feature_column_ops_test.py 文件源码

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