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  <div class="section" id="nets">
<h1>nets<a class="headerlink" href="#nets" title="Permalink to this headline">¶</a></h1>
<div class="section" id="simple-img-conv-pool">
<h2>simple_img_conv_pool<a class="headerlink" href="#simple-img-conv-pool" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.nets.</code><code class="descname">simple_img_conv_pool</code><span class="sig-paren">(</span><em>input</em>, <em>num_filters</em>, <em>filter_size</em>, <em>pool_size</em>, <em>pool_stride</em>, <em>act</em>, <em>param_attr=None</em>, <em>pool_type='max'</em>, <em>use_cudnn=True</em><span class="sig-paren">)</span></dt>
<dd></dd></dl>

</div>
<div class="section" id="sequence-conv-pool">
<h2>sequence_conv_pool<a class="headerlink" href="#sequence-conv-pool" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.nets.</code><code class="descname">sequence_conv_pool</code><span class="sig-paren">(</span><em>input</em>, <em>num_filters</em>, <em>filter_size</em>, <em>param_attr=None</em>, <em>act='sigmoid'</em>, <em>pool_type='max'</em><span class="sig-paren">)</span></dt>
<dd></dd></dl>

</div>
<div class="section" id="glu">
<h2>glu<a class="headerlink" href="#glu" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.nets.</code><code class="descname">glu</code><span class="sig-paren">(</span><em>input</em>, <em>dim=-1</em><span class="sig-paren">)</span></dt>
<dd><p>The gated linear unit composed by split, sigmoid activation and elementwise
multiplication. Specifically, Split the input into two equal sized parts
<span class="math">\(a\)</span> and <span class="math">\(b\)</span> along the given dimension and then compute as
following:</p>
<blockquote>
<div><div class="math">
\[{GLU}(a, b)= a \otimes \sigma(b)\]</div>
</div></blockquote>
<p>Refer to <a class="reference external" href="https://arxiv.org/pdf/1612.08083.pdf">Language Modeling with Gated Convolutional Networks</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li>
<li><strong>dim</strong> (<em>int</em>) &#8211; The dimension along which to split. If <span class="math">\(dim &lt; 0\)</span>, the
dimension to split along is <span class="math">\(rank(input) + dim\)</span>.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The Tensor variable with half the size of input.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Variable</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># x is a Tensor variable with shape [3, 6, 9]</span>
<span class="n">fluid</span><span class="o">.</span><span class="n">nets</span><span class="o">.</span><span class="n">glu</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># shape of output: [3, 3, 9]</span>
</pre></div>
</div>
</dd></dl>

</div>
<div class="section" id="scaled-dot-product-attention">
<h2>scaled_dot_product_attention<a class="headerlink" href="#scaled-dot-product-attention" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.nets.</code><code class="descname">scaled_dot_product_attention</code><span class="sig-paren">(</span><em>queries</em>, <em>keys</em>, <em>values</em>, <em>num_heads=1</em>, <em>dropout_rate=0.0</em><span class="sig-paren">)</span></dt>
<dd><p>The dot-product attention.</p>
<p>Attention mechanism can be seen as mapping a query and a set of key-value
pairs to an output. The output is computed as a weighted sum of the values,
where the weight assigned to each value is computed by a compatibility
function (dot-product here) of the query with the corresponding key.</p>
<p>The dot-product attention can be implemented through (batch) matrix
multipication as follows:</p>
<blockquote>
<div><div class="math">
\[Attention(Q, K, V)= softmax(QK^\mathrm{T})V\]</div>
</div></blockquote>
<p>Refer to <a class="reference external" href="https://arxiv.org/pdf/1706.03762.pdf">Attention Is All You Need</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>queries</strong> (<em>Variable</em>) &#8211; The input variable which should be a 3-D Tensor.</li>
<li><strong>keys</strong> (<em>Variable</em>) &#8211; The input variable which should be a 3-D Tensor.</li>
<li><strong>values</strong> (<em>Variable</em>) &#8211; The input variable which should be a 3-D Tensor.</li>
<li><strong>num_heads</strong> (<em>int</em>) &#8211; Head number to compute the scaled dot product
attention. Default value is 1.</li>
<li><strong>dropout_rate</strong> (<em>float</em>) &#8211; The dropout rate to drop the attention weight.
Default value is 0.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><dl class="docutils">
<dt>A 3-D Tensor computed by multi-head scaled dot product</dt>
<dd><p class="first last">attention.</p>
</dd>
</dl>
</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first">Variable</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Raises:</th><td class="field-body"><p class="first last"><code class="xref py py-exc docutils literal"><span class="pre">ValueError</span></code> &#8211; If input queries, keys, values are not 3-D Tensors.</p>
</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>1. When num_heads &gt; 1, three linear projections are learned respectively
to map input queries, keys and values into queries&#8217;, keys&#8217; and values&#8217;.
queries&#8217;, keys&#8217; and values&#8217; have the same shapes with queries, keys
and values.</p>
<p class="last">1. When num_heads == 1, scaled_dot_product_attention has no learnable
parameters.</p>
</div>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Suppose q, k, v are Tensors with the following shape:</span>
<span class="c1"># q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]</span>

<span class="n">contexts</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">nets</span><span class="o">.</span><span class="n">scaled_dot_product_attention</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="n">contexts</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># [3, 5, 10]</span>
</pre></div>
</div>
</dd></dl>

</div>
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