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

plot them:
StepByStep Method
def visualize_outputs(self, layers, n_images=10, y=None, yhat=None):
layers = filter(lambda l: l in self.visualization.keys(),
layers)
layers = list(layers)
shapes = [self.visualization[layer].shape for layer in layers]
n_rows = [shape[1] if len(shape) == 4 else 1
for shape in shapes]
total_rows = np.sum(n_rows)
fig, axes = plt.subplots(total_rows, n_images,
figsize=(1.5*n_images, 1.5*total_rows))
axes = np.atleast_2d(axes).reshape(total_rows, n_images)
# Loops through the layers, one layer per row of subplots
row = 0
for i, layer in enumerate(layers):
start_row = row
# Takes the produced feature maps for that layer
output = self.visualization[layer]
is_vector = len(output.shape) == 2
for j in range(n_rows[i]):
StepByStep._visualize_tensors(
axes[row, :],
output if is_vector else output[:, j].squeeze(),
y,
yhat,
layer_name=layers[i] \
if is_vector \
else f'{layers[i]}\nfil#{row-start_row}',
title='Image' if (row == 0) else None
)
row += 1
for ax in axes.flat:
ax.label_outer()
plt.tight_layout()
404 | Chapter 5: Convolutions