C++ NeuralNetwork::arrange_parameters方法代码示例

void RootMeanSquaredErrorTest::test_calculate_gradient(void)
{
   message += "test_calculate_gradient\n";

   NumericalDifferentiation nd;

   NeuralNetwork nn;

   Vector<double> network_parameters;

   DataSet ds;

   RootMeanSquaredError rmse(&nn, &ds);

   Vector<double> objective_gradient;
   Vector<double> numerical_objective_gradient;

   // Test

   nn.set(3, 4, 2);
   nn.initialize_parameters(0.0);

   ds.set(3, 2, 5);
   ds.initialize_data(0.0);

   // Test

   nn.set(3, 4, 2);
   nn.initialize_parameters(1.0);

   network_parameters = nn.arrange_parameters();

   ds.set(3, 2, 5);
   ds.initialize_data(1.0);

   objective_gradient = rmse.calculate_gradient();
   numerical_objective_gradient = nd.calculate_gradient(rmse, &RootMeanSquaredError::calculate_performance, network_parameters);
   assert_true((objective_gradient - numerical_objective_gradient).calculate_absolute_value() < 1.0e-3, LOG);

   // Test

   nn.set(1,1,1);

   network_parameters = nn.arrange_parameters();

   ds.set(1,1,1);
   ds.initialize_data(1.0);

   rmse.set_neural_network_pointer(&nn);

   objective_gradient = rmse.calculate_gradient();
   numerical_objective_gradient = nd.calculate_gradient(rmse, &RootMeanSquaredError::calculate_performance, network_parameters);
   assert_true((objective_gradient - numerical_objective_gradient).calculate_absolute_value() < 1.0e-3, LOG);
}

void NeuralParametersNormTest::test_calculate_performance(void)   
{
   message += "test_calculate_performance\n";

   NeuralNetwork nn;
   Vector<double> neural_parameters;

   NeuralParametersNorm npn(&nn);

   Vector<double> parameters;

   double performance;

   // Test

   nn.set(1, 1);
   nn.initialize_parameters(0.0);

   performance = npn.calculate_regularization();

   assert_true(performance == 0.0, LOG);

   // Test

   nn.set(1, 1);
   nn.initialize_parameters(3.1415927);

   parameters = nn.arrange_parameters();

   assert_true(npn.calculate_regularization() == npn.calculate_regularization(parameters), LOG);
}

void NeuralNetworkTest::test_arrange_parameters(void) {
  message += "test_arrange_parameters\n";

  NeuralNetwork nn;
  Vector<double> parameters;

  IndependentParameters* ip;

  // Test

  nn.set();
  parameters = nn.arrange_parameters();

  assert_true(parameters.size() == 0, LOG);

  // Test

  nn.set(1, 1, 1);

  ip = new IndependentParameters(1);
  nn.set_independent_parameters_pointer(ip);
  nn.initialize_parameters(0.0);
  parameters = nn.arrange_parameters();

  assert_true(parameters.size() == 5, LOG);
  assert_true(parameters == 0.0, LOG);

  // Test

  nn.set();
  ip = new IndependentParameters(1);
  nn.set_independent_parameters_pointer(ip);
  nn.initialize_parameters(0.0);
  parameters = nn.arrange_parameters();
  assert_true(parameters.size() == 1, LOG);
  assert_true(parameters == 0.0, LOG);

  // Test

  nn.set(1, 1, 1);
  ip = new IndependentParameters(1);
  nn.set_independent_parameters_pointer(ip);
  nn.initialize_parameters(0.0);
  parameters = nn.arrange_parameters();
  assert_true(parameters.size() == 5, LOG);
  assert_true(parameters == 0.0, LOG);
}

void NormalizedSquaredErrorTest::test_calculate_gradient(void)
{
   message += "test_calculate_gradient\n";

   NumericalDifferentiation nd;

   NeuralNetwork nn;

   Vector<double> network_parameters;

   DataSet ds;
   Matrix<double> data;

   NormalizedSquaredError nse(&nn, &ds);

