Keras，优化时保存状态的最佳方法

Question

我只是想知道在优化模型的同时保存模型状态的最佳方法是什么。我想这样做，这样我就可以运行它一段时间，保存它，稍后再回来。我知道有一个函数用于保存权重，另一个函数用于将模型保存为JSON。在学习过程中，我需要保存模型的权重和参数。这包括动量和学习率等参数。有没有办法将模型和权重都保存在同一个文件中。我读到用泡菜不被认为是好的做法。另外，gradident decept的动量是否包含在JSON模型中或权重中？在

Answer 1

您可以创建一个包含权重和体系结构的tar归档文件，以及一个pickle文件，其中包含model.optimizer.get_state()返回的优化器状态。在

Answer 2

from keras.models import load_model

model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
del model  # deletes the existing model

# returns a compiled model
# identical to the previous one
model = load_model('my_model.h5')

You can use model.save(filepath) to save a Keras model into a single HDF5 file which will contain:

• the architecture of the model, allowing to re-create the model
• the weights of the model
• the training configuration (loss, optimizer)
• the state of the optimizer, allowing to resume training exactly where you left off.

You can then use keras.models.load_model(filepath) to reinstantiate your model. load_model will also take care of compiling the model using the saved training configuration (unless the model was never compiled in the first place).

Keras常见问题：How can I save a Keras model?