终于把机器学习中的损失函数搞懂了！！！

1. Mean Squared Error (MSE)

MSE 是回归任务中最常用的损失函数之一。

它衡量模型预测值与实际值之间的平均平方误差。

公式:

特点:

• 对于大的误差，MSE 会给出更大的惩罚，因为误差被平方。
• 对于异常值较为敏感。

复制

import tensorflow as tf
import matplotlib.pyplot as plt

class MeanSquaredError_Loss:
    """
    This class provides two methods to calculate Mean Squared Error Loss.
    """
    def __init__(self):
        pass

    @staticmethod
    def mean_squared_error_manual(y_true, y_pred):
        
        squared_difference = tf.square(y_true - y_pred)
        loss = tf.reduce_mean(squared_difference)
        return loss

    @staticmethod
    def mean_squared_error_tf(y_true, y_pred):
        
        mse = tf.keras.losses.MeanSquaredError()
        loss = mse(y_true, y_pred)
        return loss

if __name__ == "__main__":
    def mean_squared_error_test(N=10, C=10):
    
        # Generate random data
        y_true = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.float32)
        y_pred = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.float32)


        # Test the MeanSquaredError_Loss class
        mse_manual = MeanSquaredError_Loss.mean_squared_error_manual(y_true, y_pred)
        print(f"mean_squared_error_manual: {mse_manual}")

        mse_tf = MeanSquaredError_Loss.mean_squared_error_tf(y_true, y_pred)
        print(f"mean_squared_error_tensorflow: {mse_tf}")
        print()

        # Plot the points on a graph
        plt.figure(figsize=(8, 6))
        plt.scatter(y_true.numpy(), y_pred.numpy(), color='blue', label='Predicted vs Actual')
        plt.plot([-C, C], [-C, C], 'r--', label='Ideal Line')  # Diagonal line representing ideal predictions

        plt.title(f"Predictions vs Actuals\nMean Squared Error: {mse_manual.numpy():.4f}")
        plt.xlabel('Actual Values')
        plt.ylabel('Predicted Values')
        plt.legend()
        plt.grid(True)
        plt.show()

    mean_squared_error_test()

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2. Mean Absolute Error (MAE)

MAE 也是用于回归任务的损失函数，它计算的是预测值与实际值之间误差的绝对值的平均值。

公式:

特点:

• MAE 不像 MSE 那样对异常值敏感，因为它没有平方误差。
• 更加直观，直接反映了误差的平均大小。

复制

import tensorflow as tf
import matplotlib.pyplot as plt

class MeanAbsoluteError_Loss:
    """
    This class provides two methods to calculate Mean Absolute Error Loss.
    """
    def __init__(self):
        pass

    @staticmethod
    def mean_absolute_error_manual(y_true, y_pred):
        absolute_difference = tf.math.abs(y_true - y_pred)
        loss = tf.reduce_mean(absolute_difference)
        return loss

    @staticmethod
    def mean_absolute_error_tf(y_true, y_pred):
        mae = tf.keras.losses.MeanAbsoluteError()
        loss = mae(y_true, y_pred)
        return loss

if __name__ == "__main__":
    def mean_absolute_error_test(N=10, C=10):
        # Generate random data
        y_true = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.float32)
        y_pred = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.float32)


        # Test the MeanabsoluteError_Loss class
        mae_manual = MeanAbsoluteError_Loss.mean_absolute_error_manual(y_true, y_pred)
        print(f"mean_absolute_error_manual: {mae_manual}")

        mae_tf = MeanAbsoluteError_Loss.mean_absolute_error_tf(y_true, y_pred)
        print(f"mean_absolute_error_tensorflow: {mae_tf}")
        print()

        # Plot the points on a graph
        plt.figure(figsize=(8, 6))
        plt.scatter(y_true.numpy(), y_pred.numpy(), color='blue', label='Predicted vs Actual')
        plt.plot([-C, C], [-C, C], 'r--', label='Ideal Line')  # Diagonal line representing ideal predictions

        plt.title(f"Predictions vs Actuals\nMean Absolute Error: {mae_manual.numpy():.4f}")
        plt.xlabel('Actual Values')
        plt.ylabel('Predicted Values')
        plt.legend()
        plt.grid(True)
        plt.show()

    mean_absolute_error_test()

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3. Huber Loss

Huber Loss 是介于 MSE 和 MAE 之间的一种损失函数，当误差较小时，它像 MSE 一样处理，而当误差较大时，它像 MAE 一样处理。

这样可以在处理异常值时更稳定。

公式:

特点:

• 对异常值更具有鲁棒性，同时保留了误差较小时的敏感性。

复制

import tensorflow as tf
import matplotlib.pyplot as plt

class Huber_Loss:
    """
    This class provides two methods to calculate Huber Loss.
    """
    def __init__(self, delta = 1.0):
        
        self.delta = delta

    def huber_loss_manual(self, y_true, y_pred):
        
        error = tf.math.abs(y_true - y_pred)
        is_small_error = tf.math.less_equal(error, self.delta)
        small_error_loss = tf.math.square(error) / 2
        large_error_loss = self.delta * (error - (0.5 * self.delta))
        loss = tf.where(is_small_error, small_error_loss, large_error_loss)
        loss = tf.reduce_mean(loss)
        return loss

    def huber_loss_tf(self, y_true, y_pred):
        
        huber_loss = tf.keras.losses.Huber(delta = self.delta)(y_true, y_pred)
        return huber_loss

if __name__ == "__main__":
    def huber_loss_test(N=10, C=10):
        # Generate random data
        y_true = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.float32)
        y_pred = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.float32)


        # Test the Huber_Loss class
        huber = Huber_Loss() 
        hl_manual = huber.huber_loss_manual(y_true, y_pred)
        print(f"huber_loss_manual: {hl_manual}")

        hl_tf = huber.huber_loss_tf(y_true, y_pred)
        print(f"huber_loss_tensorflow: {hl_tf}")
        print()

        # Plot the points on a graph
        plt.figure(figsize=(8, 6))
        plt.scatter(y_true.numpy(), y_pred.numpy(), color='blue', label='Predicted vs Actual')
        plt.plot([-C, C], [-C, C], 'r--', label='Ideal Line')  # Diagonal line representing ideal predictions

        plt.title(f"Predictions vs Actuals\nHuber Loss: {hl_manual.numpy():.4f}")
        plt.xlabel('Actual Values')
        plt.ylabel('Predicted Values')
        plt.legend()
        plt.grid(True)
        plt.show()

    huber_loss_test()

图片

4. Cross-Entropy Loss

Cross-Entropy Loss 是分类任务中广泛使用的损失函数，尤其是在二分类和多分类问题中。

它衡量的是模型输出的概率分布与实际类别的分布之间的差异。

公式:

对于二分类问题：

特点:

• 当预测概率与实际标签匹配时，损失较低；否则损失较高。
• 对于分类问题的优化尤为有效。

复制

import tensorflow as tf
import matplotlib.pyplot as plt

class Cross_Entropy_Loss:
    """
    This class provides two methods to calculate Cross-Entropy Loss.
    """
    def __init__(self):
        pass

    def cross_entropy_loss_manual(self, y_true, y_pred):
        y_pred /= tf.reduce_sum(y_pred)
        epsilon = tf.keras.backend.epsilon()
        y_pred_new = tf.clip_by_value(y_pred, epsilon, 1.)
        loss =  - tf.reduce_sum(y_true * tf.math.log(y_pred_new))
        return loss 

    def cross_entropy_loss_tf(self, y_true, y_pred):
        loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)
        return loss

if __name__ == "__main__":
    def cross_entropy_loss_test(N=10, C=1):
        # Generate random data
        y_true = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.float32)
        y_pred = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.float32)


