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develop/doc/_sources/api/v2/config/layer.rst.txt
浏览文件 @ 1c564a7a
...@@ -419,9 +419,14 @@ multi_binary_label_cross_entropy_cost ...@@ -419,9 +419,14 @@ multi_binary_label_cross_entropy_cost
.. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost .. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
:noindex: :noindex:
huber_cost huber_regression_cost
---------- -------------------------
.. autoclass:: paddle.v2.layer.huber_cost .. autoclass:: paddle.v2.layer.huber_regression_cost
:noindex:
huber_classification_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_classification_cost
:noindex: :noindex:
lambda_cost lambda_cost
......
develop/doc/api/v2/config/layer.html
浏览文件 @ 1c564a7a
...@@ -3326,38 +3326,119 @@ Input should be a vector of positive numbers, without normalization.</p> ...@@ -3326,38 +3326,119 @@ Input should be a vector of positive numbers, without normalization.</p>
</dd></dl> </dd></dl>
</div> </div>
<div class="section" id="huber-cost"> <div class="section" id="huber-regression-cost">
<h3>huber_cost<a class="headerlink" href="#huber-cost" title="Permalink to this headline"></a></h3> <h3>huber_regression_cost<a class="headerlink" href="#huber-regression-cost" title="Permalink to this headline"></a></h3>
<dl class="class"> <dl class="class">
<dt> <dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">huber_cost</code></dt> <em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">huber_regression_cost</code></dt>
<dd><p>A loss layer for huber loss.</p> <dd><blockquote>
<p>The example usage is:</p> <div>In statistics, the Huber loss is a loss function used in robust regression,
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> that is less sensitive to outliers in data than the squared error loss.
<span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span> Given a prediction f(x), a label y and <span class="math">\(\delta\)</span>, the loss function
is defined as:</div></blockquote>
<p>ight )^2, left | y-f(x)
ight <a href="#id2"><span class="problematic" id="id3">|</span></a>leq delta</p>
<blockquote>
<div>loss = delta left | y-f(x)</div></blockquote>
<p>ight <a href="#id4"><span class="problematic" id="id5">|</span></a>-0.5delta ^2, otherwise</p>
<blockquote>
<div><p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_regression_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>
<table class="docutils field-list" frame="void" rules="none"> <table class="docutils field-list" frame="void" rules="none">
<col class="field-name" /> <col class="field-name" />
<col class="field-body" /> <col class="field-body" />
<tbody valign="top"> <tbody valign="top">
<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">param input:</th><td class="field-body">The first input layer.</td>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer.</em>) &#8211; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float.</em>) &#8211; The coefficient affects the gradient in the backward.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</li>
</ul>
</td>
</tr> </tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer object.</p> <tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</td>
</tr> </tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer.</p> <tr class="field-odd field"><th class="field-name">param label:</th><td class="field-body">The input label.</td>
</td> </tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">The name of this layers. It is not necessary.</td>
</tr>
<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">None|basestring.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param delta:</th><td class="field-body">The difference between the observed and predicted values.</td>
</tr>
<tr class="field-even field"><th class="field-name">type delta:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param coeff:</th><td class="field-body">The coefficient affects the gradient in the backward.</td>
</tr>
<tr class="field-even field"><th class="field-name">type coeff:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">Extra Layer Attribute.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
</div></blockquote>
</dd></dl>
</div>
<div class="section" id="huber-classification-cost">
<h3>huber_classification_cost<a class="headerlink" href="#huber-classification-cost" title="Permalink to this headline"></a></h3>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">huber_classification_cost</code></dt>
<dd><blockquote>
<div>For classification purposes, a variant of the Huber loss called modified Huber
is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
a true binary class label :math:<a href="#id6"><span class="problematic" id="id7">`</span></a>yin left {-1, 1</div></blockquote>
<dl class="docutils">
<dt>ight }`, the modified Huber</dt>
<dd>loss is defined as:</dd>
<dt>ight )^2, yf(x)geq 1</dt>
<dd><blockquote class="first">
<div>loss = -4yf(x), ext{otherwise}</div></blockquote>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_classification_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<table class="last 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">param input:</th><td class="field-body">The first input layer.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param label:</th><td class="field-body">The input label.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">The name of this layers. It is not necessary.</td>
</tr>
<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">None|basestring.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param coeff:</th><td class="field-body">The coefficient affects the gradient in the backward.</td>
</tr>
<tr class="field-even field"><th class="field-name">type coeff:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">Extra Layer Attribute.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
</tbody>
</table>
</dd>
</dl>
</dd></dl> </dd></dl>
</div> </div>
...@@ -4017,7 +4098,7 @@ a_i * z_i &amp;\quad \mathrm{otherwise}\end{split}\]</div> ...@@ -4017,7 +4098,7 @@ a_i * z_i &amp;\quad \mathrm{otherwise}\end{split}\]</div>
<dd><p>The gated unit layer implements a simple gating mechanism over the input. <dd><p>The gated unit layer implements a simple gating mechanism over the input.
