3layer_nn.py

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], [1], [1], [0]])

np.random.seed(1)

# initialise weights randomly
syn0 = 2 * np.random.random((3, 4)) - 1
syn1 = 2 * np.random.random((4, 1)) - 1

for j in xrange(60000):

	# forward propagation
	l0 = X
	l1 = nonlin(np.dot(l0, syn0))
	l2 = nonlin(np.dot(l1, syn1))
	
	# error
	l2_error = y - l2
	
	if(j % 10000) == 0:
		print "Error:" + str(np.mean(np.abs(l2_error)))
	
	# multiply how much we missed by the
	# slope of the sigmoid at the values in l1
	l2_delta = l2_error * nonlin(l2, deriv=True)
	
	l1_error = l2_delta.dot(syn1.T)
	
	l1_delta = l1_error * nonlin(l1, deriv = True)
	
	# update weights
	syn1 += l1.T.dot(l2_delta)
	syn0 += l0.T.dot(l1_delta)

print "Output After Training:"
print l2