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()
