Deep-Learning-with-PyTorch

Using convolutions
to generalize
This chapter covers
• Understanding convolution
• Building a convolutional neural network
• Creating custom nn.Module subclasses
• The difference between the module and
functional APIs
• Design choices for neural networks
In the previous chapter, we built a simple neural network that could fit (or overfit)
the data, thanks to the many parameters available for optimization in the linear layers.
We had issues with our model, however, in that it was better at memorizing the
training set than it was at generalizing properties of birds and airplanes. Based on
our model architecture, we’ve got a guess as to why that’s the case. Due to the fully
connected setup needed to detect the various possible translations of the bird or
airplane in the image, we have both too many parameters (making it easier for the
model to memorize the training set) and no position independence (making it
harder to generalize). As we discussed in the last chapter, we could augment our
193