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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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TensorFlow是一个开放源代码的软件库,用于进行高性能数值计算。通过其灵活的架构,它允许用户轻松地部署计算工作在各种平台(CPUs、GPUs、TPUs)上,无论是在桌面、服务器还是移动设备上。TensorFlow最初由Google Brain团队(属于Google的人工智能部门)开发,并在2015年被发布到Apache 2.0开源许可证下。 TensorFlow的主要特点包括它的高度灵活性、可扩展性和可移植性。它支持从小到大的各种计算,从手机应用到复杂的机器学习系统。TensorFlow提供了一个全面的、灵活的生态系统的库、工具和社区资源,使研究人员能够推动人工智能领域的最前沿,并使开发人员能够轻松构建和部署由机器学习驱动的应用。 TensorFlow的核心是使用数据流图来表示计算。在数据流图中,节点表示在数据上执行的操作,而图中的边表示在操作之间流动的数据。这种表示法允许TensorFlow有效地执行并行计算,并且可以在不同的硬件平台上高效运行。此外,TensorFlow支持自动微分,这对于实现复杂的机器学习算法(如深度学习网络)至关重要。
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tensorflow_lattice-2.0.8.tar.gz (50个子文件)
tensorflow_lattice-2.0.8
setup.py 3KB
PKG-INFO 2KB
tensorflow_lattice
__init__.py 2KB
layers
__init__.py 1KB
python
utils.py 8KB
__init__.py 727B
model_info.py 4KB
pwl_calibration_test.py 52KB
aggregation_layer.py 3KB
pwl_calibration_lib.py 39KB
lattice_test.py 72KB
lattice_lib.py 120KB
test_utils.py 14KB
categorical_calibration_test.py 12KB
estimators.py 76KB
kronecker_factored_lattice_test.py 35KB
rtl_test.py 7KB
kronecker_factored_lattice_layer.py 25KB
premade_lib.py 67KB
pwl_calibration_layer.py 40KB
visualization.py 19KB
premade.py 23KB
lattice_layer.py 44KB
pwl_calibration_sonnet_module.py 22KB
categorical_calibration_lib.py 6KB
rtl_lib.py 5KB
parallel_combination_layer.py 6KB
aggregation_test.py 2KB
linear_test.py 23KB
rtl_layer.py 28KB
kronecker_factored_lattice_lib.py 30KB
linear_lib.py 18KB
categorical_calibration_layer.py 12KB
configs_test.py 9KB
linear_layer.py 17KB
premade_test.py 34KB
utils_test.py 9KB
configs.py 50KB
estimators_test.py 31KB
internal_utils_test.py 2KB
parallel_combination_test.py 5KB
internal_utils.py 6KB
sonnet_modules
__init__.py 709B
setup.cfg 38B
tensorflow_lattice.egg-info
SOURCES.txt 2KB
top_level.txt 19B
PKG-INFO 2KB
requires.txt 94B
dependency_links.txt 1B
README.md 2KB
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