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<li><a class="reference internal" href="#">Data Related API</a><ul>
<li><a class="reference internal" href="#module-paddle.v2.data_type">DataTypes</a></li>
<li><a class="reference internal" href="#module-paddle.v2.data_feeder">DataFeeder</a></li>
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  <div class="section" id="data-related-api">
<h1>Data Related API<a class="headerlink" href="#data-related-api" title="永久链接至标题"></a></h1>
<div class="section" id="module-paddle.v2.data_type">
<span id="datatypes"></span><h2>DataTypes<a class="headerlink" href="#module-paddle.v2.data_type" title="永久链接至标题"></a></h2>
<dl class="function">
<dt id="paddle.v2.data_type.dense_vector">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">dense_vector</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.dense_vector" title="永久链接至目标"></a></dt>
<dd><p>Dense Vector. It means the input feature is dense float vector. For example,
if the input is an image with 28*28 pixels, the input of Paddle neural
network should be a dense vector with dimension 784.</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>dim</strong> (<em>int</em>) &#8211; dimension of this vector.</li>
<li><strong>seq_type</strong> (<em>int</em>) &#8211; sequence type of input.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">An input type object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.dense_vector_sequence">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">dense_vector_sequence</code><span class="sig-paren">(</span><em>dim</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.dense_vector_sequence" title="永久链接至目标"></a></dt>
<dd><p>Data type of a sequence of dense vector.</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>dim</strong> (<em>int</em>) &#8211; dimension of dense vector.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">An input type object</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.integer_value">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">integer_value</code><span class="sig-paren">(</span><em>value_range</em>, <em>seq_type=0</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.integer_value" title="永久链接至目标"></a></dt>
<dd><p>Data type of integer.</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>seq_type</strong> (<em>int</em>) &#8211; sequence type of this input.</li>
<li><strong>value_range</strong> (<em>int</em>) &#8211; range of this integer.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">An input type object</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.integer_value_sequence">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">integer_value_sequence</code><span class="sig-paren">(</span><em>value_range</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.integer_value_sequence" title="永久链接至目标"></a></dt>
<dd><p>Data type of a sequence of integer.</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>value_range</strong> (<em>int</em>) &#8211; range of each element.</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.sparse_binary_vector">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_binary_vector</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.sparse_binary_vector" title="永久链接至目标"></a></dt>
<dd><p>Sparse binary vector. It means the input feature is a sparse vector and the
every element in this vector is either zero or one.</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>dim</strong> (<em>int</em>) &#8211; dimension of this vector.</li>
<li><strong>seq_type</strong> (<em>int</em>) &#8211; sequence type of this input.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">An input type object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.sparse_binary_vector_sequence">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_binary_vector_sequence</code><span class="sig-paren">(</span><em>dim</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.sparse_binary_vector_sequence" title="永久链接至目标"></a></dt>
<dd><dl class="docutils">
<dt>Data type of a sequence of sparse vector, which every element is either zero</dt>
<dd>or one.</dd>
</dl>
<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>dim</strong> (<em>int</em>) &#8211; dimension of sparse vector.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">An input type object</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.sparse_non_value_slot">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_non_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.sparse_non_value_slot" title="永久链接至目标"></a></dt>
<dd><p>Sparse binary vector. It means the input feature is a sparse vector and the
every element in this vector is either zero or one.</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>dim</strong> (<em>int</em>) &#8211; dimension of this vector.</li>
<li><strong>seq_type</strong> (<em>int</em>) &#8211; sequence type of this input.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">An input type object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.sparse_value_slot">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.sparse_value_slot" title="永久链接至目标"></a></dt>
<dd><p>Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.</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>dim</strong> (<em>int</em>) &#8211; dimension of this vector.</li>
<li><strong>seq_type</strong> (<em>int</em>) &#8211; sequence type of this input.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">An input type object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.sparse_vector">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_vector</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.sparse_vector" title="永久链接至目标"></a></dt>
<dd><p>Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.</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>dim</strong> (<em>int</em>) &#8211; dimension of this vector.</li>
<li><strong>seq_type</strong> (<em>int</em>) &#8211; sequence type of this input.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">An input type object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.data_type.sparse_vector_sequence">
