Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2299
Star 20931
Fork 5422
P
Paddle
提交 43f1c358 编写于 作者: T Travis CI
浏览文件
操作

Deploy to GitHub Pages: aa756a3d

上级 4e081a9f
展开全部 显示空白变更内容
内联 并排
Showing with 50 addition and 90 deletion
develop/doc/api/v2/config/layer.html
浏览文件 @ 43f1c358
......@@ -661,7 +661,7 @@ timestep to (t+k+1)-th timestep. Assumed that the hidden dim of input
activations are d, the activations r_t for the new layer at time-step t are:</p>
<div class="math">
\[r_{t,r} = \sum_{j=1}^{k + 1} {w_{i,j}h_{t+j-1, i}}
\quad ext{for} \quad (1 \leq i \leq d)\]</div>
\quad \text{for} \quad (1 \leq i \leq d)\]</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">The <cite>context_len</cite> is <cite>k + 1</cite>. That is to say, the lookahead step
......@@ -3424,66 +3424,46 @@ details.</li>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">factorization_machine</code></dt>
<dd><blockquote>
<div><p>The Factorization Machine models pairwise feature interactions as inner
<dd><p>The Factorization Machine models pairwise feature interactions as inner
product of the learned latent vectors corresponding to each input feature.
The Factorization Machine can effectively capture feature interactions
especially when the input is sparse.</p>
<p>This implementation only consider the 2-order feature interactions using
Factorization Machine with the formula:</p>
<div class="math">
\[y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j\]</div>
</div></blockquote>
<p>angle x_i x_j</p>
<blockquote>
<div><dl class="docutils">
<dt>Note:</dt>
<dd>X is the input vector with size n. V is the factor matrix. Each row of V
\[y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j \rangle x_i x_j\]</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">X is the input vector with size n. V is the factor matrix. Each row of V
is the latent vector corresponding to each input dimesion. The size of
each latent vector is k.</dd>
</dl>
each latent vector is k.</p>
</div>
<p>For details of Factorization Machine, please refer to the paper:
Factorization machines.</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">param input:</th><td class="field-body">The input layer. Supported input types: all input data types
on CPU, and only dense input types on GPU.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param factor_size:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">The hyperparameter that defines the dimensionality of
the latent vector size.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type context_len:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">int</td>
</tr>
<tr class="field-odd field"><th class="field-name">param act:</th><td class="field-body">Activation Type. Default is linear activation.</td>
</tr>
<tr class="field-even field"><th class="field-name">type act:</th><td class="field-body">paddle.v2.activation.Base</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param param_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">The parameter attribute. See paddle.v2.attr.ParameterAttribute for
details.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type param_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ParameterAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">Extra Layer config.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttributeNone</td>
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input layer. Supported input types: all input data types
on CPU, and only dense input types on GPU.</li>
<li><strong>factor_size</strong> &#8211; The hyperparameter that defines the dimensionality of
the latent vector size.</li>
<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type. Default is linear activation.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The parameter attribute. See paddle.v2.attr.ParameterAttribute for
details.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</li>
</ul>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer object.</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer</td>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
</td>
</tr>
</tbody>
</table>
</div></blockquote>
</dd></dl>
</div>
......@@ -4462,7 +4442,7 @@ details.</li>
<dd><p>The gated unit layer implements a simple gating mechanism over the input.
