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#include <neuralnet/train.h>
#include <neuralnet/matrix.h>
#include <neuralnet/neuralnet.h>
#include "activation.h"
#include "neuralnet_impl.h"
#include "test.h"
#include "test_util.h"
#include <assert.h>
TEST_CASE(neuralnet_train_xor_test) {
const int num_layers = 2;
const int layer_sizes[] = { 2, 2, 1 };
const nnActivation layer_activations[] = { nnRelu, nnIdentity };
nnNeuralNetwork* net = nnMakeNet(num_layers, layer_sizes, layer_activations);
assert(net);
// Train.
#define N 4
const R inputs[N][2] = { { 0., 0. }, { 0., 1. }, { 1., 0. }, { 1., 1. } };
const R targets[N] = { 0., 1., 1., 0. };
nnMatrix inputs_matrix = nnMatrixMake(N, 2);
nnMatrix targets_matrix = nnMatrixMake(N, 1);
nnMatrixInit(&inputs_matrix, (const R*)inputs);
nnMatrixInit(&targets_matrix, targets);
nnTrainingParams params = {
.learning_rate = 0.1,
.max_iterations = 500,
.seed = 0,
.weight_init = nnWeightInit01,
.debug = false,
};
nnTrain(net, &inputs_matrix, &targets_matrix, ¶ms);
// Test.
#define M 4
nnQueryObject* query = nnMakeQueryObject(net, /*num_inputs=*/M);
const R test_inputs[M][2] = { { 0., 0. }, { 1., 0. }, { 0., 1. }, { 1., 1. } };
nnMatrix test_inputs_matrix = nnMatrixMake(M, 2);
nnMatrixInit(&test_inputs_matrix, (const R*)test_inputs);
nnQuery(net, query, &test_inputs_matrix);
const R expected_outputs[M] = { 0., 1., 1., 0. };
for (int i = 0; i < M; ++i) {
const R test_output = nnMatrixAt(nnNetOutputs(query), i, 0);
printf("\nInput: (%f, %f), Output: %f, Expected: %f\n",
test_inputs[i][0], test_inputs[i][1], test_output, expected_outputs[i]);
}
for (int i = 0; i < M; ++i) {
const R test_output = nnMatrixAt(nnNetOutputs(query), i, 0);
TEST_TRUE(double_eq(test_output, expected_outputs[i], OUTPUT_EPS));
}
nnDeleteQueryObject(&query);
nnDeleteNet(&net);
}
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