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Modified: incubator/singa/site/trunk/en/docs/neural-net.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/neural-net.html?rev=1831195&r1=1831194&r2=1831195&view=diff
==============================================================================
--- incubator/singa/site/trunk/en/docs/neural-net.html (original)
+++ incubator/singa/site/trunk/en/docs/neural-net.html Tue May 8 15:05:16 2018
@@ -14,27 +14,22 @@
-
-
-
-
- <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-
+
- <link rel="index" title="Index"
- href="../genindex.html"/>
- <link rel="search" title="Search" href="../search.html"/>
- <link rel="top" title="incubator-singa 1.1.0 documentation" href="../index.html"/>
+ <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" />
<link href="../_static/style.css" rel="stylesheet" type="text/css">
@@ -43,8 +38,9 @@
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-<body class="wy-body-for-nav" role="document">
+<body class="wy-body-for-nav">
+
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@@ -59,7 +55,7 @@
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+ <img src="../_static/singa.png" class="logo" alt="Logo"/>
</a>
@@ -88,7 +84,10 @@
- <ul>
+
+
+
+ <ul>
<li class="toctree-l1"><a class="reference internal" href="../downloads.html">Download SINGA</a></li>
<li class="toctree-l1"><a class="reference internal" href="index.html">Documentation</a></li>
</ul>
@@ -116,32 +115,52 @@
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
- <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
- <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+ <nav class="wy-nav-top" aria-label="top navigation">
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+ <a href="../index.html">incubator-singa</a>
+
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-
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+
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+
<ul class="wy-breadcrumbs">
- <li><a href="../index.html">Docs</a> »</li>
-
- <li>Neural Net</li>
+
+ <li><a href="../index.html">Docs</a> »</li>
+
+ <li>Neural Net</li>
+
+
<li class="wy-breadcrumbs-aside">
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
@@ -149,17 +168,17 @@
<div class="section" id="neural-net">
<span id="neural-net"></span><h1>Neural Net<a class="headerlink" href="#neural-net" title="Permalink to this headline">¶</a></h1>
-<p><code class="docutils literal"><span class="pre">NeuralNet</span></code> in SINGA represents an instance of user’s neural net model. As the
-neural net typically consists of a set of layers, <code class="docutils literal"><span class="pre">NeuralNet</span></code> comprises
+<p><code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> in SINGA represents an instance of userâs neural net model. As the
+neural net typically consists of a set of layers, <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> comprises
a set of unidirectionally connected <a class="reference external" href="layer.html">Layer</a>s.
-This page describes how to convert an user’s neural net into
-the configuration of <code class="docutils literal"><span class="pre">NeuralNet</span></code>.</p>
+This page describes how to convert an userâs neural net into
+the configuration of <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code>.</p>
<p><img src="../_static/images/model-category.png" align="center" width="200px"/>
<span><strong>Figure 1 - Categorization of popular deep learning models.</strong></span></p>
<div class="section" id="net-structure-configuration">
<span id="net-structure-configuration"></span><h2>Net structure configuration<a class="headerlink" href="#net-structure-configuration" title="Permalink to this headline">¶</a></h2>
-<p>Users configure the <code class="docutils literal"><span class="pre">NeuralNet</span></code> by listing all layers of the neural net and
-specifying each layer’s source layer names. Popular deep learning models can be
+<p>Users configure the <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> by listing all layers of the neural net and
+specifying each layerâs source layer names. Popular deep learning models can be
categorized as Figure 1. The subsequent sections give details for each
category.</p>
<div class="section" id="feed-forward-models">
@@ -170,7 +189,7 @@ category.</p>
</div><p>Feed-forward models, e.g., CNN and MLP, can easily get configured as their layer
connections are undirected without circles. The
configuration for the MLP model shown in Figure 1 is as follows,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="p">{</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="p">{</span>
<span class="n">layer</span> <span class="p">{</span>
<span class="n">name</span> <span class="p">:</span> <span class="s1">'data"</span>
<span class="nb">type</span> <span class="p">:</span> <span class="n">kData</span>
@@ -206,12 +225,12 @@ configuration for the MLP model shown in
<span><strong>Figure 3 - Convert connections in RBM and RNN.</strong></span></p>
<p>For energy models including RBM, DBM,
etc., their connections are undirected (i.e., Category B). To represent these models using
-<code class="docutils literal"><span class="pre">NeuralNet</span></code>, users can simply replace each connection with two directed
+<code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code>, users can simply replace each connection with two directed
connections, as shown in Figure 3a. In other words, for each pair of connected layers, their source
-layer field should include each other’s name.
+layer field should include each otherâs name.
The full <a class="reference external" href="rbm.html">RBM example</a> has
detailed neural net configuration for a RBM model, which looks like</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="p">{</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="p">{</span>
<span class="n">layer</span> <span class="p">{</span>
<span class="n">name</span> <span class="p">:</span> <span class="s2">"vis"</span>
<span class="nb">type</span> <span class="p">:</span> <span class="n">kVisLayer</span>
@@ -248,10 +267,10 @@ configuration for a RNN model.</p>
<p>Typically, a training job includes three neural nets for
training, validation and test phase respectively. The three neural nets share most
layers except the data layer, loss layer or output layer, etc.. To avoid
-redundant configurations for the shared layers, users can uses the <code class="docutils literal"><span class="pre">exclude</span></code>
+redundant configurations for the shared layers, users can uses the <code class="docutils literal notranslate"><span class="pre">exclude</span></code>
filed to filter a layer in the neural net, e.g., the following layer will be
-filtered when creating the testing <code class="docutils literal"><span class="pre">NeuralNet</span></code>.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="p">{</span>
+filtered when creating the testing <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="p">{</span>
<span class="o">...</span>
<span class="n">exclude</span> <span class="p">:</span> <span class="n">kTest</span> <span class="c1"># filter this layer for creating test net</span>
<span class="p">}</span>
@@ -266,7 +285,7 @@ over multiple workers.</p>
<span id="batch-and-feature-dimension"></span><h3>Batch and feature dimension<a class="headerlink" href="#batch-and-feature-dimension" title="Permalink to this headline">¶</a></h3>
<p><img src="../_static/images/partition_fc.png" align="center" width="400px"/>
<span><strong>Figure 4 - Partitioning of a fully connected layer.</strong></span></p>
-<p>Every layer’s feature blob is considered a matrix whose rows are feature
+<p>Every layerâs feature blob is considered a matrix whose rows are feature
vectors. Thus, one layer can be split on two dimensions. Partitioning on
dimension 0 (also called batch dimension) slices the feature matrix by rows.
For instance, if the mini-batch size is 256 and the layer is partitioned into 2
@@ -287,7 +306,7 @@ partitioned into two sub-layers.</p>
<li><p class="first">Partitioning each singe layer into sub-layers on batch dimension (see
below). It is enabled by configuring the partition dimension of the layer to
0, e.g.,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span>
<span class="n">layer</span> <span class="p">{</span>
<span class="n">partition_dim</span><span class="p">:</span> <span class="mi">0</span>
<span class="p">}</span>
@@ -297,7 +316,7 @@ below). It is enabled by configuring the
<li><p class="first">Partitioning each singe layer into sub-layers on feature dimension (see
below). It is enabled by configuring the partition dimension of the layer to
1, e.g.,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span>
<span class="n">layer</span> <span class="p">{</span>
<span class="n">partition_dim</span><span class="p">:</span> <span class="mi">1</span>
<span class="p">}</span>
@@ -306,7 +325,7 @@ below). It is enabled by configuring th
</li>
<li><p class="first">Partitioning all layers into different subsets. It is enabled by
configuring the location ID of a layer, e.g.,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span>
<span class="n">layer</span> <span class="p">{</span>
<span class="n">location</span><span class="p">:</span> <span class="mi">1</span>
<span class="p">}</span>
@@ -322,7 +341,7 @@ configuring the location ID of a layer,
useful for large models. An example application is to implement the
<a class="reference external" href="http://arxiv.org/abs/1404.5997">idea proposed by Alex</a>.
Hybrid partitioning is configured like,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span>
<span class="n">layer</span> <span class="p">{</span>
<span class="n">location</span><span class="p">:</span> <span class="mi">1</span>
<span class="p">}</span>
@@ -351,16 +370,16 @@ are under test and will be released in l
<ul class="simple">
<li>sharing parameters among layers via user configuration. For example, the
visible layer and hidden layer of a RBM shares the weight matrix, which is configured through
-the <code class="docutils literal"><span class="pre">share_from</span></code> field as shown in the above RBM configuration. The
+the <code class="docutils literal notranslate"><span class="pre">share_from</span></code> field as shown in the above RBM configuration. The
configurations must be the same (except name) for shared parameters.</li>
-<li>due to neural net partitioning, some <code class="docutils literal"><span class="pre">Param</span></code> objects are replicated into
+<li>due to neural net partitioning, some <code class="docutils literal notranslate"><span class="pre">Param</span></code> objects are replicated into
different workers, e.g., partitioning one layer on batch dimension. These
workers share parameter values. SINGA controls this kind of parameter
sharing automatically, users do not need to do any configuration.</li>
-<li>the <code class="docutils literal"><span class="pre">NeuralNet</span></code> for training and testing (and validation) share most layers
-, thus share <code class="docutils literal"><span class="pre">Param</span></code> values.</li>
+<li>the <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> for training and testing (and validation) share most layers
+, thus share <code class="docutils literal notranslate"><span class="pre">Param</span></code> values.</li>
</ul>
-<p>If the shared <code class="docutils literal"><span class="pre">Param</span></code> instances resident in the same process (may in different
+<p>If the shared <code class="docutils literal notranslate"><span class="pre">Param</span></code> instances resident in the same process (may in different
threads), they use the same chunk of memory space for their values. But they
would have different memory spaces for their gradients. In fact, their
gradients will be averaged by the stub or server.</p>
@@ -369,62 +388,62 @@ gradients will be averaged by the stub o
<span id="advanced-user-guide"></span><h2>Advanced user guide<a class="headerlink" href="#advanced-user-guide" title="Permalink to this headline">¶</a></h2>
<div class="section" id="creation">
<span id="creation"></span><h3>Creation<a class="headerlink" href="#creation" title="Permalink to this headline">¶</a></h3>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">static</span> <span class="n">NeuralNet</span><span class="o">*</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">Create</span><span class="p">(</span><span class="n">const</span> <span class="n">NetProto</span><span class="o">&</span> <span class="n">np</span><span class="p">,</span> <span class="n">Phase</span> <span class="n">phase</span><span class="p">,</span> <span class="nb">int</span> <span class="n">num</span><span class="p">);</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">static</span> <span class="n">NeuralNet</span><span class="o">*</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">Create</span><span class="p">(</span><span class="n">const</span> <span class="n">NetProto</span><span class="o">&</span> <span class="n">np</span><span class="p">,</span> <span class="n">Phase</span> <span class="n">phase</span><span class="p">,</span> <span class="nb">int</span> <span class="n">num</span><span class="p">);</span>
</pre></div>
</div>
-<p>The above function creates a <code class="docutils literal"><span class="pre">NeuralNet</span></code> for a given phase, and returns a
-pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance. The phase is in {kTrain,
-kValidation, kTest}. <code class="docutils literal"><span class="pre">num</span></code> is used for net partitioning which indicates the
+<p>The above function creates a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> for a given phase, and returns a
+pointer to the <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> instance. The phase is in {kTrain,
+kValidation, kTest}. <code class="docutils literal notranslate"><span class="pre">num</span></code> is used for net partitioning which indicates the
number of partitions. Typically, a training job includes three neural nets for
training, validation and test phase respectively. The three neural nets share most
-layers except the data layer, loss layer or output layer, etc.. The <code class="docutils literal"><span class="pre">Create</span></code>
+layers except the data layer, loss layer or output layer, etc.. The <code class="docutils literal notranslate"><span class="pre">Create</span></code>
function takes in the full net configuration including layers for training,
validation and test. It removes layers for phases other than the specified
-phase based on the <code class="docutils literal"><span class="pre">exclude</span></code> field in
