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develop/doc/_sources/api/v2/fluid/layers.rst.txt
浏览文件 @ c6aa44ec
...@@ -188,12 +188,6 @@ beam_search_decode ...@@ -188,12 +188,6 @@ beam_search_decode
:noindex: :noindex:
lstm
---------
.. autofunction:: paddle.v2.fluid.layers.lstm
:noindex:
lod_rank_table lod_rank_table
--------- ---------
.. autofunction:: paddle.v2.fluid.layers.lod_rank_table .. autofunction:: paddle.v2.fluid.layers.lod_rank_table
...@@ -300,7 +294,14 @@ conv2d_transpose ...@@ -300,7 +294,14 @@ conv2d_transpose
.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose .. autofunction:: paddle.v2.fluid.layers.conv2d_transpose
:noindex: :noindex:
sequence_expand sequence_expand
--------- ---------
.. autofunction:: paddle.v2.fluid.layers.sequence_expand .. autofunction:: paddle.v2.fluid.layers.sequence_expand
:noindex: :noindex:
lstm_unit
---------
.. autofunction:: paddle.v2.fluid.layers.lstm_unit
:noindex:
develop/doc/api/v2/fluid/layers.html
浏览文件 @ c6aa44ec
...@@ -863,9 +863,6 @@ the BatchNorm layer using the configurations from the input parameters.</p> ...@@ -863,9 +863,6 @@ the BatchNorm layer using the configurations from the input parameters.</p>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">beam_search_decode</code><span class="sig-paren">(</span><em>ids</em>, <em>scores</em><span class="sig-paren">)</span></dt> <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">beam_search_decode</code><span class="sig-paren">(</span><em>ids</em>, <em>scores</em><span class="sig-paren">)</span></dt>
<dd></dd></dl> <dd></dd></dl>
</div>
<div class="section" id="lstm">
<h2>lstm<a class="headerlink" href="#lstm" title="Permalink to this headline"></a></h2>
</div> </div>
<div class="section" id="lod-rank-table"> <div class="section" id="lod-rank-table">
<h2>lod_rank_table<a class="headerlink" href="#lod-rank-table" title="Permalink to this headline"></a></h2> <h2>lod_rank_table<a class="headerlink" href="#lod-rank-table" title="Permalink to this headline"></a></h2>
...@@ -1070,7 +1067,7 @@ stride_H = stride_W = stride.</li> ...@@ -1070,7 +1067,7 @@ stride_H = stride_W = stride.</li>
<h2>sequence_expand<a class="headerlink" href="#sequence-expand" title="Permalink to this headline"></a></h2> <h2>sequence_expand<a class="headerlink" href="#sequence-expand" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt> <dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_expand</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>main_program=None</em>, <em>startup_program=None</em><span class="sig-paren">)</span></dt> <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_expand</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span></dt>
<dd><p>Sequence Expand Layer. This layer will expand the input variable <strong>x</strong> <dd><p>Sequence Expand Layer. This layer will expand the input variable <strong>x</strong>
according to LoD information of <strong>y</strong>. And the following examples will according to LoD information of <strong>y</strong>. And the following examples will
explain how sequence_expand works:</p> explain how sequence_expand works:</p>
...@@ -1116,8 +1113,6 @@ explain how sequence_expand works:</p> ...@@ -1116,8 +1113,6 @@ explain how sequence_expand works:</p>
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>x</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li> <li><strong>x</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li>
<li><strong>y</strong> (<em>Variable</em>) &#8211; The input variable which is a LoDTensor.</li> <li><strong>y</strong> (<em>Variable</em>) &#8211; The input variable which is a LoDTensor.</li>
<li><strong>main_program</strong> (<em>Program</em>) &#8211; The main program.</li>
<li><strong>startup_program</strong> (<em>Program</em>) &#8211; The startup program.</li>
</ul> </ul>
</td> </td>
</tr> </tr>
...@@ -1138,6 +1133,71 @@ explain how sequence_expand works:</p> ...@@ -1138,6 +1133,71 @@ explain how sequence_expand works:</p>
</div> </div>
</dd></dl> </dd></dl>
</div>
<div class="section" id="lstm-unit">
