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Posted to commits@singa.apache.org by wa...@apache.org on 2019/04/21 13:56:45 UTC
svn commit: r1857911 [6/27] - in /incubator/singa/site/trunk: en/
en/_static/ en/_static/css/ en/_static/fonts/ en/_static/fonts/Lato/
en/_static/fonts/RobotoSlab/ en/_static/js/ en/community/ en/develop/
en/docs/ en/docs/model_zoo/ en/docs/model_zoo/c...
Modified: incubator/singa/site/trunk/en/docs/layer.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/layer.html?rev=1857911&r1=1857910&r2=1857911&view=diff
==============================================================================
--- incubator/singa/site/trunk/en/docs/layer.html (original)
+++ incubator/singa/site/trunk/en/docs/layer.html Sun Apr 21 13:56:42 2019
@@ -18,15 +18,21 @@
-
-
+ <script type="text/javascript" src="../_static/js/modernizr.min.js"></script>
+ <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
+ <script type="text/javascript" src="../_static/jquery.js"></script>
+ <script type="text/javascript" src="../_static/underscore.js"></script>
+ <script type="text/javascript" src="../_static/doctools.js"></script>
+ <script type="text/javascript" src="../_static/language_data.js"></script>
+
+ <script type="text/javascript" src="../_static/js/theme.js"></script>
-
+
- <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+ <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
<link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" />
@@ -44,21 +50,16 @@
}
</style>
-
-
- <script src="../_static/js/modernizr.min.js"></script>
-
</head>
<body class="wy-body-for-nav">
<div class="wy-grid-for-nav">
-
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
<div class="wy-side-scroll">
- <div class="wy-side-nav-search">
+ <div class="wy-side-nav-search" >
@@ -261,40 +262,33 @@ layer using the engine attribute.</p>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">Layer</code><span class="sig-paren">(</span><em>name</em>, <em>conf=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Layer" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Base Python layer class.</p>
-<dl class="docutils">
-<dt>Typically, the life cycle of a layer instance includes:</dt>
-<dd><ol class="first last arabic simple">
-<li>construct layer without input_sample_shapes, goto 2;
-construct layer with input_sample_shapes, goto 3;</li>
-<li>call setup to create the parameters and setup other meta fields</li>
-<li>call forward or access layer members</li>
-<li>call backward and get parameters for update</li>
+<dl class="simple">
+<dt>Typically, the life cycle of a layer instance includes:</dt><dd><ol class="arabic simple">
+<li><p>construct layer without input_sample_shapes, goto 2;
+construct layer with input_sample_shapes, goto 3;</p></li>
+<li><p>call setup to create the parameters and setup other meta fields</p></li>
+<li><p>call forward or access layer members</p></li>
+<li><p>call backward and get parameters for update</p></li>
</ol>
</dd>
</dl>
-<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>name</strong> (<em>str</em>) â layer name</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>name</strong> (<em>str</em>) â layer name</p>
+</dd>
+</dl>
<dl class="method">
<dt id="singa.layer.Layer.setup">
<code class="descname">setup</code><span class="sig-paren">(</span><em>in_shapes</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Layer.setup" title="Permalink to this definition">¶</a></dt>
<dd><p>Call the C++ setup function to create params and set some meta data.</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>in_shapes</strong> â if the layer accepts a single input Tensor, in_shapes is
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>in_shapes</strong> â if the layer accepts a single input Tensor, in_shapes is
a single tuple specifying the inpute Tensor shape; if the layer
accepts multiple input Tensor (e.g., the concatenation layer),
-in_shapes is a tuple of tuples, each for one input Tensor</td>
-</tr>
-</tbody>
-</table>
+in_shapes is a tuple of tuples, each for one input Tensor</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
@@ -307,28 +301,22 @@ in_shapes is a tuple of tuples, each for
<dt id="singa.layer.Layer.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Layer.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Layer.param_names">
<code class="descname">param_names</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Layer.param_names" title="Permalink to this definition">¶</a></dt>
-<dd><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">Returns:</th><td class="field-body">a list of strings, one for the name of one parameter Tensor</td>
-</tr>
-</tbody>
-</table>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a list of strings, one for the name of one parameter Tensor</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
@@ -338,76 +326,62 @@ has multiple outputs</td>
<p>Parameter tensors are not stored as layer members because cpp Tensor
could be moved onto diff devices due to the change of layer device,
which would result in inconsistency.</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">Returns:</th><td class="field-body">a list of tensors, one for each paramter</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a list of tensors, one for each paramter</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Layer.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Layer.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Forward propagate through this 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>flag</strong> â True (kTrain) for training (kEval); False for evaluating;
