zo32O0nkhA66Jv2LL/Q/7eTJegKMhx6K/0ZkpvQ6dtTE9u9/w8cfJ9eGpIwMXQRwzTVeRxI9SZnQAztEE23JYkHp6Toyf+st7al98slw661eR2W8VtiGpO++8zqy6Nm6VT+tXnttfJ5qMlxJm9ArVtQzqyeyk07SFS8PPmj9WsyRAhuSXnpJNyS1bZu4G5JeeUWXKSd6A7qkTOhZWZrMq1TxOpLoS0/XVgCXXgrnned1NCbWiOjKlxUr4KKLDm1ICizrTQS5uTByJHTvnviDuKRL6M4l/oRosIEDdc3t0097HYmJZQ0b6sqX99+HzZsTa0PSlCnwww+Ju1QxWNIl9E2b9PyBiV4/Dzj2WPjgA+3vYkxxLr9cR+uDBiXOhqSMDG1z0TthTpxZuKRL6InWMteYSKtTR+vqgUQezxuSvvtOV3fdfHNyzB8lXULPytK6YSL1QDYmGnr00JUwd98dvxuSRo7URH7TTV5HUj6SLqH7fDq7X6uW15EYE/uqVdM16/G4IenXX/Xk2n37aukxGSRlQk+W+rkxkVJwQ1KrVlqWieUNSe+8o83okmEyNKCi1wGUp19+gXXrYMiQYg81xhQQ2JDUp4+WMG66SZPmqFHa3bMwzuna9gMH9GvBS7Ru//hjndTt1q38fkdeS6qEvnChfrUJUWNKr3lznTB95RW46y7dkNS4ceGJNTc3+jGJaK08cKlcWfeZ/O1vidu3JZSkSui2wsWYyAhsSLrwQvjXv3QFTCCRBifW4AQbzm2lfY5E7ZpaUkmX0FNToUEDryMxJjE0agTPPed1FCYgqSZFs7JsdG6MSVxJk9D37oVVqyyhG2MSV9Ik9MWLdYmVLVk0xiSqpEnoNiFqjEl0YSV0EeklIqtEZLWI3FvEcaeISK6I9ItciJGRlQVHHw1NmngdiTHGREexCV1EUoAM4AKgFTBARFoVcty/gWmRDjISAi1zk2lNqjEmuYQzQu8CrHbOrXHOHQDGAqEaUd4GvA/8HMH4IuLgQW0yZPVzY0wiCyehNwbWB32f7b8tn4g0BvoALxT1RCJyk4hkikjmli1bShprqS1frrvXrH5ujElk4ST0UEUKV+D7p4F7nHNFbvJ1zo1yznV2znVuUI67e2xC1BiTDMLZKZoNBE8lpgIFm2d2BsaKFqjrAxeKSI5z7oNIBFlWPh9Ur150AyFjjIl34ST0+UBzEWkGbAD6A1cFH+Ccyz/BmYi8DkyOlWQOmtDbt7d+D8aYxFZsycU5lwPciq5eWQGMd84tE5EhIhLzjWjz8pLrpNDGmOQVVnMu59wUYEqB20JOgDrnBpY9rMj5/nvYs8cSujEm8SX8TtHAhKgtWTTGJLqkSOiVKkHr1l5HYowx0ZXwCT0rS5N55cpeR2KMMdGV0AndOZsQNcYkj4RO6Bs3wpYtVj83xiSHhE7oWVn61UboxphkkNAJ3efT7oppaV5HYowx0ZfwCb15c6hZ0+tIjDEm+hI+oVv93BiTLBI2oW/bBj/8YPVzY0zySNiEvnChfrWEboxJFgmb0K0HujEm2SR0Qm/SBOrX9zoSY4wpHwmb0LOybHRujEkuCZnQf/0VVq2yhG6MSS4JmdAXL9Y+LrZk0RiTTBIyoduWf2NMMkrIhO7zQb16kJrqdSTGGFN+Ejahd+igfVyMMSZZJFxCP3AAli61+rkxJvkkXEJfvlyTutXPjTHJJuESuu0QNcYkq4RM6DVrattcY4xJJgmZ0NPSoELC/WTGGFO0sNKeiPQSkVUislpE7g1xf28RWSwiC0UkU0ROj3yoxcvL0y6LVm4xxiSjisUdICIpQAZwLpANzBeRSc655UGHfQpMcs45EWkHjAdaRiPgoqxeDXv2WEI3xiSncEboXYDVzrk1zrkDwFigd/ABzrk9zjnn/7YG4PBAYELUliwaY5JROAm9MbA+6Pts/22HEZE+IrIS+B8wONQTichN/pJM5pYtW0oTb5GysqBSJWjVKuJPbYwxMS+chB5qv+URI3Dn3ETnXEvgMmBEqCdyzo1yznV2znVu0KBBiQINh88HbdpA5coRf2pjjIl54ST0bKBJ0PepwMbCDnbOfQ6cICLlemoJ5w5t+TfGmGQUTkKfDzQXkWYiUhnoD0wKPkBEThTRziki0hGoDGyLdLBF2bABtm61+rkxJnkVu8rFOZcjIrcC04AU4FXn3DIRGeK//wWgL3CtiBwE9gHpQZOk5cJa5hpjkl2xCR3AOTcFmFLgtheCrv8b+HdkQysZn0+7K7Zr52UUxhjjnYTZT+nzQYsWuu3fGGOSUUIldCu3GGOSWVgll1i3bRv8+CPceqvXkRhTOgcPHiQ7O5v9+/d7HYqJEVWrViU1NZVKlSqF/ZiESOjWMtfEu+zsbGrVqkXTpk0RO9VW0nPOsW3bNrKzs2nWrFnYj0uIkosldBPv9u/fT7169SyZGwBEhHr16pX4E1vCJPTjjtMTQxsTryyZm2Cl+XtIiISelWWjc2OMifuEvmcPfPutJXRjSmvbtm20b9+e9u3bc+yxx9K4ceP87w8cOFDkYzMzM7n99tuLfY2uXbuWOK6cnBzq16/PfffdV+LHJqu4nxRdvFj7uNiWf2NKp169eixcuBCA4cOHU7NmTe666678+3NycqhYMXSq6Ny5M507dy72Nb788ssSxzV9+nRatGjB+PHj+ec//xm1klRRP1+8ifufwrb8m4QzbJieeiuS2reHp58O+/CBAwdy9NFH4/P56NixI+np