thuwarakeshm · October 26, 2021 10:42
diff --git a/freeze.py b/freeze.py
 # trainable property is available on model instances
 model.trainable = False

 # Also on individual layers
 layer.trainable = False
diff --git a/layer.py b/layer.py
 tf.keras.layers.Dense(8, activation='relu')(input1)
diff --git a/loading.py b/loading.py
 model = VGG16(
  include_top=False, 
  # Other model parameters  
 )
diff --git a/training.py b/training.py
 # Specify a small value for learning rate. 
 # This ensures the model doesn't drastically deviate from the original one.
 base_learning_rate = 0.0001

 model.compile(
  optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),
  loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
  metrics=['accuracy']
 )

 training_history = model.fit(
  train_dataset,
  epochs=initial_epochs,
  validation_data=validation_dataset
 )
	# trainable property is available on model instances
	model.trainable = False

	# Also on individual layers
	layer.trainable = False
	# Specify a small value for learning rate.
	# This ensures the model doesn't drastically deviate from the original one.
	base_learning_rate = 0.0001

	model.compile(
	optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),
	loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
	metrics=['accuracy']
	)

	training_history = model.fit(
	train_dataset,
	epochs=initial_epochs,
	validation_data=validation_dataset
	)