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apache / mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
https://mxnet.apache.org
Apache License 2.0
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How to implement gradient operators? #6055

Closed zhuchen03 closed 7 years ago

zhuchen03 commented 7 years ago

I want to implement the improved WGAN with mxnet. However, the gradient penalty is a great headache. It is a complex loss function, which contains the data gradient. For simple loss functions, we can calculate the gradient of loss with respect to the output easily, but how to calculate the data gradient with respect to the output?

It seems that tensorflow has a gradient operator, just as the common convolution operators or fully connected operators. With such an operator, things become simple - we just need to write the expression for the loss function. However, I don't have an idea of it.

Maybe mxnet can also have such an operator in the future?

szha commented 7 years ago

This issue is closed due to lack of activity in the last 90 days. Feel free to ping me to reopen if this is still an active issue. Thanks!