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    <li>Data Reader Interface and DataSets</li>
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202 203
  <div class="section" id="data-reader-interface-and-datasets">
<h1>Data Reader Interface and DataSets<a class="headerlink" href="#data-reader-interface-and-datasets" title="永久链接至标题"></a></h1>
204 205
<div class="section" id="datatypes">
<h2>DataTypes<a class="headerlink" href="#datatypes" title="永久链接至标题"></a></h2>
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<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>

238
<dl class="function">
239 240
<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>
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<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>
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<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>
263
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">InputType</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
271 272
<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>
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<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>
282
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body">InputType</td>
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</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
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<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>
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<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>
305
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">InputType</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
313 314
<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>
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<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">
327 328
<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>
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<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>
344
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">InputType</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
352 353
<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>
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<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>
366
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body">InputType</td>
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</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
373
<dt>
374 375 376
<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>
377 378 379 380 381 382 383 384 385 386 387 388 389
<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>
390
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">InputType</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
398
<dt>
399 400 401
<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>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
406
<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>
407
</tr>
408
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">An input type object</td>
409
</tr>
410
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body">InputType</td>
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</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
417
<dt>
418 419 420
<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>
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<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>
434
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">InputType</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
442
<dt>
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<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>
446 447 448 449
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
450 451 452 453 454
<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>
455
</tr>
456 457
<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>
458
</tr>
459 460
<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>
461 462 463 464 465 466
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
467 468
<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>
469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492
<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>
493 494
<div class="section" id="datafeeder">
<h2>DataFeeder<a class="headerlink" href="#datafeeder" title="永久链接至标题"></a></h2>
495
</div>
496 497
<div class="section" id="reader">
<h2>Reader<a class="headerlink" href="#reader" title="永久链接至标题"></a></h2>
498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539
<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">
540 541
<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>
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<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">
568 569
<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>
570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591
<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">
592 593
<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>
594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622
<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">
623 624
<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>
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647
<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">
648 649
<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>
650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674
<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">
675 676
<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>
677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697
<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>

698 699
<dl class="function">
<dt>
700
<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>
701 702 703 704 705 706 707 708 709 710
<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
711 712
:param order: keep the order of reader
:type order: bool
713 714 715 716
:return: the decarated reader
:rtype: callable</p>
</dd></dl>

717
</div>
718 719
<p>Creator package contains some simple reader creator, which could
be used in user program.</p>
720
<dl class="function">
721 722
<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>
723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738
<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">
739 740
<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>
741 742 743 744 745 746 747 748 749 750 751 752 753 754
<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>

755 756
<dl class="function">
<dt>
757
<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>
758
<dd><dl class="docutils">
759 760
<dt>Create a data reader that yield a record one by one from</dt>
<dd>the paths:</dd>
761 762 763 764 765
</dl>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
766
<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>
767
</tr>
768 769 770
<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>
771 772 773
</tr>
</tbody>
</table>
774 775
</dd></dl>

776
</div>
777 778
<div class="section" id="minibatch">
<h2>minibatch<a class="headerlink" href="#minibatch" title="永久链接至标题"></a></h2>
779
<dl class="function">
780 781
<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>
782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805
<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>
806 807 808
<p>Dataset package.</p>
<div class="section" id="mnist">
<h3>mnist<a class="headerlink" href="#mnist" title="永久链接至标题"></a></h3>
809 810
<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
811
parse training set and test set into paddle reader creators.</p>
812
<dl class="function">
813 814
<dt>
<code class="descclassname">paddle.v2.dataset.mnist.</code><code class="descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
815
<dd><p>MNIST training set creator.</p>
816 817 818 819 820 821
<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">
822
<tr class="field-odd field"><th class="field-name">返回:</th><td class="field-body">Training reader creator</td>
823 824 825 826 827 828 829 830
</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">
831 832
<dt>
<code class="descclassname">paddle.v2.dataset.mnist.</code><code class="descname">test</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
833
<dd><p>MNIST test set creator.</p>
834 835 836 837 838 839 840 841 842 843 844 845 846 847
<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>

