numpy方程到Keras后端损失函数方程的转换

import keras.backend as K

def numpy_to_keras_loss(numpy_loss):
    def keras_loss(y_true, y_pred):
        # 将numpy数组转换为Keras张量
        y_true = K.constant(y_true)
        y_pred = K.constant(y_pred)
        
        # 执行numpy损失函数方程
        loss = numpy_loss(y_true, y_pred)
        
        # 将Keras张量转换为numpy数组
        loss = K.eval(loss)
        
        return loss
    return keras_loss

import numpy as np

# 定义一个numpy损失函数方程
def numpy_loss(y_true, y_pred):
    return np.mean(np.square(y_true - y_pred))

# 转换为Keras后端的损失函数方程
keras_loss = numpy_to_keras_loss(numpy_loss)

model.compile(optimizer='adam', loss=keras_loss)
model.fit(x_train, y_train, epochs=10, batch_size=32)