resnet_satellite_image_classification_Code.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
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          "shell.execute_reply": "2023-06-07T11:56:09.280685Z",
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        },
        "id": "hH-b6NQJdJwI"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import glob\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import shutil\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.image import imread\n",
        "import seaborn as sns\n",
        "sns.set(rc={'figure.figsize':(10,6)})\n",
        "sns.set(font_scale=1.3)\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "\n",
        "!pip install -U -q pyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "\n",
        "!pip install tensorflow\n",
        "\n",
        "!pip install -q keras\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Activation,Input, Dropout, Flatten, Dense, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.optimizers import SGD, RMSprop\n",
        "from keras.layers import BatchNormalization, GlobalAveragePooling2D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "#from keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "from keras.regularizers import l2\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(action=\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:09.284029Z",
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          "iopub.status.idle": "2023-06-07T11:56:22.030618Z",
          "shell.execute_reply": "2023-06-07T11:56:22.029352Z",
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        },
        "id": "X9mNd9ghdJwO",
        "outputId": "c6fc8755-9260-4715-9d55-4dadeac3d8da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting split-folders\n",
            "  Downloading split_folders-0.5.1-py3-none-any.whl (8.4 kB)\n",
            "Installing collected packages: split-folders\n",
            "Successfully installed split-folders-0.5.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "! pip install split-folders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:22.033976Z",
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          "shell.execute_reply": "2023-06-07T11:56:22.043470Z",
          "shell.execute_reply.started": "2023-06-07T11:56:22.033937Z"
        },
        "id": "WJN8iFHTdJwP"
      },
      "outputs": [],
      "source": [
        "import splitfolders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:22.048367Z",
          "iopub.status.busy": "2023-06-07T11:56:22.047984Z",
          "iopub.status.idle": "2023-06-07T11:56:40.333958Z",
          "shell.execute_reply": "2023-06-07T11:56:40.333012Z",
          "shell.execute_reply.started": "2023-06-07T11:56:22.048303Z"
        },
        "id": "3aB1HCbrdJwQ",
        "outputId": "0a790894-7f9b-4029-b4e2-20c7fb3c3d7f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Copying files: 5631 files [00:18, 308.35 files/s]\n"
          ]
        }
      ],
      "source": [
        "input_folder = \"/content/gdrive/MyDrive/Colab Notebooks/data1\"\n",
        "output = \"/content/gdrive/MyDrive/Colab Notebooks/data2\"\n",
        "\n",
        "splitfolders.ratio(input_folder, output=output, seed=42, ratio=(0.7,0.3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:40.336292Z",
          "iopub.status.busy": "2023-06-07T11:56:40.335247Z",
          "iopub.status.idle": "2023-06-07T11:56:40.455237Z",
          "shell.execute_reply": "2023-06-07T11:56:40.454387Z",
          "shell.execute_reply.started": "2023-06-07T11:56:40.336254Z"
        },
        "id": "2bAD933odJwR",
        "outputId": "125790c4-b34b-452c-9c43-384f1bc500c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 3941 images belonging to 4 classes.\n"
          ]
        }
      ],
      "source": [
        "train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "training_set = train_datagen.flow_from_directory('/content/gdrive/MyDrive/Colab Notebooks/data2/train',\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'categorical',\n",
        "                                                 shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:40.457113Z",
          "iopub.status.busy": "2023-06-07T11:56:40.456799Z",
          "iopub.status.idle": "2023-06-07T11:56:40.462350Z",
          "shell.execute_reply": "2023-06-07T11:56:40.461397Z",
          "shell.execute_reply.started": "2023-06-07T11:56:40.457083Z"
        },
        "id": "lEAv_6yTdJwR"
      },
      "outputs": [],
      "source": [
        "# test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input, rescale = 1./255)\n",
        "# test_set = test_datagen.flow_from_directory('/working/dataset/test',\n",
        "#                                             target_size = (224, 224),\n",
        "#                                             batch_size = 32,\n",
        "#                                             class_mode = 'categorical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:40.464331Z",
          "iopub.status.busy": "2023-06-07T11:56:40.463772Z",
          "iopub.status.idle": "2023-06-07T11:56:40.523432Z",
          "shell.execute_reply": "2023-06-07T11:56:40.522558Z",
          "shell.execute_reply.started": "2023-06-07T11:56:40.464300Z"
        },
        "id": "FEWbaaotdJwS",
        "outputId": "e99d2ce8-d597-4708-9f1d-c512da0b384b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1690 images belonging to 4 classes.\n"
          ]
        }
      ],
      "source": [
        "val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,rescale = 1./255)\n",
        "\n",
        "val_set = val_datagen.flow_from_directory('/content/gdrive/MyDrive/Colab Notebooks/data2/val',\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'categorical',\n",
        "                                            shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:40.527621Z",
          "iopub.status.busy": "2023-06-07T11:56:40.526950Z",
          "iopub.status.idle": "2023-06-07T11:56:40.532924Z",
          "shell.execute_reply": "2023-06-07T11:56:40.531898Z",
          "shell.execute_reply.started": "2023-06-07T11:56:40.527588Z"
        },
        "id": "4tssl4JrdJwT"
      },
      "outputs": [],
      "source": [
        "early = EarlyStopping(monitor='val_loss',patience=5)\n",
        "reduce = ReduceLROnPlateau(monitor='val_loss', patience=3, factor=0.2, mil_lr=0.001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:40.535083Z",
          "iopub.status.busy": "2023-06-07T11:56:40.534797Z",
          "iopub.status.idle": "2023-06-07T11:56:40.543615Z",
          "shell.execute_reply": "2023-06-07T11:56:40.542796Z",
          "shell.execute_reply.started": "2023-06-07T11:56:40.535059Z"
        },
        "id": "nScEBL_8dJwT"
      },
      "outputs": [],
      "source": [
        "output_classes = 4\n",
        "batch_size = 32\n",
        "epochs = 10\n",
        "image_shape=(224,224,3)\n",
        "nb_train_samples = 3941\n",
        "nb_validation_samples = 1690\n",
        "# nb_test_samples = 564"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:40.549008Z",
          "iopub.status.busy": "2023-06-07T11:56:40.548588Z",
          "iopub.status.idle": "2023-06-07T11:56:46.693739Z",
          "shell.execute_reply": "2023-06-07T11:56:46.692973Z",
          "shell.execute_reply.started": "2023-06-07T11:56:40.548960Z"
        },
        "id": "rccKQ76KdJwU",
        "outputId": "828ad11e-36d1-466c-fa01-ce6197cebf64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 1s 0us/step\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \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",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
