How to use repeat() function when building data in Keras?

Question

I am training a binary classifier on a dataset of cats and dogs:
Total Dataset: 10000 images
Training Dataset: 8000 images
Validation/Test Dataset: 2000 im

Answer 1

Your problem stems from the fact that the parameters steps_per_epoch and validation_steps need to be equal to the total number of data points divided to the batch_size.

Your code would work in Keras 1.X, prior to August 2017.

Change your model.fit function to:

history = model.fit_generator(training_set,
                              steps_per_epoch=int(8000/batch_size),
                              epochs=25,
                              validation_data=test_set,
                              validation_steps=int(2000/batch_size))

As of TensorFlow2.1, fit_generator() is being deprecated. You can use .fit() method also on generators.

TensorFlow >= 2.1 code:

history = model.fit(training_set.repeat(),
                    steps_per_epoch=int(8000/batch_size),
                    epochs=25,
                    validation_data=test_set.repeat(),
                    validation_steps=int(2000/batch_size))

Notice that int(8000/batch_size) is equivalent to 8000 // batch_size (integer division)