+phase based on the <code class="docutils literal notranslate"><span class="pre">exclude</span></code> field in
<a class="reference external" href="layer.html">layer configuration</a>:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="p">{</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="p">{</span>
<span class="o">...</span>
<span class="n">exclude</span> <span class="p">:</span> <span class="n">kTest</span> <span class="c1"># filter this layer for creating test net</span>
<span class="p">}</span>
</pre></div>
</div>
-<p>The filtered net configuration is passed to the constructor of <code class="docutils literal"><span class="pre">NeuralNet</span></code>:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">NeuralNet</span><span class="p">::</span><span class="n">NeuralNet</span><span class="p">(</span><span class="n">NetProto</span> <span class="n">netproto</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span>
+<p>The filtered net configuration is passed to the constructor of <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">NeuralNet</span><span class="p">::</span><span class="n">NeuralNet</span><span class="p">(</span><span class="n">NetProto</span> <span class="n">netproto</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span>
</pre></div>
</div>
<p>The constructor creates a graph representing the net structure firstly in</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">Graph</span><span class="o">*</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">CreateGraph</span><span class="p">(</span><span class="n">const</span> <span class="n">NetProto</span><span class="o">&</span> <span class="n">netproto</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Graph</span><span class="o">*</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">CreateGraph</span><span class="p">(</span><span class="n">const</span> <span class="n">NetProto</span><span class="o">&</span> <span class="n">netproto</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span>
</pre></div>
</div>
<p>Next, it creates a layer for each node and connects layers if their nodes are
connected.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">CreateNetFromGraph</span><span class="p">(</span><span class="n">Graph</span><span class="o">*</span> <span class="n">graph</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">CreateNetFromGraph</span><span class="p">(</span><span class="n">Graph</span><span class="o">*</span> <span class="n">graph</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span>
</pre></div>
</div>
-<p>Since the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance may be shared among multiple workers, the
-<code class="docutils literal"><span class="pre">Create</span></code> function returns a pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance .</p>
+<p>Since the <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> instance may be shared among multiple workers, the
+<code class="docutils literal notranslate"><span class="pre">Create</span></code> function returns a pointer to the <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> instance .</p>
</div>
<div class="section" id="parameter-sharing">
<span id="id1"></span><h3>Parameter sharing<a class="headerlink" href="#parameter-sharing" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">Param</span></code> sharing
-is enabled by first sharing the Param configuration (in <code class="docutils literal"><span class="pre">NeuralNet::Create</span></code>)
+<p><code class="docutils literal notranslate"><span class="pre">Param</span></code> sharing
+is enabled by first sharing the Param configuration (in <code class="docutils literal notranslate"><span class="pre">NeuralNet::Create</span></code>)
to create two similar (e.g., the same shape) Param objects, and then calling
-(in <code class="docutils literal"><span class="pre">NeuralNet::CreateNetFromGraph</span></code>),</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">Param</span><span class="p">::</span><span class="n">ShareFrom</span><span class="p">(</span><span class="n">const</span> <span class="n">Param</span><span class="o">&</span> <span class="n">from</span><span class="p">);</span>
+(in <code class="docutils literal notranslate"><span class="pre">NeuralNet::CreateNetFromGraph</span></code>),</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">Param</span><span class="p">::</span><span class="n">ShareFrom</span><span class="p">(</span><span class="n">const</span> <span class="n">Param</span><span class="o">&</span> <span class="n">from</span><span class="p">);</span>
</pre></div>
</div>
-<p>It is also possible to share <code class="docutils literal"><span class="pre">Param</span></code>s of two nets, e.g., sharing parameters of
+<p>It is also possible to share <code class="docutils literal notranslate"><span class="pre">Param</span></code>s of two nets, e.g., sharing parameters of
the training net and the test net,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">NeuralNet</span><span class="p">:</span><span class="n">ShareParamsFrom</span><span class="p">(</span><span class="n">NeuralNet</span><span class="o">*</span> <span class="n">other</span><span class="p">);</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">NeuralNet</span><span class="p">:</span><span class="n">ShareParamsFrom</span><span class="p">(</span><span class="n">NeuralNet</span><span class="o">*</span> <span class="n">other</span><span class="p">);</span>
</pre></div>
</div>
-<p>It will call <code class="docutils literal"><span class="pre">Param::ShareFrom</span></code> for each Param object.</p>
+<p>It will call <code class="docutils literal notranslate"><span class="pre">Param::ShareFrom</span></code> for each Param object.</p>
</div>
<div class="section" id="access-functions">
<span id="access-functions"></span><h3>Access functions<a class="headerlink" href="#access-functions" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">NeuralNet</span></code> provides a couple of access function to get the layers and params
+<p><code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> provides a couple of access function to get the layers and params
of the net:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">const</span> <span class="n">std</span><span class="p">::</span><span class="n">vector</span><span class="o"><</span><span class="n">Layer</span><span class="o">*>&</span> <span class="n">layers</span><span class="p">()</span> <span class="n">const</span><span class="p">;</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">const</span> <span class="n">std</span><span class="p">::</span><span class="n">vector</span><span class="o"><</span><span class="n">Layer</span><span class="o">*>&</span> <span class="n">layers</span><span class="p">()</span> <span class="n">const</span><span class="p">;</span>