<h2>lstm_unit<a class="headerlink" href="#lstm-unit" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">lstm_unit</code><span class="sig-paren">(</span><em>x_t</em>, <em>hidden_t_prev</em>, <em>cell_t_prev</em>, <em>forget_bias=0.0</em>, <em>param_attr=None</em>, <em>bias_attr=None</em><span class="sig-paren">)</span></dt>
<dd><p>Lstm unit layer. The equation of a lstm step is:</p>
<blockquote>
<div><div class="math">
\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
</div></blockquote>
<p>The inputs of lstm unit includes <span class="math">\(x_t\)</span>, <span class="math">\(h_{t-1}\)</span> and
<span class="math">\(c_{t-1}\)</span>. The implementation separates the linear transformation
and non-linear transformation apart. Here, we take <span class="math">\(i_t\)</span> as an
example. The linear transformation is applied by calling a <cite>fc</cite> layer and
the equation is:</p>
<blockquote>
<div><div class="math">
\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i\]</div>
</div></blockquote>
<p>The non-linear transformation is applied by calling <cite>lstm_unit_op</cite> and the
equation is:</p>
<blockquote>
<div><div class="math">
\[i_t = \sigma(L_{i_t})\]</div>
</div></blockquote>
<p>This layer has two outputs including <span class="math">\(h_t\)</span> and <span class="math">\(o_t\)</span>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>x_t</strong> (<em>Variable</em>) &#8211; The input value of current step.</li>
<li><strong>hidden_t_prev</strong> (<em>Variable</em>) &#8211; The hidden value of lstm unit.</li>
<li><strong>cell_t_prev</strong> (<em>Variable</em>) &#8211; The cell value of lstm unit.</li>
<li><strong>forget_bias</strong> (<em>float</em>) &#8211; The forget bias of lstm unit.</li>
<li><strong>param_attr</strong> (<em>ParamAttr</em>) &#8211; The attributes of parameter weights, used to set
initializer, name etc.</li>
<li><strong>bias_attr</strong> (<em>ParamAttr</em>) &#8211; The attributes of bias weights, if not False,
bias weights will be created and be set to default value.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The hidden value and cell value of lstm unit.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first">tuple</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Raises:</th><td class="field-body"><p class="first last"><code class="xref py py-exc docutils literal"><span class="pre">ValueError</span></code> &#8211; The ranks of <strong>x_t</strong>, <strong>hidden_t_prev</strong> and <strong>cell_t_prev</strong> not be 2 or the 1st dimensions of <strong>x_t</strong>, <strong>hidden_t_prev</strong> and <strong>cell_t_prev</strong> not be the same.</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x_t</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_t_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">prev_hidden</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">prev_hidden_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">prev_cell</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">prev_cell_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">hidden_value</span><span class="p">,</span> <span class="n">cell_value</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">lstm_unit</span><span class="p">(</span><span class="n">x_t</span><span class="o">=</span><span class="n">x_t</span><span class="p">,</span>
<span class="n">hidden_t_prev</span><span class="o">=</span><span class="n">prev_hidden</span><span class="p">,</span>
<span class="n">cell_t_prev</span><span class="o">=</span><span class="n">prev_cell</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div> </div>
</div> </div>
......
develop/doc/operators.json
浏览文件 @ c6aa44ec
...@@ -1846,7 +1846,7 @@ ...@@ -1846,7 +1846,7 @@
"inputs" : [ "inputs" : [
{ {
"name" : "X", "name" : "X",
"comment" : "FC input before the non-linear activation.", "comment" : "Lstm unit only applies non-linear activations, please make surethat linear tranformation has already been applied to `X`. Linear tranformation can be applied by adding a `fc` layer",
"duplicable" : 0, "duplicable" : 0,
"intermediate" : 0 "intermediate" : 0
}, { }, {
......
