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authorjeanne <jeanne@localhost.localdomain>2022-05-11 09:54:38 -0700
committerjeanne <jeanne@localhost.localdomain>2022-05-11 09:54:38 -0700
commit411f66a2540fa17c736116d865e0ceb0cfe5623b (patch)
treefa92c69ec627642c8452f928798ff6eccd24ddd6 /src/lib/test/train_xor_test.c
parent7705b07456dfd4b89c272613e98eda36cc787254 (diff)
Initial commit.
Diffstat (limited to 'src/lib/test/train_xor_test.c')
-rw-r--r--src/lib/test/train_xor_test.c66
1 files changed, 66 insertions, 0 deletions
diff --git a/src/lib/test/train_xor_test.c b/src/lib/test/train_xor_test.c
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1#include <neuralnet/train.h>
2
3#include <neuralnet/matrix.h>
4#include <neuralnet/neuralnet.h>
5#include "activation.h"
6#include "neuralnet_impl.h"
7
8#include "test.h"
9#include "test_util.h"
10
11#include <assert.h>
12
13TEST_CASE(neuralnet_train_xor_test) {
14 const int num_layers = 2;
15 const int layer_sizes[] = { 2, 2, 1 };
16 const nnActivation layer_activations[] = { nnRelu, nnIdentity };
17
18 nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations);
19 assert(net);
20
21 // Train.
22
23 #define N 4
24 const R inputs[N][2] = { { 0., 0. }, { 0., 1. }, { 1., 0. }, { 1., 1. } };
25 const R targets[N] = { 0., 1., 1., 0. };
26
27 nnMatrix inputs_matrix = nnMatrixMake(N, 2);
28 nnMatrix targets_matrix = nnMatrixMake(N, 1);
29 nnMatrixInit(&inputs_matrix, (const R*)inputs);
30 nnMatrixInit(&targets_matrix, targets);
31
32 nnTrainingParams params = {
33 .learning_rate = 0.1,
34 .max_iterations = 500,
35 .seed = 0,
36 .weight_init = nnWeightInit01,
37 .debug = false,
38 };
39
40 nnTrain(net, &inputs_matrix, &targets_matrix, &params);
41
42 // Test.
43
44 #define M 4
45
46 nnQueryObject* query = nnMakeQueryObject(net, /*num_inputs=*/M);
47
48 const R test_inputs[M][2] = { { 0., 0. }, { 1., 0. }, { 0., 1. }, { 1., 1. } };
49 nnMatrix test_inputs_matrix = nnMatrixMake(M, 2);
50 nnMatrixInit(&test_inputs_matrix, (const R*)test_inputs);
51 nnQuery(net, query, &test_inputs_matrix);
52
53 const R expected_outputs[M] = { 0., 1., 1., 0. };
54 for (int i = 0; i < M; ++i) {
55 const R test_output = nnMatrixAt(nnNetOutputs(query), i, 0);
56 printf("\nInput: (%f, %f), Output: %f, Expected: %f\n",
57 test_inputs[i][0], test_inputs[i][1], test_output, expected_outputs[i]);
58 }
59 for (int i = 0; i < M; ++i) {
60 const R test_output = nnMatrixAt(nnNetOutputs(query), i, 0);
61 TEST_TRUE(double_eq(test_output, expected_outputs[i], OUTPUT_EPS));
62 }
63
64 nnDeleteQueryObject(&query);
65 nnDeleteNet(&net);
66}