network.py

conv_base = ResNet50(include_top=False,
                     weights='imagenet',
                     input_shape=(224, 224, 3))
for layer in conv_base.layers:
    layer.trainable = False
x = conv_base.output
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(512, activation='relu')(x)
x = layers.Dense(256, activation='softmax')(x)
predictions = layers.Dense(2, activation='softmax')(x)
model = Model(conv_base.input, predictions)
optimizer = keras.optimizers.RMSprop(lr=1e-4)
model.compile(loss='sparse_categorical_crossentropy',
              optimizer=optimizer,
              metrics=['accuracy'])