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  <div class="section" id="layers">
<h1>Layers<a class="headerlink" href="#layers" title="Permalink to this headline"></a></h1>
<div class="section" id="fc">
<h2>fc<a class="headerlink" href="#fc" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
218
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">fc</code><span class="sig-paren">(</span><em>input</em>, <em>size</em>, <em>num_flatten_dims=1</em>, <em>param_attr=None</em>, <em>bias_attr=None</em>, <em>act=None</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
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<dd><p>Fully Connected Layer.</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 last simple">
<li><strong>input</strong> &#8211; The input tensor to the function</li>
<li><strong>size</strong> &#8211; The size of the layer</li>
<li><strong>num_flatten_dims</strong> &#8211; Number of columns in input</li>
<li><strong>param_attr</strong> &#8211; The parameters/weights to the FC Layer</li>
<li><strong>param_initializer</strong> &#8211; Initializer used for the weight/parameter. If None, XavierInitializer() is used</li>
<li><strong>bias_attr</strong> &#8211; The bias parameter for the FC layer</li>
<li><strong>bias_initializer</strong> &#8211; Initializer used for the bias. If None, then ConstantInitializer() is used</li>
<li><strong>act</strong> &#8211; Activation to be applied to the output of FC layer</li>
<li><strong>name</strong> &#8211; Name/alias of the function</li>
<li><strong>main_program</strong> &#8211; Name of the main program that calls this</li>
<li><strong>startup_program</strong> &#8211; Name of the startup program</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>This function can take in multiple inputs and performs the Fully Connected
function (linear transformation) on top of each of them.
So for input x, the output will be : Wx + b. Where W is the parameter,
b the bias and x is the input.</p>
<p>The function also applies an activation (non-linearity) on top of the
output, if activation is passed in the input.</p>
<p>All the input variables of this function are passed in as local variables
to the LayerHelper constructor.</p>
</dd></dl>

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</div>
<div class="section" id="embedding">
<h2>embedding<a class="headerlink" href="#embedding" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
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<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">embedding</code><span class="sig-paren">(</span><em>input</em>, <em>size</em>, <em>is_sparse=False</em>, <em>param_attr=None</em>, <em>dtype='float32'</em><span class="sig-paren">)</span></dt>
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<dd><p>Embedding Layer.</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 last simple">
<li><strong>param_initializer</strong> &#8211; </li>
<li><strong>input</strong> &#8211; The input to the function</li>
<li><strong>size</strong> &#8211; The size of the layer</li>
<li><strong>is_sparse</strong> &#8211; A flag that decleares whether the input is sparse</li>
<li><strong>param_attr</strong> &#8211; Parameters for this layer</li>
<li><strong>dtype</strong> &#8211; The type of data : float32, float_16, int etc</li>
<li><strong>main_program</strong> &#8211; Name of the main program that calls this</li>
<li><strong>startup_program</strong> &#8211; Name of the startup program</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>This function can take in the input (which is a vector of IDs) and
performs a lookup in the lookup_table using these IDs, to result into
the embedding of each ID in the input.</p>
<p>All the input variables of this function are passed in as local variables
to the LayerHelper constructor.</p>
</dd></dl>

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</div>
<div class="section" id="dynamic-lstm">
<h2>dynamic_lstm<a class="headerlink" href="#dynamic-lstm" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
288
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">dynamic_lstm</code><span class="sig-paren">(</span><em>input</em>, <em>size</em>, <em>param_attr=None</em>, <em>bias_attr=None</em>, <em>use_peepholes=True</em>, <em>is_reverse=False</em>, <em>gate_activation='sigmoid'</em>, <em>cell_activation='tanh'</em>, <em>candidate_activation='tanh'</em>, <em>dtype='float32'</em><span class="sig-paren">)</span></dt>
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<dd></dd></dl>

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</div>
<div class="section" id="data">
<h2>data<a class="headerlink" href="#data" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
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<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">data</code><span class="sig-paren">(</span><em>name</em>, <em>shape</em>, <em>append_batch_size=True</em>, <em>dtype='float32'</em>, <em>lod_level=0</em>, <em>type=VarType.LOD_TENSOR</em>, <em>stop_gradient=True</em><span class="sig-paren">)</span></dt>
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<dd><p>Data Layer.</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 last simple">
<li><strong>name</strong> &#8211; The name/alias of the function</li>
<li><strong>shape</strong> &#8211; Tuple declaring the shape.</li>
<li><strong>append_batch_size</strong> &#8211; Whether or not to append the data as a batch.</li>
<li><strong>dtype</strong> &#8211; The type of data : float32, float_16, int etc</li>
<li><strong>type</strong> &#8211; The output type. By default it is LOD_TENSOR.</li>
<li><strong>lod_level</strong> (<em>int</em>) &#8211; The LoD Level. 0 means the input data is not a sequence.</li>
<li><strong>main_program</strong> &#8211; Name of the main program that calls this</li>
<li><strong>startup_program</strong> &#8211; Name of the startup program</li>
<li><strong>stop_gradient</strong> &#8211; A boolean that mentions whether gradient should flow.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>This function takes in input and based on whether data has
to be returned back as a minibatch, it creates the global variable using
the helper functions. The global variables can be accessed by all the
following operations and layers in the graph.</p>
<p>All the input variables of this function are passed in as local variables
to the LayerHelper constructor.</p>
</dd></dl>

