feature_column_ops_test.py 文件源码

def testBucketizedColumnHavingMultiDimensions(self):
    country = tf.contrib.layers.sparse_column_with_hash_bucket(
        "country", hash_bucket_size=5)
    bucket = tf.contrib.layers.bucketized_column(
        tf.contrib.layers.real_valued_column("price", 2),
        boundaries=[0., 10., 100.])
    with tf.Graph().as_default():
      # buckets 2, 3, 0
      features = {"price": tf.constant([[20., 210], [110, 50], [-3, -30]]),
                  "country": tf.SparseTensor(values=["US", "SV"],
                                             indices=[[0, 0], [1, 0]],
                                             shape=[3, 2])}
      output, column_to_variable, _ = (
          tf.contrib.layers.weighted_sum_from_feature_columns(features,
                                                              [bucket, country],
                                                              num_outputs=1))
      with self.test_session() as sess:
        tf.global_variables_initializer().run()
        tf.initialize_all_tables().run()

        # dimension = 2, bucket_size = 4, num_classes = 1
        sess.run(column_to_variable[bucket][0].assign(
            [[0.1], [0.2], [0.3], [0.4], [1], [2], [3], [4]]))
        self.assertAllClose(output.eval(), [[0.3 + 4], [0.4 + 3], [0.1 + 1]])