write_read_infer Program

Uses

  • program~~write_read_infer~~UsesGraph program~write_read_infer write_read_infer julienne_m julienne_m program~write_read_infer->julienne_m module~fiats_m fiats_m program~write_read_infer->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~training_data_files_m training_data_files_m module~fiats_m->module~training_data_files_m 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~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->julienne_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~activation_m activation_m module~neural_network_m->module~activation_m module~tensor_m->module~kind_parameters_m module~tensor_map_m->julienne_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_names_m->julienne_string_m module~trainable_network_m->julienne_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~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~training_data_files_m->julienne_m module~activation_m->julienne_m iso_c_binding iso_c_binding module~activation_m->iso_c_binding

This program demonstrates how to write a neural network to a JSON file, read the same network from the written file, query the network object for some of its properties, print those properties, and use the network to perform inference. The network performs an identity mapping from any non-negative inputs to the corresponding outputs using a RELU activation function.


Calls

program~~write_read_infer~~CallsGraph program~write_read_infer write_read_infer flag_value flag_value program~write_read_infer->flag_value proc~write_read_query_infer write_read_query_infer program~write_read_infer->proc~write_read_query_infer string string program~write_read_infer->string string_t string_t program~write_read_infer->string_t proc~write_read_query_infer->string file_t file_t proc~write_read_query_infer->file_t none~activation_function_name neural_network_t%activation_function_name proc~write_read_query_infer->none~activation_function_name none~infer neural_network_t%infer proc~write_read_query_infer->none~infer none~nodes_per_layer~2 neural_network_t%nodes_per_layer proc~write_read_query_infer->none~nodes_per_layer~2 none~num_inputs neural_network_t%num_inputs proc~write_read_query_infer->none~num_inputs none~num_outputs neural_network_t%num_outputs proc~write_read_query_infer->none~num_outputs none~to_json~3 neural_network_t%to_json proc~write_read_query_infer->none~to_json~3 none~values tensor_t%values proc~write_read_query_infer->none~values proc~identity_network identity_network proc~write_read_query_infer->proc~identity_network write_lines write_lines proc~write_read_query_infer->write_lines interface~default_real_activation_name neural_network_t%default_real_activation_name none~activation_function_name->interface~default_real_activation_name interface~double_precision_activation_name neural_network_t%double_precision_activation_name none~activation_function_name->interface~double_precision_activation_name interface~default_real_infer neural_network_t%default_real_infer none~infer->interface~default_real_infer interface~double_precision_infer neural_network_t%double_precision_infer none~infer->interface~double_precision_infer interface~default_real_nodes_per_layer~2 neural_network_t%default_real_nodes_per_layer none~nodes_per_layer~2->interface~default_real_nodes_per_layer~2 interface~double_precision_nodes_per_layer~2 neural_network_t%double_precision_nodes_per_layer none~nodes_per_layer~2->interface~double_precision_nodes_per_layer~2 interface~default_real_num_inputs neural_network_t%default_real_num_inputs none~num_inputs->interface~default_real_num_inputs interface~double_precision_num_inputs neural_network_t%double_precision_num_inputs none~num_inputs->interface~double_precision_num_inputs interface~default_real_num_outputs neural_network_t%default_real_num_outputs none~num_outputs->interface~default_real_num_outputs interface~double_precision_num_outputs neural_network_t%double_precision_num_outputs none~num_outputs->interface~double_precision_num_outputs interface~default_real_to_json~2 neural_network_t%default_real_to_json none~to_json~3->interface~default_real_to_json~2 interface~double_precision_to_json~2 neural_network_t%double_precision_to_json none~to_json~3->interface~double_precision_to_json~2 interface~default_real_values tensor_t%default_real_values none~values->interface~default_real_values interface~double_precision_values tensor_t%double_precision_values none~values->interface~double_precision_values proc~identity_network->string_t

Variables

Type Attributes Name Initial
type(command_line_t) :: command_line
type(string_t) :: file_name

Functions

function identity_network() result(neural_network)

Arguments

None

Return Value type(neural_network_t)


Subroutines

subroutine write_read_query_infer(output_file_name)

Arguments

Type IntentOptional Attributes Name
type(string_t), intent(in) :: output_file_name