| import torch |
| import torch.nn.functional as F |
| |
| |
| def num_all_parameters(model): |
| """Returns the number of all parameters in a model.""" |
| return sum(p.numel() for p in model.parameters()) |
| |
| |
| def num_parameters(model): |
| """Returns the number of trainable parameters in a model.""" |
| return sum(p.numel() for p in model.parameters() if p.requires_grad) |
| |
| |
| def training_loop(model, optimizer, loss_function, train, validation, epochs=10): |
| """A rudimentary PyTorch training loop for classification with training and validation data.""" |
| for epoch in range(epochs): |
| # Switch to training mode. |
| model.train() |
| tloss = 0.0 |
| num_train = len(train) # in batches |
| for inp, target in train: # batch loop (training) |
| optimizer.zero_grad() |
| output = model(inp) |
| loss = loss_function(output, target) |
| loss.backward() |
| optimizer.step() |
| tloss += loss.data.item() |
| |
| # Switch to inference mode. |
| model.eval() |
| vloss = 0.0 |
| num_validation = len(validation) # in batches |
| num_correct = 0 |
| num_total = 0 |
| for inp, target in validation: # batch loop (validation) |
| output = model(inp) |
| loss = loss_function(output, target) |
| vloss += loss.data.item() |
| correct = torch.eq( |
| torch.max(F.softmax(output, dim=1), dim=1)[1], target |
| ).view(-1) |
| num_correct += torch.sum(correct).item() |
| num_total += correct.shape[0] |
| |
| # Report stats. |
| print( |
| "Epoch {:d}, Training loss = {:.2f} #{:d}, Validation loss = {:.2f} #{:d}, Accuracy = {:.2f} #{:d}".format( |
| epoch, |
| (tloss / num_train) if num_train != 0 else 0, |
| num_train, |
| (vloss / num_validation) if num_validation != 0 else 0, |
| num_validation, |
| (num_correct / num_total) if num_total != 0 else 0, |
| num_total, |
| ) |
| ) |
