parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
+parser.add_argument("--random_regression_order", action="store_true", default=False)
+
parser.add_argument("--no_checkpoint", action="store_true", default=False)
parser.add_argument("--overwrite_results", action="store_true", default=False)
def random_order(result, fixed_len):
- order = torch.rand(result.size(), device=result.device)
- order[:, :fixed_len] = torch.linspace(-2, -1, fixed_len, device=order.device)
- return order.sort(1).indices
+ if args.random_regression_order:
+ order = torch.rand(result.size(), device=result.device)
+ order[:, :fixed_len] = torch.linspace(-2, -1, fixed_len, device=order.device)
+ return order.sort(1).indices
+ else:
+ return torch.arange(result.size(1)).unsqueeze(0).expand(result.size(0), -1)
def shuffle(x, order, reorder=False):
task.produce_results(n_epoch, model)
- exit(0)
-
##############################
for n_epoch in range(nb_epochs_finished, args.nb_epochs):