-other values for furture use.</li>
-<li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>list<Tensor></em>) â an input tensor if the layer is
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> â True (kTrain) for training (kEval); False for evaluating;
+other values for furture use.</p></li>
+<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>list<Tensor></em>) â an input tensor if the layer is
connected from a single layer; a list of tensors if the layer
-is connected from multiple layers.</li>
+is connected from multiple layers.</p></li>
</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a tensor if the layer is connected to a single layer; a list of
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a tensor if the layer is connected to a single layer; a list of
tensors if the layer is connected to multiple layers;</p>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Layer.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>flag</em>, <em>dy</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Layer.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Backward propagate gradients through this 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>flag</strong> (<em>int</em>) â for future use.</li>
-<li><strong>dy</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>list<Tensor></em>) â the gradient tensor(s) y w.r.t the
-objective loss</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><dx, <dp1, dp2..>>, dx is a (set of) tensor(s) for the gradient of x
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> (<em>int</em>) â for future use.</p></li>
+<li><p><strong>dy</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>list<Tensor></em>) â the gradient tensor(s) y w.r.t the
+objective loss</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><dx, <dp1, dp2..>>, dx is a (set of) tensor(s) for the gradient of x
, dpi is the gradient of the i-th parameter</p>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Layer.to_device">
<code class="descname">to_device</code><span class="sig-paren">(</span><em>device</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Layer.to_device" title="Permalink to this definition">¶</a></dt>
<dd><p>Move layer state tensors onto the given device.</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>device</strong> â swig converted device, created using singa.device</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>device</strong> â swig converted device, created using singa.device</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
@@ -427,32 +401,26 @@ objective loss</li>
<dt id="singa.layer.Dummy.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Dummy.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Dummy.setup">
<code class="descname">setup</code><span class="sig-paren">(</span><em>input_sample_shape</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Dummy.setup" title="Permalink to this definition">¶</a></dt>
<dd><p>Call the C++ setup function to create params and set some meta data.</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>in_shapes</strong> â if the layer accepts a single input Tensor, in_shapes is
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>in_shapes</strong> â if the layer accepts a single input Tensor, in_shapes is
a single tuple specifying the inpute Tensor shape; if the layer
accepts multiple input Tensor (e.g., the concatenation layer),
-in_shapes is a tuple of tuples, each for one input Tensor</td>
-</tr>
-</tbody>
-</table>
+in_shapes is a tuple of tuples, each for one input Tensor</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
@@ -474,26 +442,24 @@ in_shapes is a tuple of tuples, each for
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">Conv2D</code><span class="sig-paren">(</span><em>name</em>, <em>nb_kernels</em>, <em>kernel=3</em>, <em>stride=1</em>, <em>border_mode='same'</em>, <em>cudnn_prefer='fastest'</em>, <em>workspace_byte_limit=1024</em>, <em>data_format='NCHW'</em>, <em>use_bias=True</em>, <em>W_specs=None</em>, <em>b_specs=None</em>, <em>pad=None</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Conv2D" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Construct a layer for 2D convolution.</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>nb_kernels</strong> (<em>int</em>) â num of the channels (kernels) of the input Tensor</li>
-<li><strong>kernel</strong> â an integer or a pair of integers for kernel height and width</li>
-<li><strong>stride</strong> â an integer or a pair of integers for stride height and width</li>
-<li><strong>border_mode</strong> (<em>string</em>) â padding mode, case in-sensitive,
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_kernels</strong> (<em>int</em>) â num of the channels (kernels) of the input Tensor</p></li>
+<li><p><strong>kernel</strong> â an integer or a pair of integers for kernel height and width</p></li>
+<li><p><strong>stride</strong> â an integer or a pair of integers for stride height and width</p></li>
+<li><p><strong>border_mode</strong> (<em>string</em>) â padding mode, case in-sensitive,
âvalidâ -> padding is 0 for height and width
âsameâ -> padding is half of the kernel (floor), the kernel must be
-odd number.</li>
-<li><strong>cudnn_prefer</strong> (<em>string</em>) â the preferred algorithm for cudnn convolution
+odd number.</p></li>
+<li><p><strong>cudnn_prefer</strong> (<em>string</em>) â the preferred algorithm for cudnn convolution
which could be âfastestâ, âautotuneâ, âlimited_workspaceâ and
-âno_workspaceâ</li>
-<li><strong>workspace_byte_limit</strong> (<em>int</em>) â max workspace size in MB (default is 512MB)</li>