   Vector<double> objective_gradient;
   Vector<double> numerical_objective_gradient;

   // Test 

   nn.set(1,1,1);

   nn.initialize_parameters(0.0);

   ds.set(1, 1, 2);

   data.set(2, 2);
   data[0][0] = -1.0;
   data[0][1] = -1.0;
   data[1][0] = 1.0;
   data[1][1] = 1.0;

   ds.set_data(data);

   objective_gradient = nse.calculate_gradient();

   assert_true(objective_gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(objective_gradient == 0.0, LOG);

   // Test 

   nn.set(3, 4, 5);
   nn.randomize_parameters_normal();

   network_parameters = nn.arrange_parameters();

   ds.set(3, 5, 2);
   ds.randomize_data_normal();

   objective_gradient = nse.calculate_gradient();
   numerical_objective_gradient = nd.calculate_gradient(nse, &NormalizedSquaredError::calculate_performance, network_parameters);

   assert_true((objective_gradient - numerical_objective_gradient).calculate_absolute_value() < 1.0e-3, LOG);
}

void NeuralNetworkTest::test_randomize_parameters_normal(void) {
  message += "test_randomize_parameters_normal\n";

  NeuralNetwork nn;
  Vector<double> network_parameters;

  // Test

  nn.set(1, 1, 1);
  nn.randomize_parameters_normal(1.0, 0.0);
  network_parameters = nn.arrange_parameters();
  assert_true(network_parameters == 1.0, LOG);
}

void NeuralNetworkTest::test_randomize_parameters_uniform(void) {
  message += "test_randomize_parameters_uniform\n";

  NeuralNetwork nn;
  Vector<double> parameters;

  // Test

  nn.set(1, 1, 1);
  nn.randomize_parameters_uniform();
  parameters = nn.arrange_parameters();
  assert_true(parameters >= -1.0, LOG);
  assert_true(parameters <= 1.0, LOG);
}

void NeuralNetworkTest::test_set_parameters(void) {
  message += "test_set_parameters\n";

  Vector<unsigned> multilayer_perceptron_architecture;
  NeuralNetwork nn;

  unsigned parameters_number;
  Vector<double> parameters;

  // Test

  nn.set_parameters(parameters);

  parameters = nn.arrange_parameters();
  assert_true(parameters.size() == 0, LOG);

  // Test

  multilayer_perceptron_architecture.set(2, 2);
  nn.set(multilayer_perceptron_architecture);

  nn.construct_independent_parameters();

  nn.get_independent_parameters_pointer()->set_parameters_number(2);

  parameters_number = nn.count_parameters_number();

  parameters.set(0.0, 1.0, parameters_number - 1);
  nn.set_parameters(parameters);
  parameters = nn.arrange_parameters();

  assert_true(parameters.size() == parameters_number, LOG);
  assert_true(parameters[0] == 0.0, LOG);
  assert_true(parameters[parameters_number - 1] == parameters_number - 1.0,
              LOG);
}

void PerformanceFunctionalTest::test_calculate_gradient(void)
{
   message += "test_calculate_gradient\n";

   NeuralNetwork nn;

   size_t parameters_number;
   Vector<double> parameters;

   PerformanceFunctional pf(&nn);

   pf.destruct_all_terms();
   pf.set_regularization_type(PerformanceFunctional::NEURAL_PARAMETERS_NORM_REGULARIZATION);

   Vector<double> gradient;

   // Test

   nn.set(1, 1, 1);

   nn.initialize_parameters(0.0);

   parameters = nn.arrange_parameters();

   gradient = pf.calculate_gradient(parameters);

   assert_true(gradient == 0.0, LOG);

   // Test

   parameters_number = nn.count_parameters_number();
   nn.initialize_parameters(0.0);

   MockPerformanceTerm* mptp = new MockPerformanceTerm(&nn);

   pf.set_user_objective_pointer(mptp);

   gradient = pf.calculate_gradient();

   assert_true(gradient.size() == parameters_number, LOG);
   assert_true(gradient == 0.0, LOG);
}

void NeuralNetworkTest::test_initialize_parameters(void) {
  message += "test_initialize_parameters\n";

