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

StepByStep Method
@staticmethod
def _visualize_tensors(axs, x, y=None, yhat=None,
layer_name='', title=None):
# The number of images is the number of subplots in a row
n_images = len(axs)
# Gets max and min values for scaling the grayscale
minv, maxv = np.min(x[:n_images]), np.max(x[:n_images])
# For each image
for j, image in enumerate(x[:n_images]):
ax = axs[j]
# Sets title, labels, and removes ticks
if title is not None:
ax.set_title(f'{title} #{j}', fontsize=12)
shp = np.atleast_2d(image).shape
ax.set_ylabel(
f'{layer_name}\n{shp[0]}x{shp[1]}',
rotation=0, labelpad=40
)
xlabel1 = '' if y is None else f'\nLabel: {y[j]}'
xlabel2 = '' if yhat is None else f'\nPredicted: {yhat[j]}'
xlabel = f'{xlabel1}{xlabel2}'
if len(xlabel):
ax.set_xlabel(xlabel, fontsize=12)
ax.set_xticks([])
ax.set_yticks([])
# Plots weight as an image
ax.imshow(
np.atleast_2d(image.squeeze()),
cmap='gray',
vmin=minv,
vmax=maxv
)
return
setattr(StepByStep, '_visualize_tensors', _visualize_tensors)
"What is that @staticmethod thingy above the method’s definition?"
Visualizing Filters and More! | 389