Python create_and_train_model Examples

Example #1

File: 02_layers_post.py Project: usert5432/slice_lid

        'seed': 0,
        'steps_per_epoch': 500,
        'target_pdg_iscc_list': [(12, 1), (14, 1), (16, 1), (0, 0)],
        'test_size': 200000,
        # Args
        'outdir': 'prod4/02_model_selection/02_layers_post',
    })

search_space = []

for layers_post in range(10):
    search_space.append({
        'model': {
            'kwargs': {
                'layers_post': [
                    128,
                ] * layers_post,
            }
        },
    })

parse_concurrency_cmdargs(config)

setup_logging(logging.DEBUG,
              os.path.join(ROOT_OUTDIR, config['outdir'], "train.log"))

speval(lambda x: create_and_train_model(**config, extra_kwargs=x),
       search_space,
       os.path.join(ROOT_OUTDIR, config['outdir'], "trials.db"),
       timeout=24 * 60 * 60)

Example #2

File: 02_adding_numu.py Project: usert5432/slice_lid

        },
        'regularizer': {
            'name': 'l1',
            'kwargs': {
                'l': 0.0001
            },
        },
        'schedule': {
            'name': 'standard',
            'kwargs': {
                'monitor': 'val_loss',
                'factor': 0.5,
                'patience': 5,
                'cooldown': 0
            },
        },
        'seed': 0,
        'steps_per_epoch': 500,
        'target_pdg_iscc_list': [(12, 1), (14, 1)],
        'test_size': 200000,
        # Args
        'outdir': 'prod4/01_initial_studies/02_adding_numu',
    })

parse_concurrency_cmdargs(config)

setup_logging(logging.DEBUG,
              os.path.join(ROOT_OUTDIR, config['outdir'], "train.log"))

create_and_train_model(**config)