Network forward / backward equality checker

Summary:
In some cases you have an optimized network and a normal
one. And you would like to make sure they produce same results. If
math under the hood is the same, you could do this with a very high
precision compare to a traditional numerical gradient check. One of
the application - RNNs. There we can unroll RNN into Caffe2 graph and
make sure result is the same as in the optimized version using
RecurrentNetworkOp.

Another possible application - graph transformations. We can verify
that after that nets produce same gradients (cc akyrola on memonger,
bwasti on other transformation ideas)

Reviewed By: bwasti

Differential Revision: D5200855

fbshipit-source-id: 0196af187f0c2feb33de4778ea08d0d288fe1017
2 files changed
tree: af5dc70dbb262d486b1474b4165eba11708c100c
  1. .travis/
  2. caffe/
  3. caffe2/
  4. cmake/
  5. docs/
  6. scripts/
  7. third_party/
  8. .Doxyfile
  9. .Doxyfile-c
  10. .Doxyfile-python
  11. .gitignore
  12. .gitmodules
  13. .travis.yml
  14. appveyor.yml
  15. CMakeLists.txt
  16. LICENSE
  17. Makefile
  18. PATENTS
  19. README.md
  20. release-notes.md
README.md

Caffe2

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