The input <span class="math">\(X\)</span> is first projected into a new space <span class="math">\(X'\)</span>, and
it is also used to produce a gate weight <span class="math">\(\sigma\)</span>. Element-wise
product between <a href="#id5"><span class="problematic" id="id6">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p>
product between <span class="math">\(X'\)</span> and <span class="math">\(\sigma\)</span> is finally returned.</p>
<dl class="docutils">
<dt>Reference:</dt>
<dd><a class="reference external" href="https://arxiv.org/abs/1612.08083">Language Modeling with Gated Convolutional Networks</a></dd>
......
develop/doc/searchindex.js
浏览文件 @ 43f1c358
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/config/layer.html
浏览文件 @ 43f1c358
......@@ -674,7 +674,7 @@ timestep to (t+k+1)-th timestep. Assumed that the hidden dim of input
activations are d, the activations r_t for the new layer at time-step t are:</p>
<div class="math">
\[r_{t,r} = \sum_{j=1}^{k + 1} {w_{i,j}h_{t+j-1, i}}
\quad ext{for} \quad (1 \leq i \leq d)\]</div>
\quad \text{for} \quad (1 \leq i \leq d)\]</div>
<div class="admonition note">
<p class="first admonition-title">注解</p>
<p class="last">The <cite>context_len</cite> is <cite>k + 1</cite>. That is to say, the lookahead step
......@@ -3437,66 +3437,46 @@ details.</li>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">factorization_machine</code></dt>
<dd><blockquote>
<div><p>The Factorization Machine models pairwise feature interactions as inner
<dd><p>The Factorization Machine models pairwise feature interactions as inner
product of the learned latent vectors corresponding to each input feature.
The Factorization Machine can effectively capture feature interactions
especially when the input is sparse.</p>
<p>This implementation only consider the 2-order feature interactions using
Factorization Machine with the formula:</p>
<div class="math">
\[y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j\]</div>
</div></blockquote>
<p>angle x_i x_j</p>
<blockquote>
<div><dl class="docutils">
<dt>Note:</dt>
<dd>X is the input vector with size n. V is the factor matrix. Each row of V
\[y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j \rangle x_i x_j\]</div>
<div class="admonition note">
<p class="first admonition-title">注解</p>
<p class="last">X is the input vector with size n. V is the factor matrix. Each row of V
is the latent vector corresponding to each input dimesion. The size of
each latent vector is k.</dd>
</dl>
each latent vector is k.</p>
</div>
<p>For details of Factorization Machine, please refer to the paper:
Factorization machines.</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">param input:</th><td class="field-body">The input layer. Supported input types: all input data types
on CPU, and only dense input types on GPU.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param factor_size:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">The hyperparameter that defines the dimensionality of
the latent vector size.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type context_len:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">int</td>
</tr>
<tr class="field-odd field"><th class="field-name">param act:</th><td class="field-body">Activation Type. Default is linear activation.</td>
</tr>
<tr class="field-even field"><th class="field-name">type act:</th><td class="field-body">paddle.v2.activation.Base</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param param_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">The parameter attribute. See paddle.v2.attr.ParameterAttribute for
details.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type param_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ParameterAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">Extra Layer config.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttributeNone</td>
<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input layer. Supported input types: all input data types
on CPU, and only dense input types on GPU.</li>
<li><strong>factor_size</strong> &#8211; The hyperparameter that defines the dimensionality of
the latent vector size.</li>
<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type. Default is linear activation.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The parameter attribute. See paddle.v2.attr.ParameterAttribute for
details.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</li>
</ul>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer object.</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer</td>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
</td>
</tr>
</tbody>
</table>
</div></blockquote>
</dd></dl>
</div>
......@@ -4475,7 +4455,7 @@ details.</li>
<dd><p>The gated unit layer implements a simple gating mechanism over the input.
The input <span class="math">\(X\)</span> is first projected into a new space <span class="math">\(X'\)</span>, and
it is also used to produce a gate weight <span class="math">\(\sigma\)</span>. Element-wise
product between <a href="#id5"><span class="problematic" id="id6">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p>
product between <span class="math">\(X'\)</span> and <span class="math">\(\sigma\)</span> is finally returned.</p>
<dl class="docutils">
<dt>Reference:</dt>
<dd><a class="reference external" href="https://arxiv.org/abs/1612.08083">Language Modeling with Gated Convolutional Networks</a></dd>
......
develop/doc_cn/searchindex.js
浏览文件 @ 43f1c358
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或