-
Notifications
You must be signed in to change notification settings - Fork 4
/
mnist_trainer.cpp
52 lines (48 loc) · 1.61 KB
/
mnist_trainer.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
#include <cstdio>
#include <algorithm>
#include <cfloat>
#include "NN/Trainer.hpp"
#include "MNIST/Loader.hpp"
int main(int argc, char **argv)
{
if(argc != 2)
{
printf("Usage: ./MnistTrainer [output snn filename]\n");
return EXIT_FAILURE;
}
MnistLoader training_set{"MNIST/train-images.idx3-ubyte", "MNIST/train-labels.idx1-ubyte"};
//SimpleNN snn{"mnist.nn"};
SimpleNN snn({784, 300, 300, 10});
snn.UniformRandomizeWeights(0.0, 0.00001);
//snn.HeRandomizeWeights();
Trainer{snn,
0.00001, //learning rate
6000, //20 epoches
100, //batch size
}.Run(training_set.GetDataSet());
/*Trainer{snn,
0.001, //learning rate
24000, //40 epoches
100, //batch size
}.Run(training_set.GetDataSet());*/
snn.Save(argv[1]);
//SimpleNN snn{"./bests/784-200-200-10_0.nn"};
unsigned correct_count = 0;
for(const auto &i : training_set.GetDataSet())
{
snn.Evaluate(i.m_inputs);
int res = std::max_element(snn.GetOutput(), snn.GetOutput() + 10) - snn.GetOutput();
if(i.m_expected[res] > 0.5) ++correct_count;
}
printf("training set accuracy: %lf%%\n", 100.0 * (float)correct_count / (float)training_set.GetDataSet().size());
correct_count = 0;
MnistLoader validation_set{"MNIST/t10k-images-idx3-ubyte", "MNIST/t10k-labels-idx1-ubyte"};
for(const auto &i : validation_set.GetDataSet())
{
snn.Evaluate(i.m_inputs);
int res = std::max_element(snn.GetOutput(), snn.GetOutput() + 10) - snn.GetOutput();
if(i.m_expected[res] > 0.9) ++correct_count;
}
printf("validation set accuracy: %lf%%\n", 100.0 * (float)correct_count / (float)validation_set.GetDataSet().size());
return EXIT_SUCCESS;
}