vggtune.py

import tensorflow as tf
from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers, models, optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import EarlyStopping

# Load the pre-trained VGG16 model without the top layer
base_model = VGG16(input_shape=(256, 256, 3), include_top=False, weights='imagenet')

# Freeze the convolutional base
base_model.trainable = False

# Add new layers on top of the pre-trained base
model = models.Sequential([
    base_model,
    layers.Flatten(),
    layers.Dense(512, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(optimizer=optimizers.Adam(learning_rate=0.001),  # Initial learning rate
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Data augmentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

test_datagen = ImageDataGenerator(rescale=1./255)

# Load the training and validation data
train_dir = 'MURA-v1.1/train/XR_WRIST'  
validation_dir = 'MURA-v1.1/valid/XR_WRIST' 

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(256, 256),
    batch_size=32,
    class_mode='binary'
)

validation_generator = test_datagen.flow_from_directory(
    validation_dir,
    target_size=(256, 256),
    batch_size=32,
    class_mode='binary'
)

# Set up early stopping
early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)

# Train the model with data augmentation
history = model.fit(
    train_generator,
    epochs=10,  # Initial number of epochs
    validation_data=validation_generator,
    callbacks=[early_stopping]
)

# Fine-tuning the model
# Unfreeze the top 4 convolutional layers of the VGG16 base model
base_model.trainable = True
for layer in base_model.layers[:-4]:
    layer.trainable = False

# Compile the model with a lower learning rate
model.compile(optimizer=optimizers.Adam(learning_rate=1e-5),  # Lower learning rate for fine-tuning
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Continue training the model with fine-tuning
history_fine = model.fit(
    train_generator,
    epochs=10,  # Additional epochs for fine-tuning
    validation_data=validation_generator,
    callbacks=[early_stopping]
)

# Save the trained model
from tensorflow.keras.models import save_model
save_model(model, "modelVGG16.h5")