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TFL model configuration library for canned estimators.
To construct a TFL canned estimator, construct a model configuration and pass it to the canned estimator constructor:
feature_columns = ...
model_config = tfl.configs.CalibratedLatticeConfig(...)
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)
Supported models are:
Calibrated linear model: Constructed using
tfl.configs.CalibratedLinearConfig
. A calibrated linear model that 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 apply weighted averaging on calibrated inputs.Calibrated lattice model: Constructed using
tfl.configs.CalibratedLatticeConfig
. A calibrated lattice model applies piecewise-linear and categorical calibration on the input feature, followed by a lattice model and an optional output piecewise-linear calibration.Calibrated lattice ensemble model: Constructed using
tfl.configs.CalibratedLatticeEnsembleConfig
. A calibrated lattice ensemble model applies piecewise-linear and categorical calibration on the input feature, followed by an ensemble of lattice models and an optional output piecewise-linear calibration.
Feature calibration and per-feature configurations are set using
tfl.configs.FeatureConfig
. Feature configurations include monotonicity
constraints, per-feature regularization (see tfl.configs.RegularizerConfig
),
and lattice sizes for lattice models.
Classes
class AggregateFunctionConfig
: Config for aggregate function learning model.
class CalibratedLatticeConfig
: Config for calibrated lattice model.
class CalibratedLatticeEnsembleConfig
: Config for calibrated lattice model.
class CalibratedLinearConfig
: Config for calibrated lattice model.
class DominanceConfig
: Configuration for dominance constraints in TFL canned estimators.
class FeatureConfig
: Per-feature configuration for TFL canned estimators.
class RegularizerConfig
: Regularizer configuration for TFL canned estimators.
class TrustConfig
: Configuration for feature trusts in TFL canned estimators.
Functions
apply_updates(...)
: Updates a model config with the given set of (key, values) updates.
Other Members | |
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absolute_import |
Instance of __future__._Feature
|
division |
Instance of __future__._Feature
|
print_function |
Instance of __future__._Feature
|