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develop/doc/_sources/api/v2/config/optimizer.rst.txt
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.. _api_v2.optimizer:
==========
Optimizer
==========
......
develop/doc/_sources/api/v2/data.rst.txt
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========
Datasets
========
==================================
Data Reader Interface and DataSets
==================================
DataTypes
......@@ -49,7 +49,6 @@ mnist
:members:
:noindex:
cifar
+++++
......@@ -61,7 +60,7 @@ conll05
+++++++
.. automodule:: paddle.v2.dataset.conll05
:members:
:members: get_dict,get_embedding,test
:noindex:
imdb
......@@ -85,6 +84,12 @@ movielens
:members:
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.MovieInfo
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.UserInfo
:noindex:
sentiment
+++++++++
......@@ -102,7 +107,7 @@ uci_housing
wmt14
+++++
.. automodule:: paddle.v2.dataset.uci_housing
.. automodule:: paddle.v2.dataset.wmt14
:members:
:noindex:
develop/doc/_sources/api/v2/run_logic.rst.txt
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......@@ -6,18 +6,21 @@ Parameters
==========
.. automodule:: paddle.v2.parameters
:members: Parameters
:noindex:
Trainer
=======
.. automodule:: paddle.v2.trainer
:members: SGD
:noindex:
Event
=====
.. automodule:: paddle.v2.event
:members:
:noindex:
Inference
......@@ -25,3 +28,4 @@ Inference
.. autofunction:: paddle.v2.infer
:noindex:
\ No newline at end of file
develop/doc/about/index_en.html
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develop/doc/api/index_en.html
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<li class="toctree-l2"><a class="reference internal" href="v2/run_logic.html">Training and Inference</a></li>
</ul>
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<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="v2/model_configs.html">Model Configuration</a></li>
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develop/doc/api/v1/data_provider/dataprovider_en.html
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develop/doc/api/v1/data_provider/pydataprovider2_en.html
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develop/doc/api/v1/predict/swig_py_paddle_en.html
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<li class="toctree-l3"><a class="reference internal" href="../../v2/config/attr.html">Parameter Attribute</a></li>
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develop/doc/api/v1/trainer_config_helpers/data_sources.html
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develop/doc/api/v1/trainer_config_helpers/layers.html
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<li class="toctree-l3"><a class="reference internal" href="../../v2/config/attr.html">Parameter Attribute</a></li>
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</ul>
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develop/doc/api/v1/trainer_config_helpers/networks.html
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develop/doc/api/v1/trainer_config_helpers/poolings.html
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</ul>
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<li class="toctree-l3"><a class="reference internal" href="attr.html">Parameter Attribute</a></li>
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</ul>
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......
develop/doc/api/v2/config/attr.html
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......@@ -34,7 +34,7 @@
<link rel="search" title="Search" href="../../../search.html"/>
<link rel="top" title="PaddlePaddle documentation" href="../../../index.html"/>
<link rel="up" title="Model Configuration" href="../model_configs.html"/>
<link rel="next" title="Datasets" href="../data.html"/>
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
......@@ -164,7 +164,7 @@
<li class="toctree-l3 current"><a class="current reference internal" href="#">Parameter Attribute</a></li>
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......@@ -320,7 +320,7 @@ The details allocation in parallel_nn please refer to <a class="reference extern
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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......@@ -217,27 +217,177 @@
<div itemprop="articleBody">
<div class="section" id="optimizer">
<span id="api-v2-optimizer"></span><h1>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline"></a></h1>
<h1>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline"></a></h1>
<div class="section" id="momentum">
<h2>Momentum<a class="headerlink" href="#momentum" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">Momentum</code><span class="sig-paren">(</span><em>momentum=None</em>, <em>sparse=False</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>SGD Optimizer.</p>
<p>SGD is an optimization method, trying to find a neural network that
minimize the &#8220;cost/error&#8221; of it by iteration. In paddle&#8217;s implementation
SGD Optimizer is synchronized, which means all gradients will be wait to
calculate and reduced into one gradient, then do optimize operation.</p>
<p>The neural network consider the learning problem of minimizing an objective
function, that has the form of a sum</p>
<div class="math">
\[Q(w) = \sum_{i}^{n} Q_i(w)\]</div>
<p>The value of function Q sometimes is the cost of neural network (Mean
Square Error between prediction and label for example). The function Q is
parametrised by w, the weight/bias of neural network. And weights is what to
be learned. The i is the i-th observation in (trainning) data.</p>
<p>So, the SGD method will optimize the weight by</p>
<div class="math">
\[w = w - \eta \nabla Q(w) = w - \eta \sum_{i}^{n} \nabla Q_i(w)\]</div>
<p>where <span class="math">\(\eta\)</span> is learning rate. And <span class="math">\(n\)</span> is batch size.</p>
</dd></dl>
</div>
<div class="section" id="adam">
<h2>Adam<a class="headerlink" href="#adam" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">Adam</code><span class="sig-paren">(</span><em>beta1=0.9</em>, <em>beta2=0.999</em>, <em>epsilon=1e-08</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Adam optimizer.
The details of please refer <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
<div class="math">
\[\begin{split}m(w, t) &amp; = \beta_1 m(w, t-1) + (1 - \beta_1) \nabla Q_i(w) \\
v(w, t) &amp; = \beta_2 v(w, t-1) + (1 - \beta_2)(\nabla Q_i(w)) ^2 \\
w &amp; = w - \frac{\eta}{\sqrt{v(w,t) + \epsilon}}\end{split}\]</div>
<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 last simple">
<li><strong>beta1</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_1\)</span> in equation.</li>
<li><strong>beta2</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_2\)</span> in equation.</li>
<li><strong>epsilon</strong> (<em>float</em>) &#8211; the <span class="math">\(\epsilon\)</span> in equation. It is used to prevent
divided by zero.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="adamax">
<h2>Adamax<a class="headerlink" href="#adamax" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">Adamax</code><span class="sig-paren">(</span><em>beta1=0.9</em>, <em>beta2=0.999</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Adamax optimizer.</p>
<p>The details of please refer this <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
<div class="math">
\[\begin{split}m_t &amp; = \beta_1 * m_{t-1} + (1-\beta_1)* \nabla Q_i(w) \\
u_t &amp; = max(\beta_2*u_{t-1}, abs(\nabla Q_i(w))) \\
w_t &amp; = w_{t-1} - (\eta/(1-\beta_1^t))*m_t/u_t\end{split}\]</div>
<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 last simple">
<li><strong>beta1</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_1\)</span> in the equation.</li>
<li><strong>beta2</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_2\)</span> in the equation.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="adagrad">
<h2>AdaGrad<a class="headerlink" href="#adagrad" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">AdaGrad</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Adagrad(for ADAptive GRAdient algorithm) optimizer.</p>
<p>For details please refer this <a class="reference external" href="http://www.magicbroom.info/Papers/DuchiHaSi10.pdf">Adaptive Subgradient Methods for
Online Learning and Stochastic Optimization</a>.</p>
<div class="math">
\[\begin{split}G &amp;= \sum_{\tau=1}^{t} g_{\tau} g_{\tau}^T \\
w &amp; = w - \eta diag(G)^{-\frac{1}{2}} \circ g\end{split}\]</div>
</dd></dl>
</div>
<div class="section" id="decayedadagrad">
<h2>DecayedAdaGrad<a class="headerlink" href="#decayedadagrad" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">DecayedAdaGrad</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>AdaGrad method with decayed sum gradients. The equations of this method
show as follow.</p>
<div class="math">
\[\begin{split}E(g_t^2) &amp;= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\
learning\_rate &amp;= 1/sqrt( ( E(g_t^2) + \epsilon )\end{split}\]</div>
<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 last simple">
<li><strong>rho</strong> (<em>float</em>) &#8211; The <span class="math">\(\rho\)</span> parameter in that equation</li>
<li><strong>epsilon</strong> (<em>float</em>) &#8211; The <span class="math">\(\epsilon\)</span> parameter in that equation.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="adadelta">
<h2>AdaDelta<a class="headerlink" href="#adadelta" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">AdaDelta</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>AdaDelta method. The details of adadelta please refer to this
<a class="reference external" href="http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf">ADADELTA: AN ADAPTIVE LEARNING RATE METHOD</a>.</p>
<div class="math">
\[\begin{split}E(g_t^2) &amp;= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\
learning\_rate &amp;= sqrt( ( E(dx_{t-1}^2) + \epsilon ) / ( \
E(g_t^2) + \epsilon ) ) \\
E(dx_t^2) &amp;= \rho * E(dx_{t-1}^2) + (1-\rho) * (-g*learning\_rate)^2\end{split}\]</div>
<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 last simple">
<li><strong>rho</strong> (<em>float</em>) &#8211; <span class="math">\(\rho\)</span> in equation</li>
<li><strong>epsilon</strong> (<em>float</em>) &#8211; <span class="math">\(\rho\)</span> in equation</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="rmsprop">
<h2>RMSProp<a class="headerlink" href="#rmsprop" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">RMSProp</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>RMSProp(for Root Mean Square Propagation) optimizer. For details please
refer this <a class="reference external" href="http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">slide</a>.</p>
<p>The equations of this method as follows:</p>
<div class="math">
\[\begin{split}v(w, t) &amp; = \rho v(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2 \\
w &amp; = w - \frac{\eta} {\sqrt{v(w,t) + \epsilon}} \nabla Q_{i}(w)\end{split}\]</div>
<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 last simple">
<li><strong>rho</strong> (<em>float</em>) &#8211; the <span class="math">\(\rho\)</span> in the equation. The forgetting factor.</li>
<li><strong>epsilon</strong> (<em>float</em>) &#8211; the <span class="math">\(\epsilon\)</span> in the equation.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
</div>
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<li class="toctree-l3"><a class="reference internal" href="attr.html">Parameter Attribute</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="../run_logic.html">Training and Inference</a></li>
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<link rel="top" title="PaddlePaddle documentation" href="../../index.html"/>
<link rel="up" title="API" href="../index_en.html"/>
