forked from freudenreichan/info2Praktikum-NeuronalesNetz
308 lines
11 KiB
C
308 lines
11 KiB
C
#include "neuralNetwork.h"
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#include "unity.h"
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
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FILE *f = fopen(path, "wb");
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if (f == NULL)
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return;
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/* 1) Header: exakt das String, ohne '\n' oder abschließendes '\0' */
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const char header[] = "__info2_neural_network_file_format__";
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fwrite(header, sizeof(char), strlen(header), f);
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/* Wenn es keine Layer gibt, kein Dimensionspaar schreiben (loadModel
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wird beim Lesen dann 0 zurückgeben). Aber wir können auch frühzeitig
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mit einem 0-Int terminieren — beides ist in Ordnung. */
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if (nn.numberOfLayers == 0) {
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/* optional: schreibe ein 0 als next outputDimension (nicht nötig) */
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int zero = 0;
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fwrite(&zero, sizeof(int), 1, f);
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fclose(f);
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return;
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}
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/* 2) Für die erste Layer schreiben wir inputDimension und outputDimension */
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/* inputDimension == weights.cols, outputDimension == weights.rows */
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int inputDim = (int)nn.layers[0].weights.cols;
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int outputDim = (int)nn.layers[0].weights.rows;
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fwrite(&inputDim, sizeof(int), 1, f);
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fwrite(&outputDim, sizeof(int), 1, f);
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/* 3) Für jede Layer in Reihenfolge: Gewichte (output x input), Biases (output
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x 1). Zwischen Layern wird nur die nächste outputDimension (int)
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geschrieben. */
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for (int i = 0; i < nn.numberOfLayers; i++) {
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Layer layer = nn.layers[i];
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int wrows = (int)layer.weights.rows;
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int wcols = (int)layer.weights.cols;
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int wcount = wrows * wcols;
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int bcount =
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layer.biases.rows * layer.biases.cols; /* normalerweise rows * 1 */
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/* Gewichte (MatrixType binär) */
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if (wcount > 0 && layer.weights.buffer != NULL) {
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fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, f);
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}
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/* Biases (MatrixType binär) */
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if (bcount > 0 && layer.biases.buffer != NULL) {
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fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, f);
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}
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/* Für die nächste Layer: falls vorhanden, schreibe deren outputDimension */
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if (i + 1 < nn.numberOfLayers) {
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int nextOutput = (int)nn.layers[i + 1].weights.rows;
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fwrite(&nextOutput, sizeof(int), 1, f);
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} else {
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/* Letzte Layer: wir können das Ende signalisieren, indem wir ein 0
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schreiben. loadModel liest dann outputDimension = 0 und beendet die
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Schleife. */
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int zero = 0;
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fwrite(&zero, sizeof(int), 1, f);
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}
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}
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fclose(f);
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}
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void test_loadModelReturnsCorrectNumberOfLayers(void) {
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const char *path = "some__nn_test_file.info2";
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MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
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MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
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Matrix weights1 = {.buffer = buffer1, .rows = 3, .cols = 2};
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Matrix weights2 = {.buffer = buffer2, .rows = 2, .cols = 3};
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MatrixType buffer3[] = {1, 2, 3};
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MatrixType buffer4[] = {1, 2};
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Matrix biases1 = {.buffer = buffer3, .rows = 3, .cols = 1};
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Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
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Layer layers[] = {{.weights = weights1, .biases = biases1},
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{.weights = weights2, .biases = biases2}};
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NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2};
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NeuralNetwork netUnderTest;
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prepareNeuralNetworkFile(path, expectedNet);
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netUnderTest = loadModel(path);
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remove(path);
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TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers,
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netUnderTest.numberOfLayers);
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clearModel(&netUnderTest);
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}
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void test_loadModelReturnsCorrectWeightDimensions(void) {
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const char *path = "some__nn_test_file.info2";
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MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
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Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
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MatrixType biasBuffer[] = {7, 8, 9};
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Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
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Layer layers[] = {{.weights = weights, .biases = biases}};
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NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
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NeuralNetwork netUnderTest;
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prepareNeuralNetworkFile(path, expectedNet);
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netUnderTest = loadModel(path);
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remove(path);
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TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
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TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
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netUnderTest.layers[0].weights.rows);
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TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
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netUnderTest.layers[0].weights.cols);
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clearModel(&netUnderTest);
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}
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void test_loadModelReturnsCorrectBiasDimensions(void) {
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const char *path = "some__nn_test_file.info2";
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MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
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Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
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MatrixType biasBuffer[] = {7, 8, 9};
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Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
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Layer layers[] = {{.weights = weights, .biases = biases}};
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NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
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NeuralNetwork netUnderTest;
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prepareNeuralNetworkFile(path, expectedNet);
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netUnderTest = loadModel(path);
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remove(path);
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TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
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TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows,
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netUnderTest.layers[0].biases.rows);
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TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols,
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netUnderTest.layers[0].biases.cols);
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clearModel(&netUnderTest);
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}
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void test_loadModelReturnsCorrectWeights(void) {
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const char *path = "some__nn_test_file.info2";
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MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
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Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
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MatrixType biasBuffer[] = {7, 8, 9};
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Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
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Layer layers[] = {{.weights = weights, .biases = biases}};
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NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
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NeuralNetwork netUnderTest;
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prepareNeuralNetworkFile(path, expectedNet);
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netUnderTest = loadModel(path);
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remove(path);
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TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
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TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
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netUnderTest.layers[0].weights.rows);
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TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
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netUnderTest.layers[0].weights.cols);
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int n =
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netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols;
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TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer,
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netUnderTest.layers[0].weights.buffer, n);
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clearModel(&netUnderTest);
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}
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void test_loadModelReturnsCorrectBiases(void) {
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const char *path = "some__nn_test_file.info2";
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MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
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Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
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MatrixType biasBuffer[] = {7, 8, 9};
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Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
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Layer layers[] = {{.weights = weights, .biases = biases}};
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NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
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NeuralNetwork netUnderTest;
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prepareNeuralNetworkFile(path, expectedNet);
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netUnderTest = loadModel(path);
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remove(path);
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TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
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TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
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netUnderTest.layers[0].weights.rows);
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TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
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netUnderTest.layers[0].weights.cols);
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int n =
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netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols;
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TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer,
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netUnderTest.layers[0].biases.buffer, n);
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clearModel(&netUnderTest);
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}
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void test_loadModelFailsOnWrongFileTag(void) {
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const char *path = "some_nn_test_file.info2";
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NeuralNetwork netUnderTest;
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FILE *file = fopen(path, "wb");
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if (file != NULL) {
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const char *fileTag = "info2_neural_network_file_format";
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fwrite(fileTag, sizeof(char), strlen(fileTag), file);
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fclose(file);
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}
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netUnderTest = loadModel(path);
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remove(path);
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TEST_ASSERT_NULL(netUnderTest.layers);
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TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
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}
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void test_clearModelSetsMembersToNull(void) {
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const char *path = "some__nn_test_file.info2";
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MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
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Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
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MatrixType biasBuffer[] = {7, 8, 9};
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Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
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Layer layers[] = {{.weights = weights, .biases = biases}};
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NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
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NeuralNetwork netUnderTest;
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prepareNeuralNetworkFile(path, expectedNet);
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netUnderTest = loadModel(path);
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remove(path);
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TEST_ASSERT_NOT_NULL(netUnderTest.layers);
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TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
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clearModel(&netUnderTest);
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TEST_ASSERT_NULL(netUnderTest.layers);
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TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
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}
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static void someActivation(Matrix *matrix) {
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for (int i = 0; i < matrix->rows * matrix->cols; i++) {
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matrix->buffer[i] = fabs(matrix->buffer[i]);
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}
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}
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void test_predictReturnsCorrectLabels(void) {
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const unsigned char expectedLabels[] = {4, 2};
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GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
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GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
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GrayScaleImage inputImages[] = {
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{.buffer = imageBuffer1, .width = 2, .height = 2},
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{.buffer = imageBuffer2, .width = 2, .height = 2}};
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MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
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MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
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MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22,
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23, -24, 25, 26, 27, -28, -29};
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Matrix weights1 = {.buffer = weightsBuffer1, .rows = 2, .cols = 4};
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Matrix weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
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Matrix weights3 = {.buffer = weightsBuffer3, .rows = 5, .cols = 3};
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MatrixType biasBuffer1[] = {200, 0};
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MatrixType biasBuffer2[] = {0, -100, 0};
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MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
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Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
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Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
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Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
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Layer layers[] = {
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{.weights = weights1, .biases = biases1, .activation = someActivation},
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{.weights = weights2, .biases = biases2, .activation = someActivation},
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{.weights = weights3, .biases = biases3, .activation = someActivation}};
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NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
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unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
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TEST_ASSERT_NOT_NULL(predictedLabels);
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int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
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TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
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free(predictedLabels);
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}
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void setUp(void) {
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// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
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}
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void tearDown(void) {
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// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
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}
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int main() {
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UNITY_BEGIN();
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printf("\n============================\nNeural network "
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"tests\n============================\n");
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RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
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RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
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RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
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RUN_TEST(test_loadModelReturnsCorrectWeights);
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RUN_TEST(test_loadModelReturnsCorrectBiases);
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RUN_TEST(test_loadModelFailsOnWrongFileTag);
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RUN_TEST(test_clearModelSetsMembersToNull);
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RUN_TEST(test_predictReturnsCorrectLabels);
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return UNITY_END();
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} |