Deep-Learning-with-PyTorch

Training and validating the model
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By recording the label, prediction, and loss for each and every training (and later, validation)
sample, we have a wealth of detailed information we can use to investigate
the behavior of our model. For now, we’re going to focus on compiling per-class statistics,
but we could easily use this information to find the sample that is classified the
most wrongly and start to investigate why. Again, for some projects, this kind of information
will be less interesting, but it’s good to remember that you have these kinds of
options available.
11.5.2 The validation loop is similar
The validation loop in figure 11.8 looks very similar to training but is somewhat simplified.
The key difference is that validation is read-only. Specifically, the loss value
returned is not used, and the weights are not updated.
Init model
Initialized
with Random
weights
FuLly
Trained
Init data loaders
LOop over epochs
Training LOop
Load batch tuple
ClaSsify Batch
Calculate LoSs
Record metrics
Update weights
Validation LOop
Load
batch tuple
ClaSsify Batch
Calculate LoSs
Record metrics
Log Metrics
console
tensorboard
Figure 11.8 The training and validation script we will implement in this chapter, with a
focus on the per-epoch validation loop
Nothing about the model should have changed between the start and end of the function
call. In addition, it’s quite a bit faster due to the with torch.no_grad() context
manager explicitly informing PyTorch that no gradients need to be computed.
Listing 11.13
training.py:137, LunaTrainingApp.main
def main(self):
for epoch_ndx in range(1, self.cli_args.epochs + 1):
# ... line 157
valMetrics_t = self.doValidation(epoch_ndx, val_dl)