implement prepareNeuralNetworkFile()

neuralNetwork.c

@@ -164,7 +164,7 @@ NeuralNetwork loadModel(const char *path)
 
         assignActivations(model);
     }
-
+    printf("%d\n", model.numberOfLayers);
     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 file 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 dimensions and data for each layer
+        for(int i = 0; i < nn.numberOfLayers; i++){
+            
+            // Write output dimension (rows of weights)
+            fwrite(&nn.layers[i].weights.rows, sizeof(int), 1, file);
+            
+            // Write weight matrix 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 matrix data
+            int biasSize = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+            fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasSize, file);
+        }
+        
+        // Write terminating 0 to signal end of layers
+        int zero = 0;
+        fwrite(&zero, sizeof(int), 1, file);
+        
+        fclose(file);
+    }
+}
 void test_loadModelReturnsCorrectNumberOfLayers(void)
 {
     const char *path = "some__nn_test_file.info2";