<link rel="next" title="ABOUT" href="../../about/index_en.html"/>
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</ul>
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......@@ -215,21 +215,307 @@
<h1>Training and Inference<a class="headerlink" href="#training-and-inference" title="Permalink to this headline"></a></h1>
<div class="section" id="parameters">
<h2>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.parameters.</code><code class="descname">Parameters</code></dt>
<dd><p>Parameters is a dictionary contains Paddle&#8217;s parameter. The key of
Parameters is the name of parameter. The value of Parameters is a plain
<code class="code docutils literal"><span class="pre">numpy.ndarry</span></code> .</p>
<p>Basically usage is</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">paddle</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="o">...</span><span class="p">)</span>
<span class="o">...</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">paddle</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="o">...</span><span class="p">)</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
<span class="n">parameter_names</span> <span class="o">=</span> <span class="n">parameters</span><span class="o">.</span><span class="n">names</span><span class="p">()</span>
<span class="n">fc_mat</span> <span class="o">=</span> <span class="n">parameters</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fc&#39;</span><span class="p">)</span>
<span class="k">print</span> <span class="n">fc_mat</span>
</pre></div>
</div>
<dl class="method">
<dt>
<code class="descname">keys</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>keys are the names of each parameter.</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">Returns:</th><td class="field-body">list of parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">list</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">names</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>names of each parameter.</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">Returns:</th><td class="field-body">list of parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">list</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">has_key</code><span class="sig-paren">(</span><em>key</em><span class="sig-paren">)</span></dt>
<dd><p>has_key return true if there are such parameter name == key</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"><strong>key</strong> (<em>basestring</em>) &#8211; Parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">True if contains such key</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">get_shape</code><span class="sig-paren">(</span><em>key</em><span class="sig-paren">)</span></dt>
<dd><p>get shape of the parameter.</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"><strong>key</strong> (<em>basestring</em>) &#8211; parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">parameter&#8217;s shape</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">tuple</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">get</code><span class="sig-paren">(</span><em>parameter_name</em><span class="sig-paren">)</span></dt>
<dd><p>Get parameter by parameter name.</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">Note:</th><td class="field-body">It will always copy the parameter from C++ side.</td>
</tr>
<tr class="field-even field"><th class="field-name">Parameters:</th><td class="field-body"><strong>parameter_name</strong> (<em>basestring</em>) &#8211; parameter name</td>
</tr>
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The parameter matrix.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">np.ndarray</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">set</code><span class="sig-paren">(</span><em>parameter_name</em>, <em>value</em><span class="sig-paren">)</span></dt>
<dd><p>Set parameter by parameter name &amp; matrix.</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>parameter_name</strong> (<em>basestring</em>) &#8211; parameter name</li>
<li><strong>value</strong> (<em>np.ndarray</em>) &#8211; parameter matrix</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Nothing.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">append_gradient_machine</code><span class="sig-paren">(</span><em>gradient_machine</em><span class="sig-paren">)</span></dt>
<dd><p>append gradient machine to parameters. This method is used internally in
Trainer.train.</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"><strong>gradient_machine</strong> (<em>api.GradientMachine</em>) &#8211; Paddle C++ GradientMachine object.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">serialize</code><span class="sig-paren">(</span><em>name</em>, <em>f</em><span class="sig-paren">)</span></dt>
<dd><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>name</strong> &#8211; </li>
<li><strong>f</strong> (<em>file</em>) &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">deserialize</code><span class="sig-paren">(</span><em>name</em>, <em>f</em><span class="sig-paren">)</span></dt>
<dd><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>name</strong> &#8211; </li>
<li><strong>f</strong> (<em>file</em>) &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="trainer">
<h2>Trainer<a class="headerlink" href="#trainer" title="Permalink to this headline"></a></h2>
<p>Module Trainer</p>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.trainer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>cost</em>, <em>parameters</em>, <em>update_equation</em>, <em>extra_layers=None</em><span class="sig-paren">)</span></dt>
<dd><p>Simple SGD Trainer.
