kshitij12345 commented on a change in pull request #15109: [DOC] refine autograd docs URL: https://github.com/apache/incubator-mxnet/pull/15109#discussion_r290965942
########## File path: docs/api/python/autograd/autograd.md ########## @@ -76,7 +82,63 @@ Detailed tutorials are available in Part 1 of [the MXNet gluon book](http://gluon.mxnet.io/). +# Higher order gradient + +Some operators support higher order gradients. Meaning that you calculate the gradient of the +gradient. For this the operator's backward must be as well differentiable. Some operators support +differentiating multiple times, and others two, most just once. + +For calculating higher order gradients, we can use the `mx.autograd.grad` function while recording +and then call backward, or call `mx.autograd.grad` two times. If we do the later is important that +the first call uses `create_graph=True` and `retain_graph=True` and the second call uses +`create_graph=False` and `retain_graph=True`. Otherwise we will not get the results that we want. If Review comment: I am slightly confused, `create_graph` argument determines if the computation graph for the `grad`/`backward` call will be created or not. And `retain_graph`, determines if this graph will be retained or freed. So is it possible to retain the graph without creating it or maybe I am misunderstanding the argument behaviour? ---------------------------------------------------------------- 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: us...@infra.apache.org With regards, Apache Git Services
