Why we have to rescale by 1. / 255

train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
horizontal_flip=True)

I saw this process in Keras blog, and your implementation. But I don’t understand why?

def preprocess_input(x, dim_ordering='default'):
    if dim_ordering == 'default':
        dim_ordering = K.image_dim_ordering()
    assert dim_ordering in {'tf', 'th'}

    if dim_ordering == 'th':
        x[:, 0, :, :] -= 103.939
        x[:, 1, :, :] -= 116.779
        x[:, 2, :, :] -= 123.68
        # 'RGB'->'BGR'
        x = x[:, ::-1, :, :]
    else:
        x[:, :, :, 0] -= 103.939
        x[:, :, :, 1] -= 116.779
        x[:, :, :, 2] -= 123.68
        # 'RGB'->'BGR'
        x = x[:, :, :, ::-1]
return x

this is preprocess step on imagenet_util.py, It just minus the mean value of each dimension.

[Arsey]

Hi @anhlt! Rescale is a value by which we will multiply the data before any other processing. Our original images consist in RGB coefficients in the 0-255, but such values would be too high for our model to process (given a typical learning rate), so we target values between 0 and 1 instead by scaling with a 1/255. factor (the description taken from https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)

Talking about preprocess_input function, I don’t know on which step it can/should be used. I’ve tried to train model with extracting those mean values and converting to BGR, but then model does not train at all and stack on a very low accuracy (near 0.0387 for each epoch). Then I’ve tried it on prediction step - results still bad. Also I’ve noticed that this function is not used inside of Keras.

About mean value here’s interesting info - https://github.com/Lasagne/Recipes/issues/20

[Suraj023]

rescale is a value by which we will multiply the data before any other processing. Our original images consist in RGB coefficients in the 0-255, but such values would be too high for our models to process (given a typical learning rate), so we target values between 0 and 1 instead by scaling with a 1./255 factor.