Python tf.feature_column.bucketized_column用法及代碼示例

表示由 boundaries 分桶的離散化密集輸入。

用法

tf.feature_column.bucketized_column(
    source_column, boundaries
)

桶包括左邊界，不包括右邊界。即，boundaries=[0., 1., 2.] 生成桶 (-inf, 0.) , [0., 1.) , [1., 2.) 和 [2., +inf) 。

例如，如果輸入是

boundaries = [0, 10, 100]
input tensor = [[-5, 10000]
                [150,   10]
                [5,    100]]

那麽輸出將是

output = [[0, 3]
          [3, 2]
          [1, 3]]

例子：

price = tf.feature_column.numeric_column('price')
bucketized_price = tf.feature_column.bucketized_column(
    price, boundaries=[...])
columns = [bucketized_price, ...]
features = tf.io.parse_example(
    ..., features=tf.feature_column.make_parse_example_spec(columns))
dense_tensor = tf.keras.layers.DenseFeatures(columns)(features)

bucketized_column 也可以使用 crossed_column 與另一個分類列交叉：

price = tf.feature_column.numeric_column('price')
# bucketized_column converts numerical feature to a categorical one.
bucketized_price = tf.feature_column.bucketized_column(
    price, boundaries=[...])
# 'keywords' is a string feature.
price_x_keywords = tf.feature_column.crossed_column(
    [bucketized_price, 'keywords'], 50K)
columns = [price_x_keywords, ...]
features = tf.io.parse_example(
    ..., features=tf.feature_column.make_parse_example_spec(columns))
dense_tensor = tf.keras.layers.DenseFeatures(columns)(features)
linear_model = tf.keras.experimental.LinearModel(units=...)(dense_tensor)