[GitHub] [incubator-mxnet] gilbertfrancois commented on issue #10002: General support of OPs for second-order gradient

[GitBox <gi...@apache.org>]

gilbertfrancois commented on issue #10002:
URL: https://github.com/apache/incubator-mxnet/issues/10002#issuecomment-708962453


   What is the current status of support for second order derivatives in Gluon? I tried implementing the method from the paper [Improved Training of Wasserstein GANs](https://arxiv.org/pdf/1704.00028.pdf), but the training program returns an error when I add the gradient penalty to the loss function and do a back propagation. I noticed that, with mxnet version 1.7, it works for Dense layers without activation, but e.g. Conv2D and many other layers seem still unsupported. I saw a similar question here #5982, but that was around 3 years ago.
   
   Are there plans to add second order derivative support for e.g. gluon.nn.Conv2D, gluon.nn.BatchNorm, gluon.nn.Activation, gluon.nn.LeakyReLU?


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