Python實現最簡單的三層神經網路
阿新 • • 發佈:2018-11-14
import numpy as np
def sigmoid( x, deriv=False): #求導:derivation
if (deriv == True):
return x*(1-x)
return 1/(1+np.exp(-x))
x=np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1],
[0,0,1]]
)
#print(x.shape)
y=np.array([[0],
[1],
[ 1],
[0],
[0]]
)
np.random.seed(1)
w0=2*np.random.random((3,4)) -1
w1=2*np.random.random((4,1)) -1
#print(w0)
#print(w1)
for i in range(6000):
l0=x
l1=sigmoid(np.dot(l0,w0))
l2=sigmoid(np.dot(l1,w1))
l2_erroe=y-l2
#print(l2_erroe.shape)
if (i%1000)==0:
print('Error' +str(np.mean(np.abs(l2_erroe))))
l2_delta=l2_erroe*sigmoid(l2,deriv=True)
#print(l2_delta.shape)
l1_error=l2_delta.dot(w1.T)
l1_delta=l1_error*sigmoid(l1,deriv=True)
w1+=l1.T.dot(l2_delta)
w0+=l0.T.dot(l1_delta)
執行結果: