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| <li class="navelem"><a class="el" href="namespacesinga.html">singa</a></li><li class="navelem"><a class="el" href="classsinga_1_1SoftmaxCrossEntropy.html">SoftmaxCrossEntropy</a></li> </ul> |
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| |
| <p>Softmax + cross entropy for multi-category classification. |
| <a href="classsinga_1_1SoftmaxCrossEntropy.html#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="loss_8h_source.html">loss.h</a>></code></p> |
| <div class="dynheader"> |
| Inheritance diagram for singa::SoftmaxCrossEntropy:</div> |
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| <table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a> |
| Public Member Functions</h2></td></tr> |
| <tr class="memitem:a7dacf1dfecad29db29d942e874f3a8fa"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classsinga_1_1Tensor.html">Tensor</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsinga_1_1SoftmaxCrossEntropy.html#a7dacf1dfecad29db29d942e874f3a8fa">Forward</a> (int flag, const <a class="el" href="classsinga_1_1Tensor.html">Tensor</a> &prediction, const <a class="el" href="classsinga_1_1Tensor.html">Tensor</a> &target) override</td></tr> |
| <tr class="memdesc:a7dacf1dfecad29db29d942e874f3a8fa"><td class="mdescLeft"> </td><td class="mdescRight">Compute the loss values for each sample/instance given the prediction and the target. <a href="#a7dacf1dfecad29db29d942e874f3a8fa">More...</a><br /></td></tr> |
| <tr class="separator:a7dacf1dfecad29db29d942e874f3a8fa"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a1e7c9465c3ef0b732525700482a80492"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classsinga_1_1Tensor.html">Tensor</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsinga_1_1SoftmaxCrossEntropy.html#a1e7c9465c3ef0b732525700482a80492">Backward</a> () override</td></tr> |
| <tr class="memdesc:a1e7c9465c3ef0b732525700482a80492"><td class="mdescLeft"> </td><td class="mdescRight">Compute the gradients of the loss values w.r.t. <a href="#a1e7c9465c3ef0b732525700482a80492">More...</a><br /></td></tr> |
| <tr class="separator:a1e7c9465c3ef0b732525700482a80492"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="inherit_header pub_methods_classsinga_1_1Loss"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classsinga_1_1Loss')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classsinga_1_1Loss.html">singa::Loss</a></td></tr> |
| <tr class="memitem:ab1ffff2b3ddebd84bd1ea19104e1bba5 inherit pub_methods_classsinga_1_1Loss"><td class="memItemLeft" align="right" valign="top"><a id="ab1ffff2b3ddebd84bd1ea19104e1bba5"></a> |
| void </td><td class="memItemRight" valign="bottom"><b>Setup</b> (const string &conf)</td></tr> |
| <tr class="separator:ab1ffff2b3ddebd84bd1ea19104e1bba5 inherit pub_methods_classsinga_1_1Loss"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:afe5a390383602feed7b0adf3a6afb487 inherit pub_methods_classsinga_1_1Loss"><td class="memItemLeft" align="right" valign="top"><a id="afe5a390383602feed7b0adf3a6afb487"></a> |
| virtual void </td><td class="memItemRight" valign="bottom"><b>ToDevice</b> (std::shared_ptr< <a class="el" href="classsinga_1_1Device.html">Device</a> > device)</td></tr> |
| <tr class="separator:afe5a390383602feed7b0adf3a6afb487 inherit pub_methods_classsinga_1_1Loss"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a5be3a26d72200fb17ce1b2fa8c866525 inherit pub_methods_classsinga_1_1Loss"><td class="memItemLeft" align="right" valign="top"><a id="a5be3a26d72200fb17ce1b2fa8c866525"></a> |
| virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classsinga_1_1Loss.html#a5be3a26d72200fb17ce1b2fa8c866525">Setup</a> (const LossConf &conf)</td></tr> |
| <tr class="memdesc:a5be3a26d72200fb17ce1b2fa8c866525 inherit pub_methods_classsinga_1_1Loss"><td class="mdescLeft"> </td><td class="mdescRight">Set meta fields from user configurations. <br /></td></tr> |
| <tr class="separator:a5be3a26d72200fb17ce1b2fa8c866525 inherit pub_methods_classsinga_1_1Loss"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a7dfd3be50e9f20430604d4adab1664ce inherit pub_methods_classsinga_1_1Loss"><td class="memItemLeft" align="right" valign="top">float </td><td class="memItemRight" valign="bottom"><a class="el" href="classsinga_1_1Loss.html#a7dfd3be50e9f20430604d4adab1664ce">Evaluate</a> (int flag, const <a class="el" href="classsinga_1_1Tensor.html">Tensor</a> &prediction, const <a class="el" href="classsinga_1_1Tensor.html">Tensor</a> &target)</td></tr> |
| <tr class="memdesc:a7dfd3be50e9f20430604d4adab1664ce inherit pub_methods_classsinga_1_1Loss"><td class="mdescLeft"> </td><td class="mdescRight">Average loss values for all samples in the mini-batch It calls <a class="el" href="classsinga_1_1Loss.html#ad8ce9cac8b07719a2dcf2e5a50585f80" title="Compute the loss values for each sample/instance given the prediction and the target. ">Forward()</a> internally. <a href="classsinga_1_1Loss.html#a7dfd3be50e9f20430604d4adab1664ce">More...</a><br /></td></tr> |
| <tr class="separator:a7dfd3be50e9f20430604d4adab1664ce inherit pub_methods_classsinga_1_1Loss"><td class="memSeparator" colspan="2"> </td></tr> |
| </table> |
| <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2> |
| <div class="textblock"><p>Softmax + cross entropy for multi-category classification. </p> |
| </div><h2 class="groupheader">Member Function Documentation</h2> |
| <a id="a1e7c9465c3ef0b732525700482a80492"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a1e7c9465c3ef0b732525700482a80492">◆ </a></span>Backward()</h2> |
| |
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| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classsinga_1_1Tensor.html">Tensor</a> singa::SoftmaxCrossEntropy::Backward </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Compute the gradients of the loss values w.r.t. </p> |
| <p>the prediction, which is: p[i] - t[i]/ t[j] </p> |
| |
| <p>Implements <a class="el" href="classsinga_1_1Loss.html#a82a5257979b7111e283b2da846124a08">singa::Loss</a>.</p> |
| |
| </div> |
| </div> |
| <a id="a7dacf1dfecad29db29d942e874f3a8fa"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a7dacf1dfecad29db29d942e874f3a8fa">◆ </a></span>Forward()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classsinga_1_1Tensor.html">Tensor</a> singa::SoftmaxCrossEntropy::Forward </td> |
| <td>(</td> |
| <td class="paramtype">int </td> |
| <td class="paramname"><em>flag</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classsinga_1_1Tensor.html">Tensor</a> & </td> |
| <td class="paramname"><em>prediction</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classsinga_1_1Tensor.html">Tensor</a> & </td> |
| <td class="paramname"><em>target</em> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Compute the loss values for each sample/instance given the prediction and the target. </p> |
| <p>If the target consists one integer per instance, i.e. the label index (dentoed as idx_truth), the loss is -log(p[idx_truth]), p[] is the probability for each category, computed from Softmax(prediction). If the target consists one array per instance (e.g., for multiple labels), the loss is - (t[i] * log(p[i]) / t[j], t[i] is the weight of the i-th label (e.g., 1: the instance has this label, 0: the instance does not have this label).</p> |
| <p>Users can call Average(const Tensor&) to get the average loss value over all samples in the batch. </p> |
| |
| <p>Implements <a class="el" href="classsinga_1_1Loss.html#ad8ce9cac8b07719a2dcf2e5a50585f80">singa::Loss</a>.</p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this class was generated from the following file:<ul> |
| <li>/home/moaz/incubator-singa/include/singa/model/<a class="el" href="loss_8h_source.html">loss.h</a></li> |
| </ul> |
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