preprocess_and_init_VGG16.py

#Preprocess dataset per https://www.tensorflow.org/tutorials/load_data/images
train_ds = train_ds.map(lambda x, y: (preprocess_input(x), y))
val_ds = val_ds.map(lambda x, y: (preprocess_input(x), y))

# Show min/max of first image. Notice the pixel values after preprocess.
image_batch, labels_batch = next(iter(train_ds))
first_image = image_batch[0]
print('image min and max values:', np.min(first_image), np.max(first_image), '\n\n')

# Load and freeze VGG16 model.
# Include_top=False removes classification layer trained on ImageNet.
base_model = VGG16(weights="imagenet", include_top=False, input_shape=(224,224,3))
base_model.trainable = False ## Not trainable weights
base_model.summary()