| /dports/misc/opennn/opennn-5.0.5/opennn/ |
| H A D | quasi_newton_method.h | 83 parameters.resize(parameters_number); in set()
86 old_parameters.resize(parameters_number); in set()
88 parameters_difference.resize(parameters_number); in set()
90 potential_parameters.resize(parameters_number); in set()
91 parameters_increment.resize(parameters_number); in set()
95 old_gradient.resize(parameters_number); in set()
98 gradient_difference.resize(parameters_number); in set()
100 inverse_hessian.resize(parameters_number, parameters_number); in set()
103 old_inverse_hessian.resize(parameters_number, parameters_number); in set()
108 training_direction.resize(parameters_number); in set()
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| H A D | conjugate_gradient.cpp | 522 if(old_gradient_size != parameters_number) in calculate_FR_parameter()
533 if(gradient_size != parameters_number) in calculate_FR_parameter()
604 if(old_gradient_size != parameters_number) in calculate_PR_parameter()
615 if(gradient_size != parameters_number) in calculate_PR_parameter()
690 if(old_gradient_size != parameters_number) in calculate_PR_training_direction()
701 if(gradient_size != parameters_number) in calculate_PR_training_direction()
758 if(old_gradient_size != parameters_number) in calculate_FR_training_direction()
769 if(gradient_size != parameters_number) in calculate_FR_training_direction()
848 if(gradient_size != parameters_number) in calculate_conjugate_gradient_training_direction()
1943 parameters.resize(parameters_number); in set()
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| H A D | gradient_descent.h | 74 const Index parameters_number = neural_network_pointer->get_parameters_number(); in set() local
78 parameters.resize(parameters_number); in set()
81 old_parameters.resize(parameters_number); in set()
82 potential_parameters.resize(parameters_number); in set()
84 parameters_increment.resize(parameters_number); in set()
88 old_gradient.resize(parameters_number); in set()
92 training_direction.resize(parameters_number); in set()
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| H A D | loss_index.cpp | 668 return Tensor<type, 2>(parameters_number,parameters_number).setConstant(0.0); in calculate_regularization_hessian()
921 const Index parameters_number = parameters.size(); in calculate_error_gradient_numerical_differentiation() local
932 for(Index i = 0; i < parameters_number; i++) in calculate_error_gradient_numerical_differentiation()
977 const Index parameters_number = parameters.size(); in calculate_Jacobian_numerical_differentiation() local
996 for(Index j = 0; j < parameters_number; j++) in calculate_Jacobian_numerical_differentiation()
1083 const Index parameters_number = parameters.size(); in l1_norm_gradient() local
1085 Tensor<type, 1> gradient(parameters_number); in l1_norm_gradient()
1095 const Index parameters_number = parameters.size(); in l1_norm_hessian() local
1097 Tensor<type, 2> hessian(parameters_number, parameters_number); in l1_norm_hessian()
1109 Tensor<type, 1> gradient(parameters_number); in l2_norm_gradient()
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| H A D | levenberg_marquardt_algorithm.h | 72 const Index parameters_number = neural_network_pointer->get_parameters_number(); in set() local
76 parameters.resize(parameters_number); in set()
79 old_parameters.resize(parameters_number); in set()
81 parameters_difference.resize(parameters_number); in set()
83 potential_parameters.resize(parameters_number); in set()
84 parameters_increment.resize(parameters_number); in set()
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| H A D | long_short_term_memory_layer.cpp | 214 Tensor<type, 1> parameters(parameters_number); in get_parameters()
631 if(new_parameters_size != parameters_number) in set_parameters()
2148 Tensor<type, 1> error_gradient(parameters_number); in calculate_error_gradient()
2325 for(Index i = 0; i < parameters_number; i++) in calculate_forget_weights_error_gradient()
2434 for(Index i = 0; i < parameters_number; i++) in calculate_input_weights_error_gradient()
2542 for(Index i = 0; i < parameters_number; i++) in calculate_state_weights_error_gradient()
2651 for(Index i = 0; i < parameters_number; i++) in calculate_output_weights_error_gradient()
3095 const Index parameters_number = neurons_number; in calculate_forget_biases_error_gradient() local
3186 const Index parameters_number = neurons_number; in calculate_input_biases_error_gradient() local
3279 const Index parameters_number = neurons_number; in calculate_state_biases_error_gradient() local
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| H A D | loss_index.h | 98 const Index parameters_number = neural_network_pointer->get_parameters_number(); in set() local
114 gradient.resize(parameters_number); in set()
162 SecondOrderLoss(const Index& parameters_number, const Index& samples_number) in SecondOrderLoss()
165 gradient.resize(parameters_number); in SecondOrderLoss()
166 error_terms_Jacobian.resize(samples_number, parameters_number); in SecondOrderLoss()
167 hessian.resize(parameters_number, parameters_number); in SecondOrderLoss()
173 const Index parameters_number = hessian.dimension(0); in sum_hessian_diagonal() local
175 for(Index i = 0; i < parameters_number; i++) in sum_hessian_diagonal()
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| H A D | stochastic_gradient_descent.h | 70 const Index parameters_number = neural_network_pointer->get_parameters_number(); in set() local
72 parameters.resize(parameters_number); in set()
76 parameters_increment.resize(parameters_number); in set()
77 nesterov_increment.resize(parameters_number); in set()
78 last_parameters_increment.resize(parameters_number); in set()
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| H A D | recurrent_layer.cpp | 417 const Index parameters_number = get_parameters_number(); in set_parameters() local
421 if(new_parameters_size != parameters_number) in set_parameters()
846 const Index parameters_number = get_parameters_number(); in calculate_error_gradient() local
848 Tensor<type, 1> error_gradient(parameters_number); in calculate_error_gradient()
887 const Index parameters_number = inputs_number*neurons_number; in calculate_input_weights_error_gradient() local
891 Tensor<type, 2> combinations_weights_derivatives(parameters_number, neurons_number); in calculate_input_weights_error_gradient()
896 Tensor<type, 1> input_weights_gradient(parameters_number); in calculate_input_weights_error_gradient()
919 for(Index i = 0; i < parameters_number; i++) in calculate_input_weights_error_gradient()
948 const Index parameters_number = neurons_number*neurons_number; in calculate_recurrent_weights_error_gradient() local
954 Tensor<type, 1> recurrent_weights_gradient(parameters_number); in calculate_recurrent_weights_error_gradient()
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| H A D | adaptive_moment_estimation.cpp | 323 const Index parameters_number = neural_network_pointer->get_parameters_number(); in perform_training() local
349 Tensor<type, 1> minimal_selection_parameters(parameters_number); in perform_training()
1030 const Index parameters_number = neural_network_pointer->get_parameters_number(); in set() local
1032 parameters.resize(parameters_number); in set()
1035 minimal_selection_parameters.resize(parameters_number); in set()
1037 gradient_exponential_decay.resize(parameters_number); in set()
1040 square_gradient_exponential_decay.resize(parameters_number); in set()
1043 aux.resize(parameters_number); in set()
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| H A D | neural_network.cpp | 1057 Index parameters_number = 0; in get_parameters_number() local
1064 return parameters_number; in get_parameters_number()
1093 const Index parameters_number = get_parameters_number(); in get_parameters() local
1095 Tensor<type, 1> parameters(parameters_number); in get_parameters()
1170 const Index parameters_number = get_parameters_number(); in set_parameters() local
1172 if(size < parameters_number) in set_parameters()
1420 Index index = parameters_number; in forward_propagate()
1432 index += parameters_number; in forward_propagate()
2404 Tensor<type, 1> new_parameters(parameters_number); in load_parameters()
2439 Tensor<type, 1> new_parameters(parameters_number); in load_parameters_binary()
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| H A D | quasi_newton_method.cpp | 457 const Index parameters_number = optimization_data.parameters.size(); in initialize_inverse_hessian_approximation() local
459 for(Index i = 0; i < parameters_number; i++) optimization_data.inverse_hessian(i,i) = 1.0; in initialize_inverse_hessian_approximation()
560 const Index parameters_number = neural_network_pointer->get_parameters_number(); in calculate_DFP_inverse_hessian() local
