train_chest_ctscan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f3b72840",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import os\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Conv2D,BatchNormalization, Dense, MaxPooling2D, Dropout, Flatten,GlobalAveragePooling2D\n",
    "\n",
    "from matplotlib.image import imread\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "from resnet50 import ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab343565",
   "metadata": {},
   "outputs": [],
   "source": [
    "# File Directory for both the train and test\n",
    "train_path = \"./data/chest-ctscan-images/train\"\n",
    "val_path = \"./data/chest-ctscan-images/valid\"\n",
    "test_path = \"./data/chest-ctscan-images/test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "72ade536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_images(folder_path):\n",
    "    image_files = os.listdir(folder_path)\n",
    "    fig, ax = plt.subplots(2,5, figsize=(12,4))\n",
    "    \n",
    "    for i in range(2):\n",
    "        for j in range(5):\n",
    "            img_path = os.path.join(folder_path, random.choice(image_files))\n",
    "            image = imread(img_path)\n",
    "            ax[i, j].imshow(image, cmap=\"gray\")\n",
    "            ax[i, j].axis('off')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "folder_path = train_path + '/normal'\n",
    "visualize_images(folder_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a881f372",
   "metadata": {},
   "source": [
    "## Image augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12e8fc09",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_SHAPE = (224, 224, 3)\n",
    "NUM_CLASSES = 4\n",
    "\n",
    "train_datagen = ImageDataGenerator(rotation_range=10,\n",
    "                                   width_shift_range=0.2,\n",
    "                                   height_shift_range= 0.2,\n",
    "                                   shear_range=0.2,\n",
    "                                   zoom_range=0.2,\n",
    "                                   horizontal_flip=True,\n",
    "                                   vertical_flip=False,\n",
    "                                   preprocessing_function=preprocess_input,\n",
    "                                   dtype=\"float32\")\n",
    "\n",
    "test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "                                 dtype=\"float32\")\n",
    "\n",
    "val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "                                 dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1eef8843",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 613 images belonging to 4 classes.\n",
      "Found 315 images belonging to 4 classes.\n",
      "Found 72 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data = train_datagen.flow_from_directory(train_path,\n",
    "                                                 target_size=(224, 224),\n",
    "                                                 batch_size=32,\n",
    "                                                 class_mode=\"categorical\")\n",
    "test_data = test_datagen.flow_from_directory(test_path,\n",
    "                                            target_size=(224, 224),\n",
    "                                            batch_size=32,\n",
    "                                            class_mode=\"categorical\")\n",
    "val_data = val_datagen.flow_from_directory(val_path,\n",
    "                                          target_size=(224, 224),\n",
    "                                          batch_size=32,\n",
    "                                          class_mode=\"categorical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe0fbe87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib': 0,\n",
       " 'large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa': 1,\n",
       " 'normal': 2,\n",
       " 'squamous.cell.carcinoma_left.hilum_T1_N2_M0_IIIa': 3}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.class_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede6ee44",
   "metadata": {},
   "source": [
    "#  Resnet model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04b5bde1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
      "                                                                                                  \n",
      " zero_padding2d_2 (ZeroPadd  (None, 230, 230, 3)          0         ['input_3[0][0]']             \n",
      " ing2D)                                                                                           \n",
      "                                                                                                  \n",
      " conv2d_106 (Conv2D)         (None, 112, 112, 64)         9472      ['zero_padding2d_2[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_108 (B  (None, 112, 112, 64)         256       ['conv2d_106[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_98 (Activation)  (None, 112, 112, 64)         0         ['batch_normalization_108[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " max_pooling2d_4 (MaxPoolin  (None, 55, 55, 64)           0         ['activation_98[0][0]']       \n",
      " g2D)                                                                                             \n",
      "                                                                                                  \n",
      " conv2d_107 (Conv2D)         (None, 55, 55, 64)           4160      ['max_pooling2d_4[0][0]']     \n",
      "                                                                                                  \n",
      " batch_normalization_109 (B  (None, 55, 55, 64)           256       ['conv2d_107[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_99 (Activation)  (None, 55, 55, 64)           0         ['batch_normalization_109[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " conv2d_108 (Conv2D)         (None, 55, 55, 64)           36928     ['activation_99[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_110 (B  (None, 55, 55, 64)           256       ['conv2d_108[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_100 (Activation  (None, 55, 55, 64)           0         ['batch_normalization_110[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_109 (Conv2D)         (None, 55, 55, 256)          16640     ['activation_100[0][0]']      \n",
      "                                                                                                  \n",
      " conv2d_110 (Conv2D)         (None, 55, 55, 256)          16640     ['max_pooling2d_4[0][0]']     \n",
      "                                                                                                  \n",
      " batch_normalization_111 (B  (None, 55, 55, 256)          1024      ['conv2d_109[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_112 (B  (None, 55, 55, 256)          1024      ['conv2d_110[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_32 (Add)                (None, 55, 55, 256)          0         ['batch_normalization_111[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'batch_normalization_112[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " activation_101 (Activation  (None, 55, 55, 256)          0         ['add_32[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_111 (Conv2D)         (None, 55, 55, 64)           16448     ['activation_101[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_113 (B  (None, 55, 55, 64)           256       ['conv2d_111[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_102 (Activation  (None, 55, 55, 64)           0         ['batch_normalization_113[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_112 (Conv2D)         (None, 55, 55, 64)           36928     ['activation_102[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_114 (B  (None, 55, 55, 64)           256       ['conv2d_112[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_103 (Activation  (None, 55, 55, 64)           0         ['batch_normalization_114[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_113 (Conv2D)         (None, 55, 55, 256)          16640     ['activation_103[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_115 (B  (None, 55, 55, 256)          1024      ['conv2d_113[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_33 (Add)                (None, 55, 55, 256)          0         ['batch_normalization_115[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_101[0][0]']      \n",
      "                                                                                                  \n",
      " activation_104 (Activation  (None, 55, 55, 256)          0         ['add_33[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_114 (Conv2D)         (None, 55, 55, 64)           16448     ['activation_104[0][0]']      \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " batch_normalization_116 (B  (None, 55, 55, 64)           256       ['conv2d_114[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_105 (Activation  (None, 55, 55, 64)           0         ['batch_normalization_116[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_115 (Conv2D)         (None, 55, 55, 64)           36928     ['activation_105[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_117 (B  (None, 55, 55, 64)           256       ['conv2d_115[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_106 (Activation  (None, 55, 55, 64)           0         ['batch_normalization_117[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_116 (Conv2D)         (None, 55, 55, 256)          16640     ['activation_106[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_118 (B  (None, 55, 55, 256)          1024      ['conv2d_116[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_34 (Add)                (None, 55, 55, 256)          0         ['batch_normalization_118[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_104[0][0]']      \n",
      "                                                                                                  \n",