The input <span class="math">\(X\)</span> is first projected into a new space <span class="math">\(X'\)</span>, and The input <span class="math">\(X\)</span> is first projected into a new space <span class="math">\(X'\)</span>, and
it is also used to produce a gate weight <span class="math">\(\sigma\)</span>. Element-wise it is also used to produce a gate weight <span class="math">\(\sigma\)</span>. Element-wise
prodict between <a href="#id6"><span class="problematic" id="id7">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p> prodict between <a href="#id12"><span class="problematic" id="id13">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p>
<dl class="docutils"> <dl class="docutils">
<dt>Reference:</dt> <dt>Reference:</dt>
<dd>Language Modeling with Gated Convolutional Networks <dd>Language Modeling with Gated Convolutional Networks
......
develop/doc/searchindex.js
浏览文件 @ 1c564a7a
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/config/layer.rst.txt
浏览文件 @ 1c564a7a
...@@ -419,9 +419,14 @@ multi_binary_label_cross_entropy_cost ...@@ -419,9 +419,14 @@ multi_binary_label_cross_entropy_cost
.. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost .. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
:noindex: :noindex:
huber_cost huber_regression_cost
---------- -------------------------
.. autoclass:: paddle.v2.layer.huber_cost .. autoclass:: paddle.v2.layer.huber_regression_cost
:noindex:
huber_classification_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_classification_cost
:noindex: :noindex:
lambda_cost lambda_cost
......
develop/doc_cn/api/v2/config/layer.html
浏览文件 @ 1c564a7a
...@@ -3333,38 +3333,119 @@ Input should be a vector of positive numbers, without normalization.</p> ...@@ -3333,38 +3333,119 @@ Input should be a vector of positive numbers, without normalization.</p>
</dd></dl> </dd></dl>
</div> </div>
<div class="section" id="huber-cost"> <div class="section" id="huber-regression-cost">
<h3>huber_cost<a class="headerlink" href="#huber-cost" title="永久链接至标题"></a></h3> <h3>huber_regression_cost<a class="headerlink" href="#huber-regression-cost" title="永久链接至标题"></a></h3>
<dl class="class"> <dl class="class">
<dt> <dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">huber_cost</code></dt> <em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">huber_regression_cost</code></dt>
<dd><p>A loss layer for huber loss.</p> <dd><blockquote>
<p>The example usage is:</p> <div>In statistics, the Huber loss is a loss function used in robust regression,
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> that is less sensitive to outliers in data than the squared error loss.
