# KERAS

conv_base = ResNet50(include_top=False,
                     weights='imagenet')

for layer in conv_base.layers:
    layer.trainable = False

x = conv_base.output
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(128, activation='relu')(x)
predictions = layers.Dense(2, activation='softmax')(x)
model = Model(conv_base.input, predictions)

optimizer = keras.optimizers.Adam()
model.compile(loss='sparse_categorical_crossentropy',
              optimizer=optimizer,
metrics=['accuracy'])