@StevenJokes In addition, for the DCGAN issue that is related to D2L, a simple way to verify that you are correct (and also convince the others), is to write a test case that checks whether these two networks are **equivalent**.
For example, you have a network A implemented in MXNet and a network B implemented in PyTorch. There are several checks that you can do: - Just try to see if these two networks have the same number of parameters - Do a forward pass of both networks and check whether the outputs are the same. - Do a forward + backward and match the gradient. Usually, you will need to do more to convince the others that certain issues exist. There are some examples: - Here, the minimal reproducible example related to Autograd helps us locate the problem: https://github.com/apache/incubator-mxnet/issues/17989 - A minimal example that captures a potential issue of the GELU implementation in MKLDNN https://github.com/apache/incubator-mxnet/issues/18826 It will be a good practice if you can write such test cases and tell D2L people. -- You are receiving this because you were mentioned. Reply to this email directly or view it on GitHub: https://github.com/apache/incubator-mxnet/issues/18931#issuecomment-676597008