<span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span> Given a prediction f(x), a label y and <span class="math">\(\delta\)</span>, the loss function
is defined as:</div></blockquote>
<p>ight )^2, left | y-f(x)
ight <a href="#id2"><span class="problematic" id="id3">|</span></a>leq delta</p>
<blockquote>
<div>loss = delta left | y-f(x)</div></blockquote>
<p>ight <a href="#id4"><span class="problematic" id="id5">|</span></a>-0.5delta ^2, otherwise</p>
<blockquote>
<div><p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_regression_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>
<table class="docutils field-list" frame="void" rules="none"> <table class="docutils field-list" frame="void" rules="none">
<col class="field-name" /> <col class="field-name" />
<col class="field-body" /> <col class="field-body" />
<tbody valign="top"> <tbody valign="top">
<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">param input:</th><td class="field-body">The first input layer.</td>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer.</em>) &#8211; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float.</em>) &#8211; The coefficient affects the gradient in the backward.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</li>
</ul>
</td>
</tr> </tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer object.</p> <tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</td>
</tr> </tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer.</p> <tr class="field-odd field"><th class="field-name">param label:</th><td class="field-body">The input label.</td>
</td> </tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">The name of this layers. It is not necessary.</td>
</tr>
<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">None|basestring.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param delta:</th><td class="field-body">The difference between the observed and predicted values.</td>
</tr>
<tr class="field-even field"><th class="field-name">type delta:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param coeff:</th><td class="field-body">The coefficient affects the gradient in the backward.</td>
</tr>
<tr class="field-even field"><th class="field-name">type coeff:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">Extra Layer Attribute.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
</div></blockquote>
</dd></dl>
</div>
<div class="section" id="huber-classification-cost">
<h3>huber_classification_cost<a class="headerlink" href="#huber-classification-cost" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">huber_classification_cost</code></dt>
<dd><blockquote>
<div>For classification purposes, a variant of the Huber loss called modified Huber
is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
a true binary class label :math:<a href="#id6"><span class="problematic" id="id7">`</span></a>yin left {-1, 1</div></blockquote>
<dl class="docutils">
<dt>ight }`, the modified Huber</dt>
<dd>loss is defined as:</dd>
<dt>ight )^2, yf(x)geq 1</dt>
<dd><blockquote class="first">
<div>loss = -4yf(x), ext{otherwise}</div></blockquote>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_classification_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<table class="last 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">param input:</th><td class="field-body">The first input layer.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param label:</th><td class="field-body">The input label.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">The name of this layers. It is not necessary.</td>
</tr>
<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">None|basestring.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param coeff:</th><td class="field-body">The coefficient affects the gradient in the backward.</td>
</tr>
<tr class="field-even field"><th class="field-name">type coeff:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">Extra Layer Attribute.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
</tbody>
</table>
</dd>
</dl>
</dd></dl> </dd></dl>
</div> </div>
...@@ -4024,7 +4105,7 @@ a_i * z_i &amp;\quad \mathrm{otherwise}\end{split}\]</div> ...@@ -4024,7 +4105,7 @@ a_i * z_i &amp;\quad \mathrm{otherwise}\end{split}\]</div>
<dd><p>The gated unit layer implements a simple gating mechanism over the input. <dd><p>The gated unit layer implements a simple gating mechanism over the input.
The input <span class="math">\(X\)</span> is first projected into a new space <span class="math">\(X'\)</span>, and The input <span class="math">\(X\)</span> is first projected into a new space <span class="math">\(X'\)</span>, and
it is also used to produce a gate weight <span class="math">\(\sigma\)</span>. Element-wise it is also used to produce a gate weight <span class="math">\(\sigma\)</span>. Element-wise
prodict between <a href="#id6"><span class="problematic" id="id7">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p> prodict between <a href="#id12"><span class="problematic" id="id13">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p>
<dl class="docutils"> <dl class="docutils">
<dt>Reference:</dt> <dt>Reference:</dt>
<dd>Language Modeling with Gated Convolutional Networks <dd>Language Modeling with Gated Convolutional Networks
......
develop/doc_cn/searchindex.js
浏览文件 @ 1c564a7a
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