      " activation_107 (Activation  (None, 55, 55, 256)          0         ['add_34[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_117 (Conv2D)         (None, 28, 28, 128)          32896     ['activation_107[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_119 (B  (None, 28, 28, 128)          512       ['conv2d_117[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_108 (Activation  (None, 28, 28, 128)          0         ['batch_normalization_119[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_118 (Conv2D)         (None, 28, 28, 128)          147584    ['activation_108[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_120 (B  (None, 28, 28, 128)          512       ['conv2d_118[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_109 (Activation  (None, 28, 28, 128)          0         ['batch_normalization_120[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_119 (Conv2D)         (None, 28, 28, 512)          66048     ['activation_109[0][0]']      \n",
      "                                                                                                  \n",
      " conv2d_120 (Conv2D)         (None, 28, 28, 512)          131584    ['activation_107[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_121 (B  (None, 28, 28, 512)          2048      ['conv2d_119[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_122 (B  (None, 28, 28, 512)          2048      ['conv2d_120[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_35 (Add)                (None, 28, 28, 512)          0         ['batch_normalization_121[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'batch_normalization_122[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " activation_110 (Activation  (None, 28, 28, 512)          0         ['add_35[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_121 (Conv2D)         (None, 28, 28, 128)          65664     ['activation_110[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_123 (B  (None, 28, 28, 128)          512       ['conv2d_121[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_111 (Activation  (None, 28, 28, 128)          0         ['batch_normalization_123[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_122 (Conv2D)         (None, 28, 28, 128)          147584    ['activation_111[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_124 (B  (None, 28, 28, 128)          512       ['conv2d_122[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_112 (Activation  (None, 28, 28, 128)          0         ['batch_normalization_124[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_123 (Conv2D)         (None, 28, 28, 512)          66048     ['activation_112[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_125 (B  (None, 28, 28, 512)          2048      ['conv2d_123[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_36 (Add)                (None, 28, 28, 512)          0         ['batch_normalization_125[0][0\n",
      "                                                                    ]',                           \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                     'activation_110[0][0]']      \n",
      "                                                                                                  \n",
      " activation_113 (Activation  (None, 28, 28, 512)          0         ['add_36[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_124 (Conv2D)         (None, 28, 28, 128)          65664     ['activation_113[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_126 (B  (None, 28, 28, 128)          512       ['conv2d_124[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_114 (Activation  (None, 28, 28, 128)          0         ['batch_normalization_126[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_125 (Conv2D)         (None, 28, 28, 128)          147584    ['activation_114[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_127 (B  (None, 28, 28, 128)          512       ['conv2d_125[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_115 (Activation  (None, 28, 28, 128)          0         ['batch_normalization_127[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_126 (Conv2D)         (None, 28, 28, 512)          66048     ['activation_115[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_128 (B  (None, 28, 28, 512)          2048      ['conv2d_126[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_37 (Add)                (None, 28, 28, 512)          0         ['batch_normalization_128[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_113[0][0]']      \n",
      "                                                                                                  \n",
      " activation_116 (Activation  (None, 28, 28, 512)          0         ['add_37[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_127 (Conv2D)         (None, 28, 28, 128)          65664     ['activation_116[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_129 (B  (None, 28, 28, 128)          512       ['conv2d_127[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_117 (Activation  (None, 28, 28, 128)          0         ['batch_normalization_129[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_128 (Conv2D)         (None, 28, 28, 128)          147584    ['activation_117[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_130 (B  (None, 28, 28, 128)          512       ['conv2d_128[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_118 (Activation  (None, 28, 28, 128)          0         ['batch_normalization_130[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_129 (Conv2D)         (None, 28, 28, 512)          66048     ['activation_118[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_131 (B  (None, 28, 28, 512)          2048      ['conv2d_129[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_38 (Add)                (None, 28, 28, 512)          0         ['batch_normalization_131[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_116[0][0]']      \n",
      "                                                                                                  \n",
      " activation_119 (Activation  (None, 28, 28, 512)          0         ['add_38[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_130 (Conv2D)         (None, 14, 14, 256)          131328    ['activation_119[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_132 (B  (None, 14, 14, 256)          1024      ['conv2d_130[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_120 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_132[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_131 (Conv2D)         (None, 14, 14, 256)          590080    ['activation_120[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_133 (B  (None, 14, 14, 256)          1024      ['conv2d_131[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_121 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_133[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_132 (Conv2D)         (None, 14, 14, 1024)         263168    ['activation_121[0][0]']      \n",
      "                                                                                                  \n",
      " conv2d_133 (Conv2D)         (None, 14, 14, 1024)         525312    ['activation_119[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_134 (B  (None, 14, 14, 1024)         4096      ['conv2d_132[0][0]']          \n",
      " atchNormalization)                                                                               \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " batch_normalization_135 (B  (None, 14, 14, 1024)         4096      ['conv2d_133[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_39 (Add)                (None, 14, 14, 1024)         0         ['batch_normalization_134[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'batch_normalization_135[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " activation_122 (Activation  (None, 14, 14, 1024)         0         ['add_39[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_134 (Conv2D)         (None, 14, 14, 256)          262400    ['activation_122[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_136 (B  (None, 14, 14, 256)          1024      ['conv2d_134[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_123 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_136[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_135 (Conv2D)         (None, 14, 14, 256)          590080    ['activation_123[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_137 (B  (None, 14, 14, 256)          1024      ['conv2d_135[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_124 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_137[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_136 (Conv2D)         (None, 14, 14, 1024)         263168    ['activation_124[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_138 (B  (None, 14, 14, 1024)         4096      ['conv2d_136[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_40 (Add)                (None, 14, 14, 1024)         0         ['batch_normalization_138[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_122[0][0]']      \n",
      "                                                                                                  \n",
      " activation_125 (Activation  (None, 14, 14, 1024)         0         ['add_40[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_137 (Conv2D)         (None, 14, 14, 256)          262400    ['activation_125[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_139 (B  (None, 14, 14, 256)          1024      ['conv2d_137[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_126 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_139[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_138 (Conv2D)         (None, 14, 14, 256)          590080    ['activation_126[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_140 (B  (None, 14, 14, 256)          1024      ['conv2d_138[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_127 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_140[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_139 (Conv2D)         (None, 14, 14, 1024)         263168    ['activation_127[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_141 (B  (None, 14, 14, 1024)         4096      ['conv2d_139[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_41 (Add)                (None, 14, 14, 1024)         0         ['batch_normalization_141[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_125[0][0]']      \n",
      "                                                                                                  \n",
