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Created April 17, 2023 22:29
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AI Skin Assignment.ipynb
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ai-skin-assignment.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/samnatale3/b963aa69a281b8c2905eb986db16f357/ai-skin-assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Display information about the NVIDIA GPU being used by this Google Colab notebook"
],
"metadata": {
"id": "cWb5eyAxSQE3"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b9bUN__RKphq",
"outputId": "4ee2fb52-56de-43f2-a464-ba40ca59631b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mon Apr 17 16:59:33 2023 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla V100-SXM2... Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 34C P0 24W / 300W | 0MiB / 16384MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "markdown",
"source": [
"# Import the os module to interact with the filesystem\n",
"\n",
"# Download the ISIC 2020 dataset (JPEG images) and the corresponding ground truth labels\n",
"# These datasets are used for skin lesion classification tasks\n"
],
"metadata": {
"id": "qSS1YpDVSWoJ"
}
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GCTLEwapnyHE",
"outputId": "8f6d60e5-d762-460e-b120-26265ce29eb2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2023-04-17 16:59:38-- https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Training_JPEG.zip\n",
"Resolving isic-challenge-data.s3.amazonaws.com (isic-challenge-data.s3.amazonaws.com)... 52.217.49.44, 3.5.25.202, 3.5.29.160, ...\n",
"Connecting to isic-challenge-data.s3.amazonaws.com (isic-challenge-data.s3.amazonaws.com)|52.217.49.44|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 24707698022 (23G) [application/zip]\n",
"Saving to: ‘ISIC_2020_Training_JPEG.zip’\n",
"\n",
"ISIC_2020_Training_ 100%[===================>] 23.01G 33.7MB/s in 12m 57s \n",
"\n",
"2023-04-17 17:12:36 (30.3 MB/s) - ‘ISIC_2020_Training_JPEG.zip’ saved [24707698022/24707698022]\n",
"\n",
"--2023-04-17 17:12:36-- https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Training_GroundTruth.csv\n",
"Resolving isic-challenge-data.s3.amazonaws.com (isic-challenge-data.s3.amazonaws.com)... 52.217.91.28, 52.217.170.33, 3.5.27.132, ...\n",
"Connecting to isic-challenge-data.s3.amazonaws.com (isic-challenge-data.s3.amazonaws.com)|52.217.91.28|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 2056020 (2.0M) [text/csv]\n",
"Saving to: ‘ISIC_2020_Training_GroundTruth.csv’\n",
"\n",
"ISIC_2020_Training_ 100%[===================>] 1.96M 3.79MB/s in 0.5s \n",
"\n",
"2023-04-17 17:12:37 (3.79 MB/s) - ‘ISIC_2020_Training_GroundTruth.csv’ saved [2056020/2056020]\n",
"\n"
]
}
],
"source": [
"import os\n",
"\n",
"# Download and extract the datasets, deleting after unzipped to create more storage space\n",
"!wget https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Training_JPEG.zip\n",
"!wget https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Training_GroundTruth.csv\n"
]
},
{
"cell_type": "markdown",
"source": [
"# Unzip the content into a folder: /content/images\n"
],
"metadata": {
"id": "bLj9xZZrScAB"
}
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "EZCkOhoF2Qro"
},
"outputs": [],
"source": [
"!unzip -q ISIC_2020_Training_JPEG.zip -d /content\n",
"# Zip will unzip into a folder called 'train' we want this to be 'images'\n",
"!mv /content/train /content/images"
]
},
{
"cell_type": "markdown",
"source": [
"# Import dependancies"
],
"metadata": {
"id": "cDMhy9elSh4k"
}
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "A70oFkFOmA7g"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import cv2\n",
"from sklearn.model_selection import train_test_split\n",
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"import shutil\n",
"from tensorflow.keras.models import load_model\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
"from tensorflow.keras.applications import DenseNet121, DenseNet201, InceptionV3, NASNetLarge, MobileNetV2\n",
"from google.colab import drive\n",
"import itertools\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.metrics import roc_curve, auc\n",
"from sklearn.metrics import confusion_matrix, roc_auc_score, matthews_corrcoef"
]
},
{
"cell_type": "markdown",
"source": [
"# Read and process the ground truth CSV file\n",
"# Balance the dataset by selecting an equal number of benign and malignant images\n",
"# Split the balanced data into train and test sets (80% train, 20% test)\n",
"# Organize the train and test images into their respective benign and malignant folders\n"
],
"metadata": {
"id": "UpflFEhBSwwE"
}
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "p1ggcwNPECZP",
"outputId": "ad95bb49-e3a5-4d07-bccb-1b42cc225a7d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Starting benign count: 32542\n",
"Starting malignant count: 584\n",
"Minimum count to balance: 584\n",
"\n",
"Train:\n",
" Benign count: 461\n",
" Malignant count: 473\n",
"Test:\n",
" Benign count: 123\n",
" Malignant count: 111\n"
]
}
],
"source": [
"# Read the ground truth CSV\n",
"df = pd.read_csv(\"ISIC_2020_Training_GroundTruth.csv\")\n",
"\n",
"# Count the number of benign and malignant images\n",
"benign_count = sum(df['benign_malignant'] == 'benign')\n",
"malignant_count = sum(df['benign_malignant'] == 'malignant')\n",
"\n",
"print(\"Starting benign count:\", benign_count)\n",
"print(\"Starting malignant count:\", malignant_count)\n",
"\n",
"# Determine the minimum count between the two classes\n",
"min_count = min(benign_count, malignant_count)\n",
"\n",
"print(\"Minimum count to balance:\", min_count);\n",
"\n",
"# Balance the dataset by selecting an equal number of benign and malignant images\n",
"balanced_df = pd.concat([\n",
" df[df['benign_malignant'] == 'benign'].sample(min_count, random_state=42),\n",
" df[df['benign_malignant'] == 'malignant'].sample(min_count, random_state=42)\n",
"])\n",
"\n",
"# Split the balanced data into train and test sets (80% train, 20% test)\n",
"train_df, test_df = train_test_split(balanced_df, test_size=0.2, random_state=42)\n",
"\n",
"# Create folders for train and test images\n",
"for dataset in ['train', 'test']:\n",
" for folder in ['benign', 'malignant']:\n",
" path = f\"/content/{dataset}/{folder}\"\n",
" if not os.path.exists(path):\n",
" os.makedirs(path)\n",
"\n",
"# Move train images to their respective folders\n",
"for index, row in train_df.iterrows():\n",
" image_path = os.path.join(\"/content/images\", row['image_name'] + \".jpg\")\n",
" if os.path.isfile(image_path):\n",
" if row['benign_malignant'] == 'benign':\n",
" shutil.copy(image_path, os.path.join(\"/content/train/benign\", row['image_name'] + \".jpg\"))\n",
" else:\n",
" shutil.copy(image_path, os.path.join(\"/content/train/malignant\", row['image_name'] + \".jpg\"))\n",
"\n",
"# Move test images to their respective folders\n",
"for index, row in test_df.iterrows():\n",
" image_path = os.path.join(\"/content/images\", row['image_name'] + \".jpg\")\n",
" if os.path.isfile(image_path):\n",
" if row['benign_malignant'] == 'benign':\n",
" shutil.copy(image_path, os.path.join(\"/content/test/benign\", row['image_name'] + \".jpg\"))\n",
" else:\n",
" shutil.copy(image_path, os.path.join(\"/content/test/malignant\", row['image_name'] + \".jpg\"))\n",
"\n",
"\n",
"train_benign_count = len(os.listdir(\"/content/train/benign\"))\n",
"train_malignant_count = len(os.listdir(\"/content/train/malignant\"))\n",
"test_benign_count = len(os.listdir(\"/content/test/benign\"))\n",
"test_malignant_count = len(os.listdir(\"/content/test/malignant\"))\n",
"\n",
"print()\n",
"print(\"Train:\")\n",
"print(f\" Benign count: {train_benign_count}\")\n",
"print(f\" Malignant count: {train_malignant_count}\")\n",
"\n",
"print(\"Test:\")\n",
"print(f\" Benign count: {test_benign_count}\")\n",
"print(f\" Malignant count: {test_malignant_count}\")\n"
]
},
{
"cell_type": "markdown",
"source": [
"# Split the train set into train and validation sets (80% train, 20% validation)\n",
"# Create data generators for training, validation, and test sets\n",
