Fix cross entropy loss compute()

CHANGELOG.md

@@ -4,6 +4,7 @@
     - Add numerical stability to logsumexp function
     - Fix Mean Shift delta hyper-parameter
     - Fix MAPE calculation in Error Analysis report
+    - Fix Cross Entropy loss missing (1−t)·log(1−p) term
     - Fix SiLU derivative at zero
     - Remove spurious +1 in Relative Entropy gradient
     - SoftPlus derivative expressed in terms of input

src/NeuralNet/CostFunctions/CrossEntropy.php

@@ -33,9 +33,14 @@ class CrossEntropy implements ClassificationLoss
      */
     public function compute(Matrix $output, Matrix $target) : float
     {
-        $entropy = $output->clipLower(EPSILON)->log();
+        $output = $output->clip(EPSILON, 1.0 - EPSILON);
 
-        return $target->negate()->multiply($entropy)->mean()->mean();
+        $ones = Matrix::ones(...$output->shape());
+
+        $entropy = $output->log()->multiply($target)
+            ->add($ones->subtract($output)->log()->multiply($ones->subtract($target)));
+
+        return $entropy->negate()->mean()->mean();
     }
 
     /**

tests/NeuralNet/CostFunctions/CrossEntropyTest.php

@@ -63,7 +63,7 @@ public function computeProvider() : Generator
             Matrix::quick([
                 [1.0, 0.0, 0.0],
             ]),
-            0.00335011195116715,
+            0.006700227235667666,
         ];
 
         yield [
@@ -73,7 +73,7 @@ public function computeProvider() : Generator
             Matrix::quick([
                 [0.0, 1.0, 0.0],
             ]),
-            0.3054302439580517,
+            0.5500866356514519,
         ];
 
         yield [
@@ -83,7 +83,7 @@ public function computeProvider() : Generator
             Matrix::quick([
                 [1.0, 0.0, 0.0],
             ]),
-            6.140226914650789,
+            6.94287545086808,
         ];
 
         yield [
@@ -97,7 +97,7 @@ public function computeProvider() : Generator
                 [0.0, 1.0, 0.0],
                 [0.0, 0.0, 1.0],
             ]),
-            0.10809567592917217,
+            0.20764012617655228,
         ];
     }