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

Figure 6.31 - Losses
Evaluation
print(StepByStep.loader_apply(
train_loader, sbs_cnn3.correct).sum(axis=0),
StepByStep.loader_apply(
val_loader, sbs_cnn3.correct).sum(axis=0))
Output
tensor([2511, 2520]) tensor([336, 372])
Looking good! Lower losses, 99.64% training accuracy, and 90.32% validation
accuracy.
Recap
In this chapter, we’ve introduced dropout layers for regularization and focused on
the inner workings of different optimizers and the role of the learning rate in the
process. This is what we’ve covered:
• computing channel statistics using a temporary data loader to build a
Normalize() transform
• using Normalize() to standardize an image dataset
• understanding how convolutions over multiple channels work
• building a fancier model with two typical convolutional blocks and dropout
layers
Recap | 495