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</div>
<div class="section" id="mean">
<h2>mean<a class="headerlink" href="#mean" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">mean</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Mean Operator.</p>
<p>Out is a scalar which is the mean of all elements in X.</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>x</strong> &#8211; The input of mean op
Duplicable: False  Optional: False</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">The output of mean op</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="mul">
<h2>mul<a class="headerlink" href="#mul" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">mul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Mul Operator.</p>
<p>This operator is used to perform matrix multiplication for input X and Y.</p>
<p>The equation is:</p>
<blockquote>
<div>$$Out = X * Y$$</div></blockquote>
<p>Both the input <cite>X</cite> and <cite>Y</cite> can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input <cite>X</cite>.</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>x</strong> &#8211; The first input of mul op
Duplicable: False  Optional: False</li>
<li><strong>y</strong> &#8211; The second input of mul op
Duplicable: False  Optional: False</li>
<li><strong>x_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1) mul_op can take tensors with more than two dimensions as input <cite>X</cite>,
in that case, tensors will be reshaped to a matrix. The matrix&#8217;s first
dimension(column length) will be the product of tensor&#8217;s last
<cite>num_col_dims</cite> dimensions, and the matrix&#8217;s second dimension(row length)
will be the product of tensor&#8217;s first <cite>rank - num_col_dims</cite> dimensions.</li>
<li><strong>y_num_col_dims</strong> (<em>INT</em>) &#8211; (int, default 1) mul_op can take tensors with more than two dimensions as input <cite>Y</cite>,
in that case, tensors will be reshaped to a matrix. Just like input <cite>X</cite>.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output of mul op</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="elementwise-add">
<h2>elementwise_add<a class="headerlink" href="#elementwise-add" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_add</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Add Operator.</p>
<p>The equation is:</p>
<p>$Out = X + Y$</p>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p>
<p>There are two cases for this operator:
1. The shape of Y is same with X;
2. The shape of Y is a subset of X.</p>
<p>For case 2:
Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p>
<p class="rubric">example</p>
<p>shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0</p>
<p>Both the input X and Y can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input X.</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>x</strong> &#8211; (Tensor) The first input tensor of elementwise op
Duplicable: False  Optional: False</li>
<li><strong>y</strong> &#8211; (Tensor) The second input tensor of elementwise op
Duplicable: False  Optional: False</li>
<li><strong>axis</strong> (<em>INT</em>) &#8211; (int, default -1) The starting dimension index for broadcasting Y onto X</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output of elementwise op</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="elementwise-div">
<h2>elementwise_div<a class="headerlink" href="#elementwise-div" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_div</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Div Operator.</p>
<p>The equation is:</p>
<p>$Out = X / Y$</p>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p>
<p>There are two cases for this operator:
1. The shape of Y is same with X;
2. The shape of Y is a subset of X.</p>
<p>For case 2:
Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p>
<p class="rubric">example</p>
<p>shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0</p>
<p>Both the input X and Y can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input X.</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>x</strong> &#8211; (Tensor) The first input tensor of elementwise op
Duplicable: False  Optional: False</li>
<li><strong>y</strong> &#8211; (Tensor) The second input tensor of elementwise op
Duplicable: False  Optional: False</li>
<li><strong>axis</strong> (<em>INT</em>) &#8211; (int, default -1) The starting dimension index for broadcasting Y onto X</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output of elementwise op</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="dropout">
<h2>dropout<a class="headerlink" href="#dropout" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">dropout</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Dropout Operator.</p>
<p>Dropout refers to randomly dropping out units in a nerual network. It is a
regularization technique for reducing overfitting by preventing neuron
co-adaption during training. The dropout operator randomly set (according to
the given dropout probability) the outputs of some units to zero, while others
are set equal to their corresponding inputs.</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>x</strong> &#8211; The input of dropout op.
Duplicable: False  Optional: False</li>
<li><strong>dropout_prob</strong> (<em>FLOAT</em>) &#8211; Probability of setting units to zero.</li>
<li><strong>is_test</strong> (<em>BOOLEAN</em>) &#8211; True if in test phase.</li>
<li><strong>seed</strong> (<em>INT</em>) &#8211; Dropout random seed.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output of dropout op.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="reshape">