SGD Trainer combines data reader, network topolopy and update_equation together
to train/test a neural network.</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 last simple">
<li><strong>update_equation</strong> (<em>paddle.v2.optimizer.Optimizer</em>) &#8211; The optimizer object.</li>
<li><strong>cost</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Target cost that neural network should be optimized.</li>
<li><strong>parameters</strong> (<em>paddle.v2.parameters.Parameters</em>) &#8211; The parameters dictionary.</li>
<li><strong>extra_layers</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Some layers in the neural network graph are not
in the path of cost layer.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt>
<code class="descname">train</code><span class="sig-paren">(</span><em>reader</em>, <em>num_passes=1</em>, <em>event_handler=None</em>, <em>feeding=None</em><span class="sig-paren">)</span></dt>
<dd><p>Training method. Will train num_passes of input data.</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>reader</strong> (<em>collections.Iterable</em>) &#8211; A reader that reads and yeilds data items. Usually we use a
batched reader to do mini-batch training.</li>
<li><strong>num_passes</strong> &#8211; The total train passes.</li>
<li><strong>event_handler</strong> (<em></em><em>(</em><em>BaseEvent</em><em>) </em><em>=&gt; None</em>) &#8211; Event handler. A method will be invoked when event
occurred.</li>
<li><strong>feeding</strong> (<em>dict|list</em>) &#8211; Feeding is a map of neural network input name and array
index that reader returns.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">test</code><span class="sig-paren">(</span><em>reader</em>, <em>feeding=None</em><span class="sig-paren">)</span></dt>
<dd><p>Testing method. Will test input data.</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>reader</strong> (<em>collections.Iterable</em>) &#8211; A reader that reads and yeilds data items.</li>
<li><strong>feeding</strong> (<em>dict</em>) &#8211; Feeding is a map of neural network input name and array
index that reader returns.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="event">
<h2>Event<a class="headerlink" href="#event" title="Permalink to this headline"></a></h2>
<p>All training events.</p>
<p>Testing and training events.</p>
<p>There are:</p>
<ul class="simple">
<li>TestResult</li>
<li>BeginIteration</li>
<li>EndIteration</li>
<li>BeginPass</li>
<li>EndPass</li>
</ul>
<p>TODO(yuyang18): Complete it!</p>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">TestResult</code><span class="sig-paren">(</span><em>evaluator</em>, <em>cost</em><span class="sig-paren">)</span></dt>
<dd><p>Result that trainer.test return.</p>
</dd></dl>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">BeginPass</code><span class="sig-paren">(</span><em>pass_id</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Pass Training Start.</p>
</dd></dl>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">EndPass</code><span class="sig-paren">(</span><em>pass_id</em>, <em>evaluator</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Pass Training Complete.</p>
</dd></dl>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">BeginIteration</code><span class="sig-paren">(</span><em>pass_id</em>, <em>batch_id</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Batch Training Start.</p>
</dd></dl>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">EndIteration</code><span class="sig-paren">(</span><em>pass_id</em>, <em>batch_id</em>, <em>cost</em>, <em>evaluator</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Batch Training Complete.</p>
</dd></dl>
</div>
<div class="section" id="inference">
<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this headline"></a></h2>
......@@ -286,7 +572,7 @@ or max_id.</li>
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<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/attr.html">Parameter Attribute</a></li>
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......@@ -164,7 +164,7 @@
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/attr.html">Parameter Attribute</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/data.html">Datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/data.html">Data Reader Interface and DataSets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/run_logic.html">Training and Inference</a></li>
</ul>
</li>
......