"# Configure the generators to resize the images, apply rescaling, and set batch size and class mode\n"
],
"metadata": {
"id": "JHTU8ZabS-IP"
}
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "p6fKaPNaEH-y",
"outputId": "22a2a402-6f34-49a1-b0c8-e355d38ea8c6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"malignant 385\n",
"benign 362\n",
"Name: benign_malignant, dtype: int64\n",
"Found 748 images belonging to 2 classes.\n",
"Found 186 images belonging to 2 classes.\n",
"Found 234 images belonging to 2 classes.\n"
]
}
],
"source": [
"# Split the train set into train and validation sets (80% train, 20% validation)\n",
"train_df, val_df = train_test_split(train_df, test_size=0.2, random_state=42)\n",
"\n",
"print(train_df['benign_malignant'].value_counts())\n",
"IMG_SIZE = 224\n",
"\n",
"# Create data generators for training, validation and test sets\n",
"train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)\n",
"test_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"train_generator = train_datagen.flow_from_directory(\n",
" directory=\"/content/train\",\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='binary',\n",
" subset='training'\n",
")\n",
"\n",
"val_generator = train_datagen.flow_from_directory(\n",
" directory=\"/content/train\",\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='binary',\n",
" subset='validation'\n",
")\n",
"\n",
"\n",
"test_generator = test_datagen.flow_from_directory(\n",
" directory=\"/content/test\",\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='binary',\n",
" shuffle=False # Keep the order of the predictions for ensemble later\n",
")"
]
},
{
"cell_type": "markdown",
"source": [
"# Mount Google Drive to save trained models so they can be used in multiple instances.\n",
"\n",
"# Google Collab has a max of 12hrs"
],
"metadata": {
"id": "Ej_WEMIYS_1E"
}
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1ttbej5DZsL4",
"outputId": "46644049-82b2-453d-b03e-40b8cab09366"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
]
}
],
"source": [
"drive.mount('/content/gdrive')\n",
"\n",
"model_save_path = \"/content/gdrive/MyDrive/saved_models\"\n",
"if not os.path.exists(model_save_path):\n",
" os.makedirs(model_save_path)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LC015stqqM0g"
},
"source": [
"# Define the list of model architectures to train\n",
"# Train each model with their respective pre-trained weights and save the trained models\n",
"\n",
"# I selected a smaller number of epochs for the sake of time and computational resources and acknowledge that training for more epochs could potentially lead to better performance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true,
"base_uri": "https://localhost:8080/"
},
"id": "xOF0_q9ja2ro",
"outputId": "55b834b3-83c0-48f1-ef3f-540fa76bec50"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training MobileNetV2 model...\n",
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
"9406464/9406464 [==============================] - 0s 0us/step\n",
"Epoch 1/10\n",
"24/24 [==============================] - 148s 6s/step - loss: 0.6613 - accuracy: 0.6364 - val_loss: 0.5587 - val_accuracy: 0.7097\n",
"Epoch 2/10\n",
"24/24 [==============================] - 147s 6s/step - loss: 0.5093 - accuracy: 0.7620 - val_loss: 0.5211 - val_accuracy: 0.7097\n",
"Epoch 3/10\n",
"24/24 [==============================] - 130s 5s/step - loss: 0.4650 - accuracy: 0.7807 - val_loss: 0.5082 - val_accuracy: 0.7043\n",
"Epoch 4/10\n",
"24/24 [==============================] - 148s 6s/step - loss: 0.4408 - accuracy: 0.7914 - val_loss: 0.5031 - val_accuracy: 0.7312\n",
"Epoch 5/10\n",
"24/24 [==============================] - 136s 6s/step - loss: 0.4300 - accuracy: 0.7914 - val_loss: 0.5021 - val_accuracy: 0.7366\n",
"Epoch 6/10\n",
"24/24 [==============================] - 128s 5s/step - loss: 0.4135 - accuracy: 0.8128 - val_loss: 0.4963 - val_accuracy: 0.7366\n",
"Epoch 7/10\n",
"24/24 [==============================] - 129s 5s/step - loss: 0.4046 - accuracy: 0.8182 - val_loss: 0.4848 - val_accuracy: 0.7634\n",
"Epoch 8/10\n",
"24/24 [==============================] - 131s 5s/step - loss: 0.3948 - accuracy: 0.8262 - val_loss: 0.4832 - val_accuracy: 0.7796\n",
"Epoch 9/10\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.3909 - accuracy: 0.8302 - val_loss: 0.4765 - val_accuracy: 0.7903\n",
"Epoch 10/10\n",
"24/24 [==============================] - 131s 6s/step - loss: 0.3753 - accuracy: 0.8396 - val_loss: 0.4794 - val_accuracy: 0.7634\n",
"MobileNetV2 model saved.\n"
]
}
],
"source": [
"# Define the list of models to train\n",
"model_architectures = [ \n",
" {\"name\": \"DenseNet121\", \"model\": DenseNet121}, \n",
" {\"name\": \"DenseNet201\", \"model\": DenseNet201}, \n",
" {\"name\": \"InceptionV3\", \"model\": InceptionV3}, \n",
" {\"name\": \"NASNetLarge\", \"model\": NASNetLarge}, \n",
" {\"name\": \"MobileNetV2\", \"model\": MobileNetV2}, ]\n",
"\n",
"# Iterate through each model architecture\n",
"for arch in model_architectures:\n",
" print(f\"Training {arch['name']} model...\")\n",
" \n",
" # Initialize the base model with pre-trained weights\n",
" base_model = arch['model'](\n",
" input_shape=(IMG_SIZE, IMG_SIZE, 3),\n",
" include_top=False,\n",
" weights='imagenet'\n",
" )\n",
"\n",
" # Set the base model as non-trainable (use pre-trained weights)\n",
" base_model.trainable = False\n",
" \n",
" # Define the input layer\n",
" inputs = tf.keras.Input(shape=(IMG_SIZE, IMG_SIZE, 3))\n",
" \n",
" # Pass the inputs through the base model\n",
" x = base_model(inputs, training=False)\n",
" \n",
" # Add a global average pooling layer\n",
" x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
" \n",
" # Add the output layer with sigmoid activation for binary classification\n",
" outputs = tf.keras.layers.Dense(1, activation='sigmoid')(x)\n",
"\n",
" # Build the final model\n",
" model = tf.keras.Model(inputs, outputs)\n",
"\n",
" # Compile the model with Adam optimizer and binary cross-entropy loss\n",
" model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
" loss=tf.keras.losses.BinaryCrossentropy(),\n",
" metrics=['accuracy'])\n",
"\n",
" # Train the model using the training and validation data\n",
" history = model.fit(\n",
" train_generator,\n",
" validation_data=val_generator,\n",
" epochs=10,\n",
" steps_per_epoch=len(train_generator),\n",
" validation_steps=len(val_generator),\n",
" verbose=1\n",
" )\n",
" \n",
" # Save the trained model to Google Drive\n",
" model_save_path = \"/content/gdrive/MyDrive/saved_models\"\n",
" model.save(os.path.join(model_save_path, f\"{arch['name']}.h5\"))\n",
"\n",
" print(f\"{arch['name']} model saved.\")\n"
]
},
{
"cell_type": "markdown",
"source": [
"# Prepare the test images and labels by loading and preprocessing them\n",
"# Read test images from the test directory, resize, and rescale them\n",
"# Store the preprocessed images and their corresponding labels in lists\n"
],
"metadata": {
"id": "Ga19Q9YLTf9Q"
}
},
{
"cell_type": "code",
"source": [
"# Prepare the test images and labels\n",
"test_images = []\n",
"test_labels = []\n",
"test_folder = \"/content/test\"\n",
"\n",
"# Iterate over each class folder in the test directory\n",
"for label, folder in enumerate(['benign', 'malignant']):\n",
" folder_path = os.path.join(test_folder, folder)\n",
" \n",
" # Iterate over each image in the class folder\n",
" for image_name in os.listdir(folder_path):\n",
" image_path = os.path.join(folder_path, image_name)\n",
" img = cv2.imread(image_path)\n",
" if img is not None:\n",
" img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n",
" img = img / 255.0\n",
" \n",
" # Append the preprocessed image and its label to the test images and labels list\n",
" test_images.append(img)\n",
" test_labels.append(label)\n",
"\n",
"# Convert the test images list to a numpy array\n",
"test_images = np.array(test_images)"
],
"metadata": {
"id": "kuuPoiYAN1lP"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Evaluate the performance of each trained model on the test dataset\n",
"# Load each saved model, generate predictions, and calculate evaluation metrics\n"
],
"metadata": {
