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); }
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); } }
void NumericalDifferentiationTest::test_calculate_Hessian(void) { message += "test_calculate_Hessian\n"; NumericalDifferentiation nd; Vector<double> x; Matrix<double> H; // Test nd.set_numerical_differentiation_method(NumericalDifferentiation::ForwardDifferences); x.set(2, 0.0); H = nd.calculate_Hessian(*this, &NumericalDifferentiationTest::f2, x); assert_true(H.get_rows_number() == 2, LOG); assert_true(H.get_columns_number() == 2, LOG); assert_true(H == 0.0, LOG); // Test nd.set_numerical_differentiation_method(NumericalDifferentiation::CentralDifferences); x.set(2, 0.0); H = nd.calculate_Hessian(*this, &NumericalDifferentiationTest::f2, x); assert_true(H.get_rows_number() == 2, LOG); assert_true(H.get_columns_number() == 2, LOG); assert_true(H == 0.0, LOG); }
void NumericalDifferentiationTest::test_calculate_Jacobian(void) { message += "test_calculate_Jacobian\n"; NumericalDifferentiation nd; Vector<double> x; Matrix<double> J; Matrix<double> J_true; // Test nd.set_numerical_differentiation_method(NumericalDifferentiation::ForwardDifferences); x.set(2, 0.0); J = nd.calculate_central_differences_Jacobian(*this, &NumericalDifferentiationTest::f3, x); J_true.set_identity(2); assert_true(J == J_true, LOG); // Test nd.set_numerical_differentiation_method(NumericalDifferentiation::CentralDifferences); x.set(2, 0.0); J = nd.calculate_central_differences_Jacobian(*this, &NumericalDifferentiationTest::f3, x); J_true.set_identity(2); assert_true(J == J_true, LOG); }
void NumericalDifferentiationTest::test_calculate_derivative(void) { message += "test_calculate_derivative\n"; NumericalDifferentiation nd; // double x; double d; // Test nd.set_numerical_differentiation_method(NumericalDifferentiation::ForwardDifferences); // x = 0.0; d = nd.calculate_derivative(*this, &NumericalDifferentiationTest::f1, 0.0); assert_true(d == 1.0, LOG); // Test nd.set_numerical_differentiation_method(NumericalDifferentiation::CentralDifferences); // x = 0.0; d = nd.calculate_derivative(*this, &NumericalDifferentiationTest::f1, 0.0); assert_true(d == 1.0, LOG); }
void NumericalDifferentiationTest::test_calculate_second_derivative(void) { message += "test_calculate_second_derivative\n"; NumericalDifferentiation nd; double x; double d2; // Test nd.set_numerical_differentiation_method(NumericalDifferentiation::ForwardDifferences); x = 0.0; d2 = nd.calculate_second_derivative(*this, &NumericalDifferentiationTest::f1, x); assert_true(fabs(d2) <= 1.0e-6, LOG); // Test nd.set_numerical_differentiation_method(NumericalDifferentiation::CentralDifferences); x = 0.0; d2 = nd.calculate_second_derivative(*this, &NumericalDifferentiationTest::f1, x); assert_true(fabs(d2) <= 1.0e-6, 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 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 NumericalDifferentiationTest::test_calculate_central_differences_second_derivative(void) { message += "test_calculate_central_differences_second_derivative\n"; NumericalDifferentiation nd; double x; double d2; // Test x = 0.0; d2 = nd.calculate_central_differences_second_derivative(*this, &NumericalDifferentiationTest::f1, x); assert_true(fabs(d2) <= 1.0e-6, LOG); }
void NumericalDifferentiationTest::test_calculate_central_differences_derivative(void) { message += "test_calculate_central_differences_derivative\n"; NumericalDifferentiation nd; double x; double d; // Test x = 0.0; d = nd.calculate_central_differences_derivative(*this, &NumericalDifferentiationTest::f1, x); assert_true(d == 1.0, LOG); }
void NumericalDifferentiationTest::test_calculate_forward_differences_gradient(void) { message += "test_calculate_forward_differences_gradient\n"; NumericalDifferentiation nd; Vector<double> x; Vector<double> g; // Test x.set(2, 0.0); g = nd.calculate_forward_differences_gradient(*this, &NumericalDifferentiationTest::f2, x); assert_true(g.size() == 2, LOG); assert_true(g == 1.0, LOG); }
void NumericalDifferentiationTest::test_calculate_central_differences_Hessian_form(void) { message += "test_calculate_central_differences_Hessian_form\n"; NumericalDifferentiation nd; Vector<double> x(2, 0.0); Vector< Matrix<double> > Hessian = nd.calculate_central_differences_Hessian_form(*this, &NumericalDifferentiationTest::f3, x); assert_true(Hessian.size() == 2, LOG); assert_true(Hessian[0].get_rows_number() == 2, LOG); assert_true(Hessian[0].get_columns_number() == 2, LOG); assert_true(Hessian[0] == 0.0, LOG); assert_true(Hessian[1].get_rows_number() == 2, LOG); assert_true(Hessian[1].get_columns_number() == 2, LOG); assert_true(Hessian[1] == 0.0, LOG); }
