Config for calibrated lattice model.

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.


model_config = tfl.configs.CalibratedLinearConfig(
feature_analysis_input_fn = create_input_fn(num_epochs=1, ...)
train_input_fn = create_input_fn(num_epochs=100, ...)
estimator = tfl.estimators.CannedClassifier(

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.



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Returns a deserialized configuration dictionary.


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Returns existing or default FeatureConfig with the given name.


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Returns a configuration dictionary.


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Returns existing or default RegularizerConfig with the given name.