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  <div class="section" id="data-reader-interface">
<h1>Data Reader Interface<a class="headerlink" href="#data-reader-interface" title="永久链接至标题">¶</a></h1>
<div class="section" id="datatypes">
<h2>DataTypes<a class="headerlink" href="#datatypes" title="永久链接至标题">¶</a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">dense_array</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Dense Array. It means the input feature is dense array with float type.
For example, if the input is an image with 28*28 pixels, the input of
Paddle neural network could be a dense vector with dimension 784 or a
numpy array with shape (28, 28).</p>
<p>For the 2-D convolution operation, each sample in one mini-batch must have
the similarly size in PaddlePaddle now. But, it supports variable-dimension
feature across mini-batch. For the variable-dimension, the param dim is not
used. While the data reader must yield numpy array and the data feeder will
set the data shape correctly.</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">InputType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<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></dt>
<dd><p>Dense Array. It means the input feature is dense array with float type.
For example, if the input is an image with 28*28 pixels, the input of
Paddle neural network could be a dense vector with dimension 784 or a
numpy array with shape (28, 28).</p>
<p>For the 2-D convolution operation, each sample in one mini-batch must have
the similarly size in PaddlePaddle now. But, it supports variable-dimension
feature across mini-batch. For the variable-dimension, the param dim is not
used. While the data reader must yield numpy array and the data feeder will
set the data shape correctly.</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">InputType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<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></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">InputType</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<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></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">InputType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<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></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>
<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></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">InputType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<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></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">InputType</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_float_vector</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></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">InputType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_float_vector_sequence</code><span class="sig-paren">(</span><em>dim</em><span class="sig-paren">)</span></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">InputType</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<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></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">InputType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt>
<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></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">InputType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt>
<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></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="datafeeder">
<h2>DataFeeder<a class="headerlink" href="#datafeeder" title="永久链接至标题">¶</a></h2>
</div>
<div class="section" id="reader">
<h2>Reader<a class="headerlink" href="#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="id1">
<h3>Data Reader Interface<a class="headerlink" href="#id1" 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>
<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></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>
<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></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>
<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></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>
<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></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>
<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></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>
<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></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>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.reader.</code><code class="descname">xmap_readers</code><span class="sig-paren">(</span><em>mapper</em>, <em>reader</em>, <em>process_num</em>, <em>buffer_size</em>, <em>order=False</em><span class="sig-paren">)</span></dt>
<dd><p>Use multiprocess to map samples from reader by a mapper defined by user.
And this function contains a buffered decorator.
:param mapper:  a function to map sample.
:type mapper: callable
:param reader: the data reader to read from
:type reader: callable
:param process_num: process number to handle original sample
:type process_num: int
:param buffer_size: max buffer size
:type buffer_size: int
:param order: keep the order of reader
:type order: bool
:return: the decarated reader
:rtype: callable</p>
</dd></dl>

</div>
<p>Creator package contains some simple reader creator, which could
be used in user program.</p>
<dl class="function">
<dt>
<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></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>
<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></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>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.reader.creator.</code><code class="descname">cloud_reader</code><span class="sig-paren">(</span><em>paths</em>, <em>etcd_endpoints</em>, <em>timeout_sec=5</em>, <em>buf_size=64</em><span class="sig-paren">)</span></dt>
<dd><dl class="docutils">
<dt>Create a data reader that yield a record one by one from</dt>
<dd>the paths:</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">Paths:</th><td class="field-body">path of recordio files, can be a string or a string list.</td>
</tr>
<tr class="field-even field"><th class="field-name">Etcd_endpoints:</th><td class="field-body">the endpoints for etcd cluster</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回:</th><td class="field-body">data reader of recordio files.</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="minibatch">
<h2>minibatch<a class="headerlink" href="#minibatch" title="永久链接至标题">¶</a></h2>
<dl class="function">
<dt>
<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></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>


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