vgg = keras.applications.VGG16(weights='imagenet', include_top=True)
vgg.summary()

inp = vgg.input
new_classification_layer = Dense(num_classes, activation='softmax')
out = new_classification_layer(vgg.layers[-2].output)
model_new = Model(inp, out)

for l, layer in enumerate(model_new.layers[:-1]):
    layer.trainable = False

for l, layer in enumerate(model_new.layers[-1:]):
    layer.trainable = True

model_new.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model_new.summary()