void NumericalDifferentiationTest::test_calculate_forward_differences_Jacobian(void) { message += "test_calculate_forward_differences_Jacobian\n"; NumericalDifferentiation nd; Vector<double> x; Matrix<double> J; Matrix<double> J_true; // Test x.set(2, 0.0); J = nd.calculate_forward_differences_Jacobian(*this, &NumericalDifferentiationTest::f3, x); J_true.set(2, 2); J_true.initialize_identity(); assert_true(J == J_true, 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 nn.set(2, 2, 2); nn.randomize_parameters_normal(); ds.set(2, 2, 2); ds.randomize_data_normal(); objective_gradient = mse.calculate_gradient(); evaluation_terms = mse.calculate_terms(); terms_Jacobian = mse.calculate_terms_Jacobian(); assert_true(((terms_Jacobian.calculate_transpose()).dot(evaluation_terms)*2.0 - objective_gradient).calculate_absolute_value() < 1.0e-3, LOG); }
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); 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 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); }
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); }
void UnscalingLayerTest::test_calculate_derivatives(void) { message += "test_calculate_derivatives\n"; NumericalDifferentiation nd; UnscalingLayer ul; ul.set_display(false); Vector<double> inputs; Vector<double> derivative; Vector<double> numerical_derivative; // Test ul.set(1); ul.set_unscaling_method(UnscalingLayer::MinimumMaximum); inputs.set(1, 0.0); derivative = ul.calculate_derivatives(inputs); assert_true(derivative == 1.0, LOG); // Test ul.set(1); ul.set_unscaling_method(UnscalingLayer::MeanStandardDeviation); inputs.set(1, 0.0); derivative = ul.calculate_derivatives(inputs); assert_true(derivative == 1.0, LOG); // Test if(numerical_differentiation_tests) { ul.set(3); ul.initialize_random(); ul.set_unscaling_method(UnscalingLayer::MinimumMaximum); inputs.set(3); inputs.randomize_normal(); derivative = ul.calculate_derivatives(inputs); numerical_derivative = nd.calculate_derivative(ul, &UnscalingLayer::calculate_outputs, inputs); assert_true((derivative-numerical_derivative).calculate_absolute_value() < 1.0e-3, LOG); } // Test if(numerical_differentiation_tests) { ul.set(3); ul.initialize_random(); ul.set_unscaling_method(UnscalingLayer::MeanStandardDeviation); inputs.set(3); inputs.randomize_normal(); derivative = ul.calculate_derivatives(inputs); numerical_derivative = nd.calculate_derivative(ul, &UnscalingLayer::calculate_outputs, inputs); assert_true((derivative-numerical_derivative).calculate_absolute_value() < 1.0e-3, LOG); } }
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; architecture[2] = 2; nn.set(architecture); nn.initialize_parameters(0.0); ds.set(2,2,3); 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(4, 1); nn.set(architecture); nn.randomize_parameters_normal(); parameters = nn.arrange_parameters(); ds.set(1,1,1); ds.randomize_data_normal(); gradient = me.calculate_gradient(); numerical_gradient = nd.calculate_gradient(me, &MinkowskiError::calculate_error, parameters); assert_true((gradient - numerical_gradient).calculate_absolute_value() < 1.0e-3, LOG); // Test nn.set(5,4,3); nn.randomize_parameters_normal(); parameters = nn.arrange_parameters(); ds.set(2,5,3); ds.randomize_data_normal(); me.set_Minkowski_parameter(1.75); gradient = me.calculate_gradient(); numerical_gradient = nd.calculate_gradient(me, &MinkowskiError::calculate_error, parameters); assert_true((gradient - numerical_gradient).calculate_absolute_value() < 1.0e-3, LOG); }
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 nn.set(2, 3, 4); nn.initialize_parameters(0.0); ds.set(2, 4, 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 for(unsigned i = 0; i < 100; i++) { ds.initialize_data(1.0); nn.randomize_parameters_normal(); parameters = nn.arrange_parameters(); gradient.clear(); gradient = sse.calculate_gradient(); numerical_gradient = nd.calculate_gradient(sse, &SumSquaredError::calculate_error, parameters); error = (gradient - numerical_gradient).calculate_absolute_value(); assert_true(error < 1.0e-3, LOG); } // Test nn.set(1, 1, 1); nn.initialize_parameters(1.0); parameters = nn.arrange_parameters(); ds.set(1, 1, 1); ds.initialize_data(1.0); gradient.clear(); gradient = sse.calculate_gradient(); numerical_gradient = nd.calculate_gradient(sse, &SumSquaredError::calculate_error, parameters); assert_true((gradient - numerical_gradient).calculate_absolute_value() < 1.0e-3, LOG); // Test architecture.set(1000, 1); nn.set(architecture); nn.randomize_parameters_normal(); ds.set(10, 1, 1); ds.randomize_data_normal(); sse.set(&nn, &ds); gradient.clear(); gradient = sse.calculate_gradient(); }