6QwbNox9+/ZRrVo1XnvtNVq0aMHs2bN54oknmDx5MsOHD+fHH39kzZo1/PjjjwwbNix/9F6zZk327NnD7NmzGT58OPXr12fp0qV06tSJt956K2SyHjNmDHfccQcjR45k3rx5nHbaaQBMnTqV+++/n9zcXOrXr8+nn37Knj17uO2228jMzEREeOihh+jbt2/+6wK89957TJ48mddffz3sny83N5d77rmHadOmISLceOONtGrViueff56JEycC8MknnzBy5EgmTJhQtn+jCIj7hO7zQf360PiIDu3GmLL49ttvmTFjBikpKezatYvPP/+cihUrMmPGDO6//37ef//9Ix6zcuVKZs2axe7du2nRogVDhw49Yh21z+dj2bJlNGrUiG7dujF37lxOP/3ws1bu27ePTz/9lBdffJEdO3YwZswYTjvtNLZs2cKNN97I559/TrNmzfjll18AGDFiBLVr12bJkiUAbN++PSI/36hRo1i7di0+n4+KFSvyyy+/ULduXW655Ra2bNlCgwYNeO211xg0aFBpf80RlRAJvWNH7eNiTEIowUg6mq644gpSUlIA2LlzJ9dddx3fffcdIsLBgwdDPuaiiy6iSpUqVKlShd/97nf89NNPpKamHnZMly5d8m9r374969atOyKhT548mR49elC9enX69u3LiBEjeOqpp5g3bx5nnnlm/trso48+GoAZM2YwduzY/MfXrVs3Ij/fjBkzGDJkSH5JJvB611xzDW+99RaDBg3iq6++YvTo0cW+XnmI64R+4AAsXQp33ul1JMYknho1auRff/DBB+nRowcTJ05k3bp1dO/ePeRjqlSpkn89JSWFnJycUh0zZswY5s6dS9OmTQGduJ01axbOuZDlmcJuD76t4JrucH6+wp530KBBXHLJJVStWpUrrrgiZmrwcb3KZdkyOHjQ6ufGRNvOnTtp7K9rvv7661F9rV27djFnzhx+/PFH1q1bx7p168jIyMgvu3z22WesXbsWIL/kct555/H888/nP0eg5HLMMcewYsUK8vLy8mveoRT285133nm88MIL+W86gddr1KgRjRo14h//+AcDBw6M2M9eVnGd0G2HqDHl4+677+a+++6jW7du5ObmRvW1JkyYwNlnn33YSL53795MmjSJo446ilGjRnH55ZeTlpZGeno6AA888ADbt2+nTZs2pKWlMWvWLAAeffRRLr74Ys4++2waNmxY4p/vhhtu4LjjjqNdu3akpaXxzjvv5N/3xz/+kSZNmtAqhk5iLOV8Hop8nTt3dpmZmWV6jttug9dfh507oUJcvzWZZLdixQpOPvlkr8MwJXDrrbfSoUMHrr/++qi9Rqi/CxFZ4JwLuVY0Ngo/peTz6WosS+bGmPLUqVMnatSowZNPPul1KIeJ24Sem6tLdQcP9joSY0yyWbBggdchhBS3Y9vVq+HXX61+bowxAXGb0AMTorbl3xhjVNwm9KwsqFwZYmiC2RhjPBW3Cd3ngzZtoARnZzLGmIQWlwnduUNb/o0xZde9e3emTZt22G1PP/00f/rTn4p8TGDp8YUXXsiOHTuOOGb48OE88cQTRb72Bx98wPLly/O//9vf/saMGTNKEH3R7rjjDho3bkxeXl7EnjNWxWVCz87WE0PbhKgxkTFgwIDDeqEAjB07lgEDBoT1+ClTplCnTp1SvXbBhP7www9zzjnnlOq5CgrsEG3SpAmff/55RJ4zlGhvtgpXXCZ0a5lrEtmwYdC9e2Qvw4YV/Zr9+vVj8uTJ/PbbbwCsW7eOjRs3cvrppzN06FA6d+5M69ateeihh0I+vmnTpmzduhWARx55hBYtWnDOOeewatWq/GNeeuklTjnlFNLS0ujbty979+7lyy+/ZNKkSfzlL3+hffv2fP/99wwcOJD33nsPgE8//ZQOHTrQtm1bBg8enB9f06ZNeeihh+jYsSNt27Zl5cqVIeOaNWsWbdq0YejQoYwZMyb/9p9++ok+ffqQlpZGWlpafr/20aNH5+8KveaaawAOiwe0FTDA7Nmz6dGjB1dddRVt27YF4LLLLqNTp060bt2aUaNG5T9m6tSpdOzYkbS0NHr27EleXh7Nmzdny5YtgL7xnHjiifm/w9IKK6GLSC8RWSUiq0Xk3hD3/1FEFvsvX4pIWpmiKobPp90V27WL5qsYkzzq1atHly5dmDp1KqCj8/T0dESERx55hMzMTBYvXsxnn33G4sWLC32eBQsWMHbsWHw+HxMmTGD+/Pn5911++eXMnz+fRYsWcfLJJ/PKK6/QtWtXLr30Uh5//HEWLlzICSeckH/8/v37GThwIOPGjWPJkiXk5OQwcuTI/Pvr169PVlYWQ4cOLbSsM2bMGAYMGECfPn2YPHlyfhfF22+/nbPOOotFixaRlZVF69atWbZsGY888ggzZ85k0aJFPPPMM8X+3r755hseeeSR/E8Yr776KgsWLCAzM5Nnn32Wbdu25bf8ff/991m0aBHvvvsuFSpU4Oqrr+btt98GtKtjWloa9evXL/Y1i1LsxiIRSQEygHOBbGC+iExyzi0POmwtcJZzbruIXACMAk4tU2RF8PmgZUsIapZmTMLwqntuoOzSu3dvxo4dy6uvvgrA+PHjGTVqFDk5OWzatInly5fTrpDR1BdffEGfPn2oXr06AJdeemn+fUuXLuWBBx5gx44d7Nmzh/PPP7/IeFatWkWzZs046aSTALjuuuvIyMhgmP/jxuWXXw7ors1QJ5c4cOAAU6ZM4amnnqJWrVqceuqpTJ8+nYsuuoiZM2fmt7xNSUmhdu3ajB49mn79+uUn1UCr3KJ06dIlv5UvwLPPPpvfBGz9+vV89913bNmyJWTL38GDB9O7d2+GDRvGq6++GpGe6uHsFO0CrHbOrQEQkbFAbyA/oTvngs8vNQ84vAFyhPl8cMYZ0XwFY5LPZZddxp133klWVhb79u2jY8eOrF27lieeeIL58+dTt25dBg4ceEQb2oIKO1XcwIED+eCDD0hLS+P1119n9uzZRT5PcX2mAs27CmvBO3XqVHbu3JlfDtm7dy/Vq1fnoosuKvT1QsVesWLF/AlV59xh51kNbsE7e/ZsZsyYwVdffUX16tXp3r07+/fvL/R5mzRpwjHHHMPMmTP5+uuv80frZRFOyaUxsD7o+2z/bYW5Hvg41B0icpOIZIpIZqB2VFJbt8L69VY/NybSatasSffu3Rk8eHD+ZOiuXbuoUaMGtWvX5qeffuLjj0P+18535plnMnHiRPbt28fu3bv56KOP8u/bvXs3DRs25ODBg4clr1q1arF79+4jnqtly5asW7eO1atXA/Dmm29y1llnhf3zjBkzhpdffjm/Be/atWuZPn06e/fupWfPnvnlm9zcXHbt2kXPnj0ZP34827ZtAw61ym3atGn+Vv8PP/yw0JN77Ny5k7p161K9enVWrlzJvHnzAApt+QvazfHqq6/myiuvzD/ZRlmEk9BDvd2GfOsUkR5oQr8n1P3OuVHOuc7Ouc4NGjQIP8og1jLXmOgZMGAAixYton///gCkpaXRoUMHWrduzeDBg+nWrVuRjw+cn7N9+/b07duXM4I+So8YMYJTTz2Vc889l5YtW+bf3r9/fx5//HE6dOjA999/n3971apVee2117jiiito27YtFSpUYMiQIWH9HHv37mXatGmHjcZr1KjB6aefzkcffcQzzzzDrFmzaNu2LZ06dWLZsmW0bt2av/71r5x11lmkpaVxp//MOTfeeCOfffYZXbp04euvvz5sVB6sV69e5OTk0K5dOx588EH+8Ic/ANCgQYOQLX9BS1J79uyJ2Cnsim2fKyKnAcOdc+f7v78PwDn3rwLHtQMmAhc4574t7oVL2z53zhx47DFtmxtGicuYuGDtc5NTZmYmf/7zn/niiy9C3h+N9rnzgeYi0gzYAPQHrirwAscBE4BrwknmZXH66Xoxxph49uijjzJy5MiI1M4Dii25OOdygFuBacAKYLxzbpmIDBGRwOefvwH1gP+KyEIRKduZK4wxJsHde++9/PDDD0ecILsswuqH7pybAkwpcNsLQddvAG6IWFTGJKHCVkOY5FSas8nF5U5RYxJN1apV2bZtW6n+E5vE45xj27ZtVK1atUSPi9szFhmTSFJTU8nOzqa0y3lN4qlatSqpqSXb0mMJ3ZgYUKlSpcN2HBpTGlZyMcaYBGEJ3RhjEoQldGOMSRDF7hSN2guLbAF+KOXD6wNlaxwcHbEaF8RubBZXyVhcJZOIcf3eOReyd4pnCb0sRCSzsK2vXorVuCB2Y7O4SsbiKplki8tKLsYYkyAsoRtjTIKI14Q+qvhDPBGrcUHsxmZxlYzFVTJJFVdc1tCNMcYcKV5H6MYYYwqwhG6MMQki7hK6iPQSkVUislpE7vU6HgAReVVEfhaRpV7HEkxEmojILBFZISLLROQOr2MCEJGqIvKNiCzyx/V3r2MKJiIpIuITkclexxIgIutEZEmsnW9AROqIyHsistL/d3ZaDMTUwv97Clx2icgwr+MCEJE/+//ml4rIGBEpWTvF4p4/nmroIpICfAuci56sej4wwDm33OO4zgT2AKOdc228jCWYiDQEGjrnskSkFrAAuCwGfl8C1HDO7RGRSsAc4A7n3Dwv4woQkTuBzsBRzrmLvY4HNKEDnZ1zMbVJRkTeAL5wzr0sIpWB6s65HR6Hlc+fMzYApzrnSruRMVKxNEb/1ls55/aJyHhginPu9Ui9RryN0LsAq51za5xzB4CxQG+PY8I59znwS7EHljPn3CbnXJb/+m70jFONvY0KnNrj/7aS/xITIwsRSQUuAl72OpZYJyJHAWcCrwA45w7EUjL36wl873UyD1IRqCYiFYHqwMZIPnm8JfTGwPqg77OJgQQVD0SkKdAB+NrjUID8ssZC4GfgE+dcTMQFPA3cDeR5HEdBDpguIgtE5Cavg/E7HtgCvOYvUb0sIjW8DqqA/sAYr4MAcM5tAJ4AfgQ2ATudc9Mj+RrxltBDnZ8rJkZ2sUxEagLvA8Occ7u8jgfAOZfrnGsPpAJdRMTzUpWIXAz87Jxb4HUsIXRzznUELgBu8Zf5vFYR6AiMdM51AH4FYmJeC8BfAroUeNfrWABEpC5aUWgGNAJqiMjVkXyNeEvo2UCToO9TifBHlkTjr1G/D7ztnJvgdTwF+T+izwZ6eRsJAN2AS/316rHA2SLylrchKefcRv/Xn4GJaPnRa9lAdtCnq/fQBB8rLgCynHM/eR2I3znAWufcFufcQWAC0DWSLxBvCX0+0FxEmvnfffsDkzyOKWb5Jx9fAVY45/7jdTwBItJAROr4r1dD/9BXehoU4Jy7zzmX6pxriv5tzXTORXQEVRoiUsM/qY2/pHEe4PmKKufcZmC9iLTw39QT8HTCvYABxEi5xe9H4A8iUt3/f7MnOq8VMXF1CjrnXI6I3ApMA1KAV51zyzwOCxEZA3QH6otINvCQc+4Vb6MCdMR5DbDEX68GuN85N8W7kABoCLzhX4FQARjvnIuZJYIx6BhgouYAKgLvOOemehtSvtuAt/0DrDXAII/jAUBEqqOr4W72OpYA59zXIvIekAXkAD4i3AIgrpYtGmOMKVy8lVyMMcYUwhK6McYkCEvoxhiTICyhG2NMgrCEbowxCcISujHGJAhL6MYYkyD+H/PebPk03be4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "Iteration= 2\n",
