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Python, 162 lines
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162``` ```from math import * import random from random import random as r class NN: def __init__(self): # initialize node-activations self.ai = [1.0]*ni self.ah = [1.0]*nh self.ao = [1.0]*no self.Aah = [0.0]*nh self.Aao = [0.0]*no self.Ach, self.Aco = [0]*nh, [0]*no self.wi = [ [r() for j in range(nh)] for i in range(ni) ] self.wo = [ [r() for k in range(no)] for j in range(nh) ] self.ci = [[0.0]*nh]*ni self.co = [[0.0]*no]*nh def runNN (self, inputs): self.ai = inputs self.ah = [ sig(sum([ self.ai[i]*self.wi[i][j] for i in range(ni) ])) for j in range(nh)] self.ao = [ sig(sum([ self.ah[j]*self.wo[j][k] for j in range(nh) ])) for k in range(no)] return self.ao def backPropagate (self, targets, N, M): output_deltas = [0.0] * no for k in range(no): error = targets[k] - self.ao[k] output_deltas[k] = error * dsig(self.ao[k]) for j in range(nh): for k in range(no): change = output_deltas[k] * self.ah[j] self.wo[j][k] += N*change + M*self.co[j][k] self.co[j][k] = change # calc hidden deltas hidden_deltas = [0.0] * nh for j in range(nh): error = 0.0 for k in range(no): error += output_deltas[k] * self.wo[j][k] hidden_deltas[j] = error * dsig(self.ah[j]) #update input weights for i in range (ni): for j in range (nh): change = hidden_deltas[j] * self.ai[i] self.wi[i][j] += N*change + M*self.ci[i][j] self.ci[i][j] = change # calc combined error # 1/2 for differential convenience & **2 for modulus error = 0.0 for k in range(len(targets)): error = 0.5 * (targets[k]-self.ao[k])**2 return error def test(self, patterns): for p in patterns: inputs = p[0] print 'Inputs:', p[0], '-->', self.runNN(inputs), '\tTarget', p[1] def BP(self, patterns, N, M,i): for p in patterns: inputs = p[0] targets = p[1] self.runNN(inputs) error = self.backPropagate(targets, N, M) if i % 50 == 0: print str(i).zfill(len(str(max_iterations-1))),'Combined error', error def NGA(self, pat,i ): for p in pat: inputs = p[0] targets = p[1] self.astrocyteactions(inputs) def astrocyteactions (self, inputs): for m in range(M_iters): self.ai = inputs for j in range(nh): self.ah[j] = sig(sum([ self.ai[i]*self.wi[i][j] for i in range(ni) ])) if self.ah[j] > 0: self.Aah[j] +=1 else: self.Aah[j] -=1 if self.Aah[j] == Athresh: self.Ach[j] = Adur elif self.Aah[j] == -Athresh: self.Ach[j] = -Adur if self.Ach[j] > 0: for i in range(ni): self.wi[i][j] += self.wi[i][j]*0.25 self.Ach[j] -=1 elif self.Ach[j] < 0: for i in range(ni): self.wi[i][j] += self.wi[i][j]*-0.5 self.Ach[j] +=1 for k in range(no): self.ao[k] = sig(sum([ self.ah[j]*self.wo[j][k] for j in range(nh) ])) if self.ao[k] > 0: self.Aao[k] +=1 else: self.Aao[k] -=1 if self.Aao[k] == Athresh: self.Aco[k] = Adur elif self.Aao[k] == -Athresh: self.Aco[k] = -Adur if self.Aco[k] > 0: for j in range(nh): self.wo[j][k] += self.wo[j][k]*0.25 self.Aco[k] -=1 elif self.Aco[k] < 0: for j in range(nh): self.wo[j][k] += self.wo[j][k]*-0.5 self.Aco[k] +=1 return self.ao def weights(self): print 'Input weights:' for i in range(ni): print map(lambda x: round(x,2),self.wi[i]) print print 'Output weights:' for j in range(nh): print map(lambda x: round(x,2),self.wo[j]) print '' def train (self, patterns): for i in range(max_iterations): self.BP(patterns, N, M,i) self.NGA(patterns,i) self.test(patterns) self.weights() def sig (x): return tanh(x) def dsig (y): return 1 - y**2 max_iterations = 10000 N=0.2 M=0.1 M_iters = 8 Athresh, Adur = 2, 3 ni, nh, no = 2,2,1 pat = [ [[0,0], [0]], [[0,1], [1]], [[1,0], [1]], [[1,1], [0]] ] def main (): myNN = NN() myNN.train(pat) if __name__ == "__main__": main() ```
 Created by David Adler on Tue, 2 Oct 2012 (MIT)

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