simple_gp_classification_test.py 文件源码

def test_kissgp_classification_error_cuda():
    if torch.cuda.is_available():
        train_x, train_y = train_data(cuda=True)
        model = GPClassificationModel(train_x.data).cuda()
        model.condition(train_x, train_y)

        # Find optimal model hyperparameters
        model.train()
        optimizer = optim.Adam(model.parameters(), lr=0.1)
        optimizer.n_iter = 0
        for i in range(50):
            optimizer.zero_grad()
            output = model.forward(train_x)
            loss = -model.marginal_log_likelihood(output, train_y)
            loss.backward()
            optimizer.n_iter += 1
            optimizer.step()

        # Set back to eval mode
        model.eval()
        test_preds = model(train_x).mean().ge(0.5).float().mul(2).sub(1).squeeze()
        mean_abs_error = torch.mean(torch.abs(train_y - test_preds) / 2)
        assert(mean_abs_error.data.squeeze()[0] < 1e-5)