def update()

in drqa/reader/model.py [0:0]

• 23 lines of code
• 9 McCabe index (conditional complexity)

    def update(self, ex):
        """Forward a batch of examples; step the optimizer to update weights."""
        if not self.optimizer:
            raise RuntimeError('No optimizer set.')

        # Train mode
        self.network.train()

        # Transfer to GPU
        if self.use_cuda:
            inputs = [e if e is None else e.cuda(non_blocking=True)
                      for e in ex[:5]]
            target_s = ex[5].cuda(non_blocking=True)
            target_e = ex[6].cuda(non_blocking=True)
        else:
            inputs = [e if e is None else e for e in ex[:5]]
            target_s = ex[5]
            target_e = ex[6]

        # Run forward
        score_s, score_e = self.network(*inputs)

        # Compute loss and accuracies
        loss = F.nll_loss(score_s, target_s) + F.nll_loss(score_e, target_e)

        # Clear gradients and run backward
        self.optimizer.zero_grad()
        loss.backward()

        # Clip gradients
        torch.nn.utils.clip_grad_norm_(self.network.parameters(),
                                       self.args.grad_clipping)

        # Update parameters
        self.optimizer.step()
        self.updates += 1

        # Reset any partially fixed parameters (e.g. rare words)
        self.reset_parameters()

        return loss.item(), ex[0].size(0)