TensorFlow 特征列(Feature Column)使用示例-优快云博客

本文链接：https://blog.youkuaiyun.com/zhangbaoanhadoop/article/details/81943916

这段代码展示了如何在 TensorFlow 中使用不同的特征列类型，包括数值列、桶化列、词汇列表分类列、哈希桶列、嵌入列以及加权分类列，并演示了线性模型和交叉列的创建与应用。

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import tensorflow as tf
from tensorflow.python.estimator.inputs import numpy_io
import numpy as np
import collections
from tensorflow.python.framework import errors
from tensorflow.python.platform import test
from tensorflow.python.training import coordinator
from tensorflow import feature_column

from tensorflow.python.feature_column.feature_column import _LazyBuilder

def test_numeric():
    '''
    两个产生的结果相同
    _LazyBuilder
    input_layer
    :return:
    '''

    price = {'price': [[1.], [2.], [3.], [4.],[10.]]}  # 4行样本
    builder = _LazyBuilder(price)

    def transform_fn(x):
        return x + 2

    price_column = feature_column.numeric_column('price', normalizer_fn=transform_fn)

    price_transformed_tensor = price_column._get_dense_tensor(builder)

    with tf.Session() as session:
        print(session.run([price_transformed_tensor]))

    # 使用input_layer

    price_transformed_tensor = feature_column.input_layer(price, [price_column])

    with tf.Session() as session:
        print('use input_layer' + '_' * 40)
        print(session.run([price_transformed_tensor]))

def test_bucketized_column():

    price = {'price': [[5.], [15.], [25.], [35.], [55.], [45.]]}  # 4行样本

    price_column = feature_column.numeric_column('price')
    bucket_price = feature_column.bucketized_column(price_column, [0, 10, 20, 30, 40])

    price_bucket_tensor = feature_column.input_layer(price, [bucket_price])

    with tf.Session() as session:
        print(session.run([price_bucket_tensor]))

def test_categorical_column_with_vocabulary_list():

    # color_data = {'color': [['R', 'R'], ['G', 'R'], ['B', 'G'], ['A', 'A']]}  # 4行样本
    color_data = {'color': [['R'], ['R'], ['G'], ['A']]}  # 4行样本

Feature_col