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

Xenith/core.cpp

@@ -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;
-        std::vector<double> next(sizes[i + 1]);
+        for (int j = 0; j < sizes[i+1]; j++) {
-
+            double sum = biases[i][j];
-        // Ðàñïàðàëëåëèâàåì ðàñ÷åò êàæäîãî íåéðîíà â ñëîå
+            for (int k = 0; k < (int)curr.size(); k++) sum += curr[k] * weights[i][j][k];
-        OMP_PARALLEL
+            next.push_back(1.0 / (1.0 + exp(-sum)));
-            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];
-                }
-
-                // Ñèãìîèäà. Òåïåðü ïèøåì ïî èíäåêñó j — ýòî áåçîïàñíî äëÿ ïîòîêîâ
-                next[j] = 1.0 / (1.0 + exp(-sum));
-            }
-
         curr = next;
         outputs.push_back(curr);
     }
@@ -66,53 +39,30 @@ 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;
-
+    for (int i = 0; i < sizes[numLayers-1]; i++) {
-    // 2. Âû÷èñëåíèå îøèáêè íà âûõîäíîì ñëîå
-    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]);
-
+        for (int j = 0; j < sizes[i]; j++) {
-        // Ïàðàëëåëèì âû÷èñëåíèÿ äëÿ êàæäîãî íåéðîíà â ñëîå
+            double e = 0;
-        OMP_PARALLEL
+            for (int k = 0; k < sizes[i+1]; k++) e += errors[i+1][k] * weights[i][k][j];
-            for (int j = 0; j < sizes[i]; j++) {
+            errors[i][j] = e * outputs[i][j] * (1.0 - outputs[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
-                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++) {
-
+        for (int j = 0; j < sizes[i+1]; j++) {
-        // Ïàðàëëåëèì îáíîâëåíèå âåñîâ ñëåäóþùåãî ñëîÿ
+            for (int k = 0; k < sizes[i]; k++) weights[i][j][k] += lr * errors[i+1][j] * outputs[i][k];
-        OMP_PARALLEL
+            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++) {
-                    // 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];
-            }
     }
 
     return totalErr;