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

Output
[('', Sequential(
(conv1): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1))
(relu1): ReLU()
(maxp1): MaxPool2d(kernel_size=2, stride=2, padding=0,
dilation=1, ceil_mode=False)
(flatten): Flatten()
(fc1): Linear(in_features=16, out_features=10, bias=True)
(relu2): ReLU()
(fc2): Linear(in_features=10, out_features=3, bias=True)
)),
('conv1', Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1))),
('relu1', ReLU()),
('maxp1',
MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1,
ceil_mode=False)),
('flatten', Flatten()),
('fc1', Linear(in_features=16, out_features=10, bias=True)),
('relu2', ReLU()),
('fc2', Linear(in_features=10, out_features=3, bias=True))]
The first, unnamed, module is the whole model itself. The other, named, modules
are its layers. Any of those layers may be one of the inputs of the hook function. So,
we need to be able to look up the layer name, given the corresponding layer
instance—if only there was something we could use to easily look up values, right?
layer_names = {layer: name for name, layer in modules[1:]}
layer_names
398 | Chapter 5: Convolutions