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#include "ggml.h"
#include "ggml-opt.h"
#include "mnist-common.h"
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <string>
#include <thread>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
int main(int argc, char ** argv) {
srand(time(NULL));
ggml_time_init();
if (argc != 4 && argc != 5) {
fprintf(stderr, "Usage: %s mnist-fc-f32.gguf data/MNIST/raw/t10k-images-idx3-ubyte data/MNIST/raw/t10k-labels-idx1-ubyte [CPU/CUDA0]\n", argv[0]);
exit(1);
}
ggml_opt_dataset_t dataset = ggml_opt_dataset_init(GGML_TYPE_F32, GGML_TYPE_F32, MNIST_NINPUT, MNIST_NCLASSES, MNIST_NTEST, MNIST_NBATCH_PHYSICAL);
if (!mnist_image_load(argv[2], dataset)) {
return 1;
}
if (!mnist_label_load(argv[3], dataset)) {
return 1;
}
const int iex = rand() % MNIST_NTEST;
mnist_image_print(stdout, dataset, iex);
const std::string backend = argc >= 5 ? argv[4] : "";
const int64_t t_start_us = ggml_time_us();
mnist_model model = mnist_model_init_from_file(argv[1], backend, MNIST_NBATCH_LOGICAL, MNIST_NBATCH_PHYSICAL);
mnist_model_build(model);
const int64_t t_load_us = ggml_time_us() - t_start_us;
fprintf(stdout, "%s: loaded model in %.2lf ms\n", __func__, t_load_us / 1000.0);
ggml_opt_result_t result_eval = mnist_model_eval(model, dataset);
std::vector<int32_t> pred(MNIST_NTEST);
ggml_opt_result_pred(result_eval, pred.data());
fprintf(stdout, "%s: predicted digit is %d\n", __func__, pred[iex]);
double loss;
double loss_unc;
ggml_opt_result_loss(result_eval, &loss, &loss_unc);
fprintf(stdout, "%s: test_loss=%.6lf+-%.6lf\n", __func__, loss, loss_unc);
double accuracy;
double accuracy_unc;
ggml_opt_result_accuracy(result_eval, &accuracy, &accuracy_unc);
fprintf(stdout, "%s: test_acc=%.2lf+-%.2lf%%\n", __func__, 100.0*accuracy, 100.0*accuracy_unc);
ggml_opt_result_free(result_eval);
return 0;
}
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