tfl.configs.CalibratedLinearConfig

Config for calibrated lattice model.

tfl.configs.CalibratedLinearConfig( feature_configs=None, regularizer_configs=None, use_bias=True, output_min=None, output_max=None, output_calibration=False, output_calibration_num_keypoints=10, output_initialization='quantiles', output_calibration_input_keypoints_type='fixed' )

Used in the notebooks

Used in the tutorials

A calibrated linear model applies piecewise-linear and categorical calibration on the input feature, followed by a linear combination and an optional output piecewise-linear calibration. When using output calibration or when output bounds are specified, the linear layer will be apply weighted averaging on calibrated inputs.

Example:

model_config = tfl.configs.CalibratedLinearConfig( feature_configs=[...], ) feature_analysis_input_fn = create_input_fn(num_epochs=1, ...) train_input_fn = create_input_fn(num_epochs=100, ...) estimator = tfl.estimators.CannedClassifier( feature_columns=feature_columns, model_config=model_config, feature_analysis_input_fn=feature_analysis_input_fn) estimator.train(input_fn=train_input_fn)

feature_configs A list of tfl.configs.FeatureConfig instances that specify configurations for each feature. If a configuration is not provided for a feature, a default configuration will be used.
regularizer_configs A list of tfl.configs.RegularizerConfig instances that apply global regularization.
use_bias If a bias term should be used for the linear combination.
output_min Lower bound constraint on the output of the model.
output_max Upper bound constraint on the output of the model.
output_calibration If a piecewise-linear calibration should be used on the output of the lattice.
output_calibration_num_keypoints Number of keypoints to use for the output piecewise-linear calibration.
output_initialization The initial values to setup for the output of the model. When using output calibration, these values are used to initialize the output keypoints of the output piecewise-linear calibration. Otherwise the lattice parameters will be setup to form a linear function in the range of output_initialization. It can be one of:

  • String 'quantiles': Output is initliazed to label quantiles, if possible.
  • String 'uniform': Output is initliazed uniformly in label range.
  • A list of numbers: To be used for initialization of the output lattice or output calibrator.
output_calibration_input_keypoints_type One of "fixed" or "learned_interior". If "learned_interior", keypoints are initialized to the values in pwl_calibration_input_keypoints but then allowed to vary during training, with the exception of the first and last keypoint location which are fixed.

Methods

deserialize_nested_configs

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@classmethod deserialize_nested_configs( config, custom_objects=None )

Returns a deserialized configuration dictionary.

feature_config_by_name

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feature_config_by_name( feature_name )

Returns existing or default FeatureConfig with the given name.

from_config

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@classmethod from_config( config, custom_objects=None )

get_config

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get_config()

Returns a configuration dictionary.

regularizer_config_by_name

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regularizer_config_by_name( regularizer_name )

Returns existing or default RegularizerConfig with the given name.