develop/doc/searchindex.js
浏览文件 @ c6aa44ec
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
浏览文件 @ c6aa44ec
...@@ -188,12 +188,6 @@ beam_search_decode ...@@ -188,12 +188,6 @@ beam_search_decode
:noindex: :noindex:
lstm
---------
.. autofunction:: paddle.v2.fluid.layers.lstm
:noindex:
lod_rank_table lod_rank_table
--------- ---------
.. autofunction:: paddle.v2.fluid.layers.lod_rank_table .. autofunction:: paddle.v2.fluid.layers.lod_rank_table
...@@ -300,7 +294,14 @@ conv2d_transpose ...@@ -300,7 +294,14 @@ conv2d_transpose
.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose .. autofunction:: paddle.v2.fluid.layers.conv2d_transpose
:noindex: :noindex:
sequence_expand sequence_expand
--------- ---------
.. autofunction:: paddle.v2.fluid.layers.sequence_expand .. autofunction:: paddle.v2.fluid.layers.sequence_expand
:noindex: :noindex:
lstm_unit
---------
.. autofunction:: paddle.v2.fluid.layers.lstm_unit
:noindex:
develop/doc_cn/api/v2/fluid/layers.html
浏览文件 @ c6aa44ec
...@@ -882,9 +882,6 @@ the BatchNorm layer using the configurations from the input parameters.</p> ...@@ -882,9 +882,6 @@ the BatchNorm layer using the configurations from the input parameters.</p>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">beam_search_decode</code><span class="sig-paren">(</span><em>ids</em>, <em>scores</em><span class="sig-paren">)</span></dt> <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">beam_search_decode</code><span class="sig-paren">(</span><em>ids</em>, <em>scores</em><span class="sig-paren">)</span></dt>
<dd></dd></dl> <dd></dd></dl>
</div>
<div class="section" id="lstm">
<h2>lstm<a class="headerlink" href="#lstm" title="永久链接至标题"></a></h2>
</div> </div>
<div class="section" id="lod-rank-table"> <div class="section" id="lod-rank-table">
<h2>lod_rank_table<a class="headerlink" href="#lod-rank-table" title="永久链接至标题"></a></h2> <h2>lod_rank_table<a class="headerlink" href="#lod-rank-table" title="永久链接至标题"></a></h2>
...@@ -1089,7 +1086,7 @@ stride_H = stride_W = stride.</li> ...@@ -1089,7 +1086,7 @@ stride_H = stride_W = stride.</li>
<h2>sequence_expand<a class="headerlink" href="#sequence-expand" title="永久链接至标题"></a></h2> <h2>sequence_expand<a class="headerlink" href="#sequence-expand" title="永久链接至标题"></a></h2>
<dl class="function"> <dl class="function">
<dt> <dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_expand</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>main_program=None</em>, <em>startup_program=None</em><span class="sig-paren">)</span></dt> <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_expand</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span></dt>
<dd><p>Sequence Expand Layer. This layer will expand the input variable <strong>x</strong> <dd><p>Sequence Expand Layer. This layer will expand the input variable <strong>x</strong>
according to LoD information of <strong>y</strong>. And the following examples will according to LoD information of <strong>y</strong>. And the following examples will
explain how sequence_expand works:</p> explain how sequence_expand works:</p>
...@@ -1135,8 +1132,6 @@ explain how sequence_expand works:</p> ...@@ -1135,8 +1132,6 @@ explain how sequence_expand works:</p>
<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple"> <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
<li><strong>x</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li> <li><strong>x</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li>
<li><strong>y</strong> (<em>Variable</em>) &#8211; The input variable which is a LoDTensor.</li> <li><strong>y</strong> (<em>Variable</em>) &#8211; The input variable which is a LoDTensor.</li>
<li><strong>main_program</strong> (<em>Program</em>) &#8211; The main program.</li>
<li><strong>startup_program</strong> (<em>Program</em>) &#8211; The startup program.</li>
</ul> </ul>
</td> </td>
</tr> </tr>
