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(try): stochastic gradient decent

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Jakob Stornig 2023-09-23 22:05:55 +02:00
parent 86ac3e855c
commit 34a23c6eab
7 changed files with 288 additions and 27 deletions
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49
main.c
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@ -2,25 +2,36 @@
#include "matrix.h"
#include "image.h"
#include "neuronal_network.h"
#include "neural_net.h"
int main() {
Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
// img_visualize(images[4]);
Neural_Network* nn = new_network(28*28, 16, 10, 0.5);
randomize_network(nn, 20);
// save_network(nn);
// Neural_Network* nn = load_network("../networks/test1.txt");
for (int i = 0; i < 10000; ++i) {
train_network(nn, images[i], images[i]->label);
}
measure_network_accuracy(nn, images, 100);
// Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
//// img_visualize(images[4]);
//
// Neural_Network* nn = new_network(28*28, 16, 10, 0.5);
// randomize_network(nn, 20);
//// save_network(nn);
//
//// Neural_Network* nn = load_network("../networks/test1.txt");
//
//
// for (int i = 0; i < 10000; ++i) {
// train_network(nn, images[i], images[i]->label);
// }
//
// measure_network_accuracy(nn, images, 100);
// Matrix *m = matrix_create(2, 1);
// m->numbers[0][0] = 1;
// m->numbers[1][0] = 1;
// Neural_Network * net = create_network(3, 2, 3, 1);
// feedforward(net, m);
//
// int pause;
int imported_count = 0;
Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", &imported_count, 60000);
matrix_save(images[0]->pixel_values, "image1.txt");
matrix_save(images[1]->pixel_values, "images2.txt");
Neural_Network * net = create_network(3, 28*28, 30, 10);
train_network_with_batches(net, images, imported_count, 1, 10, 3);
}