You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by ha...@apache.org on 2018/08/09 22:55:01 UTC

[incubator-mxnet] branch master updated: Update fully_connected.cc documentation (#12097)

This is an automated email from the ASF dual-hosted git repository.

haibin pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
     new 470c3dc  Update fully_connected.cc documentation (#12097)
470c3dc is described below

commit 470c3dc22895823c6cd7255bb85b510cf6abf6d8
Author: Haibin Lin <li...@gmail.com>
AuthorDate: Thu Aug 9 15:54:52 2018 -0700

    Update fully_connected.cc documentation (#12097)
---
 src/operator/nn/fully_connected.cc | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index d1d84e9..f720a10 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -250,9 +250,15 @@ The learnable parameters include both ``weight`` and ``bias``.
 
 If ``no_bias`` is set to be true, then the ``bias`` term is ignored.
 
-Note that the operator also supports forward computation with `row_sparse` weight and bias,
-where the length of `weight.indices` and `bias.indices` must be equal to `num_hidden`.
-This could be used for model inference with `row_sparse` weights trained with `SparseEmbedding`.
+.. Note::
+
+    The sparse support for FullyConnected is limited to forward evaluation with `row_sparse`
+    weight and bias, where the length of `weight.indices` and `bias.indices` must be equal
+    to `num_hidden`. This could be useful for model inference with `row_sparse` weights
+    trained with importance sampling or noise contrastive estimation.
+
+    To compute linear transformation with 'csr' sparse data, sparse.dot is recommended instead
+    of sparse.FullyConnected.
 
 )code" ADD_FILELINE)
 .set_num_inputs([](const NodeAttrs& attrs) {