Do Keras epoch-counting callbacks work across several fitting sessions?

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Issue

Some Keras callbacks, like ModelCheckpoint or ReduceLROnPlateau, rely on counting the number of epochs that some condition is met until some action is taken.

For certain purposes I need to train a Keras model in several fitting sessions, so something like

for epoch in range(num_epochs):
   model.fit(data, epochs=1)

rather than

model.fit(data, epochs=num_epochs)

I was wondering if Keras callbacks work even if I use them across several fitting sessions.

Solution

Each time model.fit(...) is called callbacks.History is reset. So no, it will not work like that. While you could log yourself as @kacpo1 mentioned and save each, you may benefit from the train_on_batch(...) method. This performs a single update and you can set reset_metrics=False in the method call to retain your metrics.

https://keras.io/api/models/model_training_apis/#trainonbatch-method

Answered By – Aidan Costello

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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