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f7bd8c60 · Travis CI · e77bb920 · f7bd8c60 · f7bd8c60 · f7bd8c60
5 changed file
--- a/develop/doc/api/v2/fluid/layers.html
+++ b/develop/doc/api/v2/fluid/layers.html
@@ -1265,13 +1265,12 @@ are in NCHW format. Where N is batch size, C is the number of channels, H is the
 of the feature, and W is the width of the feature.
 The details of convolution layer, please refer UFLDL&#8217;s <a class="reference external" href="http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/">convolution,</a> .
 If bias attribution and activation type are provided, bias is added to the output of the convolution,
-and the corresponding activation function is applied to the final result.
-For each input <span class="math">\(X\)</span>, the equation is:</p>
+and the corresponding activation function is applied to the final result.</p>
+<p>For each input <span class="math">\(X\)</span>, the equation is:</p>
 <div class="math">
 \[Out = \sigma (W \ast X + b)\]</div>
 <p>In the above equation:</p>
-<blockquote>
-<div><ul class="simple">
+<ul class="simple">
 <li><span class="math">\(X\)</span>: Input value, a tensor with NCHW format.</li>
 <li><span class="math">\(W\)</span>: Filter value, a tensor with MCHW format.</li>
 <li><span class="math">\(\ast\)</span>: Convolution operation.</li>
@@ -1279,20 +1278,21 @@ For each input <span class="math">\(X\)</span>, the equation is:</p>
 <li><span class="math">\(\sigma\)</span>: Activation function.</li>
 <li><span class="math">\(Out\)</span>: Output value, the shape of <span class="math">\(Out\)</span> and <span class="math">\(X\)</span> may be different.</li>
 </ul>
-</div></blockquote>
 <p class="rubric">Example</p>
-<dl class="docutils">
-<dt>Input:</dt>
-<dd><p class="first">Input shape: $(N, C_{in}, H_{in}, W_{in})$</p>
-<p class="last">Filter shape: $(C_{out}, C_{in}, H_f, W_f)$</p>
-</dd>
-<dt>Output:</dt>
-<dd>Output shape: $(N, C_{out}, H_{out}, W_{out})$</dd>
-</dl>
+<ul>
+<li><p class="first">Input:</p>
+<p>Input shape: $(N, C_{in}, H_{in}, W_{in})$</p>
+<p>Filter shape: $(C_{out}, C_{in}, H_f, W_f)$</p>
+</li>
+<li><p class="first">Output:
+Output shape: $(N, C_{out}, H_{out}, W_{out})$</p>
+</li>
+</ul>
 <p>Where</p>
 <div class="math">
-\[\begin{split}H_{out}&amp;= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\
-W_{out}&amp;= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1\end{split}\]</div>
+\[\]</div>
+<p>H_{out}&amp;= frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \
+W_{out}&amp;= frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -2067,15 +2067,45 @@ to compute the length.</td>
 <dl class="function">
 <dt>
 <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>dilation=None</em>, <em>param_attr=None</em>, <em>use_cudnn=True</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
-<dd><p>The transpose of conv2d layer.</p>
-<p>This layer is also known as deconvolution layer.</p>
+<dd><p><strong>Convlution2D transpose layer</strong></p>
+<p>The convolution2D transpose layer calculates the output based on the input,
+filter, and dilations, strides, paddings. Input(Input) and output(Output)
+are in NCHW format. Where N is batch size, C is the number of channels,
+H is the height of the feature, and W is the width of the feature.
+Parameters(dilations, strides, paddings) are two elements. These two elements
+represent height and width, respectively. The details of convolution transpose
+layer, please refer to the following explanation and references <a class="reference external" href="http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf">therein</a>.</p>
+<p>For each input <span class="math">\(X\)</span>, the equation is:</p>
+<div class="math">
+\[Out = W \ast X\]</div>
+<p>In the above equation:</p>
+<ul class="simple">
+<li><span class="math">\(X\)</span>: Input value, a tensor with NCHW format.</li>
+<li><span class="math">\(W\)</span>: Filter value, a tensor with MCHW format.</li>
+<li><span class="math">\(\ast\)</span> : Convolution transpose operation.</li>
+<li><span class="math">\(Out\)</span>: Output value, the shape of <span class="math">\(Out\)</span> and <span class="math">\(X\)</span> may be different.</li>
+</ul>
+<p class="rubric">Example</p>
+<ul>
+<li><p class="first">Input:</p>
+<p>Input shape: $(N, C_{in}, H_{in}, W_{in})$</p>
+<p>Filter shape: $(C_{in}, C_{out}, H_f, W_f)$</p>
+</li>
+<li><p class="first">Output:</p>
+<p>Output shape: $(N, C_{out}, H_{out}, W_{out})$</p>
+</li>
+</ul>
+<p>Where</p>
+<div class="math">
+\[\begin{split}H_{out} &amp;= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\
+W_{out} &amp;= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\end{split}\]</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>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
+<li><strong>num_filters</strong> (<em>int</em>) &#8211; The number of the 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
@@ -2083,17 +2113,17 @@ 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>
+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>
+padding_H = padding_W = padding. Default: padding = 0.</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>
+stride_H = stride_W = stride. Default: stride = 1.</li>
 <li><strong>dilation</strong> (<em>int|tuple</em>) &#8211; The dilation size. If dilation is a tuple, it must
 contain two integers, (dilation_H, dilation_W). Otherwise, the
-dilation_H = dilation_W = dilation.</li>
-<li><strong>param_attr</strong> &#8211; Parameter Attribute.</li>
+dilation_H = dilation_W = dilation. Default: dilation = 1.</li>
+<li><strong>param_attr</strong> (<em>ParamAttr</em>) &#8211; The parameters to the Conv2d_transpose Layer. Default: None</li>
 <li><strong>use_cudnn</strong> (<em>bool</em>) &#8211; Use cudnn kernel or not, it is valid only when the cudnn
 library is installed. Default: True</li>
 <li><strong>name</strong> (<em>str|None</em>) &#8211; A name for this layer(optional). If set None, the layer
@@ -2101,14 +2131,22 @@ will be named automatically.</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>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The tensor variable storing the convolution transpose result.</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>
+<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 the shapes of input, filter_size, stride, padding and groups mismatch.</p>
 </td>
 </tr>
 </tbody>
 </table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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;data&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</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">conv2d_transpose</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">conv2d_transpose</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
 </dd></dl>