604 const Index parameters_number = neural_network_pointer->get_parameters_number(); in calculate_BFGS_inverse_hessian() local
620 Tensor<type, 1> BFGS(parameters_number); in calculate_BFGS_inverse_hessian()
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| H A D | convolutional_layer.cpp | 840 const Index parameters_number = get_parameters_number(); in calculate_error_gradient() local
842 layer_error_gradient.resize(parameters_number); in calculate_error_gradient()
887 …for(Index gradient_index = synaptic_weights_number; gradient_index < parameters_number; gradient_i… in calculate_error_gradient()
940 const Index parameters_number = get_parameters_number(); in calculate_error_gradient() local
942 back_propagation.synaptic_weights_derivatives.resize(1, parameters_number); in calculate_error_gradient()
989 …for(Index gradient_index = synaptic_weights_number; gradient_index < parameters_number; gradient_i… in calculate_error_gradient()
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| H A D | gradient_descent.cpp | 425 const Index parameters_number = neural_network_pointer->get_parameters_number(); in calculate_training_direction() local
429 if(gradient_size != parameters_number) in calculate_training_direction()
434 << ") is not equal to number of parameters(" << parameters_number << ").\n"; in calculate_training_direction()
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| H A D | stochastic_gradient_descent.cpp | 445 const Index parameters_number = neural_network_pointer->get_parameters_number(); in perform_training() local
471 Tensor<type, 1> minimal_selection_parameters(parameters_number); in perform_training()
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| H A D | levenberg_marquardt_algorithm.cpp | 558 const Index parameters_number = neural_network_pointer->get_parameters_number(); in perform_training() local
579 LossIndex::SecondOrderLoss terms_second_order_loss(parameters_number, training_samples_number); in perform_training()
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| /dports/misc/opennn/opennn-5.0.5/tests/ |
| H A D | neural_network_test.cpp | 1019 Index parameters_number; in test_set_parameters() local
1037 parameters_number = neural_network.get_parameters_number(); in test_set_parameters()
1038 parameters.resize(parameters_number); in test_set_parameters()
1044 assert_true(parameters.size() == parameters_number, LOG); in test_set_parameters()
1174 Index parameters_number; in test_perturbate_parameters() local
1181 parameters_number = neural_network.get_parameters_number(); in test_perturbate_parameters()
1182 parameters.resize(parameters_number); in test_perturbate_parameters()
1192 assert_true(parameters.size() == parameters_number, LOG); in test_perturbate_parameters()
1227 Index parameters_number; in test_calculate_outputs() local
1333 parameters.resize(parameters_number); in test_calculate_outputs()
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| H A D | normalized_squared_error_test.cpp | 565 const Index parameters_number = neural_network.get_parameters_number(); in test_calculate_error_terms() local
571 LossIndex::SecondOrderLoss second_order_loss(parameters_number, samples_number); in test_calculate_error_terms()
627 const Index parameters_number = neural_network.get_parameters_number(); in test_calculate_error_terms_Jacobian() local
633 LossIndex::SecondOrderLoss second_order_loss(parameters_number, samples_number); in test_calculate_error_terms_Jacobian()
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| H A D | conjugate_gradient_test.cpp | 180 Index parameters_number = neural_network.get_parameters_number(); in test_calculate_PR_training_direction() local
213 Index parameters_number = neural_network.get_parameters_number(); in test_calculate_FR_training_direction() local
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| H A D | perceptron_layer_test.cpp | 513 Index parameters_number = perceptron_layer_3.get_parameters_number(); in test_get_parameters() local
515 assert_true(parameters_number == 4, LOG); in test_get_parameters()
1070 Index parameters_number = perceptron_layer_2.get_parameters_number(); in test_calculate_combinations() local
1072 assert_true(parameters_number == 4, LOG); in test_calculate_combinations()
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