develop/doc_cn/_sources/api/v2/config/optimizer.rst.txt
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.. _api_v2.optimizer:
==========
Optimizer
==========
......
develop/doc_cn/_sources/api/v2/data.rst.txt
浏览文件 @ 52e8ebf2
========
Datasets
========
==================================
Data Reader Interface and DataSets
==================================
DataTypes
......@@ -49,7 +49,6 @@ mnist
:members:
:noindex:
cifar
+++++
......@@ -61,7 +60,7 @@ conll05
+++++++
.. automodule:: paddle.v2.dataset.conll05
:members:
:members: get_dict,get_embedding,test
:noindex:
imdb
......@@ -85,6 +84,12 @@ movielens
:members:
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.MovieInfo
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.UserInfo
:noindex:
sentiment
+++++++++
......@@ -102,7 +107,7 @@ uci_housing
wmt14
+++++
.. automodule:: paddle.v2.dataset.uci_housing
.. automodule:: paddle.v2.dataset.wmt14
:members:
:noindex:
develop/doc_cn/_sources/api/v2/run_logic.rst.txt
浏览文件 @ 52e8ebf2
......@@ -6,18 +6,21 @@ Parameters
==========
.. automodule:: paddle.v2.parameters
:members: Parameters
:noindex:
Trainer
=======
.. automodule:: paddle.v2.trainer
:members: SGD
:noindex:
Event
=====
.. automodule:: paddle.v2.event
:members:
:noindex:
Inference
......@@ -25,3 +28,4 @@ Inference
.. autofunction:: paddle.v2.infer
:noindex:
\ No newline at end of file
develop/doc_cn/api/v2/config/attr.html
浏览文件 @ 52e8ebf2
......@@ -34,7 +34,7 @@
<link rel="search" title="搜索" href="../../../search.html"/>
<link rel="top" title="PaddlePaddle 文档" href="../../../index.html"/>
<link rel="up" title="Model Configuration" href="../model_configs.html"/>
<link rel="next" title="Datasets" href="../data.html"/>
<link rel="next" title="Data Reader Interface and DataSets" href="../data.html"/>
<link rel="prev" title="Networks" href="networks.html"/>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
......@@ -327,7 +327,7 @@ The details allocation in parallel_nn please refer to <a class="reference extern
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
<a href="../data.html" class="btn btn-neutral float-right" title="Datasets" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
<a href="../data.html" class="btn btn-neutral float-right" title="Data Reader Interface and DataSets" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
<a href="networks.html" class="btn btn-neutral" title="Networks" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
......
develop/doc_cn/api/v2/config/optimizer.html
浏览文件 @ 52e8ebf2
......@@ -224,27 +224,177 @@
<div itemprop="articleBody">
<div class="section" id="optimizer">
<span id="api-v2-optimizer"></span><h1>Optimizer<a class="headerlink" href="#optimizer" title="永久链接至标题"></a></h1>
<h1>Optimizer<a class="headerlink" href="#optimizer" title="永久链接至标题"></a></h1>
<div class="section" id="momentum">
<h2>Momentum<a class="headerlink" href="#momentum" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">Momentum</code><span class="sig-paren">(</span><em>momentum=None</em>, <em>sparse=False</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>SGD Optimizer.</p>
<p>SGD is an optimization method, trying to find a neural network that
minimize the &#8220;cost/error&#8221; of it by iteration. In paddle&#8217;s implementation
SGD Optimizer is synchronized, which means all gradients will be wait to
calculate and reduced into one gradient, then do optimize operation.</p>
<p>The neural network consider the learning problem of minimizing an objective
function, that has the form of a sum</p>
<div class="math">
\[Q(w) = \sum_{i}^{n} Q_i(w)\]</div>
<p>The value of function Q sometimes is the cost of neural network (Mean
Square Error between prediction and label for example). The function Q is
parametrised by w, the weight/bias of neural network. And weights is what to
be learned. The i is the i-th observation in (trainning) data.</p>
<p>So, the SGD method will optimize the weight by</p>
<div class="math">
\[w = w - \eta \nabla Q(w) = w - \eta \sum_{i}^{n} \nabla Q_i(w)\]</div>
<p>where <span class="math">\(\eta\)</span> is learning rate. And <span class="math">\(n\)</span> is batch size.</p>
</dd></dl>
</div>
<div class="section" id="adam">
<h2>Adam<a class="headerlink" href="#adam" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">Adam</code><span class="sig-paren">(</span><em>beta1=0.9</em>, <em>beta2=0.999</em>, <em>epsilon=1e-08</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Adam optimizer.
