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

Weights as Pixels
During data preparation, we flattened the inputs from five-by-five images to 25-
element-long tensors. Here is a crazy idea: What if we take some other tensor with
25 elements in it and try to visualize it as an image?
We have some perfect candidates for this: The weights used by each unit in
Hidden Layer #0. Each unit uses 25 weights since each unit receives values from 25
features. We even have these weights in a variable already:
w_nn_hidden0.shape
Output
torch.Size([5, 25])
Five units, 25 weights each. Perfect! We only need to use view() to reshape the 25-
element-long tensors representing the weights into two-dimensional tensors
(5x5), and visualize them as if they were images:
Figure 4.10 - Weights as pixels
"What’s the point of doing that?"
Visualizing weights as images is commonplace when using convolutional neural
networks (CNNs). These images will be called filters, and trained models will likely
exhibit more recognizable characteristics in their filters. Since our model was
poorly trained, it’s no wonder the images above are not very informative.
Moreover, in our case, these are not quite "filters," since they have the same size as
the input image. In CNN-based models, real filters cover only part of the image.
We’ll get back to it in the next chapter.
Deep-ish Model | 311