 </div>

--- a/develop/doc/operators.json
+++ b/develop/doc/operators.json
@@ -649,7 +649,7 @@
 } ] 
 },{
 "type" : "conv3d_transpose",
- "comment" : "\nConvolution3D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the\nnumber of channels, D is the depth of the feature, H is the height of the feature,\nand W is the width of the feature.\nFilter(Input) is in MCDHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, D is the depth of the filter,H is the\nheight of the filter, and W is the width of the filter.\nParameters(strides, paddings) are three elements. These three elements represent\ndepth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:   \n  Input:\n       Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\\\\n       H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\\\\n       W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f\n  $$\n",
+ "comment" : "\nConvolution3D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the\nnumber of channels, D is the depth of the feature, H is the height of the feature,\nand W is the width of the feature.\nFilter(Input) is in MCDHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, D is the depth of the filter,H is the\nheight of the filter, and W is the width of the filter.\nParameters(strides, paddings) are three elements. These three elements represent\ndepth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:   \n  Input:\n       Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\\n       H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\\n       W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1\n  $$\n",
 "inputs" : [ 
 { 
   "name" : "Input",
@@ -1805,7 +1805,7 @@
 "attrs" : [  ] 
 },{
 "type" : "conv2d_transpose",
- "comment" : "\nConvolution2D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the\nnumber of channels, H is the height of the feature, and W is the width of the feature.\nFilter(Input) is in MCHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, H is the height of the filter,\nand W is the width of the filter.\nParameters(strides, paddings) are two elements. These two elements represent height\nand width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n  Input:\n       Input shape: $(N, C_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{in}, C_{out}, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + H_f \\\\\n       W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + W_f\n  $$\n",
+ "comment" : "\nConvolution2D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the\nnumber of channels, H is the height of the feature, and W is the width of the feature.\nFilter(Input) is in MCHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, H is the height of the filter,\nand W is the width of the filter.\nParameters(strides, paddings) are two elements. These two elements represent height\nand width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n  Input:\n       Input shape: $(N, C_{in}, H_{in}, W_{in})$\n       Filter shape: $(C_{in}, C_{out}, H_f, W_f)$\n  Output:\n       Output shape: $(N, C_{out}, H_{out}, W_{out})$\n  Where\n  $$\n       H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\\n       W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\n  $$\n",
 "inputs" : [ 
 { 
   "name" : "Input",

