Implement prepareNeuralNetworkFile

Startcode/neuralNetwork.c

@@ -52,8 +52,8 @@ static int checkFileHeader(FILE *file)
     int fileHeaderLen = strlen(FILE_HEADER_STRING);
     char buffer[BUFFER_SIZE] = {0};
 
-    if(BUFFER_SIZE-1 < fileHeaderLen)
-        fileHeaderLen = BUFFER_SIZE-1;
+    if(BUFFER_SIZE - 1 < fileHeaderLen)
+        fileHeaderLen = BUFFER_SIZE - 1;
 
     if(fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
         isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
@@ -67,17 +67,17 @@ static unsigned int readDimension(FILE *file)
 
     if(fread(&dimension, sizeof(int), 1, file) != 1)
         dimension = 0;
-    
+
     return dimension;
 }
 
 static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols)
 {
     Matrix matrix = createMatrix(rows, cols);
-    
+
     if(matrix.buffer != NULL)
     {
-        if(fread(matrix.buffer, sizeof(MatrixType), rows*cols, file) != rows*cols)
+        if(fread(matrix.buffer, sizeof(MatrixType), rows * cols, file) != rows * cols)
             clearMatrix(&matrix);
     }
 
@@ -110,13 +110,13 @@ static void clearLayer(Layer *layer)
 
 static void assignActivations(NeuralNetwork model)
 {
-    for(int i = 0; i < (int)model.numberOfLayers-1; i++)
+    for(int i = 0; i < (int)model.numberOfLayers - 1; i++)
     {
         model.layers[i].activation = relu;
     }
 
     if(model.numberOfLayers > 0)
-        model.layers[model.numberOfLayers-1].activation = softmax;
+        model.layers[model.numberOfLayers - 1].activation = softmax;
 }
 
 NeuralNetwork loadModel(const char *path)
@@ -128,7 +128,7 @@ NeuralNetwork loadModel(const char *path)
     {
         if(checkFileHeader(file))
         {
-            unsigned int inputDimension = readDimension(file); 
+            unsigned int inputDimension = readDimension(file);
             unsigned int outputDimension = readDimension(file);
 
             while(inputDimension > 0 && outputDimension > 0)
@@ -142,7 +142,7 @@ NeuralNetwork loadModel(const char *path)
                     clearModel(&model);
                     break;
                 }
-                
+
                 layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
 
                 if(layerBuffer != NULL)
@@ -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};
@@ -201,7 +254,7 @@ static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
         {
             Matrix biasResult;
             Matrix weightResult;
-            
+
             weightResult = multiply(model.layers[i].weights, result);
             clearMatrix(&result);
             biasResult = add(model.layers[i].biases, weightResult);
@@ -248,9 +301,9 @@ unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
     Matrix outputBatch = forward(model, inputBatch);
 
     unsigned char *result = argmax(outputBatch);
-    
+
     clearMatrix(&outputBatch);
-    
+
     return result;
 }
 
@@ -265,4 +318,4 @@ void clearModel(NeuralNetwork *model)
         model->layers = NULL;
         model->numberOfLayers = 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)
@@ -16,15 +15,15 @@ void test_loadModelReturnsCorrectNumberOfLayers(void)
     const char *path = "some__nn_test_file.info2";
     MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
     MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights1 = {.buffer=buffer1, .rows=3, .cols=2};
-    Matrix weights2 = {.buffer=buffer2, .rows=2, .cols=3};
+    Matrix weights1 = {.buffer = buffer1, .rows = 3, .cols = 2};
+    Matrix weights2 = {.buffer = buffer2, .rows = 2, .cols = 3};
     MatrixType buffer3[] = {1, 2, 3};
     MatrixType buffer4[] = {1, 2};
-    Matrix biases1 = {.buffer=buffer3, .rows=3, .cols=1};
-    Matrix biases2 = {.buffer=buffer4, .rows=2, .cols=1};
-    Layer layers[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}};
+    Matrix biases1 = {.buffer = buffer3, .rows = 3, .cols = 1};
+    Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
+    Layer layers[] = {{.weights = weights1, .biases = biases1}, {.weights = weights2, .biases = biases2}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -40,12 +39,12 @@ void test_loadModelReturnsCorrectWeightDimensions(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -63,12 +62,12 @@ void test_loadModelReturnsCorrectBiasDimensions(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -86,12 +85,12 @@ void test_loadModelReturnsCorrectWeights(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -111,12 +110,12 @@ void test_loadModelReturnsCorrectBiases(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -159,12 +158,12 @@ void test_clearModelSetsMembersToNull(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -192,23 +191,23 @@ void test_predictReturnsCorrectLabels(void)
     const unsigned char expectedLabels[] = {4, 2};
     GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
     GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
-    GrayScaleImage inputImages[] = {{.buffer=imageBuffer1, .width=2, .height=2}, {.buffer=imageBuffer2, .width=2, .height=2}};
+    GrayScaleImage inputImages[] = {{.buffer = imageBuffer1, .width = 2, .height = 2}, {.buffer = imageBuffer2, .width = 2, .height = 2}};
     MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
     MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
     MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29};
-    Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4};
-    Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
-    Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3};
+    Matrix weights1 = {.buffer = weightsBuffer1, .rows = 2, .cols = 4};
+    Matrix weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
+    Matrix weights3 = {.buffer = weightsBuffer3, .rows = 5, .cols = 3};
     MatrixType biasBuffer1[] = {200, 0};
     MatrixType biasBuffer2[] = {0, -100, 0};
     MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
-    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}, \
-                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
-                      {.weights=weights3, .biases=biases3, .activation=someActivation}};
-    NeuralNetwork netUnderTest = {.layers=layers, .numberOfLayers=3};
+    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},
+                      {.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);
     TEST_ASSERT_NOT_NULL(predictedLabels);
     int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
@@ -216,12 +215,14 @@ void test_predictReturnsCorrectLabels(void)
     free(predictedLabels);
 }
 
-void setUp(void) {
-    // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
+void setUp(void)
+{
+    // TODO: Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
-    // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
+void tearDown(void)
+{
+    // TODO: Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 
 int main()
@@ -239,4 +240,4 @@ int main()
     RUN_TEST(test_predictReturnsCorrectLabels);
 
     return UNITY_END();
-}
+}