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

StepByStep Method
def visualize_filters(self, layer_name, **kwargs):
try:
# Gets the layer object from the model
layer = self.model
for name in layer_name.split('.'):
layer = getattr(layer, name)
# We are only looking at filters for 2D convolutions
if isinstance(layer, nn.Conv2d):
# Takes the weight information
weights = layer.weight.data.cpu().numpy()
# weights -> (channels_out (filter), channels_in, H, W)
n_filters, n_channels, _, _ = weights.shape
# Builds a figure
size = (2 * n_channels + 2, 2 * n_filters)
fig, axes = plt.subplots(n_filters, n_channels,
figsize=size)
axes = np.atleast_2d(axes)
axes = axes.reshape(n_filters, n_channels)
# For each channel_out (filter)
for i in range(n_filters):
StepByStep._visualize_tensors(
axes[i, :],
1
weights[i],
2
layer_name=f'Filter #{i}',
title='Channel'
)
for ax in axes.flat:
ax.label_outer()
fig.tight_layout()
return fig
except AttributeError:
return
setattr(StepByStep, 'visualize_filters', visualize_filters)
1 The i-th row of subplots corresponds to a particular filter; each row has as many
columns as there are input channels.
Visualizing Filters and More! | 393