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From Xingjian Shi <>
Subject Re: [apache/incubator-mxnet] [Development] MXNet 2.0 Update (#18931)
Date Wed, 19 Aug 2020 18:46:21 GMT
@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:
- A minimal example that captures a potential issue of the GELU implementation in MKLDNN

It will be a good practice if you can write such test cases and tell D2L people.

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