The Softmax loss function is a multi-class generalization of the LogisticLoss Logistic loss. @author Edward Raff
| 11 | * @author Edward Raff |
| 12 | */ |
| 13 | public class SoftmaxLoss extends LogisticLoss implements LossMC |
| 14 | { |
| 15 | |
| 16 | private static final long serialVersionUID = 3936898932252996024L; |
| 17 | |
| 18 | @Override |
| 19 | public double getLoss(Vec processed, int y) |
| 20 | { |
| 21 | return -Math.log(processed.get(y)); |
| 22 | } |
| 23 | |
| 24 | @Override |
| 25 | public void process(Vec pred, Vec processed) |
| 26 | { |
| 27 | if(pred != processed) |
| 28 | pred.copyTo(processed); |
| 29 | MathTricks.softmax(processed, false); |
| 30 | } |
| 31 | |
| 32 | @Override |
| 33 | public void deriv(Vec processed, Vec derivs, int y) |
| 34 | { |
| 35 | for(int i = 0; i < processed.length(); i++) |
| 36 | if(i == y) |
| 37 | derivs.set(i, processed.get(i)-1);//-(1-p) |
| 38 | else |
| 39 | derivs.set(i, processed.get(i));//-(0-p) |
| 40 | } |
| 41 | |
| 42 | @Override |
| 43 | public CategoricalResults getClassification(Vec processed) |
| 44 | { |
| 45 | return new CategoricalResults(processed.arrayCopy()); |
| 46 | } |
| 47 | } |
nothing calls this directly
no outgoing calls
no test coverage detected