notraining

Module

Description

$Id$

CPARAM logical, parameter :: ltraining = .false.


Quick access

Variables:

calc_tau, checkpoint_output_dir, config_file, descale, dt_train, end_time, idiag_loss, idiag_tauerror, infer, inference_time, input, input_channels, input_max, input_min, isgs_emf, isgs_emfx, isgs_emfy, isgs_emfz, istat, it_train, it_train_chkpt, it_train_end, it_train_start, itau_bb, itau_bbxx, itau_bbxy, itau_bbxz, itau_bbyy, itau_bbyz, itau_bbzz, itau_hydro, itau_hydroxx, itau_hydroxy, itau_hydroxz, itau_hydroyy, itau_hydroyz, itau_hydrozz, label, lckpt_written, lfortran_launched, lmodel_saved, lroute_via_cpu, lrun_epoch, lscale, ltrain_dens, ltrain_mag, ltrained, luse_trained_tau, lwrite_sample, max_loss, model, model_device, model_file, model_output_dir, output, output_channels, output_max, output_min, save_chkpt, save_model, scale, start_infer, start_time, t_last_chkpt, t_train_chkpt, t_train_end, t_train_start, tau_pred, tauerror, train, train_loss, train_step_ckpt, training_time, uumean, val_step_ckpt, write_sample

Routines:

calc_diagnostics_training(), dtraining_dt(), finalize_training(), get_slices_training(), initialize_training(), read_training_run_pars(), register_training(), rprint_training(), training_after_boundary(), write_training_run_pars()

Needed modules

Variables

  • training/inference_time [real,public/optional/default=0]
  • training/training_time [real,public/optional/default=0]

Subroutines and functions

subroutine  training/initialize_training(f)
Parameters:

f (mx,my,mz,mfarray) [real]

subroutine  training/register_training()
subroutine  training/read_training_run_pars(iomsg)
Parameters:

iomsg [character,out]

subroutine  training/write_training_run_pars(unit)
Parameters:

unit [integer,in]

subroutine  training/training_after_boundary(f)
Parameters:

f (mx,my,mz,mfarray) [real]

subroutine  training/calc_diagnostics_training(f)
Parameters:

f (mx,my,mz,mfarray) [real]

subroutine  training/rprint_training(lreset)
Parameters:

lreset [logical]

subroutine  training/dtraining_dt(f, df)
Parameters:
subroutine  training/finalize_training()
subroutine  training/get_slices_training(f, slices)
Parameters: