提交 718e424b 编写于 作者: T Travis CI

Deploy to GitHub Pages: 43d69818

上级 7dd2ead4
......@@ -358,9 +358,9 @@ every element in this vector is either zero or one.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_non_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse binary vector. It means the input feature is a sparse vector and the
every element in this vector is either zero or one.</p>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_float_vector</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
......@@ -383,24 +383,18 @@ every element in this vector is either zero or one.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.</p>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_float_vector_sequence</code><span class="sig-paren">(</span><em>dim</em><span class="sig-paren">)</span></dt>
<dd><p>Data type of a sequence of sparse vector, which most elements are zero,
others could be any float value.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</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 class="field-odd field"><th class="field-name">Parameters:</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">Returns:</th><td class="field-body"><p class="first">An input type object.</p>
</td>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">An input type object</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">InputType</p>
</td>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">InputType</td>
</tr>
</tbody>
</table>
......@@ -408,9 +402,9 @@ elements in this vector are zero, others could be any float value.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_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>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_non_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse binary vector. It means the input feature is a sparse vector and the
every element in this vector is either zero or one.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
......@@ -433,18 +427,24 @@ elements in this vector are zero, others could be any float value.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_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>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dim</strong> (<em>int</em>) &#8211; dimension of sparse vector.</td>
<tr class="field-odd field"><th class="field-name">Parameters:</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">Returns:</th><td class="field-body">An input type object</td>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">An input type object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">InputType</td>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">InputType</p>
</td>
</tr>
</tbody>
</table>
......
因为 它太大了无法显示 source diff 。你可以改为 查看blob
......@@ -174,7 +174,7 @@ decoder_inputs = paddle.layer.fc(
1. 两者都是对梯度的截断,但截断时机不同,前者在 :code:`optimzier` 更新网络参数时应用;后者在激活函数反向计算时被调用;
2. 截断对象不同:前者截断可学习参数的梯度,后者截断回传给前层的梯度;
除此之外,还可以通过减小学习或者对数据进行归一化处理来解决这类问题。
除此之外,还可以通过减小学习或者对数据进行归一化处理来解决这类问题。
5. 如何调用 infer 接口输出多个layer的预测结果
-----------------------------------------------
......
......@@ -372,9 +372,9 @@ every element in this vector is either zero or one.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_non_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse binary vector. It means the input feature is a sparse vector and the
every element in this vector is either zero or one.</p>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_float_vector</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
......@@ -397,24 +397,18 @@ every element in this vector is either zero or one.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.</p>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_float_vector_sequence</code><span class="sig-paren">(</span><em>dim</em><span class="sig-paren">)</span></dt>
<dd><p>Data type of a sequence of sparse vector, which most elements are zero,
others could be any float value.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><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 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"><p class="first">An input type object.</p>
</td>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">An input type object</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">InputType</p>
</td>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body">InputType</td>
</tr>
</tbody>
</table>
......@@ -422,9 +416,9 @@ elements in this vector are zero, others could be any float value.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_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>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_non_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse binary vector. It means the input feature is a sparse vector and the
every element in this vector is either zero or one.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
......@@ -447,18 +441,24 @@ elements in this vector are zero, others could be any float value.</p>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_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>
<code class="descclassname">paddle.v2.data_type.</code><code class="descname">sparse_value_slot</code><span class="sig-paren">(</span><em>dim</em>, <em>seq_type=0</em><span class="sig-paren">)</span></dt>
<dd><p>Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><strong>dim</strong> (<em>int</em>) &#8211; dimension of sparse vector.</td>
<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">An input type object</td>
<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">InputType</td>
<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>
......
......@@ -414,7 +414,7 @@ layer_attr=paddle.attr.ExtraLayerAttribute(</p>
<li>两者都是对梯度的截断,但截断时机不同,前者在 <code class="code docutils literal"><span class="pre">optimzier</span></code> 更新网络参数时应用;后者在激活函数反向计算时被调用;</li>
<li>截断对象不同:前者截断可学习参数的梯度,后者截断回传给前层的梯度;</li>
</ol>
<p>除此之外,还可以通过减小学习或者对数据进行归一化处理来解决这类问题。</p>
<p>除此之外,还可以通过减小学习或者对数据进行归一化处理来解决这类问题。</p>
</div>
<div class="section" id="infer-layer">
<h2><a class="toc-backref" href="#id20">5. 如何调用 infer 接口输出多个layer的预测结果</a><a class="headerlink" href="#infer-layer" title="永久链接至标题"></a></h2>
......
因为 它太大了无法显示 source diff 。你可以改为 查看blob
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