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| <div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#ifndef SINGA_MODEL_LOSS_H_</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#define SINGA_MODEL_LOSS_H_</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <stack></span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include "singa/proto/model.pb.h"</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include "singa/core/tensor.h"</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">namespace </span><a class="code" href="namespacesinga.html">singa</a> {</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment">// template <typename T = Tensor></span></div><div class="line"><a name="l00031"></a><span class="lineno"><a class="line" href="classsinga_1_1Loss.html"> 31</a></span> <span class="keyword">class </span><a class="code" href="classsinga_1_1Loss.html">Loss</a> {</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="classsinga_1_1Loss.html">Loss</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordtype">void</span> Setup(<span class="keyword">const</span> <span class="keywordtype">string</span> &conf) {</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  LossConf loss;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  loss.ParseFromString(conf);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  Setup(loss);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  }</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keyword">virtual</span> ~<a class="code" href="classsinga_1_1Loss.html">Loss</a>() {};</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> ToDevice(std::shared_ptr<Device> device) {}</div><div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="classsinga_1_1Loss.html#a5be3a26d72200fb17ce1b2fa8c866525"> 42</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classsinga_1_1Loss.html#a5be3a26d72200fb17ce1b2fa8c866525">Setup</a>(<span class="keyword">const</span> LossConf &conf) {}</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">virtual</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> <a class="code" href="classsinga_1_1Loss.html#ad8ce9cac8b07719a2dcf2e5a50585f80">Forward</a>(<span class="keywordtype">int</span> flag, <span class="keyword">const</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> &prediction,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> &target) = 0;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="classsinga_1_1Loss.html#a7dfd3be50e9f20430604d4adab1664ce"> 52</a></span>  <span class="keywordtype">float</span> <a class="code" href="classsinga_1_1Loss.html#a7dfd3be50e9f20430604d4adab1664ce">Evaluate</a>(<span class="keywordtype">int</span> flag, <span class="keyword">const</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> &prediction, <span class="keyword">const</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> &target) {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> loss = <a class="code" href="classsinga_1_1Loss.html#ad8ce9cac8b07719a2dcf2e5a50585f80">Forward</a>(flag, prediction, target);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">return</span> Sum<float>(loss) / (1.0f * loss.<a class="code" href="classsinga_1_1Tensor.html#a1c38d62986020862905d1307c09972e0">Size</a>());</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  }</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">virtual</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> <a class="code" href="classsinga_1_1Loss.html#a82a5257979b7111e283b2da846124a08">Backward</a>() = 0;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> };</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="comment">// ============= Mean Squared Error ===========================================</span></div><div class="line"><a name="l00063"></a><span class="lineno"><a class="line" href="classsinga_1_1MSE.html"> 63</a></span> <span class="comment"></span><span class="keyword">class </span><a class="code" href="classsinga_1_1MSE.html">MSE</a> : <span class="keyword">public</span> <a class="code" href="classsinga_1_1Loss.html">Loss</a> {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> <a class="code" href="classsinga_1_1Loss.html#ad8ce9cac8b07719a2dcf2e5a50585f80">Forward</a>(<span class="keywordtype">int</span> flag, <span class="keyword">const</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a>& prediction,</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a>& target) <span class="keyword">override</span>;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> <a class="code" href="classsinga_1_1Loss.html#a82a5257979b7111e283b2da846124a08">Backward</a>() <span class="keyword">override</span>;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="comment">// to buffer intermediate data, i.e., prediction-target</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  std::stack<Tensor> buf_;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> };</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="comment">// ===============Softamx Cross Entropy =======================================</span></div><div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="classsinga_1_1SoftmaxCrossEntropy.html"> 84</a></span> <span class="comment"></span><span class="keyword">class </span><a class="code" href="classsinga_1_1SoftmaxCrossEntropy.html">SoftmaxCrossEntropy</a> : <span class="keyword">public</span> <a class="code" href="classsinga_1_1Loss.html">Loss</a> {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> <a class="code" href="classsinga_1_1Loss.html#ad8ce9cac8b07719a2dcf2e5a50585f80">Forward</a>(<span class="keywordtype">int</span> flag, <span class="keyword">const</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a>& prediction,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keyword">const</span> <a class="code" href="classsinga_1_1Tensor.html">Tensor</a>& target) <span class="keyword">override</span>;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="classsinga_1_1Tensor.html">Tensor</a> <a class="code" href="classsinga_1_1Loss.html#a82a5257979b7111e283b2da846124a08">Backward</a>() <span class="keyword">override</span>;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="comment">// to buffer intermediate data, i.e., probability for each category and</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// the target (ground truth)</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::stack<Tensor> buf_;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> };</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> } <span class="comment">// namespace singa</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="preprocessor">#endif // SINGA_MODEL_LOSS_H_</span></div><div class="ttc" id="classsinga_1_1Loss_html_ad8ce9cac8b07719a2dcf2e5a50585f80"><div class="ttname"><a href="classsinga_1_1Loss.html#ad8ce9cac8b07719a2dcf2e5a50585f80">singa::Loss::Forward</a></div><div class="ttdeci">virtual Tensor Forward(int flag, const Tensor &prediction, const Tensor &target)=0</div><div class="ttdoc">Compute the loss values for each sample/instance given the prediction and the target. </div></div> |
| <div class="ttc" id="classsinga_1_1SoftmaxCrossEntropy_html"><div class="ttname"><a href="classsinga_1_1SoftmaxCrossEntropy.html">singa::SoftmaxCrossEntropy</a></div><div class="ttdoc">Softmax + cross entropy for multi-category classification. </div><div class="ttdef"><b>Definition:</b> loss.h:84</div></div> |
| <div class="ttc" id="classsinga_1_1Loss_html_a82a5257979b7111e283b2da846124a08"><div class="ttname"><a href="classsinga_1_1Loss.html#a82a5257979b7111e283b2da846124a08">singa::Loss::Backward</a></div><div class="ttdeci">virtual Tensor Backward()=0</div><div class="ttdoc">Compute the gradients of the loss values w.r.t. the prediction. </div></div> |
| <div class="ttc" id="classsinga_1_1Tensor_html"><div class="ttname"><a href="classsinga_1_1Tensor.html">singa::Tensor</a></div><div class="ttdoc">A Tensor instance is a multi-dimensional array resident on a Device (default device is the host CPU)...</div><div class="ttdef"><b>Definition:</b> tensor.h:56</div></div> |
| <div class="ttc" id="classsinga_1_1Loss_html"><div class="ttname"><a href="classsinga_1_1Loss.html">singa::Loss</a></div><div class="ttdoc">The base loss class, which declares the APIs for computing the objective score (loss) for a pair of p...</div><div class="ttdef"><b>Definition:</b> loss.h:31</div></div> |
| <div class="ttc" id="namespacesinga_html"><div class="ttname"><a href="namespacesinga.html">singa</a></div><div class="ttdoc">Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements...</div><div class="ttdef"><b>Definition:</b> common.h:48</div></div> |
| <div class="ttc" id="classsinga_1_1Tensor_html_a1c38d62986020862905d1307c09972e0"><div class="ttname"><a href="classsinga_1_1Tensor.html#a1c38d62986020862905d1307c09972e0">singa::Tensor::Size</a></div><div class="ttdeci">size_t Size() const</div><div class="ttdoc">Return number of total elements. </div><div class="ttdef"><b>Definition:</b> tensor.h:128</div></div> |
| <div class="ttc" id="classsinga_1_1MSE_html"><div class="ttname"><a href="classsinga_1_1MSE.html">singa::MSE</a></div><div class="ttdoc">MSE is for mean squared error or squared euclidean distance. </div><div class="ttdef"><b>Definition:</b> loss.h:63</div></div> |
| <div class="ttc" id="classsinga_1_1Loss_html_a7dfd3be50e9f20430604d4adab1664ce"><div class="ttname"><a href="classsinga_1_1Loss.html#a7dfd3be50e9f20430604d4adab1664ce">singa::Loss::Evaluate</a></div><div class="ttdeci">float Evaluate(int flag, const Tensor &prediction, const Tensor &target)</div><div class="ttdoc">Average loss values for all samples in the mini-batch It calls Forward() internally. </div><div class="ttdef"><b>Definition:</b> loss.h:52</div></div> |
| <div class="ttc" id="classsinga_1_1Loss_html_a5be3a26d72200fb17ce1b2fa8c866525"><div class="ttname"><a href="classsinga_1_1Loss.html#a5be3a26d72200fb17ce1b2fa8c866525">singa::Loss::Setup</a></div><div class="ttdeci">virtual void Setup(const LossConf &conf)</div><div class="ttdoc">Set meta fields from user configurations. </div><div class="ttdef"><b>Definition:</b> loss.h:42</div></div> |
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