<span class="n">const</span> <span class="n">std</span><span class="p">::</span><span class="n">vector</span><span class="o"><</span><span class="n">Param</span><span class="o">*>&</span> <span class="n">params</span><span class="p">()</span> <span class="n">const</span> <span class="p">;</span>
<span class="n">Layer</span><span class="o">*</span> <span class="n">name2layer</span><span class="p">(</span><span class="n">string</span> <span class="n">name</span><span class="p">)</span> <span class="n">const</span><span class="p">;</span>
<span class="n">Param</span><span class="o">*</span> <span class="n">paramid2param</span><span class="p">(</span><span class="nb">int</span> <span class="nb">id</span><span class="p">)</span> <span class="n">const</span><span class="p">;</span>
@@ -435,9 +454,9 @@ of the net:</p>
<span id="partitioning"></span><h3>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this headline">¶</a></h3>
<div class="section" id="implementation">
<span id="implementation"></span><h4>Implementation<a class="headerlink" href="#implementation" title="Permalink to this headline">¶</a></h4>
-<p>SINGA partitions the neural net in <code class="docutils literal"><span class="pre">CreateGraph</span></code> function, which creates one
-node for each (partitioned) layer. For example, if one layer’s partition
-dimension is 0 or 1, then it creates <code class="docutils literal"><span class="pre">npartition</span></code> nodes for it; if the
+<p>SINGA partitions the neural net in <code class="docutils literal notranslate"><span class="pre">CreateGraph</span></code> function, which creates one
+node for each (partitioned) layer. For example, if one layerâs partition
+dimension is 0 or 1, then it creates <code class="docutils literal notranslate"><span class="pre">npartition</span></code> nodes for it; if the
partition dimension is -1, a single node is created, i.e., no partitioning.
Each node is assigned a partition (or location) ID. If the original layer is
configured with a location ID, then the ID is assigned to each newly created node.
@@ -454,9 +473,9 @@ synchronization transparent to the users
<div class="section" id="dispatching-partitions-to-workers">
<span id="dispatching-partitions-to-workers"></span><h4>Dispatching partitions to workers<a class="headerlink" href="#dispatching-partitions-to-workers" title="Permalink to this headline">¶</a></h4>
<p>Each (partitioned) layer is assigned a location ID, based on which it is dispatched to one
-worker. Particularly, the pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance is passed
+worker. Particularly, the pointer to the <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> instance is passed
to every worker within the same group, but each worker only computes over the
-layers that have the same partition (or location) ID as the worker’s ID. When
+layers that have the same partition (or location) ID as the workerâs ID. When
every worker computes the gradients of the entire model parameters
(strategy-2), we refer to this process as data parallelism. When different
workers compute the gradients of different parameters (strategy-3 or
@@ -464,8 +483,8 @@ strategy-1), we call this process model
leads to hybrid parallelism where some workers compute the gradients of the
same subset of model parameters while other workers compute on different model
parameters. For example, to implement the hybrid parallelism in for the
-<a class="reference external" href="http://arxiv.org/abs/1404.5997">DCNN model</a>, we set <code class="docutils literal"><span class="pre">partition_dim</span> <span class="pre">=</span> <span class="pre">0</span></code> for
-lower layers and <code class="docutils literal"><span class="pre">partition_dim</span> <span class="pre">=</span> <span class="pre">1</span></code> for higher layers.</p>
+<a class="reference external" href="http://arxiv.org/abs/1404.5997">DCNN model</a>, we set <code class="docutils literal notranslate"><span class="pre">partition_dim</span> <span class="pre">=</span> <span class="pre">0</span></code> for
+lower layers and <code class="docutils literal notranslate"><span class="pre">partition_dim</span> <span class="pre">=</span> <span class="pre">1</span></code> for higher layers.</p>
</div>
</div>
</div>
@@ -473,6 +492,7 @@ lower layers and <code class="docutils l
</div>
+
</div>
<footer>
@@ -481,11 +501,11 @@ lower layers and <code class="docutils l
<div role="contentinfo">
<p>
- © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+ © Copyright 2018 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
</p>
</div>
- Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+ Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
@@ -504,9 +524,11 @@ lower layers and <code class="docutils l
var DOCUMENTATION_OPTIONS = {
URL_ROOT:'../',
VERSION:'1.1.0',
+ LANGUAGE:'None',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
- HAS_SOURCE: true
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt'
};
</script>
<script type="text/javascript" src="../_static/jquery.js"></script>
@@ -520,14 +542,13 @@ lower layers and <code class="docutils l
<script type="text/javascript" src="../_static/js/theme.js"></script>
-
-
<script type="text/javascript">
jQuery(function () {
- SphinxRtdTheme.StickyNav.enable();
+
+ SphinxRtdTheme.Navigation.enableSticky();
+
});
</script>
-
<div class="rst-versions shift-up" data-toggle="rst-versions" role="note" aria-label="versions">
<span class="rst-current-version" data-toggle="rst-current-version">
Modified: incubator/singa/site/trunk/en/docs/notebook/README.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/notebook/README.html?rev=1831195&r1=1831194&r2=1831195&view=diff
==============================================================================
--- incubator/singa/site/trunk/en/docs/notebook/README.html (original)
+++ incubator/singa/site/trunk/en/docs/notebook/README.html Tue May 8 15:05:16 2018
@@ -14,27 +14,22 @@
-
-
-
-
- <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-
+
- <link rel="index" title="Index"
- href="../../genindex.html"/>
- <link rel="search" title="Search" href="../../search.html"/>
- <link rel="top" title="incubator-singa 1.1.0 documentation" href="../../index.html"/>
+ <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" />
<link href="../../_static/style.css" rel="stylesheet" type="text/css">
@@ -43,8 +38,9 @@
</head>
-<body class="wy-body-for-nav" role="document">
+<body class="wy-body-for-nav">
+
<div class="wy-grid-for-nav">
@@ -59,7 +55,7 @@
- <img src="../../_static/singa.png" class="logo" />
+ <img src="../../_static/singa.png" class="logo" alt="Logo"/>
</a>
@@ -88,7 +84,10 @@
- <ul>
+
+
+
+ <ul>
<li class="toctree-l1"><a class="reference internal" href="../../downloads.html">Download SINGA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../index.html">Documentation</a></li>
</ul>
@@ -116,32 +115,52 @@
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
- <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
- <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
- <a href="../../index.html">incubator-singa</a>
+ <nav class="wy-nav-top" aria-label="top navigation">
+
+ <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+ <a href="../../index.html">incubator-singa</a>
+
</nav>
-
<div class="wy-nav-content">
+
<div class="rst-content">
+
-
+
+
+
+
+
+
+
+
+
+
+
<div role="navigation" aria-label="breadcrumbs navigation">
+
<ul class="wy-breadcrumbs">
- <li><a href="../../index.html">Docs</a> »</li>
-
- <li><no title></li>
+
+ <li><a href="../../index.html">Docs</a> »</li>
+
+ <li><no title></li>
+
+
<li class="wy-breadcrumbs-aside">
-
+
</li>
+
</ul>
+
+
<hr/>
</div>
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
@@ -152,6 +171,7 @@
</div>
+
</div>
<footer>
@@ -160,11 +180,11 @@
<div role="contentinfo">
<p>
- © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+ © Copyright 2018 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
</p>
</div>
- Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+ Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
@@ -183,9 +203,11 @@
var DOCUMENTATION_OPTIONS = {
URL_ROOT:'../../',
VERSION:'1.1.0',
+ LANGUAGE:'None',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
- HAS_SOURCE: true
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt'
};
</script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
@@ -199,14 +221,13 @@
<script type="text/javascript" src="../../_static/js/theme.js"></script>
-
-
<script type="text/javascript">
jQuery(function () {
- SphinxRtdTheme.StickyNav.enable();
+
+ SphinxRtdTheme.Navigation.enableSticky();
+
});
</script>
-
<div class="rst-versions shift-up" data-toggle="rst-versions" role="note" aria-label="versions">
<span class="rst-current-version" data-toggle="rst-current-version">
Modified: incubator/singa/site/trunk/en/docs/optimizer.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/optimizer.html?rev=1831195&r1=1831194&r2=1831195&view=diff
==============================================================================
--- incubator/singa/site/trunk/en/docs/optimizer.html (original)
+++ incubator/singa/site/trunk/en/docs/optimizer.html Tue May 8 15:05:16 2018
@@ -14,30 +14,24 @@
-
-
-
-
- <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-
+
- <link rel="index" title="Index"
- href="../genindex.html"/>
- <link rel="search" title="Search" href="../search.html"/>
- <link rel="top" title="incubator-singa 1.1.0 documentation" href="../index.html"/>
- <link rel="up" title="Documentation" href="index.html"/>
- <link rel="next" title="Data" href="data.html"/>
- <link rel="prev" title="Metric" href="metric.html"/>
+ <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" />
+ <link rel="next" title="Data" href="data.html" />
+ <link rel="prev" title="Metric" href="metric.html" />
<link href="../_static/style.css" rel="stylesheet" type="text/css">
@@ -46,8 +40,9 @@
</head>
-<body class="wy-body-for-nav" role="document">
+<body class="wy-body-for-nav">
+
<div class="wy-grid-for-nav">
@@ -62,7 +57,7 @@
- <img src="../_static/singa.png" class="logo" />
+ <img src="../_static/singa.png" class="logo" alt="Logo"/>
</a>
@@ -91,7 +86,10 @@
- <ul class="current">
+
+
+
+ <ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../downloads.html">Download SINGA</a></li>
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Documentation</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
@@ -137,412 +135,75 @@
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
- <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
- <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
- <a href="../index.html">incubator-singa</a>
+ <nav class="wy-nav-top" aria-label="top navigation">
+
+ <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+ <a href="../index.html">incubator-singa</a>
+
</nav>
-
<div class="wy-nav-content">
+
<div class="rst-content">
+
-
+
+
+
+
+
+
+
+
+
+
+
<div role="navigation" aria-label="breadcrumbs navigation">
+
<ul class="wy-breadcrumbs">
- <li><a href="../index.html">Docs</a> »</li>
-
+
+ <li><a href="../index.html">Docs</a> »</li>
+
<li><a href="index.html">Documentation</a> »</li>
-
- <li>Optimizer</li>
+
+ <li>Optimizer</li>
+
+
<li class="wy-breadcrumbs-aside">
-
+
</li>
+
</ul>
+
+
<hr/>
</div>
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
- <div class="section" id="module-singa.optimizer">
-<span id="optimizer"></span><h1>Optimizer<a class="headerlink" href="#module-singa.optimizer" title="Permalink to this headline">¶</a></h1>
-<p>This module includes a set of optimizers for updating model parameters.</p>
-<p>Example usage:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">optimizer</span>
-<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">tensor</span>
-
-<span class="n">sgd</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">p</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">g</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">g</span><span class="o">.</span><span class="n">gaussian</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
-
-<span class="n">sgd</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="s1">'param'</span><span class="p">)</span> <span class="c1"># use the global lr=0.1 for epoch 1</span>
-<span class="n">sgd</span><span class="o">.</span><span class="n">apply_with_lr</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="s1">'param'</span><span class="p">)</span> <span class="c1"># use lr=0.03 for epoch 2</span>
-</pre></div>
-</div>
-<dl class="class">
-<dt id="singa.optimizer.Optimizer">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
-<p>The base python optimizer class.</p>
-<p>Typically, an optimizer is used as follows:</p>
-<ol class="arabic simple">
-<li>construct the optimizer</li>
-<li>(optional) register each parameter with its specs.</li>
-<li>use the optimizer to update parameter values given parameter gradients
-and other optional info</li>
-</ol>
-<p>The subclasses should override the apply_with_lr function to do the real
-parameter udpate.</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>lr</strong> (<em>float</em>) – a constant value for the learning rate</li>
-<li><strong>momentum</strong> (<em>float</em>) – a constant value for the momentum value</li>
-<li><strong>weight_decay</strong> (<em>float</em>) – the coefficent for L2 regularizer, which is
-mutually exclusive with ‘regularizer’.</li>
-<li><strong>regularizer</strong> – an instance of Regularizer or RegularizerConf; If set,
-regularization would be applied in apply_with_lr().