  NeuralNetwork nn;
  Vector<double> parameters;

  IndependentParameters* ip;

  // Test

  nn.set(1, 1, 1);

  nn.construct_independent_parameters();

  ip = nn.get_independent_parameters_pointer();
  ip->set_parameters_number(1);

  nn.randomize_parameters_normal(1.0, 0.0);
  parameters = nn.arrange_parameters();
  assert_true(parameters == 1.0, LOG);
}

void MeanSquaredErrorTest::test_calculate_Jacobian_terms(void)
{
   message += "test_calculate_Jacobian_terms\n";

   NumericalDifferentiation nd;

   NeuralNetwork nn;
   Vector<unsigned> multilayer_perceptron_architecture;
   Vector<double> parameters;

   DataSet ds;

   MeanSquaredError mse(&nn, &ds);

   Vector<double> objective_gradient;

   Vector<double> evaluation_terms;
   Matrix<double> terms_Jacobian;
   Matrix<double> numerical_Jacobian_terms;

   // Test

   nn.set(1, 1);

   nn.initialize_parameters(0.0);

   ds.set(1, 1, 1);

   ds.initialize_data(0.0);

   terms_Jacobian = mse.calculate_terms_Jacobian();

   assert_true(terms_Jacobian.get_rows_number() == ds.get_instances().count_training_instances_number(), LOG);
   assert_true(terms_Jacobian.get_columns_number() == nn.count_parameters_number(), LOG);
   assert_true(terms_Jacobian == 0.0, LOG);

   // Test 

   nn.set(3, 4, 2);
   nn.initialize_parameters(0.0);

   ds.set(3, 2, 5);
   mse.set(&nn, &ds);
   ds.initialize_data(0.0);

   terms_Jacobian = mse.calculate_terms_Jacobian();

   assert_true(terms_Jacobian.get_rows_number() == ds.get_instances().count_training_instances_number(), LOG);
   assert_true(terms_Jacobian.get_columns_number() == nn.count_parameters_number(), LOG);
   assert_true(terms_Jacobian == 0.0, LOG);

   // Test

   multilayer_perceptron_architecture.set(3);
   multilayer_perceptron_architecture[0] = 2;
   multilayer_perceptron_architecture[1] = 1;
   multilayer_perceptron_architecture[2] = 2;

   nn.set(multilayer_perceptron_architecture);
   nn.initialize_parameters(0.0);

   ds.set(2, 2, 5);
   mse.set(&nn, &ds);
   ds.initialize_data(0.0);

   terms_Jacobian = mse.calculate_terms_Jacobian();

   assert_true(terms_Jacobian.get_rows_number() == ds.get_instances().count_training_instances_number(), LOG);
   assert_true(terms_Jacobian.get_columns_number() == nn.count_parameters_number(), LOG);
   assert_true(terms_Jacobian == 0.0, LOG);

   // Test

   nn.set(1, 1, 1);
   nn.randomize_parameters_normal();
   parameters = nn.arrange_parameters();

   ds.set(1, 1, 1);
   ds.randomize_data_normal();

   terms_Jacobian = mse.calculate_terms_Jacobian();
   numerical_Jacobian_terms = nd.calculate_Jacobian(mse, &MeanSquaredError::calculate_terms, parameters);

   assert_true((terms_Jacobian-numerical_Jacobian_terms).calculate_absolute_value() < 1.0e-3, LOG);

   // Test

   nn.set(2, 2, 2);
   nn.randomize_parameters_normal();
   parameters = nn.arrange_parameters();

   ds.set(2, 2, 2);
   ds.randomize_data_normal();

   terms_Jacobian = mse.calculate_terms_Jacobian();
   numerical_Jacobian_terms = nd.calculate_Jacobian(mse, &MeanSquaredError::calculate_terms, parameters);

   assert_true((terms_Jacobian-numerical_Jacobian_terms).calculate_absolute_value() < 1.0e-3, LOG);

   // Test
//.........这里部分代码省略.........

