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

The next part of our model, the classifier part, uses these features to feed what
would be a simple neural network with a single hidden layer if considered on its
own:
Model Configuration - Classifier
1 # Classification
2 # Hidden Layer
3 model_cnn1.add_module('fc1',
4 nn.Linear(in_features=n_channels*4*4, out_features=10)
5 )
6 model_cnn1.add_module('relu2', nn.ReLU())
7 # Output Layer
8 model.add_module('fc2', nn.Linear(in_features=10, out_features=3))
See? There is a hidden layer that takes the 16 features as inputs and maps them
into a ten-dimensional space that is going to be "activated" by the ReLU.
Then, the output layer produces three distinct linear combinations of the ten
activation values, each combination corresponding to a different class. The figure
below, depicting the second half of the model, should make it more clear.
Figure 5.17 - Classifier with softmax output
386 | Chapter 5: Convolutions