snn_08.py

from numpy import exp, array, random, dot
ts_inputs = array([[0, 0, 0], [0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
ts_outputs = array([[0, 0, 0, 1, 1]]).T

#unknown input
un_input = array([1, 0, 0])

# initialize synapse_weights
random.seed(1)
sy_weights = 2 * random.random((3,1)) - 1

# train the network
for i in xrange(10000):
    output = 1 / (1 + exp(-(dot(ts_inputs, sy_weights)))) # Calculate the value for the each of the examples
    sy_weights += dot(ts_inputs.T, (ts_outputs - output) * output * (1 - output)) # Run the adjustments 

# Print the result for our unknown input 
print 1 / (1 + exp(-(dot(un_input, sy_weights))))