Keras中的自定义加权交叉熵损失

import tensorflow as tf
from tensorflow.keras.losses import Loss

class WeightedCrossEntropyLoss(Loss):
    def __init__(self, class_weights):
        super(WeightedCrossEntropyLoss, self).__init__()
        self.class_weights = class_weights

    def call(self, y_true, y_pred):
        # 计算交叉熵损失
        loss = tf.keras.backend.categorical_crossentropy(y_true, y_pred)

        # 根据类别权重调整损失
        weighted_loss = tf.multiply(loss, self.class_weights)

        return weighted_loss

# 创建模型
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(10, activation='softmax'))

# 定义类别权重
class_weights = [1.0, 2.0, 1.5]

# 创建自定义加权交叉熵损失函数实例
loss_fn = WeightedCrossEntropyLoss(class_weights)

# 编译模型
model.compile(optimizer='adam', loss=loss_fn)

# 训练模型
model.fit(x_train, y_train, epochs=10, batch_size=32)