Implement prepareNeuralNetworkFile

Startcode/neuralNetwork.c

@@ -168,6 +168,59 @@ NeuralNetwork loadModel(const char *path)
     return model;
 }
 
+static void writeFileHeader(FILE *file)
+{
+    fwrite(FILE_HEADER_STRING, sizeof(char), strlen(FILE_HEADER_STRING), file);
+}
+
+static void writeDimension(FILE *file, const unsigned int dimension)
+{
+    fwrite(&dimension, sizeof(unsigned int), 1, file);
+}
+
+static void writeMatrix(FILE *file, const Matrix matrix)
+{
+    fwrite(matrix.buffer, sizeof(MatrixType), matrix.rows * matrix.cols, file);
+}
+
+static void writeLayer(FILE *file, const Layer layer)
+{
+    writeMatrix(file, layer.weights);
+    writeMatrix(file, layer.biases);
+}
+
+void saveModel(const char *path, const NeuralNetwork nn)
+{
+    FILE *file = fopen(path, "wb");
+    if(file == NULL)
+    {
+        return;
+    }
+
+    writeFileHeader(file);
+
+    if(nn.numberOfLayers > 0)
+    {
+        const unsigned int inputDimension = nn.layers[0].weights.cols;
+        writeDimension(file, inputDimension);
+
+        for(unsigned int i = 0; i < nn.numberOfLayers; i++)
+        {
+            const Layer layer = nn.layers[i];
+
+            const unsigned int outputDimension = layer.weights.rows;
+
+            writeDimension(file, outputDimension);
+
+            writeLayer(file, layer);
+        }
+    }
+
+    writeDimension(file, 0);
+
+    fclose(file);
+}
+
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
     Matrix matrix = {NULL, 0, 0};

Startcode/neuralNetwork.h

@@ -20,6 +20,7 @@ typedef struct
 } NeuralNetwork;
 
 NeuralNetwork loadModel(const char *path);
+void saveModel(const char *path, const NeuralNetwork nn);
 unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages);
 void clearModel(NeuralNetwork *model);
 

Startcode/neuralNetworkTests.c

@@ -5,10 +5,9 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
-
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    saveModel(path, nn);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -205,8 +204,8 @@ void test_predictReturnsCorrectLabels(void)
     Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
     Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
     Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
-    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
+    Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation},
-                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
+                      {.weights = weights2, .biases = biases2, .activation = someActivation},
                       {.weights = weights3, .biases = biases3, .activation = someActivation}};
     NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
     unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
@@ -216,12 +215,14 @@ void test_predictReturnsCorrectLabels(void)
     free(predictedLabels);
 }
 
-void setUp(void) {
+void setUp(void)
-    // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
+{
+    // TODO: Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
+void tearDown(void)
-    // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
+{
+    // TODO: Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 
 int main()