[quote="wrongtest, post:3, topic:7960"] If I have some common neural network structure such as resnet50 at hand, can I just use autodiff to get backward computation graph? [/quote] graph-wise I think you can refer to [relay.transform.gradient](https://github.com/apache/incubator-tvm/blob/master/python/tvm/relay/transform/transform.py#L713) and as you lower the differentiated graph, you may leverage the tensor-level autodiff ([te.gradient](https://github.com/apache/incubator-tvm/blob/master/python/tvm/te/autodiff.py#L22)). Though tensor gradients now are mostly manually written.
[quote="wrongtest, post:3, topic:7960"] Is there some description about common ops which can be coveraged by autodiff? [/quote] You may refer to [test cases](https://github.com/apache/incubator-tvm/blob/master/tests/python/unittest/test_te_autodiff.py) [quote="wrongtest, post:3, topic:7960"] Can te.scan() be supported? [/quote] currently it is not supported. --- [Visit Topic](https://discuss.tvm.apache.org/t/rfc-differentiable-tensor-expression-create-and-verify-backward-op-automatically/7960/5) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/8df628e061512b8ab69723e918ccc46549cbf19313faa3902569142ab512b2c2).
