Hi there,
After the model is trained, I need to use the model to generate predictions with my test dataset, instead of just getting a single accuracy (so eval() won't be enough). However, the model.predict did not work. If I do model.predict(test_loader), it gives an error:
ValueError: The type of input X should be one of {{torch.Tensor, np.ndarray}}.
if I do model.predict(*test_loader) it gives an error:
[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
TypeError: forward() takes 2 positional arguments but 191 were given
Hi there, After the model is trained, I need to use the model to generate predictions with my test dataset, instead of just getting a single accuracy (so eval() won't be enough). However, the model.predict did not work. If I do
model.predict(test_loader)
, it gives an error:if I do
model.predict(*test_loader)
it gives an error: