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C++ NumericalDifferentiation::calculate_Jacobian方法代码示例

本文整理汇总了C++中NumericalDifferentiation::calculate_Jacobian方法的典型用法代码示例。如果您正苦于以下问题：C++ NumericalDifferentiation::calculate_Jacobian方法的具体用法？C++ NumericalDifferentiation::calculate_Jacobian怎么用？C++ NumericalDifferentiation::calculate_Jacobian使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类NumericalDifferentiation的用法示例。


在下文中一共展示了NumericalDifferentiation::calculate_Jacobian方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: test_calculate_Jacobian

void ProbabilisticLayerTest::test_calculate_Jacobian(void)
{
   message += "test_calculate_Jacobian\n";

   NumericalDifferentiation nd;

   ProbabilisticLayer pl;

   Vector<double> inputs;
   Matrix<double> Jacobian;
   Matrix<double> numerical_Jacobian;

   // Test

   if(numerical_differentiation_tests)
   {
      pl.set_probabilistic_method(ProbabilisticLayer::Softmax);

      pl.set(3);

      inputs.set(3);
      inputs.randomize_normal();

      Jacobian = pl.calculate_Jacobian(inputs);
      numerical_Jacobian = nd.calculate_Jacobian(pl, &ProbabilisticLayer::calculate_outputs, inputs);

      assert_true((Jacobian-numerical_Jacobian).calculate_absolute_value() < 1.0e-3, LOG);
   }
}
开发者ID:pappakrishnan，项目名称:OpenNN，代码行数:29，代码来源:probabilistic_layer_test.cpp

示例2: test_calculate_terms_Jacobian

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.........
开发者ID:PuchoDeepLearningLabs，项目名称:OpenNN，代码行数:101，代码来源:sum_squared_error_test.cpp

示例3: test_calculate_Jacobian_terms

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.........
开发者ID:Quanteek，项目名称:OpenNN-CMake，代码行数:101，代码来源:mean_squared_error_test.cpp

示例4: test_calculate_Jacobian

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

  // One layer

  NeuralNetwork nn;

  Vector<unsigned> multilayer_perceptron_architecture;

  Vector<double> inputs;
  Matrix<double> Jacobian;

  //   Vector<double> inputs_minimum;
  //   Vector<double> inputs_maximum;

  //   Vector<double> inputs_mean;
  //   Vector<double> inputs_standard_deviation;

  //   Vector<double> outputs_minimum;
  //   Vector<double> outputs_maximum;

  //   Vector<double> outputs_mean;
  //   Vector<double> outputs_standard_deviation;

  //   mmlp.set_display(false);

  NumericalDifferentiation nd;
  Matrix<double> numerical_Jacobian;

  // Test

  nn.set(1, 1, 1);
  nn.initialize_parameters(0.0);
  inputs.set(1, 0.0);
  Jacobian = nn.calculate_Jacobian(inputs);
  assert_true(Jacobian == 0.0, LOG);

  // Test

  nn.set(3, 4, 2);
  nn.initialize_parameters(0.0);
  inputs.set(3, 0.0);
  Jacobian = nn.calculate_Jacobian(inputs);
  assert_true(Jacobian == 0.0, LOG);

  // Test

  if (numerical_differentiation_tests) {
    nn.set(3, 4, 2);
    nn.initialize_parameters(0.0);
    inputs.set(3, 0.0);
    Jacobian = nn.calculate_Jacobian(inputs);
    numerical_Jacobian =
        nd.calculate_Jacobian(nn, &NeuralNetwork::calculate_outputs, inputs);
    assert_true(
        (Jacobian - numerical_Jacobian).calculate_absolute_value() < 1.0e-3,
        LOG);
  }

  // Test

  multilayer_perceptron_architecture.set(3, 1);
  nn.set(multilayer_perceptron_architecture);
  nn.initialize_parameters(0.0);
  inputs.set(1, 0.0);
  Jacobian = nn.calculate_Jacobian(inputs);
  assert_true(Jacobian == 0.0, LOG);

  // Test

  multilayer_perceptron_architecture.set(3);
  multilayer_perceptron_architecture[0] = 3;
  multilayer_perceptron_architecture[1] = 4;
  multilayer_perceptron_architecture[2] = 1;

  nn.set(multilayer_perceptron_architecture);

  nn.initialize_parameters(0.0);
  inputs.set(3, 0.0);
  Jacobian = nn.calculate_Jacobian(inputs);
  assert_true(Jacobian == 0.0, LOG);

  // Test

  if (numerical_differentiation_tests) {
    multilayer_perceptron_architecture.set(3);
    multilayer_perceptron_architecture[0] = 3;
    multilayer_perceptron_architecture[1] = 4;
    multilayer_perceptron_architecture[2] = 1;

    nn.set(multilayer_perceptron_architecture);

    inputs.set(3);
    inputs[0] = 0.0;
    inputs[1] = 1.0;
    inputs[2] = 2.0;

    Jacobian = nn.calculate_Jacobian(inputs);
    numerical_Jacobian =
        nd.calculate_Jacobian(nn, &NeuralNetwork::calculate_outputs, inputs);
//.........这里部分代码省略.........
开发者ID:jrdodson，项目名称:opennn，代码行数:101，代码来源:neural_network_test.cpp

示例5: test_calculate_Jacobian_terms

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

   NumericalDifferentiation nd;

   NeuralNetwork nn;
   Vector<int> hidden_layers_size;
   Vector<double> network_parameters;

   DataSet ds;

   NormalizedSquaredError nse(&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.randomize_parameters_normal();
   network_parameters = nn.arrange_parameters();

   ds.set(1, 1, 2);
   ds.randomize_data_normal();

   terms_Jacobian = nse.calculate_terms_Jacobian();
   numerical_Jacobian_terms = nd.calculate_Jacobian(nse, &NormalizedSquaredError::calculate_terms, network_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();
   network_parameters = nn.arrange_parameters();

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

   terms_Jacobian = nse.calculate_terms_Jacobian();
   numerical_Jacobian_terms = nd.calculate_Jacobian(nse, &NormalizedSquaredError::calculate_terms, network_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();

   ds.set(2,2,2);
   ds.randomize_data_normal();
   
   objective_gradient = nse.calculate_gradient();

   evaluation_terms = nse.calculate_terms();
   terms_Jacobian = nse.calculate_terms_Jacobian();

   assert_true(((terms_Jacobian.calculate_transpose()).dot(evaluation_terms)*2.0 - objective_gradient).calculate_absolute_value() < 1.0e-3, LOG);

}
开发者ID:Quanteek，项目名称:OpenNN-CMake，代码行数:64，代码来源:normalized_squared_error_test.cpp


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