--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
--- a/develop/doc_cn/api/v2/fluid/layers.html
+++ b/develop/doc_cn/api/v2/fluid/layers.html
@@ -1284,13 +1284,12 @@ are in NCHW format. Where N is batch size, C is the number of channels, H is the
 of the feature, and W is the width of the feature.
 The details of convolution layer, please refer UFLDL&#8217;s <a class="reference external" href="http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/">convolution,</a> .
 If bias attribution and activation type are provided, bias is added to the output of the convolution,
-and the corresponding activation function is applied to the final result.
-For each input <span class="math">\(X\)</span>, the equation is:</p>
+and the corresponding activation function is applied to the final result.</p>
+<p>For each input <span class="math">\(X\)</span>, the equation is:</p>
 <div class="math">
 \[Out = \sigma (W \ast X + b)\]</div>
 <p>In the above equation:</p>
-<blockquote>
-<div><ul class="simple">
+<ul class="simple">
 <li><span class="math">\(X\)</span>: Input value, a tensor with NCHW format.</li>
 <li><span class="math">\(W\)</span>: Filter value, a tensor with MCHW format.</li>
 <li><span class="math">\(\ast\)</span>: Convolution operation.</li>
@@ -1298,20 +1297,21 @@ For each input <span class="math">\(X\)</span>, the equation is:</p>
 <li><span class="math">\(\sigma\)</span>: Activation function.</li>
 <li><span class="math">\(Out\)</span>: Output value, the shape of <span class="math">\(Out\)</span> and <span class="math">\(X\)</span> may be different.</li>
 </ul>
-</div></blockquote>
 <p class="rubric">Example</p>
-<dl class="docutils">
-<dt>Input:</dt>
-<dd><p class="first">Input shape: $(N, C_{in}, H_{in}, W_{in})$</p>
-<p class="last">Filter shape: $(C_{out}, C_{in}, H_f, W_f)$</p>
-</dd>
-<dt>Output:</dt>
-<dd>Output shape: $(N, C_{out}, H_{out}, W_{out})$</dd>
-</dl>
+<ul>
+<li><p class="first">Input:</p>
+<p>Input shape: $(N, C_{in}, H_{in}, W_{in})$</p>
+<p>Filter shape: $(C_{out}, C_{in}, H_f, W_f)$</p>
+</li>
+<li><p class="first">Output:
+Output shape: $(N, C_{out}, H_{out}, W_{out})$</p>
+</li>
+</ul>
 <p>Where</p>
 <div class="math">
-\[\begin{split}H_{out}&amp;= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\
-W_{out}&amp;= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1\end{split}\]</div>
+\[\]</div>
+<p>H_{out}&amp;= frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \
+W_{out}&amp;= frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -2086,15 +2086,45 @@ to compute the length.</td>
 <dl class="function">
 <dt>
 <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>dilation=None</em>, <em>param_attr=None</em>, <em>use_cudnn=True</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
-<dd><p>The transpose of conv2d layer.</p>
-<p>This layer is also known as deconvolution layer.</p>
+<dd><p><strong>Convlution2D transpose layer</strong></p>
+<p>The convolution2D transpose layer calculates the output based on the input,
+filter, and dilations, strides, paddings. Input(Input) and output(Output)
+are in NCHW format. Where N is batch size, C is the number of channels,
+H is the height of the feature, and W is the width of the feature.
+Parameters(dilations, strides, paddings) are two elements. These two elements
+represent height and width, respectively. The details of convolution transpose
+layer, please refer to the following explanation and references <a class="reference external" href="http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf">therein</a>.</p>
+<p>For each input <span class="math">\(X\)</span>, the equation is:</p>
+<div class="math">
+\[Out = W \ast X\]</div>
+<p>In the above equation:</p>
+<ul class="simple">
+<li><span class="math">\(X\)</span>: Input value, a tensor with NCHW format.</li>
+<li><span class="math">\(W\)</span>: Filter value, a tensor with MCHW format.</li>
+<li><span class="math">\(\ast\)</span> : Convolution transpose operation.</li>
+<li><span class="math">\(Out\)</span>: Output value, the shape of <span class="math">\(Out\)</span> and <span class="math">\(X\)</span> may be different.</li>
+</ul>
+<p class="rubric">Example</p>
+<ul>
+<li><p class="first">Input:</p>
+<p>Input shape: $(N, C_{in}, H_{in}, W_{in})$</p>
+<p>Filter shape: $(C_{in}, C_{out}, H_f, W_f)$</p>
+</li>
+<li><p class="first">Output:</p>
+<p>Output shape: $(N, C_{out}, H_{out}, W_{out})$</p>
+</li>
+</ul>
+<p>Where</p>
+<div class="math">
+\[\begin{split}H_{out} &amp;= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\
+W_{out} &amp;= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\end{split}\]</div>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first 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
+<li><strong>num_filters</strong> (<em>int</em>) &#8211; The number of the 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
@@ -2102,17 +2132,17 @@ 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>
+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>
+padding_H = padding_W = padding. Default: padding = 0.</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>
+stride_H = stride_W = stride. Default: stride = 1.</li>
 <li><strong>dilation</strong> (<em>int|tuple</em>) &#8211; The dilation size. If dilation is a tuple, it must
 contain two integers, (dilation_H, dilation_W). Otherwise, the
-dilation_H = dilation_W = dilation.</li>
-<li><strong>param_attr</strong> &#8211; Parameter Attribute.</li>
+dilation_H = dilation_W = dilation. Default: dilation = 1.</li>
+<li><strong>param_attr</strong> (<em>ParamAttr</em>) &#8211; The parameters to the Conv2d_transpose Layer. Default: None</li>
 <li><strong>use_cudnn</strong> (<em>bool</em>) &#8211; Use cudnn kernel or not, it is valid only when the cudnn
 library is installed. Default: True</li>
 <li><strong>name</strong> (<em>str|None</em>) &#8211; A name for this layer(optional). If set None, the layer
@@ -2120,14 +2150,22 @@ will be named automatically.</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">Output image.</p>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The tensor variable storing the convolution transpose result.</p>
 </td>
 </tr>
-<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">Variable</p>
+<tr class="field-odd field"><th class="field-name">返回类型:</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 the shapes of input, filter_size, stride, padding and groups mismatch.</p>
 </td>
 </tr>
 </tbody>
 </table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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;data&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</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">conv2d_transpose</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">conv2d_transpose</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
 </dd></dl>

 </div>

--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js