848 849 850 851 852 853
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.mnist.</code><code class="descname">convert</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span></dt>
<dd><p>Converts dataset to recordio format</p>
</dd></dl>

854
</div>
855 856
<div class="section" id="cifar">
<h3>cifar<a class="headerlink" href="#cifar" title="永久链接至标题"></a></h3>
857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887
<p>CIFAR dataset.</p>
<p>This module will download dataset from
<a class="reference external" href="https://www.cs.toronto.edu/~kriz/cifar.html">https://www.cs.toronto.edu/~kriz/cifar.html</a> and parse train/test set into
paddle reader creators.</p>
<p>The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test
images.</p>
<p>The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes
containing 600 images each. There are 500 training images and 100 testing
images per class.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.cifar.</code><code class="descname">train100</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>CIFAR-100 training set creator.</p>
<p>It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 99].</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">Training 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>
<code class="descclassname">paddle.v2.dataset.cifar.</code><code class="descname">test100</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
888
<dd><p>CIFAR-100 test set creator.</p>
889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923
<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>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.cifar.</code><code class="descname">train10</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>CIFAR-10 training 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">Training 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>
<code class="descclassname">paddle.v2.dataset.cifar.</code><code class="descname">test10</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
924
<dd><p>CIFAR-10 test set creator.</p>
925 926 927 928 929 930 931 932 933 934 935 936 937 938
<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>

939 940 941 942 943 944
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.cifar.</code><code class="descname">convert</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span></dt>
<dd><p>Converts dataset to recordio format</p>
</dd></dl>

945 946 947
</div>
<div class="section" id="conll05">
<h3>conll05<a class="headerlink" href="#conll05" title="永久链接至标题"></a></h3>
948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985
<p>Conll05 dataset.
Paddle semantic role labeling Book and demo use this dataset as an example.
Because Conll05 is not free in public, the default downloaded URL is test set
of Conll05 (which is public). Users can change URL and MD5 to their Conll
dataset. And a pre-trained word vector model based on Wikipedia corpus is used
to initialize SRL model.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.conll05.</code><code class="descname">get_dict</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get the word, verb and label dictionary of Wikipedia corpus.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.conll05.</code><code class="descname">get_embedding</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get the trained word vector based on Wikipedia corpus.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.conll05.</code><code class="descname">test</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Conll05 test set creator.</p>
<p>Because the training dataset is not free, the test dataset is used for
training. It returns a reader creator, each sample in the reader is nine
features, including sentence sequence, predicate, predicate context,
predicate context flag and tagged sequence.</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">Training 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>

986 987 988
</div>
<div class="section" id="imdb">
<h3>imdb<a class="headerlink" href="#imdb" title="永久链接至标题"></a></h3>
989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
<p>IMDB dataset.</p>
<p>This module downloads IMDB dataset from
<a class="reference external" href="http://ai.stanford.edu/%7Eamaas/data/sentiment/">http://ai.stanford.edu/%7Eamaas/data/sentiment/</a>. This dataset contains a set
of 25,000 highly polar movie reviews for training, and 25,000 for testing.
Besides, this module also provides API for building dictionary.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.imdb.</code><code class="descname">build_dict</code><span class="sig-paren">(</span><em>pattern</em>, <em>cutoff</em><span class="sig-paren">)</span></dt>
<dd><p>Build a word dictionary from the corpus. Keys of the dictionary are words,
and values are zero-based IDs of these words.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.imdb.</code><code class="descname">train</code><span class="sig-paren">(</span><em>word_idx</em><span class="sig-paren">)</span></dt>
<dd><p>IMDB training set creator.</p>
<p>It returns a reader creator, each sample in the reader is an zero-based ID
sequence and label in [0, 1].</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>word_idx</strong> (<em>dict</em>) &#8211; word dictionary</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">Training reader creator</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.dataset.imdb.</code><code class="descname">test</code><span class="sig-paren">(</span><em>word_idx</em><span class="sig-paren">)</span></dt>
<dd><p>IMDB test set creator.</p>
<p>It returns a reader creator, each sample in the reader is an zero-based ID
sequence and label in [0, 1].</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>word_idx</strong> (<em>dict</em>) &#8211; word dictionary</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">Test reader creator</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>