-Users can also do regularization outside.</li>
-<li><strong>constraint</strong> – an instance of Constraint or ConstraintConf; If set,
-constraint would be applied inside apply_with_lr(). Users can
-also apply constraint outside.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="singa.optimizer.Optimizer.register">
-<code class="descname">register</code><span class="sig-paren">(</span><em>name</em>, <em>specs</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.register" title="Permalink to this definition">¶</a></dt>
-<dd><p>Register the param specs, including creating regularizer and
-constraint per param object. Param specific regularizer and constraint
-have higher priority than the global ones. If all parameters share the
-same setting for learning rate, regularizer and constraint, then there
-is no need to call this function.</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>str</em>) – parameter name</li>
-<li><strong>specs</strong> (<em>ParamSpec</em>) – protobuf obj, including regularizer and
-constraint, multipliers for learning rate and weight decay.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="singa.optimizer.Optimizer.apply_regularizer_constraint">
-<code class="descname">apply_regularizer_constraint</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply_regularizer_constraint" title="Permalink to this definition">¶</a></dt>
-<dd><p>Apply regularization and constraint if available.</p>
-<p>If there are both global regularizer (constraint) and param specific
-regularizer (constraint), it would use the param specific 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"><ul class="first simple">
-<li><strong>epoch</strong> (<em>int</em>) – training epoch ID</li>
-<li><strong>value</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – parameter value Tensor</li>
-<li><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – parameter gradient Tensor</li>
-<li><strong>name</strong> (<em>string</em>) – to get parameter specific regularizer or constraint</li>
-<li><strong>step</strong> (<em>int</em>) – iteration ID within one epoch</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the updated gradient Tensor</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="singa.optimizer.Optimizer.apply_with_lr">
-<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply_with_lr" title="Permalink to this definition">¶</a></dt>
-<dd><p>Do update of parameters with given learning rate if the grad is not
-empty.</p>
-<p>The subclass optimizer must override this function.
-This function do nothing if the grad is empty.</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>epoch</strong> (<em>int</em>) – training epoch ID</li>
-<li><strong>lr</strong> (<em>float</em>) – learning rate</li>
-<li><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – parameter gradient</li>
-<li><strong>value</strong> (<em>Tesnor</em>) – parameter value</li>
-<li><strong>name</strong> (<em>string</em>) – paramter name to index parameter specific
-updating rules (including regularizer and constraint)</li>
-<li><strong>step</strong> (<em>int</em>) – iteration ID within one epoch</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">updated parameter value</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="singa.optimizer.Optimizer.apply">
-<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>grad</em>, <em>value</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply" title="Permalink to this definition">¶</a></dt>
-<dd><p>Do update assuming the learning rate generator is set.</p>
-<p>The subclass optimizer does not need to override this function.</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>epoch</strong> (<em>int</em>) – training epoch ID</li>
-<li><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – parameter gradient</li>
-<li><strong>value</strong> (<em>Tesnor</em>) – parameter value</li>
-<li><strong>name</strong> (<em>string</em>) – paramter name to retrieval parameter specific
-updating rules (including regularizer and constraint)</li>
-<li><strong>step</strong> (<em>int</em>) – training iteration ID within one epoch</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">updated parameter value</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.SGD">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.SGD" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Optimizer</span></code></a></p>
-<p>The vallina Stochasitc Gradient Descent algorithm with momentum.</p>
-<p>See the base Optimizer for all arguments.</p>
-<dl class="method">
-<dt id="singa.optimizer.SGD.apply_with_lr">
-<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.SGD.apply_with_lr" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.Nesterov">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Nesterov</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=0.9</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Nesterov" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Optimizer</span></code></a></p>
-<p>The SGD with Nesterov momentum.</p>
-<p>See the base Optimizer for all arguments.</p>
-<dl class="method">
-<dt id="singa.optimizer.Nesterov.apply_with_lr">
-<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Nesterov.apply_with_lr" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.RMSProp">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">RMSProp</code><span class="sig-paren">(</span><em>rho=0.9</em>, <em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.RMSProp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Optimizer</span></code></a></p>
-<p>RMSProp optimizer.</p>
-<p>See the base Optimizer for all constructor args.</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>rho</strong> (<em>float</em>) – float within [0, 1]</li>
-<li><strong>epsilon</strong> (<em>float</em>) – small value for preventing numeric error</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="singa.optimizer.RMSProp.apply_with_lr">
-<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.RMSProp.apply_with_lr" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.AdaGrad">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">AdaGrad</code><span class="sig-paren">(</span><em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>lr_gen=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.AdaGrad" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Optimizer</span></code></a></p>
-<p>AdaGrad optimizer.</p>
-<p>See the base Optimizer for all constructor args.</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>epsilon</strong> (<em>float</em>) – small number for preventing numeric error.</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="singa.optimizer.AdaGrad.apply_with_lr">
-<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.AdaGrad.apply_with_lr" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.Adam">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Adam</code><span class="sig-paren">(</span><em>beta_1=0.9</em>, <em>beta_2=0.999</em>, <em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Adam" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Optimizer</span></code></a></p>
-<p>Adam optimizer.</p>
-<p>See the base Optimizer for all constructor args.</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>beta_1</strong> (<em>float</em>) – coefficient of momentum</li>
-<li><strong>beta_2</strong> (<em>float</em>) – coefficient of aggregated squared gradient</li>
-<li><strong>epsilon</strong> (<em>float</em>) – small value for preventing numeric error</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="singa.optimizer.Adam.apply_with_lr">
-<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Adam.apply_with_lr" title="Permalink to this definition">¶</a></dt>
