## 使用基于 TensorFlow Lattice 的模型实现灵活、可控且可解释的机器学习

```import numpy as np
import tensorflow as tf
import tensorflow_lattice as tfl

model = tf.keras.models.Sequential()
model.add(
tfl.layers.ParallelCombination([
# Monotonic piece-wise linear calibration with bounded output
tfl.layers.PWLCalibration(
monotonicity='increasing',
input_keypoints=np.linspace(1., 5., num=20),
output_min=0.0,
output_max=1.0),
# Diminishing returns
tfl.layers.PWLCalibration(
monotonicity='increasing',
convexity='concave',
input_keypoints=np.linspace(0., 200., num=20),
output_min=0.0,
output_max=2.0),
# Partially monotonic categorical calibration: calib(0) <= calib(1)
tfl.layers.CategoricalCalibration(
num_buckets=4,
output_min=0.0,
output_max=1.0,
monotonicities=[(0, 1)]),
]))
model.add(
tfl.layers.Lattice(
lattice_sizes=[2, 3, 2],
monotonicities=['increasing', 'increasing', 'increasing'],
# Trust: model is more responsive to input 0 if input 1 increases
edgeworth_trusts=(0, 1, 'positive')))
model.compile(...)
```

TensorFlow Lattice 是一个实现基于可解释格的受限模型的库。借助该库，您可以通过常识或政策驱动型形状限制将领域知识注入学习过程中。这是使用一系列 Keras 层实现的，可以满足单调性、凸度和特征相互作用方式等限制条件。该库还提供易于设置的预创建模型预设 Estimator

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