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Merge branch 'main' of http://bipfr.servebeer.com:3000/KoDer/BiPy

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KoDer
2026-04-29 19:33:23 +07:00
parent 83420a314a 96f0d24c58
commit c02a886327
1 changed files with 17 additions and 67 deletions
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Xenith/core.cpp
+10 -60
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@@ -2,19 +2,6 @@
#include <cmath>
#include <cstdlib>
#define USE_PARALLEL // Çàêîììåíòèðóéòå, ÷òîáû îòêëþ÷èòü ïàðàëëåëèçì
#define MAX_THREADS 0 // Óêàæèòå ÷èñëî ÿäåð (0 — èñïîëüçîâàòü âñå äîñòóïíûå)
#ifdef USE_PARALLEL
#include <omp.h>
#define OMP_PARALLEL _Pragma("omp parallel for")
#define OMP_SET_THREADS() { if (MAX_THREADS > 0) omp_set_num_threads(MAX_THREADS); }
#else
#define OMP_PARALLEL
#define OMP_SET_THREADS()
#endif
NeuralNetwork::NeuralNetwork(LayerStructure_t layers[], int count) : numLayers(count) {
for (int i = 0; i < count; i++) sizes.push_back(layers[i].size);
for (int i = 0; i < count - 1; i++) {
@@ -32,32 +19,18 @@ NeuralNetwork::NeuralNetwork(LayerStructure_t layers[], int count) : numLayers(c
}
std::vector<double> NeuralNetwork::feedForward(const std::vector<double>& input) {
OMP_SET_THREADS(); // Ïðèìåíÿåì ëèìèò ÿäåð
outputs.clear();
outputs.push_back(input);
std::vector<double> curr = input;
for (int i = 0; i < numLayers - 1; i++) {
// Çàðàíåå ãîòîâèì âåêòîð íóæíîãî ðàçìåðà äëÿ òåêóùåãî ñëîÿ
std::vector<double> next(sizes[i + 1]);
// Ðàñïàðàëëåëèâàåì ðàñ÷åò êàæäîãî íåéðîíà â ñëîå
OMP_PARALLEL
for (int j = 0; j < sizes[i + 1]; j++) {
std::vector<double> next;
for (int j = 0; j < sizes[i+1]; j++) {
double sum = biases[i][j];
// Âíóòðåííèé öèêë îáû÷íî îñòàâëÿåì ïîñëåäîâàòåëüíûì,
// òàê êàê ñîçäàíèå ïîòîêîâ çäåñü äàñò áîëüøå òîðìîçîâ, ÷åì ïîëüçû
for (int k = 0; k < (int)curr.size(); k++) {
sum += curr[k] * weights[i][j][k];
for (int k = 0; k < (int)curr.size(); k++) sum += curr[k] * weights[i][j][k];
next.push_back(1.0 / (1.0 + exp(-sum)));
}
// Ñèãìîèäà. Òåïåðü ïèøåì ïî èíäåêñó j — ýòî áåçîïàñíî äëÿ ïîòîêîâ
next[j] = 1.0 / (1.0 + exp(-sum));
}
curr = next;
outputs.push_back(curr);
}
@@ -66,52 +39,29 @@ std::vector<double> NeuralNetwork::feedForward(const std::vector<double>& input)
double NeuralNetwork::train(const std::vector<double>& input, const std::vector<double>& target, double lr) {
// 0. Óñòàíàâëèâàåì êîëè÷åñòâî ïîòîêîâ (åñëè âêëþ÷åíî â define)
OMP_SET_THREADS();
// 1. Ïðÿìîé ïðîõîä (ïîëó÷àåì ïðåäñêàçàíèå)
std::vector<double> pred = feedForward(input);
std::vector<std::vector<double>> errors(numLayers);
errors[numLayers - 1].resize(sizes[numLayers - 1]);
double totalErr = 0;
// 2. Âû÷èñëåíèå îøèáêè íà âûõîäíîì ñëîå
for (int i = 0; i < sizes[numLayers - 1]; i++) {
for (int i = 0; i < sizes[numLayers-1]; i++) {
double e = target[i] - pred[i];
// Ïðîèçâîäíàÿ ôóíêöèè àêòèâàöèè (ñèãìîèäû): pred * (1 - pred)
errors[numLayers - 1][i] = e * pred[i] * (1.0 - pred[i]);
errors[numLayers-1][i] = e * pred[i] * (1.0 - pred[i]);
totalErr += e * e;
}
// 3. Îáðàòíîå ðàñïðîñòðàíåíèå îøèáêè (Backpropagation) ïî ñêðûòûì ñëîÿì
for (int i = numLayers - 2; i > 0; i--) {
errors[i].resize(sizes[i]);
// Ïàðàëëåëèì âû÷èñëåíèÿ äëÿ êàæäîãî íåéðîíà â ñëîå
OMP_PARALLEL
for (int j = 0; j < sizes[i]; j++) {
double e = 0;
for (int k = 0; k < sizes[i + 1]; k++) {
e += errors[i + 1][k] * weights[i][k][j];
}
// outputs[i][j] — ýòî ñîõðàíåííûé ðåçóëüòàò àêòèâàöèè ýòîãî íåéðîíà èç feedForward
for (int k = 0; k < sizes[i+1]; k++) e += errors[i+1][k] * weights[i][k][j];
errors[i][j] = e * outputs[i][j] * (1.0 - outputs[i][j]);
}
}
// 4. Îáíîâëåíèå âåñîâ è ñìåùåíèé (Biases)
for (int i = 0; i < numLayers - 1; i++) {
// Ïàðàëëåëèì îáíîâëåíèå âåñîâ ñëåäóþùåãî ñëîÿ
OMP_PARALLEL
for (int j = 0; j < sizes[i + 1]; j++) {
for (int k = 0; k < sizes[i]; k++) {
// Ãðàäèåíòíûé ñïóñê: ïðèáàâëÿåì (lr * îøèáêà * âõîäíîé ñèãíàë)
weights[i][j][k] += lr * errors[i + 1][j] * outputs[i][k];
}
biases[i][j] += lr * errors[i + 1][j];
for (int j = 0; j < sizes[i+1]; j++) {
for (int k = 0; k < sizes[i]; k++) weights[i][j][k] += lr * errors[i+1][j] * outputs[i][k];
biases[i][j] += lr * errors[i+1][j];
}
}