//.........这里部分代码省略.........
   Vector<double> parameters;

   DataSet ds;

   MeanSquaredError mse(&nn, &ds);

   Vector<double> objective_gradient;
   Vector<double> numerical_objective_gradient;
   Vector<double> numerical_differentiation_error;

   // Test

   nn.set(1, 1, 1);

   nn.initialize_parameters(0.0);

   ds.set(1, 1, 1);

   ds.initialize_data(0.0);

   objective_gradient = mse.calculate_gradient();

   assert_true(objective_gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(objective_gradient == 0.0, LOG);

   // Test 

   nn.set(3, 4, 2);
   nn.initialize_parameters(0.0);

   ds.set(3, 2, 5);
   mse.set(&nn, &ds);
   ds.initialize_data(0.0);

   objective_gradient = mse.calculate_gradient();

   assert_true(objective_gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(objective_gradient == 0.0, LOG);

   // Test

   multilayer_perceptron_architecture.set(3);
   multilayer_perceptron_architecture[0] = 2;
   multilayer_perceptron_architecture[1] = 1;
   multilayer_perceptron_architecture[2] = 3;

   nn.set(multilayer_perceptron_architecture);
   nn.initialize_parameters(0.0);

   ds.set(2, 3, 5);
   mse.set(&nn, &ds);
   ds.initialize_data(0.0);

   objective_gradient = mse.calculate_gradient();

   assert_true(objective_gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(objective_gradient == 0.0, LOG);

   // Test

   nn.set(1, 1, 1);

   nn.initialize_parameters(0.0);

   ds.set(1, 1, 1);

   ds.initialize_data(0.0);

   objective_gradient = mse.calculate_gradient();

   assert_true(objective_gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(objective_gradient == 0.0, LOG);

   // Test 

   nn.set(3, 4, 2);
   nn.initialize_parameters(0.0);

   ds.set(3, 2, 5);
   mse.set(&nn, &ds);
   ds.initialize_data(0.0);

   objective_gradient = mse.calculate_gradient();

   assert_true(objective_gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(objective_gradient == 0.0, LOG);

   // Test

   nn.set(1, 1);
   nn.initialize_parameters(1.0);
   parameters = nn.arrange_parameters();

   ds.set(1, 1, 2);
   ds.initialize_data(1.0);

   objective_gradient = mse.calculate_gradient();
   numerical_objective_gradient = nd.calculate_gradient(mse, &MeanSquaredError::calculate_performance, parameters);   
   assert_true((objective_gradient - numerical_objective_gradient).calculate_absolute_value() < 1.0e-3, LOG);
}

void NeuralNetworkTest::test_calculate_outputs(void) {
  message += "test_calculate_outputs\n";

  NeuralNetwork nn;

  unsigned inputs_number;
  unsigned outputs_number;

  Vector<unsigned> architecture;

  Vector<double> inputs;
  Vector<double> outputs;

  unsigned parameters_number;

  Vector<double> parameters;

  // Test

  nn.set(3, 4, 2);
  nn.initialize_parameters(0.0);

  inputs.set(3, 0.0);

  outputs = nn.calculate_outputs(inputs);

  assert_true(outputs == 0.0, LOG);

  // Test

  nn.set(1, 1, 1);
  nn.initialize_parameters(0.0);

  inputs.set(1, 0.0);

  outputs = nn.calculate_outputs(inputs);

  assert_true(outputs == 0.0, LOG);

  // Test

  nn.set(1, 1);

  inputs.set(1);
  inputs.randomize_normal();

  parameters = nn.arrange_parameters();

  assert_true(
      nn.calculate_outputs(inputs) == nn.calculate_outputs(inputs, parameters),
      LOG);

  // Test

  nn.set(4, 3, 5);

  inputs.set(4, 0.0);

  parameters_number = nn.count_parameters_number();

  parameters.set(parameters_number, 0.0);

  outputs = nn.calculate_outputs(inputs, parameters);

  assert_true(outputs.size() == 5, LOG);
  assert_true(outputs == 0.0, LOG);

