def test_default_logger_callbacks_cpu_model(tmpdir): """ Test each of the trainer options :return: """ tutils.reset_seed() trainer_options = dict(default_save_path=tmpdir, max_nb_epochs=1, gradient_clip_val=1.0, overfit_pct=0.20, print_nan_grads=True, show_progress_bar=False, train_percent_check=0.01, val_percent_check=0.01) model, hparams = tutils.get_model() tutils.run_model_test_no_loggers(trainer_options, model, hparams, on_gpu=False) # test freeze on cpu model.freeze() model.unfreeze()
def test_lbfgs_cpu_model(tmpdir): """Test each of the trainer options.""" tutils.reset_seed() trainer_options = dict(default_save_path=tmpdir, max_epochs=1, print_nan_grads=True, show_progress_bar=False, weights_summary='top', train_percent_check=1.0, val_percent_check=0.2) model, hparams = tutils.get_model(use_test_model=True, lbfgs=True) tutils.run_model_test_no_loggers(trainer_options, model, min_acc=0.30)
def test_lbfgs_cpu_model(): """ Test each of the trainer options :return: """ tutils.reset_seed() trainer_options = dict(max_nb_epochs=1, print_nan_grads=True, show_progress_bar=False, weights_summary='top', train_percent_check=1.0, val_percent_check=0.2) model, hparams = tutils.get_model(use_test_model=True, lbfgs=True) tutils.run_model_test_no_loggers(trainer_options, model, hparams, on_gpu=False, min_acc=0.30) tutils.clear_save_dir()