c63cc933782e2de32c9fe74c18b337b2bbe0f242,test/examples/test_kissgp_variational_regression.py,TestKissGPVariationalRegression,test_kissgp_gp_mean_abs_error,#TestKissGPVariationalRegression#,61

Before Change

        gp_model.train()
        likelihood.train()

        with gpytorch.beta_features.diagonal_correction():
            optimizer = optim.SGD(list(gp_model.parameters()) + list(likelihood.parameters()), lr=0.001)
            scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[15], gamma=0.1)
            for _ in range(20):
                scheduler.step()
                batchnum = 0
                for x_batch, y_batch in loader:
                    batchnum += 1
                    x_batch = Variable(x_batch.float())
                    y_batch = Variable(y_batch.float())

                    optimizer.zero_grad()
                    output = gp_model(x_batch)
                    loss = -mll(output, y_batch)
                    loss.backward()
                    for name, param in gp_model.named_parameters():
                        print("minibatch {} grad norm".format(batchnum), name, param.grad.norm())
                    optimizer.step()

            for name, param in gp_model.named_parameters():
                print(name, param.grad.norm())
                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()

            test_preds = likelihood(gp_model(Variable(test_x))).mean()
            mean_abs_error = torch.mean(torch.abs(Variable(test_y) - test_preds))

        self.assertLess(mean_abs_error.data.squeeze().item(), 0.1)


if __name__ == "__main__":

After Change

        // Our loss object
        // We"re using the VariationalMarginalLogLikelihood object
        mll = gpytorch.mlls.VariationalMarginalLogLikelihood(likelihood, model, n_data=train_y.size(0))

        // The training loop
        def train():
            for i in range(n_iter):

Instances