I have a 2D tensor which I want to standardize. Each row contains an instance, and each instance is an array of 400 floats. I want to efficiently use mean/std functions to get means/stds of all those instances speparately, and then use them to standardize my data.
So far I was able (I think) to get means and stds of all instances with this:
means = train_input_data.mean(dim=1)
stds = train_input_data.std(dim=1)
But I don’t know how to apply subtraction and division of that data on all instances. I can do it on one:
train_input_patches[0] = (train_input_patches[0] - means[0]) / stds[0]
but that doesnt’t seem to be an optimal way to make a loop through all instances.