feature_column_ops_test.py 文件源码

def testMulticlassWithBucketizedColumnHavingMultiDimensions(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=5))
      with self.test_session() as sess:
        tf.initialize_all_variables().run()
        tf.initialize_all_tables().run()

        # dimension = 2, bucket_size = 4, num_classes = 5
        sess.run(column_to_variable[bucket][0].assign(
            [[0.1, 1, 10, 100, 1000], [0.2, 2, 20, 200, 2000],
             [0.3, 3, 30, 300, 3000], [0.4, 4, 40, 400, 4000],
             [5, 50, 500, 5000, 50000], [6, 60, 600, 6000, 60000],
             [7, 70, 700, 7000, 70000], [8, 80, 800, 8000, 80000]]))
        self.assertAllClose(
            output.eval(),
            [[0.3 + 8, 3 + 80, 30 + 800, 300 + 8000, 3000 + 80000],
             [0.4 + 7, 4 + 70, 40 + 700, 400 + 7000, 4000 + 70000],
             [0.1 + 5, 1 + 50, 10 + 500, 100 + 5000, 1000 + 50000]])