implemented cross entropy for multiple classes

Author: devshah21

little-book-of-deep-learning/cross-entropy.py

@@ -14,7 +14,8 @@ def binary_cross_entropy(y_true, y_pred):
     float: Binary cross-entropy loss.
     """
     epsilon = 1e-15  # to avoid log(0)
-    y_pred = np.clip(y_pred, epsilon, 1 - epsilon)
+    y_pred = np.clip(y_pred, epsilon, 1 - epsilon) 
+    ## we use np.clip to avoid log(0)
     loss = -np.mean(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))
     return loss
 
@@ -23,3 +24,36 @@ def binary_cross_entropy(y_true, y_pred):
 y_pred = np.array([0.9, 0.1, 0.8, 0.7, 0.3])
 loss = binary_cross_entropy(y_true, y_pred)
 print(f'Binary Cross-Entropy Loss: {loss}')
+
+
+
+def multi_cross_entropy(y_true, y_pred, classes):
+    """
+    Compute the binary cross-entropy loss.
+    
+    Parameters:
+    y_true (numpy.ndarray): True labels (0 or 1).
+    y_pred (numpy.ndarray): Predicted probabilities.
+
+    Returns:
+    float: Binary cross-entropy loss.
+    """
+    epsilon = 1e-15  # to avoid log(0)
+    y_pred = np.clip(y_pred, epsilon, 1 - epsilon) 
+    ## we use np.clip to avoid log(0)
+    loss = -np.mean(np.sum(y_true * np.log(y_pred), axis=1))
+    return loss
+
+
+y_true = np.array([
+    [1, 0, 0],
+    [0, 1, 0],
+    [0, 0, 1]
+])
+y_pred = np.array([
+    [0.9, 0.05, 0.05],
+    [0.1, 0.8, 0.1],
+    [0.2, 0.2, 0.6]
+])
+loss = multi_cross_entropy(y_true, y_pred)
+print(f'Categorical Cross-Entropy Loss: {loss}')