"id": "4PPoqrgkTmoI"
}
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "voyYmrothQSg",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ae4f1097-87d3-4875-e4f3-f6ff11e3585d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evaluating DenseNet121 model...\n",
"8/8 [==============================] - 12s 130ms/step\n",
"DenseNet121 model evaluation:\n",
" Accuracy: 0.6623931623931624\n",
" Precision: 0.625\n",
" Recall: 0.7207207207207207\n",
" F1-score: 0.6694560669456067\n",
"\n",
"Evaluating DenseNet201 model...\n",
"8/8 [==============================] - 5s 136ms/step\n",
"DenseNet201 model evaluation:\n",
" Accuracy: 0.6538461538461539\n",
" Precision: 0.5862068965517241\n",
" Recall: 0.918918918918919\n",
" F1-score: 0.7157894736842105\n",
"\n",
"Evaluating InceptionV3 model...\n",
"8/8 [==============================] - 4s 108ms/step\n",
"InceptionV3 model evaluation:\n",
" Accuracy: 0.688034188034188\n",
" Precision: 0.6319444444444444\n",
" Recall: 0.8198198198198198\n",
" F1-score: 0.7137254901960783\n",
"\n",
"Evaluating NASNetLarge model...\n",
"8/8 [==============================] - 9s 156ms/step\n",
"NASNetLarge model evaluation:\n",
" Accuracy: 0.6709401709401709\n",
" Precision: 0.6075949367088608\n",
" Recall: 0.8648648648648649\n",
" F1-score: 0.7137546468401488\n",
"\n",
"Evaluating MobileNetV2 model...\n",
"8/8 [==============================] - 2s 67ms/step\n",
"MobileNetV2 model evaluation:\n",
" Accuracy: 0.6581196581196581\n",
" Precision: 0.5950920245398773\n",
" Recall: 0.8738738738738738\n",
" F1-score: 0.708029197080292\n",
"\n"
]
}
],
"source": [
"# Load and test each model\n",
"model_predictions = []\n",
"for arch in model_architectures:\n",
" print(f\"Evaluating {arch['name']} model...\")\n",
" \n",
" # Load the saved model from Google Drive\n",
" model_path = os.path.join(model_save_path, f\"{arch['name']}.h5\")\n",
" model = load_model(model_path)\n",
"\n",
" # Generate predictions for the test images using the loaded model\n",
" predictions = model.predict(test_images)\n",
" predictions = [1 if p >= 0.5 else 0 for p in predictions]\n",
" \n",
" # Append the model's predictions to the model_predictions list\n",
" model_predictions.append(predictions)\n",
"\n",
" # Calculate evaluation metrics (accuracy, precision, recall, f1-score) for the model's predictions\n",
" accuracy = accuracy_score(test_labels, predictions)\n",
" precision = precision_score(test_labels, predictions)\n",
" recall = recall_score(test_labels, predictions)\n",
" f1 = f1_score(test_labels, predictions)\n",
"\n",
" # Print the model's evaluation metrics\n",
" print(f\"{arch['name']} model evaluation:\")\n",
" print(f\" Accuracy: {accuracy}\")\n",
" print(f\" Precision: {precision}\")\n",
" print(f\" Recall: {recall}\")\n",
" print(f\" F1-score: {f1}\\n\")"
]
},
{
"cell_type": "markdown",
"source": [
"# Find the best weights for an ensemble of models\n",
"# Generate all possible weight combinations and evaluate their performance on the test dataset\n",
"# Store the best weight combination and its corresponding accuracy\n"
],
"metadata": {
"id": "_RZHSzqHTthW"
}
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "S8JWl1NZtpVh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1983f95d-d749-4bf7-9fb8-2043dc1013af"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Best weights: (0.1, 0.1, 0.1, 0.4, 0.3)\n",
"Best accuracy: 0.6965811965811965\n"
]
}
],
"source": [
"# Define the list of possible weights\n",
"weight_values = [0.1, 0.2, 0.3, 0.4]\n",
"\n",
"# Generate all possible combinations of weights\n",
"weight_combinations = list(itertools.product(weight_values, repeat=len(model_architectures)))\n",
"\n",
"best_weights = None\n",
"best_accuracy = 0\n",
"\n",
"# Loop through all weight combinations\n",
"for weights in weight_combinations:\n",
" # Check if the sum of the weights is 1.0 (if not, skip this combination)\n",
" if round(sum(weights), 2) != 1.0:\n",
" continue\n",
"\n",
" # Calculate the weighted predictions\n",
" combined_predictions = np.sum(\n",
" [np.array(predictions) * weight for predictions, weight in zip(model_predictions, weights)],\n",
" axis=0\n",
" )\n",
"\n",
" # Convert the predictions to binary labels\n",
" threshold = 0.5\n",
" binary_predictions = (combined_predictions > threshold).astype(int)\n",
"\n",
" # Calculate the accuracy\n",
" accuracy = accuracy_score(test_labels, binary_predictions)\n",
"\n",
" # Check if the accuracy is better than the best accuracy found so far\n",
" if accuracy > best_accuracy:\n",
" best_accuracy = accuracy\n",
" best_weights = weights\n",
"\n",
"print(f\"Best weights: {best_weights}\")\n",
"print(f\"Best accuracy: {best_accuracy}\")\n"
]
},
{
"cell_type": "markdown",
"source": [
"# Summary of the Ensemble performance"
],
"metadata": {
"id": "idESEhbhTws-"
}
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "pf-RxghBtrTG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3d471f14-78f4-4619-82cc-a841bbbda6dc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ensemble model evaluation:\n",
"Accuracy: 0.6965811965811965\n",
"Precision: 0.6351351351351351\n",
"Recall: 0.8468468468468469\n",
"F1-score: 0.7258687258687259\n"
]
}
],
"source": [
"# Calculate the weighted predictions using the best weights\n",
"combined_predictions = np.sum(\n",
" [np.array(predictions) * weight for predictions, weight in zip(model_predictions, best_weights)],\n",
" axis=0\n",
")\n",
"\n",
"# Convert the predictions to binary labels\n",
"threshold = 0.5\n",
"binary_predictions = (combined_predictions > threshold).astype(int)\n",
"\n",
"# Calculate the accuracy, precision, recall, and F1-score\n",
"accuracy = accuracy_score(test_labels, binary_predictions)\n",
"precision = precision_score(test_labels, binary_predictions)\n",
"recall = recall_score(test_labels, binary_predictions)\n",
"f1 = f1_score(test_labels, binary_predictions)\n",
"\n",
"print(\"Ensemble model evaluation:\")\n",
"print(f\"Accuracy: {accuracy}\")\n",
"print(f\"Precision: {precision}\")\n",
"print(f\"Recall: {recall}\")\n",
"print(f\"F1-score: {f1}\")\n"
]
},
{
"cell_type": "markdown",
"source": [
"# The performance of the models seems to be moderate, with accuracy ranging from 65% to 69%.\n",
"\n",
"These accuracy levels may be acceptable for some use cases but for this project (critical medical diagnoses) a higher performance would be desirable.\n",
"\n",
"# Adding fine tuning and to try to improve the accuracy"
],
"metadata": {
"id": "eKtxdqsPRzgX"
}
},
{
"cell_type": "code",
"source": [
"# Iterate through each model architecture\n",
"for arch in model_architectures:\n",
" print(f\"Training {arch['name']} model...\")\n",
" \n",
" # Initialize the base model with pre-trained weights\n",
" base_model = arch['model'](\n",
" input_shape=(IMG_SIZE, IMG_SIZE, 3),\n",
" include_top=False,\n",
" weights='imagenet'\n",
" )\n",
"\n",
" # Set the base model as non-trainable (use pre-trained weights)\n",
" base_model.trainable = False\n",
" \n",
" # Define the input layer\n",
" inputs = tf.keras.Input(shape=(IMG_SIZE, IMG_SIZE, 3))\n",
" \n",
" # Pass the inputs through the base model\n",
" x = base_model(inputs, training=False)\n",
" \n",
" # Add a global average pooling layer\n",
" x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
" \n",
" # Add the output layer with sigmoid activation for binary classification\n",
" outputs = tf.keras.layers.Dense(1, activation='sigmoid')(x)\n",
"\n",
" # Build the final model\n",
" model = tf.keras.Model(inputs, outputs)\n",
"\n",
" # Compile the model with Adam optimizer and binary cross-entropy loss\n",
" model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
" loss=tf.keras.losses.BinaryCrossentropy(),\n",
" metrics=['accuracy'])\n",
"\n",
" # Train the model using the training and validation data\n",
" history = model.fit(\n",
" train_generator,\n",
" validation_data=val_generator,\n",
" epochs=10,\n",
" steps_per_epoch=len(train_generator),\n",
" validation_steps=len(val_generator),\n",
" verbose=1\n",
" )\n",
"\n",
" # Unfreeze the last few layers of the base model for fine-tuning\n",
" for layer in base_model.layers[-5:]:\n",