The details of please refer <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
<div class="math">
\[\begin{split}m(w, t) &amp; = \beta_1 m(w, t-1) + (1 - \beta_1) \nabla Q_i(w) \\
v(w, t) &amp; = \beta_2 v(w, t-1) + (1 - \beta_2)(\nabla Q_i(w)) ^2 \\
w &amp; = w - \frac{\eta}{\sqrt{v(w,t) + \epsilon}}\end{split}\]</div>
<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 last simple">
<li><strong>beta1</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_1\)</span> in equation.</li>
<li><strong>beta2</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_2\)</span> in equation.</li>
<li><strong>epsilon</strong> (<em>float</em>) &#8211; the <span class="math">\(\epsilon\)</span> in equation. It is used to prevent
divided by zero.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="adamax">
<h2>Adamax<a class="headerlink" href="#adamax" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">Adamax</code><span class="sig-paren">(</span><em>beta1=0.9</em>, <em>beta2=0.999</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Adamax optimizer.</p>
<p>The details of please refer this <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
<div class="math">
\[\begin{split}m_t &amp; = \beta_1 * m_{t-1} + (1-\beta_1)* \nabla Q_i(w) \\
u_t &amp; = max(\beta_2*u_{t-1}, abs(\nabla Q_i(w))) \\
w_t &amp; = w_{t-1} - (\eta/(1-\beta_1^t))*m_t/u_t\end{split}\]</div>
<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 last simple">
<li><strong>beta1</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_1\)</span> in the equation.</li>
<li><strong>beta2</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_2\)</span> in the equation.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="adagrad">
<h2>AdaGrad<a class="headerlink" href="#adagrad" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">AdaGrad</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Adagrad(for ADAptive GRAdient algorithm) optimizer.</p>
<p>For details please refer this <a class="reference external" href="http://www.magicbroom.info/Papers/DuchiHaSi10.pdf">Adaptive Subgradient Methods for
Online Learning and Stochastic Optimization</a>.</p>
<div class="math">
\[\begin{split}G &amp;= \sum_{\tau=1}^{t} g_{\tau} g_{\tau}^T \\
w &amp; = w - \eta diag(G)^{-\frac{1}{2}} \circ g\end{split}\]</div>
</dd></dl>
</div>
<div class="section" id="decayedadagrad">
<h2>DecayedAdaGrad<a class="headerlink" href="#decayedadagrad" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">DecayedAdaGrad</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>AdaGrad method with decayed sum gradients. The equations of this method
show as follow.</p>
<div class="math">
\[\begin{split}E(g_t^2) &amp;= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\
learning\_rate &amp;= 1/sqrt( ( E(g_t^2) + \epsilon )\end{split}\]</div>
<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 last simple">
<li><strong>rho</strong> (<em>float</em>) &#8211; The <span class="math">\(\rho\)</span> parameter in that equation</li>
<li><strong>epsilon</strong> (<em>float</em>) &#8211; The <span class="math">\(\epsilon\)</span> parameter in that equation.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="adadelta">
<h2>AdaDelta<a class="headerlink" href="#adadelta" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">AdaDelta</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>AdaDelta method. The details of adadelta please refer to this
<a class="reference external" href="http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf">ADADELTA: AN ADAPTIVE LEARNING RATE METHOD</a>.</p>
<div class="math">
\[\begin{split}E(g_t^2) &amp;= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\
learning\_rate &amp;= sqrt( ( E(dx_{t-1}^2) + \epsilon ) / ( \
E(g_t^2) + \epsilon ) ) \\
E(dx_t^2) &amp;= \rho * E(dx_{t-1}^2) + (1-\rho) * (-g*learning\_rate)^2\end{split}\]</div>
<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 last simple">
<li><strong>rho</strong> (<em>float</em>) &#8211; <span class="math">\(\rho\)</span> in equation</li>
<li><strong>epsilon</strong> (<em>float</em>) &#8211; <span class="math">\(\rho\)</span> in equation</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="rmsprop">
<h2>RMSProp<a class="headerlink" href="#rmsprop" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.optimizer.</code><code class="descname">RMSProp</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>RMSProp(for Root Mean Square Propagation) optimizer. For details please
refer this <a class="reference external" href="http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">slide</a>.</p>
<p>The equations of this method as follows:</p>
<div class="math">
\[\begin{split}v(w, t) &amp; = \rho v(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2 \\
w &amp; = w - \frac{\eta} {\sqrt{v(w,t) + \epsilon}} \nabla Q_{i}(w)\end{split}\]</div>
<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 last simple">
<li><strong>rho</strong> (<em>float</em>) &#8211; the <span class="math">\(\rho\)</span> in the equation. The forgetting factor.</li>
<li><strong>epsilon</strong> (<em>float</em>) &#8211; the <span class="math">\(\epsilon\)</span> in the equation.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
</div>
......