-<li><strong>data_format</strong> (<em>string</em>) â either âNCHWâ or âNHWCâ</li>
-<li><strong>use_bias</strong> (<em>bool</em>) â True or False</li>
-<li><strong>pad</strong> â an integer or a pair of integers for padding height and width</li>
-<li><strong>W_specs</strong> (<em>dict</em>) â used to specify the weight matrix specs, fields
+âno_workspaceâ</p></li>
+<li><p><strong>workspace_byte_limit</strong> (<em>int</em>) â max workspace size in MB (default is 512MB)</p></li>
+<li><p><strong>data_format</strong> (<em>string</em>) â either âNCHWâ or âNHWCâ</p></li>
+<li><p><strong>use_bias</strong> (<em>bool</em>) â True or False</p></li>
+<li><p><strong>pad</strong> â an integer or a pair of integers for padding height and width</p></li>
+<li><p><strong>W_specs</strong> (<em>dict</em>) â used to specify the weight matrix specs, fields
include,
ânameâ for parameter name
âlr_multâ for learning rate multiplier
@@ -502,30 +468,25 @@ include,
âxavierâ and ââ
âstdâ, âmeanâ, âhighâ, âlowâ for corresponding init methods
TODO(wangwei) âclampâ for gradient constraint, value is scalar
-âregularizerâ for regularization, currently support âl2â</li>
-<li><strong>b_specs</strong> (<em>dict</em>) â hyper-parameters for bias vector, similar as W_specs</li>
-<li><strong>name</strong> (<em>string</em>) â layer name.</li>
-<li><strong>input_sample_shape</strong> â 3d tuple for the shape of the input Tensor
+âregularizerâ for regularization, currently support âl2â</p></li>
+<li><p><strong>b_specs</strong> (<em>dict</em>) â hyper-parameters for bias vector, similar as W_specs</p></li>
+<li><p><strong>name</strong> (<em>string</em>) â layer name.</p></li>
+<li><p><strong>input_sample_shape</strong> â 3d tuple for the shape of the input Tensor
without the batchsize, e.g., (channel, height, width) or
-(height, width, channel)</li>
+(height, width, channel)</p></li>
</ul>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
<dl class="method">
<dt id="singa.layer.Conv2D.setup">
<code class="descname">setup</code><span class="sig-paren">(</span><em>in_shape</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Conv2D.setup" title="Permalink to this definition">¶</a></dt>
<dd><p>Set up the kernel, stride and padding; then call the C++ setup
function to create params and set some meta data.</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>is a tuple of int for the input sample shape</strong> (<em>in_shapes</em>) â </td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>is a tuple of int for the input sample shape</strong> (<em>in_shapes</em>) â </p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -543,15 +504,12 @@ length</p>
<dt id="singa.layer.Conv1D.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Conv1D.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -562,28 +520,22 @@ has multiple outputs</td>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>2D pooling layer providing max/avg pooling.</p>
<p>All args are the same as those for Conv2D, except the following one</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>mode</strong> â pooling type, model_pb2.PoolingConf.MAX or
-model_pb2.PoolingConf.AVE</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>mode</strong> â pooling type, model_pb2.PoolingConf.MAX or
+model_pb2.PoolingConf.AVE</p>
+</dd>
+</dl>
<dl class="method">
<dt id="singa.layer.Pooling2D.setup">
<code class="descname">setup</code><span class="sig-paren">(</span><em>in_shape</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Pooling2D.setup" title="Permalink to this definition">¶</a></dt>
<dd><p>Set up the kernel, stride and padding; then call the C++ setup
function to create params and set some meta data.</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>is a tuple of int for the input sample shape</strong> (<em>in_shapes</em>) â </td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>is a tuple of int for the input sample shape</strong> (<em>in_shapes</em>) â </p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -608,15 +560,12 @@ function to create params and set some m
<dt id="singa.layer.MaxPooling1D.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.MaxPooling1D.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -629,15 +578,12 @@ has multiple outputs</td>
<dt id="singa.layer.AvgPooling1D.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.AvgPooling1D.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -647,13 +593,11 @@ has multiple outputs</td>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">BatchNormalization</code><span class="sig-paren">(</span><em>name</em>, <em>momentum=0.9</em>, <em>beta_specs=None</em>, <em>gamma_specs=None</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.BatchNormalization" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Batch-normalization.</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>momentum</strong> (<em>float</em>) â for running average mean and variance.</li>
-<li><strong>beta_specs</strong> (<em>dict</em>) â dictionary includes the fields for the beta
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>momentum</strong> (<em>float</em>) â for running average mean and variance.</p></li>
+<li><p><strong>beta_specs</strong> (<em>dict</em>) â dictionary includes the fields for the beta
param:
ânameâ for parameter name
âlr_multâ for learning rate multiplier
@@ -662,15 +606,13 @@ param:
âxavierâ and ââ
âstdâ, âmeanâ, âhighâ, âlowâ for corresponding init methods
âclampâ for gradient constraint, value is scalar
-âregularizerâ for regularization, currently support âl2â</li>
-<li><strong>gamma_specs</strong> (<em>dict</em>) â similar to beta_specs, but for the gamma param.</li>
