def create_calibrated_linear(feature_columns, config, quantiles_dir):
feature_names = [fc.name for fc in feature_columns]
hparams = tfl.CalibratedLinearHParams(feature_names=feature_names,
num_keypoints=200,
learning_rate=lr)
hparams.set_feature_param("feature1", "monotonicity", 1)
return tfl.calibrated_linear_regressor(feature_columns=feature_columns,
model_dir=config.model_dir,
config=config,
hparams=hparams,
quantiles_dir=quantiles_dir)
def create_calibrated_linear(feature_columns, config, quantiles_dir):
feature_names = [fc.name for fc in feature_columns]
hparams = tfl.CalibratedLinearHParams(feature_names=feature_names,
num_keypoints=200,
learning_rate=1e-4)
hparams.parse(FLAGS.hparams)
hparams.set_feature_param("capital_gain", "calibration_l2_laplacian_reg",
4.0e-3)
_pprint_hparams(hparams)
return tfl.calibrated_linear_classifier(feature_columns=feature_columns,
model_dir=config.model_dir,
config=config,
hparams=hparams,
quantiles_dir=quantiles_dir)