" layer.trainable = True\n",
"\n",
" # Compile the model with a lower learning rate for fine-tuning\n",
" model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-5),\n",
" loss=tf.keras.losses.BinaryCrossentropy(),\n",
" metrics=['accuracy'])\n",
"\n",
" # Fine-tune the model using the training and validation data\n",
" fine_tuning_history = model.fit(\n",
" train_generator,\n",
" validation_data=val_generator,\n",
" epochs=5,\n",
" steps_per_epoch=len(train_generator),\n",
" validation_steps=len(val_generator),\n",
" verbose=1\n",
" )\n",
" \n",
" # Save the trained model to Google Drive\n",
" model_save_path = \"/content/gdrive/MyDrive/saved_models\"\n",
" model.save(os.path.join(model_save_path, f\"{arch['name']}_finetuned.h5\"))\n",
"\n",
" print(f\"{arch['name']} model saved.\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JdX_hsjiRLAu",
"outputId": "4ce0adf7-e897-4d33-d89f-e7723dd49caa"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training DenseNet121 model...\n",
"Epoch 1/10\n",
"24/24 [==============================] - 173s 7s/step - loss: 0.6299 - accuracy: 0.6578 - val_loss: 0.6206 - val_accuracy: 0.7097\n",
"Epoch 2/10\n",
"24/24 [==============================] - 142s 6s/step - loss: 0.5466 - accuracy: 0.7286 - val_loss: 0.6045 - val_accuracy: 0.7527\n",
"Epoch 3/10\n",
"24/24 [==============================] - 140s 6s/step - loss: 0.5198 - accuracy: 0.7460 - val_loss: 0.5787 - val_accuracy: 0.7151\n",
"Epoch 4/10\n",
"24/24 [==============================] - 142s 6s/step - loss: 0.4885 - accuracy: 0.7647 - val_loss: 0.5681 - val_accuracy: 0.7097\n",
"Epoch 5/10\n",
"24/24 [==============================] - 139s 6s/step - loss: 0.4679 - accuracy: 0.7821 - val_loss: 0.5640 - val_accuracy: 0.7258\n",
"Epoch 6/10\n",
"24/24 [==============================] - 137s 6s/step - loss: 0.4606 - accuracy: 0.7861 - val_loss: 0.5599 - val_accuracy: 0.7366\n",
"Epoch 7/10\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.4433 - accuracy: 0.7848 - val_loss: 0.5578 - val_accuracy: 0.7097\n",
"Epoch 8/10\n",
"24/24 [==============================] - 140s 6s/step - loss: 0.4338 - accuracy: 0.8102 - val_loss: 0.5534 - val_accuracy: 0.7366\n",
"Epoch 9/10\n",
"24/24 [==============================] - 144s 6s/step - loss: 0.4310 - accuracy: 0.7941 - val_loss: 0.5469 - val_accuracy: 0.7419\n",
"Epoch 10/10\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.4206 - accuracy: 0.8115 - val_loss: 0.5446 - val_accuracy: 0.7473\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/5\n",
"24/24 [==============================] - 157s 6s/step - loss: 0.4185 - accuracy: 0.7995 - val_loss: 0.5405 - val_accuracy: 0.7473\n",
"Epoch 2/5\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.4115 - accuracy: 0.8048 - val_loss: 0.5386 - val_accuracy: 0.7473\n",
"Epoch 3/5\n",
"24/24 [==============================] - 145s 6s/step - loss: 0.4028 - accuracy: 0.8249 - val_loss: 0.5388 - val_accuracy: 0.7419\n",
"Epoch 4/5\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.4003 - accuracy: 0.8222 - val_loss: 0.5386 - val_accuracy: 0.7366\n",
"Epoch 5/5\n",
"24/24 [==============================] - 144s 6s/step - loss: 0.3915 - accuracy: 0.8302 - val_loss: 0.5279 - val_accuracy: 0.7473\n",
"DenseNet121 model saved.\n",
"Training DenseNet201 model...\n",
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
"74836368/74836368 [==============================] - 3s 0us/step\n",
"Epoch 1/10\n",
"24/24 [==============================] - 181s 7s/step - loss: 0.6273 - accuracy: 0.6444 - val_loss: 0.5848 - val_accuracy: 0.6989\n",
"Epoch 2/10\n",
"24/24 [==============================] - 142s 6s/step - loss: 0.5185 - accuracy: 0.7540 - val_loss: 0.5361 - val_accuracy: 0.7581\n",
"Epoch 3/10\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.4715 - accuracy: 0.7928 - val_loss: 0.5133 - val_accuracy: 0.7473\n",
"Epoch 4/10\n",
"24/24 [==============================] - 148s 6s/step - loss: 0.4432 - accuracy: 0.8048 - val_loss: 0.4990 - val_accuracy: 0.7634\n",
"Epoch 5/10\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.4290 - accuracy: 0.8035 - val_loss: 0.4974 - val_accuracy: 0.7258\n",
"Epoch 6/10\n",
"24/24 [==============================] - 147s 6s/step - loss: 0.4181 - accuracy: 0.8075 - val_loss: 0.4918 - val_accuracy: 0.7473\n",
"Epoch 7/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.4119 - accuracy: 0.8195 - val_loss: 0.4841 - val_accuracy: 0.7473\n",
"Epoch 8/10\n",
"24/24 [==============================] - 158s 7s/step - loss: 0.3938 - accuracy: 0.8342 - val_loss: 0.4936 - val_accuracy: 0.7366\n",
"Epoch 9/10\n",
"24/24 [==============================] - 145s 6s/step - loss: 0.3868 - accuracy: 0.8356 - val_loss: 0.4698 - val_accuracy: 0.8011\n",
"Epoch 10/10\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.3770 - accuracy: 0.8302 - val_loss: 0.4685 - val_accuracy: 0.8118\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/5\n",
"24/24 [==============================] - 175s 7s/step - loss: 0.3751 - accuracy: 0.8289 - val_loss: 0.4645 - val_accuracy: 0.8172\n",
"Epoch 2/5\n",
"24/24 [==============================] - 140s 6s/step - loss: 0.3650 - accuracy: 0.8516 - val_loss: 0.4616 - val_accuracy: 0.7957\n",
"Epoch 3/5\n",
"24/24 [==============================] - 148s 6s/step - loss: 0.3598 - accuracy: 0.8382 - val_loss: 0.4554 - val_accuracy: 0.8118\n",
"Epoch 4/5\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.3533 - accuracy: 0.8356 - val_loss: 0.4577 - val_accuracy: 0.8172\n",
"Epoch 5/5\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.3558 - accuracy: 0.8556 - val_loss: 0.4516 - val_accuracy: 0.7796\n",
"DenseNet201 model saved.\n",
"Training InceptionV3 model...\n",
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
"87910968/87910968 [==============================] - 3s 0us/step\n",
"Epoch 1/10\n",
"24/24 [==============================] - 153s 6s/step - loss: 0.6589 - accuracy: 0.6083 - val_loss: 0.6021 - val_accuracy: 0.6828\n",
"Epoch 2/10\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.5546 - accuracy: 0.7152 - val_loss: 0.5619 - val_accuracy: 0.7258\n",
"Epoch 3/10\n",
"24/24 [==============================] - 142s 6s/step - loss: 0.5017 - accuracy: 0.7687 - val_loss: 0.5526 - val_accuracy: 0.7151\n",
"Epoch 4/10\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.4683 - accuracy: 0.8008 - val_loss: 0.5523 - val_accuracy: 0.6989\n",
"Epoch 5/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.4554 - accuracy: 0.7848 - val_loss: 0.5482 - val_accuracy: 0.7151\n",
"Epoch 6/10\n",
"24/24 [==============================] - 147s 6s/step - loss: 0.4406 - accuracy: 0.8115 - val_loss: 0.5478 - val_accuracy: 0.7151\n",
"Epoch 7/10\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.4290 - accuracy: 0.7981 - val_loss: 0.5372 - val_accuracy: 0.7204\n",
"Epoch 8/10\n",
"24/24 [==============================] - 145s 6s/step - loss: 0.4109 - accuracy: 0.8235 - val_loss: 0.5454 - val_accuracy: 0.6989\n",
"Epoch 9/10\n",
"24/24 [==============================] - 142s 6s/step - loss: 0.3931 - accuracy: 0.8396 - val_loss: 0.5554 - val_accuracy: 0.7097\n",
"Epoch 10/10\n",
"24/24 [==============================] - 142s 6s/step - loss: 0.3808 - accuracy: 0.8409 - val_loss: 0.5507 - val_accuracy: 0.6989\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/5\n",
"24/24 [==============================] - 155s 6s/step - loss: 0.3886 - accuracy: 0.8302 - val_loss: 0.5377 - val_accuracy: 0.7204\n",
"Epoch 2/5\n",
"24/24 [==============================] - 147s 6s/step - loss: 0.3683 - accuracy: 0.8516 - val_loss: 0.5447 - val_accuracy: 0.7312\n",
"Epoch 3/5\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.3535 - accuracy: 0.8409 - val_loss: 0.5462 - val_accuracy: 0.7366\n",
"Epoch 4/5\n",
"24/24 [==============================] - 148s 6s/step - loss: 0.3429 - accuracy: 0.8596 - val_loss: 0.5581 - val_accuracy: 0.7204\n",
"Epoch 5/5\n",
"24/24 [==============================] - 140s 6s/step - loss: 0.3322 - accuracy: 0.8690 - val_loss: 0.5586 - val_accuracy: 0.7204\n",
"InceptionV3 model saved.\n",
"Training NASNetLarge model...\n",