      "==================================================\n",
      "Create Model\n",
      "Found 19523 validated image filenames belonging to 2 classes.\n",
      "Found 3904 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/25\n",
      "305/305 [==============================] - 246s 797ms/step - loss: 0.7132 - accuracy: 0.5691 - val_loss: 0.6273 - val_accuracy: 0.6165\n",
      "Epoch 2/25\n",
      "305/305 [==============================] - 241s 791ms/step - loss: 0.6486 - accuracy: 0.6302 - val_loss: 0.7460 - val_accuracy: 0.5038\n",
      "Epoch 3/25\n",
      "305/305 [==============================] - 246s 805ms/step - loss: 0.6338 - accuracy: 0.6435 - val_loss: 0.6279 - val_accuracy: 0.6304\n",
      "Epoch 4/25\n",
      "305/305 [==============================] - 243s 797ms/step - loss: 0.6231 - accuracy: 0.6521 - val_loss: 0.7859 - val_accuracy: 0.4818\n",
      "Epoch 5/25\n",
      "305/305 [==============================] - 242s 793ms/step - loss: 0.6017 - accuracy: 0.6744 - val_loss: 0.6223 - val_accuracy: 0.6422\n",
      "Epoch 6/25\n",
      "305/305 [==============================] - 243s 795ms/step - loss: 0.6049 - accuracy: 0.6695 - val_loss: 0.6757 - val_accuracy: 0.5799\n",
      "Epoch 7/25\n",
      "305/305 [==============================] - 241s 791ms/step - loss: 0.5981 - accuracy: 0.6765 - val_loss: 0.7496 - val_accuracy: 0.5364\n",
      "Epoch 8/25\n",
      "305/305 [==============================] - 241s 794ms/step - loss: 0.5885 - accuracy: 0.6830 - val_loss: 0.4147 - val_accuracy: 0.8366\n",
      "Epoch 9/25\n",
      "305/305 [==============================] - 241s 789ms/step - loss: 0.5719 - accuracy: 0.6924 - val_loss: 0.6596 - val_accuracy: 0.5876\n",
      "Epoch 10/25\n",
      "305/305 [==============================] - 241s 790ms/step - loss: 0.5706 - accuracy: 0.6968 - val_loss: 0.4967 - val_accuracy: 0.7508\n",
      "Epoch 11/25\n",
      "305/305 [==============================] - 241s 790ms/step - loss: 0.5721 - accuracy: 0.6943 - val_loss: 0.5972 - val_accuracy: 0.6634\n",
      "Epoch 12/25\n",
      "305/305 [==============================] - 242s 792ms/step - loss: 0.5612 - accuracy: 0.7070 - val_loss: 0.6958 - val_accuracy: 0.5902\n",
      "Epoch 13/25\n",
      "305/305 [==============================] - 241s 791ms/step - loss: 0.5494 - accuracy: 0.7097 - val_loss: 0.4666 - val_accuracy: 0.7684\n",
      "Epoch 14/25\n",
