@ptrblck
Hello, i noticed this strange thing some days ago.
When i use images /= 255 to make them between 0 and 1 my code works just fine.
If instead of images /= 255 i use
    X_copy = copy.deepcopy(images)
    X_copy = Variable(torch.FloatTensor(X_copy))

    trans = transforms.Compose([transforms.ToTensor()])

    for i in range(X_copy.shape[0]):
        a = F.to_pil_image(X_copy[i])
        X_copy[i] = trans(a)

    return X_copy`

Then my program trains for 8-40 epochs in cifar100 dataset. Then at some point the train error goes higher and the learning stops. I discovered this when i was trying to find the correct way to have my images between 0 and 1. Any idea why does this happen? Is my way to make images between 0 and 1 using ToTensor wrong?