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demo.py
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demo.py
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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
k0 = X
k1 = nonlin(np.dot(k0,syn0))
k2 = nonlin(np.dot(k1,syn1))
# how much did we miss the target value?
k2_error = y - k2
if (j% 10000) == 0:
print "Error:" + str(np.mean(np.abs(k2_error)))
# in what direction is the target value?
# were we really sure? if so, don't change too much.
k2_delta = k2_error*nonlin(k2,deriv=True)
# how much did each k1 value contribute to the k2 error (according to the weights)?
k1_error = k2_delta.dot(syn1.T)
# in what direction is the target k1?
# were we really sure? if so, don't change too much.
k1_delta = k1_error * nonlin(k1,deriv=True)
syn1 += k1.T.dot(k2_delta)
syn0 += k0.T.dot(k1_delta)