-<li><strong>name</strong> (<em>string</em>) â layer name</li>
-<li><strong>input_sample_shape</strong> (<em>tuple</em>) â with at least one integer</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+âregularizerâ for regularization, currently support âl2â</p></li>
+<li><p><strong>gamma_specs</strong> (<em>dict</em>) â similar to beta_specs, but for the gamma param.</p></li>
+<li><p><strong>name</strong> (<em>string</em>) â layer name</p></li>
+<li><p><strong>input_sample_shape</strong> (<em>tuple</em>) â with at least one integer</p></li>
+</ul>
+</dd>
+</dl>
</dd></dl>
<dl class="class">
@@ -682,63 +624,52 @@ param:
<dt id="singa.layer.L2Norm.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.L2Norm.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.L2Norm.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>is_train</em>, <em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.L2Norm.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Forward propagate through this 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>flag</strong> â True (kTrain) for training (kEval); False for evaluating;
-other values for furture use.</li>
-<li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>list<Tensor></em>) â an input tensor if the layer is
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> â True (kTrain) for training (kEval); False for evaluating;
+other values for furture use.</p></li>
+<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>list<Tensor></em>) â an input tensor if the layer is
connected from a single layer; a list of tensors if the layer
-is connected from multiple layers.</li>
+is connected from multiple layers.</p></li>
</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a tensor if the layer is connected to a single layer; a list of
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a tensor if the layer is connected to a single layer; a list of
tensors if the layer is connected to multiple layers;</p>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.L2Norm.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>is_train</em>, <em>dy</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.L2Norm.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Backward propagate gradients through this 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>flag</strong> (<em>int</em>) â for future use.</li>
-<li><strong>dy</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>list<Tensor></em>) â the gradient tensor(s) y w.r.t the
-objective loss</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><dx, <dp1, dp2..>>, dx is a (set of) tensor(s) for the gradient of x
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> (<em>int</em>) â for future use.</p></li>
+<li><p><strong>dy</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>list<Tensor></em>) â the gradient tensor(s) y w.r.t the
+objective loss</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><dx, <dp1, dp2..>>, dx is a (set of) tensor(s) for the gradient of x
, dpi is the gradient of the i-th parameter</p>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -748,20 +679,16 @@ objective loss</li>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">LRN</code><span class="sig-paren">(</span><em>name</em>, <em>size=5</em>, <em>alpha=1</em>, <em>beta=0.75</em>, <em>mode='cross_channel'</em>, <em>k=1</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.LRN" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Local response normalization.</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>size</strong> (<em>int</em>) â # of channels to be crossed
-normalization.</li>
-<li><strong>mode</strong> (<em>string</em>) â âcross_channelâ</li>
-<li><strong>input_sample_shape</strong> (<em>tuple</em>) â 3d tuple, (channel, height, width)</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>size</strong> (<em>int</em>) â # of channels to be crossed
+normalization.</p></li>
+<li><p><strong>mode</strong> (<em>string</em>) â âcross_channelâ</p></li>
+<li><p><strong>input_sample_shape</strong> (<em>tuple</em>) â 3d tuple, (channel, height, width)</p></li>
+</ul>
+</dd>
+</dl>
</dd></dl>
<dl class="class">
@@ -770,14 +697,12 @@ normalization.</li>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Apply linear/affine transformation, also called inner-product or
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>num_output</strong> (<em>int</em>) â output feature length.</li>
-<li><strong>use_bias</strong> (<em>bool</em>) â add a bias vector or not to the transformed feature</li>
-<li><strong>W_specs</strong> (<em>dict</em>) â specs for the weight matrix
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_output</strong> (<em>int</em>) â output feature length.</p></li>
+<li><p><strong>use_bias</strong> (<em>bool</em>) â add a bias vector or not to the transformed feature</p></li>
+<li><p><strong>W_specs</strong> (<em>dict</em>) â specs for the weight matrix
ânameâ for parameter name
âlr_multâ for learning rate multiplier
âdecay_multâ for weight decay multiplier
@@ -785,15 +710,13 @@ fully connected layer.</p>
âxavierâ and ââ
âstdâ, âmeanâ, âhighâ, âlowâ for corresponding init methods
âclampâ for gradient constraint, value is scalar
-âregularizerâ for regularization, currently support âl2â</li>
-<li><strong>b_specs</strong> (<em>dict</em>) â specs for the bias vector, same fields as W_specs.</li>
-<li><strong>W_transpose</strong> (<em>bool</em>) â if true, output=x*W.T+b;</li>
-<li><strong>input_sample_shape</strong> (<em>tuple</em>) â input feature length</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+âregularizerâ for regularization, currently support âl2â</p></li>
