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

return fig
setattr(StepByStep, 'visualize_outputs', visualize_outputs)
Then, let’s use the method above to plot the feature maps for the layers in the
featurizer part of our model:
featurizer_layers = ['conv1', 'relu1', 'maxp1', 'flatten']
with plt.style.context('seaborn-white'):
fig = sbs_cnn1.visualize_outputs(featurizer_layers)
Figure 5.20 - Feature maps (featurizer)
Figure 5.21 - Mini-batch of images (reproduced here for an easier comparison)
Looks cool, right? Even though I’ve plotted the images in the first four rows with
the same size, they have different dimensions, as indicated by the row labels on the
left. The shade of gray is also computed per row: The maximum (white) and
minimum (black) values were computed across the ten images produced by a given
layer; otherwise, some rows would be too dark (the ranges vary a lot from one layer
to the next).
What can we learn from these images? First, convolving the learned filter with the
input image produces some interesting results:
Visualizing Filters and More! | 405