I thought I would post a couple of useful bits of code from my final year of university.
The first is a feed forward perceptron written in Python. I tried to expand the examples on the internet as much as possible to help me understand how they work.
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#!/usr/bin/python2.7 import random class Perceptron: def __init__(self, weights=None, threshold=0.5): self.weights = weights self.threshold = threshold def activation(self, input_vector): # activation of the perceptron. val = sum(value * self.weights[index] for index, value in enumerate(input_vector)) if val > self.threshold: return 1 else: return 0 # learning rate is also the alpha def train(self, training_set, learning_rate = 0.1): # set the initial weights to random numbers in the range [-0.5,0.5] or 0 self.weights = [0 for i in training_set[0][0]] while True: error_count = 0 for input_vector, desired_output in training_set: print self.weights result = self.activation(input_vector) error = desired_output - result if error != 0: error_count += 1 # weight training for index, value in enumerate(input_vector): #delta rule : learning rate * error * current value self.weights[index] += (learning_rate * error * value) if error_count == 0: break def test(self, training_set): for input_vector, desired_result in training_set: if(self.activation(input_vector) != desired_result): return False return True if __name__ == "__main__": # values for a AND gate. training_set_AND = [((0, 0), 0), ((0, 1), 0), ((1, 0), 0), ((-1, 1), 1)] training_set_OR = [((0,1), 1), ((1,0), 1), ((0,0), 0), ((1,1), 1)] # values for a NAND gate. training_set_NAND = [((1, 0, 0), 1), ((1, 1, 0), 1), ((1, 0, 1), 1), ((1, 1, 1), 0) ] p = Perceptron(None, 0.5) p.train(training_set_AND, 0.1) print p.test(training_set_AND) |
Hi, when I test the code it looks that the solution is independent from the value of the treshhold (it can be even 10.00 for AND test case). Something missing in the code. Regards. Jack