base_plus_classification_layers.py

#Add classification layers.
from tensorflow.keras import layers, models

flatten_layer = layers.Flatten()
dense_layer_1 = layers.Dense(10, activation='relu')
dropout_1 = layers.Dropout(rate=0.3)
prediction_layer = layers.Dense(1, activation='sigmoid') #sigmoid for binary activation

#Transfer learning - base_model plus classifcation layers.
model = models.Sequential([
    base_model,
    flatten_layer,
    dense_layer_1,
    dropout_1,
    prediction_layer,
])

model.summary()