      "305/305 [==============================] - 241s 788ms/step - loss: 0.5533 - accuracy: 0.7097 - val_loss: 0.5279 - val_accuracy: 0.7257\n",
      "Epoch 15/25\n",
      "305/305 [==============================] - 242s 791ms/step - loss: 0.5441 - accuracy: 0.7189 - val_loss: 0.5660 - val_accuracy: 0.6967\n",
      "Accuarcy: 83.66%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "Iteration= 3\n",
      "==================================================\n",
      "Create Model\n",
      "Found 19523 validated image filenames belonging to 2 classes.\n",
      "Found 3904 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/25\n",
      "305/305 [==============================] - 245s 794ms/step - loss: 0.6584 - accuracy: 0.6109 - val_loss: 0.8128 - val_accuracy: 0.4664\n",
      "Epoch 2/25\n",
      "305/305 [==============================] - 241s 788ms/step - loss: 0.6003 - accuracy: 0.6737 - val_loss: 0.6591 - val_accuracy: 0.6091\n",
      "Epoch 3/25\n",
      "305/305 [==============================] - 240s 787ms/step - loss: 0.5903 - accuracy: 0.6762 - val_loss: 0.4947 - val_accuracy: 0.7559\n",
      "Epoch 4/25\n",
      "305/305 [==============================] - 251s 822ms/step - loss: 0.5740 - accuracy: 0.6924 - val_loss: 0.4750 - val_accuracy: 0.7628\n",
      "Epoch 5/25\n",
      "305/305 [==============================] - 242s 793ms/step - loss: 0.5667 - accuracy: 0.6955 - val_loss: 0.3702 - val_accuracy: 0.8458\n",
      "Epoch 6/25\n",
      "305/305 [==============================] - 242s 794ms/step - loss: 0.5593 - accuracy: 0.7006 - val_loss: 0.5956 - val_accuracy: 0.6552\n",
      "Epoch 7/25\n",
      "305/305 [==============================] - 241s 790ms/step - loss: 0.5451 - accuracy: 0.7131 - val_loss: 0.4281 - val_accuracy: 0.8061\n",
      "Epoch 8/25\n",
      "305/305 [==============================] - 242s 793ms/step - loss: 0.5404 - accuracy: 0.7197 - val_loss: 0.6868 - val_accuracy: 0.5932\n",
      "Epoch 9/25\n",
      "305/305 [==============================] - 242s 792ms/step - loss: 0.5442 - accuracy: 0.7129 - val_loss: 0.6144 - val_accuracy: 0.6591\n",
      "Epoch 10/25\n",
      "305/305 [==============================] - 242s 793ms/step - loss: 0.5320 - accuracy: 0.7249 - val_loss: 0.4515 - val_accuracy: 0.7874\n",
      "Epoch 11/25\n",
      "305/305 [==============================] - 242s 793ms/step - loss: 0.5277 - accuracy: 0.7259 - val_loss: 0.4876 - val_accuracy: 0.7656\n",
      "Epoch 12/25\n",
      "305/305 [==============================] - 245s 802ms/step - loss: 0.5236 - accuracy: 0.7318 - val_loss: 0.6891 - val_accuracy: 0.6004\n",
      "Accuarcy: 84.58%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "Iteration= 4\n",
      "==================================================\n",
      "Create Model\n",
