From 286823255629ef8e337b3797ced223a6f7672a8a Mon Sep 17 00:00:00 2001 From: Dang Qingqing Date: Fri, 15 Feb 2019 14:33:02 +0800 Subject: [PATCH] Fix row_conv doc test=develop --- paddle/fluid/operators/row_conv_op.cc | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/paddle/fluid/operators/row_conv_op.cc b/paddle/fluid/operators/row_conv_op.cc index 10b1b0c899d..d283bddbe9f 100644 --- a/paddle/fluid/operators/row_conv_op.cc +++ b/paddle/fluid/operators/row_conv_op.cc @@ -109,23 +109,23 @@ from future subsequences in a computationally efficient manner to improve unidirectional recurrent neural networks. The row convolution operator is different from the 1D sequence convolution, and is computed as follows: -Given an input sequence $in$ of length $t$ and input dimension $d$, -and a filter ($W$) of size $context \times d$, +Given an input sequence $X$ of length $t$ and input dimension $D$, +and a filter ($W$) of size $context \times D$, the output sequence is convolved as: $$ -out_{i, :} = \\sum_{j=i}^{i + context} in_{j,:} \\cdot W_{i-j, :} +out_{i} = \\sum_{j=i}^{i + context - 1} X_{j} \\cdot W_{j-i} $$ In the above equation: * $Out_{i}$: The i-th row of output variable with shape [1, D]. -* $\\tau$: Future context size. +* $context$: Future context size. * $X_{j}$: The j-th row of input variable with shape [1, D]. -* $W_{i-j}$: The (i-j)-th row of parameters with shape [1, D]. +* $W_{j-i}$: The (j-i)-th row of parameters with shape [1, D]. More details about row_conv please refer to the design document -- GitLab