develop/doc_cn/api/v2/data.html
浏览文件 @ 52e8ebf2
此差异已折叠。
develop/doc_cn/api/v2/run_logic.html
浏览文件 @ 52e8ebf2
......@@ -35,7 +35,7 @@
<link rel="top" title="PaddlePaddle 文档" href="../../index.html"/>
<link rel="up" title="API" href="../index_cn.html"/>
<link rel="next" title="FAQ" href="../../faq/index_cn.html"/>
<link rel="prev" title="Datasets" href="data.html"/>
<link rel="prev" title="Data Reader Interface and DataSets" href="data.html"/>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
<link rel="stylesheet" href="../../_static/css/override.css" type="text/css" />
......@@ -222,21 +222,307 @@
<h1>Training and Inference<a class="headerlink" href="#training-and-inference" title="永久链接至标题"></a></h1>
<div class="section" id="parameters">
<h2>Parameters<a class="headerlink" href="#parameters" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.parameters.</code><code class="descname">Parameters</code></dt>
<dd><p>Parameters is a dictionary contains Paddle&#8217;s parameter. The key of
Parameters is the name of parameter. The value of Parameters is a plain
<code class="code docutils literal"><span class="pre">numpy.ndarry</span></code> .</p>
<p>Basically usage is</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">paddle</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="o">...</span><span class="p">)</span>
<span class="o">...</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">paddle</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="o">...</span><span class="p">)</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
<span class="n">parameter_names</span> <span class="o">=</span> <span class="n">parameters</span><span class="o">.</span><span class="n">names</span><span class="p">()</span>
<span class="n">fc_mat</span> <span class="o">=</span> <span class="n">parameters</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fc&#39;</span><span class="p">)</span>
<span class="k">print</span> <span class="n">fc_mat</span>
</pre></div>
</div>
<dl class="method">
<dt>
<code class="descname">keys</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>keys are the names of each parameter.</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">返回:</th><td class="field-body">list of parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">返回类型:</th><td class="field-body">list</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">names</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>names of each parameter.</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">返回:</th><td class="field-body">list of parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">返回类型:</th><td class="field-body">list</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">has_key</code><span class="sig-paren">(</span><em>key</em><span class="sig-paren">)</span></dt>
<dd><p>has_key return true if there are such parameter name == key</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"><strong>key</strong> (<em>basestring</em>) &#8211; Parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">True if contains such key</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">get_shape</code><span class="sig-paren">(</span><em>key</em><span class="sig-paren">)</span></dt>
<dd><p>get shape of the parameter.</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"><strong>key</strong> (<em>basestring</em>) &#8211; parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">parameter&#8217;s shape</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body">tuple</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">get</code><span class="sig-paren">(</span><em>parameter_name</em><span class="sig-paren">)</span></dt>
<dd><p>Get parameter by parameter name.</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">Note:</th><td class="field-body">It will always copy the parameter from C++ side.</td>
</tr>
<tr class="field-even field"><th class="field-name">参数:</th><td class="field-body"><strong>parameter_name</strong> (<em>basestring</em>) &#8211; parameter name</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回:</th><td class="field-body">The parameter matrix.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回类型:</th><td class="field-body">np.ndarray</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">set</code><span class="sig-paren">(</span><em>parameter_name</em>, <em>value</em><span class="sig-paren">)</span></dt>
<dd><p>Set parameter by parameter name &amp; matrix.</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>parameter_name</strong> (<em>basestring</em>) &#8211; parameter name</li>
<li><strong>value</strong> (<em>np.ndarray</em>) &#8211; parameter matrix</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last">Nothing.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">append_gradient_machine</code><span class="sig-paren">(</span><em>gradient_machine</em><span class="sig-paren">)</span></dt>
<dd><p>append gradient machine to parameters. This method is used internally in
Trainer.train.</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"><strong>gradient_machine</strong> (<em>api.GradientMachine</em>) &#8211; Paddle C++ GradientMachine object.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">serialize</code><span class="sig-paren">(</span><em>name</em>, <em>f</em><span class="sig-paren">)</span></dt>
<dd><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>name</strong> &#8211; </li>
<li><strong>f</strong> (<em>file</em>) &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">deserialize</code><span class="sig-paren">(</span><em>name</em>, <em>f</em><span class="sig-paren">)</span></dt>
<dd><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>name</strong> &#8211; </li>
<li><strong>f</strong> (<em>file</em>) &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="trainer">
<h2>Trainer<a class="headerlink" href="#trainer" title="永久链接至标题"></a></h2>
<p>Module Trainer</p>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.trainer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>cost</em>, <em>parameters</em>, <em>update_equation</em>, <em>extra_layers=None</em><span class="sig-paren">)</span></dt>
<dd><p>Simple SGD Trainer.
