def create_calibrated_lattice(feature_columns, config, quantiles_dir):
feature_names = [fc.name for fc in feature_columns]
hparams = tfl.CalibratedLatticeHParams(feature_names=feature_names,
num_keypoints=200,
lattice_l2_laplacian_reg=5e-4,
lattice_l2_torsion_reg=1e-4,
learning_rate=lr,
lattice_size=2)
hparams.set_feature_param("feature1", "monotonicity", 1)
return tfl.calibrated_lattice_classifier(feature_columns=feature_columns,
model_dir=config.model_dir,
config=config,
hparams=hparams,
quantiles_dir=quantiles_dir)
def create_calibrated_lattice(feature_columns, config, quantiles_dir):
"""Creates a calibrated lattice estimator."""
feature_names = [fc.name for fc in feature_columns]
hparams = tfl.CalibratedLatticeHParams(feature_names=feature_names,
num_keypoints=200,
lattice_l2_laplacian_reg=5.0e-3,
lattice_l2_torsion_reg=1.0e-4,
learning_rate=0.1,
lattice_size=2)
hparams.parse(FLAGS.hparams)
_pprint_hparams(hparams)
return tfl.calibrated_lattice_classifier(feature_columns=feature_columns,
model_dir=config.model_dir,
config=config,
hparams=hparams,
quantiles_dir=quantiles_dir)