      " activation_128 (Activation  (None, 14, 14, 1024)         0         ['add_41[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_140 (Conv2D)         (None, 14, 14, 256)          262400    ['activation_128[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_142 (B  (None, 14, 14, 256)          1024      ['conv2d_140[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_129 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_142[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_141 (Conv2D)         (None, 14, 14, 256)          590080    ['activation_129[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_143 (B  (None, 14, 14, 256)          1024      ['conv2d_141[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_130 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_143[0][0\n",
      " )                                                                  ]']                           \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv2d_142 (Conv2D)         (None, 14, 14, 1024)         263168    ['activation_130[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_144 (B  (None, 14, 14, 1024)         4096      ['conv2d_142[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_42 (Add)                (None, 14, 14, 1024)         0         ['batch_normalization_144[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_128[0][0]']      \n",
      "                                                                                                  \n",
      " activation_131 (Activation  (None, 14, 14, 1024)         0         ['add_42[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_143 (Conv2D)         (None, 14, 14, 256)          262400    ['activation_131[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_145 (B  (None, 14, 14, 256)          1024      ['conv2d_143[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_132 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_145[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_144 (Conv2D)         (None, 14, 14, 256)          590080    ['activation_132[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_146 (B  (None, 14, 14, 256)          1024      ['conv2d_144[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_133 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_146[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_145 (Conv2D)         (None, 14, 14, 1024)         263168    ['activation_133[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_147 (B  (None, 14, 14, 1024)         4096      ['conv2d_145[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_43 (Add)                (None, 14, 14, 1024)         0         ['batch_normalization_147[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_131[0][0]']      \n",
      "                                                                                                  \n",
      " activation_134 (Activation  (None, 14, 14, 1024)         0         ['add_43[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_146 (Conv2D)         (None, 14, 14, 256)          262400    ['activation_134[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_148 (B  (None, 14, 14, 256)          1024      ['conv2d_146[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_135 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_148[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_147 (Conv2D)         (None, 14, 14, 256)          590080    ['activation_135[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_149 (B  (None, 14, 14, 256)          1024      ['conv2d_147[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_136 (Activation  (None, 14, 14, 256)          0         ['batch_normalization_149[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_148 (Conv2D)         (None, 14, 14, 1024)         263168    ['activation_136[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_150 (B  (None, 14, 14, 1024)         4096      ['conv2d_148[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_44 (Add)                (None, 14, 14, 1024)         0         ['batch_normalization_150[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_134[0][0]']      \n",
      "                                                                                                  \n",
      " activation_137 (Activation  (None, 14, 14, 1024)         0         ['add_44[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_149 (Conv2D)         (None, 7, 7, 512)            524800    ['activation_137[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_151 (B  (None, 7, 7, 512)            2048      ['conv2d_149[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_138 (Activation  (None, 7, 7, 512)            0         ['batch_normalization_151[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_150 (Conv2D)         (None, 7, 7, 512)            2359808   ['activation_138[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_152 (B  (None, 7, 7, 512)            2048      ['conv2d_150[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_139 (Activation  (None, 7, 7, 512)            0         ['batch_normalization_152[0][0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_151 (Conv2D)         (None, 7, 7, 2048)           1050624   ['activation_139[0][0]']      \n",
      "                                                                                                  \n",
      " conv2d_152 (Conv2D)         (None, 7, 7, 2048)           2099200   ['activation_137[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_153 (B  (None, 7, 7, 2048)           8192      ['conv2d_151[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_154 (B  (None, 7, 7, 2048)           8192      ['conv2d_152[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_45 (Add)                (None, 7, 7, 2048)           0         ['batch_normalization_153[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'batch_normalization_154[0][0\n",
      "                                                                    ]']                           \n",
      "                                                                                                  \n",
      " activation_140 (Activation  (None, 7, 7, 2048)           0         ['add_45[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_153 (Conv2D)         (None, 7, 7, 512)            1049088   ['activation_140[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_155 (B  (None, 7, 7, 512)            2048      ['conv2d_153[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_141 (Activation  (None, 7, 7, 512)            0         ['batch_normalization_155[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_154 (Conv2D)         (None, 7, 7, 512)            2359808   ['activation_141[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_156 (B  (None, 7, 7, 512)            2048      ['conv2d_154[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_142 (Activation  (None, 7, 7, 512)            0         ['batch_normalization_156[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_155 (Conv2D)         (None, 7, 7, 2048)           1050624   ['activation_142[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_157 (B  (None, 7, 7, 2048)           8192      ['conv2d_155[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_46 (Add)                (None, 7, 7, 2048)           0         ['batch_normalization_157[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_140[0][0]']      \n",
      "                                                                                                  \n",
      " activation_143 (Activation  (None, 7, 7, 2048)           0         ['add_46[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv2d_156 (Conv2D)         (None, 7, 7, 512)            1049088   ['activation_143[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_158 (B  (None, 7, 7, 512)            2048      ['conv2d_156[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_144 (Activation  (None, 7, 7, 512)            0         ['batch_normalization_158[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_157 (Conv2D)         (None, 7, 7, 512)            2359808   ['activation_144[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_159 (B  (None, 7, 7, 512)            2048      ['conv2d_157[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " activation_145 (Activation  (None, 7, 7, 512)            0         ['batch_normalization_159[0][0\n",
      " )                                                                  ]']                           \n",
      "                                                                                                  \n",
      " conv2d_158 (Conv2D)         (None, 7, 7, 2048)           1050624   ['activation_145[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_160 (B  (None, 7, 7, 2048)           8192      ['conv2d_158[0][0]']          \n",
      " atchNormalization)                                                                               \n",
      "                                                                                                  \n",
      " add_47 (Add)                (None, 7, 7, 2048)           0         ['batch_normalization_160[0][0\n",
      "                                                                    ]',                           \n",
      "                                                                     'activation_143[0][0]']      \n",
      "                                                                                                  \n",
      " activation_146 (Activation  (None, 7, 7, 2048)           0         ['add_47[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23587712 (89.98 MB)\n",
      "Trainable params: 23534592 (89.78 MB)\n",
      "Non-trainable params: 53120 (207.50 KB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "resnet = ResNet50(input_shape=(224, 224, 3), include_top=False, weights=\"imagenet\")\n",
    "resnet.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89b15ef3",
   "metadata": {},
   "source": [
    "## transfer learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfa438e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " model_2 (Functional)        (None, 7, 7, 2048)        23587712  \n",
      "                                                                 \n",
      " batch_normalization_161 (B  (None, 7, 7, 2048)        8192      \n",
      " atchNormalization)                                              \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 3, 3, 2048)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_8 (Dropout)         (None, 3, 3, 2048)        0         \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 18432)             0         \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 1024)              18875392  \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 512)               524800    \n",
