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<hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> + <div itemprop="articleBody"> + + <hr class="docutils" /> +<p>name: GoogleNet on ImageNet +SINGA version: 1.0.1 +SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744 +parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz +parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d +license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet</p> +<hr class="docutils" /> +<div class="section" id="image-classification-using-googlenet"> +<span id="image-classification-using-googlenet"></span><h1>Image Classification using GoogleNet<a class="headerlink" href="#image-classification-using-googlenet" title="Permalink to this headline">¶</a></h1> +<p>In this example, we convert GoogleNet trained on Caffe to SINGA for image classification.</p> +<div class="section" id="instructions"> +<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<ul> +<li><p class="first">Download the parameter checkpoint file into this folder</p> +<div class="highlight-python"><div class="highlight"><pre> $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz + $ tar xvf bvlc_googlenet.tar.gz +</pre></div> +</div> +</li> +<li><p class="first">Run the program</p> +<div class="highlight-python"><div class="highlight"><pre> # use cpu + $ python serve.py -C & + # use gpu + $ python serve.py & +</pre></div> +</div> +</li> +<li><p class="first">Submit images for classification</p> +<div class="highlight-python"><div class="highlight"><pre> $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] http://localhost:9999/api +</pre></div> +</div> +</li> +</ul> +<p>image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.</p> +</div> +<div class="section" id="details"> +<span id="details"></span><h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2> +<p>We first extract the parameter values from <a class="reference external" href="http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel";>Caffe’s checkpoint file</a> into a pickle version +After downloading the checkpoint file into <code class="docutils literal"><span class="pre">caffe_root/python</span></code> folder, run the following script</p> +<div class="highlight-python"><div class="highlight"><pre><span class="c1"># to be executed within caffe_root/python folder</span> +<span class="kn">import</span> <span class="nn">caffe</span> +<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">cPickle</span> <span class="kn">as</span> <span class="nn">pickle</span> + +<span class="n">model_def</span> <span class="o">=</span> <span class="s1">'../models/bvlc_googlenet/deploy.prototxt'</span> +<span class="n">weight</span> <span class="o">=</span> <span class="s1">'bvlc_googlenet.caffemodel'</span> <span class="c1"># must be downloaded at first</span> +<span class="n">net</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">Net</span><span class="p">(</span><span class="n">model_def</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">caffe</span><span class="o">.</span><span class="n">TEST</span><span class="p">)</span> + +<span class="n">params</span> <span class="o">=</span> <span class="p">{}</span> +<span class="k">for</span> <span class="n">layer_name</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span> + <span class="n">weights</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> + <span class="n">bias</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> + <span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">'_weight'</span><span class="p">]</span><span class="o">=</span><span class="n">weights</span> + <span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">'_bias'</span><span class="p">]</span><span class="o">=</span><span class="n">bias</span> + <span class="k">print</span> <span class="n">layer_name</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span> + +<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'bvlc_googlenet.pickle'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span> + <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">fd</span><span class="p">)</span> +</pre></div> +</div> +<p>Then we construct the GoogleNet using SINGA’s FeedForwardNet structure. +Note that we added a EndPadding layer to resolve the issue from discrepancy +of the rounding strategy of the pooling layer between Caffe (ceil) and cuDNN (floor). +Only the MaxPooling layers outside inception blocks have this problem. +Refer to <a class="reference external" href="http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html";>this</a> for more detials.