Keras model.save()和model.save_weights()之间的区别? [英] Difference between Keras model.save() and model.save_weights()?

问题描述

要在Keras中保存模型，以下文件的输出文件之间有什么区别?

To save a model in Keras, what are the differences between the output files of:

1. model.save()
2. model.save_weights()
回调中的
3. ModelCheckpoint()

1. model.save()
2. model.save_weights()
3. ModelCheckpoint() in the callback

model.save()中保存的文件大于model.save_weights()中的模型，但大大大于JSON或Yaml模型体系结构文件.为什么是这样?

The saved file from model.save() is larger than the model from model.save_weights(), but significantly larger than a JSON or Yaml model architecture file. Why is this?

重新说明一下:为什么size(model.save())+ size(something)= size(model.save_weights())+ size(model.to_json())，那是什么东西"?

Restating this: Why is size(model.save()) + size(something) = size(model.save_weights()) + size(model.to_json()), what is that "something"?

仅使用model.save_weights()和model.to_json()并从中进行加载比仅使用model.save()和load_model()效率更高?

Would it be more efficient to just model.save_weights() and model.to_json(), and load from these than to just do model.save() and load_model()?

有什么区别?

推荐答案

save()将权重和模型结构保存到单个HDF5文件中.我相信它也包括诸如优化器状态之类的东西.然后，您可以将HDF5文件与load()一起使用，以重建包括权重在内的整个模型.

save() saves the weights and the model structure to a single HDF5 file. I believe it also includes things like the optimizer state. Then you can use that HDF5 file with load() to reconstruct the whole model, including weights.

save_weights()仅将权重保存为HDF5，仅此而已.您需要额外的代码才能从JSON文件重建模型.

save_weights() only saves the weights to HDF5 and nothing else. You need extra code to reconstruct the model from a JSON file.

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