三层神经网络代码实现‘

import numpy as np
def init_network():
network={}
network['W1']=np.array([[0.1,0.3,0.5],[0.2,0.4,0.6]])
network['b1']=np.array([0.1,0.2,0.3])
network['W2']=np.array([[0.1,0.4],[0.2,0.5],[0.3,0.6]])
network['b2']=np.array([0.1,0.2])
network['W3']=np.array([[0.1,0.3],[0.2,0.4]])
network['b3']=np.array([0.1,0.2])
return network
def Sigmod(x):
return 1/(1+np.exp(-x))
def forward(network,x):
W1,W2,W3=network['W1'],network['W2'],network['W3']
b1,b2,b3=network['b1'],network['b2'],network['b3']
a1=np.dot(x,W1)+b1
z1=Sigmod(a1)
a2=np.dot(z1,W2)+b2
z2=Sigmod(a2)
a3=np.dot(z2,W3)+b3
y=identity_function(a3)
return y
def identity_function(x):
return x
network=init_network()
x=np.array([1.0,0.5])
y=forward(network,x)
print(y)