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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# EfficientNet B5\n",
"## Let's Begin...."
],
"metadata": {
"id": "DGOlpli75Z_c"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "OylMWw9Sh3b3"
},
"outputs": [],
"source": [
"# Import Neccessary Lib...\n",
"import pandas as pd\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"\n",
"import os\n",
"import random\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import confusion_matrix, classification_report\n",
"import cv2\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"from tensorflow.keras.applications import VGG19\n",
"from tensorflow.keras.optimizers import Adam, Adamax\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout, BatchNormalization\n",
"from tensorflow.keras import regularizers\n",
"from tensorflow.keras.regularizers import l1, l2"
]
},
{
"cell_type": "code",
"source": [
"# Directory paths\n",
"train_dir = 'drive/MyDrive/LungCancer-IITM/Data/train'\n",
"test_dir = 'drive/MyDrive/LungCancer-IITM/Data/test'\n",
"valid_dir = 'drive/MyDrive/LungCancer-IITM/Data/valid'"
],
"metadata": {
"id": "4DHOnXmTh8a_"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PTGmzmm_iEqc",
"outputId": "6ecc5f01-8a51-4ef8-e672-ef60d5668eab"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train DataFrame:\n",
" Image_Path \\\n",
"0 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"1 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"2 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"3 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"4 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"\n",
" Label \n",
"0 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n",
"1 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n",
"2 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n",
"3 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n",
"4 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"# Function to create a DataFrame from image files in a folder\n",
"def create_dataframe(folder_path):\n",
" # Initialize an empty dictionary to store image paths and labels\n",
" data = {'Image_Path': [], 'Label': []}\n",
"\n",
" # List all subdirectories (labels) in the given folder\n",
" labels = os.listdir(folder_path)\n",
"\n",
" # Loop through each label\n",
" for label in labels:\n",
" # Construct the full path to the label folder\n",
" label_path = os.path.join(folder_path, label)\n",
"\n",
" # Check if the path is a directory\n",
" if os.path.isdir(label_path):\n",
" # List all image files in the label folder\n",
" images = os.listdir(label_path)\n",
"\n",
" # Loop through each image\n",
" for image in images:\n",
" # Construct the full path to the image\n",
" image_path = os.path.join(label_path, image)\n",
"\n",
" # Append image path and label to the dictionary\n",
" data['Image_Path'].append(image_path)\n",
" data['Label'].append(label)\n",
"\n",
" # Create a DataFrame from the dictionary\n",
" df = pd.DataFrame(data)\n",
" return df\n",
"\n",
"# Provide the path to your 'data' folder\n",
"data_folder = 'drive/MyDrive/LungCancer-IITM/Data'\n",
"\n",
"# Create DataFrames for train, test, and valid using the create_dataframe function\n",
"train_df = create_dataframe(os.path.join(data_folder, 'train'))\n",
"test_df = create_dataframe(os.path.join(data_folder, 'test'))\n",
"valid_df = create_dataframe(os.path.join(data_folder, 'valid'))\n",
"\n",
"# Print the created DataFrames for inspection\n",
"print(\"Train DataFrame:\")\n",
"print(train_df.head())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "67d41380-a800-446f-e017-98e22cb99872",
"id": "U-4wnr0O8dvF"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train DataFrame:\n",
" Image_Path \\\n",
"0 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"1 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"2 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"3 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"4 drive/MyDrive/LungCancer-IITM/Data/train/adeno... \n",
"\n",
" Label \n",
"0 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n",
"1 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n",
"2 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n",
"3 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n",
"4 adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(\"\\nTest DataFrame:\")\n",
"print(test_df.head())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "c6iuI9JXiLQd",
"outputId": "e360f402-86ec-4aba-d390-be7e5ded6110"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Test DataFrame:\n",
" Image_Path Label\n",
"0 drive/MyDrive/LungCancer-IITM/Data/test/large.... large.cell.carcinoma\n",
"1 drive/MyDrive/LungCancer-IITM/Data/test/large.... large.cell.carcinoma\n",
"2 drive/MyDrive/LungCancer-IITM/Data/test/large.... large.cell.carcinoma\n",
"3 drive/MyDrive/LungCancer-IITM/Data/test/large.... large.cell.carcinoma\n",
"4 drive/MyDrive/LungCancer-IITM/Data/test/large.... large.cell.carcinoma\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(\"\\nValid DataFrame:\")\n",
"print(valid_df.head())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0TX-BeALiOEZ",
"outputId": "68839461-8585-426f-c4e7-dce922db48bb"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Valid DataFrame:\n",
" Image_Path \\\n",
"0 drive/MyDrive/LungCancer-IITM/Data/valid/large... \n",
"1 drive/MyDrive/LungCancer-IITM/Data/valid/large... \n",
"2 drive/MyDrive/LungCancer-IITM/Data/valid/large... \n",
"3 drive/MyDrive/LungCancer-IITM/Data/valid/large... \n",
"4 drive/MyDrive/LungCancer-IITM/Data/valid/large... \n",
"\n",
" Label \n",
"0 large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa \n",
"1 large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa \n",
"2 large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa \n",
"3 large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa \n",
"4 large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Calculate the number of unique classes (labels) in the 'Label' column of the training DataFrame\n",
"num_classes = len(train_df['Label'].unique())\n",
"\n",
"# Print the number of classes in the dataset\n",
"print(f\"We have {num_classes} classes\")\n",
"\n",
"# Print the total number of images in the training DataFrame (total rows)\n",
"print(f\"We have {train_df.shape[0]} images\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dKwwZ0aXiS8Y",
"outputId": "17a1d131-9684-4e97-eb98-d836de207eb6"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"We have 4 classes\n",
"We have 613 images\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Calculate the number of unique classes (labels) in the 'Label' column of the test DataFrame\n",
"num_classes = len(test_df['Label'].unique())\n",
"\n",
"# Print the number of classes in the dataset\n",
"print(f\"We have {num_classes} classes\")\n",
"\n",
"# Print the total number of images in the test DataFrame (total rows)\n",
"print(f\"We have {test_df.shape[0]} images\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "C8DIiGIwijaq",
"outputId": "6e7a4904-4c58-4dcc-84d6-4bd02b00df6a"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"We have 4 classes\n",
"We have 315 images\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Calculate the number of unique classes (labels) in the 'Label' column of the valid DataFrame\n",
"num_classes = len(valid_df['Label'].unique())\n",
"\n",
"# Print the number of classes in the dataset\n",
"print(f\"We have {num_classes} classes\")\n",
"\n",
"# Print the total number of images in the valid DataFrame (total rows)\n",
"print(f\"We have {valid_df.shape[0]} images\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OhbkmbZqinKY",
"outputId": "4ca3a84a-3ef7-41cd-dca5-024b6afe66fd"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"We have 4 classes\n",
"We have 72 images\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Define the size of the input images\n",
"img_size = (224, 224)\n",
"\n",
"# Specify the number of color channels in the images (3 for RGB)\n",
"channels = 3\n",
"\n",
"# Specify the color representation ('rgb' for red, green, blue)\n",
"color = 'rgb'\n",
"\n",
"# Define the shape of the input images based on size, channels, and color representation\n",
"img_shape = (img_size[0], img_size[1], channels)\n",
"\n",
"# Specify the batch size for training\n",
"batch_size = 32\n",
"\n",
"# Get the length of the test DataFrame\n",
"ts_length = len(test_df)\n",
"\n",
"# Determine an optimal test batch size that evenly divides the length of the test DataFrame\n",
"test_batch_size = max(sorted([ts_length // n for n in range(1, ts_length + 1) if ts_length % n == 0 and ts_length / n <= 80]))\n",
"\n",
"# Calculate the number of steps needed to cover the entire test dataset\n",
"test_steps = ts_length // test_batch_size\n",
"\n",
"# Define a function 'scalar' that takes an image as input (placeholder, no implementation provided)\n",
"def scalar(img):\n",
" return img\n"
],
"metadata": {
"id": "7H00Xv0riwXL"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"tr_gen = ImageDataGenerator(preprocessing_function= scalar,\n",
" horizontal_flip= True)\n",
"\n",
"# Create an ImageDataGenerator for training with specified preprocessing and augmentation settings\n",
"tr_gen = ImageDataGenerator(preprocessing_function=scalar, horizontal_flip=True)\n",
"\n",
"# Create an ImageDataGenerator for testing with specified preprocessing settings\n",
"ts_gen = ImageDataGenerator(preprocessing_function=scalar)\n",
"\n",
"# Generate a flow from DataFrame for training data\n",
"train_gen = tr_gen.flow_from_dataframe(\n",
" train_df,\n",
" x_col='Image_Path',\n",
" y_col='Label',\n",
" target_size=img_size,\n",
" class_mode='categorical',\n",
" color_mode=color,\n",
" shuffle=True,\n",
" batch_size=batch_size\n",
")\n",
"\n",
"# Generate a flow from DataFrame for validation data\n",
"valid_gen = ts_gen.flow_from_dataframe(\n",
" valid_df,\n",
" x_col='Image_Path',\n",
" y_col='Label',\n",
" target_size=img_size,\n",
" class_mode='categorical',\n",
" color_mode=color,\n",
" shuffle=True,\n",
" batch_size=batch_size\n",
")\n",
"\n",
"# Generate a flow from DataFrame for test data\n",
"test_gen = ts_gen.flow_from_dataframe(\n",
" test_df,\n",
" x_col='Image_Path',\n",
" y_col='Label',\n",
" target_size=img_size,\n",
" class_mode='categorical',\n",
" color_mode=color,\n",
" shuffle=False,\n",
" batch_size=test_batch_size\n",
")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QqSOiLrxjjOD",
"outputId": "e562f193-cc5c-439f-c7b9-18bad8e76fe2"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found 613 validated image filenames belonging to 4 classes.\n",
"Found 72 validated image filenames belonging to 4 classes.\n",
"Found 315 validated image filenames belonging to 4 classes.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Using the EfficientNetB5 pre-trained model as a base model (without the fully connected layers)\n",
"base_model = tf.keras.applications.efficientnet.EfficientNetB5(\n",
" include_top=False, # Exclude the fully connected layers\n",
" weights=\"imagenet\", # Load pre-trained ImageNet weights\n",
" input_shape=img_shape, # Specify the input shape for the model\n",
" pooling='max' # Use global max pooling as the final pooling layer\n",
")\n",
"\n",
"# Constructing the complete model using Sequential API\n",
"model = Sequential([\n",
" base_model, # EfficientNetB5 as the base model\n",
" BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001), # Batch normalization layer\n",
" Dense(256,\n",
" kernel_regularizer=regularizers.l2(l=0.016),\n",
" activity_regularizer=regularizers.l1(0.006),\n",
" bias_regularizer=regularizers.l1(0.006),\n",
" activation='relu'), # Dense layer with regularization and ReLU activation\n",
" Dropout(rate=0.45, seed=123), # Dropout layer for regularization\n",
" Dense(4, activation='softmax') # Output layer with softmax activation for multi-class classification\n",
"])\n",
"\n",
"# Compile the model with specified optimizer, loss function, and evaluation metric\n",
"model.compile(\n",
" optimizer=Adamax(learning_rate=0.001),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy']\n",
")\n",
"\n",
"# Display a summary of the model architecture\n",
"model.summary()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h2iZBYVFkm0n",
"outputId": "76e92170-c977-4a26-d134-b261838ef813"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" efficientnetb5 (Functional (None, 2048) 28513527 \n",
" ) \n",
" \n",
" batch_normalization (Batch (None, 2048) 8192 \n",
" Normalization) \n",
" \n",
" dense (Dense) (None, 256) 524544 \n",
" \n",
" dropout (Dropout) (None, 256) 0 \n",
" \n",
" dense_1 (Dense) (None, 4) 1028 \n",
" \n",
"=================================================================\n",
"Total params: 29047291 (110.81 MB)\n",
"Trainable params: 28870452 (110.13 MB)\n",
"Non-trainable params: 176839 (690.78 KB)\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Retrieve the configuration of the optimizer used in the EfficientNetB5 base model\n",
"model.optimizer.get_config()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FhylF03qk8dp",
"outputId": "b9b8515d-048c-4a1f-829a-78906413760b"
},
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'name': 'Adamax',\n",
" 'weight_decay': None,\n",
" 'clipnorm': None,\n",
" 'global_clipnorm': None,\n",
" 'clipvalue': None,\n",
" 'use_ema': False,\n",
" 'ema_momentum': 0.99,\n",
" 'ema_overwrite_frequency': None,\n",
" 'jit_compile': True,\n",
" 'is_legacy_optimizer': False,\n",
" 'learning_rate': 0.001,\n",
" 'beta_1': 0.9,\n",
" 'beta_2': 0.999,\n",
" 'epsilon': 1e-07}"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"# Define early stopping to halt training if the validation loss doesn't improve for 'patience' consecutive epochs\n",
"early_stop = EarlyStopping(monitor='val_loss',\n",
" patience=5,\n",
" verbose=1)\n",
"# Define model checkpoint to save the best weights during training based on validation loss\n",
"checkpoint = ModelCheckpoint('model_weights_efficient_B5_2.h5',\n",
" monitor='val_loss',\n",
" save_best_only=True,\n",
" save_weights_only=True,\n",
" mode='min',\n",
" verbose=1)\n",
"\n",
"# Train the EfficientNetB5 base model on the training data with validation using the generator\n",
"# - x: Training generator\n",
"# - steps_per_epoch: Number of batches to process in each epoch\n",
"# - epochs: Number of training epochs\n",
"# - callbacks: List of callbacks to apply during training (early stopping and model checkpoint)\n",
"# - validation_data: Validation generator for evaluating the model's performance on a separate dataset\n",
"\n",
"history = model.fit(x= train_gen,\n",
" steps_per_epoch = 20,\n",
" epochs= 100,\n",
" callbacks=[early_stop, checkpoint],\n",
" validation_data = valid_gen)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ymbza2MYlB2j",
"outputId": "d8eea6ac-dc3e-4e0c-8525-cdfa875f115f"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"20/20 [==============================] - ETA: 0s - loss: 8.9467 - accuracy: 0.6525\n",
"Epoch 1: val_loss improved from inf to 13.82872, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 330s 12s/step - loss: 8.9467 - accuracy: 0.6525 - val_loss: 13.8287 - val_accuracy: 0.4861\n",
"Epoch 2/100\n",
"20/20 [==============================] - ETA: 0s - loss: 7.9310 - accuracy: 0.8222\n",
"Epoch 2: val_loss improved from 13.82872 to 9.65489, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 935ms/step - loss: 7.9310 - accuracy: 0.8222 - val_loss: 9.6549 - val_accuracy: 0.5000\n",
"Epoch 3/100\n",
"20/20 [==============================] - ETA: 0s - loss: 7.1907 - accuracy: 0.9086\n",
"Epoch 3: val_loss improved from 9.65489 to 8.90058, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 947ms/step - loss: 7.1907 - accuracy: 0.9086 - val_loss: 8.9006 - val_accuracy: 0.5833\n",
"Epoch 4/100\n",
"20/20 [==============================] - ETA: 0s - loss: 6.6951 - accuracy: 0.9478\n",
"Epoch 4: val_loss improved from 8.90058 to 7.97767, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 932ms/step - loss: 6.6951 - accuracy: 0.9478 - val_loss: 7.9777 - val_accuracy: 0.5833\n",
"Epoch 5/100\n",
"20/20 [==============================] - ETA: 0s - loss: 6.2736 - accuracy: 0.9755\n",
"Epoch 5: val_loss improved from 7.97767 to 7.08031, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 932ms/step - loss: 6.2736 - accuracy: 0.9755 - val_loss: 7.0803 - val_accuracy: 0.6528\n",
"Epoch 6/100\n",
"20/20 [==============================] - ETA: 0s - loss: 5.9248 - accuracy: 0.9641\n",
"Epoch 6: val_loss improved from 7.08031 to 6.62661, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 951ms/step - loss: 5.9248 - accuracy: 0.9641 - val_loss: 6.6266 - val_accuracy: 0.7500\n",
"Epoch 7/100\n",
"20/20 [==============================] - ETA: 0s - loss: 5.6432 - accuracy: 0.9739\n",
"Epoch 7: val_loss improved from 6.62661 to 6.26470, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 932ms/step - loss: 5.6432 - accuracy: 0.9739 - val_loss: 6.2647 - val_accuracy: 0.6667\n",
"Epoch 8/100\n",
"20/20 [==============================] - ETA: 0s - loss: 5.4284 - accuracy: 0.9739\n",
"Epoch 8: val_loss improved from 6.26470 to 5.88624, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 975ms/step - loss: 5.4284 - accuracy: 0.9739 - val_loss: 5.8862 - val_accuracy: 0.7361\n",
"Epoch 9/100\n",
"20/20 [==============================] - ETA: 0s - loss: 5.1599 - accuracy: 0.9821\n",
"Epoch 9: val_loss improved from 5.88624 to 5.53767, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 933ms/step - loss: 5.1599 - accuracy: 0.9821 - val_loss: 5.5377 - val_accuracy: 0.8472\n",
"Epoch 10/100\n",
"20/20 [==============================] - ETA: 0s - loss: 4.9567 - accuracy: 0.9788\n",
"Epoch 10: val_loss improved from 5.53767 to 5.29575, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 934ms/step - loss: 4.9567 - accuracy: 0.9788 - val_loss: 5.2957 - val_accuracy: 0.8750\n",
"Epoch 11/100\n",
"20/20 [==============================] - ETA: 0s - loss: 4.7625 - accuracy: 0.9804\n",
"Epoch 11: val_loss improved from 5.29575 to 5.10167, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 948ms/step - loss: 4.7625 - accuracy: 0.9804 - val_loss: 5.1017 - val_accuracy: 0.8750\n",
"Epoch 12/100\n",
"20/20 [==============================] - ETA: 0s - loss: 4.6141 - accuracy: 0.9788\n",
"Epoch 12: val_loss improved from 5.10167 to 4.96450, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 957ms/step - loss: 4.6141 - accuracy: 0.9788 - val_loss: 4.9645 - val_accuracy: 0.8750\n",
"Epoch 13/100\n",
"20/20 [==============================] - ETA: 0s - loss: 4.4517 - accuracy: 0.9902\n",
"Epoch 13: val_loss improved from 4.96450 to 4.89537, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 938ms/step - loss: 4.4517 - accuracy: 0.9902 - val_loss: 4.8954 - val_accuracy: 0.8750\n",
"Epoch 14/100\n",
"20/20 [==============================] - ETA: 0s - loss: 4.3521 - accuracy: 0.9788\n",
"Epoch 14: val_loss improved from 4.89537 to 4.61144, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 941ms/step - loss: 4.3521 - accuracy: 0.9788 - val_loss: 4.6114 - val_accuracy: 0.8611\n",
"Epoch 15/100\n",
"20/20 [==============================] - ETA: 0s - loss: 4.1907 - accuracy: 0.9837\n",
"Epoch 15: val_loss improved from 4.61144 to 4.47061, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 980ms/step - loss: 4.1907 - accuracy: 0.9837 - val_loss: 4.4706 - val_accuracy: 0.8611\n",
"Epoch 16/100\n",
"20/20 [==============================] - ETA: 0s - loss: 4.0591 - accuracy: 0.9821\n",
"Epoch 16: val_loss improved from 4.47061 to 4.35734, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 930ms/step - loss: 4.0591 - accuracy: 0.9821 - val_loss: 4.3573 - val_accuracy: 0.8750\n",
"Epoch 17/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.9479 - accuracy: 0.9837\n",
"Epoch 17: val_loss improved from 4.35734 to 4.19360, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 940ms/step - loss: 3.9479 - accuracy: 0.9837 - val_loss: 4.1936 - val_accuracy: 0.8750\n",
"Epoch 18/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.8014 - accuracy: 0.9951\n",
"Epoch 18: val_loss improved from 4.19360 to 4.07113, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 977ms/step - loss: 3.8014 - accuracy: 0.9951 - val_loss: 4.0711 - val_accuracy: 0.8750\n",
"Epoch 19/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.7042 - accuracy: 0.9918\n",
"Epoch 19: val_loss improved from 4.07113 to 4.02841, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 940ms/step - loss: 3.7042 - accuracy: 0.9918 - val_loss: 4.0284 - val_accuracy: 0.8472\n",
"Epoch 20/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.6051 - accuracy: 0.9918\n",
"Epoch 20: val_loss improved from 4.02841 to 3.87404, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 943ms/step - loss: 3.6051 - accuracy: 0.9918 - val_loss: 3.8740 - val_accuracy: 0.9028\n",
"Epoch 21/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.5299 - accuracy: 0.9902\n",
"Epoch 21: val_loss improved from 3.87404 to 3.76933, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 947ms/step - loss: 3.5299 - accuracy: 0.9902 - val_loss: 3.7693 - val_accuracy: 0.9028\n",
"Epoch 22/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.4325 - accuracy: 0.9902\n",
"Epoch 22: val_loss improved from 3.76933 to 3.64684, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 964ms/step - loss: 3.4325 - accuracy: 0.9902 - val_loss: 3.6468 - val_accuracy: 0.8889\n",
"Epoch 23/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.3194 - accuracy: 0.9967\n",
"Epoch 23: val_loss improved from 3.64684 to 3.55495, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 934ms/step - loss: 3.3194 - accuracy: 0.9967 - val_loss: 3.5549 - val_accuracy: 0.8889\n",
"Epoch 24/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.2151 - accuracy: 0.9935\n",
"Epoch 24: val_loss improved from 3.55495 to 3.47809, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 1s/step - loss: 3.2151 - accuracy: 0.9935 - val_loss: 3.4781 - val_accuracy: 0.8889\n",
"Epoch 25/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.1480 - accuracy: 0.9869\n",
"Epoch 25: val_loss improved from 3.47809 to 3.46385, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 937ms/step - loss: 3.1480 - accuracy: 0.9869 - val_loss: 3.4639 - val_accuracy: 0.8889\n",
"Epoch 26/100\n",
"20/20 [==============================] - ETA: 0s - loss: 3.0889 - accuracy: 0.9837\n",
"Epoch 26: val_loss improved from 3.46385 to 3.30259, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 935ms/step - loss: 3.0889 - accuracy: 0.9837 - val_loss: 3.3026 - val_accuracy: 0.8889\n",
"Epoch 27/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.9959 - accuracy: 0.9902\n",
"Epoch 27: val_loss improved from 3.30259 to 3.23432, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 977ms/step - loss: 2.9959 - accuracy: 0.9902 - val_loss: 3.2343 - val_accuracy: 0.9167\n",
"Epoch 28/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.8889 - accuracy: 0.9967\n",
"Epoch 28: val_loss improved from 3.23432 to 3.13419, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 952ms/step - loss: 2.8889 - accuracy: 0.9967 - val_loss: 3.1342 - val_accuracy: 0.9028\n",
"Epoch 29/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.8285 - accuracy: 0.9918\n",
"Epoch 29: val_loss improved from 3.13419 to 3.05611, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 969ms/step - loss: 2.8285 - accuracy: 0.9918 - val_loss: 3.0561 - val_accuracy: 0.9167\n",
"Epoch 30/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.7386 - accuracy: 0.9967\n",
"Epoch 30: val_loss improved from 3.05611 to 2.98006, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 930ms/step - loss: 2.7386 - accuracy: 0.9967 - val_loss: 2.9801 - val_accuracy: 0.9167\n",
"Epoch 31/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.6883 - accuracy: 0.9935\n",
"Epoch 31: val_loss improved from 2.98006 to 2.91081, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 942ms/step - loss: 2.6883 - accuracy: 0.9935 - val_loss: 2.9108 - val_accuracy: 0.9167\n",
"Epoch 32/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.6405 - accuracy: 0.9788\n",
"Epoch 32: val_loss did not improve from 2.91081\n",
"20/20 [==============================] - 18s 901ms/step - loss: 2.6405 - accuracy: 0.9788 - val_loss: 2.9625 - val_accuracy: 0.8611\n",
"Epoch 33/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.5627 - accuracy: 0.9886\n",
"Epoch 33: val_loss improved from 2.91081 to 2.88892, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 938ms/step - loss: 2.5627 - accuracy: 0.9886 - val_loss: 2.8889 - val_accuracy: 0.9028\n",
"Epoch 34/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.5646 - accuracy: 0.9804\n",
"Epoch 34: val_loss did not improve from 2.88892\n",
"20/20 [==============================] - 18s 901ms/step - loss: 2.5646 - accuracy: 0.9804 - val_loss: 2.9084 - val_accuracy: 0.8611\n",
"Epoch 35/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.4740 - accuracy: 0.9935\n",
"Epoch 35: val_loss improved from 2.88892 to 2.79603, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 955ms/step - loss: 2.4740 - accuracy: 0.9935 - val_loss: 2.7960 - val_accuracy: 0.9028\n",
"Epoch 36/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.4113 - accuracy: 0.9853\n",
"Epoch 36: val_loss improved from 2.79603 to 2.72169, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 965ms/step - loss: 2.4113 - accuracy: 0.9853 - val_loss: 2.7217 - val_accuracy: 0.8333\n",
"Epoch 37/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.3420 - accuracy: 0.9869\n",
"Epoch 37: val_loss improved from 2.72169 to 2.62496, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 932ms/step - loss: 2.3420 - accuracy: 0.9869 - val_loss: 2.6250 - val_accuracy: 0.8611\n",