SGD Trainer combines data reader, network topolopy and update_equation together
to train/test a neural network.</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 last simple">
<li><strong>update_equation</strong> (<em>paddle.v2.optimizer.Optimizer</em>) &#8211; The optimizer object.</li>
<li><strong>cost</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Target cost that neural network should be optimized.</li>
<li><strong>parameters</strong> (<em>paddle.v2.parameters.Parameters</em>) &#8211; The parameters dictionary.</li>
<li><strong>extra_layers</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Some layers in the neural network graph are not
in the path of cost layer.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt>
<code class="descname">train</code><span class="sig-paren">(</span><em>reader</em>, <em>num_passes=1</em>, <em>event_handler=None</em>, <em>feeding=None</em><span class="sig-paren">)</span></dt>
<dd><p>Training method. Will train num_passes of input data.</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>reader</strong> (<em>collections.Iterable</em>) &#8211; A reader that reads and yeilds data items. Usually we use a
batched reader to do mini-batch training.</li>
<li><strong>num_passes</strong> &#8211; The total train passes.</li>
<li><strong>event_handler</strong> (<em></em><em>(</em><em>BaseEvent</em><em>) </em><em>=&gt; None</em>) &#8211; Event handler. A method will be invoked when event
occurred.</li>
<li><strong>feeding</strong> (<em>dict|list</em>) &#8211; Feeding is a map of neural network input name and array
index that reader returns.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt>
<code class="descname">test</code><span class="sig-paren">(</span><em>reader</em>, <em>feeding=None</em><span class="sig-paren">)</span></dt>
<dd><p>Testing method. Will test input data.</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>reader</strong> (<em>collections.Iterable</em>) &#8211; A reader that reads and yeilds data items.</li>
<li><strong>feeding</strong> (<em>dict</em>) &#8211; Feeding is a map of neural network input name and array
index that reader returns.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="event">
<h2>Event<a class="headerlink" href="#event" title="永久链接至标题"></a></h2>
<p>All training events.</p>
<p>Testing and training events.</p>
<p>There are:</p>
<ul class="simple">
<li>TestResult</li>
<li>BeginIteration</li>
<li>EndIteration</li>
<li>BeginPass</li>
<li>EndPass</li>
</ul>
<p>TODO(yuyang18): Complete it!</p>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">TestResult</code><span class="sig-paren">(</span><em>evaluator</em>, <em>cost</em><span class="sig-paren">)</span></dt>
<dd><p>Result that trainer.test return.</p>
</dd></dl>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">BeginPass</code><span class="sig-paren">(</span><em>pass_id</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Pass Training Start.</p>
</dd></dl>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">EndPass</code><span class="sig-paren">(</span><em>pass_id</em>, <em>evaluator</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Pass Training Complete.</p>
</dd></dl>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">BeginIteration</code><span class="sig-paren">(</span><em>pass_id</em>, <em>batch_id</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Batch Training Start.</p>
</dd></dl>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">EndIteration</code><span class="sig-paren">(</span><em>pass_id</em>, <em>batch_id</em>, <em>cost</em>, <em>evaluator</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Batch Training Complete.</p>
</dd></dl>
</div>
<div class="section" id="inference">
<h2>Inference<a class="headerlink" href="#inference" title="永久链接至标题"></a></h2>
......@@ -293,7 +579,7 @@ or max_id.</li>
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