      "                                                                 \n",
      " dropout_10 (Dropout)        (None, 512)               0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " dropout_11 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 4)                 1028      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 43128452 (164.52 MB)\n",
      "Trainable params: 19536644 (74.53 MB)\n",
      "Non-trainable params: 23591808 (90.00 MB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "for layer in resnet.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "model = Sequential()\n",
    "model.add(resnet)\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1024, activation=\"relu\"))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(512, activation=\"relu\"))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(256, activation=\"relu\"))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(NUM_CLASSES, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cd9d46",
   "metadata": {},
   "source": [
    "## compiling the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "12efbcb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(learning_rate=0.001)\n",
    "model.compile(optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3fbd4ed",
   "metadata": {},
   "source": [
    "## training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "88bde723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "20/20 [==============================] - 81s 4s/step - loss: 4.2308 - accuracy: 0.4062 - val_loss: 4.7557 - val_accuracy: 0.4306\n",
      "Epoch 2/50\n",
      "20/20 [==============================] - 70s 3s/step - loss: 2.7414 - accuracy: 0.5204 - val_loss: 1.4242 - val_accuracy: 0.4722\n",
      "Epoch 3/50\n",
      "20/20 [==============================] - 69s 3s/step - loss: 1.8275 - accuracy: 0.5220 - val_loss: 0.9504 - val_accuracy: 0.5000\n",
      "Epoch 4/50\n",
      "20/20 [==============================] - 65s 3s/step - loss: 1.2418 - accuracy: 0.5563 - val_loss: 0.9965 - val_accuracy: 0.5000\n",
      "Epoch 5/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 1.1048 - accuracy: 0.5726 - val_loss: 0.9568 - val_accuracy: 0.5417\n",
      "Epoch 6/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 1.0970 - accuracy: 0.5693 - val_loss: 0.9054 - val_accuracy: 0.5417\n",
      "Epoch 7/50\n",
      "20/20 [==============================] - 67s 3s/step - loss: 0.9170 - accuracy: 0.6313 - val_loss: 0.7863 - val_accuracy: 0.6806\n",
      "Epoch 8/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.8955 - accuracy: 0.6362 - val_loss: 0.8010 - val_accuracy: 0.5833\n",
      "Epoch 9/50\n",
      "20/20 [==============================] - 65s 3s/step - loss: 0.8062 - accuracy: 0.6705 - val_loss: 0.7443 - val_accuracy: 0.6111\n",
      "Epoch 10/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.7171 - accuracy: 0.7145 - val_loss: 0.8692 - val_accuracy: 0.5833\n",
      "Epoch 11/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.6802 - accuracy: 0.7178 - val_loss: 0.7567 - val_accuracy: 0.6528\n",
      "Epoch 12/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.6249 - accuracy: 0.7406 - val_loss: 0.6694 - val_accuracy: 0.7222\n",
      "Epoch 13/50\n",
      "20/20 [==============================] - 69s 3s/step - loss: 0.6323 - accuracy: 0.7210 - val_loss: 0.6204 - val_accuracy: 0.7639\n",
      "Epoch 14/50\n",
      "20/20 [==============================] - 63s 3s/step - loss: 0.6008 - accuracy: 0.7635 - val_loss: 0.5976 - val_accuracy: 0.7639\n",
      "Epoch 15/50\n",
      "20/20 [==============================] - 64s 3s/step - loss: 0.5960 - accuracy: 0.7553 - val_loss: 0.6870 - val_accuracy: 0.6667\n",
      "Epoch 16/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.5371 - accuracy: 0.7749 - val_loss: 0.7310 - val_accuracy: 0.6806\n",
      "Epoch 17/50\n",
      "20/20 [==============================] - 64s 3s/step - loss: 0.4871 - accuracy: 0.8026 - val_loss: 0.7026 - val_accuracy: 0.6944\n",
      "Epoch 18/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.4340 - accuracy: 0.8418 - val_loss: 0.6323 - val_accuracy: 0.8056\n",
      "Epoch 19/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.4424 - accuracy: 0.8124 - val_loss: 0.6168 - val_accuracy: 0.7778\n",
      "Epoch 20/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.4647 - accuracy: 0.8157 - val_loss: 0.5765 - val_accuracy: 0.8056\n",
      "Epoch 21/50\n",
      "20/20 [==============================] - 67s 3s/step - loss: 0.3931 - accuracy: 0.8515 - val_loss: 0.5871 - val_accuracy: 0.7778\n",
      "Epoch 22/50\n",
      "20/20 [==============================] - 67s 3s/step - loss: 0.4418 - accuracy: 0.8059 - val_loss: 0.7245 - val_accuracy: 0.7917\n",
      "Epoch 23/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.4101 - accuracy: 0.8434 - val_loss: 0.7361 - val_accuracy: 0.7222\n",
      "Epoch 24/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.4070 - accuracy: 0.8548 - val_loss: 0.6251 - val_accuracy: 0.7639\n",
      "Epoch 25/50\n",
      "20/20 [==============================] - 70s 3s/step - loss: 0.3339 - accuracy: 0.8744 - val_loss: 0.4738 - val_accuracy: 0.8472\n",
      "Epoch 26/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.4203 - accuracy: 0.8303 - val_loss: 0.4982 - val_accuracy: 0.8472\n",
      "Epoch 27/50\n",
      "20/20 [==============================] - 67s 3s/step - loss: 0.4635 - accuracy: 0.8303 - val_loss: 0.6230 - val_accuracy: 0.7917\n",
      "Epoch 28/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.3703 - accuracy: 0.8581 - val_loss: 0.7033 - val_accuracy: 0.7222\n",
      "Epoch 29/50\n",
      "20/20 [==============================] - 67s 3s/step - loss: 0.3914 - accuracy: 0.8548 - val_loss: 0.5244 - val_accuracy: 0.8194\n",
      "Epoch 30/50\n",
      "20/20 [==============================] - 71s 3s/step - loss: 0.3277 - accuracy: 0.8662 - val_loss: 0.4388 - val_accuracy: 0.8889\n",
      "Epoch 31/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.3716 - accuracy: 0.8613 - val_loss: 0.5178 - val_accuracy: 0.8056\n",
      "Epoch 32/50\n",
      "20/20 [==============================] - 69s 3s/step - loss: 0.3852 - accuracy: 0.8711 - val_loss: 0.5727 - val_accuracy: 0.7778\n",
      "Epoch 33/50\n",
      "20/20 [==============================] - 69s 3s/step - loss: 0.3788 - accuracy: 0.8532 - val_loss: 0.4497 - val_accuracy: 0.8611\n",
      "Epoch 34/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.3730 - accuracy: 0.8760 - val_loss: 0.5590 - val_accuracy: 0.7917\n",
      "Epoch 35/50\n",
      "20/20 [==============================] - 67s 3s/step - loss: 0.3257 - accuracy: 0.8760 - val_loss: 0.4274 - val_accuracy: 0.8889\n",
      "Epoch 36/50\n",
      "20/20 [==============================] - 69s 3s/step - loss: 0.2722 - accuracy: 0.9054 - val_loss: 0.5919 - val_accuracy: 0.8333\n",
      "Epoch 37/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.2788 - accuracy: 0.8842 - val_loss: 0.7771 - val_accuracy: 0.7778\n",
      "Epoch 38/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.3615 - accuracy: 0.8401 - val_loss: 0.4620 - val_accuracy: 0.8750\n",
      "Epoch 39/50\n",
      "20/20 [==============================] - 69s 3s/step - loss: 0.3446 - accuracy: 0.8597 - val_loss: 0.3781 - val_accuracy: 0.9444\n",
      "Epoch 40/50\n",
      "20/20 [==============================] - 65s 3s/step - loss: 0.2706 - accuracy: 0.8972 - val_loss: 0.5765 - val_accuracy: 0.8611\n",
      "Epoch 41/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.2993 - accuracy: 0.8989 - val_loss: 0.4306 - val_accuracy: 0.8611\n",
      "Epoch 42/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.2582 - accuracy: 0.9184 - val_loss: 0.4938 - val_accuracy: 0.8333\n",
      "Epoch 43/50\n",
      "20/20 [==============================] - 68s 3s/step - loss: 0.2441 - accuracy: 0.9054 - val_loss: 0.5636 - val_accuracy: 0.8472\n",
      "Epoch 44/50\n",
      "20/20 [==============================] - 67s 3s/step - loss: 0.2033 - accuracy: 0.9315 - val_loss: 0.4282 - val_accuracy: 0.8611\n",
      "Epoch 45/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.2481 - accuracy: 0.9168 - val_loss: 0.4856 - val_accuracy: 0.8611\n",
      "Epoch 46/50\n",
      "20/20 [==============================] - 65s 3s/step - loss: 0.2405 - accuracy: 0.9005 - val_loss: 0.3910 - val_accuracy: 0.8472\n",
      "Epoch 47/50\n",
      "20/20 [==============================] - 66s 3s/step - loss: 0.2500 - accuracy: 0.9168 - val_loss: 0.3874 - val_accuracy: 0.8472\n",
      "Epoch 48/50\n",
      "20/20 [==============================] - 67s 3s/step - loss: 0.4017 - accuracy: 0.8907 - val_loss: 0.3449 - val_accuracy: 0.8750\n",
      "Epoch 49/50\n",
      "20/20 [==============================] - 69s 4s/step - loss: 0.2619 - accuracy: 0.9152 - val_loss: 0.4169 - val_accuracy: 0.8750\n"
     ]
    }
   ],
   "source": [
    "mc1 = ModelCheckpoint(\n",
    "    filepath=\"ct_resnet_best_model.hdf5\",\n",
    "    monitor=\"val_accuracy\",\n",
    "    save_best_only=\"True\",\n",
    "    mode = 'max',)\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy',\n",
    "                               patience=10,\n",
    "                               restore_best_weights = True)\n",
    "#Defining LR Reducing rate\n",
    "reduce_lr = ReduceLROnPlateau(monitor = 'val_accuracy',\n",
    "                             factor = 0.1,\n",
    "                             patience = 5,\n",
    "                             verbose = 1,\n",
    "                             min_delta = 0.8)\n",
    "\n",
    "history = model.fit(train_data,\n",
    "                    validation_data=val_data,\n",
    "                    epochs = 50,\n",
    "                    batch_size=32,\n",
