神经网络常见的激活函数绘图：

import matplotlib.pyplot as plt

import numpy as np

%matplotlib inline

x = np.linspace(-10,10)

y_sigmoid = 1/(1+np.exp(-x))

y_tanh = (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))

fig = plt.figure()

# plot sigmoid

ax = fig.add_subplot(221)

ax.plot(x,y_sigmoid)

ax.grid()

ax.set_title('(a) Sigmoid')

# plot tanh

ax = fig.add_subplot(222)

ax.plot(x,y_tanh)

ax.grid()

ax.set_title('(b) Tanh')

# plot relu

ax = fig.add_subplot(223)

y_relu = np.array([0*item if item<0 else item for item in x ])

ax.plot(x,y_relu)

ax.grid()

ax.set_title('(c) ReLu')

#plot leaky relu

ax = fig.add_subplot(224)

y_relu = np.array([0.2*item if item<0 else item for item in x ])

ax.plot(x,y_relu)

ax.grid()

ax.set_title('(d) Leaky ReLu')

plt.tight_layout()#自动控制间隔