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c3e3a836 · Travis CI · 296a760e · c3e3a836 · c3e3a836 · c3e3a836
4 changed file
--- a/develop/doc/api/v2/fluid/layers.html
+++ b/develop/doc/api/v2/fluid/layers.html
@@ -222,8 +222,7 @@
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">fc</code><span class="sig-paren">(</span><em>input</em>, <em>size</em>, <em>num_flatten_dims=1</em>, <em>param_attr=None</em>, <em>bias_attr=None</em>, <em>act=None</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
-<dd><blockquote>
+<dd><p><strong>Fully Connected Layer</strong></p>
-<div><p><strong>Fully Connected Layer</strong></p>
 <p>The fully connected layer can take multiple tensors as its inputs. It
 creates a variable (one for each input tensor) called weights for each input
 tensor, which represents a fully connected weight matrix from each input
@@ -235,11 +234,8 @@ created and added to the output. Finally, if activation is not None,
 it will be applied to the output as well.</p>
 <p>This process can be formulated as follows:</p>
 <div class="math">
-\[Out = Act\left({\sum_{i=0}^{N-1}W_iX_i + b}\]</div>
+\[Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})\]</div>
-</div></blockquote>
+<p>In the above equation:</p>
-<p>ight)</p>
-<blockquote>
-<div><p>In the above equation:</p>
 <ul class="simple">
 <li><span class="math">\(N\)</span>: Number of the input.</li>
 <li><span class="math">\(X_i\)</span>: The input tensor.</li>
@@ -248,13 +244,15 @@ it will be applied to the output as well.</p>
 <li><span class="math">\(Act\)</span>: The activation funtion.</li>
 <li><span class="math">\(Out\)</span>: The output tensor.</li>
 </ul>
-<dl class="docutils">
+<table class="docutils field-list" frame="void" rules="none">
-<dt>Args:</dt>
+<col class="field-name" />
-<dd><p class="first">input(Variable|list): The input tensor(s) to the fully connected layer.
+<col class="field-body" />
-size(int): The number of output units in the fully connected layer.
+<tbody valign="top">
-num_flatten_dims(int): The fc layer can accept an input tensor with more</p>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<blockquote>
+<li><strong>input</strong> (<em>Variable|list</em>) &#8211; The input tensor(s) to the fully connected layer.</li>
-<div>than two dimensions. If this happens, the
+<li><strong>size</strong> (<em>int</em>) &#8211; The number of output units in the fully connected layer.</li>
+<li><strong>num_flatten_dims</strong> (<em>int</em>) &#8211; The fc layer can accept an input tensor with more
+than two dimensions. If this happens, the
 multidimensional tensor will first be flattened
 into a 2-dimensional matrix. The parameter
 <cite>num_flatten_dims</cite> determines how the input tensor
@@ -268,37 +266,41 @@ For example, suppose <cite>X</cite> is a 6-dimensional tensor
 with a shape [2, 3, 4, 5, 6], and
 <cite>x_num_col_dims</cite> = 3. Then, the flattened matrix
 will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
-By default, <cite>x_num_col_dims</cite> is set to 1.</div></blockquote>
+By default, <cite>x_num_col_dims</cite> is set to 1.</li>
-<dl class="docutils">
+<li><strong>param_attr</strong> (<em>ParamAttr|list</em>) &#8211; The parameter attribute for learnable
-<dt>param_attr(ParamAttr|list): The parameter attribute for learnable</dt>
+parameters/weights of the fully connected
-<dd>parameters/weights of the fully connected
+layer.</li>
-layer.</dd>
+<li><strong>param_initializer</strong> (<em>ParamAttr|list</em>) &#8211; The initializer used for the
-<dt>param_initializer(ParamAttr|list): The initializer used for the</dt>
+weight/parameter. If set None,
-<dd>weight/parameter. If set None,
+XavierInitializer() will be used.</li>
-XavierInitializer() will be used.</dd>
+<li><strong>bias_attr</strong> (<em>ParamAttr|list</em>) &#8211; The parameter attribute for the bias parameter
-<dt>bias_attr(ParamAttr|list): The parameter attribute for the bias parameter</dt>
+for this layer. If set None, no bias will be
-<dd>for this layer. If set None, no bias will be
+added to the output units.</li>
-added to the output units.</dd>
+<li><strong>bias_initializer</strong> (<em>ParamAttr|list</em>) &#8211; The initializer used for the bias.