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_vector_sequence</code><span class="sig-paren">(</span><em>dim</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.sparse_vector_sequence" title="永久链接至目标"></a></dt>
<dd><p>Data type of a sequence of sparse vector, which most elements are zero,
others could be any float value.</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>dim</strong> (<em>int</em>) &#8211; dimension of sparse vector.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">An input type object</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><a class="reference internal" href="#paddle.v2.data_type.InputType" title="paddle.v2.data_type.InputType">InputType</a></td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="paddle.v2.data_type.InputType">
<em class="property">class </em><code class="descclassname">paddle.v2.data_type.</code><code class="descname">InputType</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type</em>, <em>tp</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_type.InputType" title="永久链接至目标"></a></dt>
<dd><p>InputType is the base class for paddle input types.</p>
<div class="admonition note">
<p class="first admonition-title">注解</p>
<p class="last">this is a base class, and should never be used by user.</p>
</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>dim</strong> (<em>int</em>) &#8211; dimension of input. If the input is an integer, it means the
value range. Otherwise, it means the size of layer.</li>
<li><strong>seq_type</strong> (<em>int</em>) &#8211; sequence type of input. 0 means it is not a sequence. 1
means it is a variable length sequence. 2 means it is a
nested sequence.</li>
<li><strong>type</strong> (<em>int</em>) &#8211; data type of input.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="module-paddle.v2.data_feeder">
<span id="datafeeder"></span><h2>DataFeeder<a class="headerlink" href="#module-paddle.v2.data_feeder" title="永久链接至标题"></a></h2>
<dl class="class">
<dt id="paddle.v2.data_feeder.DataFeeder">
484
<em class="property">class </em><code class="descclassname">paddle.v2.data_feeder.</code><code class="descname">DataFeeder</code><span class="sig-paren">(</span><em>data_types</em>, <em>feeding=None</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_feeder.DataFeeder" title="永久链接至目标"></a></dt>
485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518
<dd><p>DataFeeder converts the data returned by paddle.reader into a data structure
of Arguments which is defined in the API. The paddle.reader usually returns
a list of mini-batch data entries. Each data entry in the list is one sample.
Each sample is a list or a tuple with one feature or multiple features.
DataFeeder converts this mini-batch data entries into Arguments in order
to feed it to C++ interface.</p>
<p>The example usage:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data_types</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">784</span><span class="p">)),</span>
              <span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">integer_value</span><span class="p">(</span><span class="mi">10</span><span class="p">))]</span>
<span class="n">reader_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;image&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
<span class="n">feeder</span> <span class="o">=</span> <span class="n">DataFeeder</span><span class="p">(</span><span class="n">data_types</span><span class="o">=</span><span class="n">data_types</span><span class="p">,</span> <span class="n">reader_dict</span><span class="o">=</span><span class="n">reader_dict</span><span class="p">)</span>
<span class="n">minibatch_data</span> <span class="o">=</span> <span class="p">[</span>
                   <span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">2.0</span><span class="p">,</span><span class="mf">3.0</span><span class="p">,</span><span class="mf">4.0</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span> <span class="p">),</span>  <span class="c1"># first sample</span>
                   <span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">2.0</span><span class="p">,</span><span class="mf">3.0</span><span class="p">,</span><span class="mf">4.0</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span> <span class="p">)</span>   <span class="c1"># second sample</span>
                 <span class="p">]</span>
<span class="c1"># or minibatch_data = [</span>
<span class="c1">#                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ],  # first sample</span>
<span class="c1">#                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ]   # second sample</span>
<span class="c1">#                     ]</span>
<span class="n">arg</span> <span class="o">=</span> <span class="n">feeder</span><span class="p">(</span><span class="n">minibatch_data</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title">注解</p>
<p class="last">This module is for internal use only. Users should use the <cite>reader</cite>
interface.</p>
</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>data_types</strong> (<em>list</em>) &#8211; A list to specify data name and type. Each item is
a tuple of (data_name, data_type).</li>
519
<li><strong>reader_dict</strong> &#8211; A dictionary to specify the position of each data
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in the input data.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="paddle.v2.data_feeder.DataFeeder.convert">
<code class="descname">convert</code><span class="sig-paren">(</span><em>dat</em>, <em>argument=None</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.data_feeder.DataFeeder.convert" title="永久链接至目标"></a></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 last simple">
<li><strong>dat</strong> (<em>list</em>) &#8211; A list of mini-batch data. Each sample is a list or tuple
one feature or multiple features.</li>
<li><strong>argument</strong> (<em>py_paddle.swig_paddle.Arguments</em>) &#8211; An Arguments object contains this mini-batch data with
