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<li><a href="index.html">Documentation</a> »</li> + + <li>Loss</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.loss"> +<span id="loss"></span><h1>Loss<a class="headerlink" href="#module-singa.loss" title="Permalink to this headline">¶</a></h1> +<p>Loss module includes a set of training loss implmentations. Some are converted +from C++ implementation, and the rest are implemented directly using python +Tensor.</p> +<p>Example usage:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">tensor</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">loss</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">3</span><span class="p">,</span> <span class="mi">5</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="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># randomly genearte the prediction activation</span> +<span class="n">y</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span> <span class="c1"># set the truth</span> + +<span class="n">f</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropy</span><span class="p">()</span> +<span class="n">l</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="c1"># l is tensor with 3 loss values</span> +<span class="n">g</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> <span class="c1"># g is a tensor containing all gradients of x w.r.t l</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.loss.Loss"> +<em class="property">class </em><code class="descclassname">singa.loss.</code><code class="descname">Loss</code><a class="headerlink" href="#singa.loss.Loss" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>Base loss class.</p> +<p>Subclasses that wrap the C++ loss classes can use the inherited foward, +backward, and evaluate functions of this base class. Other subclasses need +to override these functions</p> +<dl class="method"> +<dt id="singa.loss.Loss.backward"> +<code class="descname">backward</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.Loss.backward" title="Permalink to this definition">¶</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns</dt> +<dd class="field-odd"><p>the grad of x w.r.t. the loss</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.loss.Loss.evaluate"> +<code class="descname">evaluate</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.Loss.evaluate" title="Permalink to this definition">¶</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>flag</strong> (<em>int</em>) â must be kEval, to be removed</p></li> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the prediction Tensor</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the ground truth Tnesor</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>the averaged loss for all samples in x.</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.loss.Loss.forward"> +<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.Loss.forward" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the loss values.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>flag</strong> â kTrain/kEval or bool. If it is kTrain/True, then the backward +function must be called before calling forward again.</p></li> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the prediction Tensor</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the ground truch Tensor, x.shape[0] must = y.shape[0]</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a tensor of floats for the loss values, one per sample</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.loss.SigmoidCrossEntropy"> +<em class="property">class </em><code class="descclassname">singa.loss.</code><code class="descname">SigmoidCrossEntropy</code><span class="sig-paren">(</span><em>epsilon=1e-08</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.SigmoidCrossEntropy" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.loss.Loss" title="singa.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.loss.Loss</span></code></a></p> +<p>This loss evaluates the cross-entropy loss between the prediction and the +truth values with the prediction probability generated from Sigmoid.</p> +<dl class="method"> +<dt id="singa.loss.SigmoidCrossEntropy.backward"> +<code class="descname">backward</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.SigmoidCrossEntropy.backward" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the gradient of loss w.r.t to x.</p> +<dl class="field-list simple"> +<dt class="field-odd">Returns</dt> +<dd class="field-odd"><p>dx = pi - yi.</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.loss.SigmoidCrossEntropy.evaluate"> +<code class="descname">evaluate</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.SigmoidCrossEntropy.evaluate" title="Permalink to this definition">¶</a></dt> +<dd><p>Compuate the averaged error.</p> +<dl class="field-list simple"> +<dt class="field-odd">Returns</dt> +<dd class="field-odd"><p>a float value as the averaged error</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.loss.SigmoidCrossEntropy.forward"> +<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.SigmoidCrossEntropy.forward" title="Permalink to this definition">¶</a></dt> +<dd><p>loss is -yi * log pi - (1-yi) log (1-pi), where pi=sigmoid(xi)</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>flag</strong> (<em>bool</em>) â true for training; false for evaluation</p></li> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the prediction Tensor</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the truth Tensor, a binary array value per sample</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a Tensor with one error value per sample</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.loss.SoftmaxCrossEntropy"> +<em class="property">class </em><code class="descclassname">singa.loss.</code><code class="descname">SoftmaxCrossEntropy</code><a class="headerlink" href="#singa.loss.SoftmaxCrossEntropy" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.loss.Loss" title="singa.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.loss.Loss</span></code></a></p> +<p>This loss function is a combination of SoftMax and Cross-Entropy loss.</p> +<p>It converts the inputs via SoftMax function and then +computes the cross-entropy loss against the ground truth values.</p> +<p>For each sample, the ground truth could be a integer as the label index; +or a binary array, indicating the label distribution. The ground truth +tensor thus could be a 1d or 2d tensor. +The data/feature tensor could 1d (for a single sample) or 2d for a batch of +samples.</p> +</dd></dl> + +<dl class="class"> +<dt id="singa.loss.SquaredError"> +<em class="property">class </em><code class="descclassname">singa.loss.</code><code class="descname">SquaredError</code><a class="headerlink" href="#singa.loss.SquaredError" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.loss.Loss" title="singa.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.loss.Loss</span></code></a></p> +<p>This loss evaluates the squared error between the prediction and the +truth values.</p> +<p>It is implemented using Python Tensor operations.</p> +<dl class="method"> +<dt id="singa.loss.SquaredError.backward"> +<code class="descname">backward</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.SquaredError.backward" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the gradient of x w.r.t the error.</p> +<dl class="field-list simple"> +<dt class="field-odd">Returns</dt> +<dd class="field-odd"><p>x - y</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.loss.SquaredError.evaluate"> +<code class="descname">evaluate</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.SquaredError.evaluate" title="Permalink to this definition">¶</a></dt> +<dd><p>Compuate the averaged error.