How to apply regularization when fine tuning on the imagenet extracted models

Question

One way of fine tuning is extracting a model ( like VGG16 trained on Imagenet) , adding a layer or so and then train the model.

Is it possible to apply regularization to the model layers apart from the added layer using Tensorflow.Keras. I don't think adding regularization to only one layer effects the outcome much.

I know we can apply the regularization for the added layer as:

x = Dense(classes, kernel_regularizer=l2(reg), name="labels")(x)

But is it possible to apply regularization for other layers as well in Keras. It could be easily done in mxnet.

Would be grateful for any help.

Answer 1

This solution should work. By iterating over the model layers we can just add a regularizer. You can then add your dense layer after this.

model = tf.keras.applications.VGG16(include_top=False, weights=None)
regularizer = tf.keras.regularizers.l2(0.001)

for i in range(len(model.layers)):
    if isinstance(model.layers[i], tf.keras.layers.Conv2D):
        print('Adding regularizer to layer {}'.format(model.layers[i].name))
        model.layers[i].kernel_regularizer = regularizer

# Add Dense layer
classes = 10
x = model.output
x = Dense(classes, kernel_regularizer=regularizer, name="labels")(x)

model = tf.keras.Model(model.input, x)