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| <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-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span> |
| <span class="kn">from</span> <span class="nn">singa</span> <span class="kn">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="bp">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"><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><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">the grad of x w.r.t. the loss</td> |
| </tr> |
| </tbody> |
| </table> |
| </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><table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>flag</strong> (<em>int</em>) – must be kEval, to be removed</li> |
| <li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – the prediction Tensor</li> |
| <li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – the ground truth Tnesor</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the averaged loss for all samples in x.</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </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> |
| <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> – kTrain/kEval or bool. If it is kTrain/True, then the backward |
| function must be called before calling forward again.</li> |
| <li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – the prediction Tensor</li> |
| <li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – the ground truch Tensor, x.shape[0] must = y.shape[0]</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a tensor of floats for the loss values, one per sample</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </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"><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"><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> |
| <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">x - y</td> |
| </tr> |
| </tbody> |
| </table> |
| </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> |
| <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 float value as the averaged error</td> |
| </tr> |
| </tbody> |
| </table> |
| </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> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>flag</strong> (<em>int</em>) – kTrain or kEval; if kTrain, then the backward must be |
| called before calling forward again.</li> |
| <li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – the prediction Tensor</li> |
| <li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – the truth Tensor, an integer value per sample, whose |
| value is [0, x.shape[1])</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a Tensor with one error value per sample</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
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