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

本文整理汇总了C++中NumericalDifferentiation::calculate_gradient方法的典型用法代码示例。如果您正苦于以下问题：C++ NumericalDifferentiation::calculate_gradient方法的具体用法？C++ NumericalDifferentiation::calculate_gradient怎么用？C++ NumericalDifferentiation::calculate_gradient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_gradient方法的7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_gradient

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

   NumericalDifferentiation nd;

   Vector<double> x;
   Vector<double> g;
	   
   // Test

   nd.set_numerical_differentiation_method(NumericalDifferentiation::ForwardDifferences);

   x.set(2, 0.0);

   g = nd.calculate_gradient(*this, &NumericalDifferentiationTest::f2, x);

   assert_true(g.size() == 2, LOG);
   assert_true(g == 1.0, LOG);

   // Test

   nd.set_numerical_differentiation_method(NumericalDifferentiation::CentralDifferences);

   x.set(2, 0.0);

   g = nd.calculate_gradient(*this, &NumericalDifferentiationTest::f2, x);

   assert_true(g.size() == 2, LOG);
   assert_true(g == 1.0, LOG);
}
开发者ID:petrarce，项目名称:MyNet，代码行数:31，代码来源:numerical_differentiation_test.cpp

示例2: test_calculate_gradient

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);
}
开发者ID:Grace，项目名称:OpenNN，代码行数:54，代码来源:root_mean_squared_error_test.cpp

示例3: test_calculate_gradient

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);
}
开发者ID:Quanteek，项目名称:OpenNN-CMake，代码行数:54，代码来源:normalized_squared_error_test.cpp

示例4: test_calculate_gradient

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

   NumericalDifferentiation nd;
   DataSet ds;
   NeuralNetwork nn;
   SumSquaredError sse(&nn, &ds);

   Vector<size_t> architecture;

   Vector<double> parameters;
   Vector<double> gradient;
   Vector<double> numerical_gradient;
   Vector<double> error;

   // Test 

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

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

   gradient = sse.calculate_gradient();

   assert_true(gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(gradient == 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);

   gradient.clear();

   gradient = sse.calculate_gradient();

   assert_true(gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(gradient == 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, 5, 2);
   sse.set(&nn, &ds);
   ds.initialize_data(0.0);

   gradient.clear();

   gradient = sse.calculate_gradient();

   assert_true(gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(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);

   gradient.clear();

   gradient = sse.calculate_gradient();

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

   // Test 

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

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

   gradient.clear();

   gradient = sse.calculate_gradient();

   assert_true(gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(gradient == 0.0, 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5: test_calculate_gradient

void MeanSquaredErrorTest::test_calculate_gradient(void)
{
   message += "test_calculate_gradient\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> 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);
//.........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6: test_calculate_gradient

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

   NumericalDifferentiation nd;
   NeuralNetwork nn;
   NeuralParametersNorm npn(&nn);

   Vector<size_t> architecture;

   Vector<double> parameters;
   Vector<double> gradient;
   Vector<double> numerical_gradient;
   Vector<double> error;

   // Test 

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

   gradient = npn.calculate_gradient();

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

   // Test 

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

   gradient = npn.calculate_gradient();

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

   // Test

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

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

   npn.set_neural_network_pointer(&nn);

   gradient = npn.calculate_gradient();

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

   // Test 

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

   npn.set_neural_network_pointer(&nn);

   gradient = npn.calculate_gradient();

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


   // Test

   nn.initialize_parameters(1.0);
   parameters = nn.arrange_parameters();

   gradient = npn.calculate_gradient();
   numerical_gradient = nd.calculate_gradient(npn, &NeuralParametersNorm::calculate_regularization, parameters);
   error = (gradient - numerical_gradient).calculate_absolute_value();

   assert_true(error < 1.0e-3, LOG);
}
开发者ID:Artelnics，项目名称:OpenNN，代码行数:76，代码来源:neural_parameters_norm_test.cpp

示例7: test_calculate_gradient

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

   NumericalDifferentiation nd;

   NeuralNetwork nn;
   Vector<size_t> architecture;

   Vector<double> parameters;

   DataSet ds;

   MinkowskiError me(&nn, &ds);

   Vector<double> gradient;
   Vector<double> numerical_gradient;

   // Test

   nn.set(1,1,1);

   nn.initialize_parameters(0.0);

   ds.set(1,1,1);

   ds.initialize_data(0.0);

   gradient = me.calculate_gradient();

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

   // Test 

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

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

   gradient = me.calculate_gradient();

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

   // Test

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

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

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

   gradient = me.calculate_gradient();

   assert_true(gradient.size() == nn.count_parameters_number(), LOG);
   assert_true(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);

   gradient = me.calculate_gradient();

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

   // Test 

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

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

   gradient = me.calculate_gradient();

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

   // Test

   architecture.set(3);
   architecture[0] = 2;
   architecture[1] = 1;
//.........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:minkowski_error_test.cpp


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