wangwei created SINGA-342:
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Summary: Support autograd
Key: SINGA-342
URL: https://issues.apache.org/jira/browse/SINGA-342
Project: Singa
Issue Type: New Feature
Reporter: wangwei
Autograd computes the partial derivatives of a complex function following chain
rule (or back-propagation).
To implement autograd, we can follow
[https://stackoverflow.com/questions/32034237/how-does-numpys-transpose-method-permute-the-axes-of-an-array]
and [https://github.com/HIPS/autograd.]
In particular, we record the operation and operands of each result tensor
during forward propagation. A graph is constructed based on the recorded
information. Once the loss.backward() is triggered, we run backward propagation
over the graph to compute the gradients of parameters.
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