Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

StepByStep Method
@staticmethod
def loader_apply(loader, func, reduce='sum'):
results = [func(x, y) for i, (x, y) in enumerate(loader)]
results = torch.stack(results, axis=0)
if reduce == 'sum':
results = results.sum(axis=0)
elif reduce == 'mean':
results = results.float().mean(axis=0)
return results
setattr(StepByStep, 'loader_apply', loader_apply)
Since it is a static method, we can call it from the class itself, passing the loader as
its first argument, and a function (or method, in this case) as its second argument. It
will call the correct() method for each mini-batch (as in the example above), and
sum all the results up:
StepByStep.loader_apply(sbs_cnn1.val_loader, sbs_cnn1.correct)
Output
tensor([[59, 67],
[55, 62],
[71, 71]])
Quite simple, right? This method will be very useful for us in the next chapter when
we normalize the images and thus need to compute the mean and standard
deviation over all images in the training loader.
From the results above, we see that our model got 185 out of 200 images correctly
classified in the validation set, an accuracy of 92.5%! Not bad, not bad at all :-)
Putting It All Together
In this chapter, we focused mostly on the model configuration part, adding
410 | Chapter 5: Convolutions