VanillaNNLayers.py

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

np.random.seed(1)

# randomly initialize our weights with mean 0
syn0 = 2*np.random.random((3,4)) - 1
b0 = np.array([[1]])
syn1 = 2*np.random.random((4,1)) - 1
b1 = np.array([[1]])

print b0
#syn0[:,3] = 1
#syn1[:,3] = -1
#syn1[:,1] = 1
#print syn1

for j in xrange(60000):

	# Feed forward through layers 0, 1, and 2
    l0 = X
    l1 = nonlin(np.dot(l0,syn0)) + b0
    l2 = nonlin(np.dot(l1,syn1)) + b1

    # 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)


print "Output After Training:" 
print l2