      "Found 19523 validated image filenames belonging to 2 classes.\n",
      "Found 3904 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/25\n",
      "305/305 [==============================] - 254s 826ms/step - loss: 0.6327 - accuracy: 0.6329 - val_loss: 0.7495 - val_accuracy: 0.5446\n",
      "Epoch 2/25\n",
      "305/305 [==============================] - 254s 832ms/step - loss: 0.5670 - accuracy: 0.6969 - val_loss: 0.6742 - val_accuracy: 0.6007\n",
      "Epoch 3/25\n",
      "305/305 [==============================] - 249s 814ms/step - loss: 0.5653 - accuracy: 0.6998 - val_loss: 0.4860 - val_accuracy: 0.7587\n",
      "Epoch 4/25\n",
      "305/305 [==============================] - 245s 801ms/step - loss: 0.5532 - accuracy: 0.7103 - val_loss: 0.4391 - val_accuracy: 0.7982\n",
      "Epoch 5/25\n",
      "305/305 [==============================] - 243s 797ms/step - loss: 0.5389 - accuracy: 0.7174 - val_loss: 0.4180 - val_accuracy: 0.8023\n",
      "Epoch 6/25\n",
      "305/305 [==============================] - 242s 794ms/step - loss: 0.5398 - accuracy: 0.7163 - val_loss: 0.8059 - val_accuracy: 0.5236\n",
      "Epoch 7/25\n",
      "305/305 [==============================] - 242s 794ms/step - loss: 0.5355 - accuracy: 0.7186 - val_loss: 0.3439 - val_accuracy: 0.8696\n",
      "Epoch 8/25\n",
      "305/305 [==============================] - 244s 799ms/step - loss: 0.5235 - accuracy: 0.7314 - val_loss: 0.6320 - val_accuracy: 0.6588\n",
      "Epoch 9/25\n",
      "305/305 [==============================] - 243s 798ms/step - loss: 0.5174 - accuracy: 0.7365 - val_loss: 0.5449 - val_accuracy: 0.7123\n",
      "Epoch 10/25\n",
      "305/305 [==============================] - 242s 794ms/step - loss: 0.5260 - accuracy: 0.7279 - val_loss: 0.5557 - val_accuracy: 0.7259\n",
      "Epoch 11/25\n",
      "305/305 [==============================] - 251s 824ms/step - loss: 0.5098 - accuracy: 0.7393 - val_loss: 0.5758 - val_accuracy: 0.6865\n",
      "Epoch 12/25\n",
      "305/305 [==============================] - 245s 802ms/step - loss: 0.4978 - accuracy: 0.7475 - val_loss: 0.5047 - val_accuracy: 0.7459\n",
      "Epoch 13/25\n",
      "305/305 [==============================] - 243s 797ms/step - loss: 0.4990 - accuracy: 0.7457 - val_loss: 0.5001 - val_accuracy: 0.7380\n",
      "Epoch 14/25\n",
      "305/305 [==============================] - 251s 821ms/step - loss: 0.4947 - accuracy: 0.7513 - val_loss: 0.4000 - val_accuracy: 0.8089\n",
      "Accuarcy: 86.96%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "Iteration= 5\n",
      "==================================================\n",
      "Create Model\n",
      "Found 19523 validated image filenames belonging to 2 classes.\n",