"Epoch 38/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.2655 - accuracy: 0.9951\n",
"Epoch 38: val_loss improved from 2.62496 to 2.49132, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 980ms/step - loss: 2.2655 - accuracy: 0.9951 - val_loss: 2.4913 - val_accuracy: 0.9167\n",
"Epoch 39/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.2046 - accuracy: 0.9967\n",
"Epoch 39: val_loss improved from 2.49132 to 2.45171, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 935ms/step - loss: 2.2046 - accuracy: 0.9967 - val_loss: 2.4517 - val_accuracy: 0.9028\n",
"Epoch 40/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.1569 - accuracy: 0.9935\n",
"Epoch 40: val_loss improved from 2.45171 to 2.36931, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 934ms/step - loss: 2.1569 - accuracy: 0.9935 - val_loss: 2.3693 - val_accuracy: 0.9306\n",
"Epoch 41/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.0928 - accuracy: 0.9967\n",
"Epoch 41: val_loss improved from 2.36931 to 2.30855, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 979ms/step - loss: 2.0928 - accuracy: 0.9967 - val_loss: 2.3086 - val_accuracy: 0.9306\n",
"Epoch 42/100\n",
"20/20 [==============================] - ETA: 0s - loss: 2.0393 - accuracy: 0.9967\n",
"Epoch 42: val_loss improved from 2.30855 to 2.24363, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 937ms/step - loss: 2.0393 - accuracy: 0.9967 - val_loss: 2.2436 - val_accuracy: 0.9306\n",
"Epoch 43/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.9881 - accuracy: 0.9984\n",
"Epoch 43: val_loss improved from 2.24363 to 2.19355, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 979ms/step - loss: 1.9881 - accuracy: 0.9984 - val_loss: 2.1935 - val_accuracy: 0.9167\n",
"Epoch 44/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.9369 - accuracy: 1.0000\n",
"Epoch 44: val_loss improved from 2.19355 to 2.13765, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 963ms/step - loss: 1.9369 - accuracy: 1.0000 - val_loss: 2.1376 - val_accuracy: 0.9306\n",
"Epoch 45/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.8963 - accuracy: 0.9967\n",
"Epoch 45: val_loss improved from 2.13765 to 2.11182, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 934ms/step - loss: 1.8963 - accuracy: 0.9967 - val_loss: 2.1118 - val_accuracy: 0.9306\n",
"Epoch 46/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.8555 - accuracy: 0.9967\n",
"Epoch 46: val_loss improved from 2.11182 to 2.08817, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 939ms/step - loss: 1.8555 - accuracy: 0.9967 - val_loss: 2.0882 - val_accuracy: 0.9306\n",
"Epoch 47/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.8443 - accuracy: 0.9869\n",
"Epoch 47: val_loss improved from 2.08817 to 2.08034, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 978ms/step - loss: 1.8443 - accuracy: 0.9869 - val_loss: 2.0803 - val_accuracy: 0.9306\n",
"Epoch 48/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.7713 - accuracy: 0.9984\n",
"Epoch 48: val_loss improved from 2.08034 to 1.98731, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 936ms/step - loss: 1.7713 - accuracy: 0.9984 - val_loss: 1.9873 - val_accuracy: 0.9306\n",
"Epoch 49/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.7160 - accuracy: 0.9984\n",
"Epoch 49: val_loss improved from 1.98731 to 1.93409, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 935ms/step - loss: 1.7160 - accuracy: 0.9984 - val_loss: 1.9341 - val_accuracy: 0.9306\n",
"Epoch 50/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.6692 - accuracy: 0.9967\n",
"Epoch 50: val_loss improved from 1.93409 to 1.88645, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 953ms/step - loss: 1.6692 - accuracy: 0.9967 - val_loss: 1.8864 - val_accuracy: 0.9306\n",
"Epoch 51/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.6335 - accuracy: 0.9967\n",
"Epoch 51: val_loss improved from 1.88645 to 1.87095, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 937ms/step - loss: 1.6335 - accuracy: 0.9967 - val_loss: 1.8709 - val_accuracy: 0.9306\n",
"Epoch 52/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.6030 - accuracy: 0.9935\n",
"Epoch 52: val_loss improved from 1.87095 to 1.81230, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 934ms/step - loss: 1.6030 - accuracy: 0.9935 - val_loss: 1.8123 - val_accuracy: 0.9306\n",
"Epoch 53/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.5754 - accuracy: 0.9951\n",
"Epoch 53: val_loss improved from 1.81230 to 1.80875, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 943ms/step - loss: 1.5754 - accuracy: 0.9951 - val_loss: 1.8088 - val_accuracy: 0.9306\n",
"Epoch 54/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.5490 - accuracy: 0.9902\n",
"Epoch 54: val_loss improved from 1.80875 to 1.77187, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 973ms/step - loss: 1.5490 - accuracy: 0.9902 - val_loss: 1.7719 - val_accuracy: 0.9167\n",
"Epoch 55/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.4940 - accuracy: 0.9967\n",