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); assert_true( (Jacobian - numerical_Jacobian).calculate_absolute_value() < 1.0e-3, LOG); } // Scaling and unscaling test // if(numerical_differentiation_tests) // { // nn.set(2, 3); // nn.set_variables_scaling_method(NeuralNetwork::MinimumMaximum); // nn.set_input_minimum(0, -0.3); // nn.set_input_minimum(1, -0.2); // nn.set_input_maximum(0, 0.0); // nn.set_input_maximum(1, 0.1); // nn.set_output_minimum(0, -1.0); // nn.set_output_minimum(1, -4.1); // nn.set_output_minimum(2, -8.2); // nn.set_output_maximum(0, 1.0); // nn.set_output_maximum(1, 7.2); // nn.set_output_maximum(2, 6.0); // inputs.set(2); // inputs.randomize_normal(); // 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); // } // Scaling and unscaling test // if(numerical_differentiation_tests) // { // nn.set(2, 3); // nn.set_variables_scaling_method(NeuralNetwork::MeanStandardDeviation); // nn.set_input_mean(0, -0.3); // nn.set_input_mean(1, -0.2); // nn.set_input_standard_deviation(0, 0.2); // nn.set_input_standard_deviation(1, 0.1); // nn.set_output_mean(0, -1.0); // nn.set_output_mean(1, -4.1); // nn.set_output_mean(2, -8.2); // nn.set_output_standard_deviation(0, 1.0); // nn.set_output_standard_deviation(1, 7.2); // nn.set_output_standard_deviation(2, 6.0); // inputs.set(2); // inputs.randomize_normal(); // 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); // } // Conditions test // mmlp.set(1, 1, 1); // mmlp.initialize_parameters(0.0); // inputs.set(1, 0.0); // Jacobian = mmlp.calculate_Jacobian(inputs); // assert_true(Jacobian == 0.0, LOG); // Conditions test // Lower and upper bounds test // Probabilistic postprocessing test }
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 nn.set(2, 2, 2); nn.randomize_parameters_normal(); ds.set(2, 2, 2); ds.randomize_data_normal(); gradient = sse.calculate_gradient(); terms = sse.calculate_terms(); terms_Jacobian = sse.calculate_terms_Jacobian(); assert_true(((terms_Jacobian.calculate_transpose()).dot(terms)*2.0 - gradient).calculate_absolute_value() < 1.0e-3, LOG); }
void LevenbergMarquardtAlgorithmTest::test_calculate_Hessian_approximation(void) { message += "test_calculate_Hessian_approximation\n"; NumericalDifferentiation nd; NeuralNetwork nn; size_t parameters_number; Vector<double> parameters; DataSet ds; PerformanceFunctional pf(&nn, &ds); pf.set_error_type(PerformanceFunctional::SUM_SQUARED_ERROR); Matrix<double> terms_Jacobian; Matrix<double> Hessian; Matrix<double> numerical_Hessian; Matrix<double> Hessian_approximation; LevenbergMarquardtAlgorithm lma(&pf); // Test nn.set(1, 2); nn.initialize_parameters(0.0); parameters_number = nn.count_parameters_number(); ds.set(1,2,2); ds.initialize_data(0.0); terms_Jacobian = pf.calculate_terms_Jacobian(); Hessian_approximation = lma.calculate_Hessian_approximation(terms_Jacobian); assert_true(Hessian_approximation.get_rows_number() == parameters_number, LOG); assert_true(Hessian_approximation.get_columns_number() == parameters_number, LOG); assert_true(Hessian_approximation.is_symmetric(), LOG); // Test pf.set_error_type(PerformanceFunctional::NORMALIZED_SQUARED_ERROR); nn.set(1,1,2); nn.randomize_parameters_normal(); parameters_number = nn.count_parameters_number(); ds.set(1,2,3); ds.randomize_data_normal(); terms_Jacobian = pf.calculate_terms_Jacobian(); Hessian_approximation = lma.calculate_Hessian_approximation(terms_Jacobian); assert_true(Hessian_approximation.get_rows_number() == parameters_number, LOG); assert_true(Hessian_approximation.get_columns_number() == parameters_number, LOG); assert_true(Hessian_approximation.is_symmetric(), LOG); // Test nn.set(2); nn.randomize_parameters_normal(); MockErrorTerm* mptp = new MockErrorTerm(&nn); pf.set_user_error_pointer(mptp); terms_Jacobian = pf.calculate_terms_Jacobian(); Hessian = pf.calculate_Hessian(); lma.set_damping_parameter(0.0); assert_true((lma.calculate_Hessian_approximation(terms_Jacobian) - Hessian).calculate_absolute_value() < 1.0e-3, LOG); // Test pf.set_error_type(PerformanceFunctional::SUM_SQUARED_ERROR); ds.set(1, 1, 1); ds.randomize_data_normal(); nn.set(1, 1); parameters = nn.arrange_parameters(); nn.randomize_parameters_normal(); numerical_Hessian = nd.calculate_Hessian(pf, &PerformanceFunctional::calculate_performance, parameters); terms_Jacobian = pf.calculate_terms_Jacobian(); Hessian_approximation = lma.calculate_Hessian_approximation(terms_Jacobian); assert_true((numerical_Hessian - Hessian_approximation).calculate_absolute_value() >= 0.0, LOG); }