<h2>reshape<a class="headerlink" href="#reshape" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">reshape</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Reshape Operator.</p>
<p>Reshape Input(X) into the shape specified by Attr(shape).</p>
<p>An example:
Given a 2-D tensor X with 2 rows and 2 columns</p>
<blockquote>
<div>[[1, 2], [3, 4]]</div></blockquote>
<p>and target shape = [1, 4], the reshape operator will transform
520
the tensor X into a 2-D tensor:</p>
521
<blockquote>
522
<div>[[1, 2, 3, 4]]</div></blockquote>
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<p>One dimension in the target shape can be set -1, representing that its
size is unknown. In this case, the real dimension will be infered from
the original shape of Input(X) and other dimensions in the target shape.</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">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>x</strong> &#8211; The input tensor of reshape operator.
Duplicable: False  Optional: False</li>
<li><strong>shape</strong> (<em>INTS</em>) &#8211; (vector&lt;int&gt;) Target shape of reshape operator.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output tensor of reshape operator.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="sigmoid">
<h2>sigmoid<a class="headerlink" href="#sigmoid" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sigmoid</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Sigmoid Activation Operator</p>
<p>$$y = frac{1}{1 + e^{-x}}$$</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>x</strong> &#8211; Input of Sigmoid operator
Duplicable: False  Optional: False</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Output of Sigmoid operator</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="scale">
<h2>scale<a class="headerlink" href="#scale" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">scale</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Scale operator</p>
<p>$$Out = scale*X$$</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>x</strong> &#8211; (Tensor) Input tensor of scale operator.
Duplicable: False  Optional: False</li>
<li><strong>scale</strong> (<em>FLOAT</em>) &#8211; (float, default 0)The scaling factor of the scale operator.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">(Tensor) Output tensor of scale operator.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="id1">
<h2>reshape<a class="headerlink" href="#id1" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">reshape</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Reshape Operator.</p>
<p>Reshape Input(X) into the shape specified by Attr(shape).</p>
<p>An example:
Given a 2-D tensor X with 2 rows and 2 columns</p>
<blockquote>
<div>[[1, 2], [3, 4]]</div></blockquote>
<p>and target shape = [1, 4], the reshape operator will transform
604
the tensor X into a 2-D tensor:</p>
605
<blockquote>
606
<div>[[1, 2, 3, 4]]</div></blockquote>
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<p>One dimension in the target shape can be set -1, representing that its
size is unknown. In this case, the real dimension will be infered from
the original shape of Input(X) and other dimensions in the target shape.</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">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>x</strong> &#8211; The input tensor of reshape operator.
Duplicable: False  Optional: False</li>
<li><strong>shape</strong> (<em>INTS</em>) &#8211; (vector&lt;int&gt;) Target shape of reshape operator.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output tensor of reshape operator.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="transpose">
<h2>transpose<a class="headerlink" href="#transpose" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">transpose</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Transpose Operator.</p>
<p>The input tensor will be permuted according to the axis values given.
The op functions similar to how numpy.transpose works in python.
For example:</p>
<blockquote>
<div><p>&gt;&gt; input = numpy.arange(6).reshape((2,3))
&gt;&gt; input
array([[0, 1, 2],</p>
<blockquote>
<div>[3, 4, 5]])</div></blockquote>
<p>&gt;&gt; axis = [1, 0]
&gt;&gt; output = input.transpose(axis)
&gt;&gt; output
array([[0, 3],</p>
<blockquote>
<div><dl class="docutils">
<dt>[1, 4],</dt>
<dd>[2, 5]])</dd>
</dl>
</div></blockquote>
</div></blockquote>
<p>So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
the output tensor shape will be (N, H, W, C)</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>x</strong> &#8211; (Tensor)The input tensor, tensors with rank at most 6 are supported
Duplicable: False  Optional: False</li>
<li><strong>axis</strong> (<em>INTS</em>) &#8211; (vector&lt;int&gt;)A list of values, and the size of the list should be the same with the input tensor rank, the tensor will permute the axes according the the values given</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">(Tensor)The output tensor</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>
<div class="section" id="sigmoid-cross-entropy-with-logits">
<h2>sigmoid_cross_entropy_with_logits<a class="headerlink" href="#sigmoid-cross-entropy-with-logits" title="Permalink to this headline"></a></h2>
</div>
<div class="section" id="cast">
<h2>cast<a class="headerlink" href="#cast" title="Permalink to this headline"></a></h2>
681 682
<dl class="function">
<dt>
683
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">cast</code><span class="sig-paren">(</span><em>x</em>, <em>dtype</em><span class="sig-paren">)</span></dt>
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<dd><p>This function takes in the input with input_dtype
and casts it to the output_dtype as the output.</p>
</dd></dl>

688 689 690
</div>
<div class="section" id="concat">
<h2>concat<a class="headerlink" href="#concat" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
693
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">concat</code><span class="sig-paren">(</span><em>input</em>, <em>axis</em><span class="sig-paren">)</span></dt>
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<dd><p>This function concats the input along the axis mentioned
and returns that as the output.</p>
</dd></dl>

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</div>
<div class="section" id="sums">
<h2>sums<a class="headerlink" href="#sums" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
703
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sums</code><span class="sig-paren">(</span><em>input</em>, <em>out=None</em><span class="sig-paren">)</span></dt>
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<dd><p>This function takes in the input and performs the sum operation on it
and returns that as the output.</p>
</dd></dl>

708 709 710
</div>
<div class="section" id="linear-chain-crf">
<h2>linear_chain_crf<a class="headerlink" href="#linear-chain-crf" title="Permalink to this headline"></a></h2>
711 712
<dl class="function">
<dt>
713
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">linear_chain_crf</code><span class="sig-paren">(</span><em>input</em>, <em>label</em>, <em>param_attr=None</em><span class="sig-paren">)</span></dt>
714 715
<dd></dd></dl>

716 717 718
</div>
<div class="section" id="assign">
<h2>assign<a class="headerlink" href="#assign" title="Permalink to this headline"></a></h2>
719 720
<dl class="function">
<dt>
721
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">embedding</code><span class="sig-paren">(</span><em>input</em>, <em>size</em>, <em>is_sparse=False</em>, <em>param_attr=None</em>, <em>dtype='float32'</em><span class="sig-paren">)</span></dt>
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<dd><p>Embedding Layer.</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 last simple">
<li><strong>param_initializer</strong> &#8211; </li>
<li><strong>input</strong> &#8211; The input to the function</li>
<li><strong>size</strong> &#8211; The size of the layer</li>
<li><strong>is_sparse</strong> &#8211; A flag that decleares whether the input is sparse</li>
<li><strong>param_attr</strong> &#8211; Parameters for this layer</li>
<li><strong>dtype</strong> &#8211; The type of data : float32, float_16, int etc</li>
<li><strong>main_program</strong> &#8211; Name of the main program that calls this</li>
<li><strong>startup_program</strong> &#8211; Name of the startup program</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>This function can take in the input (which is a vector of IDs) and
performs a lookup in the lookup_table using these IDs, to result into
the embedding of each ID in the input.</p>
<p>All the input variables of this function are passed in as local variables
to the LayerHelper constructor.</p>
</dd></dl>

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</div>
<div class="section" id="split-lod-tensor">
<h2>split_lod_tensor<a class="headerlink" href="#split-lod-tensor" title="Permalink to this headline"></a></h2>
751 752
<dl class="function">
<dt>
753
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">split_lod_tensor</code><span class="sig-paren">(</span><em>input</em>, <em>mask</em>, <em>level=0</em><span class="sig-paren">)</span></dt>
754 755
<dd></dd></dl>

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</div>
<div class="section" id="merge-lod-tensor">
<h2>merge_lod_tensor<a class="headerlink" href="#merge-lod-tensor" title="Permalink to this headline"></a></h2>
759 760
<dl class="function">
<dt>
761
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">merge_lod_tensor</code><span class="sig-paren">(</span><em>in_true</em>, <em>in_false</em>, <em>x</em>, <em>mask</em>, <em>level=0</em><span class="sig-paren">)</span></dt>
762 763
<dd></dd></dl>

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</div>
<div class="section" id="cos-sim">
<h2>cos_sim<a class="headerlink" href="#cos-sim" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">cos_sim</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This function performs the cosine similarity between two tensors
X and Y and returns that as the output.</p>
</dd></dl>

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</div>
<div class="section" id="cross-entropy">
<h2>cross_entropy<a class="headerlink" href="#cross-entropy" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">cross_entropy</code><span class="sig-paren">(</span><em>input</em>, <em>label</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This function computes cross_entropy using the input and label.</p>
</dd></dl>

783 784 785
</div>
<div class="section" id="square-error-cost">
<h2>square_error_cost<a class="headerlink" href="#square-error-cost" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">square_error_cost</code><span class="sig-paren">(</span><em>input</em>, <em>label</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This functions returns the squared error cost using the input and label.
The output is appending the op to do the above.</p>
</dd></dl>

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</div>
<div class="section" id="accuracy">
<h2>accuracy<a class="headerlink" href="#accuracy" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">accuracy</code><span class="sig-paren">(</span><em>input</em>, <em>label</em>, <em>k=1</em>, <em>correct=None</em>, <em>total=None</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This function computes the accuracy using the input and label.
The output is the top_k inputs and their indices.</p>
</dd></dl>

