aae_semisupervised.py 文件源码

python
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项目:AAE_pytorch 作者: fducau 项目源码 文件源码
def classification_accuracy(Q, data_loader):
    Q.eval()
    labels = []
    test_loss = 0
    correct = 0

    for batch_idx, (X, target) in enumerate(data_loader):
        X = X * 0.3081 + 0.1307
        X.resize_(data_loader.batch_size, X_dim)
        X, target = Variable(X), Variable(target)
        if cuda:
            X, target = X.cuda(), target.cuda()

        labels.extend(target.data.tolist())
        # Reconstruction phase
        output = Q(X)[0]

        test_loss += F.nll_loss(output, target).data[0]

        pred = output.data.max(1)[1]
        correct += pred.eq(target.data).cpu().sum()

    test_loss /= len(data_loader)
    return 100. * correct / len(data_loader.dataset)


####################
# Train procedure
####################
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