            "                                )                                                                 \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 (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \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 (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \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 (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \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",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \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",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \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",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \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",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \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",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \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",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \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 (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \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 (GlobalAveragePooling  (None, 2048)        0           ['conv5_block3_out[0][0]']       \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 1024)         2098176     ['avg_pool[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 1024)        4096        ['dense[0][0]']                  \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " activation (Activation)        (None, 1024)         0           ['batch_normalization[0][0]']    \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 1024)         0           ['activation[0][0]']             \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 1024)         1049600     ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 1024)        4096        ['dense_1[0][0]']                \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_1 (Activation)      (None, 1024)         0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)            (None, 1024)         0           ['activation_1[0][0]']           \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 4)            4100        ['dropout_1[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 26,747,780\n",
            "Trainable params: 26,690,564\n",
            "Non-trainable params: 57,216\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "base_model = ResNet50(weights='imagenet', include_top=False, pooling = 'avg', input_tensor=Input(shape=image_shape))\n",
        "\n",
        "x = base_model.output\n",
        "# x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1024, kernel_regularizer=l2(0.0001), bias_regularizer=l2(0.0001))(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation(\"relu\")(x)\n",
        "x = Dropout(0.6)(x)\n",
        "x = Dense(1024, kernel_regularizer=l2(0.0001), bias_regularizer=l2(0.0001))(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation(\"relu\")(x)\n",
        "x = Dropout(0.6)(x)\n",
        "prediction = Dense(output_classes, activation=tf.nn.softmax)(x)\n",
        "\n",
        "model = Model(inputs=base_model.input,outputs=prediction)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:46.695237Z",
          "iopub.status.busy": "2023-06-07T11:56:46.694826Z",
          "iopub.status.idle": "2023-06-07T11:56:46.760326Z",
          "shell.execute_reply": "2023-06-07T11:56:46.759567Z",
          "shell.execute_reply.started": "2023-06-07T11:56:46.695211Z"
        },
        "id": "Cwf3YjugdJwU"
      },
      "outputs": [],
      "source": [
        "adam_opt=Adam(lr = 0.00001, beta_1=0.6, beta_2=0.9,amsgrad=True)\n",
        "\n",
        "model.compile(optimizer= adam_opt, loss = 'categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T11:56:46.761758Z",
          "iopub.status.busy": "2023-06-07T11:56:46.761386Z",
          "iopub.status.idle": "2023-06-07T12:08:10.148482Z",
          "shell.execute_reply": "2023-06-07T12:08:10.147525Z",
          "shell.execute_reply.started": "2023-06-07T11:56:46.761724Z"
        },
        "id": "3h-2J9nOdJwV",
        "outputId": "4e38432b-38c2-4d31-9686-459eaffb26b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training started it takes few minutes\n",
            "Epoch 1/10\n",
            "123/123 [==============================] - 107s 456ms/step - loss: 0.4275 - accuracy: 0.9412 - val_loss: 10.6048 - val_accuracy: 0.2548 - lr: 0.0010\n",
            "Epoch 2/10\n",
            "123/123 [==============================] - 54s 442ms/step - loss: 0.3348 - accuracy: 0.9749 - val_loss: 25.7705 - val_accuracy: 0.2548 - lr: 0.0010\n",
            "Epoch 3/10\n",
            "123/123 [==============================] - 55s 450ms/step - loss: 0.2627 - accuracy: 0.9913 - val_loss: 18.6794 - val_accuracy: 0.2548 - lr: 0.0010\n",
            "Epoch 4/10\n",
            "123/123 [==============================] - 55s 448ms/step - loss: 0.2692 - accuracy: 0.9898 - val_loss: 9.5823 - val_accuracy: 0.2548 - lr: 0.0010\n",
            "Epoch 5/10\n",
            "123/123 [==============================] - 55s 446ms/step - loss: 0.2536 - accuracy: 0.9900 - val_loss: 17.9596 - val_accuracy: 0.2548 - lr: 0.0010\n",
            "Epoch 6/10\n",
            "123/123 [==============================] - 55s 444ms/step - loss: 0.2328 - accuracy: 0.9974 - val_loss: 20.9365 - val_accuracy: 0.2548 - lr: 0.0010\n",
            "Epoch 7/10\n",
            "123/123 [==============================] - 56s 456ms/step - loss: 0.2343 - accuracy: 0.9946 - val_loss: 10.4126 - val_accuracy: 0.2548 - lr: 0.0010\n",
            "Epoch 8/10\n",
            "123/123 [==============================] - 56s 455ms/step - loss: 0.2278 - accuracy: 0.9982 - val_loss: 7.3263 - val_accuracy: 0.4483 - lr: 2.0000e-04\n",
            "Epoch 9/10\n",
            "123/123 [==============================] - 55s 447ms/step - loss: 0.2289 - accuracy: 0.9964 - val_loss: 3.9428 - val_accuracy: 0.5264 - lr: 2.0000e-04\n",
            "Epoch 10/10\n",
            "123/123 [==============================] - 55s 444ms/step - loss: 0.2304 - accuracy: 0.9959 - val_loss: 0.6247 - val_accuracy: 0.9171 - lr: 2.0000e-04\n",
            "successfully completed the traing session\n",
            "--- took 11:23 minutes ---\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "print(\"Training started it takes few minutes\")\n",
        "start_time = time.time()\n",
        "\n",
        "history = model.fit(\n",
        "  training_set,\n",
        "  steps_per_epoch = nb_train_samples // batch_size,\n",
        "  epochs = epochs,\n",
        "  validation_data = val_set,\n",
        "  validation_steps = nb_validation_samples // batch_size,\n",
        "  callbacks = [early, reduce])\n",
        "\n",
        "print(\"successfully completed the traing session\")\n",
        "print(\"--- took %d:%.2d minutes ---\" % divmod(time.time() - start_time, 60))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T12:09:25.498545Z",
          "iopub.status.busy": "2023-06-07T12:09:25.498174Z",
          "iopub.status.idle": "2023-06-07T12:09:26.407626Z",
          "shell.execute_reply": "2023-06-07T12:09:26.406731Z",
          "shell.execute_reply.started": "2023-06-07T12:09:25.498517Z"
        },
        "id": "taEbP7LadJwV",
        "outputId": "dc69f826-7f8a-4a9f-bf08-3fc87d5840c8"
      },
      "outputs": [
        {
          "data": {
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8eFB2u11JSUm6+uqrNXHixFaoHAAAAADQ1nwiyL799tvKzs5WSEiIoqOjlZWV1azjbNiwQfPnz1dAQIBGjx6t0NBQbdmyRS+++KKOHz+uu+66q2ULBwAAAAC0OZ8Isj/+8Y/VuXNnxcfH64svvtCCBQuafIySkhL94x//kMVi0dy5c9W7d29J0q233qqnnnpKy5YtU1pamvr169fS5QMAAAAA2pBP3CM7aNAgxcfHX9Qx1q9fr8LCQo0ePdoVYiUpICBAM2bMkCQtX778op4D3vXJJ59owoQJ+uSTTy7qONu3b9eECRP08ssvt0xhAACg3TOcThmVFTJKimUU5svIOysj75yMgjwZRQUySopklJXIKC+XUVkpw+lg20SgFfnEGdmWsGvXLknS4MGD3fr69++vwMBA7d69u42ran8mTJjQpPHz58/3+J6gfv/617/02muvyWw266233lJcXJy3SwKAehmGIVVWSGWlUnlZ1d/n/xhlpVJZiVReKpWd76v+2HBKFqtksXj421JH+/m/zVUfm9we4+nj+o9tMrMyu7cYhiE5nZLdLjkqJXtl1cd2u+Q4/7errbJWm1FrTPW4GmMcdqmystYYw1FZ+5gXPofb5+f/djqb9wJNZsl8wZ/qNoul/n6zuWqemz187vY4i0x1HqOOY5kuPLaHcXXWZ5HJUs9xar7GBo5TNe7b4xiBAXKWhcooL6t6j00mSee3vDSd/4/JVOtjtsTseNpNkM3MzJQkdenSxa3PYrEoPj5eJ06cUHl5uQIDA+s8jtXa8JfE5MVvFm9/k86aNcutbcmSJSouLtbNN9+s0NDQWn2dO3dusZrHjh2rAQMGKCYm5qKO2b9/f73yyiuKiIjw+tfTE4fDoY8//lgmk0lOp1Mff/yxx6+7N1TvKdsU1d9TjfneAnxNe56/VcGzUiorqQqa5WVVH5dWh8+SC4JojUDq1na+vbk/6F/sa2mJg5hMNYKtteqH6vMfm+oIwqZabec/tlrP/4Bvqd1mqQ7d1qof2q1Wt2Oa6gzgVpms5z821+i3Vj+mRrCv8VxWq1WGYcgiQ0bNcOawy3CFu29Do2GvdAtzVeMqawVFw0PgM+y1x9R+jtpB1KgZDqv72vOZS8MpOZySow2eqvWfok2er1zSqYs5QHXwNV3wcXUAdo05/3ettpqf13hMrcebXLna83OZznfV9Vye6qn5XBfWc+FzNfB66njtNeuxTJkmy6DhTfqytpXG/j+33fyfuaSkRJIUHBzssb+6vaSkpN4gGxUV1eBzFRYWeuWHGl/4Qep//ud/3No+/fRTFRcXa8aMGUpMTGy1546MjFRkZORFHyc0NNQtcPuSDRs2KCcnRzfddJM+++wzLVu2THfffbfMZu/fCWCz2Zp9drgx31uAr/KV+WtUVshZUiKjtFjO0hIZpSXn//72c6Pk/Mdl5/tKSuQsLa4xtvpxxZKjDX6y9heG8W2wurCrroe0bkUXrdxsUZGT9xgdkGFIMrz+Tepr/0bUrCfympsU4udX/Hk/GbWR6nsUGjoDl5ubK7uH/4nVVFlZ2eCYlma1Wtv8ORur+mtrt9tr1XjrrbdKqrpM9uWXX9aaNWuUk5OjmTNn6u6771ZOTo4+/PBDff3118rMzFRhYaGioqI0bNgwzZo1yy0Uf/zxx3ruuef0yCOPaMqUKa728ePH67LLLtPTTz+tl156SevWrVNhYaGSkpJ066231horSdu2bdP999+vWbNm6e6773ar99VXX9Urr7yilStX6ty5c4qLi9P111+v2267zW3+GIah//znP/rwww+VkZGhiIgIjRkzRvfcc49++MMfSqpalbspPvjgA0nSDTfcoIqKCi1btkzr16/XqFGjPI7PysrSm2++qU2bNik7O1udOnVSUlKSxo8f73pN1Y4ePao333xT27ZtU25urkJDQ9WtWzdNmTJF1157bYO1VVZWKjs7u0mvx2q1KioqqlHfW4Cvudj5a9grq85altY4k1leKpU2fHbTva206uwW0FiEWAB1KCwqVEkTf6ZrK9X/721wXBvU0iZqnnH1pLS0VJLUqVOneo9jt9tVWVlZ7xjDMOq9ed/xm4frfXxzOEymFlswwPLY71rkOJ5cWKPdbtf999+vwsJCDR8+XCEhIercubMMw9COHTv09ttva8iQIerbt68CAgJ09OhRffbZZ1q/fr3+9re/KSEhoVHPU1RUpHvvvVdWq1Xjxo1TRUWFVq9ereeee06S6gxpFx7H4XDooYceUk5OjkaMGCGLxaIvv/xSf//731VRUeF2ie/8+fP1wQcfuMKuzWbTV199pb1798rhcMhisTTpfcvJydGGDRt06aWXqmfPnrrmmmu0bNkyffTRRxo5cqTb+N27d+vRRx9VYWGhhgwZonHjxqmkpERHjhzRokWLau3F/OWXX+qpp56S3W5XWlqaevTooYKCAh04cEBLlizRNddc02B9hmG4fX8YZSXSuRwp96yM3Jyqj/NqfJx7VkWlxVWXt7juhzF9ez9NdfuF9894anc91sM9OA0cx2S2uNdgtrgfs6EaGlWbxW2MqbHHdHttjflaVb8ZrjfqgoZ6+mpNT6P2GE9zt67H1xp74bE9HK/Ovout2e0J6n499dV8vs9ptagsOFjlWadlLy6sdd9n9b2grqBZfmFfqcezewAAeJvd4ZCzgczj69pNkE1MTNShQ4eUkZGhXr161epzOBw6c+aMYmJi6r2suMUc3tfih/S1SxMa6+zZs+revbv++Mc/uv0SYciQIXr33Xfd2rds2aI5c+Zo0aJFevDBBxv1PIcOHdJ1112nBx54QBZL1YId06ZN0w9/+EO99dZbjTrbKFUFyd69e+vf//63a67MmjVLd955p9555x3dcccdrku8v/nmG33wwQfq3r27XnjhBYWEhEiS7rnnHlcYriuI1+Xjjz+W0+l01Tto0CAlJSVp/fr1OnfunKKjo11jKysr9dRTT6mwsFBz587VuHHjah2r5pnTvLw8PfvsszIMQ3/+85+VkpJS59iaDKej9r1S2VlyvvdKVUjNPSvl5kilnn955H4w4/xljN45Q+Dt7yFvPz+ap0KSb/6+uh2wWKTATlW/rHE4qv6dcTg564yWZzJJVtv5P+fvKbZW/7HV+Pz8xzbb+fuSrbX7qh9jsX17L7KhqjPfhrPqHvGaf6rbHHX0nx9jOB0XtDvcj+H2p74xNZ7P4ZSMC44PSPr2t+D+q90E2ZSUFK1bt07bt2/XmDFjavXt2bNH5eXlHs9oofX99Kc/9XgmvK5LBoYNG6YePXpoy5YtjX6OoKAgzZ492xViJalHjx5KTU3VN998o5KSkjrvn77Qz3/+81q/8IiKitIVV1yhzz77TCdOnFDPnj0lVd0bLEkzZ850hVip6j7Se+65R/fdd1+j65ckp9OpZcuWyWq1atKkSa72q6++Wv/+97/16aef6rbbbnO1f/XVV8rKytK4cePcQqykWveyfvrppyopKdGtt96qlJSUb1eIdFSF1NigABl5Z2sv3uHwsEJj9mkZ69jGCuiQzGYpqFPVn8BOtT42BdX43NUe5N5e83FWm8fbfWr/++Tw8LejjnZ71Q/w5z82mvpYh+Pbx9vtno/jvPBYno7v4WOjHYcH18JT1guCYu1Q+G0ItMrkKRTWFTJrjDG5Bc/zfbaa4dI9eLIidW1GfQG4rkBc3ziPIb5qjOGo5zhG3cc0m6SQoCAVFxfL6XCo1v2ute5/Nao+r/mxaozzNPb8h7U/v3Bs05/H8Di2ruepcXxXez1ja9VTx9gL/77weS547aYQ310vprH8LsiWl5crJydHgYGBio2NdbVffvnleuONN/TVV19pypQprr1kKyoq9NZbb0mSJk+e7JWaO7KAgIBa+/pe6Msvv9SHH36o/fv3Kz8/X84awakpq+MmJSXVCpPVqvcnLioqalSQDQkJUVJSUp3HKSwsdLUdOHBAkjRw4EC38QMGDKgVqhtj8+bNOn36tMaOHauIiAhX+zXXXKOXX35Zy5Yt04wZM1w/+O3Zs0eSNGLEiFrHqfVD4PlAunvHdknS8D49ZZw65jmkAmhfTGb3cOkKnkHuwbK+UFpP8Gzxsl2rB19c+PClcw1G9Q/q9YXoRoRsw15PcHa6P8ZsGOoUGqrSiko5XSsl2+TpbKTJUyisGUo9BU/L+e1e4Fdct7q0xXM183E2m00RcXGqyM5u8JY/dFw+EWRXrlypvXv3SpJOnz4tSfr6669dlzsmJSXppptukiQdPHhQ8+bN04ABAzR37lzXMYKDg/U///M/mj9/vubOnasrrrhCoaGh2rx5szIyMjRlyhT169evTV8Xqs5m1vWDzzvvvKMFCxYoPDxcw4cPV3x8vOtM6CeffKKsrKxGP09dqxBXh0lnI0NbU45TXFwsyfOZZYvFovDw8EY9Z7WlS5dKqjoDW1NCQoKGDBmirVu3avv27Ro8aJDksKswL1eSFBMUICMn69uzqB5CanF+viQpNrhT1T6PAHxPdfAMDHILkSZP7YGdpKDgqlB6YSAN6iTZAnxyi7GOyBUcrE3bvsztOE0cb7PZFBUXJzthAEA75BNBdu/evVq9enWttmPHjunYsWOSqs5uVQfZ+qSlpenJJ5/Uu+++q/Xr18vhcCgpKUk//vGPNXHixNYo3bNefVv8kKYWXOzJFzgcDr3yyiuKiYnRP/7xD7cwuGrVKi9V1njVZ4Bzc3Pd7oV1OBwqKCioddVAfXJzc/XVV19Jkp544ok6xy19520NjqoK26Hnf6LJOXlC6t2j/lqDqy7tzsnNVfcurbdFkovFIkXGSFGxMsfEKTi+s0pLS+W027+9lKj6zPEFlxe5zigbF/S5xjsueKzx7SVJno5pOCWnUc/xLri8CWgsk6lGoKwdJk21wqanUHr+Ma5xwVIAwRMAgMbyiSB777336t57723U2JSUFC1evLjO/gEDBmjAgAEtVVqztPSqwCaTybX9TnsJs/n5+SouLtbQoUPdQmx2drYyMjK8VFnj9enTRwcOHNDOnTvdguzu3bvlqLE/o6fLfWv+/cl/P5Ddblffnj3Uu2tXj8/3xdebtebrLfr57TMUHhqi/r2q7tX9On2Xpo4bW2+t/Xv10prNW/V1+m4NS7nI7w+LVYqMlqJjZYqKlaJipKg4maKqgquiY6WwCNflZv52RsCoK+C6ArajKhjXG7breqyHtguOYziNGvcTeQjgnh5rGHKdq6m1aXrNhpofXrh5es2vwAV9Fx633j6T+5i6nrPWU9bxnM2uueFjmxr5eixWiyKiY1VQUSG71VY7mAYEEjwBAPASnwiy6HgiIyMVFBSk/fv3q7S01LUYVFlZmebPn18rBPqqq6++Wh9//LEWLVqktJEjFRIYKDnsqiwr1Usvvlg1yOGouie1+kxkHZZ9/oUk6f7v36HkHt09jrFYLfrw89Va/tUG3Xz1JI0ecpniY6K1ZvNWrdm8VVcOH1prfPa5XMVFV/2S4JorLtdrH3yk91as0thhQ5TSp7fnsSbTtwtlWKxuC3QoPFrmBUva9T1RLXV/XrOf3yvPirpYbDZ1iotTUXa2TH7wixgAADoKgiy8wmw266abbtJbb72le+65R6NHj1Z5ebk2b94si8WiPn366ODBg94u03V2zji/F6RRUlS19Yzdocs6x+n6CeP10edf6Ad33aWxw4bIYrFo/Y5vFNKpk2IiI2U2qcF7Urfv3aeTWWfUp9sldYZYSZp65Rh9+PlqLV2zVjdfPUk2q1VP/vRHeuQPf9bcF17U0AH9lNyjh8orKnTk5CkdPnFS7/91viQpMjxMj/34h3pqwd/0i9/8TmlDh6hHt24qKi3VwSNHVFFZqb//7W+S2VLvGSZTeUW7DrEAAADwDwRZeM0999yj8PBwffzxx/rvf/+riIgIpaWl6Z577tGTTz7ZJjUYhiHZK6vCpuGsWp08+/T57Rcqqy79NQypqGqxJBXkSfm5rsf/cuYMXZIQp4++WKMPv1ij8NAQjRk6RD+8+Sbd+uAjigyL8/zENSxdvVaSGrw8OLlHd/XpdokOHj+hXQcPKaVPb/Xv3Ut/n/e43lj6iTbt3KVv9h1QcHAnde3SRTNn3CrFJ7rOql7RvY9eHDRUb7zxhrZv365N23coLCxMPXr00I3fvVYmC/8cAAAAwD+YjPZy02ULyW7EfXwFBQVNXpH2YrTHe2S9xXA6pYpyqbxMKi+t+rsVLmM+cTpLsx57QhNGjdATP/mfiztY9UbulvObr7u2P6ixd18DZ1JbSnPmvs1mU1xcXKO+twBfw/yFP2P+wl8xdzu26ve/IZyCQbtm2O21Q2tFee3NpS/Sufx8RYWH1wqRpeXl+uvrVXsXjx06pP4DmMx134/qCqlmFpQBAAAAaiDIot0wDKPqEuHyMqnsfHC1t+5v8ZZ8tlKfb/xal/VNVnRkhHLz87Vl9x7l5OZp5KBUjRt9+bcbyXtaRImQCgAAADQZQRZ+y3A6pPLyb8+2lpe13T6g58+kDh8yWIczMrV59x4VFhXJYrGo6yVd9b1pt2jaLbfIbLO1TT0AAABAB0KQhV+oWpTpwsuEKyS1wj3DNS/3revv82dShyV117Crrmn5GgAAAADUiSALn2QYzqqgWvMyYYe9ZZ/EZJICAqXAICmwk2Sz1QqpAAAAAHwTQRY+wXA4ap9tLS+v2g6nJVks34bWwCApIJA9UQEAAAA/RJBFm6talKmy9r2tlRUt/0S2gKrQGhRUFVytNs60AgAAAO0AQRat7tu9W0ulsupFmVp471aT+fzZ1m//mCyWln0OAAAAAD6BIIsWV7V3a42zrS28d6ukqntZgzp9G1wDAjnbCgAAAHQQBFlcFMMwzi/KVCO4tvjerTUXZQqqulTYYiW4AgAAAB0UQRZNYjgcUsX5wFpWVvVxS+/dara4XybMokwAAAAAziPIok7f7t1a8zLhVti71RZQO7jaAjjbCgAAAKBOBFm4fLsoU9m3f1pl79YalwgHBslkYRoCAAAAaDwSRAdmOOzfXiJcfv4y4RZflMny7b6t7N0KAAAAoAUQZDuIqr1bK2qfbW3G3q0T7/5Rk8b/4Q9/0JAhvZr8PPX55S9/qR07dujzzz9v9jGKi4s1bdo0lZWV6frrr9eDDz7YghUCAAAAaE0E2Xaq6jLhGmdbW2jv1u/feP23n5hMksWq/3z8qYpLSvS9731PYWFhtcYnJiZe9HO2hpUrV6qsrEwmk0mrVq3S7Nmz1alTJ2+XBQAAAKARCLLthGGv/DawlpVJla2wd6vVprvuuMNt79ZP132l4pIS3XLLLercuXPLPmcr+eijj2Sz2XTjjTdqyZIlWrVqlaZOnertsgAAAAA0AkG2Fcz59GgLH9Ekk6k6lxpViwYbhmQ4q7a+cTrVlJWEnxtkacRTmiRboGtBJgUGyWS1Nbny9evX691339W+fftUVlamzp07a+LEibrtttsUGBhYa+y6dev07rvv6ujRoyoqKlJERISSkpI0ceJE3XDDDTp9+rRuu+021/gJEya4Pr7mmmv06KOPNqqm/fv368CBA7ryyis1bdo0/ec//9HSpUvrDbLLly/XsmXLdPDgQZWXlysmJkYDBw7Ubbfdpp49e7rGORwOffDBB1qxYoWOHj0qh8OhuLg4DRkyRHfccYcSEhIa+6UDAAAAUAeCbCvYl1Pm7RKaruberUFBUsDF7936r3