"Epoch 55: val_loss improved from 1.77187 to 1.72648, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 943ms/step - loss: 1.4940 - accuracy: 0.9967 - val_loss: 1.7265 - val_accuracy: 0.9167\n",
"Epoch 56/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.4628 - accuracy: 0.9951\n",
"Epoch 56: val_loss improved from 1.72648 to 1.66311, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 932ms/step - loss: 1.4628 - accuracy: 0.9951 - val_loss: 1.6631 - val_accuracy: 0.9306\n",
"Epoch 57/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.4283 - accuracy: 0.9951\n",
"Epoch 57: val_loss improved from 1.66311 to 1.58719, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 956ms/step - loss: 1.4283 - accuracy: 0.9951 - val_loss: 1.5872 - val_accuracy: 0.9306\n",
"Epoch 58/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.4072 - accuracy: 0.9967\n",
"Epoch 58: val_loss improved from 1.58719 to 1.56380, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 932ms/step - loss: 1.4072 - accuracy: 0.9967 - val_loss: 1.5638 - val_accuracy: 0.9306\n",
"Epoch 59/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.3953 - accuracy: 0.9902\n",
"Epoch 59: val_loss did not improve from 1.56380\n",
"20/20 [==============================] - 18s 935ms/step - loss: 1.3953 - accuracy: 0.9902 - val_loss: 1.5837 - val_accuracy: 0.9306\n",
"Epoch 60/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.3637 - accuracy: 0.9902\n",
"Epoch 60: val_loss improved from 1.56380 to 1.55265, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 941ms/step - loss: 1.3637 - accuracy: 0.9902 - val_loss: 1.5526 - val_accuracy: 0.9444\n",
"Epoch 61/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.3116 - accuracy: 0.9918\n",
"Epoch 61: val_loss improved from 1.55265 to 1.48927, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 955ms/step - loss: 1.3116 - accuracy: 0.9918 - val_loss: 1.4893 - val_accuracy: 0.9444\n",
"Epoch 62/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.2852 - accuracy: 0.9951\n",
"Epoch 62: val_loss improved from 1.48927 to 1.46638, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 947ms/step - loss: 1.2852 - accuracy: 0.9951 - val_loss: 1.4664 - val_accuracy: 0.9306\n",
"Epoch 63/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.2581 - accuracy: 0.9935\n",
"Epoch 63: val_loss improved from 1.46638 to 1.45661, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 934ms/step - loss: 1.2581 - accuracy: 0.9935 - val_loss: 1.4566 - val_accuracy: 0.9306\n",
"Epoch 64/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.2234 - accuracy: 0.9967\n",
"Epoch 64: val_loss improved from 1.45661 to 1.42951, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 932ms/step - loss: 1.2234 - accuracy: 0.9967 - val_loss: 1.4295 - val_accuracy: 0.9306\n",
"Epoch 65/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.1978 - accuracy: 0.9967\n",
"Epoch 65: val_loss improved from 1.42951 to 1.40270, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 972ms/step - loss: 1.1978 - accuracy: 0.9967 - val_loss: 1.4027 - val_accuracy: 0.9306\n",
"Epoch 66/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.1966 - accuracy: 0.9935\n",
"Epoch 66: val_loss did not improve from 1.40270\n",
"20/20 [==============================] - 18s 896ms/step - loss: 1.1966 - accuracy: 0.9935 - val_loss: 1.4201 - val_accuracy: 0.9167\n",
"Epoch 67/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.1682 - accuracy: 0.9984\n",
"Epoch 67: val_loss did not improve from 1.40270\n",
"20/20 [==============================] - 19s 931ms/step - loss: 1.1682 - accuracy: 0.9984 - val_loss: 1.4158 - val_accuracy: 0.9306\n",
"Epoch 68/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.1414 - accuracy: 0.9918\n",
"Epoch 68: val_loss improved from 1.40270 to 1.36896, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 939ms/step - loss: 1.1414 - accuracy: 0.9918 - val_loss: 1.3690 - val_accuracy: 0.9306\n",
"Epoch 69/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.1103 - accuracy: 0.9984\n",
"Epoch 69: val_loss improved from 1.36896 to 1.32771, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 20s 977ms/step - loss: 1.1103 - accuracy: 0.9984 - val_loss: 1.3277 - val_accuracy: 0.9306\n",
"Epoch 70/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.1277 - accuracy: 0.9886\n",
"Epoch 70: val_loss did not improve from 1.32771\n",
"20/20 [==============================] - 18s 897ms/step - loss: 1.1277 - accuracy: 0.9886 - val_loss: 1.3546 - val_accuracy: 0.9167\n",
"Epoch 71/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.0964 - accuracy: 0.9902\n",
"Epoch 71: val_loss improved from 1.32771 to 1.27567, saving model to model_weights_efficient_B5_2.h5\n",
"20/20 [==============================] - 19s 938ms/step - loss: 1.0964 - accuracy: 0.9902 - val_loss: 1.2757 - val_accuracy: 0.9306\n",
"Epoch 72/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.0688 - accuracy: 0.9886\n",
"Epoch 72: val_loss did not improve from 1.27567\n",
"20/20 [==============================] - 18s 907ms/step - loss: 1.0688 - accuracy: 0.9886 - val_loss: 1.3252 - val_accuracy: 0.9028\n",
"Epoch 73/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.0428 - accuracy: 0.9984\n",