+<li><p><strong>b_specs</strong> (<em>dict</em>) â specs for the bias vector, same fields as W_specs.</p></li>
+<li><p><strong>W_transpose</strong> (<em>bool</em>) â if true, output=x*W.T+b;</p></li>
+<li><p><strong>input_sample_shape</strong> (<em>tuple</em>) â input feature length</p></li>
+</ul>
+</dd>
+</dl>
</dd></dl>
<dl class="class">
@@ -801,18 +724,14 @@ fully connected layer.</p>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">Dropout</code><span class="sig-paren">(</span><em>name</em>, <em>p=0.5</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Dropout" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Droput 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>p</strong> (<em>float</em>) â probability for dropping out the element, i.e., set to 0</li>
-<li><strong>name</strong> (<em>string</em>) â layer name</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>p</strong> (<em>float</em>) â probability for dropping out the element, i.e., set to 0</p></li>
+<li><p><strong>name</strong> (<em>string</em>) â layer name</p></li>
+</ul>
+</dd>
+</dl>
</dd></dl>
<dl class="class">
@@ -820,19 +739,15 @@ fully connected layer.</p>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">Activation</code><span class="sig-paren">(</span><em>name</em>, <em>mode='relu'</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Activation" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Activation layers.</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> (<em>string</em>) â layer name</li>
-<li><strong>mode</strong> (<em>string</em>) â âreluâ, âsigmoidâ, or âtanhâ</li>
-<li><strong>input_sample_shape</strong> (<em>tuple</em>) â shape of a single sample</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> (<em>string</em>) â layer name</p></li>
+<li><p><strong>mode</strong> (<em>string</em>) â âreluâ, âsigmoidâ, or âtanhâ</p></li>
+<li><p><strong>input_sample_shape</strong> (<em>tuple</em>) â shape of a single sample</p></li>
+</ul>
+</dd>
+</dl>
</dd></dl>
<dl class="class">
@@ -840,19 +755,15 @@ fully connected layer.</p>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">Softmax</code><span class="sig-paren">(</span><em>name</em>, <em>axis=1</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Softmax" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Apply softmax.</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>axis</strong> (<em>int</em>) â reshape the input as a matrix with the dimension
-[0,axis) as the row, the [axis, -1) as the column.</li>
-<li><strong>input_sample_shape</strong> (<em>tuple</em>) â shape of a single sample</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>int</em>) â reshape the input as a matrix with the dimension
+[0,axis) as the row, the [axis, -1) as the column.</p></li>
+<li><p><strong>input_sample_shape</strong> (<em>tuple</em>) â shape of a single sample</p></li>
+</ul>
+</dd>
+</dl>
</dd></dl>
<dl class="class">
@@ -860,19 +771,15 @@ fully connected layer.</p>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">Flatten</code><span class="sig-paren">(</span><em>name</em>, <em>axis=1</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Flatten" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Reshape the input tensor into a matrix.</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>axis</strong> (<em>int</em>) â reshape the input as a matrix with the dimension
-[0,axis) as the row, the [axis, -1) as the column.</li>
-<li><strong>input_sample_shape</strong> (<em>tuple</em>) â shape for a single sample</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>int</em>) â reshape the input as a matrix with the dimension
+[0,axis) as the row, the [axis, -1) as the column.</p></li>
+<li><p><strong>input_sample_shape</strong> (<em>tuple</em>) â shape for a single sample</p></li>
+</ul>
+</dd>
+</dl>
</dd></dl>
<dl class="class">
@@ -880,45 +787,36 @@ fully connected layer.</p>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">Merge</code><span class="sig-paren">(</span><em>name</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Merge" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Sum all input tensors.</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>input_sample_shape</strong> â sample shape of the input. The sample shape of all
-inputs should be the same.</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>input_sample_shape</strong> â sample shape of the input. The sample shape of all
+inputs should be the same.</p>
+</dd>
+</dl>
<dl class="method">
<dt id="singa.layer.Merge.setup">
<code class="descname">setup</code><span class="sig-paren">(</span><em>in_shape</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Merge.setup" title="Permalink to this definition">¶</a></dt>
<dd><p>Call the C++ setup function to create params and set some meta data.</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>in_shapes</strong> â if the layer accepts a single input Tensor, in_shapes is
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>in_shapes</strong> â if the layer accepts a single input Tensor, in_shapes is
a single tuple specifying the inpute Tensor shape; if the layer
accepts multiple input Tensor (e.g., the concatenation layer),
-in_shapes is a tuple of tuples, each for one input Tensor</td>
-</tr>
-</tbody>
-</table>
+in_shapes is a tuple of tuples, each for one input Tensor</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Merge.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Merge.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
@@ -929,30 +827,25 @@ has multiple outputs</td>
:param flag: not used.
