VGG16.py

from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras import layers
from tensorflow.keras.models import Model

base_model = VGG16(weights='imagenet', #pre-trained weights
                      include_top=False,  #remove original classifier
                      input_shape=(HEIGHT, WIDTH, 3))

#Freeze all layers
for layer in base_model.layers: 
  layer.trainable = False

#Add binary classifier
x = base_model.output
x = layers.Flatten()(base_model.output)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dropout(0.2)(x)                  
x = layers.Dense (1, activation='sigmoid')(x)           

model = Model(inputs=base_model.input, outputs=x)