Created
October 17, 2016 08:43
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import numpy as np | |
# sigmoid function | |
def nonlin(x, deriv=False): | |
if(deriv==True): | |
return x*(1-x) | |
return 1/(1+np.exp(-x)) | |
# input dataset | |
X = np.array([ [0,0,1], | |
[0,1,1], | |
[1,0,1], | |
[1,1,1]]) | |
# output dataset | |
y = np.array([[0,0,1,1]]).T | |
# seed random numbers to make calculation | |
# deterministic (just a good practice) | |
np.random.seed(1) | |
# initialize weights randomly with mean 0 | |
syn0 = 2*np.random.random((3,1)) - 1 | |
for iter in xrange(10000): | |
#forward propagation | |
l0 = X | |
l1 = nonlin(np.dot(l0,syn0)) | |
# how much did we miss? | |
l1_error = y - l1 | |
# multiply how much we missed by the | |
# slope of the sigmoid at the values in l1 | |
l1_delta = l1_error*nonlin(l1,True) | |
# update weights | |
syn0 += np.dot(l0.T, l1_delta) | |
print "Output After Training:" | |
print l1 |
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