  // Test

  architecture.set(5);

  architecture.randomize_uniform(5, 10);

  nn.set(architecture);

  inputs_number = nn.get_inputs_pointer()->get_inputs_number();
  outputs_number = nn.get_outputs_pointer()->get_outputs_number();

  inputs.set(inputs_number, 0.0);

  parameters_number = nn.count_parameters_number();

  parameters.set(parameters_number, 0.0);

  outputs = nn.calculate_outputs(inputs, parameters);

  assert_true(outputs.size() == outputs_number, LOG);
  assert_true(outputs == 0.0, LOG);
}

void PerformanceFunctionalTest::test_calculate_performance(void)
{
   message += "test_calculate_performance\n";

   DataSet ds;

   NeuralNetwork nn;

   Vector<double> parameters;

   PerformanceFunctional pf(&nn);

   double performance;

   Vector<double> direction;
   double rate;

   // Test

   pf.destruct_all_terms();
   pf.set_regularization_type(PerformanceFunctional::NEURAL_PARAMETERS_NORM_REGULARIZATION);

   NeuralParametersNorm* neural_parameters_norm = pf.get_neural_parameters_norm_regularization_pointer();

   double neural_parameters_norm_weight = neural_parameters_norm->get_neural_parameters_norm_weight();

   nn.set(1, 1);

   nn.initialize_parameters(1.0);

   parameters = nn.arrange_parameters();

   assert_true(fabs(pf.calculate_performance() - neural_parameters_norm_weight*sqrt(2.0)) < 1.0e-3, LOG);

   assert_true(fabs(pf.calculate_performance() - pf.calculate_performance(parameters)) < 1.0e-3, LOG);

   // Test

   parameters = nn.arrange_parameters();

   assert_true(pf.calculate_performance() != pf.calculate_performance(parameters*2.0), LOG);

   // Test

   direction.set(2, -0.5);
   rate = 2.0;

   assert_true(pf.calculate_performance(direction, rate) == 0.0, LOG);

   // Test

   parameters = nn.arrange_parameters();

   direction.set(2, -1.5);
   rate = 2.3;

   assert_true(pf.calculate_performance(direction, rate) == pf.calculate_performance(parameters + direction*rate), LOG);

   // Test

   ds.set(1, 1, 1);
   ds.randomize_data_normal();

   pf.set_data_set_pointer(&ds);

   pf.destruct_all_terms();
   pf.set_objective_type(PerformanceFunctional::SUM_SQUARED_ERROR_OBJECTIVE);

   nn.set(1, 1);

   nn.initialize_parameters(1.0);

   parameters = nn.arrange_parameters();

   assert_true(fabs(pf.calculate_performance() - pf.calculate_performance(parameters)) < 1.0e-3, LOG);

   // Test

   parameters = nn.arrange_parameters();

   assert_true(pf.calculate_performance() != pf.calculate_performance(parameters*2.0), LOG);

   // Test

   parameters = nn.arrange_parameters();

   direction.set(2, -1.5);
   rate = 2.3;

   assert_true(pf.calculate_performance(direction, rate) == pf.calculate_performance(parameters + direction*rate), LOG);

   // Test

   nn.initialize_parameters(0.0);

   MockPerformanceTerm* mptp = new MockPerformanceTerm(&nn);

   pf.set_user_objective_pointer(mptp);

   performance = pf.calculate_performance();
//.........这里部分代码省略.........

void SumSquaredErrorTest::test_calculate_terms_Jacobian(void)
{   
   message += "test_calculate_terms_Jacobian\n";

   NumericalDifferentiation nd;

   NeuralNetwork nn;
   Vector<size_t> architecture;
   Vector<double> parameters;

   DataSet ds;

   SumSquaredError sse(&nn, &ds);

   Vector<double> gradient;

   Vector<double> terms;
   Matrix<double> terms_Jacobian;
   Matrix<double> numerical_Jacobian_terms;