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/nasnet/NASNet-large-no-top.h5\n",
"343610240/343610240 [==============================] - 11s 0us/step\n",
"Epoch 1/10\n",
"24/24 [==============================] - 174s 6s/step - loss: 0.6187 - accuracy: 0.6564 - val_loss: 0.6188 - val_accuracy: 0.6720\n",
"Epoch 2/10\n",
"24/24 [==============================] - 139s 6s/step - loss: 0.5097 - accuracy: 0.7727 - val_loss: 0.5962 - val_accuracy: 0.6989\n",
"Epoch 3/10\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.4695 - accuracy: 0.7968 - val_loss: 0.5955 - val_accuracy: 0.6882\n",
"Epoch 4/10\n",
"24/24 [==============================] - 139s 6s/step - loss: 0.4426 - accuracy: 0.8021 - val_loss: 0.5882 - val_accuracy: 0.6882\n",
"Epoch 5/10\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.4189 - accuracy: 0.8168 - val_loss: 0.5891 - val_accuracy: 0.6882\n",
"Epoch 6/10\n",
"24/24 [==============================] - 141s 6s/step - loss: 0.4079 - accuracy: 0.8302 - val_loss: 0.5867 - val_accuracy: 0.6935\n",
"Epoch 7/10\n",
"24/24 [==============================] - 144s 6s/step - loss: 0.3928 - accuracy: 0.8329 - val_loss: 0.5870 - val_accuracy: 0.6989\n",
"Epoch 8/10\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.3797 - accuracy: 0.8476 - val_loss: 0.5830 - val_accuracy: 0.6882\n",
"Epoch 9/10\n",
"24/24 [==============================] - 143s 6s/step - loss: 0.3673 - accuracy: 0.8543 - val_loss: 0.5841 - val_accuracy: 0.7097\n",
"Epoch 10/10\n",
"24/24 [==============================] - 137s 6s/step - loss: 0.3618 - accuracy: 0.8503 - val_loss: 0.5781 - val_accuracy: 0.6882\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/5\n",
"24/24 [==============================] - 173s 6s/step - loss: 0.3594 - accuracy: 0.8476 - val_loss: 0.5973 - val_accuracy: 0.7312\n",
"Epoch 2/5\n",
"24/24 [==============================] - 142s 6s/step - loss: 0.3388 - accuracy: 0.8636 - val_loss: 0.5873 - val_accuracy: 0.6989\n",
"Epoch 3/5\n",
"24/24 [==============================] - 140s 6s/step - loss: 0.3332 - accuracy: 0.8730 - val_loss: 0.5834 - val_accuracy: 0.6720\n",
"Epoch 4/5\n",
"24/24 [==============================] - 144s 6s/step - loss: 0.3216 - accuracy: 0.8730 - val_loss: 0.5868 - val_accuracy: 0.6935\n",
"Epoch 5/5\n",
"24/24 [==============================] - 140s 6s/step - loss: 0.3167 - accuracy: 0.8703 - val_loss: 0.5977 - val_accuracy: 0.7258\n",
"NASNetLarge model saved.\n",
"Training MobileNetV2 model...\n",
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
"9406464/9406464 [==============================] - 1s 0us/step\n",
"Epoch 1/10\n",
"24/24 [==============================] - 146s 6s/step - loss: 0.6431 - accuracy: 0.6217 - val_loss: 0.5707 - val_accuracy: 0.6828\n",
"Epoch 2/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.5221 - accuracy: 0.7313 - val_loss: 0.5654 - val_accuracy: 0.6935\n",
"Epoch 3/10\n",
"24/24 [==============================] - 153s 6s/step - loss: 0.4873 - accuracy: 0.7607 - val_loss: 0.5328 - val_accuracy: 0.7527\n",
"Epoch 4/10\n",
"24/24 [==============================] - 137s 6s/step - loss: 0.4703 - accuracy: 0.7714 - val_loss: 0.5244 - val_accuracy: 0.7581\n",
"Epoch 5/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.4489 - accuracy: 0.7914 - val_loss: 0.5176 - val_accuracy: 0.7634\n",
"Epoch 6/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.4357 - accuracy: 0.7955 - val_loss: 0.5131 - val_accuracy: 0.7581\n",
"Epoch 7/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.4221 - accuracy: 0.8075 - val_loss: 0.5101 - val_accuracy: 0.7634\n",
"Epoch 8/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.4145 - accuracy: 0.8195 - val_loss: 0.5269 - val_accuracy: 0.7312\n",
"Epoch 9/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.4086 - accuracy: 0.8182 - val_loss: 0.5017 - val_accuracy: 0.7688\n",
"Epoch 10/10\n",
"24/24 [==============================] - 138s 6s/step - loss: 0.4035 - accuracy: 0.8222 - val_loss: 0.5032 - val_accuracy: 0.7796\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/5\n",
"24/24 [==============================] - 148s 6s/step - loss: 0.3930 - accuracy: 0.8262 - val_loss: 0.5387 - val_accuracy: 0.7204\n",
"Epoch 2/5\n",
"24/24 [==============================] - 152s 6s/step - loss: 0.3806 - accuracy: 0.8329 - val_loss: 0.5167 - val_accuracy: 0.7419\n",
"Epoch 3/5\n",
"24/24 [==============================] - 136s 6s/step - loss: 0.3729 - accuracy: 0.8382 - val_loss: 0.4888 - val_accuracy: 0.7849\n",
"Epoch 4/5\n",
"24/24 [==============================] - 136s 6s/step - loss: 0.3655 - accuracy: 0.8369 - val_loss: 0.5008 - val_accuracy: 0.7581\n",
"Epoch 5/5\n",
"24/24 [==============================] - 148s 6s/step - loss: 0.3557 - accuracy: 0.8463 - val_loss: 0.4915 - val_accuracy: 0.7688\n",
"MobileNetV2 model saved.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Function to plot ROC curve\n",
"def plot_roc_curve(fpr, tpr, roc_auc, model_name):\n",
" plt.plot(fpr, tpr, label=f'{model_name} (AUC = {roc_auc:.2f})')\n",
" plt.plot([0, 1], [0, 1], 'k--')\n",
" plt.xlim([0.0, 1.0])\n",
" plt.ylim([0.0, 1.05])\n",
" plt.xlabel('False Positive Rate')\n",
" plt.ylabel('True Positive Rate')\n",
" plt.title('Receiver Operating Characteristic (ROC)')\n",
" plt.legend(loc=\"lower right\")\n",
"\n",
"# Function to plot confusion matrix\n",
"def plot_confusion_matrix(cm, model_name):\n",
" sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
" plt.xlabel('Predicted')\n",
" plt.ylabel('True')\n",
" plt.title(f'Confusion Matrix for {model_name}')\n",
"\n",
"# Initialize an empty dictionary to store MCC scores\n",
"mcc_scores = {}\n",
"\n",
"# Load and test each model\n",
"model_predictions = []\n",
"for arch in model_architectures:\n",
" print(f\"Evaluating {arch['name']} finetuned model...\")\n",
" \n",
" # Load the saved model from Google Drive\n",
" model_path = os.path.join(model_save_path, f\"{arch['name']}_finetuned.h5\")\n",
" model = load_model(model_path)\n",
"\n",
" # Generate predictions for the test images using the loaded model\n",
" predictions = model.predict(test_images)\n",
" predictions_binary = [1 if p >= 0.5 else 0 for p in predictions]\n",
" \n",
" # Append the model's predictions to the model_predictions list\n",
" model_predictions.append(predictions_binary)\n",
"\n",
" # Calculate evaluation metrics (accuracy, precision, recall, f1-score, confusion matrix, AUC-ROC, and MCC) for the model's predictions\n",
" accuracy = accuracy_score(test_labels, predictions_binary)\n",
" precision = precision_score(test_labels, predictions_binary)\n",
" recall = recall_score(test_labels, predictions_binary)\n",
" f1 = f1_score(test_labels, predictions_binary)\n",
" cm = confusion_matrix(test_labels, predictions_binary)\n",
" auc_roc = roc_auc_score(test_labels, predictions)\n",
" mcc = matthews_corrcoef(test_labels, predictions_binary)\n",
"\n",
" # Print the model's evaluation metrics\n",
" print(f\"{arch['name']} model evaluation:\")\n",
" print(f\" Accuracy: {accuracy}\")\n",
" print(f\" Precision: {precision}\")\n",
" print(f\" Recall: {recall}\")\n",
" print(f\" F1-score: {f1}\")\n",
" print(f\" Confusion matrix: \\n{cm}\")\n",
" print(f\" AUC-ROC: {auc_roc}\")\n",
" print(f\" MCC: {mcc}\\n\")\n",
"\n",
" # Store the MCC score in the dictionary\n",
" mcc_scores[arch['name']] = mcc\n",
"\n",
" # Plot the confusion matrix\n",
" plt.figure()\n",
" plot_confusion_matrix(cm, arch['name'])\n",
" plt.show()\n",
"\n",
" # Plot the ROC curve\n",
" fpr, tpr, _ = roc_curve(test_labels, predictions)\n",
" roc_auc = auc(fpr, tpr)\n",
" plt.figure()\n",
" plot_roc_curve(fpr, tpr, roc_auc, arch['name'])\n",
" plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "AXAHFzIzAYl4",
"outputId": "95e89898-85c9-4a37-907e-d74e0bc81613"
},
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evaluating DenseNet121 finetuned model...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"8/8 [==============================] - 5s 39ms/step\n",
"DenseNet121 model evaluation:\n",
" Accuracy: 0.6623931623931624\n",
" Precision: 0.6012658227848101\n",
" Recall: 0.8558558558558559\n",
" F1-score: 0.7063197026022304\n",
" Confusion matrix: \n",
"[[60 63]\n",
" [16 95]]\n",
" AUC-ROC: 0.7453306965502088\n",
" MCC: 0.36644484679082756\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evaluating DenseNet201 finetuned model...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"8/8 [==============================] - 5s 57ms/step\n",
"DenseNet201 model evaluation:\n",
" Accuracy: 0.6239316239316239\n",
" Precision: 0.56353591160221\n",
" Recall: 0.918918918918919\n",
" F1-score: 0.6986301369863014\n",
" Confusion matrix: \n",
"[[ 44 79]\n",
" [ 9 102]]\n",
" AUC-ROC: 0.7639346663736908\n",
" MCC: 0.33003174636621696\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evaluating InceptionV3 finetuned model...\n",
"8/8 [==============================] - 2s 31ms/step\n",
"InceptionV3 model evaluation:\n",
" Accuracy: 0.688034188034188\n",
" Precision: 0.6759259259259259\n",
" Recall: 0.6576576576576577\n",
" F1-score: 0.6666666666666667\n",
" Confusion matrix: \n",
"[[88 35]\n",
" [38 73]]\n",
" AUC-ROC: 0.7499450670182377\n",
" MCC: 0.37372120906708856\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evaluating NASNetLarge finetuned model...\n",
"8/8 [==============================] - 8s 119ms/step\n",
"NASNetLarge model evaluation:\n",
" Accuracy: 0.6239316239316239\n",
" Precision: 0.5688622754491018\n",
" Recall: 0.8558558558558559\n",
" F1-score: 0.683453237410072\n",
" Confusion matrix: \n",
"[[51 72]\n",
" [16 95]]\n",
" AUC-ROC: 0.7489928953343588\n",
" MCC: 0.29879245429862344\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evaluating MobileNetV2 finetuned model...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"8/8 [==============================] - 1s 21ms/step\n",
"MobileNetV2 model evaluation:\n",
" Accuracy: 0.6410256410256411\n",
" Precision: 0.5859872611464968\n",
" Recall: 0.8288288288288288\n",
" F1-score: 0.6865671641791046\n",
" Confusion matrix: \n",
"[[58 65]\n",
" [19 92]]\n",
" AUC-ROC: 0.7635684464952758\n",
" MCC: 0.31921297473985993\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Define the list of possible weights\n",
"weight_values = [0.1, 0.2, 0.3, 0.4]\n",
"\n",
"# Generate all possible combinations of weights\n",
"weight_combinations = list(itertools.product(weight_values, repeat=len(model_architectures)))\n",
"\n",
"best_weights = None\n",
"best_accuracy = 0\n",
"\n",
"# Loop through all weight combinations\n",
"for weights in weight_combinations:\n",
" # Check if the sum of the weights is 1.0 (if not, skip this combination)\n",
" if round(sum(weights), 2) != 1.0:\n",
" continue\n",
"\n",
" # Calculate the weighted predictions\n",
" combined_predictions = np.sum(\n",
" [np.array(predictions) * weight for predictions, weight in zip(model_predictions, weights)],\n",
" axis=0\n",
" )\n",
"\n",
" # Convert the predictions to binary labels\n",
" threshold = 0.5\n",
" binary_predictions = (combined_predictions > threshold).astype(int)\n",
"\n",
" # Calculate the accuracy\n",
" accuracy = accuracy_score(test_labels, binary_predictions)\n",
"\n",
" # Check if the accuracy is better than the best accuracy found so far\n",
" if accuracy > best_accuracy:\n",
" best_accuracy = accuracy\n",
" best_weights = weights\n",
"\n",
"print(f\"Best weights: {best_weights}\")\n",
"print(f\"Best accuracy: {best_accuracy}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WEcBBLroA3v2",
"outputId": "ec127e5c-9bcc-408a-94bb-7c8949389784"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Best weights: (0.1, 0.1, 0.3, 0.1, 0.4)\n",
"Best accuracy: 0.688034188034188\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Calculate the weighted predictions using the best weights\n",
"combined_predictions = np.sum(\n",
" [np.array(predictions) * weight for predictions, weight in zip(model_predictions, best_weights)],\n",
" axis=0\n",
")\n",
"\n",
"# Convert the predictions to binary labels\n",
"threshold = 0.5\n",
"binary_predictions = (combined_predictions > threshold).astype(int)\n",
"\n",
"# Calculate the accuracy, precision, recall, and F1-score\n",
"accuracy = accuracy_score(test_labels, binary_predictions)\n",
"precision = precision_score(test_labels, binary_predictions)\n",
"recall = recall_score(test_labels, binary_predictions)\n",
"f1 = f1_score(test_labels, binary_predictions)\n",
"cm = confusion_matrix(test_labels, binary_predictions)\n",
"auc_roc = roc_auc_score(test_labels, binary_predictions)\n",
"mcc = matthews_corrcoef(test_labels, binary_predictions)\n",
"\n",
"print(\"Ensemble model evaluation:\")\n",
"print(f\"Accuracy: {accuracy}\")\n",
"print(f\"Precision: {precision}\")\n",
"print(f\"Recall: {recall}\")\n",
"print(f\"F1-score: {f1}\")\n",
"print(f\" Confusion matrix: \\n{cm}\")\n",
"print(f\" AUC-ROC: {auc_roc}\")\n",
"print(f\" MCC: {mcc}\\n\")\n",
"\n",
"mcc_scores['Ensemble'] = mcc\n",
"\n",
"plt.figure()\n",
"plt.barh(list(mcc_scores.keys()), list(mcc_scores.values()), color='blue')\n",
"plt.xlabel('MCC Score')\n",
"plt.ylabel('Model')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 656
},
"id": "KaQQnqaUCeLo",
"outputId": "57be0fa1-360d-4656-ac6a-57831fc3e0d0"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ensemble model evaluation:\n",
"Accuracy: 0.688034188034188\n",
"Precision: 0.6266666666666667\n",
"Recall: 0.8468468468468469\n",
"F1-score: 0.7203065134099618\n",
" Confusion matrix: \n",
"[[67 56]\n",
" [17 94]]\n",
" AUC-ROC: 0.6957811470006592\n",
" MCC: 0.4075957404067199\n",
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0, 0.5, 'Model')"
]
},
"metadata": {},
"execution_count": 33
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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vwoQJmj17tlJSUtS/f39Jkq+vr8aMGaNRo0YpNzdXd955p+x2u+Li4uTn56eoqCiFhITIZrNp7dq1uueee+Tt7a3KlStLkipXrqz+/ftrwoQJSk9Pz/f8wB9++EF/+tOfFB4ertGjRystLc3xugUGBhZprgAAoGIr8yt/kuTn5yc/P78C66ZPn66+ffvqkUceUbt27XTo0CF98cUXqlq1ar52Tz/9tG677TalpaXpX//6l2OV7Woee+wxzZkzR/Pnz1fLli3VpUsXLViwoNCVP+nS7wrHjRunCxcuOO1/+eWX9eKLL2ratGlq2rSpIiIitG7dOkdfderU0aRJkzR+/HjVrFnT8ZV3nujoaJ06dUrh4eEKCgpyqluxYoVOnDihjz76SLVr13aUP/zhD0WaJwAAqPhsxhhT2oNA2ZKenv5/N7PYJRUcugEAZRuf7taT9/ltt9sLXTSTysnKHwAAAIoH4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAtxK+0BoOyy2yU/v9IeBQAAKE6s/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWwt/2RaH8/Ut7BABQcRlT2iOAVbHyBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAspE+FvyJAhstlsmj59utP+NWvWyGaz5WvfpEkTeXp6Ki0tLV9dSkqKHn74YQUFBcnLy0t169ZVZGSk9u/f72hjs9nk5eWln3/+2enYPn36aMiQIUUed2xsrGw2m06fPp1vPn369ClyPwAAADdLmQh/kuTl5aVXX31Vp06dumK7bdu26fz583rwwQe1cOFCp7qsrCz16NFDdrtdq1at0oEDB7Rs2TK1bNkyX0Cz2WyaOHFicU+jROTk5Cg3N7e0hwEAACqAMhP+unfvrlq1amnatGlXbDd37lw9/PDDeuSRRzRv3jynuoSEBCUnJ+vdd99Vx44dFRISok6dOumVV15Rx44dndqOGDFCH330kX744YdCz5Wbm6tp06apQYMG8vb2VuvWrbVixQpJUmpqqrp27SpJqlq1qmw2W5FXDV9//XW1bNlSlSpVUnBwsP76178qIyPDUb9gwQJVqVJFn332mZo1ayZPT08dPnxYx44d07333itvb281aNBAS5YsUf369fXmm286jj19+rQee+wxBQYGys/PT3/605+0Z8+eIo0LAABUfGUm/Lm6umrq1Kl6++239csvvxTY5syZM1q+fLkGDRrkWOHbunWroz4wMFAuLi5asWKFcnJyrni+Tp06qVevXho/fnyhbaZNm6ZFixbp/fffV0JCgkaNGqVBgwZp8+bNCg4O1sqVKyVJBw4c0LFjx/TWW28Vaa4uLi76xz/+oYSEBC1cuFBfffWVxo4d69Tm3LlzevXVVzVnzhwlJCSoRo0aGjx4sI4eParY2FitXLlSH374oY4fP+50XL9+/XT8+HF9/vnn2rVrl9q1a6du3brp5MmThY4nMzNT6enpTgUAAFRQpgyIiooykZGRxhhjOnbsaIYOHWqMMWb16tXm90P88MMPTZs2bRzbTz/9tImKinLq65133jE+Pj7G19fXdO3a1UyePNkkJyc7tZFkVq9ebRISEoyrq6vZsmWLMcaYyMhIR38XLlwwPj4+Zvv27U7HRkdHmwEDBhhjjNm0aZORZE6dOlXofIpi+fLlplq1ao7t+fPnG0kmPj7esS8pKclIMt9++61j38GDB40k88YbbxhjjNm6davx8/MzFy5ccOq/YcOG5oMPPij0/DExMUZSAcVuJEOhUCiUEihAcbPb7UaSsdvtV2xXZlb+8rz66qtauHChkpKS8tXNmzdPgwYNcmwPGjRIy5cv15kzZxz7hg8frrS0NC1evFhhYWFavny5mjdvrg0bNuTrr1mzZho8eHCBq3+HDh3SuXPn1KNHD1WuXNlRFi1apOTk5Bua47///W9169ZNderUka+vrx555BH99ttvOnfunKONh4eHWrVq5dg+cOCA3Nzc1K5dO8e+Ro0aqWrVqo7tPXv2KCMjQ9WqVXMac0pKyhXHPGHCBNntdkc5cuTIDc0PAACUXW6lPYDL3XXXXQoPD9eECROcfkOXmJior7/+Wt98843GjRvn2J+Tk6OlS5dq2LBhjn2+vr7q3bu3evfurVdeeUXh4eF65ZVX1KNHj3znmzRpkkJDQ7VmzRqn/Xm/wVu3bp3q1KnjVOfp6Xnd80tNTVWvXr305JNPasqUKQoICNC2bdsUHR2tixcvysfHR5Lk7e1d4J3OV5KRkaHatWsrNjY2X12VKlUKPc7T0/OG5gQAAMqPMhf+JGn69Olq06aNGjdu7Ng3d+5c3XXXXZo1a5ZT2/nz52vu3LlO4e/3bDabmjRpou3btxdYHxwcrBEjRui5555Tw4YNHft/f6NFly5dCjzWw8NDkq