"Epoch 73: val_loss did not improve from 1.27567\n",
"20/20 [==============================] - 18s 901ms/step - loss: 1.0428 - accuracy: 0.9984 - val_loss: 1.3001 - val_accuracy: 0.9028\n",
"Epoch 74/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.0202 - accuracy: 0.9951\n",
"Epoch 74: val_loss did not improve from 1.27567\n",
"20/20 [==============================] - 18s 895ms/step - loss: 1.0202 - accuracy: 0.9951 - val_loss: 1.4571 - val_accuracy: 0.8472\n",
"Epoch 75/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.0117 - accuracy: 0.9902\n",
"Epoch 75: val_loss did not improve from 1.27567\n",
"20/20 [==============================] - 19s 930ms/step - loss: 1.0117 - accuracy: 0.9902 - val_loss: 1.2980 - val_accuracy: 0.8889\n",
"Epoch 76/100\n",
"20/20 [==============================] - ETA: 0s - loss: 1.0119 - accuracy: 0.9918\n",
"Epoch 76: val_loss did not improve from 1.27567\n",
"20/20 [==============================] - 19s 968ms/step - loss: 1.0119 - accuracy: 0.9918 - val_loss: 1.2769 - val_accuracy: 0.9028\n",
"Epoch 76: early stopping\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Calculate the total number of samples in the test dataset\n",
"ts_length = len(test_df)\n",
"# Determine the optimal test batch size within a reasonable range (1 to 80)\n",
"test_batch_size = max(sorted([ts_length // n for n in range(1, ts_length + 1) if ts_length%n == 0 and ts_length/n <= 80]))\n",
"# Calculate the number of steps to cover the entire test dataset using the determined test batch size\n",
"test_steps = ts_length // test_batch_size\n",
"\n",
"# Evaluate the EfficientNetB5base model on the training dataset and print the results\n",
"train_score = model.evaluate(train_gen, steps= test_steps, verbose= 1)\n",
"# Evaluate the EfficientNetB5 base model on the validation dataset and print the results\n",
"valid_score = model.evaluate(valid_gen, steps= test_steps, verbose= 1)\n",
"# Evaluate the EfficientNetB5 base model on the test dataset and print the results\n",
"test_score = model.evaluate(test_gen, steps= test_steps, verbose= 1)\n",
"\n",
"# Print the evaluation results for the training dataset\n",
"print(\"Train Loss: \", train_score[0])\n",
"print(\"Train Accuracy: \", train_score[1])\n",
"print('-' * 20)\n",
"\n",
"# Print the evaluation results for the validation dataset\n",
"print(\"Validation Loss: \", valid_score[0])\n",
"print(\"Validation Accuracy: \", valid_score[1])\n",
"print('-' * 20)\n",
"\n",
"# Print the evaluation results for the test dataset\n",
"print(\"Test Loss: \", test_score[0])\n",
"print(\"Test Loss: \", test_score[0])\n",
"print(\"Test Accuracy: \", test_score[1])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mf1mrDrLpGXF",
"outputId": "85ada66f-d5a3-4cd5-b1ad-b6ab756c89bf"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"5/5 [==============================] - 2s 276ms/step - loss: 0.9960 - accuracy: 1.0000\n",
"3/5 [=================>............] - ETA: 0s - loss: 1.2769 - accuracy: 0.9028"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 5 batches). You may need to use the repeat() function when building your dataset.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"5/5 [==============================] - 1s 213ms/step - loss: 1.2769 - accuracy: 0.9028\n",
"5/5 [==============================] - 150s 37s/step - loss: 1.2655 - accuracy: 0.9111\n",
"Train Loss: 0.9959659576416016\n",
"Train Accuracy: 1.0\n",
"--------------------\n",
"Validation Loss: 1.2768819332122803\n",
"Validation Accuracy: 0.9027777910232544\n",
"--------------------\n",
"Test Loss: 1.2654653787612915\n",
"Test Accuracy: 0.9111111164093018\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# EfficientNet B5\n",
"## (The Above model is EfficientNetB5 which shows best accuracy compare to other models)\n",
"## Train Accuracy: 100%\n",
"## Validation Accuracy: 90.2%\n",
"## Test Accuracy: 91.11%"
],
"metadata": {
"id": "3aYDXYnm71Wd"
}
},
{
"cell_type": "markdown",
"source": [
"# VGG19\n",
"## Train Accuracy: 100%\n",
"## Validation Accuracy: 80.56%\n",
"## Test Accuracy: 79.05%"
],
"metadata": {
"id": "av1hgCOj-VLh"
}
},
{
"cell_type": "markdown",
"source": [
"# VGG16\n",
"## Train Accuracy: 100%\n",
"## Validation Accuracy: 79.16%\n",
"## Test Accuracy: 76.19%"
],
"metadata": {
"id": "shJGEpmM-iSU"
}
},
{
"cell_type": "code",
"source": [
"import shutil\n",
"\n",
"# Source path\n",
"source_path = \"content/model_weights_efficient_B5_2.h5\"\n",
"\n",
"# Destination path (Data folder)\n",
"destination_path = \"drive/MyDrive/LungCancer-IITM/Data/model_weights_efficient_B5_2.h5\"\n",
"\n",
"# Move the file\n",
"shutil.move(source_path, destination_path)\n",
"\n",
"print(f\"File moved from {source_path} to {destination_path}\")"
],
"metadata": {
"id": "nF-O7RYjEFCi"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# EfficientNetB5 model link:-\n",
"google_drive_link = \"https://drive.google.com/file/d/1ppJ_h5jE3tr2-n0x1TBzx8CEfCdAg9TD/view?usp=drive_link\""
],
"metadata": {
"id": "qXQfOXJGEo9R"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#Thank You..."
],
"metadata": {
"id": "F1XWcOHaE8gc"
}
}
]
} |