:param inputs: a list of tensors
:type inputs: list</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">Returns:</th><td class="field-body">A single tensor as the sum of all input tensors</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>A single tensor as the sum of all input tensors</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Merge.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>flag</em>, <em>grad</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Merge.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Replicate the grad for each input source 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"><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">A list of replicated grad, one per source layer</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A list of replicated grad, one per source layer</p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -962,86 +855,70 @@ has multiple outputs</td>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">Split</code><span class="sig-paren">(</span><em>name</em>, <em>num_output</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Split" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Replicate the input tensor.</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>num_output</strong> (<em>int</em>) â number of output tensors to generate.</li>
-<li><strong>input_sample_shape</strong> â includes a single integer for the input sample
-feature size.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_output</strong> (<em>int</em>) â number of output tensors to generate.</p></li>
+<li><p><strong>input_sample_shape</strong> â includes a single integer for the input sample
+feature size.</p></li>
+</ul>
+</dd>
+</dl>
<dl class="method">
<dt id="singa.layer.Split.setup">
<code class="descname">setup</code><span class="sig-paren">(</span><em>in_shape</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Split.setup" title="Permalink to this definition">¶</a></dt>
<dd><p>Call the C++ setup function to create params and set some meta data.</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>in_shapes</strong> â if the layer accepts a single input Tensor, in_shapes is
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>in_shapes</strong> â if the layer accepts a single input Tensor, in_shapes is
a single tuple specifying the inpute Tensor shape; if the layer
accepts multiple input Tensor (e.g., the concatenation layer),
-in_shapes is a tuple of tuples, each for one input Tensor</td>
-</tr>
-</tbody>
-</table>
+in_shapes is a tuple of tuples, each for one input Tensor</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Split.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Split.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Split.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>input</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Split.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Replicate the input tensor into mutiple tensors.</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>flag</strong> â not used</li>
-<li><strong>input</strong> â a single input tensor</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list a output tensor (each one is a copy of the input)</p>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> â not used</p></li>
+<li><p><strong>input</strong> â a single input tensor</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list a output tensor (each one is a copy of the input)</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Split.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>flag</em>, <em>grads</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Split.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Sum all grad tensors to generate a single output tensor.</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>grads</strong> (<em>list of Tensor</em>) â </td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a single tensor as the sum of all grads</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>grads</strong> (<em>list of Tensor</em>) â </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a single tensor as the sum of all grads</p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -1052,63 +929,50 @@ has multiple outputs</td>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Concatenate tensors vertically (axis = 0) or horizontally (axis = 1).</p>
<p>Currently, only support tensors with 2 dimensions.</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>axis</strong> (<em>int</em>) â 0 for concat row; 1 for concat columns;</li>
-<li><strong>input_sample_shapes</strong> â a list of sample shape tuples, one per input tensor</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>int</em>) â 0 for concat row; 1 for concat columns;</p></li>
+<li><p><strong>input_sample_shapes</strong> â a list of sample shape tuples, one per input tensor</p></li>
+</ul>
+</dd>
+</dl>
<dl class="method">
<dt id="singa.layer.Concat.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Concat.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Concatenate all input tensors.</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>flag</strong> â same as Layer::forward()</li>
-<li><strong>input</strong> â a list of tensors</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a single concatenated tensor</p>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> â same as Layer::forward()</p></li>
+<li><p><strong>input</strong> â a list of tensors</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a single concatenated tensor</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Concat.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>flag</em>, <em>dy</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Concat.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Backward propagate gradients through this 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>flag</strong> â same as Layer::backward()</li>
