Line data    Source code

       1             : //* This file is part of the MOOSE framework
       2             : //* https://mooseframework.inl.gov
       3             : //*
       4             : //* All rights reserved, see COPYRIGHT for full restrictions
       5             : //* https://github.com/idaholab/moose/blob/master/COPYRIGHT
       6             : //*
       7             : //* Licensed under LGPL 2.1, please see LICENSE for details
       8             : //* https://www.gnu.org/licenses/lgpl-2.1.html
       9             : 
      10             : #ifdef MOOSE_LIBTORCH_ENABLED
      11             : 
      12             : #include "DRLControlNeuralNetParameters.h"
      13             : #include "LibtorchDRLControlTrainer.h"
      14             : 
      15             : registerMooseObject("StochasticToolsApp", DRLControlNeuralNetParameters);
      16             : 
      17             : InputParameters
      18          16 : DRLControlNeuralNetParameters::validParams()
      19             : {
      20          16 :   InputParameters params = GeneralReporter::validParams();
      21             : 
      22          16 :   params.addClassDescription("Outputs the parameters of a LibtorchArtificialNeuralNetwork within a "
      23             :                              "LibtorchDRLControlTrainer.");
      24             : 
      25          32 :   params.addRequiredParam<UserObjectName>("trainer_name",
      26             :                                           "The control object holding the neural network.");
      27             : 
      28          16 :   return params;
      29           0 : }
      30             : 
      31           8 : DRLControlNeuralNetParameters::DRLControlNeuralNetParameters(const InputParameters & params)
      32           8 :   : GeneralReporter(params), SurrogateModelInterface(this)
      33             : {
      34             :   auto & network =
      35          24 :       declareValueByName<const Moose::LibtorchArtificialNeuralNet *>(name(), REPORTER_MODE_ROOT);
      36           8 :   network = &getSurrogateTrainer<LibtorchDRLControlTrainer>("trainer_name").controlNeuralNet();
      37           8 : }
      38             : 
      39             : #endif