diff --git a/neuralNetwork.c b/neuralNetwork.c
index b3eee0b..e4ed1d6 100644
--- a/neuralNetwork.c
+++ b/neuralNetwork.c
@@ -133,6 +133,7 @@ NeuralNetwork loadModel(const char *path)
 
             while(inputDimension > 0 && outputDimension > 0)
             {
+				printf("\nID: %d, OD: %d\n", inputDimension, outputDimension);
                 Layer layer = readLayer(file, inputDimension, outputDimension);
                 Layer *layerBuffer = NULL;
 
@@ -155,7 +156,7 @@ NeuralNetwork loadModel(const char *path)
 
                 model.layers[model.numberOfLayers] = layer;
                 model.numberOfLayers++;
-
+				printf("\nNumber of Layers is now: %d\n", model.numberOfLayers);
                 inputDimension = outputDimension;
                 outputDimension = readDimension(file);
             }
diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c
index 95e5447..d210113 100644
--- a/neuralNetworkTests.c
+++ b/neuralNetworkTests.c
@@ -17,17 +17,18 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 
 	const char* header = "__info2_neural_network_file_format__";
 	fwrite(header, sizeof(const char), strlen(header), file);
-	fwrite(&(nn.numberOfLayers), sizeof(unsigned int), 1, file);
-	fwrite(&(nn.numberOfLayers), sizeof(unsigned int), 1, 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.rows), sizeof(unsigned int), 1, file);
-		fwrite(&(nn.layers[i].weights.cols), sizeof(unsigned int), 1, file);
 		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.rows), sizeof(unsigned int), 1, file);
-		fwrite(&(nn.layers[i].biases.cols), sizeof(unsigned int), 1, file);
 		fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), nn.layers[i].biases.rows * nn.layers[i].biases.cols, file);
 	}
 	fclose(file);