Initial commit

net/loss.py

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+import numpy as np
+
+class Loss:
+    @staticmethod
+    def mean_squared_error(Y, A):
+        """ Mean Squared Error (MSE) """
+        return np.mean((Y - A) ** 2)
+    
+    @staticmethod
+    def mean_absolute_error(Y, A):
+        """ Mean Absolute Error (MAE) """
+        return np.mean(np.abs(Y - A))
+    
+    @staticmethod
+    def huber_loss(Y, A, delta=1.0):
+        """ Huber Loss """
+        error = Y - A
+        is_small_error = np.abs(error) <= delta
+        squared_loss = 0.5 * error ** 2
+        linear_loss = delta * (np.abs(error) - 0.5 * delta)
+        return np.where(is_small_error, squared_loss, linear_loss).mean()
+    
+    @staticmethod
+    def binary_cross_entropy_loss(Y, A):
+        """ Binary Cross-Entropy Loss """
+        m = Y.shape[1]
+        return -np.sum(Y * np.log(A + 1e-8) + (1 - Y) * np.log(1 - A + 1e-8)) / m
+    
+    @staticmethod
+    def categorical_cross_entropy_loss(Y, A):
+        """ Categorical Cross-Entropy Loss (for softmax) """
+        m = Y.shape[1]
+        return -np.sum(Y * np.log(A + 1e-8)) / m
+    
+    @staticmethod
+    def hinge_loss(Y, A):
+        """ Hinge Loss (used in SVM) """
+        return np.mean(np.maximum(0, 1 - Y * A))
+    
+    @staticmethod
+    def kl_divergence(P, Q):
+        """ Kullback-Leibler Divergence """
+        return np.sum(P * np.log(P / (Q + 1e-8)))
+    
+    @staticmethod
+    def poisson_loss(Y, A):
+        """ Poisson Loss """
+        return np.mean(A - Y * np.log(A + 1e-8))
+    
+    @staticmethod
+    def cosine_proximity_loss(Y, A):
+        """ Cosine Proximity Loss """
+        dot_product = np.sum(Y * A, axis=0)
+        norms = np.linalg.norm(Y, axis=0) * np.linalg.norm(A, axis=0)
+        return -np.mean(dot_product / (norms + 1e-8))
+
+    @classmethod
+    def get_function_name(cls, func):
+        return func.__name__
+
+    @classmethod
+    def get_all_loss_names(cls):
+        return [name for name, func in cls.__dict__.items() 
+                if callable(func) and not name.startswith("__") and 
+                not name in ['get_function_name', 'get_all_loss_names']]