train.py 文件源码

def test(args, epoch, net, testLoader, optimizer, testF):
    net.eval()
    test_loss = 0
    incorrect = 0
    for data, target in testLoader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = net(data)
        test_loss += F.nll_loss(output, target).data[0]
        pred = output.data.max(1)[1] # get the index of the max log-probability
        incorrect += pred.ne(target.data).cpu().sum()

    test_loss = test_loss
    test_loss /= len(testLoader) # loss function already averages over batch size
    nTotal = len(testLoader.dataset)
    err = 100.*incorrect/nTotal
    print('\nTest set: Average loss: {:.4f}, Error: {}/{} ({:.0f}%)\n'.format(
        test_loss, incorrect, nTotal, err))

    testF.write('{},{},{}\n'.format(epoch, test_loss, err))
    testF.flush()