# Unfreeze some layers
for layer in base_model.layers[-4:]:
    layer.trainable = True

# Recompile the model (necessary after modifying layer.trainable)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Continue training
model.fit(train_data, epochs=10, validation_data=validation_data)