Redes Neurais Artificiais
De Aulas
Links relacionados: Inteligência Artificial
Neurônio Artificial Portas Lógicas em Python
"""
PROGRAM: Perceptron
author: Saulo Popov Zambiasi
created: 2019.05.17
modified: 2022.04.28
"""
from random import *
class Perceptron:
N = 0.01
epoch = 100
weight = []
option = 0
# example by columns
# bias (-1) | x1 | x2 | y_or | y_not_or | y_and | y_not_and | y_xor
example = [
[-1, 0, 0, 0, 1, 0, 1, 0],
[-1, 0, 1, 1, 0, 0, 1, 1],
[-1, 1, 0, 1, 0, 0, 1, 1],
[-1, 1, 1, 1, 0, 1, 0, 0]
]
def __init__(self, option):
self.option = option
if self.option < 0 or self.option > 4:
self.option = 0
self.option += 3
for i in range(3):
self.weight += [randint(1, 100) / 100.0]
def adjust_weight(self, e, out):
for i in range(3):
self.weight[i] += self.N * \
(self.example[e][self.option] - out) * self.example[e][i]
def training(self):
trained = False
epoch = 0
while not trained and epoch < self.epoch:
trained = True
for i in range(4):
y = self.net(self.example[i][1], self.example[i][2])
if y != self.example[i][self.option]:
self.adjust_weight(i, y)
trained = False
epoch += 1
if trained:
print("Trained in", epoch, "epochs.")
return True
else:
print("There was not possible to learn.")
return False
def net(self, x1, x2):
y = (-1 * self.weight[0]) + \
(x1 * self.weight[1]) + (x2 * self.weight[2])
if y > 0:
return 1
return 0
# MAIN PROGRAM -----------------------------------------
def main():
print("PERCEPTRON: training to logic ports")
o = 0
while o != 9:
print("Choose operation [ 0: OR | 1: NOT OR |",
"2: AND | 3: NOT AND | 4: XOR ] - Or 9 to exit")
o = int(input("operation: "))
if o != 9:
neuron = Perceptron(o)
if neuron.training():
exit_loop = False
while not exit_loop:
print("-- Type 0 or 1 - Or 9 to other training")
x1 = int(input("x1: "))
if x1 != 9:
x2 = int(input("x2: "))
y = neuron.net(x1, x2)
print("y = ", y)
else:
exit_loop = True
print("Terminated...")
# -------------------------------------------------------
if __name__ == "__main__":
main()
Neurônio Artificial Porta OU em C++11
#include <iostream>
using namespace std;
class Neuronio
{
private:
int saida;
float N = 0.01;
int epocas = 100;
float peso[3];
int exemplo[4][4]
{
{ -1, 0, 0, 0 },
{ -1, 0, 1, 1 },
{ -1, 1, 0, 1 },
{ -1, 1, 1, 1 }
};
public:
Neuronio();
void transferencia();
void treinamento_pesos(int j);
void treinamento();
int net(int j);
int executar(int x[2]);
};
Neuronio::Neuronio()
{
for (int i = 0; i < 3; i++)
{
peso[i] = ((rand() % 200) - 100) / 100.0;
}
}
int Neuronio::net(int j)
{
float y = 0;
for (int i = 0; i < 3; i++)
{
y += exemplo[j][i] * peso[i];
}
if (y > 0)
{
return 1;
}
return 0;
}
void Neuronio::treinamento_pesos(int j)
{
for (int i = 0; i < 3; i++)
{
peso[i] = peso[i] + (N * (exemplo[j][3] - saida) * exemplo[j][i]);
}
}
void Neuronio::treinamento()
{
int e = 0;
bool errou;
do
{
cout << "Epoca(" << e << ")" << endl;
errou = false;
for (int i = 0; i < 4; i++)
{
saida = net(i);
if (saida != exemplo[i][3])
{
treinamento_pesos(i);