76+8Lf27Vrl3Jzc/Xaa6/JxeXSwusnn3xy1eMaN26s7Oxs7d69W7fddpukS6uTv787ul27dkpLS5Obm5vq169f5DEBAADrKJPhr2XLlho4cKD+8Y9/SLr0CJd//vOfmjx5slq0aOHU9rHHHtPrr7+uhIQEZWVlKSYmRo888oiaNWsmDw8Pbd68WfPmzXNaLbzchAkTNHv2bKWkpKh///6SLq0ejhkzRqNGjVJubq7uvPNO2e12xcXFyc/PT1FRUQoJCZHNZtPatWt1zz33yNvbW5UrV5Yk2e12xcfHO52nWrVqatSokbKysvT222+rd+/eiouL0/vvv3/V16RJkybq3r27Hn/8cb333ntyd3fXM88847RC2L17d4WFhalPnz6aMWOGQkNDdfToUa1bt07333+/2rdvX+RrAAAAKqib9BvEKyroBomUlBTj4eFhJJkVK1YYFxcXk5aWVuDxTZs2NaNGjTInTpwwI0eONC1atDCVK1c2vr6+pmXLlmbmzJkmJyfH0V66dMPH702dOtVIcrqBJDc317z55pumcePGxt3d3QQGBprw8HCzefNmR5vJkyebWrVqGZvN5jg2KirKSPlvoIiOjjbGGPP666+b2rVrG29vbxMeHm4WLVpkpP+/cWT+/PnG398/3zyPHj1q7r77buPp6WlCQkLMkiVLTI0aNcz777/vaJOenm6eeuopExQUZNzd3U1wcLAZOHCgOXz48FWuwv/L+8EoN3xQKBRKyRWguBX1hg+bMcaUUu7EDfrll18UHBzsuIGkuKSnp8vf31+SXZJfsfULAPh/fPqiuOV9ftvtdvn5Ff75XSa/9kXBvvrqK2VkZKhly5Y6duyYxo4dq/r16+uuu+4q7aEBAIBygvBXjmRlZem5557TTz/9JF9fX91xxx1avHix3N3dS3toAACgnOBrX+TD174AUPL49EVxK+rXvmXuIc8AAAAoOYQ/AAAACyH8AQAAWEiRb/jIe+ByUYwcOfK6BgMAAICSVeQbPho0aFC0Dm02/fTTTzc0KJQubvgAgJLHDR8obsX+nL+UlJRiGRgAAABKzw395u/ixYs6cOCAsrOzi2s8AAAAKEHXFf7OnTun6Oho+fj4qHnz5jp8+LAk6amnntL06dOLdYAAAAAoPtcV/iZMmKA9e/YoNjZWXl5ejv3du3fXsmXLim1wAAAAKF7X9efd1qxZo2XLlqljx46y2WyO/c2bN1dycnKxDQ4AAADF67pW/k6cOKEaNWrk23/27FmnMAgAAICy5brCX/v27bVu3TrHdl7gmzNnjsLCwopnZAAAACh21/W179SpU3X33XcrMTFR2dnZeuutt5SYmKjt27dr8+bNxT1GAAAAFJPrCn933nmn4uPjNX36dLVs2VJffvml2rVrpx07dqhly5bFPUaUErtdusIzIgEAQDlU5L/wAeso6hPCAQBA2VHsf+EjPT29yCcnMAAAAJRNRQ5/VapUKfKdvDk5Odc9IAAAAJScIoe/TZs2Of6dmpqq8ePHa8iQIY67e3fs2KGFCxdq2rRpxT9KAAAAFIvr+s1ft27d9Nhjj2nAgAFO+5csWaIPP/xQsbGxxTU+lAJ+8wcAQPlT1M/v63rO344dO9S+fft8+9u3b69vvvnmeroEAADATXBd4S84OFizZ8/Ot3/OnDkKDg6+4UEBAACgZFzXc/7eeOMN9e3bV59//rk6dOggSfrmm2908OBBrVy5slgHCAAAgOJzXSt/99xzjw4ePKjevXvr5MmTOnnypHr37q0ff/xR99xzT3GPEQAAAMWEhzwjH274AACg/Cn2hzxf7vTp05o7d66SkpIkSc2bN9fQoUPl7+9/vV0CAACghF3Xyt93332n8PBweXt76/bbb5ckffvttzp//rzj7/yi/Mr7fw6SXRIrfwCA8sWq32kWdeXvusJf586d1ahRI82ePVtubpcWD7Ozs/XYY4/pp59+0pYtW65/5Ch1hD8AQHlG+CuB8Oft7a3du3erSZMmTvsTExPVvn17nTt37tpHjDKD8AcAKM8IfyXwkGc/Pz8dPnw43/4jR47I19f3eroEAADATXBd4a9///6Kjo7WsmXLdOTIER05ckRLly4t8E++AQAAoOy4rrt9Z86cKZvNpsGDBys7O1vGGHl4eOjJJ5/U9OnTi3uMAAAAKCY39Jy/c+fOKTk5WZLUsGFD+fj4FNvAUHr4zR8AoDzjN3/F+Jy/oUOHFqndvHnzrqVbAAAA3CTXFP4WLFigkJAQtW3bVvxhEAAAgPLnmsLfk08+qY8//lgpKSl69NFHNWjQIAUEBJTU2AAAAFDMrulu31mzZunYsWMaO3as/vWvfyk4OFgPPfSQvvjiC1YCAQAAyoEbuuHj559/1oIFC7Ro0SJlZ2crISFBlStXLs7xoRRwwwcAoDyz6npUiT7k2XGwi4tsNpuMMcrJybmRrgAAAHATXHP4y8zM1Mcff6wePXooNDRU+/bt0zvvvKPDhw+z6gcAAFDGXdMNH3/961+1dOlSBQcHa+jQofr4449VvXr1khobAAAAitk1/ebPxcVF9erVU9u2bWWz2Qptt2rVqmIZHEoHv/kDAJRn/OavGB/yPHjw4CuGPgAAAJRt1/yQZwAAAJRfN3S3Lwr20ksvqU2bNqU9DAAAgHzKVfgbMmSI+vTpU9rDcGKz2bRmzRqnfWPGjNHGjRuL7RwrV66Uq6ur/vOf/xRYf+utt2r06NGSLgXPJk2aqFKlSqpataq6d++unTt3FttYAABA+Vauwl95UblyZVWrVq3Y+rvvvvtUrVo1LVy4MF/dli1bdOjQIUVHR0uSQkND9c4772jfvn3atm2b6tevr549e+rEiRPFNh4AAFB+ldvw98c//lEjR47U2LFjFRAQoFq1aumll15yanP69Gk98cQTqlmzpry8vNSiRQutXbvWUb9t2zZ17txZ3t7eCg4O1siRI3X27FlHff369fXyyy9rwIABqlSpkurUqaNZs2Y51UvS/fffL5vN5ti+/Gvf3NxcTZ48WXXr1pWnp6fatGmj9evXO+pTU1Nls9m0atUqde3aVT4+PmrdurV27NghSXJ3d9cjjzxS4G8u582bpw4dOqh58+aSpIcffljdu3fXLbfcoubNm+v1119Xenq69u7dez0vMwAAqGDKbfiTpIULF6pSpUrauXOnZsyYocmTJ2vDhg2SLgWuu+++W3Fxcfroo4+UmJio6dOny9XVVZKUnJysiIgI9e3bV3v37tWyZcu0bds2jRgxwukcf//739W6dWvt3r1b48eP19NPP+04x7fffitJmj9/vo4dO+bYvtxbb72l1157TTNnztTevXsVHh6u++67TwcPHnRq9/zzz2vMmDGKj49XaGioBgwYoOzsbElSdHS0Dh48qC1btjjaZ2RkaMWKFY5Vv8tdvHhRH374ofz9/dW6detCX8fMzEylp6c7FQAAUEGZciQqKspERkYaY4zp0qWLufPOO53q//CHP5hx48YZY4z54osvjIuLizlw4ECBfUVHR5vHH3/cad/WrVuNi4uLOX/+vDHGmJCQEBMREeHUpn///ubuu+92bEsyq1evdmoTExNjWrdu7dgOCgoyU6ZMyTfWv/71r8YYY1JSUowkM2fOHEd9QkKCkWSSkpIc+zp27GiioqIc23PnzjU+Pj4mPT3dqe9//etfplKlSsZms5mgoCDzzTffFPga/H68kgoodnPpaUkUCoVCoZSfYlV2u91IMna7/YrtyvXKX6tWrZy2a9eurePHj0uS4uPjVbduXYWGhhZ47J49e7RgwQJVrlzZUcLDw5Wbm6uUlBRHu7CwMKfjwsLClJSUVOQxpqen6+jRo+rUqZPT/k6dOuXr5/fzqV27tiQ55iNJQ4cO1YoVK3TmzBlJl77y7devn3x9fZ366dq1q+Lj47V9+3ZFRETooYcecurnchMmTJDdbneUI0eOFHl+AACgfCnX4c/d3d1p22azKTc3V5Lk7e19xWMzMjL0xBNPKD4+3lH27NmjgwcPqmHDhiU25iv5/XzyHqadNx9J+vOf/yxJ+uSTT3Tw4EHFxcUV+JVvpUqV1KhRI3Xs2FFz586Vm5ub5s6dW+h5PT095efn51QAAEDFdE0PeS5PWrVqpV9++UU//vhjgat/7dq1U2Jioho1anTFfr7++ut8202bNnVsu7u7Kycnp9Dj/fz8FBQUpLi4OHXp0sWxPy4uTrfffntRpyNJ8vX1Vb9+/TRv3jwlJycrNDRUnTt3vupxubm5yszMvKZzAQCAiqnChr8uXbrorrvuUt++ffX666+rUaNG2r9/v2w2myIiIjRu3Dh17NhRI0aM0GOPPaZKlSopMTFRGzZs0DvvvOPoJy4uTjNmzFCfPn20YcMGLV++XOvWrXPU169fXxs3blSnTp3k6empqlWr5hvLs88+q5iYGDVs2FBt2rTR/PnzFR8fr8WLF1/zvKKjo9W5c2clJSVp3LhxTnVnz57VlClTdN9996l27dr673//q1mzZuk///mP+vXrd83nAgAAFU+5/tr3alauXKk//OEPGjBggJo1a6axY8c6VulatWqlzZs368cff1Tnzp3Vtm1bTZw4UUFBQU59PPPMM/ruu+/Utm1bvfLKK3r99dcVHh7uqH/ttde0YcMGBQcHq23btgWOY+TIkRo9erSeeeYZtWzZUuvXr9dnn32mW2+99ZrndOedd6px48ZKT0/X4MGDnepcXV21f/9+9e3bV6Ghoerdu7d+++03bd261fEoGAAAYG02Y4wp7UGUVfXr19ff/vY3/e1vfyvtodxU6enp8vf3l2SXxO//AADli1WTTd7nt91uv+Lv9yv0yh8AAACcEf4AAAAspMLe8FEcUlNTS3sIAAAAxYqVPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwELfSHgDKLrtd8vMr7VEAAIDixMofAACAhRD+AAAALITwBwAAYCGEPwAAAAsh/AEAAFgI4Q8AAMBCCH8AAAAWQvgDAACwEMIfAACAhRD+AAAALITwBwAAYCH8bV8Uyt+/tEcAALhZjCntEeBmYeUPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFlKq4W/IkCGy2Wyy2Wxyd3dXzZo11aNHD82bN0+5ubmlObR8bDabvLy89PPPPzvt79Onj4YMGVLkfmJjY2Wz2XT69Gmn/dOmTdMf/vAH+fr6qkaNGurTp48OHDjg1ObChQsaPny4qlWrpsqVK6tv37769ddfndqMHDlSt912mzw9PdWmTZtrmSIAALCAUl/5i4iI0LFjx5SamqrPP/9cXbt21dNPP61evXopOzu7tIfnxGazaeLEiSXS9+bNmzV8+HB9/fXX2rBhg7KystSzZ0+dPXvW0WbUqFH617/+peXLl2vz5s06evSoHnjggXx9DR06VP379y+RcQIAgHLOlKKoqCgTGRmZb//GjRuNJDN79mxjjDGnTp0y0dHRpnr16sbX19d07drVxMfHO9rHxMSY1q1bm0WLFpmQkBDj5+dn+vfvb9LT0x1tli9fblq0aGG8vLxMQECA6datm8nIyHDUz5492zRp0sR4enqaxo0bm1mzZjmNSZIZM2aMcXFxMfv27XPsj4yMNFFRUY7tnJwcM3XqVFO/fn3j5eVlWrVqZZYvX26MMSYlJcVIciq/P/b3jh8/biSZzZs3G2OMOX36tHF3d3f0ZYwxSUlJRpLZsWNHvuPzXpPrYbfb/298diMZCoVCoVigoPzL+/y22+1XbFfqK38F+dOf/qTWrVtr1apVkqR+/frp+PHj+vzzz7Vr1y61a9dO3bp108mTJx3HJCcna82aNVq7dq3Wrl2rzZs3a/r06ZKkY8eOacCAARo6dKiSkpIUGxurBx54QMYYSdLixYs1ceJETZkyRUlJSZo6dapefPFFLVy40GlcnTp1Uq9evTR+/PhCxz5t2jQtWrRI77//vhISEjRq1CgNGjRImzdvVnBwsFauXClJOnDggI4dO6a33nqrwH7sdrskKSAgQJK0a9cuZWVlqXv37o42TZo0Ub169bRjx45ren0vl5mZqfT0dKcCAAAqJrfSHkBhmjRpor1792rbtm365ptvdPz4cXl6ekqSZs6cqTVr1mjFihV6/PHHJUm5ublasGCBfH19JUmPPPKINm7cqClTpujYsWPKzs7WAw88oJCQEElSy5YtHeeKiYnRa6+95vgKtUGDBkpMTNQHH3ygqKgop3FNmzZNrVq10tatW9W5c2enuszMTE2dOlX//ve/FRYWJkm65ZZbtG3bNn3wwQfq0qWLI8zVqFFDVapUKXDuubm5+tvf/qZOnTqpRYsWkqS0tDR5eHjkO6ZmzZpKS0u7ptf2ctOmTdOkSZNuqA8AAFA+lNnwZ4yRzWbTnj17lJGRoWrVqjnVnz9/XsnJyY7t+vXrO4KfJNWuXVvHjx+XJLVu3VrdunVTy5YtFR4erp49e+rBBx9U1apVdfbsWSUnJys6OlrDhg1zHJ+dnS1/f/9842rWrJkGDx6s8ePHKy4uzqnu0KFDOnfunHr06OG0/+LFi2rbtm2R5z58+HD98MMP2rZtW5GPuRETJkzQ6NGjHdvp6ekKDg6+KecGAAA3V5kNf0lJSWrQoIEyMjJUu3ZtxcbG5mvz+1Uwd3d3pzqbzea4Y9jV1VUbNmzQ9u3b9eWXX+rtt9/W888/r507d8rHx0eSNHv2bHXo0MGpD1dX1wLHNmnSJIWGhmrNmjVO+zMyMiRJ69atU506dZzq8lYtr2bEiBFau3attmzZorp16zr216pVSxcvXtTp06ed5v3rr7+qVq1aReq7MJ6enkUeHwAAKN/KZPj76quvtG/fPo0aNUp169ZVWlqa3NzcVL9+/evu02azqVOnTurUqZMmTpyokJAQrV69WqNHj1ZQUJB++uknDRw4sEh9BQcHa8SIEXruuefUsGFDx/5mzZrJ09NThw8fVpcuXQo81sPDQ5KUk5PjtN8Yo6eeekqrV69WbGysGjRo4FR/2223yd3dXRs3blTfvn0lXfrd4OHDhx1fMQMAAFxNqYe/zMxMpaWlKScnR7/++qvWr1+vadOmqVevXho8eLBcXFwUFhamPn36aMaMGQoNDdXRo0e1bt063X///Wrfvv1Vz7Fz505t3LhRPXv2VI0aNbRz506dOHFCTZs2lXRpJW/kyJHy9/dXRESEMjMz9d133+nUqVNOX4f+3oQJEzR79mylpKQ4Hqvi6+urMWPGaNSoUcrNzdWdd94pu92uuLg4+fn5KSoqSiEhIbLZbFq7dq3uueceeXt7q3Llyho+fLiWLFmiTz/9VL6+vo7f8fn7+8vb21v+/v6Kjo7W6NGjFRAQID8/Pz311FMKCwtTx44dHeM6dOiQMjIylJaWpvPnzys+Pl7SpWCaFzwBAICF3YxbjwsTFRVlpEuPPHFzczOBgYGme/fuZt68eSYnJ8fRLj093Tz11FMmKCjIuLu7m+DgYDNw4EBz+PBhY0zBjzV54403TEhIiDHGmMTERBMeHm4CAwONp6enCQ0NNW+//bZT+8WLF5s2bdoYDw8PU7VqVXPXXXeZVatWOeolmdWrVzsdM3XqVCM5P64lNzfXvPnmm6Zx48bG3d3dBAYGmvDwcMcjW4wxZvLkyaZWrVrGZrM5js17HS4v8+fPdxx3/vx589e//tVUrVrV+Pj4mPvvv98cO3bMaUxdunQpsJ+UlJQiXJFLeNQLhUKhWK+g/Cvqo15sxhhz0xMnyrT09PT/u9nFLsmvtIcDALgJSAPlX97nt91ul59f4Z/fZfI5fwAAACgZhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACzErbQHgLLLbpf8/Ep7FAAAoDix8gcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCH/bF4Xy9y/tEQAA4MyY0h5B+cfKHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACykVMPfkCFDZLPZZLPZ5O7urpo1a6pHjx6aN2+ecnNzS3No+dhsNnl5eennn3922t+nTx8NGTKkyP3ExsbKZrPp9OnTTvu3bNmi3r17KygoSDabTWvWrHGqz8rK0rhx49SyZUtVqlRJQUFBGjx4sI4ePerUbsqUKbrjjjvk4+OjKlWqXMMMAQCAFZT6yl9ERISOHTum1NRUff755+ratauefvpp9erVS9nZ2aU9PCc2m00TJ04skb7Pnj2r1q1ba9asWQXWnzt3Tt9//71efPFFff/991q1apUOHDig++67z6ndxYsX1a9fPz355JMlMk4AAFDOmVIUFRVlIiMj8+3fuHGjkWRmz55tjDHm1KlTJjo62lSvXt34+vqarl27mvj4eEf7mJgY07p1a7No0SITEhJi/Pz8TP/+/U16erqjzfLly02LFi2Ml5eXCQgIMN26dTMZGRmO+tmzZ5smTZoYT09P07hxYzNr1iynMUkyY8aMMS4uLmbfvn2O/ZGRkSYqKsqxnZOTY6ZOnWrq169vvLy8TKtWrczy5cuNMcakpKQYSU7l98f+/lyrV6++6uv3zTffGEnm559/zlc3f/584+/vf9U+CmK32/9vfHYjGQqFQqFQykxB4fI+v+12+xXblfrKX0H+9Kc/qXXr1lq1apUkqV+/fjp+/Lg+//xz7dq1S+3atVO3bt108uRJxzHJyclas2aN1q5dq7Vr12rz5s2aPn26JOnYsWMaMGCAhg4dqqSkJMXGxuqBBx6QMUaStHjxYk2cOFFTpkxRUlKSpk6dqhdffFELFy50GlenTp3Uq1cvjR8/vtCxT5s2TYsWLdL777+vhIQEjRo1SoMGDdLmzZsVHByslStXSpIOHDigY8eO6a233rru18lut8tms93w17uZmZlKT093KgAAoIK6OVm0YIWt/BljTP/+/U3Tpk3N1q1bjZ+fn7lw4YJTfcOGDc0HH3xgjLm08ufj4+O00vfss8+aDh06GGOM2bVrl5FkUlNTCzxXw4YNzZIlS5z2vfzyyyYsLMyxLV1ajUtISDCurq5my5Ytxhjnlb8LFy4YHx8fs337dqe+oqOjzYABA4wxxmzatMlIMqdOnSr0dck715WcP3/etGvXzjz88MMF1l/Lyl9MTIyRVEBh5Y9CoVAoZaugcEVd+XMrrdB5NcYY2Ww27dmzRxkZGapWrZpT/fnz55WcnOzYrl+/vnx9fR3btWvX1vHjxyVJrVu3Vrdu3dSyZUuFh4erZ8+eevDBB1W1alWdPXtWycnJio6O1rBhwxzHZ2dny9/fP9+4mjVrpsGDB2v8+PGKi4tzqjt06JDOnTunHj16OO2/ePGi2rZte/0vxmWysrL00EMPyRij995774b7mzBhgkaPHu3YTk9PV3Bw8A33CwAAyp4yG/6SkpLUoEEDZWRkqHbt2oqNjc3X5vdfd7q7uzvV2Ww2xx3Drq6u2rBhg7Zv364vv/xSb7/9tp5//nnt3LlTPj4+kqTZs2erQ4cOTn24uroWOLZJkyYpNDQ03x25GRkZkqR169apTp06TnWenp5XnXNR5AW/n3/+WV999ZX8/PxuuE9PT89iGx8AACjbymT4++qrr7Rv3z6NGjVKdevWVVpamtzc3FS/fv3r7tNms6lTp07q1KmTJk6cqJCQEK1evVqjR49WUFCQfvrpJw0cOLBIfQUHB2vEiBF67rnn1LBhQ8f+Zs2aydPTU4cPH1aXLl0KPNbDw0OSlJOTc81zyAt+Bw8e1KZNm/KthgIAAFxNqYe/zMxMpaWlKScnR7/++qvWr1+vadOmqVevXho8eLBcXFwUFhamPn36aMaMGQoNDdXRo0e1bt063X///Wrfvv1Vz7Fz505t3LhRPXv2VI0aNbRz506dOHFCTZs2lXRpJW/kyJHy9/dXRESEMjMz9d133+nUqVNOX4f+3oQJEzR79mylpKSof//+kiRfX1+NGTNGo0aNUm5uru68807Z7XbFxcXJz89PUVFRCgkJkc1m09q1a3XPPffI29tblStXVkZGhg4dOuToPyUlRfHx8QoICFC9evWUlZWlBx98UN9//73Wrl2rnJwcpaWlSZICAgIcofLw4cM6efKkDh8+rJycHMXHx0uSGjVqpMqVK1/3dQIAABXETfkFYiGioqKMdOnmAjc3NxMYGGi6d+9u5s2bZ3Jychzt0tPTzVNPPWWCgoKMu7u7CQ4ONgMHDjSHDx82xvz/o15+74033jAhISHGGGMSExNNeHi4CQwMNJ6eniY0NNS8/fbbTu0XL15s2rRpYzw8PEzVqlXNXXfdZVatWuWol/LfhDF16lQjOT+uJTc317z55pumcePGxt3d3QQGBprw8HCzefNmR5vJkyebWrVqGZvN5jg270aQy0tefUGPickrmzZtKvA1LazN1fCoFwqFQqGU1YLCFfWGD5sxxtzMsImyLz09/f9udrFLuvHfFAIAUFxILYXL+/y22+1XvCegTD7nDwAAACWD8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIW4lfYAUHbZ7ZKfX2mPAgAAFCdW/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBDCHwAAgIUQ/gAAACyE8AcAAGAhhD8AAAALIfwBAABYCOEPAADAQgh/AAAAFkL4AwAAsBC30h4Ayh5jjCQpPT29lEcCAACKKu9zO+9zvDCEP+Tz22+/SZKCg4NLeSQAAOBanTlzRv7+/oXWE/6QT0BAgCTp8OHDV/wvT3mXnp6u4OBgHTlyRH5+fqU9nBLDPCsW5llxWGGOEvO8mYwxOnPmjIKCgq7YjvCHfFxcLv0U1N/fv0K/UfP4+fkxzwqEeVYsVpinFeYoMc+bpSiLNtzwAQAAYCGEPwAAAAsh/CEfT09PxcTEyNPTs7SHUqKYZ8XCPCsWK8zTCnOUmGdZZDNXux8YAAAAFQYrfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPBnAbNmzVL9+vXl5eWlDh066Jtvvrli++XLl6tJkyby8vJSy5Yt9T//8z9O9cYYTZw4UbVr15a3t7e6d++ugwcPluQUiqS45zlkyBDZbDanEhERUZJTKJJrmWdCQoL69u2r+vXry2az6c0337zhPm+W4p7nSy+9lO96NmnSpARnUDTXMs/Zs2erc+fOqlq1qqpWraru3bvna18R3p9FmWdFeH+uWrVK7du3V5UqVVSpUiW1adNG//znP53aVITrWZR5VoTr+XtLly6VzWZTnz59nPaXmetpUKEtXbrUeHh4mHnz5pmEhAQzbNgwU6VKFfPrr78W2D4uLs64urqaGTNmmMTERPPCCy8Yd3d3s2/fPkeb6dOnG39/f7NmzRqzZ88ec99995kGDRqY8+fP36xp5VMS84yKijIRERHm2LFjjnLy5MmbNaUCXes8v/nmGzNmzBjz8ccfm1q1apk33njjhvu8GUpinjExMaZ58+ZO1/PEiRMlPJMru9Z5Pvzww2bWrFlm9+7dJikpyQwZMsT4+/ubX375xdGmIrw/izLPivD+3LRpk1m1apVJTEw0hw4dMm+++aZxdXU169evd7SpCNezKPOsCNczT0pKiqlTp47p3LmziYyMdKorK9eT8FfB3X777Wb48OGO7ZycHBMUFGSmTZtWYPuHHnrI3HvvvU77OnToYJ544gljjDG5ubmmVq1a5u9//7uj/vTp08bT09N8/PHHJTCDoinueRpz6X+MLn/jlrZrnefvhYSEFBiKbqTPklIS84yJiTGtW7cuxlHeuBt97bOzs42vr69ZuHChMabivD8vd/k8jal47888bdu2NS+88IIxpuJeT2Oc52lMxbme2dnZ5o477jBz5szJN6eydD352rcCu3jxonbt2qXu3bs79rm4uKh79+7asWNHgcfs2LHDqb0khYeHO9qnpKQoLS3NqY2/v786dOhQaJ8lrSTmmSc2NlY1atRQ48aN9eSTT+q3334r/gkU0fXMszT6vFElOaaDBw8qKChIt9xyiwYOHKjDhw/f6HCvW3HM89y5c8rKylJAQICkivP+vNzl88xTkd6fxhht3LhRBw4c0F133SWpYl7PguaZpyJcz8mTJ6tGjRqKjo7OV1eWrqfbTT0bbqr//ve/ysnJUc2aNZ3216xZU/v37y/wmLS0tALbp6WlOerz9hXW5mYriXlKUkREhB544AE1aNBAycnJeu6553T33Xdrx44dcnV1Lf6JXMX1zLM0+rxRJTWmDh06aMGCBWrcuLGOHTumSZMmqXPnzvrhhx/k6+t7o8O+ZsUxz3HjxikoKMjxYVJR3p+Xu3yeUsV5f9rtdtWpU0eZmZlydXXVu+++qx49ekiqWNfzSvOUKsb13LZtm+bOnav4+PgC68vS9ST8AYX485//7Ph3y5Yt1apVKzVs2FCxsbHq1q1bKY4M1+Puu+92/LtVq1bq0KGDQkJC9MknnxT4/9LLuunTp2vp0qWKjY2Vl5dXaQ+nxBQ2z4ry/vT19VV8fLwyMjK0ceNGjR49Wrfccov++Mc/lvbQitXV5lner+eZM2f0yCOPaPbs2apevXppD+eq+Nq3AqtevbpcXV3166+/Ou3/9ddfVatWrQKPqVWr1hXb5/3ntfRZ0kpingW55ZZbVL16dR06dOjGB30drmeepdHnjbpZY6pSpYpCQ0PL5fWcOXOmpk+fri+//FKtWrVy7K8o7888hc2zIOX1/eni4qJGjRqpTZs2euaZZ/Tggw9q2rRpkirW9bzSPAtS3q5ncnKyUlNT1bt3b7m5ucnNzU2LFi3SZ599Jjc3NyUnJ5ep60n4q8A8PDx02223aePGjY59ubm52rhxo8LCwgo8JiwszKm9JG3YsMHRvkGDBqpVq5ZTm/T0dO3cubPQPktaScyzIL/88ot+++031a5du3gGfo2uZ56l0eeNulljysjIUHJycrm7njNmzNDLL7+s9evXq3379k51FeX9KV15ngWpKO/P3NxcZWZmSqpY1/Nyv59nQcrb9WzSpIn27dun+Ph4R7nvvvvUtWtXxcfHKzg4uGxdz5t6ewluuqVLlxpPT0+zYMECk5iYaB5//HFTpUoVk5aWZowx5pFHHjHjx493tI+LizNubm5m5syZJikpycTExBT4qJcqVaqYTz/91Ozdu9dERkaWiUcPFOc8z5w5Y8aMGWN27NhhUlJSzL///W/Trl07c+utt5oLFy6UyhyNufZ5ZmZmmt27d5vdu3eb2rVrmzFjxpjdu3ebgwcPFrnP0lAS83zmmWdMbGysSUlJMXFxcaZ79+6mevXq5vjx4zd9fnmudZ7Tp083Hh4eZsWKFU6PxDhz5oxTm/L+/rzaPCvK+3Pq1Knmyy+/NMnJySYxMdHMnDnTuLm5mdmzZzvaVITrebV5VpTrebmC7mAuK9eT8GcBb7/9tqlXr57x8PAwt99+u/n6668ddV26dDFRUVFO7T/55BMTGhpqPDw8TPPmzc26deuc6nNzc82LL75oatasaTw9PU23bt3MgQMHbsZUrqg453nu3DnTs2dPExgYaNzd3U1ISIgZNmxYqQaiPNcyz5SUFCMpX+nSpUuR+ywtxT3P/v37m9q1axsPDw9Tp04d079/f3Po0KGbOKOCXcs8Q0JCCpxnTEyMo01FeH9ebZ4V5f35/PPPm0aNGhkvLy9TtWpVExYWZpYuXerUX0W4nlebZ0W5npcrKPyVletpM8aYm7vWCAAAgNLCb/4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHACVoyJAhstls+stf/pKvbvjw4bLZbBoyZIjT/rS0ND311FO65ZZb5OnpqeDgYPXu3dvpD8JL0u7du9WvXz/VrFlTXl5euvXWWzVs2DD9+OOPhY4nJSVFDz/8sIKCguTl5aW6desqMjJS+/fvL5b5Aij7CH8AUMKCg4O1dOlSnT9/3rHvwoULWrJkierVq+fUNjU1Vbfddpu++uor/f3vf9e+ffu0fv16de3aVcOHD3e0W7t2rTp27KjMzEwtXrxYSUlJ+uijj+Tv768XX3yxwHFkZWWpR48estvtWrVqlQ4cOKBly5apZcuWOn36dInMPe+8AMqQm/7XhAHAQvL+uHuLFi3MRx995Ni/ePFi06pVKxMZGen0x+HvvvtuU6dOHZORkZGvr1OnThljjDl79qypXr266dOnT4HnzGt3ud27dxtJJjU19YpjPnLkiPnzn/9sqlatanx8fMxtt93m9Aft3333XXPLLbcYd3d3ExoaahYtWuR0vCTz7rvvmt69exsfHx8TExNjjDFmzZo1pm3btsbT09M0aNDAvPTSSyYrK+uKYwFQ/Fj5A4CbYOjQoZo/f75je968eXr00Ued2pw8eVLr16/X8OHDValSpXx9VKlSRZL0xRdf6L///a/Gjh1b4Lny2l0uMDBQLi4uWrFihXJycgpsk5GRoS5duug///mPPvvsM+3Zs0djx45Vbm6uJGn16tV6+umn9cwzz+iHH37QE088oUcffVSbNm1y6uell17S/fffr3379mno0KHaunWrBg8erKefflqJiYn64IMPtGDBAk2ZMqXAcQAoQaWdPgGgIstb+Tt+/Ljx9PQ0qampJjU11Xh5eZkTJ044rfzt3LnTSDKrVq26Yp+vvvqqkWROnjx5zeN55513jI+Pj/H19TVdu3Y1kydPNsnJyY76Dz74wPj6+prffvutwOPvuOMOM2zYMKd9/fr1M/fcc49jW5L529/+5tSmW7duZurUqU77/vnPf5ratWtf8xwA3BhW/gDgJggMDNS9996rBQsWaP78+br33ntVvXp1pzbGmCL1VdR2BRk+fLjS0tK0ePFihYWFafny5WrevLk2bNggSYqPj1fbtm0VEBBQ4PFJSUnq1KmT075OnTopKSnJaV/79u2dtvfs2aPJkyercuXKjjJs2DAdO3ZM586du+75ALh2bqU9AACwiqFDh2rEiBGSpFmzZuWrv/XWW2Wz2a56521oaKgkaf/+/QoLC7vmcfj6+qp3797q3bu3XnnlFYWHh+uVV15Rjx495O3tfc39FeTyr60zMjI0adIkPfDAA/naenl5Fcs5ARQNK38AcJNERETo4sWLysrKUnh4eL76gIAAhYeHa9asWTp79my++rw7cnv27Knq1atrxowZBZ7nWu7ctdlsatKkieN8rVq1Unx8vE6ePFlg+6ZNmyouLs5pX1xcnJo1a3bF87Rr104HDhxQo0aN8hUXFz6KgJuJdxwA3CSurq5KSkpSYmKiXF1dC2wza9Ys5eTk6Pbbb9fKlSt18OBBJSUl6R//+Idjla9SpUqaM2eO1q1bp/vuu0///ve/lZqaqu+++05jx44t8JmC0qWvdCMjI7VixQolJibq0KFDmjt3rubNm6fIyEhJ0oABA1SrVi316dNHcXFx+umnn7Ry5Urt2LFDkvTss89qwYIFeu+993Tw4EG9/vrrWrVqlcaMGXPFuU+cOFGLFi3SpEmTlJCQoKSkJC1dulQvvPDC9b6cAK5Xaf/oEAAqsrwbPgpz+aNejDHm6NGjZvjw4SYkJMR4eHiYOnXqmPvuu89s2rTJqd23335rHnjgARMYGGg8PT1No0aNzOOPP24OHjxY4LlOnDhhRo4caVq0aGEqV65sfH19TcuWLc3MmTNNTk6Oo11qaqrp27ev8fPzMz4+PqZ9+/Zm586djvqiPOpl9erV+c6/fv16c8cddxhvb2/j5+dnbr/9dvPhhx8W+toAKBk2Y27gl8MAAAAoV/jaFwAAwEIIfwAAABZC+AMAALAQwh8AAICFEP4AAAAshPAHAABgIYQ/AAAACyH8AQAAWAjhDwAAwEIIfwAAABZC+AMAALCQ/wUWVJsLEuyokQAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyO3QIpzJfPtbT7VTKsS/Yyp",
"include_colab_link": true
},
"gpuClass": "premium",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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