导入必要的库

import mathimport matplotlib.pyplot as pltimport numpy as npimport matplotlib as mplmpl.rcParams['axes.unicode_minus'] = False

绘制softmax函数图像

fig = plt.figure(figsize=(6,4))ax = fig.add_subplot(111)x = np.linspace(-10,10)y = sigmoid(x)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 = 'Softmax',linestyle='-',color='blue')plt.legend(['Softmax'])plt.savefig('softmax.png')

绘制Relu激活函数图像

fig =  plt.figure(figsize=(6,4))ax = fig.add_subplot(111)x = np.arange(-10,10)y = np.where(x<0,0,x) # 小于0输出0，大于0输出yplt.xlim(-11,11)plt.ylim(-11,11)ax = plt.gca() # 获得当前axis坐标轴对象ax.spines['right'].set_color('none') # 去除右边界线ax.spines['top'].set_color('none') # 去除上边界线ax.xaxis.set_ticks_position('bottom') # 指定下边的边作为x轴ax.yaxis.set_ticks_position('left') # 指定左边的边为y轴ax.spines['bottom'].set_position(('data',0)) # 指定data 设置的bottom（也就是指定的x轴）绑定到y轴的0这个点上ax.spines['left'].set_position(('data',0))  # 指定y轴绑定到x轴的0这个点上plt.plot(x,y,label = 'ReLU',linestyle='-',color='darkviolet')plt.legend(['ReLU'])plt.savefig('relu.png')

绘制Tanh激活函数图像

x = np.arange(-10,10)a = np.array(x)y = (math.e**(x) - math.e**(-x)) / (math.e**(x) + math.e**(-x))plt.xlim(-11,11)ax = plt.gca()ax.spines['right'].set_color('none')ax.spines['top'].set_color('none')ax.xaxis.set_ticks_position('bottom')ax.yaxis.set_ticks_position('left')ax.spines['bottom'].set_position(('data',0))ax.spines['left'].set_position(('data',0))plt.plot(x,y,label='Tanh',linestyle='-',color='green')plt.legend(['Tanh'])plt.savefig('Tanh.png',dpi=500) # 指定分辨率