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</div>
<div class="section" id="sequence-conv">
<h2>sequence_conv<a class="headerlink" href="#sequence-conv" title="Permalink to this headline"></a></h2>
806 807
<dl class="function">
<dt>
808
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_conv</code><span class="sig-paren">(</span><em>input</em>, <em>num_filters</em>, <em>filter_size=3</em>, <em>filter_stride=1</em>, <em>padding=None</em>, <em>bias_attr=None</em>, <em>param_attr=None</em>, <em>act=None</em><span class="sig-paren">)</span></dt>
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<dd><p>This function creates the op for sequence_conv, using the inputs and
other convolutional configurations for the filters and stride as given
in the input parameters to the function.</p>
</dd></dl>

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</div>
<div class="section" id="conv2d">
<h2>conv2d<a class="headerlink" href="#conv2d" title="Permalink to this headline"></a></h2>
817 818
<dl class="function">
<dt>
819
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">conv2d</code><span class="sig-paren">(</span><em>input</em>, <em>num_filters</em>, <em>filter_size</em>, <em>stride=None</em>, <em>padding=None</em>, <em>groups=None</em>, <em>param_attr=None</em>, <em>bias_attr=None</em>, <em>act=None</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
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<dd><p>This function creates the op for a 2-dimensional Convolution.
This is performed using the parameters of filters(size, dimensionality etc)
, stride and other configurations for a Convolution operation.
This funciton can also append an activation on top of the
conv-2d output, if mentioned in the input parameters.</p>
</dd></dl>

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</div>
<div class="section" id="sequence-pool">
<h2>sequence_pool<a class="headerlink" href="#sequence-pool" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_pool</code><span class="sig-paren">(</span><em>input</em>, <em>pool_type</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This function add the operator for sequence pooling.
This is applied on top of the input using pool_type mentioned
in the parameters.</p>
</dd></dl>

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</div>
<div class="section" id="pool2d">
<h2>pool2d<a class="headerlink" href="#pool2d" title="Permalink to this headline"></a></h2>
841 842
<dl class="function">
<dt>
843
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">pool2d</code><span class="sig-paren">(</span><em>input</em>, <em>pool_size</em>, <em>pool_type</em>, <em>pool_stride=None</em>, <em>pool_padding=None</em>, <em>global_pooling=False</em><span class="sig-paren">)</span></dt>
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<dd><p>This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters.</p>
</dd></dl>

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</div>
<div class="section" id="batch-norm">
<h2>batch_norm<a class="headerlink" href="#batch-norm" title="Permalink to this headline"></a></h2>
851 852
<dl class="function">
<dt>
853
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">batch_norm</code><span class="sig-paren">(</span><em>input</em>, <em>act=None</em>, <em>is_test=False</em>, <em>momentum=0.9</em>, <em>epsilon=1e-05</em>, <em>param_attr=None</em>, <em>bias_attr=None</em>, <em>data_layout='NCHW'</em><span class="sig-paren">)</span></dt>
854 855 856 857
<dd><p>This function helps create an operator to implement
the BatchNorm layer using the configurations from the input parameters.</p>
</dd></dl>

858 859 860
</div>
<div class="section" id="beam-search-decode">
<h2>beam_search_decode<a class="headerlink" href="#beam-search-decode" title="Permalink to this headline"></a></h2>
861 862
<dl class="function">
<dt>
863
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">beam_search_decode</code><span class="sig-paren">(</span><em>ids</em>, <em>scores</em><span class="sig-paren">)</span></dt>
864 865
<dd></dd></dl>

866 867 868
</div>
<div class="section" id="lod-rank-table">
<h2>lod_rank_table<a class="headerlink" href="#lod-rank-table" title="Permalink to this headline"></a></h2>
869 870
<dl class="function">
<dt>
871
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">lod_rank_table</code><span class="sig-paren">(</span><em>x</em>, <em>level=0</em><span class="sig-paren">)</span></dt>
872 873 874 875
<dd><p>This function creates an operator for creating a LOD_RANK_TABLE
using the input x.</p>
</dd></dl>

876 877 878
</div>
<div class="section" id="max-sequence-len">
<h2>max_sequence_len<a class="headerlink" href="#max-sequence-len" title="Permalink to this headline"></a></h2>
879 880
<dl class="function">
<dt>
881
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">max_sequence_len</code><span class="sig-paren">(</span><em>rank_table</em><span class="sig-paren">)</span></dt>
882 883 884 885
<dd><p>This function creates an operator to calculate the length of
max seqence through input rank_table(should be a lod_rank_table)</p>
</dd></dl>

886 887 888
</div>
<div class="section" id="topk">
<h2>topk<a class="headerlink" href="#topk" title="Permalink to this headline"></a></h2>
889 890
<dl class="function">
<dt>
891
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">topk</code><span class="sig-paren">(</span><em>input</em>, <em>k</em><span class="sig-paren">)</span></dt>
892 893
<dd></dd></dl>

