Test commit

neuralNetwork.c

@@ -155,7 +155,6 @@ NeuralNetwork loadModel(const char *path)
 
                 model.layers[model.numberOfLayers] = layer;
                 model.numberOfLayers++;
-
                 inputDimension = outputDimension;
                 outputDimension = readDimension(file);
             }
@@ -170,7 +169,7 @@ NeuralNetwork loadModel(const char *path)
 
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
-    Matrix matrix = {NULL, 0, 0};
+    Matrix matrix = {0, 0, NULL}; // TODO changed this line to fit our functionality
 
     if(count > 0 && images != NULL)
     {

neuralNetworkTests.c

@@ -9,6 +9,29 @@
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
     // TODO
+	FILE* file = fopen(path, "wb");
+	if(file == NULL) {
+		printf("Failed to open file");
+		return;
+	}
+	printf("\nLayers in pNNF: %d\n", nn.numberOfLayers);
+	const char* header = "__info2_neural_network_file_format__";
+	fwrite(header, sizeof(const char), strlen(header), file);
+
+	for (int i = 0; i < nn.numberOfLayers; i++) {
+
+        fwrite(&(nn.layers[i].weights.cols), sizeof(unsigned int), 1, file);
+        fwrite(&(nn.layers[i].weights.rows), sizeof(unsigned int), 1, file);
+    }
+
+	for(int i = 0; i < nn.numberOfLayers; i++) {
+		//write everything to do with weights
+		fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), nn.layers[i].weights.rows * nn.layers[i].weights.cols, file);
+
+		//write everything to do with biases
+		fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), nn.layers[i].biases.rows * nn.layers[i].biases.cols, file);
+	}
+	fclose(file);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -25,6 +48,7 @@ void test_loadModelReturnsCorrectNumberOfLayers(void)
     Layer layers[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}};
 
     NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
+	printf("\nexpectedNetLayers: %d", expectedNet.numberOfLayers);
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);