-<dt>bias_initializer(ParamAttr|list): The initializer used for the bias.</dt>
+If set None, then ConstantInitializer()
-<dd>If set None, then ConstantInitializer()
+will be used.</li>
-will be used.</dd>
+<li><strong>act</strong> (<em>str</em>) &#8211; Activation to be applied to the output of the fully connected
-<dt>act(str): Activation to be applied to the output of the fully connected</dt>
+layer.</li>
-<dd>layer.</dd>
+<li><strong>name</strong> (<em>str</em>) &#8211; Name/alias of the fully connected layer.</li>
-</dl>
+</ul>
-<p class="last">name(str): Name/alias of the fully connected layer.</p>
+</td>
-</dd>
+</tr>
-<dt>Returns:</dt>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The output tensor variable.</p>
-<dd>Variable: The output tensor variable.</dd>
+</td>
-<dt>Raises:</dt>
+</tr>
-<dd>ValueError: If rank of the input tensor is less than 2.</dd>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first">Variable</p>
-<dt>Examples:</dt>
+</td>
-<dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
+</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; If rank of the input tensor is less than 2.</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
 <span class="n">fc</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">data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s2">&quot;tanh&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-</dd>
-</dl>
-</div></blockquote>
 </dd></dl>
 </div>

--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
--- a/develop/doc_cn/api/v2/fluid/layers.html
+++ b/develop/doc_cn/api/v2/fluid/layers.html
@@ -235,8 +235,7 @@
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">fc</code><span class="sig-paren">(</span><em>input</em>, <em>size</em>, <em>num_flatten_dims=1</em>, <em>param_attr=None</em>, <em>bias_attr=None</em>, <em>act=None</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
-<dd><blockquote>
+<dd><p><strong>Fully Connected Layer</strong></p>
-<div><p><strong>Fully Connected Layer</strong></p>
 <p>The fully connected layer can take multiple tensors as its inputs. It
 creates a variable (one for each input tensor) called weights for each input
 tensor, which represents a fully connected weight matrix from each input
@@ -248,11 +247,8 @@ created and added to the output. Finally, if activation is not None,
 it will be applied to the output as well.</p>
 <p>This process can be formulated as follows:</p>
 <div class="math">
-\[Out = Act\left({\sum_{i=0}^{N-1}W_iX_i + b}\]</div>
+\[Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})\]</div>
-</div></blockquote>
+<p>In the above equation:</p>
-<p>ight)</p>
-<blockquote>
-<div><p>In the above equation:</p>
 <ul class="simple">
 <li><span class="math">\(N\)</span>: Number of the input.</li>
 <li><span class="math">\(X_i\)</span>: The input tensor.</li>
@@ -261,13 +257,15 @@ it will be applied to the output as well.</p>
 <li><span class="math">\(Act\)</span>: The activation funtion.</li>
 <li><span class="math">\(Out\)</span>: The output tensor.</li>
 </ul>
-<dl class="docutils">
+<table class="docutils field-list" frame="void" rules="none">
-<dt>Args:</dt>
+<col class="field-name" />
-<dd><p class="first">input(Variable|list): The input tensor(s) to the fully connected layer.
+<col class="field-body" />
-size(int): The number of output units in the fully connected layer.
+<tbody valign="top">
-num_flatten_dims(int): The fc layer can accept an input tensor with more</p>
+<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
-<blockquote>
+<li><strong>input</strong> (<em>Variable|list</em>) &#8211; The input tensor(s) to the fully connected layer.</li>
-<div>than two dimensions. If this happens, the
+<li><strong>size</strong> (<em>int</em>) &#8211; The number of output units in the fully connected layer.</li>
+<li><strong>num_flatten_dims</strong> (<em>int</em>) &#8211; The fc layer can accept an input tensor with more
+than two dimensions. If this happens, the
 multidimensional tensor will first be flattened
 into a 2-dimensional matrix. The parameter
 <cite>num_flatten_dims</cite> determines how the input tensor
@@ -281,37 +279,41 @@ For example, suppose <cite>X</cite> is a 6-dimensional tensor
 with a shape [2, 3, 4, 5, 6], and
 <cite>x_num_col_dims</cite> = 3. Then, the flattened matrix
 will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
-By default, <cite>x_num_col_dims</cite> is set to 1.</div></blockquote>
+By default, <cite>x_num_col_dims</cite> is set to 1.</li>
-<dl class="docutils">
+<li><strong>param_attr</strong> (<em>ParamAttr|list</em>) &#8211; The parameter attribute for learnable
-<dt>param_attr(ParamAttr|list): The parameter attribute for learnable</dt>
+parameters/weights of the fully connected
-<dd>parameters/weights of the fully connected
+layer.</li>
-layer.</dd>
+<li><strong>param_initializer</strong> (<em>ParamAttr|list</em>) &#8211; The initializer used for the
-<dt>param_initializer(ParamAttr|list): The initializer used for the</dt>
+weight/parameter. If set None,
-<dd>weight/parameter. If set None,
+XavierInitializer() will be used.</li>
-XavierInitializer() will be used.</dd>
+<li><strong>bias_attr</strong> (<em>ParamAttr|list</em>) &#8211; The parameter attribute for the bias parameter
-<dt>bias_attr(ParamAttr|list): The parameter attribute for the bias parameter</dt>
+for this layer. If set None, no bias will be
-<dd>for this layer. If set None, no bias will be
+added to the output units.</li>
-added to the output units.</dd>
+<li><strong>bias_initializer</strong> (<em>ParamAttr|list</em>) &#8211; The initializer used for the bias.
-<dt>bias_initializer(ParamAttr|list): The initializer used for the bias.</dt>
+If set None, then ConstantInitializer()
-<dd>If set None, then ConstantInitializer()
+will be used.</li>
-will be used.</dd>
+<li><strong>act</strong> (<em>str</em>) &#8211; Activation to be applied to the output of the fully connected
-<dt>act(str): Activation to be applied to the output of the fully connected</dt>
+layer.</li>
-<dd>layer.</dd>
+<li><strong>name</strong> (<em>str</em>) &#8211; Name/alias of the fully connected layer.</li>
-</dl>
+</ul>
-<p class="last">name(str): Name/alias of the fully connected layer.</p>
+</td>
-</dd>
+</tr>
-<dt>Returns:</dt>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The output tensor variable.</p>
-<dd>Variable: The output tensor variable.</dd>
+</td>
-<dt>Raises:</dt>
+</tr>
-<dd>ValueError: If rank of the input tensor is less than 2.</dd>
+<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first">Variable</p>
-<dt>Examples:</dt>
+</td>
-<dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
+</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; If rank of the input tensor is less than 2.</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
 <span class="n">fc</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">data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s2">&quot;tanh&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-</dd>
-</dl>
-</div></blockquote>
 </dd></dl>
 </div>

--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js