        # Test the Cross-Entropy_Loss class
        cross_entropy = Cross_Entropy_Loss() 
        ce_manual = cross_entropy.cross_entropy_loss_manual(y_true, y_pred)
        print(f"cross_entropy_loss_manual: {ce_manual}")

        ce_tf = cross_entropy.cross_entropy_loss_tf(y_true, y_pred)
        print(f"cross_entropy_loss_tensorflow: {ce_tf}")
        print()

        # Plot the points on a graph
        plt.figure(figsize=(8, 6))
        plt.scatter(y_true.numpy(), y_pred.numpy(), color='blue', label='Predicted vs Actual')
        plt.plot([-C, C], [-C, C], 'r--', label='Ideal Line')  # Diagonal line representing ideal predictions

        plt.title(f"Predictions vs Actuals\nCross-Entropy Loss: {ce_manual.numpy():.4f}")
        plt.xlabel('Actual Values')
        plt.ylabel('Predicted Values')
        plt.legend()
        plt.grid(True)
        plt.show()

    cross_entropy_loss_test()

5. Hinge Loss

Hinge Loss 通常用于支持向量机（SVM）中。

它鼓励模型使得正确类别的得分高于错误类别至少一个边距（通常是1）。

公式:

特点:

• 强制模型为正确类别创造一个“边距”，使得分类更加鲁棒。
• 适用于线性分类器的优化。

复制

import tensorflow as tf
import matplotlib.pyplot as plt

class Hinge_Loss:
    """
    This class provides two methods to calculate Hinge Loss.
    """
    def __init__(self):
        pass

    def hinge_loss_manual(self, y_true, y_pred):
        
        pos = tf.reduce_sum(y_true * y_pred, axis=-1)
        neg = tf.reduce_max((1 - y_true) * y_pred, axis=-1)
        loss = tf.maximum(0, neg - pos + 1)
        return loss 

    def hinge_loss_tf(self, y_true, y_pred):
        
        loss = tf.keras.losses.CategoricalHinge()(y_true, y_pred)
        return loss

if __name__ == "__main__":
    def hinge_loss_test(N=10, C=10):
       
        # Generate random data
        y_true = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.int32)
        y_pred = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.int32)


        # Test the Hinge_Loss class
        cross_entropy = Hinge_Loss() 
        hl_manual = cross_entropy.hinge_loss_manual(y_true, y_pred)
        print(f"hinge_loss_manual: {hl_manual}")

        hl_tf = cross_entropy.hinge_loss_tf(y_true, y_pred)
        print(f"hinge_loss_tensorflow: {hl_tf}")
        print()

        # Plot the points on a graph
        plt.figure(figsize=(8, 6))
        plt.scatter(y_true.numpy(), y_pred.numpy(), color='blue', label='Predicted vs Actual')
        plt.plot([-C, C], [-C, C], 'r--', label='Ideal Line')  # Diagonal line representing ideal predictions

        plt.title(f"Predictions vs Actuals\nHinge Loss: {hl_manual.numpy()}")
        plt.xlabel('Actual Values')
        plt.ylabel('Predicted Values')
        plt.legend()
        plt.grid(True)
        plt.show()

    hinge_loss_test()

6. Intersection Over Union (IoU)

IoU 通常用于目标检测任务中，衡量预测的边界框与实际边界框之间的重叠程度。

公式:

特点:

• 值域在0到1之间，1表示完美重叠，0表示没有重叠。
• 用于评估边界框预测的准确性。

复制

import tensorflow as tf
import matplotlib.pyplot as plt

class IOU:
    def __init__(self):
        pass

    def IOU_manual(self, y_true, y_pred):
        intersection = tf.reduce_sum(tf.cast(tf.logical_and(tf.equal(y_true, 1), tf.equal(y_pred, 1)), dtype=tf.float32))
        union = tf.reduce_sum(tf.cast(tf.logical_or(tf.equal(y_true, 1), tf.equal(y_pred, 1)), dtype=tf.float32))
        iou = intersection / union
        return iou

    def IOU_tf(self, y_true, y_pred):
        iou_metric = tf.keras.metrics.IoU(num_classes=2, target_class_ids=[1])
        iou_metric.update_state(y_true, y_pred)
        iou = iou_metric.result()
        return iou

if __name__ == "__main__":
    def IOU_test(N=10, C=10):
        # Generate random data
        y_true = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.int32)
        y_pred = tf.random.uniform(shape=(N, ), minval=-C, maxval=C, dtype=tf.int32)

        y_true = tf.constant([[0, 1, 1, 0], 
                              [0, 1, 1, 0], 
                              [0, 0, 0, 0], 
                              [0, 0, 0, 0]], dtype=tf.float32)  # Example binary mask (ground truth)

        y_pred = tf.constant([[0, 1, 1, 0], 
                              [1, 1, 0, 0], 
                              [0, 0, 0, 0], 
                              [0, 0, 0, 0]], dtype=tf.float32)  # Example binary mask (prediction)

        iou = IOU()

        iou_manual = iou.IOU_manual(y_true, y_pred)
        print(f"IOU_manual: {iou_manual}")

        iou_tf = iou.IOU_tf(y_true, y_pred)
        print(f"IOU_tensorflow: {iou_tf}")

    IOU_test()

7. Kullback-Leibler (KL) Divergence

KL 散度是一种衡量两个概率分布之间差异的非对称性度量，通常用于生成模型和变分自编码器中。

公式:

特点:

• 当 P 和 Q 完全相同时，KL 散度为0。
• 适用于评估模型预测的概率分布与目标概率分布之间的差异。

复制

import tensorflow as tf
import matplotlib.pyplot as plt

class Kullback_Leibler:
    """
    This class provides two methods to calculate Kullback-Leibler Loss.
    """
    def __init__(self):
        pass

    def kullback_leibler_manual(self, y_true, y_pred):
        epsilon = tf.keras.backend.epsilon()
        y_true = tf.clip_by_value(y_true, epsilon, 1)
        y_pred = tf.clip_by_value(y_pred, epsilon, 1)
        
        loss = tf.reduce_sum(y_true * tf.math.log(y_true / y_pred), axis=-1)
        return loss

    def kullback_leibler_tf(self, y_true, y_pred):
        loss = tf.reduce_sum(tf.keras.losses.KLDivergence()(y_true, y_pred))
        return loss

if __name__ == "__main__":
    def kullback_leibler_test(N=5, C=1):
        # Generate random data
        y_true = tf.random.uniform(shape=(N, ), minval=0, maxval=C, dtype=tf.float32)
        y_pred = tf.random.uniform(shape=(N, ), minval=0, maxval=C, dtype=tf.float32)

        #converting them to probabilities
        y_true /= tf.reduce_sum(y_true)
        y_pred /= tf.reduce_sum(y_pred)

        # Test the kullback_leibler class
        kl = Kullback_Leibler() 
        kl_manual = kl.kullback_leibler_manual(y_true, y_pred)
        print(f"kullback_leibler_manual: {kl_manual}")

        kl_tf = kl.kullback_leibler_tf(y_true, y_pred)
        print(f"kullback_leibler_tensorflow: {kl_tf}")
        print()

        # Plot the points on a graph
        plt.figure(figsize=(8, 6))
        plt.scatter(y_true.numpy(), y_pred.numpy(), color='blue', label='Predicted vs Actual')
        plt.plot([0, C], [0, C], 'r--', label='Ideal Line')  # Diagonal line representing ideal predictions

        plt.title(f"Predictions vs Actuals\nKullback-Leibler Loss: {kl_manual.numpy()}")
        plt.xlabel('Actual Values')
        plt.ylabel('Predicted Values')
        plt.legend()
        plt.grid(True)
        plt.show()

    kullback_leibler_test()

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