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

The meow() method is totally independent of the Cat class. We do not even
need to create a cat! That’s what I meant by "called on an uninstantiated class
object."
Cat.meow()
Output
Meow
See? The meow() method could well be an independent function because it
works like one. But, in the context of a Cat class, it makes sense to have that
method attached to it since they belong together, conceptually speaking.
Most of the _visualize_tensors() method’s body consists of handling titles,
labels, and axes' ticks before using imshow() to actually plot the image, so it is not
that interesting. Let’s check its arguments:
• axs: an array of subplots, corresponding to one row of subplots as returned by
Matplotlib’s subplot
• x: a Numpy array containing at least as many images / filters as subplots in axs
• y: optional, a Numpy array containing at least as many labels as subplots in axs
• yhat: optional, a Numpy array containing at least as many predicted labels as
subplots in axs
• layer_name: label for the row of subplots
• title: title prefix for each subplot
We’ll call our static method from other (regular) methods to plot the images we’re
interested in. Let’s start with the filters.
Visualizing Filters
We can apply the same principle to the weights of the filter learned by our
convolutional layer. We can access the weights of any given layer using dot
notation:
Visualizing Filters and More! | 391