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class Training():
def __init__(self, epochs):
self.epochs = epochs
def run(self):
for epoch in range(1, self.epochs + 1):
self.run_epoch(self.data_loader["train"], phase="train")
loss_training = self.run_epoch(self.data_loader["train"], phase="eval")
loss_validation = self.run_epoch(self.data_loader["validation"], phase="eval")
# ToDo: log outputs and time
self.lr_scheduler.step()
if epoch % self.snapshot_epoch:
self.store_snapshot(epoch)
def run_epoch(self, data_loader, phase):
self.model.train() if phase == "train" else self.model.eval()
epoch_loss = 0
for inputs, labels in self.data_loader:
inputs = inputs.to(self.device)
labels = labels.to(self.device)
self.optimizer.zero_grad()
with torch.set_grad_enabled(phase == "train"):
outputs = self.model(inputs)
loss = self.loss(outputs, labels)
if phase == "train":
loss.backward()
optimizer.step()
epoch_loss += loss.item() / inputs.size[0]
return epoch_loss
def store_snapshot(self, epoch):
pass