errou = true;
}
cout << "...Pesos: " << peso[0] << " " << peso[1] << " " << peso[2] << endl;
}
e++;
}
while((e < epocas) && (errou));
}
int Neuronio::executar(int x[2])
{
cout << "-1 * " << peso[0] << " + " << x[0] << " * " << peso[1] << " + " << x[1] << " + " << peso[2] << endl;
float y = (-1 * peso[0]) + (x[0] * peso[1]) + (x[1] * peso[2]);
if (y > 0)
{
return 1;
}
return 0;
}
int main()
{
Neuronio n;
n.treinamento();
int continuar = 1;
while (continuar == 1)
{
int in[2];
cout << "x1: ";
cin >> in[0];
cout << "x2: ";
cin >> in[1];
cout << "retorno: " << n.executar(in) << endl;
cout << "continuar? ";
cin >> continuar;
}
return 0;
}
Neurônio Artificial para Portas Lógicas em Java
import java.util.*;
public class Perceptron {
private double[] w = new double[3];
private double y = 0;
private double N = 0.1;
private final int BIAS = -1;
private final int MAX_EPOCAS = 1000;
private int operacao = 0;
private Random rand = new Random();
Scanner entrada;
private int[][] e = {
{ 0, 0, 0, 0, 1, 1, 0 },
{ 0, 1, 1, 0, 0, 1, 1 },
{ 1, 0, 1, 0, 0, 1, 1 },
{ 1, 1, 1, 1, 0, 0, 0 }
};
Perceptron(int op) {
if ((op >= 0) && (op < 5)) {
operacao = op + 2;
}
for (int i = 0; i < 3; i++) {
w[i] = ((rand.nextInt(20) + 1) / 10) - 1;
}
entrada = new Scanner(System.in);
}
int executar(int x1, int x2) {
// Somatorio NET
y = ((BIAS) * w[0]) + (x1 * w[1]) + (x2 * w[2]);
// Funcao de Transferencia
if (y > 0) {
return 1;
}
return 0;
}
boolean treinar() {
boolean treinou;
int epoca = 0;
do {
treinou = true;
for (int i = 0; i < 4; i++) {
int s = executar(e[i][0], e[i][1]);
if (s != e[i][operacao]) {
corrigirPeso(i, s);
treinou = false;
}
}
epoca++;
} while ((treinou == false) && (epoca < MAX_EPOCAS));
System.out.println("O algoritmo treinou " + epoca + " epocas...");
if (treinou == false) {
System.out.println("O algoritmo nao conseguiu convergir...");
}
return treinou;
}
void corrigirPeso(int exemplo, int saida) {
w[0] = w[0] + (N * (e[exemplo][operacao] - saida) * (BIAS));
w[1] = w[1] + (N * (e[exemplo][operacao] - saida) * e[exemplo][0]);
w[2] = w[2] + (N * (e[exemplo][operacao] - saida) * e[exemplo][1]);
}
void testar() {
boolean sair = false;
while (!sair) {
System.out.println("-- Digite 9 para sair --");
System.out.print("x1 : ");
int x1 = entrada.nextInt();
if (x1 == 9) {
sair = true;
} else {
System.out.print("x2 : ");
int x2 = entrada.nextInt();
int y = executar(x1, x2);
System.out.println(" y : " + y);
}
}
}
public static void main(String[] arguments) {
boolean erro = false;
if (arguments.length == 1) {
int op = Integer.valueOf(arguments[0]);
erro = ((op < 0) || (op > 4));
if (!erro) {
Perceptron p = new Perceptron(op);
if (p.treinar()) {
p.testar();
}
}
} else {
erro = true;
}
if (erro) {
System.out.println("Use: Perceptron <operacao>");
System.out.println("operacao:");
System.out.println("\t0 - ou");
System.out.println("\t1 - e");
System.out.println("\t2 - nao ou");
System.out.println("\t3 - nao e");
System.out.println("\t4 - ou exclusivo");
}
}
}
