How does data normalization work in keras during prediction?

Question

I see that the imageDataGenerator allows me to specify different styles of data normalization, e.g. featurewise_center, samplewise_center, etc.

I see from the exampl

Answer 1

Yes - this is a really huge downside of Keras.ImageDataGenerator that you couldn't provide the standarization statistics on your own. But - there is an easy method on how to overcome this issue.

Assuming that you have a function normalize(x) which is normalizing an image batch (remember that generator is not providing a simple image but an array of images - a batch with shape (nr_of_examples_in_batch, image_dims ..) you could make your own generator with normalization by using:

def gen_with_norm(gen, normalize):
    for x, y in gen:
        yield normalize(x), y

Then you might simply use gen_with_norm(datagen.flow, normalize) instead of datagen.flow.

Moreover - you might recover the mean and std computed by a fit method by getting it from appropriate fields in datagen (e.g. datagen.mean and datagen.std).