Keras: What if the size of data is not divisible by batch_size?

Question

I am new to Keras and just started working on some examples. I am dealing with the following problem: I have 4032 samples and use about 650 of them as for the fit or basical

Answer 1

The simplest solution is to use fit_generator instead of fit. I write a simple dataloader class that can be inherited to do more complex stuff. It would look something like this with get_next_batch_data redefined to whatever your data is including stuff like augmentation etc..

class BatchedLoader():
    def __init__(self):
        self.possible_indices = [0,1,2,...N] #(say N = 33)
        self.cur_it = 0
        self.cur_epoch = 0

    def get_batch_indices(self):
        batch_indices = self.possible_indices [cur_it : cur_it + batchsize]
        # If len(batch_indices) < batchsize, the you've reached the end
        # In that case, reset cur_it to 0 and increase cur_epoch and shuffle possible_indices if wanted
        # And add remaining K = batchsize - len(batch_indices) to batch_indices


    def get_next_batch_data(self):
        # batch_indices = self.get_batch_indices()
        # The data points corresponding to those indices will be your next batch data