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net.py
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net.py
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import math
import random
import string
class Net:
def __init__(self, ni, nh, no):
ni += 1 #bias node
self.ni, self.nh, self.no = ni, nh, no
self.wi = self.make_matrix(ni, nh)
self.wo = self.make_matrix(nh, no)
self.ai = [1.] * ni
self.ah = [1.] * nh
self.ao = [1.] * no
# set them to random vaules
for i in range(self.ni):
for j in range(self.nh):
self.wi[i][j] = self.rand(-0.2, 0.2)
for j in range(self.nh):
for k in range(self.no):
self.wo[j][k] = self.rand(-2.0, 2.0)
self.ci = self.make_matrix(ni, nh)
self.co = self.make_matrix(nh, no)
def rand(self, a, b):
return (b-a)*random.random() + a
def make_matrix(self, I, J, fill=0.):
return [[fill for x in range(J)] for x in range(I)]
def sigmoid(self, x):
return math.tanh(x)
def dsigmoid(self, y):
return 1.0 - y**2
def update(self, inputs):
# input activations
for i in range(self.ni-1):
self.ai[i] = inputs[i]
# hidden activations
for j in range(self.nh):
sum = 0.0
for i in range(self.ni):
sum += self.ai[i] * self.wi[i][j]
self.ah[j] = self.sigmoid(sum)
# output activations
for k in range(self.no):
sum = 0.0
for j in range(self.nh):
sum += self.ah[j] * self.wo[j][k]
self.ao[k] = self.sigmoid(sum)
return self.ao[:]
def back_propagate(self, targets, N, M):
# calculate error terms for output
output_deltas = [0.0] * self.no
for k in range(self.no):
error = targets[k]-self.ao[k]
output_deltas[k] = self.dsigmoid(self.ao[k]) * error
# calculate error terms for hidden
hidden_deltas = [0.0] * self.nh
for j in range(self.nh):
error = 0.0
for k in range(self.no):
error = error + output_deltas[k]*self.wo[j][k]
hidden_deltas[j] = self.dsigmoid(self.ah[j]) * error
# update output weights
for j in range(self.nh):
for k in range(self.no):
change = output_deltas[k]*self.ah[j]
self.wo[j][k] = self.wo[j][k] + N*change + M*self.co[j][k]
self.co[j][k] = change
# update input weights
for i in range(self.ni):
for j in range(self.nh):
change = hidden_deltas[j]*self.ai[i]
self.wi[i][j] = self.wi[i][j] + N*change + M*self.ci[i][j]
self.ci[i][j] = change
# calculate error
error = 0.0
for k in range(len(targets)):
error = error + 0.5*(targets[k]-self.ao[k])**2
return error
def test(self, i):
o = self.update(i)
print(i, '->', o)
return o
def train(self, data, iterations=1000, N=0.5, M=0.1):
# N: learning rate
# M: momentum factor
for i in range(iterations):
error = 0.
for inputs, outputs in data.items():
self.update(inputs)
error += self.back_propagate(outputs, N, M)
#if i % 100 == 0:
if True:
print('error %-.5f' % error)
def demo():
# Teach network XOR function
data = {
'00': '0',
'01': '1',
'10': '1',
'11': '0'
}
process = lambda x: tuple(map(int,x))
data = {process(k): process(v) for k, v in data.items()}
# create a network with two input, two hidden, and one output nodes
n = Net(2, 2, 1)
# train it with some patterns
n.train(data)
# test it
for i in data.keys():
n.test(i)
if __name__ == '__main__':
demo()