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#include <neuralnet/train.h>
#include "neuralnet_impl.h"
#include <neuralnet/matrix.h>
#include <neuralnet/neuralnet.h>
#include "test.h"
#include "test_util.h"
#include <assert.h>
TEST_CASE(neuralnet_train_linear_perceptron_non_origin_test) {
const int num_layers = 1;
const int input_size = 1;
const nnLayer layers[] = {
{.type = nnLinear, .linear = {.input_size = 1, .output_size = 1}}
};
nnNeuralNetwork* net = nnMakeNet(layers, num_layers, input_size);
assert(net);
// Train.
// Try to learn the Y = 2X + 1 line.
#define N 2
const R inputs[N] = {0., 1.};
const R targets[N] = {1., 3.};
nnMatrix inputs_matrix = nnMatrixMake(N, 1);
nnMatrix targets_matrix = nnMatrixMake(N, 1);
nnMatrixInit(&inputs_matrix, inputs);
nnMatrixInit(&targets_matrix, targets);
nnTrainingParams params = {
.learning_rate = 0.7,
.max_iterations = 20,
.seed = 0,
.weight_init = nnWeightInit01,
.debug = false,
};
nnTrain(net, &inputs_matrix, &targets_matrix, ¶ms);
const R weight = nnMatrixAt(&net->layers[0].linear.weights, 0, 0);
const R expected_weight = 2.0;
printf(
"\nTrained network weight: %f, Expected: %f\n", weight, expected_weight);
TEST_TRUE(double_eq(weight, expected_weight, WEIGHT_EPS));
const R bias = nnMatrixAt(&net->layers[0].linear.biases, 0, 0);
const R expected_bias = 1.0;
printf("Trained network bias: %f, Expected: %f\n", bias, expected_bias);
TEST_TRUE(double_eq(bias, expected_bias, WEIGHT_EPS));
// Test.
nnQueryObject* query = nnMakeQueryObject(net, 1);
const R test_input[] = {2.3};
R test_output[1];
nnQueryArray(net, query, test_input, test_output);
const R expected_output = test_input[0] * expected_weight + expected_bias;
printf("Output: %f, Expected: %f\n", test_output[0], expected_output);
TEST_TRUE(double_eq(test_output[0], expected_output, OUTPUT_EPS));
nnDeleteQueryObject(&query);
nnDeleteNet(&net);
}
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