   // Test

   nn.set(1, 1);

   nn.initialize_parameters(0.0);

   ds.set(1, 1, 1);

   ds.initialize_data(0.0);

   terms_Jacobian = sse.calculate_terms_Jacobian();

   assert_true(terms_Jacobian.get_rows_number() == ds.get_instances().get_instances_number(), LOG);
   assert_true(terms_Jacobian.get_columns_number() == nn.count_parameters_number(), LOG);
   assert_true(terms_Jacobian == 0.0, LOG);

   // Test 

   nn.set(3, 4, 2);
   nn.initialize_parameters(0.0);

   ds.set(3, 2, 5);
   sse.set(&nn, &ds);
   ds.initialize_data(0.0);

   terms_Jacobian = sse.calculate_terms_Jacobian();

   assert_true(terms_Jacobian.get_rows_number() == ds.get_instances().count_training_instances_number(), LOG);
   assert_true(terms_Jacobian.get_columns_number() == nn.count_parameters_number(), LOG);
   assert_true(terms_Jacobian == 0.0, LOG);

   // Test

   architecture.set(3);
   architecture[0] = 5;
   architecture[1] = 1;
   architecture[2] = 2;

   nn.set(architecture);
   nn.initialize_parameters(0.0);

   ds.set(5, 2, 3);
   sse.set(&nn, &ds);
   ds.initialize_data(0.0);

   terms_Jacobian = sse.calculate_terms_Jacobian();

   assert_true(terms_Jacobian.get_rows_number() == ds.get_instances().count_training_instances_number(), LOG);
   assert_true(terms_Jacobian.get_columns_number() == nn.count_parameters_number(), LOG);
   assert_true(terms_Jacobian == 0.0, LOG);

   // Test

   nn.set(1, 1, 1);
   nn.randomize_parameters_normal();
   parameters = nn.arrange_parameters();

   ds.set(1, 1, 1);
   ds.randomize_data_normal();

   terms_Jacobian = sse.calculate_terms_Jacobian();
   numerical_Jacobian_terms = nd.calculate_Jacobian(sse, &SumSquaredError::calculate_terms, parameters);

   assert_true((terms_Jacobian-numerical_Jacobian_terms).calculate_absolute_value() < 1.0e-3, LOG);

   // Test

   nn.set(2, 2, 2);
   nn.randomize_parameters_normal();
   parameters = nn.arrange_parameters();

   ds.set(2, 2, 2);
   ds.randomize_data_normal();

   terms_Jacobian = sse.calculate_terms_Jacobian();
   numerical_Jacobian_terms = nd.calculate_Jacobian(sse, &SumSquaredError::calculate_terms, parameters);

   assert_true((terms_Jacobian-numerical_Jacobian_terms).calculate_absolute_value() < 1.0e-3, LOG);

   // Test
//.........这里部分代码省略.........

void SumSquaredErrorTest::test_calculate_performance(void) {
  message += "test_calculate_performance\n";

  NeuralNetwork nn;
  Vector<double> parameters;

  DataSet ds;
  Matrix<double> data;

  SumSquaredError sse(&nn, &ds);

  double performance;

  // Test

  nn.set(1, 1);
  nn.initialize_parameters(0.0);

  ds.set(1, 1, 1);
  ds.initialize_data(0.0);

  performance = sse.calculate_performance();

  assert_true(performance == 0.0, LOG);

  // Test

  nn.set(1, 1, 1);
  nn.initialize_parameters(0.0);

  ds.set(1, 1, 1);
  ds.initialize_data(1.0);

  performance = sse.calculate_performance();

  assert_true(performance == 1.0, LOG);

  // Test

  nn.set(1, 1);
  nn.randomize_parameters_normal();

  parameters = nn.arrange_parameters();

  ds.set(1, 1, 1);
  ds.randomize_data_normal();

  assert_true(
      sse.calculate_performance() == sse.calculate_performance(parameters),
      LOG);

  // Test

  nn.set(1, 1);
  nn.randomize_parameters_normal();

  parameters = nn.arrange_parameters();

  ds.set(1, 1, 1);
  ds.randomize_data_normal();

  assert_true(sse.calculate_performance() !=
                  sse.calculate_performance(parameters * 2.0),
              LOG);
}