ActivationFunction

import numpy as np
import matplotlib.pyplot as plt

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

def lrelu(x,a):
    y=x.copy()
    for i in range(y.shape[0]):
        if y[i]<0:
            y[i]=a*y[i]
    return y
def tanh(x):
    y=(1.0-np.exp(-2*x))/(1.0+np.exp(-2*x))
    return y
def softplus(x):
    y=np.log(np.exp(x)+1)
    return y
def relu(x):
    y=x.copy()
    y[y<0]=0
    return y
def softsign(x):
    y=x/(np.abs(x)+1)
    return y
def elu(x,a):
    y=x.copy()
    for i in range(y.shape[0]):
        if y[i]<0:
            y[i]=a*(np.exp(y[i])-1)
    return y
x=np.linspace(start=-10,stop=10,num=100)
x1=np.linspace(start=-1,stop=1,num=100)
y=np.linspace(start=-2,stop=2,num=10)
#y=lrelu(x,0.25)
#print(x)
#print(y)

#plt.plot(fpr, tpr, lw=1, label='ROC fold %d (auc(area) = %0.4f)'
         
#plt.plot(x,y)
#plt.grid(True)
#plt.show()
         
#sub_axix = filter(lambda x:x%200 == 0, x_axix)
plt.title('ActivationFunction')
plt.plot(x, lrelu(x,0.25), color='green', label='lrelu')
plt.plot(x, sigmoid(x), color='red', label='sigmoid')
plt.plot(x, tanh(x), color='blue', label='tanh')
#plt.plot(x, train_pn_dis,  color='skyblue', label='PN distance')
#plt.plot(x, thresholds, color='blue', label='threshold')
plt.legend() # 显示图例
 
plt.xlabel('iteration times')
plt.ylabel('rate')
plt.show()