在Python中使用Keras实现奖励的自定义损失函数

import keras.backend as K
import numpy as np

def custom_loss(y_true, y_pred):
    # 计算基本损失
    base_loss = K.mean(K.square(y_true - y_pred))
    
    # 计算奖励项
    reward = K.mean(K.square(y_true - y_pred) < 0.1)
    
    # 调整损失值
    adjusted_loss = base_loss - reward
    
    return adjusted_loss

from keras.models import Sequential
from keras.layers import Dense

# 创建模型
model = Sequential()
model.add(Dense(10, input_dim=5, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# 编译模型并使用自定义损失函数
model.compile(loss=custom_loss, optimizer='adam')

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