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

Recap
In this chapter, we’ve introduced convolutions and related concepts and built a
convolutional neural network to tackle a multiclass classification problem. This is
what we’ve covered:
• understanding the role of a kernel / filter in a convolution
• understanding the role of a stride and its impact on the shape of the output
• realizing that there are as many filters as combinations of input and output
channels
• using padding to preserve the shape of the output
• using pooling to shrink the shape of the output
• assembling convolution, activation function, and pooling into a typical
convolutional block
• using a sequence of convolutional blocks to pre-process images, converting
them into features
• (re)building Yann LeCun’s LeNet-5
• generating a dataset of 1,000 images for a multiclass classification problem
• understanding how a softmax function transforms logits into probabilities
• understanding the difference between PyTorch’s negative log-likelihood and
cross-entropy losses
• highlighting the importance of choosing the correct combination of last layer
and loss function (again)
• using the loss function to handle imbalanced datasets
• building our own convolutional neural network, with a featurizer made of a
typical convolutional block, followed by a traditional classifier with a single
hidden layer
• visualizing the learned filters
• understanding and using (forward) hooks to capture the outputs of
intermediate layers of our model
• removing the hooks after they served their purpose so as to not harm the
model speed
• using the captured outputs to visualize feature maps and understanding how
414 | Chapter 5: Convolutions