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save_test.c
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save_test.c
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/**********************************************************************************/
/* Copyright (c) 2023 Mark Seminatore */
/* All rights reserved. */
/* */
/* Permission is hereby granted, free of charge, to any person obtaining a copy */
/* of this software and associated documentation files(the "Software"), to deal */
/* in the Software without restriction, including without limitation the rights */
/* to use, copy, modify, merge, publish, distribute, sublicense, and / or sell */
/* copies of the Software, and to permit persons to whom the Software is */
/* furnished to do so, subject to the following conditions: */
/* */
/* The above copyright notice and this permission notice shall be included in all */
/* copies or substantial portions of the Software. */
/* */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR */
/* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, */
/* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE */
/* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER */
/* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, */
/* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE */
/* SOFTWARE. */
/**********************************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <time.h>
#include "ann.h"
//------------------------------
// main program start
//------------------------------
int main(int argc, char *argv[])
{
char *network_filename = "mnist-fashion.nna";
char *test_filename = "fashion-mnist_test.csv";
real *test_data;
int test_rows, test_stride;
if (argc > 1)
network_filename = argv[1];
if (argc > 2)
test_filename = argv[2];
PNetwork pnet = ann_load_network(network_filename);
if (!pnet)
return ERR_FAIL;
// load the test data
printf("Loading %s...", test_filename);
CHECK_OK(ann_load_csv(test_filename, CSV_HAS_HEADER, &test_data, &test_rows, &test_stride));
puts("done.");
PTensor y_test_labels = tensor_create_from_array(test_rows, test_stride, test_data);
free(test_data);
PTensor x_test = tensor_slice_cols(y_test_labels, 1);
if (!x_test)
return ERR_FAIL;
PTensor y_test = tensor_onehot(y_test_labels, 10);
if (!y_test)
return ERR_FAIL;
// normalize inputs
tensor_mul_scalar(x_test, (real)(1.0 / 255.0));
// evaluate the network against the test data
real acc = ann_evaluate_accuracy(pnet, x_test, y_test);
printf("\nTest accuracy: %g%%\n", acc * 100);
// free memory
ann_free_network(pnet);
tensor_free(y_test_labels);
tensor_free(x_test);
tensor_free(y_test);
return ERR_OK;
}