-<li><strong>dy</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the gradient tensors of y w.r.t objective loss</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><dl class="docutils">
-<dt><dx, []>, dx is a list tensors for the gradient of the inputs; []</dt>
-<dd><p class="first last">is an empty list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> â same as Layer::backward()</p></li>
+<li><p><strong>dy</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the gradient tensors of y w.r.t objective loss</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><dl class="simple">
+<dt><dx, []>, dx is a list tensors for the gradient of the inputs; []</dt><dd><p>is an empty list.</p>
</dd>
</dl>
</p>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -1119,80 +983,64 @@ has multiple outputs</td>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Slice the input tensor into multiple sub-tensors vertially (axis=0) or
horizontally (axis=1).</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>axis</strong> (<em>int</em>) â 0 for slice rows; 1 for slice columns;</li>
-<li><strong>slice_point</strong> (<em>list</em>) â positions along the axis to do slice; there are n-1
-points for n sub-tensors;</li>
-<li><strong>input_sample_shape</strong> â input tensor sample shape</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>int</em>) â 0 for slice rows; 1 for slice columns;</p></li>
+<li><p><strong>slice_point</strong> (<em>list</em>) â positions along the axis to do slice; there are n-1
+points for n sub-tensors;</p></li>
+<li><p><strong>input_sample_shape</strong> â input tensor sample shape</p></li>
+</ul>
+</dd>
+</dl>
<dl class="method">
<dt id="singa.layer.Slice.get_output_sample_shape">
<code class="descname">get_output_sample_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Slice.get_output_sample_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Called after setup to get the shape of the output sample(s).</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">Returns:</th><td class="field-body">a tuple for a single output Tensor or a list of tuples if this layer
-has multiple outputs</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a tuple for a single output Tensor or a list of tuples if this layer
+has multiple outputs</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Slice.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Slice.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Slice the input tensor on the given axis.</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>flag</strong> â same as Layer::forward()</li>
-<li><strong>x</strong> â a single input tensor</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list a output tensor</p>
-</td>
-</tr>
-</tbody>
-</table>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> â same as Layer::forward()</p></li>
+<li><p><strong>x</strong> â a single input tensor</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a list a output tensor</p>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.Slice.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>flag</em>, <em>grads</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.Slice.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Concate all grad tensors to generate a single output tensor</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>flag</strong> â same as Layer::backward()</li>
-<li><strong>grads</strong> â a list of tensors, one for the gradient of one sliced tensor</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><dl class="docutils">
-<dt>a single tensor for the gradient of the original user, and an empty</dt>
-<dd><p class="first last">list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> â same as Layer::backward()</p></li>
+<li><p><strong>grads</strong> â a list of tensors, one for the gradient of one sliced tensor</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><dl class="simple">
+<dt>a single tensor for the gradient of the original user, and an empty</dt><dd><p>list.</p>
</dd>
</dl>
</p>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -1202,99 +1050,85 @@ has multiple outputs</td>
<em class="property">class </em><code class="descclassname">singa.layer.</code><code class="descname">RNN</code><span class="sig-paren">(</span><em>name</em>, <em>hidden_size</em>, <em>rnn_mode='lstm'</em>, <em>dropout=0.0</em>, <em>num_stacks=1</em>, <em>input_mode='linear'</em>, <em>bidirectional=False</em>, <em>param_specs=None</em>, <em>input_sample_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.RNN" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#singa.layer.Layer" title="singa.layer.Layer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></a></p>
<p>Recurrent layer with 4 types of units, namely lstm, gru, tanh and relu.</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>hidden_size</strong> â hidden feature size, the same for all stacks of layers.</li>
-<li><strong>rnn_mode</strong> â decides the rnn unit, which could be one of âlstmâ, âgruâ,
-âtanhâ and âreluâ, refer to cudnn manual for each mode.</li>
-<li><strong>num_stacks</strong> â num of stacks of rnn layers. It is different to the
-unrolling seqence length.</li>
-<li><strong>input_mode</strong> â âlinearâ convert the input feature x by by a linear
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>hidden_size</strong> â hidden feature size, the same for all stacks of layers.</p></li>
+<li><p><strong>rnn_mode</strong> â decides the rnn unit, which could be one of âlstmâ, âgruâ,
+âtanhâ and âreluâ, refer to cudnn manual for each mode.</p></li>
+<li><p><strong>num_stacks</strong> â num of stacks of rnn layers. It is different to the
+unrolling seqence length.</p></li>
