Sun’iy intellekt va neyron tarmoqlari”fanidan sun’iy neyron tarmoqlari modeli

3 qatlamli neyron tarmoq:

import numpy as np

def nonlin(x,deriv=False):

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]])
y = np.array([[0],
[1],
[1],
[0]])

np.random.seed(1)

# randomly initialize our weights with mean 0

syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1

for j in xrange(60000):

# Feed forward through layers 0, 1, and 2

l0 = X
l1 = nonlin(np.dot(l0,syn0))
l2 = nonlin(np.dot(l1,syn1))

# how much did we miss the target value?

l2_error = y - l2
if (j% 10000) == 0:
print "Error:" + str(np.mean(np.abs(l2_error)))
# in what direction is the target value?
# were we really sure? if so, don't change too much.
l2_delta = l2_error*nonlin(l2,deriv=True)

# how much did each l1 value contribute to the l2 error (according to the weights)?

l1_error = l2_delta.dot(syn1.T)
# in what direction is the target l1?
# were we really sure? if so, don't change too much.
l1_delta = l1_error * nonlin(l1,deriv=True)

syn1 += l1.T.dot(l2_delta)

syn0 += l0.T.dot(l1_delta)

Xato: 0.496410031903

Xato: 0.00858452565325
Xato: 0.00578945986251
Xato: 0.00462917677677
Xato: 0.00395876528027
Xato: 0.00351012256786