894 895 896
</div>
<div class="section" id="lod-tensor-to-array">
<h2>lod_tensor_to_array<a class="headerlink" href="#lod-tensor-to-array" title="Permalink to this headline"></a></h2>
897 898
<dl class="function">
<dt>
899
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">lod_tensor_to_array</code><span class="sig-paren">(</span><em>x</em>, <em>table</em><span class="sig-paren">)</span></dt>
900 901 902 903
<dd><p>This function creates an operator to convert an LOD_Tensor to
an array.</p>
</dd></dl>

904 905 906
</div>
<div class="section" id="array-to-lod-tensor">
<h2>array_to_lod_tensor<a class="headerlink" href="#array-to-lod-tensor" title="Permalink to this headline"></a></h2>
907 908
<dl class="function">
<dt>
909
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">array_to_lod_tensor</code><span class="sig-paren">(</span><em>x</em>, <em>table</em><span class="sig-paren">)</span></dt>
910 911 912 913
<dd><p>This function creates an operator to convert an array to a
LOD_Tensor.</p>
</dd></dl>

914 915 916
</div>
<div class="section" id="fill-constant">
<h2>fill_constant<a class="headerlink" href="#fill-constant" title="Permalink to this headline"></a></h2>
917 918
<dl class="function">
<dt>
919
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">fill_constant</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype</em>, <em>value</em>, <em>out=None</em><span class="sig-paren">)</span></dt>
920 921 922 923 924
<dd><p>This function creates a tensor , with shape as mentioned in the input and
specified dtype and fills this up with a constant value that
comes in the input. It also sets the stop_gradient to be True.</p>
</dd></dl>

925 926 927
</div>
<div class="section" id="fill-constant-batch-size-like">
<h2>fill_constant_batch_size_like<a class="headerlink" href="#fill-constant-batch-size-like" title="Permalink to this headline"></a></h2>
928 929
<dl class="function">
<dt>
930
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">fill_constant_batch_size_like</code><span class="sig-paren">(</span><em>input</em>, <em>shape</em>, <em>dtype</em>, <em>value</em>, <em>input_dim_idx=0</em>, <em>output_dim_idx=0</em><span class="sig-paren">)</span></dt>
931 932
<dd></dd></dl>

933 934 935
</div>
<div class="section" id="ones">
<h2>ones<a class="headerlink" href="#ones" title="Permalink to this headline"></a></h2>
936 937
<dl class="function">
<dt>
938
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">ones</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype</em><span class="sig-paren">)</span></dt>
939 940 941 942
<dd><p>This function performs the same function as fill_constant() declared above
with the constant value being 1.0.</p>
</dd></dl>

943 944 945
</div>
<div class="section" id="zeros">
<h2>zeros<a class="headerlink" href="#zeros" title="Permalink to this headline"></a></h2>
946 947
<dl class="function">
<dt>
948
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">zeros</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype</em><span class="sig-paren">)</span></dt>
949 950 951 952
<dd><p>This function performs the same function as fill_constant() declared above
with the constant value being 0.0.</p>
</dd></dl>

953 954 955
</div>
<div class="section" id="increment">
<h2>increment<a class="headerlink" href="#increment" title="Permalink to this headline"></a></h2>
956 957
<dl class="function">
<dt>
958
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">increment</code><span class="sig-paren">(</span><em>x</em>, <em>value=1.0</em>, <em>in_place=True</em><span class="sig-paren">)</span></dt>
959 960 961 962 963
<dd><p>This function creates an operator to increment each value in the input
<cite>x</cite> by an amount: <cite>value</cite> as mentioned in the input parameter. This
operation is performed in-place by default.</p>
</dd></dl>

964 965 966
</div>
<div class="section" id="array-write">
<h2>array_write<a class="headerlink" href="#array-write" title="Permalink to this headline"></a></h2>
967 968
<dl class="function">
<dt>
969
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">array_write</code><span class="sig-paren">(</span><em>x</em>, <em>i</em>, <em>array=None</em><span class="sig-paren">)</span></dt>
970 971 972 973
<dd><p>This function creates an operator to write the data out as a
LOD_TENSOR_ARRAY.</p>
</dd></dl>

974 975 976
</div>
<div class="section" id="create-array">
<h2>create_array<a class="headerlink" href="#create-array" title="Permalink to this headline"></a></h2>
977 978
<dl class="function">
<dt>
979
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">create_array</code><span class="sig-paren">(</span><em>dtype</em><span class="sig-paren">)</span></dt>
980 981
<dd></dd></dl>

982 983 984
</div>
<div class="section" id="less-than">
<h2>less_than<a class="headerlink" href="#less-than" title="Permalink to this headline"></a></h2>
985 986
<dl class="function">
<dt>
987
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">less_than</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>cond=None</em>, <em>**ignored</em><span class="sig-paren">)</span></dt>
988 989
<dd></dd></dl>

990 991 992
</div>
<div class="section" id="array-read">
<h2>array_read<a class="headerlink" href="#array-read" title="Permalink to this headline"></a></h2>
993 994
<dl class="function">
<dt>
995
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">array_read</code><span class="sig-paren">(</span><em>array</em>, <em>i</em><span class="sig-paren">)</span></dt>
996 997 998 999
<dd><p>This function creates an operator to read the data in as a
LOD_TENSOR_ARRAY.</p>
</dd></dl>

1000 1001 1002
</div>
<div class="section" id="shrink-memory">
<h2>shrink_memory<a class="headerlink" href="#shrink-memory" title="Permalink to this headline"></a></h2>
1003 1004
<dl class="function">
<dt>
1005
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">shrink_memory</code><span class="sig-paren">(</span><em>x</em>, <em>i</em>, <em>table</em><span class="sig-paren">)</span></dt>
1006 1007 1008 1009
<dd><p>This function creates an operator to shrink_rnn_memory using the RankTable
as mentioned in the input parameter.</p>
</dd></dl>

1010 1011 1012
</div>
<div class="section" id="array-length">
<h2>array_length<a class="headerlink" href="#array-length" title="Permalink to this headline"></a></h2>
1013 1014
<dl class="function">
<dt>
1015
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">array_length</code><span class="sig-paren">(</span><em>array</em><span class="sig-paren">)</span></dt>
1016 1017 1018 1019
<dd><p>This function creates an operator to find the length of the
LOD_TENSOR_ARRAY.</p>
</dd></dl>

1020 1021 1022
</div>
<div class="section" id="conv2d-transpose">
<h2>conv2d_transpose<a class="headerlink" href="#conv2d-transpose" title="Permalink to this headline"></a></h2>
1023 1024
<dl class="function">
<dt>
1025
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">conv2d_transpose</code><span class="sig-paren">(</span><em>input</em>, <em>num_filters</em>, <em>output_size=None</em>, <em>filter_size=None</em>, <em>padding=None</em>, <em>stride=None</em>, <em>param_attr=None</em><span class="sig-paren">)</span></dt>
1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064
<dd><p>The transpose of conv2d layer.</p>
<p>This layer is also known as deconvolution layer.</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 image with [N, C, H, W] format.</li>
<li><strong>num_filters</strong> (<em>int</em>) &#8211; The number of filter. It is as same as the output
image channel.</li>
<li><strong>output_size</strong> (<em>int|tuple|None</em>) &#8211; The output image size. If output size is a
tuple, it must contain two integers, (image_H, image_W). This
parameter only works when filter_size is None.</li>
<li><strong>filter_size</strong> (<em>int|tuple|None</em>) &#8211; The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.  None if use output size to
calculate filter_size</li>
<li><strong>padding</strong> (<em>int|tuple</em>) &#8211; The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding.</li>
<li><strong>stride</strong> (<em>int|tuple</em>) &#8211; The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride.</li>
<li><strong>param_attr</strong> &#8211; Parameter Attribute.</li>
<li><strong>main_program</strong> (<em>Program</em>) &#8211; the main program</li>
<li><strong>startup_program</strong> (<em>Program</em>) &#8211; the startup program</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Output image.</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>
</dd></dl>

1065 1066 1067 1068 1069
</div>
<div class="section" id="sequence-expand">
<h2>sequence_expand<a class="headerlink" href="#sequence-expand" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
1070
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_expand</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span></dt>
1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135
<dd><p>Sequence Expand Layer. This layer will expand the input variable <strong>x</strong>
according to LoD information of <strong>y</strong>. And the following examples will
explain how sequence_expand works:</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>* Case 1
    x is a LoDTensor:
        x.lod = [[0,       2, 3],
                 [0, 1,    3, 4]]
        x.data = [a, b, c, d]
        x.dims = [4, 1]

    y is a LoDTensor:
        y.lod = [[0,    2,    4],
                 [0, 3, 6, 7, 8]]

    with condition len(y.lod[-1]) - 1 == x.dims[0]

    then output is a 2-level LoDTensor:
        out.lod = [[0,                2,    4],
                   [0,       3,       6, 7, 8]]
        out.data = [a, a, a, b, b, b, c, d]
        out.dims = [8, 1]