1041 1042 1043 1044 1045 1046
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.imdb.</code><code class="descname">convert</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span></dt>
<dd><p>Converts dataset to recordio format</p>
</dd></dl>

1047
</div>
1048 1049
<div class="section" id="imikolov">
<h3>imikolov<a class="headerlink" href="#imikolov" title="永久链接至标题"></a></h3>
1050 1051 1052 1053 1054 1055
<p>imikolov&#8217;s simple dataset.</p>
<p>This module will download dataset from
<a class="reference external" href="http://www.fit.vutbr.cz/~imikolov/rnnlm/">http://www.fit.vutbr.cz/~imikolov/rnnlm/</a> and parse training set and test set
into paddle reader creators.</p>
<dl class="function">
<dt>
1056
<code class="descclassname">paddle.v2.dataset.imikolov.</code><code class="descname">build_dict</code><span class="sig-paren">(</span><em>min_word_freq=50</em><span class="sig-paren">)</span></dt>
1057 1058 1059 1060 1061 1062
<dd><p>Build a word dictionary from the corpus,  Keys of the dictionary are words,
and values are zero-based IDs of these words.</p>
</dd></dl>

<dl class="function">
<dt>
1063
<code class="descclassname">paddle.v2.dataset.imikolov.</code><code class="descname">train</code><span class="sig-paren">(</span><em>word_idx</em>, <em>n</em>, <em>data_type=1</em><span class="sig-paren">)</span></dt>
1064 1065 1066 1067 1068 1069 1070 1071 1072
<dd><p>imikolov training set creator.</p>
<p>It returns a reader creator, each sample in the reader is a word ID
tuple.</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>word_idx</strong> (<em>dict</em>) &#8211; word dictionary</li>
1073
<li><strong>n</strong> (<em>int</em>) &#8211; sliding window size if type is ngram, otherwise max length of sequence</li>
1074
<li><strong>data_type</strong> (<em>member variable of DataType</em><em> (</em><em>NGRAM</em><em> or </em><em>SEQ</em><em>)</em>) &#8211; data type (ngram or sequence)</li>
1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">Training reader creator</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>
1090
<code class="descclassname">paddle.v2.dataset.imikolov.</code><code class="descname">test</code><span class="sig-paren">(</span><em>word_idx</em>, <em>n</em>, <em>data_type=1</em><span class="sig-paren">)</span></dt>
1091 1092 1093 1094 1095 1096 1097 1098 1099
<dd><p>imikolov test set creator.</p>
<p>It returns a reader creator, each sample in the reader is a word ID
tuple.</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>word_idx</strong> (<em>dict</em>) &#8211; word dictionary</li>
1100
<li><strong>n</strong> (<em>int</em>) &#8211; sliding window size if type is ngram, otherwise max length of sequence</li>
1101
<li><strong>data_type</strong> (<em>member variable of DataType</em><em> (</em><em>NGRAM</em><em> or </em><em>SEQ</em><em>)</em>) &#8211; data type (ngram or sequence)</li>
1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">Test reader creator</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>

1115 1116 1117 1118 1119 1120
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.imikolov.</code><code class="descname">convert</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span></dt>
<dd><p>Converts dataset to recordio format</p>
</dd></dl>

1121
</div>
1122 1123
<div class="section" id="movielens">
<h3>movielens<a class="headerlink" href="#movielens" title="永久链接至标题"></a></h3>
1124
<p>Movielens 1-M dataset.</p>
1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171
<p>Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000
movies, which was collected by GroupLens Research. This module will download
Movielens 1-M dataset from
<a class="reference external" href="http://files.grouplens.org/datasets/movielens/ml-1m.zip">http://files.grouplens.org/datasets/movielens/ml-1m.zip</a> and parse training
set and test set into paddle reader creators.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">get_movie_title_dict</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get movie title dictionary.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">max_movie_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get the maximum value of movie id.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">max_user_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get the maximum value of user id.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">max_job_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get the maximum value of job id.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">movie_categories</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get movie categoriges dictionary.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">user_info</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get user info dictionary.</p>
</dd></dl>