-<dd><p>Update one parameter object.</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>step</strong> (<em>int</em>) – the accumulated training iterations, not the iteration ID</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.Regularizer">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Regularizer</code><a class="headerlink" href="#singa.optimizer.Regularizer" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
-<p>Base Python regularizer for parameter gradients.</p>
-<dl class="method">
-<dt id="singa.optimizer.Regularizer.apply">
-<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Regularizer.apply" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.CppRegularizer">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">CppRegularizer</code><span class="sig-paren">(</span><em>conf</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppRegularizer" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Regularizer" title="singa.optimizer.Regularizer"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Regularizer</span></code></a></p>
-<p>Wrapper for regularizer implemented using C++.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>conf</strong> (<em>RegularizerConf</em>) – protobuf message for the configuration.</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="singa.optimizer.CppRegularizer.apply">
-<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppRegularizer.apply" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.L2Regularizer">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">L2Regularizer</code><span class="sig-paren">(</span><em>coefficient</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Regularizer" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Regularizer" title="singa.optimizer.Regularizer"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Regularizer</span></code></a></p>
-<p>L2 regularization</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>coefficient</strong> (<em>float</em>) – regularization coefficient.</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="singa.optimizer.L2Regularizer.apply">
-<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Regularizer.apply" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.Constraint">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Constraint</code><a class="headerlink" href="#singa.optimizer.Constraint" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
-<p>Base Python constraint class for paramter gradients</p>
-<dl class="method">
-<dt id="singa.optimizer.Constraint.apply">
-<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Constraint.apply" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.CppConstraint">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">CppConstraint</code><span class="sig-paren">(</span><em>conf</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppConstraint" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Constraint" title="singa.optimizer.Constraint"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Constraint</span></code></a></p>
-<p>Wrapper for constraints implemented using C++.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>conf</strong> (<em>ConstraintConf</em>) – protobuf message for the configuration.</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="singa.optimizer.CppConstraint.apply">
-<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppConstraint.apply" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="singa.optimizer.L2Constraint">
-<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">L2Constraint</code><span class="sig-paren">(</span><em>threshold=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Constraint" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Constraint" title="singa.optimizer.Constraint"><code class="xref py py-class docutils literal"><span class="pre">singa.optimizer.Constraint</span></code></a></p>
-<p>Rescale the gradient to make the L2 norm <= a given threshold</p>
-<dl class="method">
-<dt id="singa.optimizer.L2Constraint.apply">
-<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Constraint.apply" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
+ <div class="section" id="optimizer">
+<h1>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline">¶</a></h1>
</div>
</div>
+
</div>
<footer>
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
- <a href="data.html" class="btn btn-neutral float-right" title="Data" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+ <a href="data.html" class="btn btn-neutral float-right" title="Data" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
- <a href="metric.html" class="btn btn-neutral" title="Metric" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+ <a href="metric.html" class="btn btn-neutral" title="Metric" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
</div>
@@ -551,11 +212,11 @@ updating rules (including regularizer an
<div role="contentinfo">
<p>
- © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+ © Copyright 2018 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
</p>
</div>
- Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+ Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
@@ -574,9 +235,11 @@ updating rules (including regularizer an
var DOCUMENTATION_OPTIONS = {
URL_ROOT:'../',
VERSION:'1.1.0',
+ LANGUAGE:'None',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
- HAS_SOURCE: true
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt'
};
</script>
<script type="text/javascript" src="../_static/jquery.js"></script>
@@ -590,14 +253,13 @@ updating rules (including regularizer an
<script type="text/javascript" src="../_static/js/theme.js"></script>
-
-
<script type="text/javascript">
jQuery(function () {
- SphinxRtdTheme.StickyNav.enable();
+
+ SphinxRtdTheme.Navigation.enableSticky();
+
});
</script>
-
<div class="rst-versions shift-up" data-toggle="rst-versions" role="note" aria-label="versions">
<span class="rst-current-version" data-toggle="rst-current-version">
Modified: incubator/singa/site/trunk/en/docs/snapshot.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/snapshot.html?rev=1831195&r1=1831194&r2=1831195&view=diff
==============================================================================
--- incubator/singa/site/trunk/en/docs/snapshot.html (original)
+++ incubator/singa/site/trunk/en/docs/snapshot.html Tue May 8 15:05:16 2018
@@ -14,30 +14,24 @@
-
-
-
-
- <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-
+
- <link rel="index" title="Index"
- href="../genindex.html"/>
- <link rel="search" title="Search" href="../search.html"/>
- <link rel="top" title="incubator-singa 1.1.0 documentation" href="../index.html"/>
- <link rel="up" title="Documentation" href="index.html"/>
- <link rel="next" title="Caffe Converter" href="converter.html"/>
- <link rel="prev" title="Image Tool" href="image_tool.html"/>
+ <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" />
+ <link rel="next" title="Caffe Converter" href="converter.html" />
+ <link rel="prev" title="Image Tool" href="image_tool.html" />
<link href="../_static/style.css" rel="stylesheet" type="text/css">
@@ -46,8 +40,9 @@
</head>
-<body class="wy-body-for-nav" role="document">
+<body class="wy-body-for-nav">
+
<div class="wy-grid-for-nav">
@@ -62,7 +57,7 @@
- <img src="../_static/singa.png" class="logo" />
+ <img src="../_static/singa.png" class="logo" alt="Logo"/>
</a>
@@ -91,7 +86,10 @@
- <ul class="current">
+
+
+
+ <ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../downloads.html">Download SINGA</a></li>
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Documentation</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