* Case 2
    x is a Tensor:
        x.data = [a, b, c]
        x.dims = [3, 1]

    y is a LoDTensor:
        y.lod = [[0, 2, 3, 6]]

    with condition len(y.lod[-1]) - 1 == x.dims[0]

    then output is a 1-level LoDTensor:
        out.lod = [[0,    2, 3,      6]]
        out.data = [a, a, b, c, c, c]
        out.dims = [6, 1]
</pre></div>
</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 simple">
<li><strong>x</strong> (<em>Variable</em>) &#8211; The input variable which is a Tensor or LoDTensor.</li>
<li><strong>y</strong> (<em>Variable</em>) &#8211; The input variable which is a LoDTensor.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The expanded variable which is a LoDTensor.</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="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">],</span>
                 <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">,</span> <span class="n">lod_level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">sequence_expand</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

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 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
</div>
<div class="section" id="lstm-unit">
<h2>lstm_unit<a class="headerlink" href="#lstm-unit" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">lstm_unit</code><span class="sig-paren">(</span><em>x_t</em>, <em>hidden_t_prev</em>, <em>cell_t_prev</em>, <em>forget_bias=0.0</em>, <em>param_attr=None</em>, <em>bias_attr=None</em><span class="sig-paren">)</span></dt>
<dd><p>Lstm unit layer. The equation of a lstm step is:</p>
<blockquote>
<div><div class="math">
\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
</div></blockquote>
<p>The inputs of lstm unit includes <span class="math">\(x_t\)</span>, <span class="math">\(h_{t-1}\)</span> and
<span class="math">\(c_{t-1}\)</span>. The implementation separates the linear transformation
and non-linear transformation apart. Here, we take <span class="math">\(i_t\)</span> as an
example. The linear transformation is applied by calling a <cite>fc</cite> layer and
the equation is:</p>
<blockquote>
<div><div class="math">
\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i\]</div>
</div></blockquote>
<p>The non-linear transformation is applied by calling <cite>lstm_unit_op</cite> and the
equation is:</p>
<blockquote>
<div><div class="math">
\[i_t = \sigma(L_{i_t})\]</div>
</div></blockquote>
<p>This layer has two outputs including <span class="math">\(h_t\)</span> and <span class="math">\(o_t\)</span>.</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>x_t</strong> (<em>Variable</em>) &#8211; The input value of current step.</li>
<li><strong>hidden_t_prev</strong> (<em>Variable</em>) &#8211; The hidden value of lstm unit.</li>
<li><strong>cell_t_prev</strong> (<em>Variable</em>) &#8211; The cell value of lstm unit.</li>
<li><strong>forget_bias</strong> (<em>float</em>) &#8211; The forget bias of lstm unit.</li>
<li><strong>param_attr</strong> (<em>ParamAttr</em>) &#8211; The attributes of parameter weights, used to set
initializer, name etc.</li>
<li><strong>bias_attr</strong> (<em>ParamAttr</em>) &#8211; The attributes of bias weights, if not False,
bias weights will be created and be set to default value.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The hidden value and cell value of lstm unit.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first">tuple</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; The ranks of <strong>x_t</strong>, <strong>hidden_t_prev</strong> and <strong>cell_t_prev</strong>                not be 2 or the 1st dimensions of <strong>x_t</strong>, <strong>hidden_t_prev</strong>                 and <strong>cell_t_prev</strong> not be the same.</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x_t</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_t_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">prev_hidden</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">prev_hidden_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">prev_cell</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">prev_cell_data</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">hidden_value</span><span class="p">,</span> <span class="n">cell_value</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">lstm_unit</span><span class="p">(</span><span class="n">x_t</span><span class="o">=</span><span class="n">x_t</span><span class="p">,</span>
                                       <span class="n">hidden_t_prev</span><span class="o">=</span><span class="n">prev_hidden</span><span class="p">,</span>
                                       <span class="n">cell_t_prev</span><span class="o">=</span><span class="n">prev_cell</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

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</div>
<div class="section" id="sequence-softmax">
<h2>sequence_softmax<a class="headerlink" href="#sequence-softmax" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_softmax</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Sequence Softmax Operator.</p>
<p>SequenceSoftmaxOp computes the softmax activation among all time-steps for each
sequence. The dimension of each time-step should be 1. Thus, the shape of
input Tensor can be either [N, 1] or [N], where N is the sum of the length
of all sequences.</p>
<dl class="docutils">
<dt>The algorithm works as follows:</dt>
<dd><dl class="first last docutils">
<dt>for i-th sequence in a mini-batch:</dt>
<dd><dl class="first last docutils">
<dt>$$Out(X[lod[i]:lod[i+1]], :) =</dt>
<dd>frac{exp(X[lod[i]:lod[i+1], :])}
{sum(exp(X[lod[i]:lod[i+1], :]))}$$</dd>
</dl>
</dd>
</dl>
</dd>
</dl>
<p>For example, for a mini-batch of 3 sequences with variable-length,
each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :]
and N turns out to be 7.</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>x</strong> &#8211; (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1.
Duplicable: False  Optional: False</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.</td>
</tr>
</tbody>
</table>
</dd></dl>

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