<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">movie_info</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>Get movie info dictionary.</p>
</dd></dl>

1172 1173 1174 1175 1176 1177
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">convert</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span></dt>
<dd><p>Converts dataset to recordio format</p>
</dd></dl>

1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">MovieInfo</code><span class="sig-paren">(</span><em>index</em>, <em>categories</em>, <em>title</em><span class="sig-paren">)</span></dt>
<dd><p>Movie id, title and categories information are stored in MovieInfo.</p>
</dd></dl>

<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.dataset.movielens.</code><code class="descname">UserInfo</code><span class="sig-paren">(</span><em>index</em>, <em>gender</em>, <em>age</em>, <em>job_id</em><span class="sig-paren">)</span></dt>
<dd><p>User id, gender, age, and job information are stored in UserInfo.</p>
</dd></dl>

1190
</div>
1191 1192
<div class="section" id="sentiment">
<h3>sentiment<a class="headerlink" href="#sentiment" title="永久链接至标题"></a></h3>
1193 1194 1195
<p>The script fetch and preprocess movie_reviews data set that provided by NLTK</p>
<p>TODO(yuyang18): Complete dataset.</p>
<dl class="function">
1196 1197
<dt>
<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></dt>
1198 1199 1200 1201 1202 1203 1204
<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">
1205 1206
<dt>
<code class="descclassname">paddle.v2.dataset.sentiment.</code><code class="descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
1207
<dd><p>Default training set reader creator</p>
1208 1209 1210
</dd></dl>

<dl class="function">
1211 1212
<dt>
<code class="descclassname">paddle.v2.dataset.sentiment.</code><code class="descname">test</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
1213 1214 1215
<dd><p>Default test set reader creator</p>
</dd></dl>

1216 1217 1218 1219 1220 1221
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.sentiment.</code><code class="descname">convert</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span></dt>
<dd><p>Converts dataset to recordio format</p>
</dd></dl>

1222
</div>
1223 1224 1225
<div class="section" id="uci-housing">
<h3>uci_housing<a class="headerlink" href="#uci-housing" title="永久链接至标题"></a></h3>
<p>UCI Housing dataset.</p>
1226
<p>This module will download dataset from
1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
<a class="reference external" href="https://archive.ics.uci.edu/ml/machine-learning-databases/housing/">https://archive.ics.uci.edu/ml/machine-learning-databases/housing/</a> and
parse training set and test set into paddle reader creators.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.uci_housing.</code><code class="descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>UCI_HOUSING training set creator.</p>
<p>It returns a reader creator, each sample in the reader is features after
normalization and price number.</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">Training 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>
<code class="descclassname">paddle.v2.dataset.uci_housing.</code><code class="descname">test</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>UCI_HOUSING test set creator.</p>
<p>It returns a reader creator, each sample in the reader is features after
normalization and price number.</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>

1265 1266 1267
</div>
<div class="section" id="wmt14">
<h3>wmt14<a class="headerlink" href="#wmt14" title="永久链接至标题"></a></h3>
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<p>WMT14 dataset.
The original WMT14 dataset is too large and a small set of data for set is
provided. This module will download dataset from
<a class="reference external" href="http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz">http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz</a> and
parse training set and test set into paddle reader creators.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.wmt14.</code><code class="descname">train</code><span class="sig-paren">(</span><em>dict_size</em><span class="sig-paren">)</span></dt>
<dd><p>WMT14 training set creator.</p>
<p>It returns a reader creator, each sample in the reader is source language
word ID sequence, target language word ID sequence and next word ID
sequence.</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">Training 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>
<code class="descclassname">paddle.v2.dataset.wmt14.</code><code class="descname">test</code><span class="sig-paren">(</span><em>dict_size</em><span class="sig-paren">)</span></dt>
<dd><p>WMT14 test set creator.</p>
<p>It returns a reader creator, each sample in the reader is source language
word ID sequence, target language word ID sequence and next word ID
sequence.</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>

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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.dataset.wmt14.</code><code class="descname">convert</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span></dt>
<dd><p>Converts dataset to recordio format</p>
</dd></dl>

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