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dlib
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Made tests more robust and accurate

This commit is contained in:
Davis King 2017-02-18 23:23:16 -05:00
parent 1c8728b7bc
commit a525e8d431
1 changed files with 20 additions and 16 deletions
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36
dlib/test/dnn.cpp
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@ -1743,15 +1743,16 @@ namespace
void test_simple_linear_regression()
{
::std::vector<matrix<double>> x(100);
::std::vector<float> y(100);
const int num_samples = 1000;
::std::vector<matrix<double>> x(num_samples);
::std::vector<float> y(num_samples);
::std::default_random_engine generator(16);
::std::normal_distribution<float> distribution(0,5);
::std::normal_distribution<float> distribution(0,0.1);
const float true_intercept = 50.0;
const float true_slope = 10.0;
for ( int ii = 0; ii < 100; ++ii )
for ( int ii = 0; ii < num_samples; ++ii )
{
const double val = static_cast<double>(ii);
const double val = static_cast<double>(ii)/10;
matrix<double> tmp(1,1);
tmp = val;
x[ii] = tmp;
@ -1761,9 +1762,10 @@ namespace
using net_type = loss_mean_squared<fc<1, input<matrix<double>>>>;
net_type net;
layer<1>(net).layer_details().set_bias_learning_rate_multiplier(300);
sgd defsolver;
sgd defsolver(0,0.9);
dnn_trainer<net_type> trainer(net, defsolver);
trainer.set_learning_rate(0.00001);
trainer.set_learning_rate(1e-5);
trainer.set_min_learning_rate(1e-6);
trainer.set_mini_batch_size(50);
trainer.set_max_num_epochs(170);
trainer.train(x, y);
@ -1772,7 +1774,7 @@ namespace
const float slope_error = abs(true_slope - slope);
const float intercept = layer<1>(net).layer_details().get_biases().host()[0];
const float intercept_error = abs(true_intercept - intercept);
const float eps_slope = 0.5, eps_intercept = 1.0;
const float eps_slope = 0.05, eps_intercept = 0.1;
DLIB_TEST_MSG(slope_error <= eps_slope,
"Expected slope = " << true_slope << " Estimated slope = " << slope << " Error limit = " << eps_slope);
@ -1786,10 +1788,11 @@ namespace
void test_multioutput_linear_regression()
{
const int num_outputs = 2;
::std::vector<matrix<double>> x(100);
::std::vector<matrix<float>> y(100);
const int num_samples = 1000;
::std::vector<matrix<double>> x(num_samples);
::std::vector<matrix<float>> y(num_samples);
::std::default_random_engine generator(16);
::std::normal_distribution<float> distribution(0,5);
::std::normal_distribution<float> distribution(0,0.1);
::std::normal_distribution<float> slope_distribution(10,5);
::std::normal_distribution<float> intercept_distribution(50,10);
::std::vector<float> true_intercepts(num_outputs);
@ -1800,9 +1803,9 @@ namespace
true_intercepts[jj] = intercept_distribution(generator);
}
matrix<float> ytmp(num_outputs, 1);
for ( int ii = 0; ii < 100; ++ii )
for ( int ii = 0; ii < num_samples; ++ii )
{
const double val = static_cast<double>(ii);
const double val = static_cast<double>(ii)/10;
matrix<double> tmp(1,1);
tmp = val;
x[ii] = tmp;
@ -1815,16 +1818,17 @@ namespace
using net_type = loss_mean_squared_multioutput<fc<num_outputs, input<matrix<double>>>>;
net_type net;
layer<1>(net).layer_details().set_bias_learning_rate_multiplier(900);
sgd defsolver;
sgd defsolver(0,0.9);
dnn_trainer<net_type> trainer(net, defsolver);
trainer.set_learning_rate(0.000015);
trainer.set_learning_rate(1e-5);
trainer.set_min_learning_rate(1e-6);
trainer.set_mini_batch_size(50);
trainer.set_max_num_epochs(170);
trainer.train(x, y);
float slope_error = 0.0;
float intercept_error = 0.0;
const float eps_slope = 0.5, eps_intercept = 2.0;
const float eps_slope = 0.05, eps_intercept = 0.1;
for ( int jj = 0; jj < num_outputs; ++jj )
{