sxjscience edited a comment on issue #18931: URL: https://github.com/apache/incubator-mxnet/issues/18931#issuecomment-676595439
@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 I think it will be a good practice if you can write such test cases and post it under the D2L issue. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
