train_cnn.py 文件源码

def freeze_all_but_mid_and_top(model):
    """After we fine-tune the dense layers, train deeper."""
    # we chose to train the top 2 inception blocks, i.e. we will freeze
    # the first 172 layers and unfreeze the rest:
    for layer in model.layers[:172]:
        layer.trainable = False
    for layer in model.layers[172:]:
        layer.trainable = True

    # we need to recompile the model for these modifications to take effect
    # we use SGD with a low learning rate
    model.compile(
        optimizer=SGD(lr=0.0001, momentum=0.9),
        loss='categorical_crossentropy',
        metrics=['accuracy', 'top_k_categorical_accuracy'])

    return model