Merge branch 'main' of https://git.efi.th-nuernberg.de/gitea/bruennerda98937/info2NeuronalesNetzBruennnerKobNew

matrix.c

@@ -1,9 +1,6 @@
 #include <stdlib.h>
 #include <string.h>
 #include "matrix.h"
-#include <stdbool.h>
-
-// TODO Matrix-Funktionen implementieren
 
 Matrix createMatrix(size_t rows, size_t cols)
 {
@@ -17,7 +14,7 @@ Matrix createMatrix(size_t rows, size_t cols)
         return m;
     }
 
-    // Single allocation for entire matrix
+    //Allocate Matrix buffer
     m.buffer = malloc(rows * cols * sizeof(MatrixType));
     
     if(!m.buffer){
@@ -25,7 +22,7 @@ Matrix createMatrix(size_t rows, size_t cols)
         return m;
     }
     
-    // Initialize (optional)
+    //Initialize matrix with default value
     for(unsigned int i = 0; i < rows * cols; i++){
         m.buffer[i] = UNDEFINED_MATRIX_VALUE;
     }
@@ -75,7 +72,7 @@ MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int co
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
 
-    bool doBroadcast = false;
+    unsigned int doBroadcast = 0;
     Matrix larger, smaller;
     
     if(matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols){
@@ -86,13 +83,13 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
     {
         larger = matrix1;
         smaller = matrix2;
-        doBroadcast = true;
+        doBroadcast = 1;
     }
     else if (matrix1.rows == matrix2.rows && matrix1.cols == 1)
     {
         larger = matrix2;
         smaller = matrix1;
-        doBroadcast = true;
+        doBroadcast = 1;
     }
     else{
         Matrix m = {NULL, 0, 0};
@@ -101,6 +98,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
 
     Matrix outputMatrix = createMatrix(larger.rows, larger.cols);
     if(doBroadcast){
+        //Broadcasting
         for(int i = 0; i < outputMatrix.rows; i++){
             MatrixType broadcastValue = smaller.buffer[i];
             for(int j = 0; j < outputMatrix.cols; j++){
@@ -108,6 +106,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
             }
         }
     } else{
+        //Classic execution
         for (int i = 0; i < matrix1.rows;i++) {
             for (int j = 0; j < matrix1.cols; j++) {
                 // how this should work in normal Matrix version:

neuralNetwork.c

@@ -164,7 +164,6 @@ NeuralNetwork loadModel(const char *path)
 
         assignActivations(model);
     }
-
     return model;
 }
 

neuralNetwork.sh

@@ -0,0 +1 @@
+make clean && make && make neuralNetworkTests 

neuralNetworkTests.c

@@ -8,9 +8,41 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
-}
+    FILE *file = fopen(path, "wb");
 
+    if(file != NULL){
+        const char *fileTag = "__info2_neural_network_file_format__";
+        
+        //Write header
+        fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+        
+        //Write the input dimension of the first layer
+        if(nn.numberOfLayers > 0){
+            fwrite(&nn.layers[0].weights.cols, sizeof(int), 1, file);
+        }
+        
+        //Write each layer into file
+        for(int i = 0; i < nn.numberOfLayers; i++){
+            
+            //Write output dimension
+            fwrite(&nn.layers[i].weights.rows, sizeof(int), 1, file);
+            
+            //Write weight data
+            int weightSize = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+            fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightSize, file);
+            
+            //Write bias data
+            int biasSize = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+            fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasSize, file);
+        }
+        
+        //EOF Terminator
+        int zero = 0;
+        fwrite(&zero, sizeof(int), 1, file);
+        
+        fclose(file);
+    }
+}
 void test_loadModelReturnsCorrectNumberOfLayers(void)
 {
     const char *path = "some__nn_test_file.info2";