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Copyright 2020 The TensorFlow Lattice Authors.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n     http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n=============================================================================--\u003e\n# TensorFlow Lattice\n\nTensorFlow Lattice is a library that implements constrained and interpretable\nlattice based models. It is an implementation of\n[Monotonic Calibrated Interpolated Look-Up Tables](http://jmlr.org/papers/v17/15-243.html)\nin [TensorFlow](https://www.tensorflow.org).\n\nThe library enables you to inject domain knowledge into\nthe learning process through common-sense or policy-driven shape constraints.\nThis is done using a collection of Keras layers that can satisfy constraints\nsuch as monotonicity, convexity and pairwise trust:\n\n* PWLCalibration: piecewise linear calibration of signals.\n* CategoricalCalibration: mapping of categorical inputs into real values.\n* Lattice: interpolated look-up table implementation.\n* Linear: linear function with monotonicity and norm constraints.\n\nThe library also provides easy to setup canned estimators for common use cases:\n\n* Calibrated Linear\n* Calibrated Lattice\n* Random Tiny Lattices (RTL)\n* Crystals\n\nWith TF Lattice you can use domain knowledge to better extrapolate to the parts\nof the input space not covered by the training dataset. This helps avoid\nunexpected model behaviour when the serving distribution is different from the\ntraining distribution.\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"docs/images/model_comparison.png\"\u003e\n\u003c/div\u003e\n\nYou can install our prebuilt pip package using\n\n```bash\npip install tensorflow-lattice\n```\n","funding_links":[],"categories":["Libraries","Tools","Deep Learning Framework","Python","Technical Resources"],"sub_categories":["Interpretable Models","High-Level DL APIs","Open Source/Access Responsible AI Software Packages"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftensorflow%2Flattice","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftensorflow%2Flattice","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftensorflow%2Flattice/lists"}