vgg16first.py

from keras.applications import VGG16

# VGG16 was designed to work on 224 x 224 pixel input images sizes
img_rows = 224
img_cols = 224 

#Loads the VGG16 model 
model = VGG16(weights = 'imagenet', 
                 include_top = False, 
                 input_shape = (img_rows, img_cols, 3))
                 
# Let's print our layers 
for (i,layer) in enumerate(model.layers):
    print(str(i) + " "+ layer.__class__.__name__, layer.trainable)
    
from keras.applications import VGG16
# VGG16 was designed to work on 224 x 224 pixel input images sizes
img_rows = 224
img_cols = 224 

# Re-loads the VGG16 model without the top or FC layers
model = VGG16(weights = 'imagenet', 
                 include_top = False, 
                 input_shape = (img_rows, img_cols, 3))

# Here we freeze the last 4 layers 
# Layers are set to trainable as True by default
for layer in model.layers:
    layer.trainable = False
    
# Let's print our layers 
for (i,layer) in enumerate(model.layers):
    print(str(i) + " "+ layer.__class__.__name__, layer.trainable)