4275d25af066b667a0787f9639b652789f9a0338,test/examples/test_batch_gp_regression.py,TestBatchGPRegression,test_train_on_single_set_test_on_batch,#TestBatchGPRegression#,62

Before Change

    def test_train_on_single_set_test_on_batch(self):
        // We"re manually going to set the hyperparameters to something they shouldn"t be
        likelihood = GaussianLikelihood()
        gp_model = ExactGPModel(train_x1, train_y1, likelihood)
        mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)
        gp_model.covar_module.base_kernel.initialize(log_lengthscale=-1)
        gp_model.mean_module.initialize(constant=0)
        likelihood.initialize(log_noise=0)

        // Find optimal model hyperparameters
        gp_model.train()
        likelihood.train()
        optimizer = optim.Adam(list(gp_model.parameters()) + list(likelihood.parameters()), lr=0.1)
        optimizer.n_iter = 0
        for _ in range(50):
            optimizer.zero_grad()
            output = gp_model(train_x1)
            loss = -mll(output, train_y1)
            loss.backward()
            optimizer.n_iter += 1
            optimizer.step()

        for param in gp_model.parameters():
            self.assertTrue(param.grad is not None)
            self.assertGreater(param.grad.norm().item(), 0)
        for param in likelihood.parameters():
            self.assertTrue(param.grad is not None)
            self.assertGreater(param.grad.norm().item(), 0)
        optimizer.step()

        // Test the model
        gp_model.eval()
        likelihood.eval()

After Change

    def test_train_on_single_set_test_on_batch(self):
        // We"re manually going to set the hyperparameters to something they shouldn"t be
        likelihood = GaussianLikelihood(
            log_noise_prior=gpytorch.priors.NormalPrior(loc=torch.zeros(1), scale=torch.ones(1), log_transform=True)
        )
        gp_model = ExactGPModel(train_x1, train_y1, likelihood)
        mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)

        // Find optimal model hyperparameters
        gp_model.train()
        likelihood.train()
        optimizer = optim.Adam(list(gp_model.parameters()) + list(likelihood.parameters()), lr=0.1)
        optimizer.n_iter = 0
        gp_model.train()
        likelihood.train()
        optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
        for _ in range(50):
            optimizer.zero_grad()
            output = gp_model(train_x1)
            loss = -mll(output, train_y1).sum()
            loss.backward()
            optimizer.step()

            for param in gp_model.parameters():
                self.assertTrue(param.grad is not None)
                self.assertGreater(param.grad.norm().item(), 0)
            for param in likelihood.parameters():
                self.assertTrue(param.grad is not None)
                self.assertGreater(param.grad.norm().item(), 0)

Instances