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neural_network_4.py
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neural_network_4.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 18 19:29:33 2021
@author: saidivyesh, varun
"""
import numpy as np
import math
class NeuralNetwork:
# L = # of layers
# sizes = []*L (number of neurons in each layer)
# network = []*L (network[i] is an array with all of the neuron
# values)
# weights = []*(L-1) (weights[i] is a matrix with all of the
# weights between each pair of neurons
# in each layer)
# biases = []*(L-1) (biases[i] is an array with all of the
# biases of the (i+1)th layer)
# error = []*L (basically network but with all of the errors)
# expected_out = [] (array with the expected output of the last layer)
# delta = []*L (basically error but with the derivatives of the errors)
def __init__(self, L, sizes, expected_out, inp):
self.L = L
self.sizes = sizes
self.expected_out = expected_out
self.network = []*L
self.error = []*L
self.delta = []*L
for i in range(0, L-1):
self.weights[i] = [[]*sizes[i+1]]*sizes[i]
self.biases[i] = []*sizes[i+1]
self.network[i] = []*sizes[i]
self.network[L-1] = []*sizes[L-1]
self.network[0] = inp
def forward(self):
for l in range(1, self.L):
for i in range(0, self.sizes[l]):
self.network[l][i] = self.biases[l-1][i];
for j in range(0, self.sizes[l-1]):
self.network[l][i] += self.weights[l-1][j][i] * self.network[l-1][j]
# for i in range(0, self.sizes[l]):
# self.network[l][i] = (1/(1 + np.exp(-self.network[l][i])))
def derivative(output):
return (output) * (1 - output)
def backward(self):
for l in reversed(range(0, self.L)):
if (l == self.L - 1):
for i in range(0, self.sizes[l]):
self.error[l][i] = (self.expected_out[i] - self.network[l][i])
else:
for i in range(0, self.sizes[l]):
for j in range (0, self.sizes[l+1]):
self.error[l][i] += self.weights[l][i][j] * self.delta[l+1][j]
for i in range(0, self.sizes[l]):
self.delta[l][i] = self.error[l][i] * self.derivative(self.network[l][i])
n_1 = 4
n_2 = 3
n_3 = 4
inp = [1, 1, 1, 1]
w_1 = [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
w_2 = [[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]]
b_1 = [0, 0, 0]
b_2 = [0, 0, 0, 0]
nn = NeuralNetwork(n_1, n_2, n_3, inp, w_1, w_2, b_1, b_2)
nn.run()