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# TensorFlow Lattice
TensorFlow Lattice is a library that implements constrained and interpretable
lattice based models. It is an implementation of
[Monotonic Calibrated Interpolated Look-Up Tables](http://jmlr.org/papers/v17/15-243.html)
in [TensorFlow](https://www.tensorflow.org).
The library enables you to inject domain knowledge into
the learning process through common-sense or policy-driven shape constraints.
This is done using a collection of Keras layers that can satisfy constraints
such as monotonicity, convexity and pairwise trust:
* PWLCalibration: piecewise linear calibration of signals.
* CategoricalCalibration: mapping of categorical inputs into real values.
* Lattice: interpolated look-up table implementation.
* Linear: linear function with monotonicity and norm constraints.
The library also provides easy to setup canned estimators for common use cases:
* Calibrated Linear
* Calibrated Lattice
* Random Tiny Lattices (RTL)
* Crystals
With TF Lattice you can use domain knowledge to better extrapolate to the parts
of the input space not covered by the training dataset. This helps avoid
unexpected model behaviour when the serving distribution is different from the
training distribution.
<div align="center">
<img src="docs/images/model_comparison.png">
</div>
You can install our prebuilt pip package using
```bash
pip install tensorflow-lattice
```
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