one or multiple features. The Arguments definition is
in the API.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-paddle.v2.reader">
<span id="reader"></span><h2>Reader<a class="headerlink" href="#module-paddle.v2.reader" title="永久链接至标题"></a></h2>
<p>At training and testing time, PaddlePaddle programs need to read data. To ease
the users&#8217; work to write data reading code, we define that</p>
<ul class="simple">
<li>A <em>reader</em> is a function that reads data (from file, network, random number
generator, etc) and yields data items.</li>
<li>A <em>reader creator</em> is a function that returns a reader function.</li>
<li>A <em>reader decorator</em> is a function, which accepts one or more readers, and
returns a reader.</li>
<li>A <em>batch reader</em> is a function that reads data (from <em>reader</em>, file, network,
random number generator, etc) and yields a batch of data items.</li>
</ul>
<div class="section" id="data-reader-interface">
<h3>Data Reader Interface<a class="headerlink" href="#data-reader-interface" title="永久链接至标题"></a></h3>
<p>Indeed, <em>data reader</em> doesn&#8217;t have to be a function that reads and yields data
items. It can be any function with no parameter that creates a iterable
(anything can be used in <code class="code docutils literal"><span class="pre">for</span> <span class="pre">x</span> <span class="pre">in</span> <span class="pre">iterable</span></code>):</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">iterable</span> <span class="o">=</span> <span class="n">data_reader</span><span class="p">()</span>
</pre></div>
</div>
<p>Element produced from the iterable should be a <strong>single</strong> entry of data,
<strong>not</strong> a mini batch. That entry of data could be a single item, or a tuple of
items.
Item should be of <a class="reference external" href="http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types">supported type</a> (e.g., numpy 1d
array of float32, int, list of int)</p>
<p>An example implementation for single item data reader creator:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reader_creator_random_image</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">reader</span><span class="p">():</span>
        <span class="k">while</span> <span class="bp">True</span><span class="p">:</span>
            <span class="k">yield</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">width</span><span class="o">*</span><span class="n">height</span><span class="p">)</span>
<span class="k">return</span> <span class="n">reader</span>
</pre></div>
</div>
<p>An example implementation for multiple item data reader creator:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reader_creator_random_image_and_label</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">reader</span><span class="p">():</span>
        <span class="k">while</span> <span class="bp">True</span><span class="p">:</span>
            <span class="k">yield</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">width</span><span class="o">*</span><span class="n">height</span><span class="p">),</span> <span class="n">label</span>
<span class="k">return</span> <span class="n">reader</span>
</pre></div>
</div>
<p>TODO(yuyang18): Should we add whole design doc here?</p>
<dl class="function">
<dt id="paddle.v2.reader.map_readers">
<code class="descclassname">paddle.v2.reader.</code><code class="descname">map_readers</code><span class="sig-paren">(</span><em>func</em>, <em>*readers</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.reader.map_readers" title="永久链接至目标"></a></dt>
<dd><p>Creates a data reader that outputs return value of function using
output of each data readers as arguments.</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>func</strong> &#8211; function to use. The type of func should be (Sample) =&gt; Sample</li>
<li><strong>readers</strong> &#8211; readers whose outputs will be used as arguments of func.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Type:</th><td class="field-body"><p class="first">callable</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the created data reader.</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">callable</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.reader.buffered">
<code class="descclassname">paddle.v2.reader.</code><code class="descname">buffered</code><span class="sig-paren">(</span><em>reader</em>, <em>size</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.reader.buffered" title="永久链接至目标"></a></dt>
<dd><p>Creates a buffered data reader.</p>
<p>The buffered data reader will read and save data entries into a
buffer. Reading from the buffered data reader will proceed as long
as the buffer is not empty.</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>callable</em>) &#8211; the data reader to read from.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; max buffer size.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last">the buffered data reader.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.reader.compose">
<code class="descclassname">paddle.v2.reader.</code><code class="descname">compose</code><span class="sig-paren">(</span><em>*readers</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.reader.compose" title="永久链接至目标"></a></dt>
<dd><p>Creates a data reader whose output is the combination of input readers.</p>
<p>If input readers output following data entries:
(1, 2)    3    (4, 5)
The composed reader will output:
(1, 2, 3, 4, 5)</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>readers</strong> &#8211; readers that will be composed together.</li>
<li><strong>check_alignment</strong> (<em>bool</em>) &#8211; if True, will check if input readers are aligned
correctly. If False, will not check alignment and trailing outputs
will be discarded. Defaults to True.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the new data reader.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">引发:</th><td class="field-body"><p class="first last"><strong>ComposeNotAligned</strong> &#8211; outputs of readers are not aligned.
