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
Summary: this is useful to test rnn cells
Reviewed By: dzhulgakov
Differential Revision: D4720641
fbshipit-source-id: baa7df43357ed8af72ede64be3e0a642a40472df
Summary:
If we go to prod some of the sparse features might be empty or for some reason
batch might be empty. It's a good idea to be sure that we can run empty
batches.
Reviewed By: dzhulgakov
Differential Revision: D4197297
fbshipit-source-id: 1a154ebf625d1a39fd15354a154cf100f525ae9a