+<li><p><strong>input_mode</strong> â âlinearâ convert the input feature x by by a linear
transformation to get a feature vector of size hidden_size;
âskipâ does nothing but requires the input feature size equals
-hidden_size</li>
-<li><strong>bidirection</strong> â True for bidirectional RNN</li>
-<li><strong>param_specs</strong> â config for initializing the RNN parameters.</li>
-<li><strong>input_sample_shape</strong> â includes a single integer for the input sample
-feature size.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+hidden_size</p></li>
+<li><p><strong>bidirection</strong> â True for bidirectional RNN</p></li>
+<li><p><strong>param_specs</strong> â config for initializing the RNN parameters.</p></li>
+<li><p><strong>input_sample_shape</strong> â includes a single integer for the input sample
+feature size.</p></li>
+</ul>
+</dd>
+</dl>
<dl class="method">
<dt id="singa.layer.RNN.forward">
<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.RNN.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Forward inputs through the RNN.</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>flag</strong> â True(kTrain) for training; False(kEval) for evaluation;
-others values for future use.</li>
-<li><strong><x1</strong><strong>, </strong><strong>x2</strong><strong>,</strong><strong>..xn</strong><strong>, </strong><strong>hx</strong><strong>, </strong><strong>cx></strong><strong>, </strong><strong>where xi is the input tensor for the</strong> (<em>inputs</em><em>,</em>) â i-th position, its shape is (batch_size, input_feature_length);
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flag</strong> â True(kTrain) for training; False(kEval) for evaluation;
+others values for future use.</p></li>
+<li><p><strong><x1</strong><strong>, </strong><strong>x2</strong><strong>,</strong><strong>..xn</strong><strong>, </strong><strong>hx</strong><strong>, </strong><strong>cx></strong><strong>, </strong><strong>where xi is the input tensor for the</strong> (<em>inputs</em><em>,</em>) â i-th position, its shape is (batch_size, input_feature_length);
the batch_size of xi must >= that of xi+1; hx is the initial
hidden state of shape (num_stacks * bidirection?2:1, batch_size,
hidden_size). cx is the initial cell state tensor of the same
shape as hy. cx is valid for only lstm. For other RNNs there is
no cx. Both hx and cx could be dummy tensors without shape and
-data.</li>
+data.</p></li>
</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><dl class="docutils">
-<dt><y1, y2, ⦠yn, hy, cy>, where yi is the output tensor for the i-th</dt>
-<dd><p class="first last">position, its shape is (batch_size,
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><dl class="simple">
+<dt><y1, y2, ⦠yn, hy, cy>, where yi is the output tensor for the i-th</dt><dd><p>position, its shape is (batch_size,
hidden_size * bidirection?2:1). hy is the final hidden state
tensor. cx is the final cell state tensor. cx is only used for
lstm.</p>
</dd>
</dl>
</p>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
</dd></dl>
<dl class="method">
<dt id="singa.layer.RNN.backward">
<code class="descname">backward</code><span class="sig-paren">(</span><em>flag</em>, <em>grad</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.layer.RNN.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Backward gradients through the RNN.</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>for future use.</strong> (<em>flag</em><em>,</em>) â </li>
-<li><strong><dy1</strong><strong>, </strong><strong>dy2</strong><strong>,</strong><strong>..dyn</strong><strong>, </strong><strong>dhy</strong><strong>, </strong><strong>dcy></strong><strong>, </strong><strong>where dyi is the gradient for the</strong> (<em>grad</em><em>,</em>) â </li>
-<li><strong>output</strong><strong>, </strong><strong>its shape is</strong><strong> (</strong><strong>batch_size</strong><strong>, </strong><strong>hidden_size*bidirection?2</strong> (<em>i-th</em>) â 1);
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>for future use.</strong> (<em>flag</em><em>,</em>) â </p></li>
+<li><p><strong><dy1</strong><strong>, </strong><strong>dy2</strong><strong>,</strong><strong>..dyn</strong><strong>, </strong><strong>dhy</strong><strong>, </strong><strong>dcy></strong><strong>, </strong><strong>where dyi is the gradient for the</strong> (<em>grad</em><em>,</em>) â </p></li>
+<li><p><strong>output</strong><strong>, </strong><strong>its shape is</strong><strong> (</strong><strong>batch_size</strong><strong>, </strong><strong>hidden_size*bidirection?2</strong> (<em>i-th</em>) â 1);
dhy is the gradient for the final hidden state, its shape is
(num_stacks * bidirection?2:1, batch_size,
hidden_size). dcy is the gradient for the final cell state.
cx is valid only for lstm. For other RNNs there is
no cx. Both dhy and dcy could be dummy tensors without shape and
-data.</li>
+data.</p></li>
</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><dl class="docutils">
-<dt><dx1, dx2, ⦠dxn, dhx, dcx>, where dxi is the gradient tensor for</dt>
-<dd><p class="first last">the i-th input, its shape is (batch_size,
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><dl class="simple">
+<dt><dx1, dx2, ⦠dxn, dhx, dcx>, where dxi is the gradient tensor for</dt><dd><p>the i-th input, its shape is (batch_size,
input_feature_length). dhx is the gradient for the initial
hidden state. dcx is the gradient for the initial cell state,
which is valid only for lstm.</p>
</dd>
</dl>
</p>
-</td>
-</tr>
-</tbody>
-</table>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
@@ -1335,7 +1169,7 @@ supported layers</p>
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@@ -1344,7 +1178,7 @@ supported layers</p>
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@@ -1361,36 +1195,17 @@ supported layers</p>
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