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Update gpytorch and gpflow benchmark #19

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Update gpytorch and gpflow benchmark #19

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8 changes: 4 additions & 4 deletions benchmark/gpytorch_variational_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import gpytorch
from gpytorch.models import ApproximateGP
from gpytorch.variational import CholeskyVariationalDistribution
from gpytorch.variational import VariationalStrategy, UnwhitenedVariationalStrategy
from gpytorch.variational import VariationalStrategy

__all__ = ("get_rbf_kernel", "RegressionVGP", "TwoClassVGP", "MultiClassVGP")

Expand Down Expand Up @@ -43,7 +43,7 @@ def _choose_var_strat(model, var_strat, var_dist, ind_pt, learn_ind=True, num_cl
num_tasks=num_classes, task_dim=0,
)
else:
return UnwhitenedVariationalStrategy(model, ind_pt, var_dist, learn_inducing_locations=learn_ind)
return VariationalStrategy(model, ind_pt, var_dist, learn_inducing_locations=learn_ind)


def get_rbf_kernel(ard=None, batch_shape=1):
Expand Down Expand Up @@ -189,9 +189,9 @@ def do_train(self, Xtr, Ytr, Xval, Yval):
# Define dataset iterators
train_dataset = torch.utils.data.TensorDataset(Xtr, Ytr)
if self.mb_size == 1:
train_loader = torch.utils.data.DataLoader(train_dataset, shuffle=True, num_workers=8)
train_loader = torch.utils.data.DataLoader(train_dataset, shuffle=True, num_workers=0)
else:
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.mb_size, shuffle=True, num_workers=8)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.mb_size, shuffle=True, num_workers=0)

# Start training
t_elapsed = 0
Expand Down