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import os
import argparse
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.applications import (VGG16, VGG19, ResNet50, ResNet101, InceptionV3,
DenseNet121, DenseNet201, MobileNetV2, Xception, InceptionResNetV2,
NASNetLarge, NASNetMobile, EfficientNetB0, EfficientNetB7)
from tensorflow.keras.layers import Dense, Flatten, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import numpy as np
def load_and_preprocess_image(filename, label, image_size):
# Load image
image = tf.io.read_file(filename)
image = tf.image.decode_image(image, channels=3)
# Ensure the image tensor has shape
if not tf.executing_eagerly():
image.set_shape([None, None, 3])
# Resize image to the specified size
image = tf.image.resize(image, [image_size[0], image_size[1]]) # Use height and width from the tuple
# Normalize image to [0, 1]
image = image / 255.0
image.set_shape([image_size[0], image_size[1], 3])
return image, label
def create_dataset(data_dir, labels, image_size, batch_size):
image_files = []
image_labels = []
for label in labels:
label_dir = os.path.join(data_dir, label)
for image_file in os.listdir(label_dir):
image_files.append(os.path.join(label_dir, image_file))
image_labels.append(label)
# Create a mapping from labels to indices
label_map = {label: idx for idx, label in enumerate(labels)}
image_labels = [label_map[label] for label in image_labels]
# Convert to TensorFlow datasets
dataset = tf.data.Dataset.from_tensor_slices((image_files, image_labels))
dataset = dataset.map(lambda x, y: load_and_preprocess_image(x, y, image_size))
dataset = dataset.shuffle(buffer_size=len(image_files))
dataset = dataset.batch(batch_size).prefetch(buffer_size=tf.data.AUTOTUNE)
return dataset
def create_and_train_model(base_model, model_name, shape, X_train, X_val, num_classes, labels, log_dir, model_dir,
epochs, optimizer_name, learning_rate, step_gamma, alpha, batch_size, patience):
# Freeze the base model layers
for layer in base_model.layers:
layer.trainable = False
# Add custom layers on top
x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.25)(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.25)(x)
x = Dense(256, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.25)(x)
predictions = Dense(num_classes, activation='softmax')(x) # Use the number of classes
model = Model(inputs=base_model.input, outputs=predictions)
# Learning rate schedule
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=learning_rate,
decay_steps=1000, # Adjust this according to your needs
decay_rate=step_gamma
)
# Select the optimizer
if optimizer_name.lower() == 'adam':
optimizer = Adam(learning_rate=lr_schedule)
elif optimizer_name.lower() == 'sgd':
optimizer = SGD(learning_rate=lr_schedule, momentum=alpha) # Example settings for SGD
else:
raise ValueError(f"Unsupported optimizer: {optimizer_name}")
# Compile the model
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Set up callbacks
checkpoint = ModelCheckpoint(os.path.join(model_dir, f'{model_name}_best_model.keras'),
monitor='val_accuracy', save_best_only=True, save_weights_only=False,
mode='max', verbose=1)
early_stopping = EarlyStopping(monitor='val_accuracy', patience=patience, verbose=1)
# Train the model
history = model.fit(X_train, epochs=epochs, validation_data=X_val, batch_size=batch_size,
callbacks=[checkpoint, early_stopping])
# Save training logs
with open(os.path.join(log_dir, f'{model_name}_training.log'), 'w') as f:
num_epochs = len(history.history['loss']) # Get the actual number of epochs completed
for epoch in range(num_epochs):
f.write(f"Epoch {epoch + 1}, "
f"Train Loss: {history.history['loss'][epoch]:.4f}, "
f"Train Accuracy: {history.history['accuracy'][epoch]:.4f}, "
f"Val Loss: {history.history['val_loss'][epoch]:.4f}, "
f"Val Accuracy: {history.history['val_accuracy'][epoch]:.4f}\n")
# Save labels in the model directory
with open(os.path.join(model_dir, 'labels.txt'), 'w') as f:
f.write('\n'.join(labels))
# Evaluate the model
test_loss, test_accuracy = model.evaluate(X_val)
print(f'Test Accuracy for {model_name}: {test_accuracy:.4f}')
print(f'Test Loss for {model_name}: {test_loss:.4f}')
# Optionally, save the trained model
model.save(os.path.join(model_dir, f'{model_name}_final_model.keras'))
def main(base_model_names, shape, data_path, log_dir, model_dir, epochs, optimizer, learning_rate, step_gamma, alpha, batch_size, patience):
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
# Extract labels from folder names
labels = sorted([d for d in os.listdir(os.path.join(data_path, 'train')) if os.path.isdir(os.path.join(data_path, 'train', d))])
num_classes = len(labels)
# Load data
X_train = create_dataset(os.path.join(data_path, 'train'), labels, shape, batch_size)
X_val = create_dataset(os.path.join(data_path, 'val'), labels, shape, batch_size)
if not base_model_names:
print("No base models specified. Exiting.")
return
# Define base models
base_models_dict = {
model_name: globals()[model_name](weights='imagenet', include_top=False, input_shape=shape)
for model_name in base_model_names
}
for model_name in base_model_names:
print(f'Training {model_name}...')
base_model = base_models_dict.get(model_name)
if base_model is None:
print(f"Model {model_name} not supported.")
continue
create_and_train_model(base_model, model_name, shape, X_train, X_val, num_classes, labels, log_dir, model_dir,
epochs, optimizer, learning_rate, step_gamma, alpha, batch_size, patience)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Train models using transfer learning")
parser.add_argument('--base_models', type=str, nargs='+', default=[],
help='List of base models to use for training. Leave empty to skip model training.')
parser.add_argument('--shape', type=int, nargs=3, default=(224, 224, 3), help='Input shape of the images')
parser.add_argument('--data_path', type=str, required=True, help='Path to the image data')
parser.add_argument('--log_dir', type=str, required=True, help='Directory to save logs')
parser.add_argument('--model_dir', type=str, required=True, help='Directory to save models')
parser.add_argument('--epochs', type=int, default=100, help='Number of epochs to train')
parser.add_argument('--optimizer', type=str, default='adam', help='Optimizer to use (adam or sgd)')
parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate')
parser.add_argument('--step_gamma', type=float, default=0.96, help='Gamma value for step learning rate schedule')
parser.add_argument('--alpha', type=float, default=0.9, help='Alpha for the optimizer (used for SGD)')
parser.add_argument('--batch_size', type=int, default=32, help='Batch size for training')
parser.add_argument('--patience', type=int, default=10, help='Patience for early stopping')
args = parser.parse_args()
main(args.base_models, tuple(args.shape), args.data_path, args.log_dir, args.model_dir,
args.epochs, args.optimizer, args.learning_rate, args.step_gamma, args.alpha, args.batch_size, args.patience)
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