perceptron – Acodemics

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.

#!/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)

#!/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)

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