keras中layer和model 的区别

共同点很多，那么区别是什么？官方文档灵魂问答

The Model class has the same API as Layer, with the following differences:

• It exposes built-in training, evaluation, and prediction loops (model.fit(), model.evaluate(), model.predict()).
• It exposes the list of its inner layers, via the model.layers property.
• It exposes saving and serialization APIs (save(), save_weights()...)

So if you're wondering, "should I use the Layer class or the Model class?", ask yourself: will I need to call fit() on it? Will I need to call save() on it? If so, go with Model. If not (either because your class is just a block in a bigger system, or because you are writing training & saving code yourself), use Layer.

也就是说 如果需要使用fit，使用save方法，那就使用model类，其他的其实你仅仅写的是一个block，是大模型的一个组成部分