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概述
您可以使用自定义 Estimator 通过 TFL 层创建任意单调模型。本指南概述了创建此类 Estimator 所需的步骤。
设置
安装 TF Lattice 软件包：
pip install tensorflow-lattice导入所需的软件包：
import tensorflow as tf
import logging
import numpy as np
import pandas as pd
import sys
import tensorflow_lattice as tfl
from tensorflow import feature_column as fc
from tensorflow_estimator.python.estimator.canned import optimizers
from tensorflow_estimator.python.estimator.head import binary_class_head
logging.disable(sys.maxsize)
下载 UCI Statlog (Heart) 数据集：
csv_file = tf.keras.utils.get_file(
'heart.csv', 'http://storage.googleapis.com/download.tensorflow.org/data/heart.csv')
df = pd.read_csv(csv_file)
target = df.pop('target')
train_size = int(len(df) * 0.8)
train_x = df[:train_size]
train_y = target[:train_size]
test_x = df[train_size:]
test_y = target[train_size:]
df.head()
设置用于在本指南中进行训练的默认值：
LEARNING_RATE = 0.1
BATCH_SIZE = 128
NUM_EPOCHS = 1000
特征列
与任何其他 TF Estimator 一样，数据通常需要通过 input_fn 传递给 Estimator，并使用 FeatureColumns 进行解析。
# Feature columns.
# - age
# - sex
# - ca number of major vessels (0-3) colored by flourosopy
# - thal 3 = normal; 6 = fixed defect; 7 = reversable defect
feature_columns = [
fc.numeric_column('age', default_value=-1),
fc.categorical_column_with_vocabulary_list('sex', [0, 1]),
fc.numeric_column('ca'),
fc.categorical_column_with_vocabulary_list(
'thal', ['normal', 'fixed', 'reversible']),
]
请注意，分类特征不需要用密集特征列包装，因为 tfl.laysers.CategoricalCalibration 层可以直接使用分类索引。
创建 input_fn
与任何其他 Estimator 一样，您可以使用 input_fn 将数据馈送给模型进行训练和评估。
train_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
x=train_x,
y=train_y,
shuffle=True,
batch_size=BATCH_SIZE,
num_epochs=NUM_EPOCHS,
num_threads=1)
test_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
x=test_x,
y=test_y,
shuffle=False,
batch_size=BATCH_SIZE,
num_epochs=1,
num_threads=1)
创建 model_fn
您可以通过多种方式创建自定义 Estimator。在这里，我们将构造一个在已解析的输入张量上调用 Keras 模型的 model_fn。要解析输入特征，您可以使用 tf.feature_column.input_layer、tf.keras.layers.DenseFeatures 或 tfl.estimators.transform_features。如果使用后者，则不需要使用密集特征列包装分类特征，并且生成的张量不会串联，这样可以更轻松地在校准层中使用特征。
要构造模型，您可以搭配使用 TFL 层或任何其他 Keras 层。在这里，我们从 TFL 层创建一个校准点阵 Keras 模型，并施加一些单调性约束。随后，我们使用 Keras 模型创建自定义 Estimator。
def model_fn(features, labels, mode, config):
"""model_fn for the custom estimator."""
del config
input_tensors = tfl.estimators.transform_features(features, feature_columns)
inputs = {
key: tf.keras.layers.Input(shape=(1,), name=key) for key in input_tensors
}
lattice_sizes = [3, 2, 2, 2]
lattice_monotonicities = ['increasing', 'none', 'increasing', 'increasing']
lattice_input = tf.keras.layers.Concatenate(axis=1)([
tfl.layers.PWLCalibration(
input_keypoints=np.linspace(10, 100, num=8, dtype=np.float32),
# The output range of the calibrator should be the input range of
# the following lattice dimension.
output_min=0.0,
output_max=lattice_sizes[0] - 1.0,
monotonicity='increasing',
)(inputs['age']),
tfl.layers.CategoricalCalibration(
# Number of categories including any missing/default category.
num_buckets=2,
output_min=0.0,
output_max=lattice_sizes[1] - 1.0,
)(inputs['sex']),
tfl.layers.PWLCalibration(
input_keypoints=[0.0, 1.0, 2.0, 3.0],
output_min=0.0,
output_max=lattice_sizes[0] - 1.0,
# You can specify TFL regularizers as tuple
# ('regularizer name', l1, l2).
kernel_regularizer=('hessian', 0.0, 1e-4),
monotonicity='increasing',
)(inputs['ca']),
tfl.layers.CategoricalCalibration(
num_buckets=3,
output_min=0.0,
output_max=lattice_sizes[1] - 1.0,
# Categorical monotonicity can be partial order.
# (i, j) indicates that we must have output(i) <= output(j).
# Make sure to set the lattice monotonicity to 'increasing' for this
# dimension.
monotonicities=[(0, 1), (0, 2)],
)(inputs['thal']),
])
output = tfl.layers.Lattice(
lattice_sizes=lattice_sizes, monotonicities=lattice_monotonicities)(
lattice_input)
training = (mode == tf.estimator.ModeKeys.TRAIN)
model = tf.keras.Model(inputs=inputs, outputs=output)
logits = model(input_tensors, training=training)
if training:
optimizer = optimizers.get_optimizer_instance_v2('Adagrad', LEARNING_RATE)
else:
optimizer = None
head = binary_class_head.BinaryClassHead()
return head.create_estimator_spec(
features=features,
mode=mode,
labels=labels,
optimizer=optimizer,
logits=logits,
trainable_variables=model.trainable_variables,
update_ops=model.updates)
训练和 Estimator
使用 model_fn，我们可以创建和训练 Estimator。
estimator = tf.estimator.Estimator(model_fn=model_fn)
estimator.train(input_fn=train_input_fn)
results = estimator.evaluate(input_fn=test_input_fn)
print('AUC: {}'.format(results['auc']))
AUC: 0.5701754689216614