feature_column_ops_test.py 文件源码

def testCrossColumnByItself(self):
    language = tf.contrib.layers.sparse_column_with_hash_bucket(
        "language", hash_bucket_size=3)
    language_language = tf.contrib.layers.crossed_column(
        [language, language], hash_bucket_size=10)
    with tf.Graph().as_default():
      features = {
          "language": tf.SparseTensor(values=["english", "spanish"],
                                      indices=[[0, 0], [0, 1]],
                                      shape=[1, 2]),
      }
      output, column_to_variable, _ = (
          tf.contrib.layers.weighted_sum_from_feature_columns(
              features, [language_language],
              num_outputs=1))
      with self.test_session() as sess:
        tf.global_variables_initializer().run()
        tf.initialize_all_tables().run()

        weights = column_to_variable[language_language][0]
        sess.run(weights.assign(weights + 0.4))
        # There are two features inside language. If we cross it by itself we'll
        # have four crossed features.
        self.assertAllClose(output.eval(), [[1.6]])