Deep Learning Quick Reference

Training with a generator

If you haven't used a generator before, it works like an iterator. Every time you call the ImageDataGenerator .flow() method, it will produce a new training minibatch, with random transformations applied to the images it was fed.

The Keras Model class comes with a .fit_generator() method that allows us to fit with a generator rather than a given dataset:

model.fit_generator(data_generator.flow(data["train_X"], data["train_y"], batch_size=32),                    steps_per_epoch=len(data["train_X"]) // 32,                    epochs=200,                    validation_data=(data["val_X"], data["val_y"]),                    verbose=1,                    callbacks=callbacks)

Here, we've replaced the traditional x and y parameters with the generator. Most importantly, notice the steps_per_epoch parameter. You can ...