Python/Keras - Saving model weights after every N batches

Question

I'm new to Python and Keras, and I have successfully built a neural network that saves weight files after every Epoch. However, I want more granularity (I'm visualizing layer weight distributions in time series) and would like to save the weights after every N batches, rather than every epoch.

Does anyone have any suggestions?

Answer 1

You can create your own callback (https://keras.io/callbacks/). Something like:

from keras.callbacks import Callback

class WeightsSaver(Callback):
    def __init__(self, N):
        self.N = N
        self.batch = 0

    def on_batch_end(self, batch, logs={}):
        if self.batch % self.N == 0:
            name = 'weights%08d.h5' % self.batch
            self.model.save_weights(name)
        self.batch += 1

I use self.batch instead of the batch argument provided because the later restarts at 0 at each epoch.

Then add it to your fit call. For example, to save weights every 5 batches:

model.fit(X_train, Y_train, callbacks=[WeightsSaver(5)])