python绘制神经网络中的Sigmoid和Tanh激活函数图像(附代码)

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最近在研究神经网络，用python绘制了一下常见的Sigmoid函数和Tanh函数，别的不多说，直接上代码：

#!/usr/bin/python #encoding:utf-8
import math
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
mpl.rcParams['axes.unicode_minus']=False


def  sigmoid(x):
    return 1.0 / (1.0 + np.exp(-x))

fig = plt.figure(figsize=(6,4))
ax = fig.add_subplot(111)

x = np.linspace(-10, 10)
y = sigmoid(x)
tanh = 2*sigmoid(2*x) - 1

plt.xlim(-11,11)
plt.ylim(-1.1,1.1)

ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')

ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.set_xticks([-10,-5,0,5,10])
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
ax.set_yticks([-1,-0.5,0.5,1])

plt.plot(x,y,label="Sigmoid",color = "blue")
plt.plot(2*x,tanh,label="Tanh", color = "red")
plt.legend()
plt.show()

在这段代码中，包含了不显示绘制图像的上边框和右边框，坐标轴居中显示，自己定制坐标轴的刻度等，最终结果如下：