Custom Keras Data Generator with yield

You are using the Sequence API, which works a bit different than plain generators. In a generator function, you would use the yield keyword to perform iteration inside a while True: loop, so each time Keras calls the generator, it gets a batch of data and it automatically wraps around the end of the data.

But in a Sequence, there is an index parameter to the __getitem__ function, so no iteration or yield is required, this is performed by Keras for you. This is made so the sequence can run in parallel using multiprocessing, which is not possible with old generator functions.

So you are doing things the right way, there is no change needed.

Example of generator in Keras:

def datagenerator(images, labels, batchsize, mode="train"):
    while True:
        start = 0
        end = batchsize

        while start  < len(images): 
            # load your images from numpy arrays or read from directory
            x = images[start:end] 
            y = labels[start:end]
            yield x, y

            start += batchsize
            end += batchsize

Keras wants you to have the infinite loop running in the generator.

If you want to learn about Python generators, then the link in the comments is actually a good place to start.