</p> +</div> +</div> + + + </div> + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="../../../../develop/schedule.html" class="btn btn-neutral float-right" title="Development Schedule" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="../alexnet/README.html" class="btn btn-neutral" title="Train 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The RBM model and its hyper-parameters are set following +<a class="reference external" href="http://www.cs.toronto.edu/~hinton/science.pdf";>Hinton’s paper</a></p> +<div class="section" id="running-instructions"> +<span id="running-instructions"></span><h2>Running instructions<a class="headerlink" href="#running-instructions" title="Permalink to this headline">¶</a></h2> +<ol> +<li><p class="first">Download the pre-processed <a class="reference external" href="https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz";>MNIST dataset</a></p> +</li> +<li><p class="first">Start the training</p> +<div class="highlight-python"><div class="highlight"><pre> python train.py mnist.pkl.gz +</pre></div> +</div> +</li> +</ol> +<p>By default the training code would run on CPU. To run it on a GPU card, please start +the program with an additional argument</p> +<div class="highlight-python"><div class="highlight"><pre> python train.py mnist.pkl.gz --use_gpu +</pre></div> +</div> +</div> +</div> + + + </div> + </div> + <footer> + + + <hr/> + + <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.. + + </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>. + +</footer> + + </div> + </div> + + </section> + + </div> + + + + + + <script type="text/javascript"> + var DOCUMENTATION_OPTIONS = { + URL_ROOT:'../../../', + VERSION:'1.1.0', + COLLAPSE_INDEX:false, + FILE_SUFFIX:'.html', + HAS_SOURCE: true + }; + </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/js/theme.js"></script> + + + + + <script type="text/javascript"> + jQuery(function () { + SphinxRtdTheme.StickyNav.enable(); + 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+</a> + + + + +</body> +</html> \ No newline at end of file Added: incubator/singa/site/trunk/v1.1.0/docs/net.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/net.html?rev=1782721&view=auto ============================================================================== --- incubator/singa/site/trunk/v1.1.0/docs/net.html (added) +++ incubator/singa/site/trunk/v1.1.0/docs/net.html Mon Feb 13 05:13:19 2017 @@ -0,0 +1,524 @@ + + + +<!DOCTYPE html> +<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]--> +<head> + <meta charset="utf-8"> + + <meta name="viewport" content="width=device-width, initial-scale=1.0"> + + <title>FeedForward Net — incubator-singa 1.1.0 documentation</title> + + + + + + + + + + + + + + + + <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" /> + + + + + + <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="Initializer" href="initializer.html"/> + <link rel="prev" title="Layer" href="layer.html"/> + <link href="../_static/style.css" rel="stylesheet" type="text/css"> + + + + <script src="../_static/js/modernizr.min.js"></script> + +</head> + +<body class="wy-body-for-nav" role="document"> + + <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"> + + + + <a href="../index.html" class="icon icon-home"> incubator-singa + + + + + <img src="../_static/singa.png" class="logo" /> + + </a> + + + + + <div class="version"> + latest + </div> + + + + +<div role="search"> + <form id="rtd-search-form" class="wy-form" action="../search.html" method="get"> + <input type="text" name="q" placeholder="Search docs" /> + <input type="hidden" name="check_keywords" value="yes" /> + <input type="hidden" name="area" value="default" 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+<ul> +<li class="toctree-l1"><a class="reference internal" href="../develop/schedule.html">Development Schedule</a></li> +<li class="toctree-l1"><a class="reference internal" href="../develop/how-contribute.html">How to Contribute to SINGA</a></li> +<li class="toctree-l1"><a class="reference internal" href="../develop/contribute-code.html">How to Contribute Code</a></li> +<li class="toctree-l1"><a class="reference internal" href="../develop/contribute-docs.html">How to Contribute to Documentation</a></li> +</ul> +<p class="caption"><span class="caption-text">Community</span></p> +<ul> +<li class="toctree-l1"><a class="reference internal" href="../community/source-repository.html">Source Repository</a></li> +<li class="toctree-l1"><a class="reference internal" href="../community/mail-lists.html">Project Mailing Lists</a></li> +<li class="toctree-l1"><a class="reference internal" href="../community/issue-tracking.html">Issue Tracking</a></li> +<li class="toctree-l1"><a class="reference internal" href="../community/team-list.html">The SINGA Team</a></li> +</ul> + + + + </div> + </div> + </nav> + + <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> + + + + <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">Documentation</a> »</li> + + <li>FeedForward Net</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.net"> +<span id="feedforward-net"></span><h1>FeedForward Net<a class="headerlink" href="#module-singa.net" title="Permalink to this headline">¶</a></h1> +<p>Nerual net class for constructing the nets using layers and providing access +functions for net info, e.g., parameters.</p> +<p>Example usages:</p> +<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">net</span> <span class="k">as</span> <span class="n">ffnet</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">metric</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">loss</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">layer</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">device</span> + +<span class="c1"># create net and add layers</span> +<span class="n">net</span> <span class="o">=</span> <span class="n">ffnet</span><span class="o">.</span><span class="n">FeedForwardNet</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropy</span><span class="p">(),</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">())</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="s1">'conv1'</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">input_sample_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">,)))</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu1'</span><span class="p">))</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="s1">'pool1'</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="s1">'flat'</span><span class="p">))</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="s1">'dense'</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> + +<span class="c1"># init parameters</span> +<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">param_values</span><span class="p">():</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">p</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">p</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="c1"># move net onto gpu</span> +<span class="n">dev</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu</span><span class="p">()</span> +<span class="n">net</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">dev</span><span class="p">)</span> + +<span class="c1"># training (skipped)</span> + +<span class="c1"># do prediction after training</span> +<span class="n">x</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="n">dev</span><span class="p">)</span> +<span class="n">x</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">y</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> +<span class="k">print</span> <span class="n">tensor</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.net.FeedForwardNet"> +<em class="property">class </em><code class="descclassname">singa.net.</code><code class="descname">FeedForwardNet</code><span class="sig-paren">(</span><em>loss=None</em>, <em>metric=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet" 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> +<dl class="method"> +<dt id="singa.net.FeedForwardNet.to_device"> +<code class="descname">to_device</code><span class="sig-paren">(</span><em>dev</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.to_device" title="Permalink to this definition">¶</a></dt> +<dd><p>Move the net onto the given device, including +all parameters and intermediate data.</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.add"> +<code class="descname">add</code><span class="sig-paren">(</span><em>lyr</em>, <em>src=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.add" title="Permalink to this definition">¶</a></dt> +<dd><p>Append a layer into the layer list.</p> +<p>This function will get the sample shape from the src layers to setup the +newly added layer. For the first layer, it is setup outside. The calling +function should ensure the correctness of the layer order. If src is +None, the last layer is the src layer. If there are multiple src layers, +the src is a list of the src 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>lyr</strong> (<a class="reference internal" href="layer.html#singa.layer.Layer" title="singa.layer.Layer"><em>Layer</em></a>) – the layer to be added</li> +<li><strong>src</strong> (<a class="reference internal" href="layer.html#singa.layer.Layer" title="singa.layer.Layer"><em>Layer</em></a>) – the source layer of lyr</li> +</ul> +</td> +</tr> +</tbody> +</table> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.param_values"> +<code class="descname">param_values</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.param_values" title="Permalink to this definition">¶</a></dt> +<dd><p>Return a list of tensors for all parameters</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.param_specs"> +<code class="descname">param_specs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.param_specs" title="Permalink to this definition">¶</a></dt> +<dd><p>Return a list of ParamSpec for all parameters</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.param_names"> +<code class="descname">param_names</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.param_names" title="Permalink to this definition">¶</a></dt> +<dd><p>Return a list for the names of all params</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.train"> +<code class="descname">train</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.train" title="Permalink to this definition">¶</a></dt> +<dd><p>Run BP for one iteration.</p> +<p>Currently only support nets with a single output layer, and a single +loss objective and metric. +TODO(wangwei) consider multiple loss objectives and metrics.</p> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> +<li><strong>x</strong> – input data, a single input Tensor or a dict: layer name -> Tensor</li> +<li><strong>y</strong> – label data, 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">gradients of parameters and the loss and metric values.</p> +</td> +</tr> +</tbody> +</table> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.evaluate"> +<code class="descname">evaluate</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.evaluate" title="Permalink to this definition">¶</a></dt> +<dd><p>Evaluate the loss and metric of the given data.</p> +<p>Currently only support nets with a single output layer, and a single +loss objective and metric. +TODO(wangwei) consider multiple loss objectives and metrics.</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>x</strong> – input data, a single input Tensor or a dict: layer name -> Tensor</li> +<li><strong>y</strong> – label data, a single input Tensor.</li> +</ul> +</td> +</tr> +</tbody> +</table> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.predict"> +<code class="descname">predict</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.predict" title="Permalink to this definition">¶</a></dt> +<dd><p>Forward the input data through each layer to get the values of the +output layers.</p> +<p>Currently only support nets with a single output 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>x</strong> – input data, a single input Tensor or a dict: layer name -> Tensor</td> +</tr> +<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a single output tensor as the prediction result.</td> +</tr> +</tbody> +</table> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.topo_sort"> +<code class="descname">topo_sort</code><span class="sig-paren">(</span><em>layers</em>, <em>src_of_layer</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.topo_sort" title="Permalink to this definition">¶</a></dt> +<dd><p>Topology sort of layers.</p> +<p>It would try to preserve the orders of the input 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 simple"> +<li><strong>layers</strong> – a list of layers; the layers from the output of the same +layer (e.g., slice layer) should be added by users in correct +order; This function would not change their order.</li> +<li><strong>src_of_layer</strong> – a dictionary: src layer name -> a list of src layers</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 of ordered layer</p> +</td> +</tr> +</tbody> +</table> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.forward"> +<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em>, <em>output=[]</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.forward" title="Permalink to this definition">¶</a></dt> +<dd><p>Forward the input(s) through every layer.</p> +<p>If a layer has inputs from other layers and from x, the data from x is +ordered before the data from other layers, e.g., if layer 1 -> layer 2, +and x[‘layer 2’] has data, then the input of layer 2 is +flatten([x[‘layer 2’], output of layer 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 simple"> +<li><strong>flag</strong> – True for training; False for evaluation; could also be +model_pb2.kTrain or model_pb2.kEval, or other values for future +use.</li> +<li><strong>x</strong> – a single SINGA tensor or a dictionary: layer name-> singa tensor</li> +<li><strong>output</strong> (<em>list</em>) – a list of layer names whose output would be returned +in addition to the default output</li> +</ul> +</td> +</tr> +<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">if there is only one output layer, return its output tensor(s); +else return a dictionary: layer name -> output tensor(s)</p> +</td> +</tr> +</tbody> +</table> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.backward"> +<code class="descname">backward</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.backward" title="Permalink to this definition">¶</a></dt> +<dd><p>Run back-propagation after forward-propagation.</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 gradient tensor for all parameters</td> +</tr> +</tbody> +</table> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.save"> +<code class="descname">save</code><span class="sig-paren">(</span><em>f</em>, <em>buffer_size=10</em>, <em>use_pickle=False</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.save" title="Permalink to this definition">¶</a></dt> +<dd><p>Save model parameters using io/snapshot.</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>f</strong> – file name</li> +<li><strong>buffer_size</strong> – size (MB) of the IO, default setting is 10MB; Please +make sure it is larger than any single parameter object.</li> +<li><strong>use_pickle</strong> (<em>Boolean</em>) – if true, it would use pickle for dumping; +otherwise, it would use protobuf for serialization, which uses +less space.</li> +</ul> +</td> +</tr> +</tbody> +</table> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.load"> +<code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>buffer_size=10</em>, <em>use_pickle=False</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.load" title="Permalink to this definition">¶</a></dt> +<dd><p>Load model parameters using io/snapshot.</p> +<p>Please refer to the argument description in save().</p> +</dd></dl> + +</dd></dl> + +</div> + + + </div> + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="initializer.html" class="btn btn-neutral float-right" title="Initializer" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="layer.html" class="btn btn-neutral" title="Layer" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2017 The Apache Software Foundation. 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