feature_column_ops_test.py 文件源码

def testDeepColumnsSucceedForDNN(self):
    real_valued = tf.contrib.layers.real_valued_column("income", 3)
    bucket = tf.contrib.layers.bucketized_column(
        tf.contrib.layers.real_valued_column("price", 2),
        boundaries=[0., 10., 100.])
    hashed_sparse = tf.contrib.layers.sparse_column_with_hash_bucket("wire", 10)
    features = {
        "income": tf.constant([[20., 10, -5], [110, 0, -7], [-3, 30, 50]]),
        "price": tf.constant([[20., 200], [110, 2], [-20, -30]]),
        "wire": tf.SparseTensor(values=["omar", "stringer", "marlo"],
                                indices=[[0, 0], [1, 0], [2, 0]],
                                shape=[3, 1])
    }
    embeded_sparse = tf.contrib.layers.embedding_column(
        hashed_sparse,
        10, initializer=tf.constant_initializer(133.7))
    output = tf.contrib.layers.input_from_feature_columns(
        features, [real_valued, bucket, embeded_sparse])
    with self.test_session():
      tf.initialize_all_variables().run()
      # size of output = 3 (real_valued) + 2 * 4 (bucket) + 10 (embedding) = 21
      self.assertAllEqual(output.eval().shape, [3, 21])