Hands-On Transfer Learning with Python

Pretrained CNN model with fine-tuning and image augmentation

We will now leverage our VGG-16 model object stored in the vgg_model variable and unfreeze convolution blocks 4 and 5 while keeping the first three blocks frozen. The following code helps us achieve this:

vgg_model.trainable = True set_trainable = False for layer in vgg_model.layers:     if layer.name in ['block5_conv1', 'block4_conv1']:         set_trainable = True     if set_trainable:         layer.trainable = True     else:         layer.trainable = False print("Trainable layers:", vgg_model.trainable_weights) Trainable layers: [<tf.Variable 'block4_conv1/kernel:0' shape=(3, 3, 256, 512) dtype=float32_ref>, <tf.Variable 'block4_conv1/bias:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'block4_conv2/kernel:0' ...