@@ -137,104 +135,75 @@
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
- <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
- <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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- <li>Snapshot</li>
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- <div class="section" id="module-singa.snapshot">
-<span id="snapshot"></span><h1>Snapshot<a class="headerlink" href="#module-singa.snapshot" title="Permalink to this headline">¶</a></h1>
-<p>This script includes io::snapshot class and its methods.</p>
-<p>Example usages:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">snapshot</span>
-
-<span class="n">sn1</span> <span class="o">=</span> <span class="n">snapshot</span><span class="o">.</span><span class="n">Snapshot</span><span class="p">(</span><span class="s1">'param'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-<span class="n">params</span> <span class="o">=</span> <span class="n">sn1</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> <span class="c1"># read all params as a dictionary</span>
-
-<span class="n">sn2</span> <span class="o">=</span> <span class="n">snapshot</span><span class="o">.</span><span class="n">Snapshot</span><span class="p">(</span><span class="s1">'param_new'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">iteritems</span><span class="p">():</span>
- <span class="n">sn2</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
-</pre></div>
-</div>
-<dl class="class">
-<dt id="singa.snapshot.Snapshot">
-<em class="property">class </em><code class="descclassname">singa.snapshot.</code><code class="descname">Snapshot</code><span class="sig-paren">(</span><em>f</em>, <em>mode</em>, <em>buffer_size=10</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot" title="Permalink to this definition">¶</a></dt>
-<dd><p>Class and member functions for singa::Snapshot.</p>
-<dl class="method">
-<dt id="singa.snapshot.Snapshot.read">
-<code class="descname">read</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot.read" title="Permalink to this definition">¶</a></dt>
-<dd><p>Call read method to load all (param_name, param_val)</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 dict of (parameter name, parameter Tensor)</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="method">
-<dt id="singa.snapshot.Snapshot.write">
-<code class="descname">write</code><span class="sig-paren">(</span><em>param_name</em>, <em>param_val</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot.write" title="Permalink to this definition">¶</a></dt>
-<dd><p>Call Write method to write a parameter</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>param_name</strong> (<em>string</em>) – name of the parameter</li>
-<li><strong>param_val</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – value tensor of the parameter</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</dd></dl>
-
+ <div class="section" id="snapshot">
+<h1>Snapshot<a class="headerlink" href="#snapshot" title="Permalink to this headline">¶</a></h1>
</div>
</div>
+
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<footer>
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- <a href="converter.html" class="btn btn-neutral float-right" title="Caffe Converter" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
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@@ -243,11 +212,11 @@
<div role="contentinfo">
<p>
- © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+ © Copyright 2018 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
</p>
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+ Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
@@ -266,9 +235,11 @@
var DOCUMENTATION_OPTIONS = {
URL_ROOT:'../',
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+ LANGUAGE:'None',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
- HAS_SOURCE: true
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt'
};
</script>
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@@ -282,14 +253,13 @@
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-
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Modified: incubator/singa/site/trunk/en/docs/software_stack.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/software_stack.html?rev=1831195&r1=1831194&r2=1831195&view=diff
==============================================================================
--- incubator/singa/site/trunk/en/docs/software_stack.html (original)
+++ incubator/singa/site/trunk/en/docs/software_stack.html Tue May 8 15:05:16 2018
@@ -14,30 +14,24 @@
-
-
-
-
- <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-
+
- <link rel="index" title="Index"
- href="../genindex.html"/>
- <link rel="search" title="Search" href="../search.html"/>
- <link rel="top" title="incubator-singa 1.1.0 documentation" href="../index.html"/>
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+ <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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+ <link rel="prev" title="Installation" href="installation.html" />
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@@ -46,8 +40,9 @@
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+<body class="wy-body-for-nav">
+
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@@ -62,7 +57,7 @@
- <img src="../_static/singa.png" class="logo" />
+ <img src="../_static/singa.png" class="logo" alt="Logo"/>
</a>
@@ -91,7 +86,10 @@
- <ul class="current">
+
+
+
+ <ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../downloads.html">Download SINGA</a></li>
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Documentation</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
@@ -142,34 +140,54 @@
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
- <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
- <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+ <li><a href="../index.html">Docs</a> »</li>
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-
- <li>Software Stack</li>
+
+ <li>Software Stack</li>
+
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+
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@@ -177,7 +195,7 @@
<div class="section" id="software-stack">
<span id="software-stack"></span><h1>Software Stack<a class="headerlink" href="#software-stack" title="Permalink to this headline">¶</a></h1>
-<p>SINGA’s software stack includes three major components, namely, core, IO and
+<p>SINGAâs software stack includes three major components, namely, core, IO and
model. Figure 1 illustrates these components together with the hardware.
The core component provides memory management and tensor operations;
IO has classes for reading (and writing) data from (to) disk and network; The
@@ -267,15 +285,16 @@ model parameter values using parameter g
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+
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@@ -284,11 +303,11 @@ model parameter values using parameter g
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<p>
- © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+ © Copyright 2018 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
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@@ -307,9 +326,11 @@ model parameter values using parameter g
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+ LANGUAGE:'None',
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+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt'
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@@ -323,14 +344,13 @@ model parameter values using parameter g
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