power_series Module

Define a function that produces the desired network output for a given network input


Uses

  • module~~power_series~~UsesGraph module~power_series power_series assert_m assert_m module~power_series->assert_m module~fiats_m fiats_m module~power_series->module~fiats_m module~double_precision_file_m double_precision_file_m module~fiats_m->module~double_precision_file_m module~double_precision_string_m double_precision_string_m module~fiats_m->module~double_precision_string_m module~hyperparameters_m hyperparameters_m module~fiats_m->module~hyperparameters_m module~input_output_pair_m input_output_pair_m module~fiats_m->module~input_output_pair_m module~kind_parameters_m kind_parameters_m module~fiats_m->module~kind_parameters_m module~metadata_m metadata_m module~fiats_m->module~metadata_m module~mini_batch_m mini_batch_m module~fiats_m->module~mini_batch_m module~network_configuration_m network_configuration_m module~fiats_m->module~network_configuration_m module~neural_network_m neural_network_m module~fiats_m->module~neural_network_m module~tensor_m tensor_m module~fiats_m->module~tensor_m module~tensor_map_m tensor_map_m module~fiats_m->module~tensor_map_m module~tensor_names_m tensor_names_m module~fiats_m->module~tensor_names_m module~trainable_network_m trainable_network_m module~fiats_m->module~trainable_network_m module~training_configuration_m training_configuration_m module~fiats_m->module~training_configuration_m module~double_precision_file_m->module~double_precision_string_m julienne_m julienne_m module~double_precision_file_m->julienne_m module~double_precision_string_m->julienne_m module~hyperparameters_m->module~double_precision_string_m module~hyperparameters_m->module~kind_parameters_m julienne_string_m julienne_string_m module~hyperparameters_m->julienne_string_m module~input_output_pair_m->module~kind_parameters_m module~input_output_pair_m->module~tensor_m module~metadata_m->module~double_precision_string_m module~metadata_m->julienne_string_m module~mini_batch_m->module~input_output_pair_m module~mini_batch_m->module~kind_parameters_m module~network_configuration_m->module~double_precision_string_m module~network_configuration_m->julienne_string_m module~neural_network_m->module~double_precision_file_m module~neural_network_m->module~kind_parameters_m module~neural_network_m->module~metadata_m module~neural_network_m->module~mini_batch_m module~neural_network_m->module~tensor_m module~neural_network_m->module~tensor_map_m module~neural_network_m->julienne_m module~activation_m activation_m module~neural_network_m->module~activation_m module~tensor_m->module~kind_parameters_m module~tensor_map_m->module~double_precision_string_m module~tensor_map_m->module~kind_parameters_m module~tensor_map_m->module~tensor_m module~tensor_map_m->julienne_m module~tensor_names_m->julienne_string_m module~trainable_network_m->module~input_output_pair_m module~trainable_network_m->module~kind_parameters_m module~trainable_network_m->module~mini_batch_m module~trainable_network_m->module~neural_network_m module~trainable_network_m->module~tensor_map_m module~trainable_network_m->module~training_configuration_m module~trainable_network_m->julienne_m module~training_configuration_m->module~double_precision_file_m module~training_configuration_m->module~hyperparameters_m module~training_configuration_m->module~kind_parameters_m module~training_configuration_m->module~network_configuration_m module~training_configuration_m->module~tensor_names_m julienne_file_m julienne_file_m module~training_configuration_m->julienne_file_m module~training_configuration_m->julienne_string_m module~training_configuration_m->module~activation_m module~activation_m->julienne_m iso_c_binding iso_c_binding module~activation_m->iso_c_binding

Used by

  • module~~power_series~~UsedByGraph module~power_series power_series program~learn_power_series learn_power_series program~learn_power_series->module~power_series

Functions

public elemental function y(x_in) result(a)

Arguments

Type IntentOptional Attributes Name
type(tensor_t), intent(in) :: x_in

Return Value type(tensor_t)