用numpy数组实现softmax函数 [英] Implement softmax function with numpy array

问题描述

我当前的功能如下:

def soft_max(z):
    t = np.exp(z)
    a = np.exp(z) / np.sum(t, axis=1)
    return a

但是我收到错误:ValueError: operands could not be broadcast together with shapes (20,10) (20,)，因为np.sum(t，axis = 1)不是标量.

However I get the error: ValueError: operands could not be broadcast together with shapes (20,10) (20,) since np.sum(t, axis=1) isn't a scalar.

我想拥有t / the sum of each row，但是我不知道该怎么做.

I want to have t / the sum of each row but I don't know how to do this.

推荐答案

从1.2.0版开始，scipy包含softmax作为特殊功能:

As of version 1.2.0, scipy includes softmax as a special function:

https://scipy.github.io/devdocs/genic /scipy.special.softmax.html

使用axis参数可以在行上运行它.

Use the axis argument do run it over rows.

from scipy.special import softmax
softmax(arr, axis=0)

这篇关于用numpy数组实现softmax函数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！