      "Found 3904 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/25\n",
      "305/305 [==============================] - 260s 843ms/step - loss: 0.6002 - accuracy: 0.6573 - val_loss: 0.6120 - val_accuracy: 0.6650\n",
      "Epoch 2/25\n",
      "305/305 [==============================] - 260s 853ms/step - loss: 0.5371 - accuracy: 0.7205 - val_loss: 0.4868 - val_accuracy: 0.7490\n",
      "Epoch 3/25\n",
      "305/305 [==============================] - 260s 851ms/step - loss: 0.5294 - accuracy: 0.7174 - val_loss: 0.5981 - val_accuracy: 0.6755\n",
      "Epoch 4/25\n",
      "305/305 [==============================] - 241s 791ms/step - loss: 0.5258 - accuracy: 0.7312 - val_loss: 0.5472 - val_accuracy: 0.6947\n",
      "Epoch 5/25\n",
      "305/305 [==============================] - 243s 796ms/step - loss: 0.5181 - accuracy: 0.7373 - val_loss: 0.6517 - val_accuracy: 0.6383\n",
      "Epoch 6/25\n",
      "305/305 [==============================] - 242s 792ms/step - loss: 0.5077 - accuracy: 0.7401 - val_loss: 0.5894 - val_accuracy: 0.6908\n",
      "Epoch 7/25\n",
      "305/305 [==============================] - 247s 810ms/step - loss: 0.4975 - accuracy: 0.7480 - val_loss: 0.4548 - val_accuracy: 0.7707\n",
      "Epoch 8/25\n",
      "305/305 [==============================] - 243s 797ms/step - loss: 0.4898 - accuracy: 0.7517 - val_loss: 0.4503 - val_accuracy: 0.7756\n",
      "Epoch 9/25\n",
      "305/305 [==============================] - 244s 800ms/step - loss: 0.4882 - accuracy: 0.7553 - val_loss: 0.4267 - val_accuracy: 0.8007\n",
      "Epoch 10/25\n",
      "305/305 [==============================] - 246s 807ms/step - loss: 0.4841 - accuracy: 0.7582 - val_loss: 0.4124 - val_accuracy: 0.8020\n",
      "Epoch 11/25\n",
      "305/305 [==============================] - 246s 805ms/step - loss: 0.4772 - accuracy: 0.7605 - val_loss: 0.6021 - val_accuracy: 0.6821\n",
      "Epoch 12/25\n",
      "305/305 [==============================] - 246s 806ms/step - loss: 0.4742 - accuracy: 0.7612 - val_loss: 0.4959 - val_accuracy: 0.7433\n",
      "Epoch 13/25\n",
      "305/305 [==============================] - 241s 788ms/step - loss: 0.4741 - accuracy: 0.7661 - val_loss: 0.5936 - val_accuracy: 0.6875\n",
      "Epoch 14/25\n",
      "305/305 [==============================] - 240s 787ms/step - loss: 0.4683 - accuracy: 0.7702 - val_loss: 0.3476 - val_accuracy: 0.8458\n",
      "Epoch 15/25\n",
      "305/305 [==============================] - 240s 786ms/step - loss: 0.4657 - accuracy: 0.7702 - val_loss: 0.6623 - val_accuracy: 0.6455\n",
      "Epoch 16/25\n",
      "305/305 [==============================] - 241s 788ms/step - loss: 0.4581 - accuracy: 0.7744 - val_loss: 0.5491 - val_accuracy: 0.7111\n",
