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develop/doc/api/v2/data.html
浏览文件 @ cac0f6f5
......@@ -477,84 +477,6 @@ nested sequence.</li>
</div>
<div class="section" id="datafeeder">
<h2>DataFeeder<a class="headerlink" href="#datafeeder" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<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></dt>
<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 simple usage shows below</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">feeding</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">]</span>
<span class="n">data_types</span> <span class="o">=</span> <span class="n">enumerate_data_types_of_data_layers</span><span class="p">(</span><span class="n">topology</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">feeding</span><span class="o">=</span><span class="n">feeding</span><span class="p">)</span>
<span class="n">minibatch_data</span> <span class="o">=</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="o">...</span><span class="p">],</span> <span class="mi">5</span><span class="p">)]</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>
<p>If mini-batch data and data layers are not one to one mapping, we
could pass a dictionary to feeding parameter to represent the mapping
relationship.</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">feeding</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">feeding</span><span class="o">=</span><span class="n">feeding</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="o">.</span><span class="n">convert</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">Note</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">Parameters:</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>
<li><strong>feeding</strong> (<em>dict|collections.Sequence|None</em>) &#8211; A dictionary or a sequence to specify the position of each
data in the input data.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt>
<code class="descname">convert</code><span class="sig-paren">(</span><em>dat</em>, <em>argument=None</em><span class="sig-paren">)</span></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first 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="reader">
<h2>Reader<a class="headerlink" href="#reader" title="Permalink to this headline"></a></h2>
......
develop/doc/searchindex.js
浏览文件 @ cac0f6f5
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
develop/doc_cn/api/v2/data.html
浏览文件 @ cac0f6f5
......@@ -482,84 +482,6 @@ nested sequence.</li>
</div>
<div class="section" id="datafeeder">
<h2>DataFeeder<a class="headerlink" href="#datafeeder" title="永久链接至标题"></a></h2>
<dl class="class">
<dt>
<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></dt>
<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 simple usage shows below</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">feeding</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">]</span>
<span class="n">data_types</span> <span class="o">=</span> <span class="n">enumerate_data_types_of_data_layers</span><span class="p">(</span><span class="n">topology</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">feeding</span><span class="o">=</span><span class="n">feeding</span><span class="p">)</span>
<span class="n">minibatch_data</span> <span class="o">=</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="o">...</span><span class="p">],</span> <span class="mi">5</span><span class="p">)]</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>
<p>If mini-batch data and data layers are not one to one mapping, we
could pass a dictionary to feeding parameter to represent the mapping
relationship.</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">feeding</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">feeding</span><span class="o">=</span><span class="n">feeding</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="o">.</span><span class="n">convert</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>
<li><strong>feeding</strong> (<em>dict|collections.Sequence|None</em>) &#8211; A dictionary or a sequence to specify the position of each
data in the input data.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt>
<code class="descname">convert</code><span class="sig-paren">(</span><em>dat</em>, <em>argument=None</em><span class="sig-paren">)</span></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first 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="reader">
<h2>Reader<a class="headerlink" href="#reader" title="永久链接至标题"></a></h2>
......
develop/doc_cn/searchindex.js
浏览文件 @ cac0f6f5
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
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