trainable_network_m Module


Uses

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

Used by

  • module~~trainable_network_m~~UsedByGraph module~trainable_network_m trainable_network_m module~fiats_m fiats_m module~fiats_m->module~trainable_network_m module~trainable_network_s trainable_network_s module~trainable_network_s->module~trainable_network_m module~addition_m addition_m module~addition_m->module~fiats_m module~exponentiation_m exponentiation_m module~exponentiation_m->module~fiats_m module~multiply_inputs multiply_inputs module~multiply_inputs->module~fiats_m module~power_series power_series module~power_series->module~fiats_m module~saturated_mixing_ratio_m saturated_mixing_ratio_m module~saturated_mixing_ratio_m->module~fiats_m program~concurrent_inferences concurrent_inferences program~concurrent_inferences->module~fiats_m program~learn_addition learn_addition program~learn_addition->module~fiats_m program~learn_addition->module~addition_m program~learn_exponentiation learn_exponentiation program~learn_exponentiation->module~fiats_m program~learn_exponentiation->module~exponentiation_m program~learn_multiplication learn_multiplication program~learn_multiplication->module~fiats_m program~learn_multiplication->module~multiply_inputs program~learn_power_series learn_power_series program~learn_power_series->module~fiats_m program~learn_power_series->module~power_series program~print_training_configuration print_training_configuration program~print_training_configuration->module~fiats_m program~read_query_infer read_query_infer program~read_query_infer->module~fiats_m program~train_and_write train_and_write program~train_and_write->module~fiats_m program~train_saturated_mixture_ratio train_saturated_mixture_ratio program~train_saturated_mixture_ratio->module~fiats_m program~train_saturated_mixture_ratio->module~saturated_mixing_ratio_m program~write_read_infer write_read_infer program~write_read_infer->module~fiats_m

Interfaces

public interface trainable_network_t

  • private module function default_real_construct_from_components(metadata, weights, biases, nodes, input_map, output_map) result(trainable_network)

    Arguments

    Type IntentOptional Attributes Name
    type(string_t), intent(in) :: metadata(:)
    real, intent(in) :: weights(:,:,:)
    real, intent(in) :: biases(:,:)
    integer, intent(in) :: nodes(0:)
    type(tensor_map_t), intent(in), optional :: input_map
    type(tensor_map_t), intent(in), optional :: output_map

    Return Value type(trainable_network_t)

  • private impure elemental module function default_real_from_json(file) result(trainable_network)

    Arguments

    Type IntentOptional Attributes Name
    type(file_t), intent(in) :: file

    Return Value type(trainable_network_t)

  • private pure module function default_real_network(neural_network) result(trainable_network)

    Arguments

    Type IntentOptional Attributes Name
    type(neural_network_t), intent(in) :: neural_network

    Return Value type(trainable_network_t)

  • private module function perturbed_identity_network(training_configuration, perturbation_magnitude, metadata, input_map, output_map) result(trainable_network)

    Arguments

    Type IntentOptional Attributes Name
    type(training_configuration_t), intent(in) :: training_configuration
    real, intent(in) :: perturbation_magnitude
    type(string_t), intent(in) :: metadata(:)
    type(tensor_map_t) :: input_map
    type(tensor_map_t) :: output_map

    Return Value type(trainable_network_t)

interface

interface

  • private pure module subroutine default_real_train(self, mini_batches_arr, cost, adam, learning_rate)

    Arguments

    Type IntentOptional Attributes Name
    class(trainable_network_t), intent(inout) :: self
    type(mini_batch_t), intent(in) :: mini_batches_arr(:)
    real, intent(out), optional, allocatable :: cost(:)
    logical, intent(in) :: adam
    real, intent(in) :: learning_rate

Derived Types

type, public, extends(neural_network_t) ::  trainable_network_t

Components

Type Visibility Attributes Name Initial
integer, public, kind :: k = default_real
integer, public, kind :: m = default_real
type(workspace_t), private :: workspace_

Constructor

private module function default_real_construct_from_components (metadata, weights, biases, nodes, input_map, output_map)
private impure, elemental, module function default_real_from_json (file)
private pure, module function default_real_network (neural_network)
private module function perturbed_identity_network (training_configuration, perturbation_magnitude, metadata, input_map, output_map)

Type-Bound Procedures

generic, public :: activation_function_name => default_real_activation_name, double_precision_activation_name
generic, public :: infer => default_real_infer, double_precision_infer
generic, public :: map_from_output_range => default_real_map_from_output_range, double_precision_map_from_output_range
generic, public :: map_to_input_range => default_real_map_to_input_range, double_precision_map_to_input_range
generic, public :: map_to_training_ranges => default_real_map_to_training_ranges
generic, public :: nodes_per_layer => default_real_nodes_per_layer, double_precision_nodes_per_layer
generic, public :: num_hidden_layers => default_real_num_hidden_layers, double_precision_num_hidden_layers
generic, public :: num_inputs => default_real_num_inputs, double_precision_num_inputs
generic, public :: num_outputs => default_real_num_outputs, double_precision_num_outputs
generic, public :: operator(==) => default_real_approximately_equal, double_precision_approximately_equal
generic, public :: skip => default_real_skip, double_precision_skip
generic, public :: to_json => default_real_to_json, double_precision_to_json
generic, public :: train => default_real_train
procedure, private, non_overridable :: default_real_map_to_training_ranges
procedure, private, non_overridable :: default_real_train