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| def train_epoch_loop(data_loader:DataLoader, opt:torch.optim, model:PyTorchForecast, takes_target:bool, forward_params={}) | |
| i = 0 | |
| running_loss = 0.0 | |
| for src, trg in data_loader: | |
| opt.zero_grad() | |
| # Convert to CPU/GPU/TPU | |
| src = src.to(model.device) | |
| trg = trg.to(model.device) | |
| # TODO figure how to avoid | |
| if takes_target: | |
| forward_params["t"] = trg | |
| output = model.model(src, **forward_params) | |
| labels = trg[:, :, 0] | |
| loss = criterion(output, labels.float()) | |
| if loss > 100: | |
| print("Warning: high loss detected") | |
| loss.backward() | |
| #torch.nn.utils.clip_grad_norm_(s.parameters(), 0.5) | |
| opt.step() | |
| running_loss += loss.item() | |
| i+=1 | |
| if torch.isnan(loss) or loss==float('inf'): | |
| raise "Error infinite or NaN loss detected. Try normalizing data or performing interpolation" | |
| print("The loss for epoch " + str(epoch)) | |
| total_loss = running_loss/float(i) | |
| print(total_loss) | |
| return total_loss |
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