Example #1
0
void test_mnist_csv(char *filename, char *weightfile)
{
    network net = parse_network_cfg(filename);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    srand(time(0));

    data test;
    test = load_mnist_data("data/mnist/t10k-images.idx3-ubyte", "data/mnist/t10k-labels.idx1-ubyte", 10000);

    matrix pred = network_predict_data(net, test);

    int i;
    for(i = 0; i < test.X.rows; ++i){
        image im = float_to_image(32, 32, 3, test.X.vals[i]);
        flip_image(im);
    }
    matrix pred2 = network_predict_data(net, test);
    scale_matrix(pred, .5);
    scale_matrix(pred2, .5);
    matrix_add_matrix(pred2, pred);

    matrix_to_csv(pred);
    fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
    free_data(test);
}
Example #2
0
void test_cifar_csvtrain(char *filename, char *weightfile)
{
    network net = parse_network_cfg(filename);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    srand(time(0));

    data test = load_all_cifar10();

    matrix pred = network_predict_data(net, test);

    int i;
    for(i = 0; i < test.X.rows; ++i){
        image im = float_to_image(32, 32, 3, test.X.vals[i]);
        flip_image(im);
    }
    matrix pred2 = network_predict_data(net, test);
    scale_matrix(pred, .5);
    scale_matrix(pred2, .5);
    matrix_add_matrix(pred2, pred);

    matrix_to_csv(pred);
    fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
    free_data(test);
}
Example #3
0
void matrix_array_to_csv(std::ostream &out, flann::Matrix<T>* mats, size_t n) {
    for (size_t i = 0; i < n; i++) {
        matrix_to_csv(out, mats[i]);
        out << "\n";
    }
    out << "\n";
}