</p> +<dl class="field-list simple"> +<dt class="field-odd">Returns</dt> +<dd class="field-odd"><p>a float value as the averaged error</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.loss.SquaredError.forward"> +<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.loss.SquaredError.forward" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the error as 0.5 * ||x-y||^2.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>flag</strong> (<em>int</em>) â kTrain or kEval; if kTrain, then the backward must be +called before calling forward again.</p></li> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the prediction Tensor</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the truth Tensor, an integer value per sample, whose +value is [0, x.shape[1])</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a Tensor with one error value per sample</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="metric.html" class="btn btn-neutral float-right" title="Metric" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="initializer.html" class="btn btn-neutral float-left" title="Initializer" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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<li><a href="index.html">Documentation</a> »</li> + + <li>Metric</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.metric"> +<span id="metric"></span><h1>Metric<a class="headerlink" href="#module-singa.metric" title="Permalink to this headline">¶</a></h1> +<p>This module includes a set of metric classes for evaluating the modelâs +performance. The specific metric classes could be converted from C++ +implmentation or implemented directly using Python.</p> +<p>Example usage:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">tensor</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">metric</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">3</span><span class="p">,</span> <span class="mi">5</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="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># randomly genearte the prediction activation</span> +<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">SoftMax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># normalize the prediction into probabilities</span> +<span class="n">y</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span> <span class="c1"># set the truth</span> + +<span class="n">f</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> +<span class="n">acc</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="c1"># averaged accuracy over all 3 samples in x</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.metric.Metric"> +<em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Metric</code><a class="headerlink" href="#singa.metric.Metric" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>Base metric class.</p> +<p>Subclasses that wrap the C++ loss classes can use the inherited foward, +and evaluate functions of this base class. Other subclasses need +to override these functions. Users need to feed in the <strong>predictions</strong> and +ground truth to get the metric values.</p> +<dl class="method"> +<dt id="singa.metric.Metric.forward"> +<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Metric.forward" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the metric for each sample.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â predictions, one row per sample</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â ground truth values, one row per sample</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a tensor of floats, one per sample</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.metric.Metric.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.metric.Metric.evaluate" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the averaged metric over all samples.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â predictions, one row per sample</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â ground truth values, one row per sample</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a float value for the averaged metric</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.metric.Accuracy"> +<em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Accuracy</code><a class="headerlink" href="#singa.metric.Accuracy" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.metric.Metric" title="singa.metric.Metric"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.metric.Metric</span></code></a></p> +<p>Compute the top one accuracy for single label prediction tasks.</p> +<p>It calls the C++ functions to do the calculation.</p> +</dd></dl> + +<dl class="class"> +<dt id="singa.metric.Precision"> +<em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Precision</code><span class="sig-paren">(</span><em>top_k</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Precision" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.metric.Metric" title="singa.metric.Metric"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.metric.Metric</span></code></a></p> +<p>Make the top-k labels of max probability as the prediction</p> +<p>Compute the precision against the groundtruth labels</p> +<dl class="method"> +<dt id="singa.metric.Precision.forward"> +<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Precision.forward" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the precision for each sample.</p> +<p>Convert tensor to numpy for computation</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â predictions, one row per sample</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â ground truth labels, one row per sample</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a tensor of floats, one per sample</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.metric.Precision.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.metric.Precision.evaluate" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the averaged precision over all samples.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â predictions, one row per sample</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â ground truth values, one row per sample</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a float value for the averaged metric</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.metric.Recall"> +<em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Recall</code><span class="sig-paren">(</span><em>top_k</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Recall" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.metric.Metric" title="singa.metric.Metric"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.metric.Metric</span></code></a></p> +<p>Make the top-k labels of max probability as the prediction</p> +<p>Compute the recall against the groundtruth labels</p> +<dl class="method"> +<dt id="singa.metric.Recall.forward"> +<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Recall.forward" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the recall for each sample.</p> +<p>Convert tensor to numpy for computation</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â predictions, one row per sample</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â ground truth labels, one row per sample</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a tensor of floats, one per sample</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.metric.Recall.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.metric.Recall.evaluate" title="Permalink to this definition">¶</a></dt> +<dd><p>Compute the averaged precision over all samples.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â predictions, one row per sample</p></li> +<li><p><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â ground truth values, one row per sample</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a float value for the averaged metric</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="optimizer.html" class="btn btn-neutral float-right" title="Optimizer" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="loss.html" class="btn btn-neutral float-left" title="Loss" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--><div class="section" id="use-parameters-pre-trained-from-caffe-in-singa"> +<h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline">¶</a></h1> +<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p> +<div class="section" id="run-this-example"> +<h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline">¶</a></h2> +<p>You can run this example by simply executing <code class="docutils literal notranslate"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal notranslate"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code> +The script does the following work.</p> +<div class="section" id="obtain-the-caffe-model"> +<h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline">¶</a></h3> +<ul class="simple"> +<li><p>Download caffe model prototxt and parameter binary file.</p></li> +<li><p>Currently we only support the latest caffe format, if your model is in +previous version of caffe, please update it to current format.(This is +supported by caffe)</p></li> +<li><p>After updating, we can obtain two files, i.e., the prototxt and parameter +binary file.</p></li> +</ul> +</div> +<div class="section" id="prepare-test-images"> +<h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline">¶</a></h3> +<p>A few sample images are downloaded into the <code class="docutils literal notranslate"><span class="pre">test</span></code> folder.</p> +</div> +<div class="section" id="predict"> +<h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline">¶</a></h3> +<p>The <code class="docutils literal notranslate"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">usage</span><span class="p">:</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> <span class="p">[</span><span class="o">-</span><span class="n">h</span><span class="p">]</span> <span class="n">model_txt</span> <span class="n">model_bin</span> <span class="n">imgclass</span> +</pre></div> +</div> +<p>where <code class="docutils literal notranslate"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models. +You can start the prediction program by executing the following command:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg16</span><span class="o">.</span><span class="n">prototxt</span> <span class="n">vgg16</span><span class="o">.</span><span class="n">caffemodel</span> <span class="n">synset_words</span><span class="o">.</span><span class="n">txt</span> +</pre></div> +</div> +<p>Then you type in the image path, and the program would output the top-5 labels.</p> +<p>More Caffe models would be tested soon.</p> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/densenet/README.html#image-classification-using-densenet">Image Classification using DenseNet</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html">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</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html#image-classification-using-googlenet">Image Classification using GoogleNet</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/inception/README.html">name: Inception V4 on ImageNet +SINGA version: 1.1.1 +SINGA commit: +parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz +parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56 +license: https://github.com/tensorflow/models/tree/master/slim</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/inception/README.html#image-classification-using-inception-v4">Image Classification using Inception V4</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/resnet/README.html">name: Resnets on ImageNet +SINGA version: 1.1 +SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f +parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz +license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/resnet/README.html#image-classification-using-residual-networks">Image Classification using Residual Networks</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/vgg/README.html">name: VGG models on ImageNet +SINGA version: 1.1.1 +SINGA commit: +license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py</a></li> +<li class="toctree-l3"><a class="reference internal" href="../imagenet/vgg/README.html#image-classification-using-vgg">Image Classification using VGG</a></li> +</ul> +</li> +<li class="toctree-l2"><a class="reference internal" href="../../security.html">Security</a></li> +</ul> +</li> +<li class="toctree-l1"><a class="reference internal" href="../../../downloads.html">Download SINGA</a></li> +</ul> +<p class="caption"><span class="caption-text">Development</span></p> +<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/how-contribute.html#how-to-become-a-singa-committer">How to become a SINGA committer</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> +<li class="toctree-l1"><a class="reference internal" href="../../../develop/how-to-release.html">How to prepare a release</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" 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><a href="../index.html">Model Zoo</a> »</li> + + <li>Train Char-RNN over plain text</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"> + + <!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--><div class="section" id="train-char-rnn-over-plain-text"> +<h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1> +<p>Recurrent neural networks (RNN) are widely used for modelling sequential data, +e.g., natural language sentences. This example describes how to implement a RNN +application (or model) using SINGAâs RNN layers. +We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn";>char-rnn</a> model as an +example, which trains over sentences or +source code, with each character as an input unit. Particularly, we will train +a RNN using GRU over Linux kernel source code. After training, we expect to +generate meaningful code from the model.</p> +<div class="section" id="instructions"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<ul> +<li><p>Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05.</p></li> +<li><p>Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/";>kernel source code</a>. +Other plain text files can also be used.</p></li> +<li><p>Start the training,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">linux_input</span><span class="o">.</span><span class="n">txt</span> +</pre></div> +</div> +<p>Some hyper-parameters could be set through command line,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="o">-</span><span class="n">h</span> +</pre></div> +</div> +</li> +<li><p>Sample characters from the model by providing the number of characters to sample and the seed string.</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="s1">'model.bin'</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s1">'#include <std'</span> +</pre></div> +</div> +<p>Please replace âmodel.binâ with the path to one of the checkpoint paths.</p> +</li> +</ul> +</div> +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="../mnist/README.html" class="btn btn-neutral float-right" title="Train a RBM model against MNIST dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="../cifar10/README.html" class="btn btn-neutral float-left" title="Train CNN over Cifar-10" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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