How do you properly ensure that a PyTorch model's Dropout and BatchNorm modules behave correctly during the evaluation/testing phase?

Question

Answer

By calling the  eval()  method on the model (e.g.,  network.eval() ).

Answer

By calling the  train()  method on the model before making predictions.

Answer

By setting  requires_grad=False  on all model parameters.

Answer

By executing the forward pass within a  with torch.no_grad():  block.

Answer

By explicitly moving the model to the CPU using  network.to('cpu') .

Crowdly