/9S6+99pqioqJ0xRVXKDw8XHv37tUrr7yibdu26fnnn5fVWjUFP/jgA82fP1/R0dEaPXq0IiIilJeXp0OHDunTTz/VDTfcoNDQUM2aNUuffPKJsrKyNGvWLNdz9enTp9F1LV26VFJV+E1ISNDgwYO1bds2HT58WL161b6f1zAMPfvss1qxYoWioqI0btw4hYeHKysrS5s3b1ZycrIryFZUVOjRRx/Vtm3b1LlzZ02ePFmdOnVSZmamVq1apZEjRxJkAQAAgBZAkO2oWnnv1i1btui1117T4MGD9cwzzyg4ONjV99prr+lf//qX3nvvPd1yyy2Svr3U96WXXlJUVFStY+Xn50uSQkNDddddd2n79u3KysrSXXfd1eS6ysrKtHLlSkVFRWnUqFGSpGuvvVbbtm3TRx99pPvuu6/W+A8//FArVqxQamqqnnvuuVqvw263q6CgwPX5yy+/rG3btunKK6/UE0884Qrp1c9bUdH0xbUAAAAAuGMflI7okp4yJXWXKTZBprAImc7f69qS3nvvPUnSgw8+WCv8SdLtt9+uiIgIrVy5sla7xWKpFf6qRUREtFhdq1atUnFxsSZNmiSLpeoS6yuvvFLBwcFasWKFW9h87733ZDKZ9MADD7i9DqvVqujoaEnfXlIcFBSkX/ziF26vIygoSOHh4S32OgAAAICOjDOyHZDJ0vpv++7du2Wz2bRixQqP/TabTcePH3d9ftVVV2nhwoW6++67NWHCBF122WVKTU1VZGRki9ZVfVnxtdde62oLCgrSuHHj9PHHH2v16tWaPHmyJKm0tFRHjx5VbGxsrftgPTl+/LiKi4uVmprqCrcAAAAAWgdBthX0jQ1q4SOaqrbOqWvfV5O5anEms7nqj0xSy55gbbKCggI5HA698sorjRo/ffp0RURE6L///a/effddLVmyRCaTSYMHD9ZPfvITJScnX3RNR44c0e7du9WnTx/17t27Vt+UKVP08ccfa+nSpa4gW1RUJEmKjY1t8NhNGQsAAADg4hBkW8Fvr+nRosczmUyySKo8sr8qqAacX5ApsFPVFjiWRqxC3MZCQkJUWVmppUuXNvqy5WuuuUbXXHONioqKtHPnTq1du1affvqp5syZo1deeeWiLzH+6KOPJEkHDx6steJxTTt27NCJEyd0ySWXKDQ0VJKUk5PT4LGbMhYAAADAxSHI+gmT1SpTUncZVluL38/aGvr376+NGzfq0KFDTVpRWKoKhZdffrkuv/xyGYahTz75RDt37tSYMWMkSebzqyk7HA7Xfa4Nqaio0IoVK2Q2m2tdVlzTqVOntGPHDi1dulQ/+clP1KlTJ/Xo0UPHjh3TkSNH6r28uFu3bgoJCdHBgwd17tw5Li8GAAAAWhGLPfmR1liUqbXcfPPNkqTnn39e586dc+svKirSgQMHXJ9v2rRJDoej1hjDMJSbmyup6j7WatWLJmVnZze6ntWrV6ugoEBpaWl6+OGHPf55/PHHZTab9dlnn8lut0uSvvvd78owDP3hD39QSUlJrWM6HA7Xa7NYLLrxxhtVVlamP/3pT67HVysvL6+1wjEAAACA5uOMLFrFiBEjdNddd+nll1/WzJkzNWrUKHXu3FnFxcXKzMzUjh07dO211+qBBx6QJD311FMKCAjQwIED1blzZzmdTu3cuVP79u1Tv379NGTIENexhwwZotWrV2vevHkaNWqUAgIC1LNnT11++eV11lO9yNN1111X55jY2FiNGjVK69ev15dffqlx48bpO9/5jr755hutXLlSM2fOdO2Hm52dra1bt2rGjBmaNm2aJGnWrFnavXu31qxZozvvvFNpaWkKCgpy7Tk7Z84c11llAAAAAM1HkEWrmTVrlgYNGqT33ntPO3bs0Lp16xQaGqr4+Hjdcsstuuaaa1xjf/SjH2nTpk06cOCANm7cqICAAHXu3Fk//vGPdeONN9a6hPj666/X6dOn9fnnn2vRokVyOBy65ppr6gyyJ06c0I4dOxQTE6O0tLR6a546darWr1+vjz76SOPGjZPJZNL/+3//T8OHD9eyZcu0atUq2e12xcTEaPjw4Ro2bJjrsQEBAfrd736n999/X8uXL9cnn3wiSYqLi9OECRNaZMEqAAAAAJLJMAzD20X4kuzsbFVWVtY7pqCgoE33BDWZTLJarbLb7eLtgjc1Z+7bbDbFxcU16nsL8DXMX/gz5i/8FXO3Y6t+/xvCPbIAAAAAAL9CkAUAAAAA+BWCLAAAAADArxBkAQAAAAB+hSALAAAAAPArBFkAAAAAaOcczva1+wn7yAIAAABAO/fB3nNafbRAk3pFaFyPcIUH+XcU9O/qvcgwDJlMJm+XAbQZ9jAGAADwT4ZhaOXhfJ3Ir9BLW87o5W1nNCIpVFf1jtTwpFBvl9csBNlmsFqtqqioUGBgoLdLAdpMRUWFrFb+yQAAAPA3B86W6UR+hetzu1Naf6JIxZVOvw2y3CPbDEFBQSorK1N5eTlnqdDuGYah8vJylZWVKSgoyNvlAAAAoIlWHc732D6pV0QbV9JyOL3SDGazWWFhYSorK1NhYWGrP5/JZJLNZlNlZSXBGV5htVoVFhYms5nffQEAAPiTCodTa44VuLUH28y6/JIwL1TUMnwmyB4/flyLFy/Wnj17VFZWpoSEBI0fP15Tp06VxWJp1DHsdruWLl2qdevWKTMzUxaLRd26ddOUKVM0evToFq3XbDYrODi4RY9ZF5vNpri4OGVnZ6uysrJNnhMAAACA/9twokjFFU639rHdwxVo9d+TFD4RZPft26enn35aDodDaWlpiomJ0a5du7Ro0SLt27dPDz30UIMLK9ntdj3zzDPatWuX4uPjNWHCBDkcDm3btk1//OMfdeLECd16661t9IoAAAAAwPtW1nFZ8UQ/vqxY8oEg63Q6tXDhQlVUVGjOnDkaPny4q33+/PnauHGj1q1bp7Fjx9Z7nE8++US7du1ScnKynnjiCddCTCUlJXriiSf07rvvavjw4erdu3ervyYAAAAA8Lbs4krtyCx2a08KD1DfWP9e+8Tr55LT09OVkZGh/v37u0KsVHXp7syZMyVJy5cvb/A4mzZtkiR973vfq7WacHBwsG666SYZhqFPP/20hasHAAAAAN/0+ZF8eVphZ1KvCL/fStTrQXbXrl2SpMGDB7v1JSQkKDExUfv372/w3tC8vDxJUnx8vFtfdVt6evrFFQsAAAAAfsAwDI+rFZtN0vie4V6oqGV5/dLizMxMSVJiYqLH/sTERGVmZiorK0tdu3at8zgRERE6ffq0zpw54zbuzJkzkqScnBxVVFQoICCgzuP44j6Z1TX5Ym1AQ5i/8GfMX/gz5i/8FXO3ZaRnFSuz0P1k4LCkMHWOaJtFa5ujse+712dHSUmJJNW5AnB1e/W4ugwZMkT79u3Te++9p9TUVFdYLS0t1QcffOAaV1xcXG+QjYqKalL9bcmXawMawvyFP2P+wp8xf+GvmLsXZ+3mHI/tNw/trri4uDaupuV5Pcg2pLH7pl533XXauHGj9u3bpwcffFCDBw+WYRjasmWLnE6nQkNDVVRU1OA+mLm5ubLb7S1ReouxWq2KioryydqAhjB/4c+Yv/BnzF/4K+buxSutdGj53iy39rBAi/pHGMrOzvZCVY1T/f43OK4NaqlXQ2dcS0tLa42rS1BQkObNm6f3339f69ev18qVKxUUFKTLLrtMt912mx588EFZLBaFhITUexy73e6ze7X6cm1AQ5i/8GfMX/gz5i/8FXO3+dYczleZ3X3v2Ct7hEtOhyqdDi9U1bK8HmSr742tvlf2QpmZmTKbzUpISGjwWEFBQZoxY4ZmzJhRq/3o0aMqLy9Xr169uNYeAAAAQLu28lCex/ar/Hzv2Jq8vmpxSkqKJGn79u1ufVlZWcrMzFRycrJsNluzn2PlypWSpDFjxjT7GAAAAADg604XVij9TKlbe8+oQPWK9u+9Y2vyepBNTU1VYmKi9uzZo82bN7vanU6nXn/9dUnS5MmTXe3l5eU6deqUcnLcb172dHnyxo0btXz5ciUkJOiqq65qhVcAAAAAAL5hpYctd6SqvWPbE69fZ2s2mzV79mw99dRTev7555WWlqbY2Filp6fr0KFDGjFiRK0zqQcPHtS8efM0YMAAzZ07t9ax7r//fnXv3l1dunSR1WrVwYMHtXv3bkVFRWnOnDkKCmo/v4EAAAAAgJqchqHPPQRZi+n8/bHtiNeDrCT17dtXzz77rBYvXqwdO3aovLxc8fHxmjlzpqZOnSqTydSo44wZM0bbt2/X3r175XA4FBcXpxtvvFE33nijQkNDW/lVAAAAAID3fHO6RNkl7is9j+gaqoggn4h+LcZkNHZ/mw4iOzvb51ZHs9lsiouL88nagIYwf+HPmL/wZ8xf+CvmbvM9/2WG1hwtcGv/f+OSNLJrmBcqarrq978hXr9HFgAAAABwcYoqHNpwotCtPTLIomFd2t/VqQRZAAAAAPBz644VqMLhfrHt+J4Rspgbd6umPyHIAgAAAICfW3mojtWKe7ev1YqrEWQBAAAAwI+dyC/X/rNlbu2XxgSpW0SgFypqfQRZAAAAAPBjqzrI3rE1EWQBAAAAwE85nJ73jg2wmDS2ne0dWxNBFgAAAAD81LbMYuWWOdza07qGKTTA4oWK2gZBFgAAAAD81Io6Fnma2E4XeapGkAUAAAAAP1RQZtfXp9z3jo0JtmpQQrAXKmo7BFkAAAAA8EOrjxbI7nRvn9hO946tiSALAAAAAH5oZV2rFbfzy4olgiwAAAAA+J3D58p0JLfcrX1AXCclhgV4oaK2RZAFAAAAAD/Tkc/GSgRZAAAAAPArlQ5Dq48WuLUHWU26olv73Tu2JoIsAAAAAPiRzaeKVFjuvnfs6G7h6mTrGBGvY7xKAAAAAGgnVh7O89h+Va+OcVmxRJAFAAAAAL+RW2rXloxit/bOoTYNiO/khYq8gyALAAAAAH7i8yP5chru7ZN6Rchkat97x9ZEkAUAAAAAP2AYhlYecl+t2CRpQge6rFgiyAIAAACAX9h/tkwnCyrc2gd1DlZciM0LFXkPQRYAAAAA/ICns7FS1WXFHQ1BFgAAAAB8XLndqXXH3PeODbGZlXZJmBcq8i6CLAAAAAD4uA0nClVc6XRrH9M9XIHWjhfrOt4rBgAAAAA/s/JwHZcV9+54lxVLBFkAAAAA8GnZxZX65nSJW3vX8AAlxwR5oSLvI8gCAAAAgA/7/HC+PGwdq0m9O9besTURZAEAAADARxmG4fGyYrNJmtCzY15WLBFkAQAAAMBn7T5TqtNFlW7tw7qEKKqT1QsV+QaCLAAAAAD4qBV1LPI0sQPuHVsTQRYAAAAAfFBppVNfHXffOzYs0KIRSR1v79iaCLIAAAAA4IO+PF6gMrv7Mk/jeoTLZumYizxVI8gCAAAAgA9aeaiOvWM7+GXFEkEWAAAAAHxOZmGFdmeXurX3jApUr+iOuXdsTQRZAAAAAPAxnI2tH0EWAAAAAHyIw2lo1RH3IGs1V90fC4IsAAAAAPiUnVklOltid2sfkRSm8KCOu3dsTQRZAAAAAPAhdV1WfFVvLiuuRpAFAAAAAB9RVOHQhpOFbu1RnawakhjihYp8E0EWAAAAAHzE2qMFqnC47x07oWe4LOaOvXdsTQRZAAAAAPARKw97vqx4IqsV10KQBQAAAAAfcDy/XAfOlrm1J8cE6ZKIQC9U5LsIsgAAAADgA+rcO5ZFntwQZAEAAADAy+xOQ1942Ds2wGLS2O7sHXshgiwAAAAAeNnWjCLllTnc2tMuCVNIgMULFfk2giwAAAAAeFldizxNYpEnj6zeLqDa8ePHtXjxYu3Zs0dlZWVKSEjQ+PHjNXXqVFksjfsNhNPp1Nq1a7VixQqdPn1aZWVliomJUWpqqm644QbFx8e38qsAAAAAgKbJL7Pr65NFbu1xwVYN6hzshYp8n08E2X379unpp5+Ww+FQWlqaYmJitGvXLi1atEj79u3TQw89JJOp4T2T/vjHP2rDhg2Ki4tTWlqaAgMDdfjwYX322Wdau3atfv3rX+uSSy5pg1cEAAAAAI2z5miBPGwdqwm9ImRuRA7qiLweZJ1OpxYuXKiKigrNmTNHw4cPd7XPnz9fGzdu1Lp16zR27Nh6j3Pw4EFt2LBB3bt31zPPPKOAgABX37vvvqu33npLH374oWbPnt2qrwcAAAAAmoLLipvO6/fIpqenKyMjQ/3793eFWEkym82aOXOmJGn58uUNHufMmTOSpIEDB9YKsZI0bNgwSVJBQUFLlQ0AAAAAF+3wuTIdyS13a0+N76TOYQEeHgHJB87I7tq1S5I0ePBgt76EhAQlJiZq//79qqyslM1mq/M4Xbt2lVQVjC8cu3XrVklVIbchVqvXvyRuqmvyxdqAhjB/4c+Yv/BnzF/4q442dz8/mu2xffKlMfXmn/aqse+712dHZmamJCkxMdFjf2JiojIzM5WVleUKq55069ZN1113nZYtW6YHHnhAQ4YMUUBAgI4ePapdu3bp6quv1rXXXttgPVFRUc17IW3Al2sDGsL8hT9j/sKfMX/hrzrC3K2wO7X6yB639k42i24a3kvBAV6Paz7L61+ZkpISSVJwsOfVuKrbq8fV56677lJiYqJeffVVffLJJ672/v37Ky0trVGrH+fm5sputzem9DZjtVoVFRXlk7UBDWH+wp8xf+HPmL/wVx1p7q47mq/8MvfXOKZ7uIrzc1XshZq8rfr9b3BcG9RyUQzDw/JddYz797//rc8++0wzZszQ2LFjFRISoqNHj+qVV17R008/rXvuuUeTJ0+u9zh2u12VlZUtUXqL8+XagIYwf+HPmL/wZ8xf+KuOMHc/3X/WY/uEnmHt/rVfLK8v9tTQGdfS0tJa4+qyevVqffLJJ5oyZYpuuukmxcTEKCgoSP369dMjjzwim82m1157TWVlZS37AgAAAACgic6WVGpbpvs518QwmwbEdfJCRf7F60G2+t7Y6ntlL5SZmSmz2ayEhIR6j7NlyxZJUkpKiltfZGSkkpKSVFZWpoyMjIusGAAAAAAuzhdHCuT0cPHpxF4RMrF3bIO8HmSrg+f27dvd+rKyspSZmank5OQGV+yqPvVe1xY71e0dZfUzAAAAAL7JMAyPe8eaJE3oyd6xjeH1IJuamqrExETt2bNHmzdvdrU7nU69/vrrklTrvtby8nKdOnVKOTk5tY7Tv39/SdLSpUvdLlNevny5zp49q8jIyHpXPgYAAACA1rb/bJlOFVS4tV+WGKK4kI635U5zeP30pNls1uzZs/XUU0/p+eefV1pammJjY5Wenq5Dhw5pxIgRGjNmjGv8wYMHNW/ePA0YMEBz5851tV9zzTVat26djh07pl/84hcaNmyYQkJCdOTIEaWnp8tsNuuHP/yhzGavZ3cAAAAAHdjKQ+5nYyVpUi/OxjaW14OsJPXt21fPPvusFi9erB07dqi8vFzx8fGaOXOmpk6d2qhrxIOCgvT000/ro48+0saNG/Xll1/KbrcrPDxcaWlpuuGGG9SnT582eDUAAAAA4Fm53am1x9xvhwwJMCvtklAvVOSfTEZj97fpILKzs31uqWubzaa4uDifrA1oCPMX/oz5C3/G/IW/au9z94sj+Zr/lftCt1MujdRPRnb2QkW+pfr9bwjX2QIAAABAG/G0yJNUtVoxGo8gCwAAAABt4ExRpXaeLnFrvyQiQJfGBHmhIv9FkAUAAACANrDqSL483dc5ib1jm4wgCwAAAACtzGkYWuXhsmKzSRrP3rFNRpAFAAAAgFa260yJsorcF68a1iVUUZ18YjMZv0KQBQAAAIBWVufesb05G9scBFkAAAAAaEUllQ59dbzQrT080KLhXdg7tjkIsgAAAADQir46Xqhyh/syT+N6hstmYZGn5iDIAgAAAEArquuy4qvYO7bZCLIAAAAA0EoyCiq0O7vUrb13dKB6RLF3bHMRZAEAAACglaz0sOWOJE3kbOxFIcgCAAAAQCtwOA197iHIWs0mXdmDIHsxCLIAAAAA0Ap2nC7W2VK7W/vIrqEKD7R4oaL2gyALAAAAAK2grsuKJ3FZ8UUjyAIAAABACysqd2jjiSK39qhOVg1JDPFCRe0LQRYAAAAAWtiaYwWqdLrvHTuhZ7gsZvaOvVgEWQAAAABoYXXtHTupN5cVtwSCLAAAAAC0oGN55Tp4rsytvW9sJ3UND/RCRe0PQRYAAAAAWtCqOhZ5uoqzsS2GIAsAAAAALcTuNPT5EfcgG2AxaUz3MC9U1D4RZAEAAACghWzJKFJ+mcOtffQlYQq2sXdsSyHIAgAAAEALqWuRp4lcVtyiCLIAAAAA0ALyyuzafMp979j4EKsGJgR7oaL2iyALAAAAAC1g9ZECOdy3jtWEXhEym9g7tiURZAEAAADgIhmGoZV1rFY8qReXFbc0giwAAAAAXKRD58p1LK/crT01IVgJoQFeqKh9I8gCAAAAwEVaeTjPYztnY1sHQRYAAAAALkKFw6k1Rwvc2jtZzRrdjb1jWwNBFgAAAAAuwtcni1RU4XRrv6J7mIKsRK7WwFcVAAAAAC5CXYs8XcVlxa2GIAsAAAAAzXS2pFLbMovd2ruE2dQvrpMXKuoYCLIAAAAA0EyfHymQ08PesRN7RcjE3rGthiALAAAAAM1gGIZWHnK/rNhskiZwWXGrIsgCAAAAQDPszSlVRmGFW/tlnUMUG2zzQkUdB0EWAAAAAJrB09lYib1j2wJBFgAAAACaqMzu1LpjhW7tIQFmjbok1AsVdSwEWQAAAABoovXHC1Vqd9879sru4QqwELNaG19hAAAAAGiiuvaOndSby4rbAkEWAAAAAJogq6hCO7NK3Nq7RwSqT3SQFyrqeAiyAAAAANAEnx8u8Ng+qTd7x7YVgiwAAAAANJLTMDxeVmwxSeN6hnuhoo6JIAsAAAAAjZSeVaIzxZVu7cOSQhUZZPVCRR0TQRYAAAAAGqnORZ7YO7ZNEWQBAAAAoBFKKh366rj73rERgRYNT2Lv2LbkM+e+jx8/rsWLF2vPnj0qKytTQkKCxo8fr6lTp8pisTT4+MWLF2vJkiX1jklNTdWvfvWrlioZAAAAQAey7lihKhyGW/u4nuGymlnkqS35RJDdt2+fnn76aTkcDqWlpSkmJka7du3SokWLtG/fPj300EMNrv6VkpJSZ99XX32ljIwMDR48uIUrBwAAANBRrDzEZcW+wutB1ul0auHChaqoqNCcOXM0fPhwV/v8+fO1ceNGrVu3TmPHjq33OCkpKR7DbHl5uZYuXSqLxaJx48a1ymsAAAAA0L6dLCjX3pxSt/be0UHqEcXesW3N6/fIpqenKyMjQ/3793eFWEkym82aOXOmJGn58uXNPv5XX32l0tJSjRgxQhER/KYEAAAAQNOt4mysT/F6kN21a5ckebzsNyEhQYmJidq/f78qK92XuG6MlStXSpImTZrU7BoBAAAAdFwOp6HPjxS4tVvNJl3Zg71jvcHrlxZnZmZKkhITEz32JyYmKjMzU1lZWeratWuTjn3y5Ent379fcXFxGjhwYKMeY7V6/UvipromX6wNaAjzF/6M+Qt/xvyFv/LFubvjZKHOldrd2i/vFq7oUC4rbkmNfd+9PjtKSkokScHBwR77q9urxzXFihUrJEkTJ06U2dy4k89RUVFNfp624su1AQ1h/sKfMX/hz5i/8Fe+NHfXrM/y2D5teA/FxcW0cTWQfCDINsQw3Je3bgy73a61a9fKbDZrwoQJjX5cbm6u7Hb337Z4k9VqVVRUlE/WBjSE+Qt/xvyFP2P+wl/52twtLLfriwPZbu0xwVb1CnYoO9u9D81X/f43OK4NaqlXQ2dcS0tLa41rrI0bN6qwsFDDhg1TdHR0ox9nt9ubfT9ua/Pl2oCGMH/hz5i/8GfMX/grX5m7Kw/kyu50P7k2vke4nA67nA4vFAXvL/ZUfW9s9b2yF8rMzJTZbFZCQkKTjrtq1SpJLPIEAAAAoPlWHq5jteLekW1bCGrxepCt3vt1+/btbn1ZWVnKzMxUcnKybDZbo4+ZlZWl9PR0RUVFaejQoS1VKgAAAIAO5GhumQ6dK3Nr7xfbSUnhAV6oCNW8HmRTU1OVmJioPXv2aPPmza52p9Op119/XZI0efJkV3t5eblOnTqlnJycOo+5atUqGYahCRMmNHqRJwAAAACoqe6zsewd621ev0fWbDZr9uzZeuqpp/T8888rLS1NsbGxSk9P16FDhzRixAiNGTPGNf7gwYOaN2+eBgwYoLlz57odz+l06osvvpDJZNLEiRPb8JUAAAAAaC/sTkOrPewdG2AxaUz3MC9UhJq8HmQlqW/fvnr22We1ePFi7dixQ+Xl5YqPj9fMmTM1depUmUymRh9r69atys3N1aBBgxQfH9+KVQMAAABorzafKlJ+uftKTqO7hSnYZvFCRaipyUF2w4YNGjlyZItfstu9e3c9/PDDDY5LSUnR4sWL6+wfPnx4vf0AAAAA0JA6LyvuxWXFvqDJQXb+/PmKjo7W5MmTNWnSJEVE8EYCAAAAaD/ySu3afKrIrT0+xKbUhKZtC4rW0eTTqk8++aQuvfRSvfPOO5o9e7b+8pe/aP/+/a1RGwAAAAC0udVHC+Rh61hN6hUhcxNue0TrafIZ2QEDBmjAgAHKzc3VZ599plWrVmndunXq0aOHpkyZoiuuuKJJW+UAAAAAgK8wDEMrD3m+rHhCr/A2rgZ1afaNrlFRUbr11lu1YMEC/fznP5fZbNbChQv1k5/8RG+88YZyc3Nbsk4AAAAAaHUHz5XpWH65W/vAhGAlhLJ3rK+46BWbzpw5o4MHDyozM1Nms1ndunXTsmXL9Itf/KLWvrAAAAAA4OvqOhvLIk++pVnb7xiGoS1btujTTz/Vzp07FRoaqmuvvVZXX321oqOjlZ+fr4ULF+qVV17R8OHDW7pmAAAAAGhxFQ6n1hxz3zu2k9Ws0d3YO9aXNDnIvv/++1q+fLlycnLUvXt3/fjHP9aYMWNq3RcbERGhG264QfPmzWvRYgEAAACgtWw8UaTiCqdb+5juYQq0tuz2o7g4TQ6yb7/9toYNG6Z7771XAwYMqHNc586dNW3atIsqDgAAAADaSp17x/bmsmJf0+Qg++c//1lxcXENjouOjtYtt9zSrKIAAAAAoC3llFRqe2axW3tSeID6xXbyQkWoT5PPj0dFRamsrMxjX1lZmex2+0UXBQAAAABt6fPD+fKwdawm9oqQib1jfU6Tg+zf/vY3vfjiix77/v73v+ull1666KIAAAAAoK0YhuHxsmKzSZrQk71jfVGTg+yuXbvqXIl42LBh2rlz50UXBQAAAABtZW92qTILK93ahySGKCbY5uER8LYmB9n8/HxFRUV57IuMjFReXt7F1gQAAAAAbWZFXYs8sXesz2pykA0ODtbp06c99p0+fVqdOnEjNAAAAAD/UGZ3at2xQrf20ACzRnYN9UJFaIwmB9mUlBS9//77KioqqtVeVFSk999/X6mpqS1WHAAAAAC0pq+OF6rM7r537JU9wmWzsHesr2ry9jvTp0/XY489pvvuu0+jR49WdHS0zp49qw0bNshut2v69OmtUScAAAAAtLg6947tFdm2haBJmhxku3Tponnz5unVV1/VypUr5XQ6ZTabNWDAAH3/+99Xly5dWqNOAAAAAGhRpwsrlJ5V4tbePTJQvaMDvVARGqvJQVaSevTooV/96leqqKhQUVGRQkNDFRAQ0NK1AQAAAECrWXWk7kWe2DvWtzUryFYLCAhQdHR0S9UCAAAAAG3CaRj63MNlxRaTNJ69Y31es4Ks0+nUtm3bdOrUKVVUVLj1T5s27aILAwAAAIDWsjOrRGeK7W7tw5NCFRF0Uef70Aaa/A4VFhbqV7/6lTIyMuocQ5AFAAAA4MtWHqrjsuLe7B3rD5q8nvSbb76pgIAAvfDCC5KkZ555Rn/60590/fXXq0uXLlq4cGGLFwkAAAAALaW4wqH1J9z3jo0IsmhYF/aO9QdNDrLp6emaOnWq695Ys9mszp07684779TAgQP16quvtniRAAAAANBSvjxeqAqH4dY+oWeErGYWefIHTQ6yZ8+eVXx8vMxms0wmk8rKylx9w4YN086dO1u0QAAAAABoSSvquKx4Yi8uK/YXTQ6y4eHhKimp2mspKipKJ06ccPUVFRXJ4XC0XHUAAAAA0IJO5pdrX06pW3uf6CB1j2TvWH/R5MWeevbsqRMnTmjo0KEaMmSIlixZok6dOslqterNN9/UpZde2hp1AgAAAMBFW+lhyx2JRZ78TZOD7LXXXqusrCxJ0owZM3TgwAHXwk8JCQm6++67W7ZCAAAAAGgBDqehz48UuLXbzCZd2Z29Y/1Jk4PsoEGDXB+Hh4frt7/9revy4qSkJFkslparDgAAAABayLbMYuWWuu8dO+qSUIUGkmP8SZPuka2oqNATTzyhb775xtVmMpnUrVs3devWjRALAAAAwGfVeVkxizz5nSYF2YCAAB0/fpzACgAAAMCvFJQ7tOlkkVt7TLBVl3UO8UJFuBhNXrU4OTlZBw8ebI1aAAAAAKBVrDmaL7vT896xFvaO9TtNDrJ33nmnVqxYodWrV9faQxYAAAAAfNXKOvaO5bJi/9TkxZ4ef/xx2e12LViwQAsWLFBgYKBMptq/wXjllVdarEAAAAAAuBhHcst0OLfcrX1AXCd1CQ/wQkW4WE0OsqNGjXILrgAAAADgq9g7tv1pcpC99957W6MOAAAAAGhxlQ5Dqz3sHRtoMWl0tzAvVISW0OR7ZAEAAADAX2zOKFJBucOtfXS3MAXb2I3FXzX5jOzq1asbHDNu3LhmFQMAAAAALanORZ64rNivNTnILliwoMExBFkAAAAA3pZbateWDPe9YxNCbUqJD/ZCRWgpTQ6yf/3rX93aCgsL9fXXX+urr77SL3/5y5aoCwAAAAAuyhdH8uVh61hN7BUhMwvY+rUmB9m4uDiPbb169ZLD4dCyZctYEAoAAACAVxmG4XG1YpOkiT25rNjftehiT6mpqdq8eXNLHhIAAAAAmuzA2TKdyK9wax/YOVjxoTYvVISW1KJBNicnR2YzCyEDAAAA8K46947txdnY9qDJlxbv3r3brc1ut+vYsWN6//33lZqa2iKFAQAAAEBzVDicWnvUfe/YYJtZl1/C3rHtQZOD7Lx58+rsGzhwoH7wgx9cVEEAAAAAcDE2nChScaXTrX1s93AFWrmCtD1ocpB98skn3dpsNpvi4uIUGRnZEjUBAAAAQLPVeVkxe8e2G00OsgMGDGiNOnT8+HEtXrxYe/bsUVlZmRISEjR+/HhNnTpVFoulScdat26dVq5cqaNHj6qiokKRkZHq3bu3ZsyYoS5durRK/QAAAAC8L7u4Ujsyi93ak8IDlBwT5IWK0BqaHGQzMjKUl5fnMdDu3r1bUVFRSkxMbNIx9+3bp6effloOh0NpaWmKiYnRrl27tGjRIu3bt08PPfSQTI3Y58lut+vPf/6zNmzYoM6dO+uKK65QcHCwcnNztXfvXmVmZhJkAQAAgHbs8yP58rB1rCb1imhUpoB/aHKQffXVV5WYmOgxyG7evFmZmZl65JFHGn08p9OphQsXqqKiQnPmzNHw4cNd7fPnz9fGjRu1bt06jR07tsFjvf3229qwYYOmTJmiWbNmua2g7HA4Gl0XAAAAAP9iGIZWebis2GySxvcM90JFaC1NvtP50KFD6t+/v8e+AQMG6NChQ006Xnp6ujIyMtS/f39XiJUks9msmTNnSpKWL1/e4HHy8vL00Ucf6ZJLLvEYYiU1+RJlAAAAAP5jd3apMgsr3dqHJIYoJpi9Y9uTJp+RLSkpUVCQ52vLAwICVFzsfj16fXbt2iVJGjx4sFtfQkKCEhMTtX//flVWVspmq3vybdiwQQ6HQ2PHjlV5ebm2bNmi7OxshYaGKiUlpdGXFFutTf6StLrqmnyxNqAhzF/4M+Yv/BnzF/7qYubu50cKPbZfkxxTb5aA72js+97k2REdHa2DBw9q0KBBbn0HDx5s8srFmZmZklTnfbWJiYnKzMxUVlaWunbtWudxDh48KKnqPtn77rtP+fnfXlJgMpk0adIk3XPPPR7P1NYUFRXVpPrbki/XBjSE+Qt/xvyFP2P+wl81de6WVNi17thut/aIIKumDumlALbdaVeaHGRHjBih//73v0pOTlZqaqqrfdeuXfrvf/+riRMnNul4JSUlkqTg4GCP/dXt1ePqUlBQteHxkiVLNGDAAD3++ONKSEjQoUOH9Pe//10rVqxQRESEbr311nqPk5ubK7vd3qTX0NqsVquioqJ8sjagIcxf+DPmL/wZ8xf+qrlzd/mBXJVWuq+JM65nhPJzz7ZkiWhF1e9/g+OaeuBp06Zpx44devrpp9WlSxdFR0fr3LlzysjIUNeuXXXLLbc0q+C6GIanNcfcOZ1VGx6HhYXp4YcfVqdOnSRJKSkpevDBB/Xwww9r6dKluvnmm+s9XW2321VZ6X5dvS/w5dqAhjB/4c+Yv/BnzF/4q6bO3c8OeA6rE3qE8T3QDjX5/HpwcLCeeeYZ3XLLLQoNDVVOTo5CQ0M1ffp0PfPMM3WeWa3veFLdZ1xLS0trjatLSEiIpKrgWh1iq3Xr1k3x8fEqKyvTyZMnm1QfAAAAAN+WWVihXWdK3dp7RgWqVzR7x7ZHzbr7PygoSNOmTdO0adMuuoDqe2Or75W9UGZmpsxmsxISEuo9TvViTtWB9kKhoaHKyspSRUXFRVQLAAAAwNd42nJHqto7Fu1Tk8/IFhQUKCMjw2NfRkaG617VxkpJSZEkbd++3a0vKytLmZmZSk5ObnCVsYEDB0qSxzOudrvdFZTj4+ObVB8AAAAA3+U0DH3uIchazdK4Huwd2141Oci+9NJL+uCDDzz2ffTRR/rXv/7VpOOlpqYqMTFRe/bs0ebNm13tTqdTr7/+uiRp8uTJrvby8nKdOnVKOTk5tY7Tr18/devWze04kvTuu++qpKREAwYMaPKqygAAAAB81zenS5Rd4r4o1PCkUIUHsf1Ue9Xkd3bfvn26++67PfZddtllevnll5t0PLPZrNmzZ+upp57S888/r7S0NMXGxio9PV2HDh3SiBEjNGbMGNf4gwcPat68eRowYIDmzp1b6zj33nuv5s6dq9/97ncaMWKE4uPjdfjwYe3evVthYWH60Y9+1NSXCwAAAMCHreSy4g6pyUG2sLBQoaGhHvtCQkKafGmxJPXt21fPPvusFi9erB07dqi8vFzx8fGaOXOmpk6dKpPJ1Kjj9OzZU88995wWL16snTt3asuWLYqIiNDEiRM1bdo0xcbGNrk2AAAAAL6pqMKhDScK3dojgywa1sVzZkH70OQgGxERoePHj9faQ7ba8ePH6wy5DenevbsefvjhBselpKRo8eLFdfZ37txZ9913X7NqAAAAAOA/1h0rUIXDfbvO8T0jZDE37mQY/FOT75EdPHiw3nvvPbcFnzIzM/X+++9ryJAhLVYcAAAAANRl5aE6LivuzWXF7V2Tz8jecsst2rp1qx5++GGlpKQoOjpa586d065du1z7yQIAAABAazqRX679Z8vc2i+NCVK3iEAvVIS21OQgGx0drd/85jd6++23tX37du3cuVPh4eEaO3asbr31VlksltaoEwAAAABc6jwbyyJPHUKz1qOOjo7WT3/6U9fnTqdT27dv1z//+U9t3bpVb7zxRosVCAAAAAA1OZyGvjjiHmQDLCaNZe/YDuGiNlY6ffq0Pv/8c61evVq5ubmyWq0aNWpUS9UGAAAAAG62ZRYrt8zh1p7WNUyhAVwh2hE0OchWVFRow4YNWrVqlfbs2eNqv/7663XTTTcpLCysRQsEAAAAgJpWsMhTh9foIHvw4EGtWrVKX331lUpLSxUUFKTx48dr1KhReu655zRs2DBCLAAAAIBWVVBm19en3PeOjQm2amBCsBcqgjc0Ksg+9NBDOnHihCQpOTlZEyZM0OjRoxUUFKSSkpJWLRAAAAAAqq0+WiC70719InvHdiiNCrLVIXbo0KG644471LVr11YtCgAAAAA8WXmYy4rRyCA7a9YsffHFF9q6dau2bt2qPn36aOLEiRo9enRr1wcAAAAAkqTD58p0JLfcrX1AXCclhgV4oSJ4S6OC7HXXXafrrrtOhw4dct0n+/e//10vv/yyhg4dKkkymTiNDwAAAKD1cDYW1Zq0anHv3r3Vu3dvzZo1y7Vy8YYNGyRJL774oq666iqNHz+eRZ8AAAAAtKhKh6HVRwvc2oOsJl3Rjb1jO5pm7SMbEBCgK6+8UldeeaVOnz6tVatWac2aNVq0aJHefvttLVq0qKXrBAAAANCBfX2qUIXl7nvHju4Wrk42sxcqgjc1K8jW1LlzZ91+++2aMWOGtm3bps8//7wl6gIAAAAAl5V17B17VS8uK+6ILjrIVjObzRo2bJiGDRvWUocEAAAAAJ0rtWtrZrFbe+dQmwbEd/JCRfA2zsEDAAAA8GlfHMmX03Bvn9QrgkVnOyiCLAAAAACfZRiGx8uKTZImcFlxh0WQBQAAAOCz9p8t08mCCrf2QZ2DFRdi80JF8AUEWQAAAAA+q65FniZxNrZDI8gCAAAA8EnldqfWHXPfOzbEZlbaJWFeqAi+giALAAAAwCdtOFGo4kqnW/uY7uEKtBJlOjLefQAAAAA+aeXhOi4r7s1lxR0dQRYAAACAzzlTVKFvTpe4tXcND1ByTJAXKoIvIcgCAAAA8DkrDuXKw9axmtSbvWNBkAUAAADgYwzD0IqDeW7tZpM0oSeXFYMgCwAAAMDHbDuZp9OF7nvHDusSoqhOVi9UBF9DkAUAAADgUz5Mz/TYPqlXZNsWAp9FkAUAAADgM0orHVq5L9utPSzQouFJoV6oCL6IIAsAAADAZ6w9mq/SSodb+7ge4bJZWOQJVQiyAAAAAHzG8oO5Htsn9WKRJ3yLIAsAAADAJ2QWVmhXlvvesT2jAtUrmr1j8S2CLAAAAACfsPJQvsd2zsbiQqxdDQAAAMCriiscen/POX2w95xbn9VcdX8sUBNBFgAAAIBXlNudWrY/V//ZdVaFFU6PY0YkhSk8iNiC2pgRAAAAANqUw2lo5eF8vfVNjs6W2usde1VvLiuGO4IsAAAAgDbhNAx9eaxQb3yTrYzCygbH944O1JDEkDaoDP6GIAsAAACgVRmGoa0ZxVq0I1uHc8sb9ZixPSL0o2FxspjZOxbuCLIAAAAAWs2e7BK9tj1bu86UNmr8sC6h+sWkfoq1lKuysuGztuiYCLIAAAAAWtzR3DIt2pGtr08VN2p839gg3Tk4TkO7RiouLlzZ2dmtXCH8GUEWAAAAQIs5XVihN77J0ZqjBTIaMb57RKDuGByrkUmhMpm4jBiNQ5AFAAAAcNHOldq1eGeOPjuYJ0cjEmx8iE13XBarsd3DuQ8WTUaQBQAAANBsReUOvbv7rD7cl6uKRiTYyCCLpqfG6uo+kbJZCLBoHoIsAAAAgCYrszv10d5cvbvnrIornA2OD7GZ9b0BMbq+X5SCrOY2qBDtGUEWAAAAQKNVOgwtP5SnxTtzlFvmaHB8gMWk6/tG6XsDYhQWaGmDCtER+EyQPX78uBYvXqw9e/aorKxMCQkJGj9+vKZOnSqLpXETfvr06XX2jR49Wr/85S9bqFoAAACgY3E4Da09VqA3v8nR6aKGt8WxmKTJfSI1PTVGMcG2NqgQHYlPBNl9+/bp6aeflsPhUFpammJiYrRr1y4tWrRI+/bt00MPPdToFczi4uI0btw4t/Zu3bq1dNkAAABAu2cYhr4+VaRFO3J0LK+8wfEmSWN7hOv2QbFKDAto/QLRIXk9yDqdTi1cuFAVFRWaM2eOhg8f7mqfP3++Nm7cqHXr1mns2LGNOl5cXFy9Z2YBAAAANE56Vole256tvTmljRo/IilEMy+LU4+ooFauDB2d14Nsenq6MjIy1L9/f1eIlSSz2ayZM2dq48aNWr58eaODLAAAAICLc/hcmV7bnq2tmcWNGj8grpO+PzhO/eODW7kyoIrXg+yuXbskSYMHD3brS0hIUGJiovbv36/KykrZbA1fW19cXKxVq1YpLy9PISEhuvTSS9WrV6+WLhsAAABod04VVOj1Hdn68nhho8b3jArUnZfFaWiXkEbfCgi0BK8H2czMTElSYmKix/7ExERlZmYqKytLXbt2bfB4x44d04svvlirrW/fvvr5z3+u+Pj4Bh9vtXr9S+KmuiZfrA1oCPMX/oz5C3/G/EVT5BRX6o0dWfrsQK6cDW8Fqy5hAbpzSILG9oyQuYUDLHO3Y2vs++712VFSUiJJCg72fBlCdXv1uPpcf/31SktLU5cuXWQ2m3XixAktWbJEO3bs0FNPPaXf//73Cgqq/3r9qKioJr6CtuPLtQENYf7CnzF/4c+Yv6hPXmmlXt5wVO9sO6UKR8N7wcaFBuie0T11Q2qirJbW3QuWuYv6eD3INsQwGvErofO+//3v1/q8b9++euyxx/Tkk09q3759WrFiha6//vp6j5Gbmyu73d6sWluL1WpVVFSUT9YGNIT5C3/G/IU/Y/6iPqWVDr23O0fvpueopLLhABsaYNH0QXH6Tr8YBVrNyj13ttVqY+52bNXvf4Pj2qCWejV0xrW0tLTWuKYym82aMGGC9u3bp7179zYYZO12uyorG94Xyxt8uTagIcxf+DPmL/wZ8xc1VTqc+uRAnt5JP6v8ckeD44OsJt3QL1o39Y9WSIBFMhyqrGz4cS2BuYv6eD3IVt8bW32v7IUyMzNlNpuVkJDQ7OcICwuTJJWXN7zvFQAAANDeOJyGvjiSrze/yVF2ScNnOa1m6ZpLozQ9JUaRnbweGQA3rXtheyOkpKRIkrZv3+7Wl5WVpczMTCUnJzdqxeK6HDhwQJIatdgTAAAA0F4YhqH1xwt139Ij+vOG0w2GWJOkib3CteA7vfSj4QmEWPgsr8/M1NRUJSYmas+ePdq8ebNrL1mn06nXX39dkjR58mTX+PLycuXk5CgwMFCxsbGu9iNHjqhLly4KDAysdfzdu3dr6dKlkqQxY8a09ssBAAAAfMKO08V6bXu2Dpwta9T4UV1DNfOyOHWLDGx4MOBlXg+yZrNZs2fP1lNPPaXnn39eaWlpio2NVXp6ug4dOqQRI0bUCqAHDx7UvHnzNGDAAM2dO9fVvmzZMm3atEmpqamKjY11rVr8zTffSJJuvvlm9e/fv61fHgAAANCmDpwt1avbs/XN6YZ3/ZCkgQnBunNwnPrGdmrlyoCW4/UgK1WtLvzss89q8eLF2rFjh8rLyxUfH6+ZM2dq6tSpjdpcecSIESotLdWxY8e0c+dOVVZWKjw8XCNHjtS1116r1NTUNnglAAAAgHecyC/X6zuytf5EUaPG944O0vcHx+myzsGN+nkb8CUmoyn723QA2dnZPrc6ms1mU1xcnE/WBjSE+Qt/xvyFP2P+dhxniir15s4cfXEkX85G/GTfNTxAd1wWq8svCfPJAMvc7diq3/+G+MQZWQAAAABNk1dm15L0s/r4QJ7sjUiwscFW3TYoVhN6Rshi9r0ACzQFQRYAAADwI8UVDr2/55w+2JurMruzwfHhgRbdkhqjay+NVIDF65uWAC2CIAsAAAD4gXK7Ux8fyNWSXedUWO5ocHwnq1k39Y/WDf2jFGyztEGFQNshyAIAAAA+zOE0tPJwvt76JkdnS+vfB1aSbGaTrkuO1LSUGIUH8eM+2idmNgAAAOCDnIahr44X6vUdOcoorGhwvNkkTewVoRkDYxUXYmuDCgHvIcgCAAAAPsQwDG3LLNZr27N1OLe8UY+5oluYbr8sVl3DA1u5OsA3EGQBAAAAH7E3u1SvbT+j9DOljRo/ODFEd14Wpz4xQa1cGeBbCLIAAACAlx3NLdOiHTn6+lRRo8b3jQ3SnYPjNDAhpJUrA3wTQRYAAADwktOFFXrzmxytPlqghneClbpFBGjmZXEa2TVUJhN7waLjIsgCAAAAbexcqV2Ld+Zo+aE8NWIrWMWH2HT7oFhd2SNcFjMBFiDIAgAAAG2kqMKh93af04d7z6nc0fA52Mggi6anxurqPpGyWQiwQDWCLAAAANDKyu1OfbgvV+/uPqviioZPwYbYzPrugGh9p1+0gqzmNqgQ8C8EWQAAAKCV2J2Glh/M09vpZ5Vbam9wfIDFpOv7Rul7A2IUFmhpgwoB/0SQBQAAAFqY0zC05miB3vwmR6eLKhscbzFJk/tEanpqjGKCbW1QIeDfCLIAAABACzEMQ5tPFWvRjmwdzStv1GOu7BGu2wfFKjEsoJWrA9oPgiwAAADQAnZllei1Hdnak13aqPHDu4Ro5uA49YwKauXKgPaHIAsAAABchMPnyrRoR7a2ZBQ3avyAuE66c3CcBsQHt3JlQPtFkAUAAACaIaOgQq9/k611xwobNb5nVKBmXhanYV1CZDKxlQ5wMQiyAAAAQBOcLanU2zvPavmhPDkb3gpWnUNtuuOyOI3pHiYzARZoEQRZAAAAoJG2nCrSb9dlqMze8F6w0Z2sunVgjK7qHSmrmQALtCSCLAAAANAIR3PL9H9rT6nCUf9p2NAAs25OidHU5CgFWs1tVB3QsRBkAQAAgAaUVDr03NqMekNsoMWkG/pF66YB0QoNsLRhdUDHQ5AFAAAA6mEYhv664bQyCis89lvN0jWXRml6SowiO/HjNdAW+E4DAAAA6rFsf56+PO55ZeJRXUP1w2HxSggNaOOqgI6NIAsAAADUYV9Oqf61NctjX3JMkB4ekySbhYWcgLbG3ecAAACABwXlDv127Sl5WqA4LMCsOWMJsYC3EGQBAACACzgNQ3/8KkM5JXa3PpOkB67oorgQW9sXBkASQRYAAABws2TXWW3JKPbYd0tqjIZ2CW3jigDURJAFAAAAavjmdLHe/CbHY9+ghGDNGBjbxhUBuBBBFgAAADjvbEmlfv9lhpwetouN6mTVg1d0kcXMfbGAtxFkAQAAAEkOp6Hfr8tQfpnDrc9skh4e04V9YgEfQZAFAAAAJC3aka3d2aUe+74/OE4p8cFtXBGAuhBkAQAA0OFtPFmod3ef89g3qmuobuof3cYVAagPQRYAAAAd2unCCv1pfabHvoRQm+67PFEmE/fFAr6EIAsAAIAOq8Lh1G/XnVJxhdOtz2Y26ZGxSQoNsHihMgD1IcgCAACgw3pp8xkdOlfuse9/hieod3RQG1cEoDEIsgAAAOiQvjiSr08P5nnsG98zXFf3iWjbggA0GkEWAAAAHc7xvHIt2HjaY1+3iAD9dGRn7osFfBhBFgAAAB1KaaVTz609pXKH4dYXZK26LzbIyo/JgC/jOxQAAAAdhmEYemFjpk4WVHjsv3dUorpGBLZxVQCaiiALAACADuPjA3lae6zQY991yZG6skd4G1cEoDkIsgAAAOgQDpwt1T+3nPHYd2lMkH4wNL6NKwLQXARZAAAAtHuF5Q79du0p2Z3u98WGBpg1Z0ySbBZ+NAb8Bd+tAAAAaNechqE/fpWhM8V2j/33j+6i+FBbG1cF4GJYvV1AtePHj2vx4sXas2ePysrKlJCQoPHjx2vq1KmyWCzNOuaSJUu0ePFiSdKTTz6plJSUliwZAAAAfuDd3ee0OaPYY9+0lBgNTwpt44oAXCyfCLL79u3T008/LYfDobS0NMXExGjXrl1atGiR9u3bp4ceeqjJ+3gdPnxY//nPfxQUFKSysrJWqhwAAAC+bGdWsV7fke2xLzUhWLcPim3jigC0BK8HWafTqYULF6qiokJz5szR8OHDXe3z58/Xxo0btW7dOo0dO7bRx6yoqNBf//pX9e7dW507d9aaNWtaq3wAAAD4qHOldv1+XYY83BarqCCLHrqiiyzmpp0sAeAbvH6PbHp6ujIyMtS/f39XiJUks9msmTNnSpKWL1/epGO+8cYbOnPmjGbPnt3kM7kAAADwfw6noefXnVJemcOtz2ySHhqTpKhOXj+nA6CZvB5kd+3aJUkaPHiwW19CQoISExO1f/9+VVZWNup46enp+vjjj3X77berS5cuLVkqAAAA/MTrO7KVfqbUY9/My+KUmhDcxhUBaEle/zVUZmamJCkxMdFjf2JiojIzM5WVlaWuXbvWe6ySkhItWLBA/fr105QpU5pVj9Xq9S+Jm+qafLE2oCHMX/gz5i/8WUeevxtPFOg/u8957Bt1SZimX5YgM1ft+ayOPHfR+Pfd67OjpKREkhQc7Pm3YtXt1ePq869//UuFhYV68sknm31JcVRUVLMe1xZ8uTagIcxf+DPmL/xZR5u/Gfmlmv/lHo99XSKC9OxNgxUexFY7/qCjzV00jdeDbEMMw8Pd+R5s3LhRa9as0Q9/+EMlJCQ0+/lyc3Nlt3veY8xbrFaroqKifLI2oCHMX/gz5i/8WUecv5UOpx5adlgFZe6v12o2ac7YJJUX5im70AvFodE64tzFt6rf/wbHtUEt9WrojGtpaWmtcZ4UFRXp73//u1JTU3X11VdfVD12u73R9+O2NV+uDWgI8xf+jPkLf9aR5u+Lm07rwFnP98XeMyxePSNsHeZr0R50pLmLpvN6kK2+N7b6XtkLZWZmymw213uWNScnR4WFhUpPT9ett97qccy8efMkSbNnz9b48eMvrmgAAAD4lNVH8vXxgTyPfVf2CNe1l0a2aT0AWpfXg2xKSoree+89bd++Xd/97ndr9WVlZSkzM1P9+vWTzVb3vQyhoaGaOHGix749e/YoMzNTQ4YMUVRUVJ2LSgEAAMA/ncgv14JNpz32dQ0P0OyRndmSEWhnvB5kU1NTlZiYqD179mjz5s2uvWSdTqdef/11SdLkyZNd48vLy5WTk6PAwEDFxsZKkmJjY/WTn/zE4/FfeOEFZWZm6oYbblBKSkorvxoAAAC0pdJKp/5vzSmV2d3XVQm0mPTIlUnqZPP6jpMAWpjXg6zZbNbs2bP11FNP6fnnn1daWppiY2OVnp6uQ4cOacSIERozZoxr/MGDBzVv3jwNGDBAc+fO9V7hAAAA8CrDMLRw02mdLKjw2H/vqM7qFhHYxlUBaAteD7KS1LdvXz377LNavHixduzYofLycsXHx2vmzJmaOnUql4IAAADAzacH87T6aIHHvimXRmpcz4g2rghAWzEZjd3fpoPIzs72udXRbDab4uLifLI2oCHMX/gz5i/8WXufvwfPlumRz47J7nT/UbZ3dJCeu7qbbBYuKfZH7X3uon7V739D+O4GAACAXykqd+i36055DLEhAWY9MrYLIRZo5/gOBwAAgN9wGob+uD5TWUWez9T98vJEJYQGtHFVANoaQRYAAAB+4/3d5/T1qSKPfd8bEK2RXcPauCIA3kCQBQAAgF/YlVWi13Zke+xLie+kmZc1fF8dgPaBIAsAAACfl1dq1+++zJCH22IVGWTRQ2OSZDGz0wXQURBkAQAA4NMcTkO//zJDuaV2tz6zSXrwii6K7uQTu0oCaCMEWQAAAPi0N7/J0c6sEo99tw+K1aDOIW1cEQBvI8gCAADAZ20+VaR3dp312DesS4huTolp44oA+AKCLAAAAHzSmaJK/fGrDI998SFW3T+6i8wm7osFOiKCLAAAAHxOpcPQb9edUmGF063PapYeHpOksECLFyoD4AsIsgAAAPA5/952RgfOlnns+8HQBCXHdmrjigD4EoIsAAAAfMraowVaui/XY9/Y7mG6LjmybQsC4HMIsgAAAPAZJwvK9deNpz32JYUHaPaozjJxXyzQ4RFkAQAA4BPK7E49t+aUyuzu98UGWkx6dGySgm3cFwuAIAsAAAAfYBiGFm46reP5FR77fzqys7pFBrZxVQB8FUEWAAAAXrf8UL6+OFLgse+aPpGa0CuijSsC4MsIsgAAAPCqw+fK9Pevszz29YoK1D3D49u4IgC+jiALAAAArymqcOi5tadU6TTc+kJsZs0Zm6QACz+yAqiNfxUAAADgFYZh6M/rM3W6qNJj/y8uT1RiWEAbVwXAHxBkAQAA4BX/3XtOG08Weey7qX+0Rl0S1sYVAfAXBFkAAAC0ud1nSvTKtmyPfQPiOunOwXFtXBEAf0KQBQAAQJvKK7Prd+sy5OG2WEUEWfTQmC6ymk1tXxgAv0GQBQAAQJtxOA09/2WGzpXa3fpMkh68ootigm1tXxgAv0KQBQAAQJt5Oz1H35wu8dh3+6BYXdY5pI0rAuCPCLIAAABoE1szirR451mPfUMTQzQtNaaNKwLgrwiyAAAAaHXZxZX6w1eZ8nBbrGKDrbp/dKLMJu6LBdA4BFkAAAC0qkqHod+tO6XCcodbn9UszRmbpPAgqxcqA+CvCLIAAABoVa9sO6N9OWUe++4aEq++sZ3auCIA/o4gCwAAgFbz5fECfbgv12PfFd3CdH3fqDauCEB7QJAFAABAqzhVUKG/rD/tsa9LmE0/S+ssE/fFAmgGgiwAAABaXLndqefWnlKp3enWF2Ax6ZGxSQq2WbxQGYD2gCALAACAFve3r7N0LK/cY99PR3ZWj6igNq4IQHtCkAUAAECLWnEoTysP53vsu6p3hCb2imjjigC0NwRZAAAAtJgjuWX629dZHvt6RgXqR8MT2rgiAO0RQRYAAAAtorjCoefWnlKFw3DrC7aZ9cjYJAVa+fETwMXjXxIAAABcNMMw9JcNmcosrPTYf19aohLDAtq4KgDtFUEWAAAAF+3Dfblaf6LIY9+N/aJ0ebewNq4IQHtGkAUAAMBF2ZNdope3nvHY1y+2k74/JL6NKwLQ3hFkAQAA0Gz5ZXb9bm2GPNwWq/BAix4e20VWs6ntCwPQrhFkAQAA0CwOp6E/fJWps6V2tz6TpAev6KLYYFvbFwag3SPIAgAAoFneST+r7ZnFHvtmDIzV4MSQNq4IQEdBkAUAAECTbc8s1ls7czz2DU4M0S2pMW1cEYCOhCALAACAJskpqdTzX2bIw22xiulk1QOjE2XhvlgArYggCwAAgEazOw39bm2GCsodbn0Wk/Tw2C6KCLJ6oTIAHQlBFgAAAI326rYz2ptT6rHvrqHx6h8X3MYVAeiIfObXZcePH9fixYu1Z88elZWVKSEhQePHj9fUqVNlsVgafHxBQYGWLFmiQ4cOKTs7W0VFRQoNDVVCQoImTZqkMWPGyGr1mZcLAADgd9YfL9R/9+Z67Lv8kjB9p29UG1cEoKPyiTOy+/bt0//+7/9qy5YtGjRokKZMmaLAwEAtWrRIf/jDH2QYnu7AqO3cuXP64osvFBQUpOHDh+v666/X8OHDlZOTowULFuj//u//5HQ62+DVAAAAtD+ZhRX684ZMj32JYTb9PK2zTCbuiwXQNrx+itLpdGrhwoWqqKjQnDlzNHz4cFf7/PnztXHjRq1bt05jx46t9ziXXHKJXn75ZZnNtbO53W7XM888o2+++UZff/21Ro0a1WqvBQAAoD0qtzv13NpTKql0PykQYDHpkbFJCglo+Ao6AGgpXj8jm56eroyMDPXv398VYiXJbDZr5syZkqTly5c3eByLxeIWYiXJarVqxIgRkqTMTM+/RQQAAEDd/r45S0dyyz32/XhEgnpGBbVxRQA6Oq8H2V27dkmSBg8e7NaXkJCgxMRE7d+/X5WVlc06vtPp1LZt2yRJ3bt3b3adAAAAHdHKQ3lacSjfY9+kXhG6qndk2xYEAPKBS4urz5ImJiZ67E9MTFRmZqaysrLUtWvXBo9XXFyspUuXSqpaAGrnzp3KzMzUlVdeqSFDhjT4eF9cEKq6Jl+sDWgI8xf+jPkLf9YS8/fIuVL97essj309ooJ07+iuslm9fl4E7Qz/9nZsjX3fvT47SkpKJEnBwZ6Xaq9urx7XkOLiYi1ZssT1uclk0g033KAZM2Y06vFRUb672p4v1wY0hPkLf8b8hT9r7vwtKrfruf8eVLnDfdHNkACLfv+9wbokmq120Hr4txf18XqQbUhjViyuKT4+XosXL5bT6dS5c+e0efNmvfXWW9q7d68ee+wxhYSE1Pv43Nxc2e32iym5xVmtVkVFRflkbUBDmL/wZ8xf+LOLmb+GYeg3q0/oeK7n/WJ/MTpJwY5iZWcXt0SpQC3829uxVb//DY5rg1rq1dAZ19LS0lrjGstsNis2NlbXXnutwsPD9cc//lHvvPOO7rrrrnofZ7fbm30/bmvz5dqAhjB/4c+Yv/BnzZm/H+49p3VHPd8X+52+URqVFMz3BFod//aiPl6/qaH63ti6VhTOzMyU2WxWQkJCs5+jeiGp6oWlAAAA4Nm+nFK9vO2Mx76+sUGaNSS+jSsCAHdeD7IpKSmSpO3bt7v1ZWVlKTMzU8nJybLZbM1+jnPnzkmq2qIHAAAAnhWU2fXbtadkd98uVmGBFj08Jkk2i6ntCwOAC3g9yKampioxMVF79uzR5s2bXe1Op1Ovv/66JGny5Mmu9vLycp06dUo5OTm1jnPo0CGVlZW5Hb+srEyvvPKKJDVq1WIAAICOyGkY+sNXmcopcb8n0STpgdGJigtp/okFAGhJXr9H1mw2a/bs2Xrqqaf0/PPPKy0tTbGxsUpPT9ehQ4c0YsQIjRkzxjX+4MGDmjdvngYMGKC5c+e62j/55BNt2rRJAwYMUGxsrAIDA3Xu3Dlt27ZNxcXF6tu3r2688UYvvEIAAADftyT9rLZlel68afrAGA3tEtrGFQFA3bweZCWpb9++evbZZ7V48WLt2LFD5eXlio+P18yZMzV16lSZTA1fwnLllVfKMAwdPHhQu3fvVkVFhYKDg9WzZ0+NHj1aEyZM4NJiAAAAD3acLtYb3+R47Lusc7BuTY1t44oAoH4mo6n727Rz2dnZPrc6ms1mU1xcnE/WBjSE+Qt/xvyFP2vs/D1bUqn7Pz6q/DKHW190J6vmX9dDkUE+ce4DHQT/9nZs1e9/Q7x+jywAAAC8w+409Pt1GR5DrNkkPTymCyEWgE8iyAIAAHRQr23P1u7sUo99s4bEaUB8cBtXBACNQ5AFAADogDacKNT7e8557BvVNVQ39otu44oAoPEIsgAAAB1MZmGF/rw+02Nf51Cb7rs8sVGLbQKAtxBkAQAAOpAKh1O/XXtKxZVOtz6b2aRHxiYpNICdHgD4NoIsAABAB/LS5jM6nFvuse9HIxLUKzqojSsCgKYjyAIAAHQQnx/O16cH8zz2TegZrsm9I9q2IABoJoIsAABAB3Asr1wLN5322Nc9IlA/GdmZ+2IB+A2CLAAAQDtXUunQc2tPqdxhuPUFWc2ac2UXBVn5sRCA/+BfLAAAgHbMMAy9sPG0ThVUeOz/2ajO6hoe2MZVAcDFIcgCAAC0Yx/tPad1xwo99k1NjtTYHuFtXBEAXDyCLAAAQDu1K7NA//ja836xl8YE6e6h8W1cEQC0DKu3CwAAAEDLKyiz69Fl+2V3ut8XGxZg1pwxSbJZOKcBwD8RZAEAANoZp2Ho+XWndLrA836x94/uovhQWxtXBQAthyDrJ46eLdbuk4Vy2O3eLgVoEovVqohCs/Lzmb/wP8xf+Ktvskr09UnP98XekhKjYUmhbVwRALQsgqyf+GBnpl77+ri3ywAAAH5sUEKwbhsU6+0yAOCicWMEAABABxDVyaoHr+gii9nk7VIA4KIRZAEAANo5s0l6+IouiuzExXgA2geCLAAAQDt352VxSkkI9nYZANBiCLIAAADt2M0psbppQLS3ywCAFsX1JX7ixkFd1DfKLIfd4e1SgCaxWC2KjIhUXn4e8xd+h/kLf2azWjWkdxcZpQWqrKz0djkA0KIIsn6ie3Swgh1h/I8IfsdmsykuLlrZ2Q7mL/wO8xf+zGazKTY0UNml3q4EAFoelxYDAAAAAPwKQRYAAAAA4FcIsgAAAAAAv0KQBQAAAAD4FYIsAAAAAMCvEGQBAAAAAH6FIAsAAAAA8CsEWQAAAACAXyHIAgAAAAD8CkEWAAAAAOBXCLIAAAAAAL9CkAUAAAAA+BWCLAAAAADArxBkAQAAAAB+hSALAAAAAPArBFkAAAAAgF8hyAIAAAAA/IrV2wX4GqvV974k1TX5Ym1AQ5i/8GfMX/gz5i/8FXO3Y2vs+24yDMNo5VoAAAAAAGgxXFoMAAAAAPArBFkAAAAAgF8hyAIAAAAA/ApBFgAAAADgVwiyAAAAAAC/wprWPuz48eNavHix9uzZo7KyMiUkJGj8+PGaOnWqLBaLt8sDPCosLNSmTZu0detWHT9+XOfOnZPValW3bt00YcIEjR8/XmYzv0ODf1mzZo3++te/SpJmz56t8ePHe7cgoAHbtm3Tp59+qgMHDqi0tFQRERHq0aOHvvvd7yo5Odnb5QF12rx5s5YtW6ZTp06pqKhI0dHRSk5O1o033qhu3bp5uzz4EIKsj9q3b5+efvppORwOpaWlKSYmRrt27dKiRYu0b98+PfTQQzKZTN4uE3Czfv16vfTSS4qKilJKSopiY2OVl5enTZs26cUXX9S2bdv0wAMPMH/hN3JycvSvf/1LQUFBKisr83Y5QINefvllLVu2TFFRURoxYoTCw8NVUFCgAwcO6PDhwwRZ+KzXXntNH374ocLDwzVixAiFhobq5MmTWrdundavX6/HHntMAwcO9HaZ8BEEWR/kdDq1cOFCVVRUaM6cORo+fLirff78+dq4caPWrVunsWPHerlSwF2XLl00Z84cDR06tNaZ19tvv12PPfaYNm7cqI0bNyotLc2LVQKNYxiGFi5cqLCwMI0cOVIffviht0sC6vXZZ59p2bJlGjVqlH7+858rICCgVr/dbvdSZUD98vLy9NFHHykqKkq///3vFRYW5upbu3at/vKXv+jdd98lyMKF6/t8UHp6ujIyMtS/f39XiJUks9msmTNnSpKWL1/urfKAeqWmpmr48OFulw9HRkZq8uTJkqTdu3d7ozSgyT7++GOlp6frpz/9qQIDA71dDlCvyspKvf322woLC9O9997rFmIlyWrlHAZ8U3Z2tgzD0KWXXlorxErSsGHDJEkFBQXeKA0+in/NfNCuXbskSYMHD3brS0hIUGJiovbv36/KykrZbLY2rg5ovuofoLhHFv7g5MmTev311zVlyhQNGDBA6enp3i4JqNc333yjwsJCTZ48WRaLRZs2bVJGRoaCgoLUr18/9ejRw9slAnVKTEyU1WrVwYMHVVRUpNDQUFff1q1bJYmzsaiFIOuDMjMzJVV9Q3uSmJiozMxMZWVlqWvXrm1ZGtBsDodDq1evluT5lzSAL3E4HPrrX/+q2NhY3X777d4uB2iUQ4cOSZKCgoL04IMP6vTp07X6R44cqZ/97GcKCgryRnlAvUJDQ3XHHXfo1Vdf1QMPPKDhw4crJCREp06d0rZt25SWlqYZM2Z4u0z4EIKsDyopKZEkBQcHe+yvbq8eB/iD119/XSdOnNCQIUMIsvB5S5Ys0ZEjR/T00097vDwT8EX5+fmSpKVLl6pbt2769a9/rW7duunUqVP65z//qU2bNumll17Sz372My9XCng2depUxcbG6sUXX9SKFStc7d27d9fo0aP5JQxq4fo+P2QYhrdLAJpk2bJl+uijj9SlSxfde++93i4HqNfBgwf13nvv6Tvf+Q6ru8KvOJ1OSZLFYtGcOXOUnJysoKAg9e7dW3PmzFFQUJDWrl2rc+fOeblSwLP3339ff/jDHzR+/Hj95S9/0Wuvvab/+7//U2RkpP7whz/ozTff9HaJ8CEEWR/U0BnX0tLSWuMAX/bJJ5/o5ZdfVlJSkubOnavw8HBvlwTUyeFw6C9/+YsSExN16623erscoElCQkIkST169FBsbGytvsjISF166aUyDMN1CTLgS3bt2qU33nhDI0aM0KxZs5SQkKDAwED16tVLDz30kKKjo/X+++8rKyvL26XCR3BpsQ+qvje2+l7ZC2VmZspsNishIaEtywKabOnSpXrllVd0ySWX6Fe/+pUiIiK8XRJQr7KyMte/vXfccYfHMQsWLNCCBQs0bdo0TZ8+vS3LA+rVpUsXSd8G2gtVt1dUVLRZTUBjbdmyRZKUkpLi1hcYGKg+ffpo06ZNOnLkCD8DQxJB1ielpKTovffe0/bt2/Xd7363Vl9WVpYyMzPVr18/ViyGT3v//ff1xhtvqEePHnr88cc5Ewu/YLPZNHHiRI99R44c0ZEjR9S/f38lJiaqV69ebVwdUL/U1FRJ0qlTp2QYhkwmU63+EydOSJLi4uLavDagIdV7HNe1xU51O1tIoRozwQelpqYqMTFRe/bs0ebNm117yTqdTr3++uuS5NqPE/BFS5Ys0eLFi9WrVy89/vjjtZbQB3xZQECAfvKTn3jsW7x4sY4cOaIJEyZo/PjxbVsY0Ajx8fEaOnSotm7dqk8//VTXXnutq+/zzz/XqVOnlJCQoD59+nixSsCzfv366ZNPPtGKFSs0efJkRUdHu/q2bdumffv2yWazqW/fvl6sEr6EIOuDzGazZs+eraeeekrPP/+80tLSFBsbq/T0dB06dEgjRozQmDFjvF0m4NEXX3yhxYsXy2w2q1+/flq2bJnbmPj4eIIAALSCe+65R48//rj+9a9/acuWLa5Vi7du3arAwEDde++97OUNn5SWlqaBAwdq586duv/++zVixAhFRka65q9hGLrjjjsUFhbm7VLhI0wGS+D6rGPHjmnx4sXas2ePysvLFR8frwkTJmjq1KmyWCzeLg/waPHixVqyZEm9YwYMGKC5c+e2TUFAC6me27Nnz+YXMfBpeXl5euedd7R161bl5eUpNDRUKSkpmjZtGvvPw6fZ7XZ9+umn+uqrr3Ty5EmVl5crNDRUffr00ZQpU3TZZZd5u0T4EIIsAAAAAMCvcG0JAAAAAMCvEGQBAAAAAH6FIAsAAAAA8CsEWQAAAACAXyHIAgAAAAD8CkEWAAAAAOBXCLIAAAAAAL9CkAUAAAAA+BWCLAAAAADAr1i9XQAAAB3JF198oQULFtTZ/+STTyolJaUNK/rWmTNn9LOf/UwzZ87UDTfc4JUaAABoDIIsAABeMHv2bHXp0sWtvWvXrl6oBgAA/0KQBQDACy655BL17t3b22UAAOCXCLIAAPig6dOn65prrlG3bt300UcfKTs7WwkJCZo2bZquuOKKWmOPHz+ut956S3v27FFFRYW6dOmiqVOnavz48bXGFRcX6z//+Y82bdqkc+fOKTg4WL1799b3v/99JSUl1Rr70Ucf6eOPP1ZBQYG6deumWbNmKTk5ubVfNgAAjUKQBQDAC5xOpxwOR602k8kks/nbdRg3b96sXbt2afr06QoMDNRnn32mP/3pT7JYLEpLS5MkZWRk6IknnlB4eLjuvvtuhYaGau3atVqwYIHy8/N14403SpJKS0v1q1/9SmfOnNGNN96oSy+9VGVlZdqzZ49yc3NrBdlPP/1USUlJuuuuuyRJb7/9tn7zm9/ohRdeUHBwcCt/ZQAAaBhBFgAAL/h//+//ubWZzWa99dZbrs8LCwv1m9/8RpGRkZKkoUOH6sEHH9Qbb7zhCrKLFy+W3W7Xk08+qdjYWNe4kpISLVmyRJMnT1ZwcLCWLl2qEydO6PHHH9egQYNczzFq1Ci3Ojp16qRHH33UFaqjoqL0v//7v9q2bZvb2WAAALyBIAsAgBf87Gc/c7uc12Qy1fo8NTXVFWKlqqB7+eWXa8mSJTp79qxiYmK0a9cupaamukJstXHjxmnbtm3av3+/Bg8erO3btysxMbFWiK3L0KFDa50Z7t69uyQpOzu7qS8TAIBWQZAFAMALkpKSGlzsqWaIvbCtsLBQMTExKiwsVFRUlNu46Oho1zhJKigocAu7dQkNDa31uc1mkyRVVFQ06vEAALQ2c8NDAACAN+Tl/f/27lYltigMA/B7kgg6xeCgmJxoEJlkVTBMEfxJNoNgGLwFxzYXYDMrzC2YLNMmbeYGRCz+nDzBOUHOhsFzmjpueJ6yYfGFb8WXtda3f/93bX5+vvy+vr5+qHt5eZmoq9VqeX5+/ppGAeCbCbIA8EMVRTERZt/e3tLv97O4uJiFhYUk79ePi6Iog+tfd3d3mZmZKScNr6+v5/HxMUVRfFv/APBVXC0GgCm4v7//MLU4Ser1emq1WpL309ROp5O9vb1yavHDw0POzs7K+oODgwwGg5yfn2d/f7+cWjwYDHJ0dFROGW61Wun3++l2u9nd3U2j0choNMpwOMzGxkbW1ta+Zd8A8BkEWQCYgsvLy3+un5ycZGtrK0nSbDazsrKSm5ubPD09pV6vp91uZ3Nzs6xfWlrKxcVFrq+vc3V1ldFolOXl5Zyenk78R3Z2djadTie9Xi+3t7fp9XqZm5vL6upqtre3v3SvAPDZfo3H4/G0mwAAJh0eHmZnZyfHx8fTbgUAfhxvZAEAAKgUQRYAAIBKcbUYAACASnEiCwAAQKUIsgAAAFSKIAsAAEClCLIAAABUiiALAABApQiyAAAAVIogCwAAQKUIsgAAAFTKHy5crDMJZS+pAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Training Acc', 'Test Acc'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Training Loss', 'Test Loss'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T12:09:33.717975Z",
          "iopub.status.busy": "2023-06-07T12:09:33.717587Z",
          "iopub.status.idle": "2023-06-07T12:09:38.962784Z",
          "shell.execute_reply": "2023-06-07T12:09:38.961736Z",
          "shell.execute_reply.started": "2023-06-07T12:09:33.717943Z"
        },
        "id": "monNBB5_dJwW",
        "outputId": "cfaaec4b-2737-445c-8659-c92edd2b8a95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation Score:  0.640999436378479\n",
            "Validation Accuracy:  0.9137499928474426\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate_generator(val_set, steps=50)\n",
        "\n",
        "print ('Validation Score: ', score[0])\n",
        "print ('Validation Accuracy: ',score[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZCL0Co2ndJwW"
      },
      "outputs": [],
      "source": [
        "# accuracy_score = model.evaluate_generator(test_set, steps=50)\n",
        "# print(\"Accuracy Score:\", accuracy_score[0])\n",
        "# print(\"Accuracy:\",accuracy_score[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T12:09:44.556556Z",
          "iopub.status.busy": "2023-06-07T12:09:44.556104Z",
          "iopub.status.idle": "2023-06-07T12:09:49.794691Z",
          "shell.execute_reply": "2023-06-07T12:09:49.793577Z",
          "shell.execute_reply.started": "2023-06-07T12:09:44.556525Z"
        },
        "id": "JvLKu1VHdJwX",
        "outputId": "63d61152-ca68-4dc8-99da-ab721ffdd370"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "53/53 [==============================] - 5s 87ms/step - loss: 0.6184 - accuracy: 0.9183\n",
            "> 91.834\n"
          ]
        }
      ],
      "source": [
        "_, acc = model.evaluate(val_set, verbose=1)\n",
        "print('> %.3f' % (acc * 100.0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-06-07T12:09:54.315391Z",
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        },
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      },
      "outputs": [],
      "source": [
        "model.save(\"Test\" + '.h5')"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### Model Evaluation\n",
        "\n",
        "The evaluate_model function loads a trained model from a saved file and evaluates it using the validation generator. It calculates overall metrics (accuracy, precision, recall, F1-score) and class-specific metrics. These metrics provide a detailed view of model performance."
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "def evaluate_model(saved_model_path, validation_generator):\n",
        "    # Load the saved model\n",
        "    loaded_model = tf.keras.models.load_model(saved_model_path)\n",
        "\n",
        "    # Evaluate the model on the test set\n",
        "    test_loss, test_accuracy = loaded_model.evaluate(validation_generator)\n",
        "    print('Test Loss:', test_loss)\n",
        "    print('Test Accuracy:', test_accuracy)\n",
        "\n",
        "    # Perform thorough evaluation\n",
        "    y_true = validation_generator.classes\n",
        "    y_pred = loaded_model.predict(validation_generator)\n",
        "    y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "\n",
        "    # Calculate overall metrics\n",
        "    accuracy = metrics.accuracy_score(y_true, y_pred_classes)\n",
        "    precision = metrics.precision_score(y_true, y_pred_classes, average='weighted')\n",
        "    recall = metrics.recall_score(y_true, y_pred_classes, average='weighted')\n",
        "    f1_score = metrics.f1_score(y_true, y_pred_classes, average='weighted')\n",
        "\n",
        "    print('Overall Metrics:')\n",
        "    print('Accuracy:', accuracy)\n",
        "    print('Precision:', precision)\n",
        "    print('Recall:', recall)\n",
        "    print('F1-Score:', f1_score)\n",
        "\n",
        "    # Calculate metrics per class\n",
        "    class_metrics = metrics.classification_report(y_true, y_pred_classes, target_names=validation_generator.class_indices.keys())\n",
        "    print('\\nMetrics per Class:')\n",
        "    print(class_metrics)"
      ]
    },
    {
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        "execution": {
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        "outputId": "de4b61a0-2874-492f-dceb-e3a99ae7f17c"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "53/53 [==============================] - 5s 82ms/step - loss: 0.6184 - accuracy: 0.9183\n",
            "Test Loss: 0.6184009909629822\n",
            "Test Accuracy: 0.918343186378479\n",
            "53/53 [==============================] - 5s 82ms/step\n",
            "Overall Metrics:\n",
            "Accuracy: 0.9183431952662722\n",
            "Precision: 0.9375075473976572\n",
            "Recall: 0.9183431952662722\n",
            "F1-Score: 0.9179678866226003\n",
            "\n",
            "Metrics per Class:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      cloudy       1.00      0.98      0.99       450\n",
            "      desert       1.00      0.98      0.99       340\n",
            "  green_area       1.00      0.73      0.84       450\n",
            "       water       0.77      1.00      0.87       450\n",
            "\n",
            "    accuracy                           0.92      1690\n",
            "   macro avg       0.94      0.92      0.92      1690\n",
            "weighted avg       0.94      0.92      0.92      1690\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import sklearn.metrics as metrics\n",
        "saved_model_path = '/content/gdrive/MyDrive/Colab Notebooks/Test.h5'\n",
        "\n",
        "evaluate_model(saved_model_path, val_set)\n",
        "# plot_training_history(history)"
      ]
    }
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