Q:

Calling "fit" multiple times in Keras

I've working on a CNN over several hundred GBs of images. I've created a training function that bites off 4Gb chunks of these images and calls fit over each of these pieces. I'm worried that I'm only training on the last piece on not the entire dataset.
Effectively, my pseudo-code looks like this:
DS = lazy_load_400GB_Dataset()
for section in DS:
    X_train = section.images
    Y_train = section.classes

    model.fit(X_train, Y_train, batch_size=16, nb_epoch=30)

I know that the API and the Keras forums say that this will train over the entire dataset, but I can't intuitively understand why the network wouldn't relearn over just the last training chunk.
Some help understanding this would be much appreciated.
Best,
Joe

A:

For datasets that do not fit into memory, there is an answer in the Keras Documentation FAQ section

You can do batch training using model.train_on_batch(X, y) and
  model.test_on_batch(X, y). See the models documentation.
Alternatively, you can write a generator that yields batches of
  training data and use the method model.fit_generator(data_generator, samples_per_epoch, nb_epoch).
You can see batch training in action in our CIFAR10 example.

So if you want to iterate your dataset the way you are doing, you should probably use model.train_on_batch and take care of the batch sizes and iteration yourself.
One more thing to note is that you should make sure the order in which the samples you train your model with is shuffled after each epoch. The way you have written the example code seems to not shuffle the dataset. You can read a bit more about shuffling here and here

A:

This question was raised at the Keras github repository in Issue #4446: Quick Question: can a model be fit for multiple times?  It was closed by François Chollet with the following statement:

Yes, successive calls to fit will incrementally train the model.

So, yes, you can call fit multiple times.