Will not raise when check_alignment is set to False.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.reader.chain">
<code class="descclassname">paddle.v2.reader.</code><code class="descname">chain</code><span class="sig-paren">(</span><em>*readers</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.reader.chain" title="永久链接至目标"></a></dt>
<dd><p>Creates a data reader whose output is the outputs of input data
readers chained together.</p>
<p>If input readers output following data entries:
[0, 0, 0]
[1, 1, 1]
[2, 2, 2]
The chained reader will output:
[0, 0, 0, 1, 1, 1, 2, 2, 2]</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>readers</strong> &#8211; input readers.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">the new data reader.</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body">callable</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.reader.shuffle">
<code class="descclassname">paddle.v2.reader.</code><code class="descname">shuffle</code><span class="sig-paren">(</span><em>reader</em>, <em>buf_size</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.reader.shuffle" title="永久链接至目标"></a></dt>
<dd><p>Creates a data reader whose data output is shuffled.</p>
<p>Output from the iterator that created by original reader will be
buffered into shuffle buffer, and then shuffled. The size of shuffle buffer
is determined by argument buf_size.</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>callable</em>) &#8211; the original reader whose output will be shuffled.</li>
<li><strong>buf_size</strong> (<em>int</em>) &#8211; shuffle buffer size.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the new reader whose output is shuffled.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">callable</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.reader.firstn">
<code class="descclassname">paddle.v2.reader.</code><code class="descname">firstn</code><span class="sig-paren">(</span><em>reader</em>, <em>n</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.reader.firstn" title="永久链接至目标"></a></dt>
<dd><p>Limit the max number of samples that reader could return.</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>callable</em>) &#8211; the data reader to read from.</li>
<li><strong>n</strong> (<em>int</em>) &#8211; the max number of samples that return.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the decorated reader.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">callable</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<span class="target" id="module-paddle.v2.reader.creator"></span><p>Creator package contains some simple reader creator, which could be used in user
program.</p>
<dl class="function">
<dt id="paddle.v2.reader.creator.np_array">
<code class="descclassname">paddle.v2.reader.creator.</code><code class="descname">np_array</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.reader.creator.np_array" title="永久链接至目标"></a></dt>
<dd><p>Creates a reader that yields elements of x, if it is a
numpy vector. Or rows of x, if it is a numpy matrix.
Or any sub-hyperplane indexed by the highest dimension.</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>x</strong> &#8211; the numpy array to create reader from.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">data reader created from x.</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.reader.creator.text_file">
<code class="descclassname">paddle.v2.reader.creator.</code><code class="descname">text_file</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.reader.creator.text_file" title="永久链接至目标"></a></dt>
<dd><p>Creates a data reader that outputs text line by line from given text file.
Trailing new line (&#8216;\n&#8217;) of each line will be removed.</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">Path:</th><td class="field-body">path of the text file.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">data reader of text file</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="module-paddle.v2.minibatch">
<span id="minibatch"></span><h2>minibatch<a class="headerlink" href="#module-paddle.v2.minibatch" title="永久链接至标题"></a></h2>
<dl class="function">
<dt id="paddle.v2.minibatch.batch">
<code class="descclassname">paddle.v2.minibatch.</code><code class="descname">batch</code><span class="sig-paren">(</span><em>reader</em>, <em>batch_size</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.minibatch.batch" title="永久链接至目标"></a></dt>
<dd><p>Create a batched reader.</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>callable</em>) &#8211; the data reader to read from.</li>
<li><strong>batch_size</strong> (<em>int</em>) &#8211; size of each mini-batch</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the batched reader.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">callable</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="dataset">
<h2>Dataset<a class="headerlink" href="#dataset" title="永久链接至标题"></a></h2>
