未验证 提交 4da291c6 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #15726 from qingqing01/fix_api_doc

Fix row_conv doc
......@@ -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
......
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