forward_thinking.py 文件源码

def test_(self, epoch):
        self.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            if args.cuda:
                data, target = data.cuda(), target.cuda()
            data, target = Variable(data, volatile=True), Variable(target)
            output = self(data)
            test_loss += F.nll_loss(output, target).data[0]
            pred = output.data.max(1)[1] # get the index of the max log-probability
            correct += pred.eq(target.data).cpu().sum()

        test_loss = test_loss
        test_loss /= len(test_loader) # loss function already averages over batch size
        accuracy = 100. * correct / len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.4f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            accuracy))

        self.test_acc.append(accuracy)