Saving model weights after each forward pass

Question

I am doing research in explainable AI by looking at the patterns in weights as a function of model hyperparameters and input data. One of the things I'm examining is how weights progress from randomness (or initializer starting values) to stabilization, after learning completes. I'd like to, instead of saving weights every epoch, save them at every other or third forward pass. How would I do that? Must I tweak the 'period' argument to the Keras model checkpoint method? If so, what's an easy formula to set that argument? Thx and have a great day.

Answer 1

just pass save_freq=3 when instantiating the tf.keras.callbacks.ModelCheckpoint.

Quoting the docs:

https://keras.io/api/callbacks/model_checkpoint/

save_freq: 'epoch' or integer. When using 'epoch', the callback saves the model after each epoch. When using integer, the callback saves the model at end of this many batches. If the Model is compiled with steps_per_execution=N, then the saving criteria will be checked every Nth batch. Note that if the saving isn't aligned to epochs, the monitored metric may potentially be less reliable (it could reflect as little as 1 batch, since the metrics get reset every epoch). Defaults to 'epoch'.