neural-net-4-min.py

import numpy as np

def nonlin( x, deriv=False ):
    if( deriv==True ):
        return (x*(1-x))
    
    return 1 / (1+np.exp(-x))

#input data
X = np.array( [ [0,0,1],
                [0,1,1],
                [1,0,1],
                [1,1,1] ] )

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

np.random.seed( 1 )                                          

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

# training step
for j in range(60000):
    l0 = X
    l1 = nonlin( np.dot(l0,syn0) )
    l2 = nonlin( np.dot(l1,syn1) )    

    l2_error = y - l2

    if(j % 10000) == 0:
        print ("Error: " + str(np.mean(np.abs(l2_error))))
    
    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 )