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authorDeterminant <[email protected]>2016-03-09 10:32:40 +0800
committerDeterminant <[email protected]>2016-03-09 10:32:40 +0800
commit4a6872601f05e9ecc059f83fb64a0a4887992b99 (patch)
treeb768fc437cbdd1be707c861c3595e78dff0d1fab /nerv
parent7bc5e6936f4d81a35e0f2de2227eb2ff6479dae2 (diff)
parent18990c8d90ad8e57fed2e5fd4d4acd4af491f880 (diff)
Merge branch 'master' of github.com:Nerv-SJTU/nervalpha-1
Diffstat (limited to 'nerv')
-rw-r--r--nerv/doc/nerv_matrix.md4
1 files changed, 2 insertions, 2 deletions
diff --git a/nerv/doc/nerv_matrix.md b/nerv/doc/nerv_matrix.md
index 8ae97f9..3782eb3 100644
--- a/nerv/doc/nerv_matrix.md
+++ b/nerv/doc/nerv_matrix.md
@@ -83,8 +83,8 @@ Fill the content of __Matrix__ `self` to be `value`.
Set the element of __Matrix__ `self` to be elementwise-sigmoid of `ma`.
* __void Matrix.sigmoid_grad(Matrix self, Matrix err, Matrix output)__
Set the element of __Matrix__ `self`, to be `self[i][j]=err[i][j]*output[i][j]*(1-output[i][j])`. This function is used to propagate sigmoid layer error.
-* __void Matrix.softmax(Matrix self, Matrix a)__
-Calculate a row-by-row softmax of __Matrix__ `a` and save the result in `self`.
+* __Matrix Matrix.softmax(Matrix self, Matrix a)__
+Calculate a row-by-row softmax of __Matrix__ `a` and save the result in `self`. Returns a new `self.nrow*1` index matrix that stores the index of the maximum value of each row.
* __void Matrix.mul_elem(Matrix self, Matrix ma, Matrix mb)__
Calculate element-wise multiplication of __Matrix__ `ma` and `mb`, store the result in `self`.
* __void Matrix.log_elem(Matrix self, Matrix ma)__