Transformers, Pytorch, Machine Learning, Deep Dives, Data Science.
history_acc.append(accuracy.item()) tepoch.set_postfix(loss=loss.item(), accuracy=100. * accuracy.item()) return losses / len(list(loader)), acc / len(list(loader)), history_loss, history_acc def evaluate(model, loader, loss_fn): model.eval() losses = 0 acc = 0 history_loss = [] history_acc = []