Deep-Learning-with-PyTorch

296 CHAPTER 11 Training a classification model to detect suspected tumors
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 11.7 The training and validation script we will implement in this chapter, with
a focus on the nested loops over each epoch and batches in the epoch
The code is relatively compact (the doTraining function is only 12 statements; it’s longer
here due to line-length limitations).
Listing 11.10
training.py:137, LunaTrainingApp.main
def main(self):
# ... line 143
for epoch_ndx in range(1, self.cli_args.epochs + 1):
trnMetrics_t = self.doTraining(epoch_ndx, train_dl)
self.logMetrics(epoch_ndx, 'trn', trnMetrics_t)
# ... line 165
def doTraining(self, epoch_ndx, train_dl):
self.model.train()
trnMetrics_g = torch.zeros(
METRICS_SIZE,
len(train_dl.dataset),
device=self.device,
)
Initializes an empty
metrics array
batch_iter = enumerateWithEstimate(
train_dl,
"E{} Training".format(epoch_ndx),
start_ndx=train_dl.num_workers,
)
for batch_ndx, batch_tup in batch_iter:
self.optimizer.zero_grad()
Sets up our batch looping
with time estimate
Frees any leftover
gradient tensors