Difference between Keras model.save() and model.save_weights()?

Just to add what ModelCheckPoint's output is, if it's relevant for anyone else: used as a callback during model training, it can either save the whole model or just the weights depending on what state the save_weights_only argument is set to. TRUE and weights only are saved, akin to calling model.save_weights(). FALSE (default) and the whole model is saved, as in calling model.save().

• model.save_weights(): Will only save the weights so if you need, you are able to apply them on a different architecture
• mode.save(): Will save the architecture of the model + the the weights + the training configuration + the state of the optimizer

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