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#!/usr/bin/env python
# Import numpy for using matrix operations
import numpy as np
# define an actovation function
def sigmoid(x,deriv=False):
if(deriv==True):
return x*(1-x)
return 1/(1+np.exp(-x))
# 0 0 1 0
# 0 1 1 0
# 1 0 1 1
# 1 1 1 1
# define an input matrix
X = np.array([ [0,0,1], [0,1,1], [1,0,1], [1,1,1] ])
# define an output matrix
y = np.array([[0,0,1,1]]).T
# seed numpy
np.random.seed(1)
# generate some weights
syn0 = 2*np.random.random((3,4)) - 1
# define how often the calculations should be run
n = 100000
# loop
for i in range(n):
# define the first layer
l0 = X
# define the second layer using the first layer and the weights
l1 = sigmoid(np.dot(l0,syn0))
# calculate an error
l1_error = y - l1
# calculate how fatal the error is
l1_delta = l1_error * sigmoid(l1,True)
# adjust the weights
syn0 += np.dot(l0.T,l1_delta)
# print some information
if (i % (n / 10000) == 0):
print("l1: " + str(l1))
print("")
print("Output After Training:")
print(l1)
l0 = np.array([0, 1, 0])
l1 = sigmoid(np.dot(l0, syn0))
print(l1)
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