From bebdad9c403293d5384fdc03cd23ca5ff869f557 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Thu, 4 Jan 2018 02:59:09 +0000 Subject: [PATCH] Deploy to GitHub Pages: 89bbc4f6e53c3636241f9decfd26a730e290d386 --- develop/doc/api/v2/fluid/layers.html | 30 ++++++++++++++----------- develop/doc_cn/api/v2/fluid/layers.html | 30 ++++++++++++++----------- 2 files changed, 34 insertions(+), 26 deletions(-) diff --git a/develop/doc/api/v2/fluid/layers.html b/develop/doc/api/v2/fluid/layers.html index 06e44d9d72..d8e56d2f4d 100644 --- a/develop/doc/api/v2/fluid/layers.html +++ b/develop/doc/api/v2/fluid/layers.html @@ -1007,7 +1007,7 @@ the given labels as soft labels, default False. Returns:

A 2-D tensor with shape [N x 1], the cross entropy loss.

-Raises:

ValueError – 1) the 1st dimension of input and label are not equal; 2) when soft_label == True, and the 2nd dimension of input and label are not equal; 3) when soft_label == False, and the 2nd dimension of label is not 1.

+Raises:

ValueError – 1) the 1st dimension of input and label are not equal; 2) when soft_label == True, and the 2nd dimension of input and label are not equal; 3) when soft_label == False, and the 2nd dimension of label is not 1.

@@ -2020,16 +2020,17 @@ explain how sequence_expand works:

Lstm unit layer. The equation of a lstm step is:

-\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
+\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
-

The inputs of lstm unit includes \(x_t\), \(h_{t-1}\) and -\(c_{t-1}\). The implementation separates the linear transformation -and non-linear transformation apart. Here, we take \(i_t\) as an -example. The linear transformation is applied by calling a fc layer and -the equation is:

+

The inputs of lstm unit include \(x_t\), \(h_{t-1}\) and +\(c_{t-1}\). The 2nd dimensions of \(h_{t-1}\) and \(c_{t-1}\) +should be same. The implementation separates the linear transformation and +non-linear transformation apart. Here, we take \(i_t\) as an example. +The linear transformation is applied by calling a fc layer and the +equation is:

-\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i\]
+\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i\]

The non-linear transformation is applied by calling lstm_unit_op and the equation is:

@@ -2043,9 +2044,12 @@ equation is:

Parameters: