This example pytho program define the popular activation functions and display the functions.

Code:

import numpy as np
import matplotlib.pyplot as plt
#-------------------------------
# define the activation functions
def relu(x):
    v=x.copy()
    v[x<0]=0
    return(v)
def sigmoid(x):
    v=1/(1+np.exp(-x))
    return(v)
def softmax(x,values):
    exponent_sum = sum([np.exp(x) for x in values])
    return(np.exp(x)/exponent_sum)
def tanh(x):
    v=(np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))
    return(v)
def softplus(x):
    v=np.log(np.exp(x)+1)
    return(v)
def elu(x,alpha):
    v=x.copy()
    v[x>=0]=x[x>=0]
    v[x<0]=alpha*(np.exp(x[x<0])-1)
    return(v)
def leakyRelu(x,alpha):
    v=x.copy()
    v[x>=alpha*x]=x[x>=alpha*x]
    v[x=0]=scale*x[x>=0]
    v[x<0]=scale*alpha*(np.exp(x[x<0])-1)
    return(v)
def exponential(x):
    return(np.exp(x))
#-----------------------------
#prepare data for displaying the functions
x=np.arange(-5,5,0.1)
y=np.empty((9,100))
y[0,:]=relu(x)
y[1,:]=elu(x,1.0)
y[2,:]=selu(x)
y[3,:]=sigmoid(x)
y[4,:]=tanh(x)
y[5,:]=softmax(x,x)
y[6,:]=softplus(x)
y[7,:]=softsign(x)
y[8,:]=exponential(x)
abc=['abcdefghijklmn']
titletext=['relu','elu','selu','sigmoid','tanh','softmax','softplus','softsign','exponential']
#--------------------------------------------------------------------
#display the functions
fig=plt.figure(figsize=(10,10))
abc='abcdefghijklmnopqrstuvwxyz'
for i in range(9):
    plt.subplot(3,3,i+1)
    plt.plot(x,y[i,:])
    plt.text(-3,max(y[i,:])*0.8,'('+abc[i]+') '+titletext[i], fontsize=14)

plt.show()
fig.savefig('activation_functions_keras.png',dpi=150)

Note: path of this code: /media/Data1/zwWorkDir/feature_engineering/activation_function_keras.ipynb