1344dee35dbacaaaaabdaf452f0dfe74e3ab50e4,torchsample/modules/super_module.py,SuperModule,evaluate_loader,#SuperModule#Any#,144

Before Change

            loss = self.loss(y_pred, y_batch)
            losses.append(loss.data[0])

        return torch.mean(torch.FloatTensor(losses))   

After Change

        return loss.data[0]

    def evaluate_loader(self, loader):
        nb_batches = int(math.ceil(len(loader.dataset.inputs)/loader.batch_size))
        losses = torch.FloatTensor(nb_batches)
        for batch_idx, (x_batch, y_batch) in enumerate(loader):
            x_batch = Variable(x_batch)
            y_batch = Variable(y_batch)

Instances