feature_column_ops_test.py 文件源码

def testCrossWithBucketizedColumn(self):
    price_bucket = tf.contrib.layers.bucketized_column(
        tf.contrib.layers.real_valued_column("price"),
        boundaries=[0., 10., 100.])
    country = tf.contrib.layers.sparse_column_with_hash_bucket(
        "country", hash_bucket_size=5)
    country_price = tf.contrib.layers.crossed_column(
        [country, price_bucket], hash_bucket_size=10)
    with tf.Graph().as_default():
      features = {
          "price": tf.constant([[20.]]),
          "country": tf.SparseTensor(values=["US", "SV"],
                                     indices=[[0, 0], [0, 1]],
                                     shape=[1, 2])
      }
      output, column_to_variable, _ = (
          tf.contrib.layers.weighted_sum_from_feature_columns(features,
                                                              [country_price],
                                                              num_outputs=1))
      with self.test_session() as sess:
        tf.initialize_all_variables().run()
        tf.initialize_all_tables().run()

        weights = column_to_variable[country_price][0]
        sess.run(weights.assign(weights + 0.4))
        # There are two crosses each with 0.4 weight.
        # score = 0.4 + 0.4
        self.assertAllClose(output.eval(), [[0.8]])