numpy中的均方误差? [英] Mean Squared Error in Numpy?

问题描述

numpy中是否有一种方法可以计算两个矩阵之间的均方误差?

Is there a method in numpy for calculating the Mean Squared Error between two matrices?

我尝试搜索，但没有找到.是另一个名字吗?

I've tried searching but found none. Is it under a different name?

如果没有，您如何克服呢?您是自己编写还是使用其他库?

If there isn't, how do you overcome this? Do you write it yourself or use a different lib?

推荐答案

您可以使用:

mse = ((A - B)**2).mean(axis=ax)

或

mse = (np.square(A - B)).mean(axis=ax)

• 与ax=0一起，沿着行执行平均值，对于每一列，返回一个数组
• 与ax=1一起，沿着列执行平均值，对于每一行，返回一个数组
• 使用ax=None沿数组逐元素执行平均值，并返回标量值
• with ax=0 the average is performed along the row, for each column, returning an array
• with ax=1 the average is performed along the column, for each row, returning an array
• with ax=None the average is performed element-wise along the array, returning a scalar value

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