    "                    callbacks=[mc1, early_stopping]\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "acf10223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hCiGEsMPagydYc/AE3gaddcdPU+cMiObWCUkA3P/5NgrKa+x/kZJjUJ4Lei+IS7XtmiaJyx9tyADghnGJpA2OYWh8CJGBPhLsnOTetP6E+HmzN6eMa+avpbLWxKheYVwzKqH9i7sQt87NzZ49mzfffJP33nuPPXv2cOedd1JRUcHMmTMBmDFjBnPmzLGev379ehYtWsShQ4f49ddfueCCCzCbzfz1r391168ghBDiJJqm8eLSfQBcNyax1e3KD144kAExQRSU1/LXz7e32Q6hRZbZnZih4O1n2zUNAU95RjrpmcV46XXd7oO7s4UFGLm3YUkyu6Qag17HP64Y2u0CQ7cGPNOnT+f555/n0UcfZfjw4aSnp7NkyRJrInNGRgbZ2Y0VMaurq3n44YcZPHgwV1xxBfHx8axatYrQ0FA3/QZCCOEkJcfgt5/cPQrHFB2Fg8utP/76WwEbjxRh9NIza/KpszsWvt4GXr5+OEYvPcv35vHf9Rn2va49+TsWDQGPLmcnoDF1SKxLCgB6mhvP6EW/6EAAbp+UzMDYYDePyH46ze6QunsrLS0lJCSEkpISgoO73x+YEMJDLbgQMtbArd9D0kR3j8Y+b58Pmeth5g9oieO5/N9r2JZZzG0Tk3l0WvvVd99edZgnv9uNj5eexX+aZHuNG8vrXvE6pF5n2zW1lWjz4tFpZsZU/5uXfjeViX0jbbv2NJdxopKV+/O4dkyCzdvbnamjn9/dK91cCCE8UV0VHGtoq5O53r1jsZfZBFnp6njfDyzbk8e2zGL8vA3ceU4fm24xc0ISZ/aLpKbezJ8+TqemvvVSI1b1tZC9TR3H2zHDY/SnzD8RgLNCchnfO8L2a09ziRH+3Dw+yS3BjjNIwCOEEO6WvQ3MDeX7HagC7FbFGWBSCcfaoZ+tO3humZBk81KRpUhemL83u7NLrbu72pS7E+qr1TbzCNsCK4vtdao55tU9i7tdHopwnAQ8QgjhbpbkW+h+AU/Bb9ZDXc4O8rIzCfTx4v9a6MTdluhgX565KgWAt3491H6D0ab5O3a0adh5vIR1FXEAjPBpuzGm8CwS8AghhLtZqgWDqgJc78AWbXc58VuzHyfod3LbxCTCHKjPcv6QWEYmhmLWYPH27LZPtqf+ThMfb8hgr6aWtHxPtFzVX3gmCXiEEMLdmgY8mgnyu9EHcYFafjLpVYAzxbiL28+0b3anqUtSVJ+q77ZntX2itaWE7fk7FTX1fJ2exR6zCnjI36dygcRpQQIeIYRwp9JsKD0GOj3EDVePdadlrYYlraX6SQCkGXcR4ut4Ef+LU+LQ6WBLRjHHiipbPqmyEApV53LiR9p872+3ZVFeU48xoheaTzCY66wBm/B8bmstIYQQAjjeMFMRNQgSz4Ds9C4V8Py8N4/jxVWtPn9V9l78gLcqJnGOz68E1uarmZPogQ69XkywL2OTwll/uJDF27P5v7NbSEi25O9E9AV/2xtXWiorXz8uEd2BIZCxVr3XsUMdGqvoXiTgEUJ4jILyGnZnlXJW/yh3D8V2lqWZnqObtD3Y6b7xNPHttiz++PHWVp8PpoKbfFVDyT1aL/LDRpFQtA4O/exwwAMwLbUH6w8X8u32rJYDHut7Znv+zs7jJWw/VoLRoOeqkT2hzBLw7ASmOzzW08re72Htvxp3FLYlehBMe9n1Y7KDBDxCCI9QUlXHVf9Zw9ETlbw1YzRpg2PcPSTbNAt4GmYausAMz4nyGuZ+o8YxqlcYkYGnJiEnVe+B41BkiOCaCYOIDbsQlq1TVZfPuNPh175waCxzv9nFzuOlHC6oIDkyoPkJOdvV9x62L2dZZnemDo0lItCnS73X3UJ1KXxzN1SesO38LljTWAIeIUS3p2ka93+2jaMnVM7Hl1uPd4+Ax1QPWQ0zKD3HQGgvlctTkQ9luRDk2O9wIK8cTdPoF2NjxeIWzP1mF4UVtQyMDeLjO87A6NVCymf6fjgOYYlDeOzSIZBjhmVz4chqlQzs5Vgn7YhAHyb0ieDX3wr4blsWfzy3X/MTLHk3Uf1tul95TT1fb1Vb0G8Y25CwbA14usZsWpe3+mUV7ET0hbTH2j/fL8zlQ7KXBDxCiG7vjV8O8ePuXAx6HSazxrK9uVTU1BPg08X/F5e/B+oqwBgEkf1Bb4DwPmqrd+5OuwOeytp6nv/fft5ZcxiDTsf7t49lQh/72yYs2ZnDd9uzMeh1PHd1asvBDjQGHpENgUf0EAiIUgHbsQ2QNMnu17aYltJDBTzbs5sHPPW1UHi4+eu245v0LCpqTfSODOCM3g05P9GD1PfyXCjPh8ButAza2cpyYO1r6jjtMRg0za3DcZTs0hJCdGvrD53g2f+pztyPXzqE5MgAquvM/LQn180js4F1a/VIFexAkzwe+5ZaVh8oYOpLv7Bg9WE0DerNGnf+dwuHCyrsuk9xZS0Pf6VmPf7vrN4M6xnS+smWooMRDQGJXg+9J6vjJs1EHTF1SCzeBh37csvYn1vW+ETREbV13xgIQXE23etjS7Ly2ER0liKFPoEQlqyO82RZq00r5kF9FSSMg4GXuHs0DpOARwjRbeWVVnP3x1sxmTWuGBHPjeMSuSRFfQh+u62dwnVdQZP8neLKWjYfLcQUbV/AU1JVx4NfbOfGt9aTWVhFfKgfb9w8iuEJoZRU1XH7uxspqayzeUhPfLubgvIa+kYH8qeTl5JOZgl4Ipuc18cS8Pxs82u2JMTfm7P6qVmX77Y1qcljmVWK6GtTheUdx0rYcbwhWXlUz+ZPOhhcnlby98OWD9Rx2uN2VbXuaiTgEUJ0S/UmM3/8eCv5ZTX0jwnkH1cMRafTMS1VFa77ZX8+JVW2f9C7RcOW9D2GAaS9+AtX/Wct965UO2DKMra220Rz6e5czv/nSj7ZmAnAjPG9+N+9Z3H+kFjemDGKHiG+HCqo4K6PNlNnMrc7nOV7c1m09Th6HTx7dQq+3m00iTTVQ+Ehddx0aan3Oep71lZVL6cDLH+W323PRrMkwVoqO9u4nPXRhqMAXDgslvCTqz/HDlPfcySPp1XLHlczagMugl7j3T2aDpGARwjRLT3/437WHy4kwGjgPzeNwt+o8nX6xwTRPyaQWpOZH3fluHmUbaguQctXS3Ez/meioLwGg17H5ir1Ie9TdIAznlzCPZ9sZcnOHKpqG4OfE+U13P3RFu54fxO5pTUkRwbw6f+N54nLhhLYkLcUHeTLW7eMwd9oYPWBEzz+7a7GoKEFJVV1zFm0A4DbJyUzMrGdpNPio6pwn5cfBMc3Ph7cQ9UUQoPDKx14YxqlDY7Bx0vPoYIKdmWVqgdbmlVqRXlDZWVQy1mn6GJlALqcjPWw9zuVSH/uXHePpsO6eEafEEKcaunuXOavVJV2n706lT5Rgc2en5bSgxeW7ue77dlcMzrBHUNsV+nBDQSjkWGOIl8L4epRPXns0iHsyCym+qO/4WuuILomg6/T4ev0LPy8DUweGMXQ+BDe/OUQRZV16HVwx1m9uTetf4uzMYN7BPPydSP4/Qeb+O+6DPpGBXLrxOQWx/PU4j3kltaQFOHP7PMGtP8LWBOW+6rcnab6TFYJ2Qd/hiFX2PnONAr08WLKwGh+aEiiHhof0uR12w94vk4/TmWtiT5RAYxLbqFAoSXgyd8LJccb86haYjDaVeSww2oroaa0/fN8Q8Hb1/mvr2mw9FF1POKmDtVV6iok4BFCdCtHT1Qw+9N0AGZOTOLilFMTVy9JVQHPqgMFFFbUnrqU4WabjxayadEX/B+wg348e3UK1zYEZuP7RkLPFMhYy/zzfPiwMpkfduZwvLiK73fk8P0ONWs1MDaIZ69OIaVnaJuvdd7gGB68YCDzftjLE9/tJikygHMGRMPm9+C7P8PNX/JL/RAWbspEp1MBpJ+xjQ9+i5MTlpvqPRnW/VsFPJrWobyPS1J6NAQ8WTwwtT+6AtuWtDRN46P1LSQrNxWapJKfa8vhn4PbH8zkh+Hs++38DRyw6yv46i61g689vqFwzbuNuVPOsu97yFynZvDOmePce7uJLGkJIbqN6joTd/53C2XV9YxMDGXOhYNaPC85MoCh8cGYzBpLdrpuWcts1jhcUEF1Xdu5NhaapvHWr4eY/vo6+tSqBqGjJ51vDXasGmrEJJuO8PAlg1n1wGS+uXsid57ThzN6h3P/1AF8c/ekdoMdi9+f1ZtrRvXErMEfP9rKb7llsPML0MzUpS+0LmXdMj6JsS3NhLTk5C3pTSVNBL03lGQ05vk4aMrAaPyNBo4VVbHzt4NQXQzoILztBqU/78tjV1YpRq+Gysot0eth5AzQe4HO0MZXw0flry9AaTtNTTtq87vw2a0q2NHp2x9XdTF8dC3s/tp5YzDVw0+Pq+Mz7lTLlB5AZniEEN3GY9/sYnd2KeEBRl67cWTr9WFQMwM7j5fy7bYsbhjXQv6GE7y4dD//+vkA3gYdg+OCGZEYxvCEUEYkhpIY7t9sVqGkqo77PtvG0t25gMY4v0NggphBLdSqsSy1NCTT6nQ6UnqG2hzgnEyn0/GPK4ZxtLCSDYcLuf3djaxgJ3qgcP8ajhdfQkK4H/dPtWEpy6KtXBpjgOoLduRXtT09ooX2EDbyMxpIGxTDN9uy2Lx5A8MAQhPB26/Va44VVTL7020A3DSuF2FtzfBdME99tUXT4J0LVSuKn5+Cy/5l/y/SHk2DVf9UScIAo26Fi19se5mtvgYW3aGCnc9uhUv+qa7rqPQPoWAf+IXDpD93/H5dhMzwCCG6hc82ZfLJRrXs8vJ1w4kLaf0DD+DiYWqpa93hE+SVVjt9PMeKKnnjFzV7UWfS2HashHfXHOHPC9M5+7kVjPr7T8x8ZwOvLPuNr9OPc8mrv7J0dy5Gg55/nh9GkKlEzYJYdgo15YK2B0YvPfNvGkViuD9VRdnoq1SLgKiqowRRyTNXpthXqPFEO8nDlt1ah1Y4PGYLy26trIPb235NoKbexF0fbqG4so7UhFAeuNCOIK41Op3akg0qGMjb2/F7NqVp8OPDjcHOpNlwyUttBzsAXj5w9Tsw8hbQzPDtPfDrix1r61BbqeruAJx1P/i2UYepm5GARwjRpWmaxqcbM3mooRjevWn9ObNf+1VxE8L9GZEYiqbB9zucX5PnxR/3U2syc0bvcH65fzIvXzecmROTGJ4QitGgp7Cilp/35fPi0v3c80k6mYVV9Azz4/M7x3NFZMOySFxKywmn1irAOVBR4LQxhwcYefuW0Yz0OWZ9TK/T+PPgMib0taMic8WJxp5KEX1bPqfPFPX98C9qiaQDzuofSZCvF1E1Kienrfydv3+3h+3HSgj19+a1G0bg42VDPpItEhuK7mnmxsDEGUz18PUs1ZQT4Py/Q9pc2/Oe9AbVpHPSbPXzssdh6SOOBz3r/wNl2WoWbcztjt2ji5IlLSGEQ6rrTBgNevR61xUiq6yt55GvdvHFFvUBfdGwWO6e3MoHbAsuSenB1oxivtue3eruJEfszirly3TVm2nOhYNIjPAnMcKfy4ar7dk19SZ2Z5WSnlnM1oxidmeXktIzhLmXDCHE3xt2WCosj275BSxVgIsOq1me3mc7bez9YoL46wgTpDc+dlN8nn03sczuhCSo5auWxKWqfkpVRXB8swoYHOTjZWDqkFh6b28IXFuZ4fk6/TgfrDuKTgf/nD6cnmH+Dr9mi9Ieg30/qITeo2s7Xpemrho+vw32LVY5OZe+CiNutP8+Op0KkvzD1UzRmlehskgFQgY7PuYrTsCql9TxlEfUDJIHkRkeIYTd0jOLSXn8R574brfLXuNAXhmXv7aaL7YcQ6+Dv14wgH9dP9KuAOviYXHodLDpaBFZxVVOG9vTS/aiaXBxShypCaGnPO/jZWBEYhgzJybzyvUj+Gn22bx47XAV7ECTCstjWn+RWNc1t+xjPgJAoU7V2vHJ3WrfDaw7tNoIPvUGSG4I1A51rOoywCUpcfTRqZkxU9ipr7s/t4wHv1DJ13+c3JfJA6I7/JqniOwHI29Wxx2ZRQHVffzDq1WwY/CB6R84Fuw0NeGPcNlrKpk5/b/w6QwVVNnq1+fVVvjYYTD06o6NpQuSgEcIYbf5Kw5SW29m4cbMZgXxnOXr9ONc+q/V7M8tJzrIh4/uOIO7zulr92xSbIgvY5LUrqPF252zrLX6QAG/7M/HS6/j/vMdyA+pr4GchlyUnqNaP88FeTxWDfcMn3Sb+vnYRvs+vNvaodWUZVmrg321ACYmBZGgVzNRmyqaL2mW19Tzh/9upqrOxKS+kdyTZlsVZoecMwe8/dV7tvc7x+5Rng/vXaKSuo1BcNMXMPBi54xvxE1w7QcqiNq3WAVV1TbU8yk6AhveVMdpj59aW8kDyJKWEMIuWcVV/LhbbfWuqjOxcn8eFwy1rYlje6rrTDzx3W5r/ZQJfSJ4+boRRAXZOLVeckwtOZgbg7D7QwpZbMhGW78SjA1bmfUG1fE5KNau8ZnNGk//oBJWbxyXSFJkK8s5bcnZAaZa8I9obF7ZEldVAa6vhYYKz6Te0LD8cUJ94IXbuOxna7VjS22YY5uguqRDCbDeJUcAjVLNny/31zIuRT2uaRoPfLGdQ/kVxAb78vJ1wzG4cJmVoFgYPwt+eU5t3e5/oX3LRsUZ8MEVcOIA+EeqYKfHcOeOcdAl6r4fX6+CqvcuUX/Wbdn/g6qc3fsc6Huuc8fTRUjAI4Swy0frMzA3mQz4fkeOUwKeoycquOvDLezKKkWnU8sS96T1t+/D6+u7T1k+GQOM8QYqgCVNnjiwDG74xK4xfrcjmx3HSwgwGvhje401W3Nso/oeP7rtxFRLwJO3VyW22vOh2paC/eqDzSdYbRePHaZybI5tsj3gaW+HlkVoIoT3gcKDcPhX9UHckXEDh7Q4ftiVyxOXmzF66XlvzREWb8/GS6/jtRtHEhHYCXknE/4Emxao92Hr+zD6Ntuuy9urgp2yLJX/dPNXqlK1KySfCbd+C/+9CrK3qS9bpDkxIbuLkYBHCGGzmnoTn2xUsy+/P6s3b/xyiGV7cqmuM7XdaLIdS3Zmc/9n2ymrqSc8wMg/pw/n7P7t78RqRtPUBzdA/wvUskODdYdOkF9ew8DYYPqFe6up/kMrVH6DjWX5a+vNPP8/NTPyf2f3IdLRD1Zb8negeRXgwoMQ5YTt1dC4RBYzRAVcPceo9+34Jki5pv3r62uh8LA6tqWBZ58pavyHfnZKwHPMkEBJVR2rDxQQ4u/NP77fA8DfLhrEqF7t9P9yFt9gOOuvsOQBWPE0pExvPXnb4thm+PAqlcQdOQBu/hJC4tu+pqN6jIDbl8KaV2xb1ko+y/mzTV2IBDxCCJv9sCOHgvJaYoJ9uO/8AXy3LYuskmp+2Z/P+UPsWx6y+HLrMe5dqP71ObpXGK/eMKLdGjstKslUCZd6b5XD4NVYbO7oxgwe+GIHA+uCWHLdmfDCQLXlO3NdY72Ydny4/igZhZVEBfnwuzM7sOPLMsPTVv4OqByK6MFwbINaBnNawNOwRGaZQbLsFLOMqz1FR1T3bGMgBNkws9dnMmx8U7WZ6IiCAwD4xvaHw/DBuqPsyS6lzqRx8bA4Zk5M6tj97TX6NtU+o/gorP132y0nDv4Mn9yoqifHj4IbP++8vlwRfdRuLSFJy0J4gmV7ckl57H98s821Ze/fX3sEgBvH9cLopefChuJ+PzjYvkHTNF77WTUBvWFcIh///gzHgh2wViUmamCzYAdg6pBYvA069uaUcSC/vDG3xMYP4bLqOl5drj5w/5zWz9qZ3W7l+eoDEp364GuPNY/HiYnL1hmehqTong0BT84OlVDdHkvCckRf22rFJE1SW64LD0LRUfvHe9LrJg0cAcDyvXlkl1TTOzKAp68a1nKvLFfyMsK5Dc01V7/cer2k3V+r1g91FSq4nvFN5zYhFVYS8AjhAV5dfoDS6nr+/t1um/s62Wvn8RK2ZBTjbdBx3VjV++miYWpW56fdudTU2/+6m44WcSCvHD9vAw9eOBBvQwf+l9R0qeYkof5Ga7HCb7dlq+aWYPN26ddXHqKwopbeUQFM70j39eMNy1mR/W1L4O2MgCcsSSVQm2pV0NMeW3doWfiGNC7fObo9XdNUki/Qe+AI4kLUMqSft4H/3DSKIF9vx+7bUUOuhLjhUFsGK5899XlLXyxTLQy+DG74VNVYEm4hAY8Q3dz+3DLSM4sByCur4b/rOvCv6DZYZncuGBpHdJD6wBmREEZssC9lNfWs+s3+isCW3ViXpvYguKMfWicv1ZxkWqqajfp2exaapZBf9rZ2Kxnnllbz1irVQuKvUwfi1ZGgzNb8HQtnb02vKFBLedBYzdmSxwO2LWs1BB7tJiw3ZeeM2inKc9VypU6PPqI3t01Mxuil55mrUxgQG+TYPZ1Br4fzGpJ8Ny1obJSqaarFw7f3qMrMo25VLSA8rJBfdyMBjxDd3KcbMwHVNgDgPysOUlHTsVL+JyuurOXrdLVcdsv4XtbH9XodFwxVszzf77BvWauoopbFDS0fnNLcs40ZHoC0QTEYvfQcyq9gT5l/YzDRTq+nl37aT3WdmVG9wpg6JKZjY7Q1f8ciZrD6XnoMKgs79trQGBSGJTefabDm8Wxq/x7WGR47Ah7rjNqKZiUDbGZ5zbAk8PLhjrN6s/vxqVya2gW6ePc+RyVmm+tg+d9VsLP0Efv7YgmXk4BHiG6stt7Moq2qxcG8K4eRFOHPiYpa3l1zxKmv89mmY9TUmxkUF3zKTpiLGvJ4lu7OobbebPM9F209Tm29mcFxwaT07GCDwtpKlSMCLTfjBIJ8vZk8QC1rfbc9q0lzy9ZnHQ7klbGwIaCcc+HAjuWJmE1wfIs6tnWGxzdEbe0GyHNCVWtLUGip4mzR08bEZU1rksNjR8ATP0ptg68uhux026+zsFZ2bnzNDs20OVva44AOdn6h8nXWvKoet7cvlnCpLvQ3Rghhr+V7cymsqCU6yIdzB0ZzT5r6QHjjl0OUVtc55TXMZo0PGpbJZozvdcqH/qheYUQF+VBaXc/qg7Yta2maxkfr1T1vGJfY8YTT/D1q6SAgCgJbbylg6br97fYsNGsV4J9brTL87JJ9mDU4b3AMo5M6mGhasF/lenj7Q9Qg269z5rLWyfk7FvEjAZ1KqC7Pb/36igJVQBCd2v1jK4OX2vIMji1r2Vro0F3iUiDlWnX8248qSfuyf6tWD6LLkIBHiG7s002qqeZVo3riZdBzaWo8faMDKamqY8Gqw055jZX788korCTY14vLhp+6hGDQ67iwYVnrBxu7km84XMjB/Ar8jYYW72m3dpazLKYMjMbP20BmYRU7DENU+f3S440fqE1sOlLIj7tz0evggQucsCXcslzUY6R9RQSdWXG5tTwn35DGJOTjbSxrWWZ3QhPB287ddJYZNYcCHjsTpd1h8kPgHeC8vljC6aQOjxDdVE5JNSv2qd5C14zqCajg4960/sz6aAtv/3qYWyckEepvbOs27bIkK18zOqHV7dgXDo3j/bVH+XF3Lv8wmdvdbfXxhsZkZafssGlt5uIk/kYv0gbH8O22LG58bxtv6wYwlu28MH8+nxma9zIqa5ghu3Z0An2jnZAYa2/+joWzZnhM9arSL7QcGPYcAwX7VGA24MKW72FrheWWWGbUMtdDTbl9u5U68rqdJawX3LUWDN4Q3AVyi8QpZIZHiG7qiy3HMGswJimM3lGNHx4XDo1lYGwQZTX1vPHLoQ69xtETFazYr5Y4bj6jV6vnjU0OJzLQSHFlHWsPnmjznkUVtXzfULfHKcnKYPMMD8B1Y9S28rLqepbVqvNTa7eQU1rd7Kui1kSwrxf3nuekWQVLFWhb83csrAHPbscSfi1OHABTjSoYGJp06vOWQKytPB7r0pID70l4bzUzZK6Do2tsv66uCoozHX/dzhTWS4KdLkxmeITohjRN47NN6kPg2pPqwuj1Omaf15/ff7CZd9cc4fZJyQ73F/rvuqNoGpzdP6rNRpkGvY6pQ2L5cH0GP+zM5qw22kJ8seUYtfVmhsYHk9Iz1KFxNaNpjfVj2pnhAZjYN5JVD0ymuLIO34JQ+PJjJvvuZ/EdY9EMzWfDeoT6WXe/dUhNeWPSsWVHlK3Ck8HLD+qrVEsHR3svWZazoge33AnbEogd36ICq5Z2FTmyQ8tCp1O7tba8p7qn9z/ftutOHAQ08AtT9YKEcJDM8AjRDW04XMiRE5UEGA3WXVJNnTc4hpSeIVTWmpi/8qBDr1FVa7LmCM0Y3/rsjoVlHP/blUu9qeXdWpqm8VHDctb1Y500u1OapXb/6Aw2t1/oGebP0PgQ+g4bD/6RGOoqGGLez9D4kGZfTgl2ALK2qqTq4J4QbGejVb2hsWZOR/J42psFixqkEqpryxoDm5O1sFvKLpZlLXsKEDbdFSa7nUQHSMAjRDdkCUSmpfYgwOfUiVqdTmddinl/7VHySqvtfo1vt2VRUlVHQrgf5wxofeeTxbjkcMIDjBRW1LL+cMs1Y9YfLuRQfgUBRgOXDXdS40TLB3lkf/sLu+n1Nm1P7zBH83csnFFxuZ3CjBi8VEI1tFyPp666oS0Gji8tJZ8F6CB/rwpUbdGRZTQhmpCAR4hupqy6ju8bdkNd00abg3P6RzGqVxg19WZe+/mAXa+haRrvNSQr3zSuFwZ9+/+y9jLorYX5FreyW8taWXl4PIEtBGoOae+DvD0drQJsC0fzdywstYWcMcPTSp0ioO08nsJDapbKJ6TNrf9t8g9XHbzB9vfbmrDs4FKeEA0k4BGim/luezZVdSb6RAUwMjG01fN0Oh1/aZjl+XhDJseLq2x+jS0ZxezKKsXHS39KjlBbLhzasKy1MweTuXltm8KKWpZYkpWdtZwFjUHAycX0bGWpApy1BaqKnDOmpjStMYCwN3/HoqNb0ysL1fZ7aFwea4k1j2fzqc81DTw6srRk77JWd9iSLroFCXiE6GY+bUhWnj4mod2CfRP6RnJG73BqTWb+tdz2WZ4PGmZ3Lk3tQZgdeSzj+0QQ6u/NiYpaNpy0rPXF5mPUmswMiw9hWEcrKzdl45b0VoXEQ+QANXtx+Bfnjcui5JjqBaUzQFyqY/eIbmgxUZzRUPjPTpaE6dDEtpuWWgKyvN0q0bopZwUefZq2mWinMremQYGld5cEPKJjJOARohv5LbeMrRnFeOl1XDGip03X/OV8lcj72aZMMk5Utnt+flmNdUlqxvgku8bnbdBz/mC1rPV9k2UtTdOstXecthUdVF6JJcfD0SUtcO2yVt4e9T2yPxj9HbuHfzgExze/nz1sDQqD41RitWZWidZNWQKPiA4uLfUcqwr0VeS3P2NVmgV1FaD3Un20hOgACXiE6EYssztTBkYTFWRbgu6YpHDO7BdJvVnjleWnVhQGFZAcLqjgy63HeOCL7dSZNIYnhDo0E3Nhw26tJbsal7XWHjrBoQKVrDzNmQ0fC/aBZlJbloPs3P3UlLXNxHLnjKspy8xIVAdnKCzBimULvj3s2LZvzeM5ueKys2Z4vIyQNEkdt7esZW0amqwK+gnRAVKHR4huorbezKItKg/DnrwaULM8v/5WwKItx7jznD5EBviQfqyYrRlFpGcWsy2zmKLK5r23Zk5McmicE/tEEuzrRX5ZDZuPFjE2OZyPN6hA7bIRTkxWBsixJCwP7VheSa+JoPdWu5AKD6kiec5ywkm7jGKGwG//c2ynlh2FGYkfDbu/br5TS9Ocu1uqz2T1uxz8GSbe0/p5JyzLWV24wrLoNiTgEaKbWL43jxMVtUQF+XDOgNYL+7VkeEIoaYOi+WlPHpf/azVlNfWnnGP00jMsPoThCaGMSw7nvIalKXsZvfScNziWL7Yc4/sd2fSJCmDJTrW85dRkZeh4/o6FTyAkjIWjq9WHsDMDno7WrrFwdGu62dS4DGbTDE9D4vKxjSrQ0elUDlJtGej0qhBiR1lm1I6uUZWUW+vL1ZFCh0KcRAIeIboJS2Xlq0aqRqH2uve8/izfm2cNdpIi/BmeEMqIxDBGJIYyMDYYo5dzVrkvGqYCnh92ZhMT7EudSSOlpyrm51Qd3ZLeVJ/JDQHPchhze8fvZ+GsTt+WYCVvt0r2balacksKD6sqzV5+tgUrcakqwbo8VyVchyY0WVpKsr/WUUsi+0NQDyjLgoy1jQHQyWSHlnAiCXiE6AZyS6v52dIodLRtyconG9IjhM/vnEBJZR2pCaHOqyLcgkn9Igny8SK3tIZ/NeQNOX12R9OcG/D0ngLL/w6Hf1WNNu3paN6aqiKoUH9uHQ54IvqqTty15WrpzdaZFmtLiUEtt4s4mdFfbfHP3qbyeJoGPM4KPHQ6FWCmf6hm1FoNeCyJ0jLDIzpOkpaF6AaaNgrt06RRqL1GJoYxeWC0S4MdAB8vA2kNS2IVtSYCfbycm6wMUJ4HlSfUMktbtWVs1WM4+IZCTcmpO5QcZfnADooDnw52XDd4QfRAdWxPPR5H6hRZtqdb8nictUOrKWuieCuJyzXlUKoqisuSlnAGCXiE6OJUo1D1P/5WKyvXVcHub9Q27Y4qy4W9i9UMSgdcODTWenzZ8JZbYHRIbsPOo4i+reeA2ENvgN5nq2Nn7dY64aTlLAtr53Q78ngcyXOy5vFYAh4XLC0lN7zXuTtU8HoyS8Kyf4Tali9EB7k94HnttddISkrC19eXcePGsWHDhjbPf+mllxgwYAB+fn4kJCRw7733Ul3thP/JC9FFbTxSxOGGLd0Xt9AoFID1r8OnN8O3bex4sYXZDB9dA5/cABvf6tCtzuofRYifNzqdk2vvWNiz88hWlqrLzuqr1bTxpTNYgpYDy2wPSB1Z9uvZMMOTnQ6mOucHbgCBUY1tLg6tPPV56w4tyd8RzuHWgGfhwoXMnj2buXPnsmXLFlJTU5k6dSp5eS1E+8BHH33Egw8+yNy5c9mzZw9vv/02Cxcu5G9/+1snj1yIzmOpvXNJShuzJDnb1fftCyF7u+MvtvNzlbsBsOJpqC51+Fa+3gY+/N04/nv7OIb0cHKyMrgm4LEUIDy2sUO/u5WzG18OuRy8fOHYBti/pP3zq0tUdWaw730K76OW9+qr1XtRrP4OOj34aKv+kezQEk7m1oDnxRdf5I477mDmzJkMHjyY+fPn4+/vz4IFC1o8f82aNUycOJEbbriBpKQkzj//fK6//vp2Z4WE6K6OF1fxzTbVVfraMW0kKxcebjjQ4Ke5jr1YfQ0sf1Id672gsgDWvOrYvRoMjQ9hYt/IDt2jVc7akt5UWJLakm6uhyOrOn4/Z+3QsgjuAeP+oI5/ekwlV7fFsh09uKcqzmgrvR7iGwoQbl8IaOp6/wh7R9y2pjNqJ89YOWs7vxAN3Bbw1NbWsnnzZtLS0hoHo9eTlpbG2rVrW7xmwoQJbN682RrgHDp0iO+//56LLrqo1depqamhtLS02ZcQ3cWLP+6ntt7MuORwRia28YFVdKTx+OByx1okbHxbzQYExcFl/1aPrf0XlOXYfy9Xq6+F/H3q2JkzPOC8ZS1TvSpiCM6dpZh0rwo+8vfCto/bPtdaYdmB98iSx7PzS/U9ol/Hiju2JHG8mrEqy27887Rw9uyYOO25LeApKCjAZDIRE9O8uFlMTAw5OS3/D/aGG27giSeeYNKkSXh7e9OnTx/OOeecNpe05s2bR0hIiPUrIcG+CrVCuMue7FIWbVXJynMuGtR6o9DqEqhqaNQ5/Cb1/ae57TdmPPkevzynjs+ZAynXqg+8ukpY+YyDv4ELFewHcx34hECIk/+bbm/3kK2Kj6oxevmpGRZn8QuFM+9Txz8/BbVt9EfryLKfJY+npqFZqSsCD29f6DVBHTdd1jKbpcqycDq3Jy3bY8WKFTz11FP8+9//ZsuWLSxatIjFixfz5JNPtnrNnDlzKCkpsX5lZmZ24oiFcNwzS/aiaXDxsDiGJ4S2fqJlOSsgCs57HIxBKg9n1yLbX2zVSypoiuwPw29U/5I/7wn13Ob3Gv+13VU0/SB39qxD8pmq8N6J3xpzVxxhzUHpa3uRQFuN+Z0K9MqyYP381s/rSMBjWdKycFXg0dKMWukxVSxR7w2hvVzzuuK047aAJzIyEoPBQG5ubrPHc3NziY2NbfGaRx55hJtvvpnf/e53DBs2jCuuuIKnnnqKefPmYW7lX7M+Pj4EBwc3+xKiq1tzsIAV+/Lx0uu4f+qAtk+2LGeFJUFAZGNvomVPqKWf9pRmwbr/qOO0xxoL7vWaAP0vVM05lz3uwG/hQs4sOHgy35DGD/uOLGs5e4dWU96+MOVhdbzqJagsPPUcs1lVZYbG3VD28A9XycsWrgp4LIniR1apPDJo8t71cU4BSCFwY8BjNBoZNWoUy5Ytsz5mNptZtmwZ48ePb/GayspK9Cf9S8lgUJVDtQ7WDBGiqzCbNZ7+YS+gtnMnRQa0fUFRwwxPWEPl3fF3QWCsWlLZ1PIGgGZWzFP/mk44AwaclA+XNlcV9tvzLWR2oc0Brgx4wDnd012dgzLsWogZppacfn3h1OeLj6iqzAaf5oGLPSzLWuC63yN6CAREq+VTy98xa8KyEwsditOeW5e0Zs+ezZtvvsl7773Hnj17uPPOO6moqGDmzJkAzJgxgzlz5ljPnzZtGv/5z3/45JNPOHz4MEuXLuWRRx5h2rRp1sBHiO5u8Y5sth8rIcBo4E/n2vCvasuSlqXVgDEAznlQHf/ybNvbq/P2wtb/quPznjh1eSh6EAy/QR0vnWtfMUKzGVY+B0vmdLiI4SksSzWOzFzYwjLrcGilfblQTTl7h9bJ9Ho47zF1vOENKDra/HnLexQ90PFZEkvist5LzSC6gl4Pvc9Rx5YZNUlYFi7g1oBn+vTpPP/88zz66KMMHz6c9PR0lixZYk1kzsjIIDs723r+ww8/zF/+8hcefvhhBg8ezO23387UqVN5/fXX3fUrCOFUtfVmnvuf2q3y+7P6EBloQ6PGpktaFiNuVksplSdgzSutX7vsCdDMMPASSBzX8jnn/E3tpMlYY1vtF1BLaYvugJ//Duv+DVlbbLvOFuX5qrElOoga6Lz7NhU/CoyBKq8pf49j93BFsb6T9TkXks8CUy38/I/mzzlj237y2SrY6TkGDN6O36c9lgDTkiguTUOFC7g9afnuu+/m6NGj1NTUsH79esaNa/yf7ooVK3j33XetP3t5eTF37lwOHDhAVVUVGRkZvPbaa4SGhnb+wIVwgY/WHyWjsJLIQB9+d6aNzSFPXtIC9S/6tIZ6PGtfa3lrecY62LdYJeie20btnpB4+2q/1FaqSs07P2987Njmdn8Nm+U1fJCHJ4OP433F2mTwhviR6tjSXsEeFSdUsAmuXZZpmly+/dPmRSety34dCHii+sMfVsO1Hzh+D1tYEpeztqp8JNmhJVzA7QGPEEIpq67jleXqf/R/TutnW++p+looaWiweHL37IGXQM+xKjdixbzmz2ka/PiIOh55s/pga4uttV+qiuCDy+HAUrUdu8+56vHjDgQNrXFFheWWWBtobrT/WsvsTnBPtcToSj1GwNCrUEUnH2t83FnvU/RA1QbClYLjIGoQoMHe71RdHpAcHuFUEvAI0UW88cshCitq6R0ZwPQxNtaWKclUS1JefhDYvKZVs3/9b/kA8vc3Prd3sWpP4OUHZz/Y/uvYUvulLAfeuQgy16udTjO+VgnU4FjQ0JocJ8xc2MKSsHvcgdkpV+fvnGzKw2oL98FlcGiF6jRuye1ydWDoLJZE8Q1vqO8B0ervnRBOIgGPEF1AXmk1b/2qPqDunzoAb4ON/2lal7OSWq5H02u82nnVdGu5qb7xePws9a9rW7RV+6XwELx9vtoGHRgLM39QOUGW7d2Fh1reOu0IZyzV2MIyw5O3x/6+Wp2dgxLeG0bfpo6Xzm2Y3dHUn0WAi1p7OJslj8dSHVryd4STScAjRBfwz59+o6rOxIjEUC4Y2nIdqhadvEOrJec2bC3f+x1krIetH6gPZL/wxpo9tmit9kvODnh7qtoGH5YMty1pnFXwC2usQ+NILszJTPVqWQ1cP3MRFAMhiYCmckvs0dkzPABn/7Wh6GR6YwJzrIuDQmfqNQEMxsafJX9HOJkEPEK42YG8cmtH9DkXttFCoiUt7dA6WfRAVT0Z4MeHVBd0UB+QvnYW4jy59svRtfDOxVCRp2ZcbvvfqcGXZWuzM/J4ThxQO5KMgZ1Tgbeng3k8nbFD62QBkTDxT+r48Er1vbssZ4HKdUposlNQAh7hZBLwCOFmz/1vLyazRtqgaMYmh9t3sTXgaWdH1+S/qXydYxuhPEcFC5YlEHvo9aoaM6hciw+uUMFP4ni4dbGaFTlZz4ZlLWfk8ViWs6IHO79dQ0scyeOpr22ceevsZZnxs5rncrl62c/ZLMtaIEtawukk4BHCjTYfLeR/u3LR6+CBCxyoKWPLkhZAcA84487Gn899FLxsqPHTkr5Nar/UV0G/qXDTotYTTK0zPJsdL+JnYQl4OmupxjL2YxttL55YdETlTBkDVef5ztS06CR0rxkeaNyeDjLDI5xOmpQI4SaapjHve5WPcs2oBPrFBNl7A9uWtCwm/Rl+W6rq6gy50r7Xakqngwuehk9uhN5nw0XPt12ULnqIml2qLlFLUu1tgW9LZ21Jt4hNUbufKvKhOAPCbFhGs/aB6uv8xqa2GDEDdn8D5nqIbKcPW1cTNxz6pgG6hvwpIZxHAh4h3OSzzcfYdLQIX289957nQBBQkQ91FYAOQm34cPANgTtX2f86LYkZAvek23auwQt6DIeMtSqPxykBTyfN8Hj7qvYVWVvULI89AY+7ZigMXjDjK/e8dkfp9XDTF+4ehfBQsqQlhBvszirlka/U8swfp/QjNsTX/ptYlrNCejq+PNVZHE3+baqyEEqPq+PoQR0fk63szeOxVgmWHBQhuhIJeIToZKXVddz14WZq6s2cMyCKO892sJN10xo8XZ21anEHdmpZZndCE9VsVWdpmsdjC3fP8AghWiQBjxCdSNM07v9sG0dOVBIf6sc/rx2OXu9gnoc9+TvuZgkacne1XKXZFtblLBd1SG+NpXhi9naor2n7XE1rksMjAY8QXYkEPEJ0ord+Pcz/duXibdDx2o0jCQswtn9Ra2zdodUVhMSrHUuaSRXGc4S1wnIn7zwK762KNJpqGttatKaiQCVno4MIB2fuhBAuIQGPEJ1kw+FCnl6idmU9eslghieEduyG3WlJCzqex9PZO7QsdLomeTztLMlZZndCE8Hbz7XjEkLYRXZpCWFRVw27v4ba8rZPM2voEsbh1cP2pZW8smru/mgLJrPGZcN7cNMZTqgSbGvRwa4ifjTs+daxPB6zSfW0AvcU0+s5Bn77UQVr4/6v9fPcUWFZCGETCXiEsFj9EqyY1+5p3kARQSya8jM3TeiNj5ehzfPrTWbu+TidvLIa+kUHMu/KYfa1j2hJbQWU56rj7rCkBU2Sfx0IeHJ3qSKH3v7u+X0teTztjd3aQ0t2aAnR1UjAIwSoZNP0D9Vxr0ngH9biaceKqojO/pkwXRnv/LCKd9Zmct/5A7g0tUeryccvLt3P2kMn8Dca+M9No/A3OuE/O8vsjm+IatDZHfQYDjqD6rZeclzl9dhqx2fqe99zQd92gOkSloCn6DBUnICAiJbPa1p0UAjRpUjAIwRAxjpVSdcYBDd+Bkb/Fk974K11PKLtY6Auk9EB+XxVFM2fF6bzxi+HePDCgZzVP6rZ+T/tzuXfKw4C8MxVKfSNDnTOeLvbchaotgcxg1V39eObbA94zCbY8bk6TpnuuvG1xS9UzdoU7Fdj7z+15fNkhkeILkuSloUA2L5QfR98aavBTl5pNWsOnuCgpvojPXuOH/dPHUCQjxe7s0uZsWADN721np3HSwDILKxk9qfpANw6IYlpqT2cN97utEOrKUfq8Rz5Vc0K+YZCv/NdMiybtDf2+hooPqqOJeARosuRgEeI+hrY9aU6Trm21dO+2ZaFpkFlkAoyjEUHmTW5Lyv/OpnbJibjbdCx6kABl7y6ij99vJU7P9xMaXU9IxJD+dtFTq4M3N12aFk4ksez/VP1fcgV7q0o3d4us8JDoJnBJxgCoztvXEIIm0jAI8RvP0J1MQT1gKQzWz3t6/QsAGL7pKgHGpYvwgOMPDptMMv/cg6XD1ezON9sy2Ln8VLCA4y8dsNIjF5O/k+tOy5pQWPQkLUVTPXtn19bqXbOgfuWsyysW9O3tNz1vWmFZXc0DRVCtEkCHiG2faK+D7u61YTYg/nl7DhegkGvY1hKwwefZQtyg4Rwf166bgTf/XESZ/WPItTfm1euG0GPUBfUY+muS1oR/cAnRO24ytvV/vn7vldlAkITIfEM14+vLZau7zUlp/zZA00CHlnOEqIrkqRlcXqrLFQzPACp17V6mmV256x+kYQmDFQPludCVbFKaG1iaHwI7982Fk3TOr79vCVmk0qwhu63pKXXQ/xIOPSzWtaKS237fEtuVcp098+aGLygxwjIWKPGHjWg+fMFDU1DZYeWEF2SzPCI09vur8BUq4rZtVLBV9M0vk5XXbovGx4PvsGqTQI0dsZugUuCHVAdw811oPeGYDu2dncVtubxlOfDgWXq2N3LWRZt5fHIDI8QXZoEPOL0ZkmIbeMDNT2zmKMnKvHzNnDe4Bj1oOVf8QUtLG24mmU5K6yXe2rSdJStbRp2LVK9t3qM7DqVi1sbu6bJlnQhujgJeMTpq+gIZKwFdCp/pxWW5azzh8QQ4NOwCmz5ULP8q74zddcdWhaW7d0F+6GqqPXzLLlVbSw1drpmXd8rGh8vz4XaMtDpu19elRCnCQl4xOlre0P13uSzILjlGjn1JjPfbVcBz+XDmywfuTXgOaK+d7cdWhYBEY1jP76l5XMKfoOsLaoy85ArO29s7QnuoXbzaWbISm983PL3ICzJvVvnhRCtkoBHnJ40Dba3P4Ow+uAJCsprCQ8wMqlfZOMTkQ1LWm3k8LhMd92h1VTPdor4WZKV+6ZBYFTL57hLS3k8luWsiC6y9CaEOIUEPOL0lLVFBStefjBoWqunfb1VJStfkhKHt6HJfy6WGZ4TB22rJ+NM3X1JCxqXhlrK49G0JruzWi8E6TYt5fEUSJd0Ibo6CXjEaWHe93u48a11FFbUqge2NXygDrwYfIJavKaq1sT/duUAcNnwk5a8gnuqYMlc19hOoLN09yUtaN6mQdOaP9e0r9mAizp/bO1pqcWE7NASosuTgEd4vCU7s3n9l0OsPnCCez7ZiqmuFnZ+oZ5sY3fW0j25VNSaSAj3Y2TiSR3J9Xr37NSqLIRq1aurW8/wxA4Dgw9UFaqWDE3Z0NfMraxd37NV13doLEQoMzxCdFkS8AiPVlRRy8Nf7bT+/OtvBXy76L9QWQD+kdBnSqvXWpazLkuNb7mmjuXDrTMTly3LWYExXTMYsJWXEeIaWnQ0nSmxsa+ZW1m6voPK46mthOJM9bPM8AjRZUnAIzzaE9/tpqC8lr7RgTxz1TAA9Dsbau8Mu1pVz21BUUUtK/fnA3D5iFa6nFsCnpbaDLiKJyxnWbSUx2NjXzO3azr2woOABn5h4B/h1mEJIVonAY/wWMv25PLl1uPodfDc1SlMH5PIbaMjOF+vPmDzel/e6rWLd2RTb9YY0iOYvtEt5/g0bk3vxIDHE3ZoWcSPUt+b7nayoa9Zl9A0j6fpDi13t78QQrRKAh7hkUqq6vjblzsA+N2ZvRnRkIMzJ+k3fHV1HDTHccdSEzX1phavt7SSaFZ752TuXNLqzvk7FpZZkpwdUFdlc1+zLsEy9qx0yNujjmU5S4guTQIe4ZH+sXg3uaU1JEcGMPu8xg8i712q2OD3+rPZdqyEpxbvOeXaY0WVbDxShE4H01JbWc6CxqTlyhPqw7ozFDXsCPOEJa3QRAiIAnM9ZG+H3V+329esy4jo29j1fffX6rFIaRoqRFcmAY/wOCv35/PppmPodPDs1Sn4ejcsjZQch8O/ADDqkjsAeG/tUb7ZltXseksriTOSI4gN8W39hYwBans6dN6ylictael0zXNhmnZG7+r0eujZsCRXsE99lxkeIbo0CXiERymrrmPOF9sBuGV8EmOSwhuf3Pk5oEHieCaMHs3dk9W/yB/8Yju/5ZZZT/umIeBpNVm5qc5c1qqvUZ3SwTOWtKAxj2fnIpv6mnUpljweCwl4hOjSJOARHuXpH/aSVVJNQrgff71gQPMntzWfQbj3vP5M6BNBZa2JOz/cQkVNPXuyS9mXW4bRoOeCoXHtv2Bn7tQqzgA08A5QS0Ge4OSdWr3PbrWvWZdjGTuA3stzglAhPJQEPMJjrDlQwIfrMwB45qoU/I1Ntpzn7IS8XWAwwpDLATDodbxy/Qhign04kFfOg4t28FVDsvKUgdGE+Hm3/6KduVOr6XKWp+wG6jECaPK7dIflLAvL7BSonCqDDX9fhBBuIwGP8AgVNfU8sEgtZd04LpEJfSKbn2BpFNp/qqqX0iAy0IfXbhiJl17Ht9uyeGfVEcDG5SxoUm25E5a0PGmHloVvMEQPUsft9DXrcgIiILy3OpblLCG6PAl4hEd47n/7yCysIj7UjzkXDTr1BMtOmmGnVu8dnRTOgxcOBKDWZCbI14tzBkTb9sKWD7qiI2Cqc2DkdrAWHUxy7et0NsvSUBt9zbqsnmPV96gBbZ8nhHA7hwKen3/+2dnjEMJhG48U8t7aIwA8deUwAn1Oqp5cmq3yX3R66DO5xXvcPimZC4fGAmorunVnV3uCe6icGnN945KTq3jSDq2mzvwLjPkdnPe4u0div8lzYOzv4Yy73D0SIUQ7Wq6r344LLriAnj17MnPmTG655RYSEhKcPS4h2mQ2axzML2drZjGv/XwATYNrR/fk7P4tJPNaEmKjBrU6g6DT6fjn9OFckpLHWf0jWzynlQtV/ZXsbSpxOcqFSxueuKQFENYLLn7B3aNwTFgSXPScu0chhLCBQwHP8ePH+eCDD3jvvfd4/PHHmTJlCrfffjuXX345RqPR2WMUghPlNaRnFpOeWczWjGK2ZRZTVlNvfT4m2IeHLh7c8sWW5pQ9R7f8fANfbwMXp9iwM+tkkf1VwFOwH7jY/uttoWme1UdLCCE6mUMBT2RkJPfeey/33nsvW7Zs4Z133uGuu+7irrvu4oYbbuD2228nNTXV2WMVp5n9uWW89vMBtmYUk1FYecrzft4GhvUMYURCKDeO69X6riobAx6HdcZOrbIcqK9Wy3Khia57HSGE8FAOBTxNjRw5ktjYWCIiInj66adZsGAB//73vxk/fjzz589nyJAuXiJedFmPfLWT9YcbWzb0iQpgRGIYwxNCGZEYyoCYILwM7aShmeoha6s6blo3xZmsO7VcGPBYlrNCesr2ZyGEcIDDAU9dXR1ff/01CxYsYOnSpYwePZp//etfXH/99eTn5/Pwww9zzTXXsHv3bmeOV5wmckur2XBEBTuv3zyKM5IjCPF34IM+fw/UVYAxyHVbh60zPPvV0pMrauTIcpYQ3YbJZKKuzsW7Nj2U0WhEr3fNBnKHAp4//vGPfPzxx2iaxs0338yzzz7L0KFDrc8HBATw/PPP06NHN6mYKrqcxduz0TQY1SuMqUNiHb+RZTkrfiTobdx5Za+IPoAOqouhogACXVAF2VN3aAnhQTRNIycnh+LiYncPpdvS6/UkJye7JB/YoYBn9+7dvPrqq1x55ZX4+Pi0eE5kZKRsXxcO+3a76mc1zZEk4qZcnb8D4O0HoQlq6/uJ31wT8HjqDi0hPIgl2ImOjsbf3x+dp1RE7yRms5msrCyys7NJTEx0+vvnUMCzbNmy9m/s5cXZZ5/tyO3FaS6zsJKtGcXodHDRsA4GPJYt6Sc3enS2yP4q4CnYD70mOP/+sqQlRJdmMpmswU5ERIS7h9NtRUVFkZWVRX19Pd7ezs1XdGihbN68eSxYsOCUxxcsWMAzzzzT4UGJ09viHdkAjEsOJzrY1/EbVZdA/j517MoZHoAIS9d0FyUuy5KWEF2aJWfH39/fzSPp3ixLWSaTyen3dijgef311xk4cOApjw8ZMoT58+fbfb/XXnuNpKQkfH19GTduHBs2bGj13HPOOQedTnfK18UXu6j+ieh031mWs1I7mAN2fAugqW3cgTa2inBUpAsDnpoyqCxQx7KkJUSXJstYHePK98+hgCcnJ4e4uFOXGqKiosjOzrbrXgsXLmT27NnMnTuXLVu2kJqaytSpU8nLy2vx/EWLFpGdnW392rlzJwaDgWuuucaRX0V0MYcLKth5vBSDXseFQ520nOWq7ehNNd2p5WyW5Sy/cPANcf79hRDiNOBQwJOQkMDq1atPeXz16tV278x68cUXueOOO5g5cyaDBw9m/vz5+Pv7t7hkBhAeHk5sbKz1a+nSpfj7+0vA4yG+26Zmdyb2jSQ8oINZ+sc6KX8HGmd4io9CfY1z7y3LWUKIbiIpKYmXXnrJ3cNokUNJy3fccQd//vOfqaurY8qUKYBKZP7rX//KX/7yF5vvU1tby+bNm5kzZ471Mb1eT1paGmvXrrXpHm+//TbXXXcdAQEBLT5fU1NDTU3jB1BpaanN4xOd77vtaobwko7uztK0Jju0OmGGJzAGfIKhphQKD0F0Cx3bHSU7tIQQLnTOOecwfPhwpwQqGzdubPXz2N0cCnjuv/9+Tpw4wV133UVtbS0Avr6+PPDAA82Cl/YUFBRgMpmIiYlp9nhMTAx79+5t9/oNGzawc+dO3n777VbPmTdvHo8/3g27MJ+G9ueWsS+3DG+DrmO1d0AtA1UWgN4bYoc5ZXxt0ulUxeWsLWpZy6kBzxH1XXZoCSHcQNM0TCYTXl7thwxRUS4oy+EkDi1p6XQ6nnnmGfLz81m3bh3btm2jsLCQRx991Nnja9Pbb7/NsGHDGDt2bKvnzJkzh5KSEutXZmZmJ45Q2MOynHV2/6jW+2LZ6vhm9T0uBbw7sNPLHq7qqSVLWkIIF7n11ltZuXIlL7/8snUT0LvvvotOp+OHH35g1KhR+Pj4sGrVKg4ePMhll11GTEwMgYGBjBkzhp9++qnZ/U5e0tLpdLz11ltcccUV+Pv7069fP7755ptO/i2VDvXSsvzCjoqMjMRgMJCbm9vs8dzcXGJj2/4XfkVFBZ988glPPPFEm+f5+Pi0WhxRdB2apvFtw3JWh3dnARzbqL53Rv6OhSt2apXnQ+5OdSxLWkJ0G5qmUVXn/K3VtvDzNti82+nll19m//79DB061Pp5umvXLgAefPBBnn/+eXr37k1YWBiZmZlcdNFF/OMf/8DHx4f333+fadOmsW/fPhITW29q/Pjjj/Pss8/y3HPP8eqrr3LjjTdy9OhRwsPDO/7L2sHhgGfTpk18+umnZGRkWJe1LBYtWmTTPYxGI6NGjWLZsmVcfvnlgKq0uGzZMu6+++42r/3ss8+oqanhpptucmj8omvZlVXK4YIKfLz0nDsopv0L2tOZ+TsW1oDHSTu1io7CB1dART4ExkLccOfcVwjhclV1JgY/+j+3vPbuJ6bib7Tt4z0kJASj0Yi/v791osGSUvLEE09w3nnnWc8NDw8nNTXV+vOTTz7Jl19+yTfffNPmZ/att97K9ddfD8BTTz3FK6+8woYNG7jgggvs/t06wqElrU8++YQJEyawZ88evvzyS+rq6ti1axfLly8nJMS+bbOzZ8/mzTff5L333mPPnj3ceeedVFRUMHPmTABmzJjRYl7Q22+/zeWXXy4VLT2EpZXEuYOiCfTp0MSj2iWVs10d9xzVwZHZwbKkdeKASpruiLy9sOACKDwIIYkw83vwCez4GIUQwkajRzefIS8vL+e+++5j0KBBhIaGEhgYyJ49e8jIyGjzPikpKdbjgIAAgoODWy0940oOfbI89dRT/POf/2TWrFkEBQXx8ssvk5yczP/93/+1WJ+nLdOnTyc/P59HH32UnJwchg8fzpIlS6yJzBkZGad0Tt23bx+rVq3ixx9/dGT4oovRNI3vtll2ZzlhOStnB5hqwT+icxN9w3uDTq92apXnQpCDidfHNsGHV0NVEUQNhJu/hGBpxCtEd+LnbWD3E1Pd9trOcPJuq/vuu4+lS5fy/PPP07dvX/z8/Lj66qtPWeU52cktInQ6HWaz2SljtIdDAc/BgwetlY2NRiMVFRXodDruvfdepkyZYveuqLvvvrvV6bAVK1ac8tiAAQPQOvovaNFlbM0s5nhxFQFGA5MHOKEictP8nc6seurlA6G91Dbygv2OBTwHl8MnN0FdhRr/jZ+Bf+eucwshOk6n09m8rORuRqPRplYOq1ev5tZbb+WKK64A1IzPkSNHXDw653FoSSssLIyysjIA4uPj2blTJVUWFxdTWVnpvNGJ04JldidtcAx+Rif8y8Qd+TsWHdmptetL+PBaFez0ngwzvpZgRwjhcklJSaxfv54jR45QUFDQ6uxLv379WLRoEenp6Wzbto0bbrjBLTM1jnIo4DnrrLNYunQpANdccw333HMPd9xxB9dffz3nnnuuUwcoPJvZrLF4R0PvLGcsZ0GTlhKdmL9j4ehOrU3vwGczwVwHgy+HGxZKzo4QolPcd999GAwGBg8eTFRUVKs5OS+++CJhYWFMmDCBadOmMXXqVEaOHNnJo3WcQ/Nt//rXv6iurgbgoYcewtvbmzVr1nDVVVfx8MMPO3WAwrNtPFJIbmkNQb5enNk/suM3rChoKNSng3g3BjwnbAx4NA1WvQjLGsorjJoJF78AeueswQshRHv69+9/SneDW2+99ZTzkpKSWL58ebPHZs2a1eznk5e4Wko/KS4udmicHWV3wFNfX893333H1KkqGUuv1/Pggw86fWCiCyrOVMFE8plOu6WllcQFQ2Lx8XLiclZkf/c02rSniajZDEsfgbX/Uj+feR9Mebhz846EEOI0YfeSlpeXF3/4wx+sMzziNPLpDHjvEsjd7ZTb1ZvMfL+jYXeWM4oNQmPCsjvydwAiLE1EM6GuqvXzTPXw9azGYGfqU3DuIxLsCCGEiziUwzN27FjS09OdPBTRpWka5O9Tx9nbnHLLdYcKOVFRS5i/NxP6OKmekjvzdwACIsE3FNDgxMGWz6mrgk9vhm0fgc4Al/8Hxs9q+VwhhBBO4VAOz1133cXs2bPJzMxk1KhRp+zVb1pkSHiI6hK1ewicVkn424beWRcOi8Pb4FDs3ZzZDMe3qOPObCnRlE6nlrWObVDvU+zQ5s9Xl8DHN8DRVWDwgWvehYEXuWWoQghxOnEo4LnuuusA+NOf/mR9TKfToWkaOp3Opv38opspzWo8dkLAU1tvZsmuHAAuSbGvWGWrCvaron/e/hA92Dn3dERkv4aA56TE5fJ8+O+Vqgq0TzBc/zEkTXLPGIUQ4jTjUMBz+PBhZ49DdHVlTQKeEwc6fLtVB/IpqaojKsiHcclOWs6y5O/0GAEGNxb8ammnVnEGvH+5ahUREAU3fQFxqS1eLoQQwvkc+lTo1auXs8churqmMzwnDqqk2w4EFZZigxcPi8Ogd1KirjV/x03LWRYRJzURzdurmoCWZam+WDO+gog+bhueEEKcjhz6xHr//ffbfH7GjBkODUZ0YU0DHnMdFB91+EO7sraeH3fnAjAt1UnLWdC4Jd1d+TsW1q3pB07qizUIbl4kfbGEEMINHAp47rnnnmY/19XVUVlZaW0xLwGPByo93vzngt8cDngWbTlOeU09vSL8GZEQ5oTBATXlkNewXd5dW9ItwpNB76WSvN+5CEw1akw3fCqtIoQQwk0c2hpTVFTU7Ku8vJx9+/YxadIkPv74Y2ePUXQFpWoJCl1DcUAHE5fNZo0Fq1UO2K0TktA7azkrOx00MwTHQ7ATZ40cYfCGsCR1bKqBPlOkL5YQwmMlJSXx0ksvuXsY7XLCXmClX79+PP3006fM/ggPYVnSim/om2Jr64STrPwtn0P5FQT5eHHN6AQnDY4mBQfdvJxlYWlrMeRKuH4hGAPaPl8IIYRLOXUri5eXF1lZWe2fKLofy5JW73NUcOFIN3BgwSo1uzN9TAKBPk7869dV8ncsLnwGht8ASWdKXywhhOgCHPrE+eabb5r9rGka2dnZ/Otf/2LixIlOGZjoQmorobpYHSefDb8859CS1v7cMn79rQC9Dm6ZkOS88WlaY8Dj7vwdC78wFRwKIUQX9sYbb/DYY49x7Ngx9PrGRZ/LLruMiIgIHnroIWbPns26deuoqKhg0KBBzJs3j7S0NDeO2jEOBTyXX355s591Oh1RUVFMmTKFF154wRnjEl1JWUP+jndA45JW5QmoLLQrL+WdhtydqUNiSQj3d974So9DeY7KL5LaNkKIrkDToK7SPa/t7W9zX75rrrmGP/7xj/z888+ce+65ABQWFrJkyRK+//57ysvLueiii/jHP/6Bj48P77//PtOmTWPfvn0kJia68rdwOocCHrPZ7OxxiK7MspwV3EPlooQkQEmmWtZKHGfTLQoralm0Rd3ntknJzh2fJX8ndigYnRhICSGEo+oq4Sk3laD4W5bNeYNhYWFceOGFfPTRR9aA5/PPPycyMpLJkyej1+tJTW38h+STTz7Jl19+yTfffMPdd9/tkuG7itOSloUHsyQsW+rHRPRV3+1Y1vpo/VFq6s2k9AxhdC8nbUW36Gr5O0II0Y3ceOONfPHFF9TU1ADw4Ycfct1116HX6ykvL+e+++5j0KBBhIaGEhgYyJ49e8jIyHDzqO3n0AzPVVddxdixY3nggQeaPf7ss8+yceNGPvvsM6cMTnQR1oAnXn2P7A+HfrZ5p1ZtvZn31x4F4LaJyehsnGq1WVfL3xFCCG9/NdPirte2w7Rp09A0jcWLFzNmzBh+/fVX/vnPfwJw3333sXTpUp5//nn69u2Ln58fV199NbW1ta4YuUs5FPD88ssvPPbYY6c8fuGFF0oOjyeyBjwN9W0svaJs3Km1eEcWeWU1xAT7cNEwJ9fIMdWpGjzQdbakCyGETtdtylH4+vpy5ZVX8uGHH3LgwAEGDBjAyJEqX3P16tXceuutXHHFFQCUl5dz5MgRN47WcQ4FPOXl5RiNxlMe9/b2prS0tMODEl3MyUtakSf1imqDpmm83bAVfcb4JIxeTl5Fzd0J9dXgGwrh0p9KCCEcceONN3LJJZewa9cubrrpJuvj/fr1Y9GiRUybNg2dTscjjzzSbfN4Hfr0GTZsGAsXLjzl8U8++YTBgwd3eFCii7EmLTdZ0gIoOqJmWNqw8UgRO4+X4uOl5/qxLsjot+bvjAK9pKQJIYQjpkyZQnh4OPv27eOGG26wPv7iiy8SFhbGhAkTmDZtGlOnTrXO/nQ3Ds3wPPLII1x55ZUcPHiQKVOmALBs2TI+/vhjyd/xRJZt6ZYZnqA4MAZCbTkUHoao/q1e+vaqQwBcObIn4QGnzgp2mOTvCCFEh+n1+hYLByclJbF8+fJmj82aNavZz91licuhfxJPmzaNr776igMHDnDXXXfxl7/8hWPHjvHTTz+dUqNHdHP1tVCep46DGgIena5xp1YbicsZJyqtXdFvm5jkmvEdtwQ8kr8jhBCidQ7X9r/44ou5+OKLnTkW0RWV5wAaGIzgH9H4eGR/lSxcsB9o+e/Be2uPoGlwVv8o+sUEOX9slYVw4oA6tvSuEkIIIVrg0AzPxo0bWb9+/SmPr1+/nk2bNnV4UKILsSQsB8U1z5FpZ6dWWXUdCzdmAq6c3dmivof3kU7kQggh2uRQwDNr1iwyMzNPefz48eOnrO2Jbu7kGjwW7QQ8n206RnlNPX2jAzm7f5RrxibLWUIIIWzkUMCze/fuFrO0R4wYwe7duzs8KNGFnFyDxyKiydZ0TWv2lMms8c4atRV95sQk5xcatLC0lJCEZSFEF6Gd9P9DYR9Xvn8OBTw+Pj7k5uae8nh2djZeXg6nBYmu6OQaPBYRfQCd6qJeUdDsqZ/25JJZWEWovzdXjujpmnE17ZAu+TtCCDfz9vYGoLLSTQ1DPYSlgrPBYHD6vR2KTs4//3zmzJnD119/TUhICADFxcX87W9/47zzznPqAIWbnVyDx8LbD0ITofio2qkV2LhsZSk0eMPYRPyMzv9LC8CJgyrY8vKFmKGueQ0hhLCRwWAgNDSUvDy1q9Xf3991s9seymw2k5+fj7+/v0smTxy64/PPP89ZZ51Fr169GDFiBADp6enExMTwwQcfOHWAws1OrsHTVGQ/FfAU7IdeE8grreabbVlsOFyIl17HjPFJrhuXJX8nLhW8XFDfRwgh7BQbGwtgDXqE/fR6PYmJiS4JFh0KeOLj49m+fTsffvgh27Ztw8/Pj5kzZ3L99ddbp/WEh7Du0mop4OkPB35ix7bNPLGxP5uOFlnTeS4d3oPYEF/XjUvyd4QQXYxOpyMuLo7o6Gjq6tquQi9aZjQa0buoar7Dc0YBAQFMmjSJxMRE65rbDz/8AMCll17qnNEJ9zKbWpzhOVJQwQ87c2CHjjuBvMM72Fh3AQDDE0K5aFisa2d3QPJ3hBBdlsFgcEkOiugYhwKeQ4cOccUVV7Bjxw50Oh2apjWbfjKZTE4boHCjinww14NOD4ExLNmZzcvLDrAnWzWIPUMfxp1GGGLMYe4Fg5k6JJYeoX6uH1ddlWoaCjLDI4QQwiYOzRvdc889JCcnk5eXh7+/Pzt37mTlypWMHj2aFStWOHmIwm0sy1mBsWh6Aw9/tZM92aUY9Dom9Y3k6qmqj1qsOZeZ43p0TrADkL1NBWKBMRDiol1gQgghPIpDMzxr165l+fLlREZGotfrMRgMTJo0iXnz5vGnP/2JrVu3Onucwh2a1OA5mF9BQXktPl561jw4hYhAH7U1fE0w1JRC4SGIHtQ542qavyO7IIQQQtjAoRkek8lEUJDqjRQZGWntsNqrVy/27dvnvNEJ92pSg2fD4UIARiSGqmAHVLAR2aQAYWeR/B0hhBB2cmiGZ+jQoWzbto3k5GTGjRvHs88+i9Fo5I033qB3797OHqNwl7LGthIbDp8AYGxyRPNzIvvD8c2ttphwCUvAI/k7QgghbORQwPPwww9TUVEBwBNPPMEll1zCmWeeSUREBAsXLnTqAIUbNczwaEFxrE9XMzxnJJ/UpDOir/reWQFPaTaUHlOJ1D1GdM5rCiGE6PYcCnimTp1qPe7bty979+6lsLCQsLAwqSzpSRoCnkJDJNkl1XjpdYxIDGt+TmR/9f1EJwU8loKD0YPBJ7BzXlMIIUS357TazeHh4e2fJLqXhrYSO8pUYJHSM+TUVhFNu6ZrmuuTiCV/RwghhANcU85QdH+appaPgHUFKkn5lPwdgPDeanmpphTKT20o63SSvyOEEMIBEvCIllUVQX0VAMuPqb8m407O3wHw8oGwJHXs6jweUz1kNZQ86Dnata8lhBDCo0jAI1rWkL9j9otgf2E9Oh2MSgpr+dyITtqanr8H6irAJxgiB7j2tYQQQngUCXhEyxoCnjJjNACD44IJ9m2lMWzTPB5Xsixn9RgBLmouJ4QQwjPJp4ZoWUMNnmxNLWONayl/x8IS8Lh6p5Y1f0eWs4QQQthHAh7RsoYZngPVqqL22JbydywsW9NtXdKqrYD5k+CtNKivtX1MxyVhWQghhGMk4BEta9iSvrdCBTxjWsvfgcaApzhTdTJvz7p/Q84O1RNr8zu2jae6BPIb2pbEywyPEEII+0jAI1rWMMOTQzj9ogMb+2e1xD8CfEMBDU4cbPu+FQWw6uXGn1c+A9Wl7Y/n+BZ1/9BeEBjV/vlCCCFEExLwiJY11ODJ1sLbXs6ChiaiNi5r/fIc1JZBbIra3VV5Ata80v54JH9HCCFEB7g94HnttddISkrC19eXcePGsWHDhjbPLy4uZtasWcTFxeHj40P//v35/vvvO2m0pxHLDI8tAQ80aTFxoPVzCg/DxrfV8XlPQNpcdbz2NSjLafv+kr8jhBCiA9wa8CxcuJDZs2czd+5ctmzZQmpqKlOnTiUvL6/F82traznvvPM4cuQIn3/+Ofv27ePNN98kPj6+k0fu4WrKoaYEUAFPmzu0LCItTUTbmOFZ/ncw10HvydBnMgy8BHqOhbpKWPF069dpmsr3AcnfEUII4RC3Bjwvvvgid9xxBzNnzmTw4MHMnz8ff39/FixY0OL5CxYsoLCwkK+++oqJEyeSlJTE2WefTWpqaieP3MOVqeWsUs2PyIgIYkN827+mvSWtrK2w83N1fN7j6rtOp2Z6ALa833odn6IjaunLYIS4FNt+ByGEEKIJtwU8tbW1bN68mbS0tMbB6PWkpaWxdu3aFq/55ptvGD9+PLNmzSImJoahQ4fy1FNPYTKZWn2dmpoaSktLm32JdjTs0MrRwhmbZGNTWGu15QNqRuZkPz2mvg+7FuKaBKi9xsOAi0AzwbLHW763JX8ndphqZSGEEELYyW0BT0FBASaTiZiYmGaPx8TEkJPTcj7HoUOH+PzzzzGZTHz//fc88sgjvPDCC/z9739v9XXmzZtHSEiI9SshIcGpv4dHsjd/ByA8GfReqvVDw/VWB5bBoRVqhmbKQ6dee+5c1YB0z7eQ2UIOl+TvCCGE6CC3Jy3bw2w2Ex0dzRtvvMGoUaOYPn06Dz30EPPnz2/1mjlz5lBSUmL9yszM7MQRd091RccAO/J3AAzeEJasjptWXDab4aeG5OQxv2tsNNpU9EAYfqM6XvroqTNEkr8jhBCig9wW8ERGRmIwGMjNzW32eG5uLrGxsS1eExcXR//+/TEYDNbHBg0aRE5ODrW1LVfs9fHxITg4uNmXaNuJ7COA6qOVEO5n+4Ut9dTa+bkqMugTDGfe1/q158wBL1/IWAv7lzQ+Xl+jrgfZki6EEMJhbgt4jEYjo0aNYtmyZdbHzGYzy5YtY/z48S1eM3HiRA4cOIDZbLY+tn//fuLi4jAajS4f8+mioiADgICoRHQ6ne0XRp7UNb2+BpY/qY4n/RkC2pgtComHM+5Uxz89BqZ6dZy9HUy1qrhhS7NDQgghhA3cuqQ1e/Zs3nzzTd577z327NnDnXfeSUVFBTNnzgRgxowZzJkzx3r+nXfeSWFhIffccw/79+9n8eLFPPXUU8yaNctdv4JH0jfs0orpmWzfhREnzfBsfAuKMyAoDsbd2f71E/8MfmGQvxe2faQea5q/Y0/wJYQQQjTh5c4Xnz59Ovn5+Tz66KPk5OQwfPhwlixZYk1kzsjIQK9vjMkSEhL43//+x7333ktKSgrx8fHcc889PPDAA+76FTxObb2Z4No80EGfPv3tu9i6Nf031fvql+fUz+fMAaN/+9f7haplrx8fgp/nwdCrJX9HCCGEU+g0raU9xJ6rtLSUkJAQSkpKJJ+nBVsOZTPy/YEAaPcfQtfWMtTJKgvh2YZZoXF/gPXzIXIA3LkGDDbG1vU18OpoKMlQu7c2vwvFR+Hmr1SxQiGEEKeljn5+d6tdWsL1du1T+Te1OiM6fxu3pFv4h6tcG1DBDqj2EbYGO6Dq7Ex5WB3/+oIKdtBB/Ej7xiKEEEI0IQGPaCbziOqFVe0b41jOTGSTZbCEM1RRQXsNuwZihkFtufo5agD4hth/HyGEEKKBBDzCymTWKMo5AoAh1MH+ZJadWqDaRjgSNOn1cN5jjT9L/o4QQogOkoBHWO3JLiW0vgAAv8hEx24SP0p9HzQNEsc5Ppg+56omowC9z3b8PkIIIQRu3qUlupZ1h04QpysEQB/cw7GbDL8JgnpA8pkdG4xOB9M/gIx1KvgRQgghOkBmeITVhsOFxDYEPDga8Bi8oP/54G1HhebW+ARBv/PUEpcQQgjRAfJJIgAwmzU2Him0zvA4HPAIIYQQXZAEPAKAA/nlFFXWEasrUg9IwCOEEMKDSMAjAFh/uBADJqKtAY+Du7SEEEKILkiSlj3cq8t+441fDtEnOpARiaGMSAxjREIoPcP8mjUG3XC4kEhKMGAGnQECotw4aiGEEMK5JODxYG+vOswLS1Xl5PTMYtIzi3ln9REAIgONDE9QAdDwhFDWN9mhRVAc6A1uGrUQQgjhfBLweKgvNh/jye92A3D35L70jQ4kPbOYrRlF7M4upaC8lp/25PHTnjzrNWO9itWB5O8IIYTwMBLweKCfdufy1y+2A3D7pGT+cn5/dDodl49QeTnVdSZ2ZZVaA6D0zGKOFVVxdlwt5CMBjxBCCI8jAY+HWX/oBLM+2oLJrHHlyHgeumhQs1wdAF9vA6N6hTGqVxigupuXVdcR+Ms6CXiEEEJ4JAl4PMiurBJ+994maurNpA2K5pmrUtDrbetlFeTrDWXZ6gcJeIQQQngY2ZbuIQ4XVHDLgg2U1dQzNimcf90wEm+DnX+8pVnquwQ8QgghPIwEPB4gp6Sam99eT0F5LYPignnr1tH4ejuwy6r0uPouNXiEEEJ4GAl4urniylpmLFjPsaIqkiL8ef+2sQT7ett/I02D0oYlraA45w5SCCGEcDMJeLqxytp6Zr67kf255UQH+fDB7eOICvJx8GaFYKpRxxLwCCGE8DAS8HRTmqZx90db2ZpRTIifNx/cPo6EcH/Hb2hZzgqIBi+jcwYphBBCdBES8HRTaw+dYPnePHy89Cy4dQwDYoM6dkNJWBZCCOHBJODpphasOgLANaN7NtTT6SBrwrIEPEIIITyPBDzd0JGCCpbtzQXg1gnJzrmp1OARQgjhwSTg6YbeXXMETYNzBkTRNzrQOTeVJS0hhBAeTAKebqakqo5PN2UCqk+W00gNHiGEEB5MAp5u5tONmVTWmugfE8ikvpHOu7Flhke2pAshhPBAEvB0I/UmM++uOQLAbROTT2kK2iGWooMywyOEEMIDScDTjfy4O5fjxVWEBxi5fIQTA5PqUqgtU8fBMsMjhBDC80i39G5kwarDANw4LtH2XlnHt8CaV8BU1/o5tRXqu28IGAM6OEohhBCi65GAp5vYllnMpqNFeBt03HxGL9svXDIHMtfZdm7UQMcGJ4QQQnRxEvB0EwtWq9mdaSk9iA72te2iwsMNwY4OLnwWDG00FdXpoc+Ujg9UCCGE6IIk4OkGckqqWbxdJRXfZs9W9B2fqe+9z4Zxv3fByIQQQojuQZKWu4H31x6h3qwxNjmcofEhtl2kabB9oTpOuc51gxNCCCG6AQl4uriqWhMfbcgA1FZ0m2VtgRMHwMsPBl3iotEJIYQQ3YMEPF3coq3HKK6sIyHcj/MGx9h+4baG2Z2BF4NPBzupCyGEEN2cBDxdmNmsWbei3zohGYPexkKDpjrY+YU6TpXlLCGEEEICni7sl9/yOZhfQaCPF9eO7mn7hQeXQ2UBBERB78muG6AQQgjRTUjA04W93TC7c+3oBIJ829hSfjJLsvLQq8EgG/GEEEIICXi6qP25Zfz6WwE6Hdw6Icn2C6tLYe9idZxyrUvGJoQQQnQ3EvB0Ue80FBo8f3AMiRH+tl+451uor4bI/tBjhItGJ4QQQnQvst7RRZjNGgUVNeSUVHO8qIpFW44Ddm5FB9j+ifqeci04s5u6EEII0Y1JwNPJjhVVsmRnDjkl1WSXVpNTor5yS6upN2vNzh0aH8zY5HDbb15yHA7/qo6HyXKWEEIIYSEBTyf708db2ZJR3OJzOh1EB/kQG+xLj1A/7jynDzp7Zml2fAZokDgBwuxoMCqEEEJ4OAl4OpGmaezLKQPg+rGJ9I0OJC7El5hgX+JCfIkK8sHb0IG0qu2fqu+SrCyEEEI0IwFPJyqsqKWi1gTA3GmD8fU2OO/mOTshbxcYjDDkcufdVwghhPAAskurE2UUVgIQG+zr3GAHGpOV+08FvzDn3lsIIYTo5iTg6USWgCcx3I5t5rYwm2DH5+pYOqMLIYQQp5CApxMdK6oCoGe4n3NvfPgXKMsG31Dod55z7y2EEEJ4AAl4OlHGCRfN8FiSlYdeCV4+zr23EEII4QEk4OlELlnSqq2EPd+o45TpzruvEEII4UEk4OlEmUUuCHj2fQ+15RDaCxLGOe++QgghhAeRgKeT1JnMZBWrHJ4EZwY82yytJKZLKwkhhBCiFV0i4HnttddISkrC19eXcePGsWHDhlbPfffdd9HpdM2+fH19O3G0jskqrsKsgY+XnqhAJ+XZlOfBweXqWJazhBBCiFa5PeBZuHAhs2fPZu7cuWzZsoXU1FSmTp1KXl5eq9cEBweTnZ1t/Tp69GgnjtgxlvydhHB/9HonzcTs/AI0E8SPgsi+zrmnEEII4YHcXmn5xRdf5I477mDmzJkAzJ8/n8WLF7NgwQIefPDBFq/R6XTExsbadP+amhpqamqsP5eWlnZ80A7ILFTLWTbl75Tlwk+PQW1Z2+cd26S+y+yOEEII0Sa3Bjy1tbVs3ryZOXPmWB/T6/WkpaWxdu3aVq8rLy+nV69emM1mRo4cyVNPPcWQIUNaPHfevHk8/vjjTh+7vawzPGE21OD5399g5+e23djgA0Ov6sDIhBBCCM/n1oCnoKAAk8lETExMs8djYmLYu3dvi9cMGDCABQsWkJKSQklJCc8//zwTJkxg165d9OzZ85Tz58yZw+zZs60/l5aWkpCQ4NxfxAaZTZa02pS1tTHYSXscfALbPj9uBAREOmGEQgghhOdy+5KWvcaPH8/48eOtP0+YMIFBgwbx+uuv8+STT55yvo+PDz4+7i/GZ3MNnp8eU9+HXQuT/uzSMQkhhBCnC7cmLUdGRmIwGMjNzW32eG5urs05Ot7e3owYMYIDBw64YohOY63BE9FGwHNgGRxaoTqeT3m4cwYmhBBCnAbcGvAYjUZGjRrFsmXLrI+ZzWaWLVvWbBanLSaTiR07dhAXF+eqYXZYSVUdxZV1ACSEtRLwmM3w01x1POYOCOvVSaMTQgghPJ/bl7Rmz57NLbfcwujRoxk7diwvvfQSFRUV1l1bM2bMID4+nnnz5gHwxBNPcMYZZ9C3b1+Ki4t57rnnOHr0KL/73e/c+Wu0yZK/ExFgJMCnlbd8x2eQswN8guHMv3Ti6IQQQgjP5/aAZ/r06eTn5/Poo4+Sk5PD8OHDWbJkiTWROSMjA72+cSKqqKiIO+64g5ycHMLCwhg1ahRr1qxh8ODB7voV2nWsqJ2E5foaWP53dTzpzxAQ0TkDE0IIIU4TOk3TNHcPojOVlpYSEhJCSUkJwcHBnfKab/xykKe+38ulqT145foRp56w9jW1FT0oDv64BYxO7qYuhBBCdHMd/fx2e6Xl00FjleUWavBUFcMvz6njc+ZIsCOEEEK4gAQ8nSCjrSrLq1+GqiKIHADDb+zkkQkhhBCnBwl4OsGx1ooOlmbBuv+o47THwOD2lCohhBDCI0nA42Ims8axolZmeH5+CuqrIOEMGHChG0YnhBBCnB4k4HGx3NJqak1mvPQ64kKa5PDk7YX0D9XxeU+Azkkd1IUQQghxCgl4XMxSgyc+zA+DvklQs+xx0Mww8BJIHOem0QkhhBCnBwl4XKzFHlpH18K+70FngHPnumlkQgghxOlDAh4XO6VLuqbB0kfU8cibIaq/m0YmhBBCnD4k4HExaw0eSw+tvd/BsY3g7a/q7gghhBDC5STgcbHMk3dorXxGfT/jLgiyrSO8EEIIITpGAh4Xa5bDk7NTNQg1GGH8LDePTAghhDh9SMDjQlW1JvLLaoCGgGf7QvVE/6ngH+7GkQkhhBCnFwl4XCizoUt6kK8XIb562PGZeiJluhtHJYQQQpx+JOBxocymy1lHfoWybPANhX7nu3dgQgghxGlGAh4Xapa/s61hOWvIFeDl48ZRCSGEEKcfCXhcyBLw9A7Rw55v1IOp17lxREIIIcTpSQIeF8osVFvSz6hbB7XlEJoICdJGQgghhOhsEvC4kCWHZ3DBEvVAynRpEiqEEEK4gQQ8LqJpGhmFlURSQnj2r+pB2Z0lhBBCuIUEPC5SUF5LVZ2JaV5r0Wkm6DESIvu5e1hCCCHEaUkCHhex1OC51nu1ekCSlYUQQgi3kYDHRTILK+mjO84g7SDoDDDkSncPSQghhDhtScDjIhknKrnc0DC70zcNAqPcOyAhhBDiNCYBj4tknijncn1DwJNyrXsHI4QQQpzmJOBxEf/cTSTo86nzCoABF7l7OEIIIcRpTQIeFxle/CMAJUkXgtHfzaMRQgghTm8S8LhAbXUVk+tXAeA14no3j0YIIYQQEvC4QPG27wjRVZKjhRMy8Bx3D0cIIYQ47UnA4wL6HZ8CsNLnHHQGLzePRgghhBAS8DhbZSFhx5cDsDvqAjcPRgghhBAgAY/z7f4Kg1bPbnMv9LFD3T0aIYQQQiABj/NtWwjAl6aJJIbL7iwhhBCiK5CAx5kKD0PmOszo+MY0gYQwCXiEEEKIrkACHmfa8RkA6xlKLuEkRkjAI4QQQnQFEvA4i6bBdrWc9XntRACZ4RFCCCG6CAl4nOX4FjhxALPBlyXmMUQF+eBnNLh7VEIIIYQApEiMs+h00O98smr8qNjvx4AwP3ePSAghhBANZIbHWeJHwo2f8V3vRwBkh5YQQgjRhUjA42SZRdWABDxCCCFEVyIBj5NlFFYCkCABjxBCCNFlSMDjZJkS8AghhBBdjgQ8TmQyaxwvrgJkSUsIIYToSiTgcaKc0mrqTBpGg56YYF93D0cIIYQQDSTgcaKME2o5Kz7MD4Ne5+bRCCGEEMJCAh4nkvwdIYQQomuSgMeJMotUwJMYLkUHhRBCiK5EAh4nsmxJl4RlIYQQomuRgMeJrDV4pGmoEEII0aVIwONEksMjhBBCdE0S8DhJZW09BeW1ACRGSMAjhBBCdCUS8DhJZqEqOBjq702wr7ebRyOEEEKIprpEwPPaa6+RlJSEr68v48aNY8OGDTZd98knn6DT6bj88stdO0AbFFbUEuLnLfk7QgghRBfk9oBn4cKFzJ49m7lz57JlyxZSU1OZOnUqeXl5bV535MgR7rvvPs4888xOGmnbxveJYNvc8/nsD+PdPRQhhBBCnMTtAc+LL77IHXfcwcyZMxk8eDDz58/H39+fBQsWtHqNyWTixhtv5PHHH6d3796dONr2+Xob3D0EIYQQQpzErQFPbW0tmzdvJi0tzfqYXq8nLS2NtWvXtnrdE088QXR0NLfffnu7r1FTU0NpaWmzLyGEEEKcXtwa8BQUFGAymYiJiWn2eExMDDk5OS1es2rVKt5++23efPNNm15j3rx5hISEWL8SEhI6PG4hhBBCdC9uX9KyR1lZGTfffDNvvvkmkZGRNl0zZ84cSkpKrF+ZmZkuHqUQQgghuhovd754ZGQkBoOB3NzcZo/n5uYSGxt7yvkHDx7kyJEjTJs2zfqY2WwGwMvLi3379tGnT59m1/j4+ODj4+OC0QshhBCiu3DrDI/RaGTUqFEsW7bM+pjZbGbZsmWMH3/qbqeBAweyY8cO0tPTrV+XXnopkydPJj09XZarhBBCCNEit87wAMyePZtbbrmF0aNHM3bsWF566SUqKiqYOXMmADNmzCA+Pp558+bh6+vL0KFDm10fGhoKcMrjQgghhBAWbg94pk+fTn5+Po8++ig5OTkMHz6cJUuWWBOZMzIy0Ou7VaqREEIIIboYnaZpmrsH0ZlKS0sJCQmhpKSE4OBgdw9HCCGEEDbo6Oe3TJ0IIYQQwuNJwCOEEEIIjycBjxBCCCE8ngQ8QgghhPB4EvAIIYQQwuO5fVt6Z7NsSpMmokIIIUT3YfncdnRz+WkX8JSVlQFIVWYhhBCiGyorKyMkJMTu6067Ojxms5msrCyCgoLQ6XROvXdpaSkJCQlkZmZKjZ9OJO+7e8j77h7yvruHvO/u0fR9DwoKoqysjB49ejhUkPi0m+HR6/X07NnTpa8RHBws/0G4gbzv7iHvu3vI++4e8r67h+V9d2Rmx0KSloUQQgjh8STgEUIIIYTHk4DHiXx8fJg7dy4+Pj7uHsppRd5395D33T3kfXcPed/dw5nv+2mXtCyEEEKI04/M8AghhBDC40nAI4QQQgiPJwGPEEIIITyeBDxCCCGE8HgS8DjJa6+9RlJSEr6+vowbN44NGza4e0ge55dffmHatGn06NEDnU7HV1991ex5TdN49NFHiYuLw8/Pj7S0NH777Tf3DNZDzJs3jzFjxhAUFER0dDSXX345+/bta3ZOdXU1s2bNIiIigsDAQK666ipyc3PdNGLP8J///IeUlBRrsbXx48fzww8/WJ+X97xzPP300+h0Ov785z9bH5P33vkee+wxdDpds6+BAwdan3fWey4BjxMsXLiQ2bNnM3fuXLZs2UJqaipTp04lLy/P3UPzKBUVFaSmpvLaa6+1+Pyzzz7LK6+8wvz581m/fj0BAQFMnTqV6urqTh6p51i5ciWzZs1i3bp1LF26lLq6Os4//3wqKiqs59x77718++23fPbZZ6xcuZKsrCyuvPJKN466++vZsydPP/00mzdvZtOmTUyZMoXLLruMXbt2AfKed4aNGzfy+uuvk5KS0uxxee9dY8iQIWRnZ1u/Vq1aZX3Oae+5Jjps7Nix2qxZs6w/m0wmrUePHtq8efPcOCrPBmhffvml9Wez2azFxsZqzz33nPWx4uJizcfHR/v444/dMELPlJeXpwHaypUrNU1T77G3t7f22WefWc/Zs2ePBmhr16511zA9UlhYmPbWW2/Je94JysrKtH79+mlLly7Vzj77bO2ee+7RNE3+vrvK3LlztdTU1Bafc+Z7LjM8HVRbW8vmzZtJS0uzPqbX60lLS2Pt2rVuHNnp5fDhw+Tk5DT7cwgJCWHcuHHy5+BEJSUlAISHhwOwefNm6urqmr3vAwcOJDExUd53JzGZTHzyySdUVFQwfvx4ec87waxZs7j44oubvccgf99d6bfffqNHjx707t2bG2+8kYyMDMC57/lp1zzU2QoKCjCZTMTExDR7PCYmhr1797ppVKefnJwcgBb/HCzPiY4xm838+c9/ZuLEiQwdOhRQ77vRaCQ0NLTZufK+d9yOHTsYP3481dXVBAYG8uWXXzJ48GDS09PlPXehTz75hC1btrBx48ZTnpO/764xbtw43n33XQYMGEB2djaPP/44Z555Jjt37nTqey4BjxDCJrNmzWLnzp3N1taF6wwYMID09HRKSkr4/PPPueWWW1i5cqW7h+XRMjMzueeee1i6dCm+vr7uHs5p48ILL7Qep6SkMG7cOHr16sWnn36Kn5+f015HlrQ6KDIyEoPBcErGeG5uLrGxsW4a1enH8l7Ln4Nr3H333Xz33Xf8/PPP9OzZ0/p4bGwstbW1FBcXNztf3veOMxqN9O3bl1GjRjFv3jxSU1N5+eWX5T13oc2bN5OXl8fIkSPx8vLCy8uLlStX8sorr+Dl5UVMTIy8950gNDSU/v37c+DAAaf+fZeAp4OMRiOjRo1i2bJl1sfMZjPLli1j/PjxbhzZ6SU5OZnY2Nhmfw6lpaWsX79e/hw6QNM07r77br788kuWL19OcnJys+dHjRqFt7d3s/d93759ZGRkyPvuZGazmZqaGnnPXejcc89lx44dpKenW79Gjx7NjTfeaD2W9971ysvLOXjwIHFxcc79+96BxGrR4JNPPtF8fHy0d999V9u9e7f2+9//XgsNDdVycnLcPTSPUlZWpm3dulXbunWrBmgvvviitnXrVu3o0aOapmna008/rYWGhmpff/21tn37du2yyy7TkpOTtaqqKjePvPu68847tZCQEG3FihVadna29auystJ6zh/+8ActMTFRW758ubZp0yZt/Pjx2vjx49046u7vwQcf1FauXKkdPnxY2759u/bggw9qOp1O+/HHHzVNk/e8MzXdpaVp8t67wl/+8hdtxYoV2uHDh7XVq1draWlpWmRkpJaXl6dpmvPecwl4nOTVV1/VEhMTNaPRqI0dO1Zbt26du4fkcX7++WcNOOXrlltu0TRNbU1/5JFHtJiYGM3Hx0c799xztX379rl30N1cS+83oL3zzjvWc6qqqrS77rpLCwsL0/z9/bUrrrhCy87Odt+gPcBtt92m9erVSzMajVpUVJR27rnnWoMdTZP3vDOdHPDIe+9806dP1+Li4jSj0ajFx8dr06dP1w4cOGB93lnvuU7TNM0JM1BCCCGEEF2W5PAIIYQQwuNJwCOEEEIIjycBjxBCCCE8ngQ8QgghhPB4EvAIIYQQwuNJwCOEEEIIjycBjxBCCCE8ngQ8QgghhPB4EvAIIU47K1asQKfTndKQUAjhuSTgEUIIIYTHk4BHCCGEEB5PAh4hRKczm83MmzeP5ORk/Pz8SE1N5fPPPwcal5sWL15MSkoKvr6+nHHGGezcubPZPb744guGDBmCj48PSUlJvPDCC82er6mp4YEHHiAhIQEfHx/69u3L22+/3eyczZs3M3r0aPz9/ZkwYQL79u2zPrdt2zYmT55MUFAQwcHBjBo1ik2bNrnoHRFCuJoEPEKITjdv3jzef/995s+fz65du7j33nu56aabWLlypfWc+++/nxdeeIGNGzcSFRXFtGnTqKurA1Sgcu2113LdddexY8cOHnvsMR555BHeffdd6/UzZszg448/5pVXXmHPnj28/vrrBAYGNhvHQw89xAsvvMCmTZvw8vLitttusz5344030rNnTzZu3MjmzZt58MEH8fb2du0bI4RwHec1eBdCiPZVV1dr/v7+2po1a5o9fvvtt2vXX3+99vPPP2uA9sknn1ifO3HihObn56ctXLhQ0zRNu+GGG7Tzzjuv2fX333+/NnjwYE3TNG3fvn0aoC1durTFMVhe46effrI+tnjxYg3QqqqqNE3TtKCgIO3dd9/t+C8shOgSZIZHCNGpDhw4QGVlJeeddx6BgYHWr/fff5+DBw9azxs/frz1ODw8nAEDBrBnzx4A9uzZw8SJE5vdd+LEifz222+YTCbS09MxGAycffbZbY4lJSXFehwXFwdAXl4eALNnz+Z3v/sdaWlpPP30083GJoTofiTgEUJ0qvLycgAWL15Menq69Wv37t3WPJ6O8vPzs+m8pktUOp0OUPlFAI899hi7du3i4osvZvny5QwePJgvv/zSKeMTQnQ+CXiEEJ1q8ODB+Pj4kJGRQd++fZt9JSQkWM9bt26d9bioqIj9+/czaNAgAAYNGsTq1aub3Xf16tX0798fg8HAsGHDMJvNzXKCHNG/f3/uvfdefvzxR6688kreeeedDt1PCOE+Xu4egBDi9BIUFMR9993Hvffei9lsZtKkSZSUlLB69WqCg4Pp1asXAE888QQRERHExMTw0EMPERkZyeWXXw7AX/7yF8aMGcOTTz7J9OnTWbt2Lf/617/497//DUBSUhK33HILt912G6+88gqpqakcPXqUvLw8rr322nbHWFVVxf3338/VV19NcnIyx44dY+PGjVx11VUue1+EEC7m7iQiIcTpx2w2ay+99JI2YMAAzdvbW4uKitKmTp2qrVy50ppQ/O2332pDhgzRjEajNnbsWG3btm3N7vH5559rgwcP1ry9vbXExETtueeea/Z8VVWVdu+992pxcXGa0WjU+vbtqy1YsEDTtMak5aKiIuv5W7du1QDt8OHDWk1NjXbddddpCQkJmtFo1Hr06KHdfffd1oRmIUT3o9M0TXNzzCWEEFYrVqxg8uTJFBUVERoa6u7hCCE8hOTwCCGEEMLjScAjhBBCCI8nS1pCCCGE8HgywyOEEEIIjycBjxBCCCE8ngQ8QgghhPB4EvAIIYQQwuNJwCOEEEIIjycBjxBCCCE8ngQ8QgghhPB4EvAIIYQQwuP9PxI/IIL2E/uDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'], label=\"train\")\n",
    "plt.plot(history.history['val_accuracy'], label=\"val\")\n",
    "plt.legend(loc=\"right\")\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d7760c",
   "metadata": {},
   "source": [
    "# test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f99d3d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_model = load_model(\"ct_resnet_best_model.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b4b6ad99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 15s 1s/step - loss: 0.5754 - accuracy: 0.7810\n",
      "Test loss: 0.5753685235977173\n",
      "Test accuracy: 0.7809523940086365\n"
     ]
    }
   ],
   "source": [
    "score = saved_model.evaluate(test_data, verbose = 1)\n",
    "print(\"Test loss:\", score[0])\n",
    "print(\"Test accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "386a29ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}