...@@ -1157,6 +1152,71 @@ explain how sequence_expand works:</p> ...@@ -1157,6 +1152,71 @@ explain how sequence_expand works:</p>
</div> </div>
</dd></dl> </dd></dl>
</div>
<div class="section" id="lstm-unit">
<h2>lstm_unit<a class="headerlink" href="#lstm-unit" title="永久链接至标题"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">lstm_unit</code><span class="sig-paren">(</span><em>x_t</em>, <em>hidden_t_prev</em>, <em>cell_t_prev</em>, <em>forget_bias=0.0</em>, <em>param_attr=None</em>, <em>bias_attr=None</em><span class="sig-paren">)</span></dt>
<dd><p>Lstm unit layer. The equation of a lstm step is:</p>
<blockquote>
<div><div class="math">
\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
</div></blockquote>
<p>The inputs of lstm unit includes <span class="math">\(x_t\)</span>, <span class="math">\(h_{t-1}\)</span> and
<span class="math">\(c_{t-1}\)</span>. The implementation separates the linear transformation
and non-linear transformation apart. Here, we take <span class="math">\(i_t\)</span> as an
example. The linear transformation is applied by calling a <cite>fc</cite> layer and
the equation is:</p>
<blockquote>
<div><div class="math">
\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i\]</div>
</div></blockquote>
<p>The non-linear transformation is applied by calling <cite>lstm_unit_op</cite> and the
equation is:</p>
<blockquote>
<div><div class="math">
\[i_t = \sigma(L_{i_t})\]</div>
</div></blockquote>
<p>This layer has two outputs including <span class="math">\(h_t\)</span> and <span class="math">\(o_t\)</span>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
<li><strong>x_t</strong> (<em>Variable</em>) &#8211; The input value of current step.</li>
<li><strong>hidden_t_prev</strong> (<em>Variable</em>) &#8211; The hidden value of lstm unit.</li>
<li><strong>cell_t_prev</strong> (<em>Variable</em>) &#8211; The cell value of lstm unit.</li>
<li><strong>forget_bias</strong> (<em>float</em>) &#8211; The forget bias of lstm unit.</li>
<li><strong>param_attr</strong> (<em>ParamAttr</em>) &#8211; The attributes of parameter weights, used to set
initializer, name etc.</li>
<li><strong>bias_attr</strong> (<em>ParamAttr</em>) &#8211; The attributes of bias weights, if not False,
bias weights will be created and be set to default value.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The hidden value and cell value of lstm unit.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first">tuple</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Raises:</th><td class="field-body"><p class="first last"><code class="xref py py-exc docutils literal"><span class="pre">ValueError</span></code> &#8211; The ranks of <strong>x_t</strong>, <strong>hidden_t_prev</strong> and <strong>cell_t_prev</strong> not be 2 or the 1st dimensions of <strong>x_t</strong>, <strong>hidden_t_prev</strong> and <strong>cell_t_prev</strong> not be the same.</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x_t</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_t_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">prev_hidden</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">prev_hidden_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">prev_cell</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">prev_cell_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">hidden_value</span><span class="p">,</span> <span class="n">cell_value</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">lstm_unit</span><span class="p">(</span><span class="n">x_t</span><span class="o">=</span><span class="n">x_t</span><span class="p">,</span>
<span class="n">hidden_t_prev</span><span class="o">=</span><span class="n">prev_hidden</span><span class="p">,</span>
<span class="n">cell_t_prev</span><span class="o">=</span><span class="n">prev_cell</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div> </div>
</div> </div>
......
develop/doc_cn/searchindex.js
浏览文件 @ c6aa44ec
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