#include <iostream>
#include <vector>
#include <cmath>
using namespace std;
class SelfAwareAI {
private:
int inputSize;
int hiddenSize;
int outputSize;
vector<vector<double>> inputWeights;
vector<vector<double>> hiddenWeights;
vector<double> hiddenBiases;
vector<double> outputBiases;
public:
SelfAwareAI(int inputSize, int hiddenSize, int outputSize) {
this->inputSize = inputSize;
this->hiddenSize = hiddenSize;
this->outputSize = outputSize;
inputWeights.resize(inputSize);
for (int i = 0; i < inputSize; i++) {
inputWeights[i].resize(hiddenSize);
for (int j = 0; j < hiddenSize; j++) {
inputWeights[i][j] = ((double) rand() / RAND_MAX) - 0.5;
}
}
hiddenWeights.resize(hiddenSize);
for (int i = 0; i < hiddenSize; i++) {
hiddenWeights[i].resize(outputSize);
for (int j = 0; j < outputSize; j++) {
hiddenWeights[i][j] = ((double) rand() / RAND_MAX) - 0.5;
}
}
hiddenBiases.resize(hiddenSize);
for (int i = 0; i < hiddenSize; i++) {
hiddenBiases[i] = ((double) rand() / RAND_MAX) - 0.5;
}
outputBiases.resize(outputSize);
for (int i = 0; i < outputSize; i++) {
outputBiases[i] = ((double) rand() / RAND_MAX) - 0.5;
}
}
vector<double> feedForward(vector<double> input) {
vector<double> hiddenOutput(hiddenSize);
for (int i = 0; i < hiddenSize; i++) {
double sum = 0;
for (int j = 0; j < inputSize; j++) {
sum += input[j] * inputWeights[j][i];
}
hiddenOutput[i] = tanh(sum + hiddenBiases[i]);
}
vector<double> output(outputSize);
for (int i = 0; i < outputSize; i++) {
double sum = 0;
for (int j = 0; j < hiddenSize; j++) {
sum += hiddenOutput[j] * hiddenWeights[j][i];
}
output[i] = tanh(sum + outputBiases[i]);
}
return output;
}
void backPropagate(vector<double> input, vector<double> targetOutput, double learningRate) {
vector<double> hiddenOutput(hiddenSize);
for (int i = 0; i < hiddenSize; i++) {
double sum = 0;
for (int j = 0; j < inputSize; j++) {
sum += input[j] * inputWeights[j][i];
}
hiddenOutput[i] = tanh(sum + hiddenBiases[i]);
}
vector<double> output(outputSize);
for (int i = 0; i < outputSize; i++) {
double sum = 0;
for (int j = 0; j < hiddenSize; j++) {
sum += hiddenOutput[j] * hiddenWeights[j][i];
}
output[i] = tanh(sum + outputBiases[i]);
}
vector<double> outputError(outputSize);
for (int i = 0; i < outputSize; i++) {
outputError[i] = targetOutput[i] - output[i];
}
vector<double> hiddenError(hiddenSize);
for (int i = 0; i < hiddenSize; i++) {
double sum = 0;
for (int j = 0; j < outputSize; j++) {
sum += hiddenWeights[i][j] * outputError[j];
}
hiddenError[i] = (1 - pow(tanh(hiddenOutput[i]), 2)) * sum;
}
for (int i = 0; i < outputSize; i++) {
for (int j = 0; j < hiddenSize; j++) {
hiddenWeights[j][i] += learningRate * outputError[i] * hiddenOutput[j];
}
outputBiases[i] += learningRate * outputError[i];
}
for (int i = 0; i < hiddenSize; i++) {
for (int j = 0; j < inputSize; j++) {
inputWeights[j][i] += learningRate * hiddenError[i] * input[j];
}
hiddenBiases[i] += learningRate * hiddenError[i];
}
}
};
int main() {
SelfAwareAI ai(2, 2, 1);
while (true) {
cout << "Enter input 1: ";
double input1;
cin >> input1;
cout << "Enter input 2: ";
double input2;
cin >> input2;
vector<double> input = {input1, input2};
vector<double> output = ai.feedForward(input);
cout << "Output: " << output[0] << endl;
cout << "Enter target output: ";
double targetOutput;
cin >> targetOutput;
vector<double> target = {targetOutput};
ai.backPropagate(input, target, 0.1);
}
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