<span class="target" id="module-paddle.v2.dataset"></span><p>Dataset package.</p>
<div class="section" id="module-paddle.v2.dataset.mnist">
<span id="mnist"></span><h3>mnist<a class="headerlink" href="#module-paddle.v2.dataset.mnist" title="永久链接至标题"></a></h3>
<p>MNIST dataset.</p>
<p>This module will download dataset from <a class="reference external" href="http://yann.lecun.com/exdb/mnist/">http://yann.lecun.com/exdb/mnist/</a> and
parse train set and test set into paddle reader creators.</p>
<dl class="function">
<dt id="paddle.v2.dataset.mnist.train">
<code class="descclassname">paddle.v2.dataset.mnist.</code><code class="descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.dataset.mnist.train" title="永久链接至目标"></a></dt>
<dd><p>MNIST train set creator.</p>
<p>It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].</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">Train reader creator</td>
</tr>
<tr class="field-even field"><th class="field-name">返回类型:</th><td class="field-body">callable</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.dataset.mnist.test">
<code class="descclassname">paddle.v2.dataset.mnist.</code><code class="descname">test</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.dataset.mnist.test" title="永久链接至目标"></a></dt>
<dd><p>MNIST test set cretor.</p>
<p>It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].</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">Test reader creator.</td>
</tr>
<tr class="field-even field"><th class="field-name">返回类型:</th><td class="field-body">callable</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="module-paddle.v2.dataset.cifar">
<span id="cifar"></span><h3>cifar<a class="headerlink" href="#module-paddle.v2.dataset.cifar" title="永久链接至标题"></a></h3>
<p>CIFAR dataset: <a class="reference external" href="https://www.cs.toronto.edu/~kriz/cifar.html">https://www.cs.toronto.edu/~kriz/cifar.html</a></p>
<p>TODO(yuyang18): Complete the comments.</p>
</div>
<div class="section" id="conll05">
<h3>conll05<a class="headerlink" href="#conll05" title="永久链接至标题"></a></h3>
<span class="target" id="module-paddle.v2.dataset.conll05"></span></div>
<div class="section" id="module-paddle.v2.dataset.imdb">
<span id="imdb"></span><h3>imdb<a class="headerlink" href="#module-paddle.v2.dataset.imdb" title="永久链接至标题"></a></h3>
<p>IMDB dataset: <a class="reference external" href="http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz">http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz</a></p>
<p>TODO(yuyang18): Complete comments.</p>
</div>
<div class="section" id="module-paddle.v2.dataset.imikolov">
<span id="imikolov"></span><h3>imikolov<a class="headerlink" href="#module-paddle.v2.dataset.imikolov" title="永久链接至标题"></a></h3>
<p>imikolov&#8217;s simple dataset: <a class="reference external" href="http://www.fit.vutbr.cz/~imikolov/rnnlm/">http://www.fit.vutbr.cz/~imikolov/rnnlm/</a></p>
<p>Complete comments.</p>
</div>
<div class="section" id="module-paddle.v2.dataset.movielens">
<span id="movielens"></span><h3>movielens<a class="headerlink" href="#module-paddle.v2.dataset.movielens" title="永久链接至标题"></a></h3>
<p>Movielens 1-M dataset.</p>
<p>TODO(yuyang18): Complete comments.</p>
</div>
<div class="section" id="module-paddle.v2.dataset.sentiment">
<span id="sentiment"></span><h3>sentiment<a class="headerlink" href="#module-paddle.v2.dataset.sentiment" title="永久链接至标题"></a></h3>
<p>The script fetch and preprocess movie_reviews data set that provided by NLTK</p>
<p>TODO(yuyang18): Complete dataset.</p>
<dl class="function">
<dt id="paddle.v2.dataset.sentiment.get_word_dict">
<code class="descclassname">paddle.v2.dataset.sentiment.</code><code class="descname">get_word_dict</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.dataset.sentiment.get_word_dict" title="永久链接至目标"></a></dt>
<dd><p>Sorted the words by the frequency of words which occur in sample
:return:</p>
<blockquote>
<div>words_freq_sorted</div></blockquote>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.dataset.sentiment.train">
<code class="descclassname">paddle.v2.dataset.sentiment.</code><code class="descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.dataset.sentiment.train" title="永久链接至目标"></a></dt>
<dd><p>Default train set reader creator</p>
</dd></dl>

<dl class="function">
<dt id="paddle.v2.dataset.sentiment.test">
<code class="descclassname">paddle.v2.dataset.sentiment.</code><code class="descname">test</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#paddle.v2.dataset.sentiment.test" title="永久链接至目标"></a></dt>
<dd><p>Default test set reader creator</p>
</dd></dl>

</div>
<div class="section" id="module-paddle.v2.dataset.uci_housing">
<span id="uci-housing"></span><h3>uci_housing<a class="headerlink" href="#module-paddle.v2.dataset.uci_housing" title="永久链接至标题"></a></h3>
<p>UCI Housing dataset.</p>
<p>TODO(yuyang18): Complete comments.</p>
</div>
</div>
</div>


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