      "Epoch 17/25\n",
      "305/305 [==============================] - 240s 787ms/step - loss: 0.4519 - accuracy: 0.7772 - val_loss: 0.4753 - val_accuracy: 0.7546\n",
      "Epoch 18/25\n",
      "305/305 [==============================] - 241s 789ms/step - loss: 0.4470 - accuracy: 0.7745 - val_loss: 0.7141 - val_accuracy: 0.6217\n",
      "Epoch 19/25\n",
      "305/305 [==============================] - 241s 789ms/step - loss: 0.4508 - accuracy: 0.7764 - val_loss: 0.5481 - val_accuracy: 0.7195\n",
      "Epoch 20/25\n",
      "305/305 [==============================] - 241s 790ms/step - loss: 0.4448 - accuracy: 0.7763 - val_loss: 0.4322 - val_accuracy: 0.7894\n",
      "Epoch 21/25\n",
      "305/305 [==============================] - 241s 789ms/step - loss: 0.4387 - accuracy: 0.7821 - val_loss: 0.7044 - val_accuracy: 0.6276\n",
      "Accuarcy: 84.58%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV_Scores:83.66%(+/- 2.79%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(83.66291165351868, 2.787959553846132)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Resnet50_with_cv(trainB, cross_validation=5,\n",
    "                 activation='softmax', classes=2, optimizer='sgd', \n",
    "                 loss='categorical_crossentropy',metrics=['accuracy'], \n",
    "                 epochs=25, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 12796 validated image filenames belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "model_test = load_model(\"LandUse_best_cv_model.h5\")\n",
    "data_generator = ImageDataGenerator(preprocessing_function=preprocess_input)\n",
    "test_generator = data_generator.flow_from_dataframe(dataframe=testB, \n",
    "                                                    directory='../Data',\n",
    "                                                    x_col=\"filepath\",\n",
    "                                                    y_col='category',\n",
    "                                                    class_mode='categorical',\n",
    "                                                    batch_size=1,\n",
    "                                                    target_size=(img_size, img_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test trained model\n",
    "step_test=test_generator.n//test_generator.batch_size\n",
    "test_score=model_test.evaluate_generator(test_generator, steps=step_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9951999187469482, 0.5467333793640137]\n"
     ]
    }
   ],
   "source": [
    "print(test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "name": "tf2-gpu.2-4.m65",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-4:m65"
  },
  "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}