feature_column_ops_test.py 文件源码

def testEmbeddingColumnWithWeightedSparseColumnForDNN(self):
    ids = feature_column.sparse_column_with_keys("ids",
                                                 ["marlo", "omar", "stringer"])
    ids_tensor = sparse_tensor.SparseTensor(
        values=["stringer", "stringer", "marlo"],
        indices=[[0, 0], [1, 0], [1, 1]],
        dense_shape=[3, 2])
    weighted_ids = feature_column.weighted_sparse_column(ids, "weights")
    weights_tensor = sparse_tensor.SparseTensor(
        values=[10.0, 20.0, 30.0],
        indices=[[0, 0], [1, 0], [1, 1]],
        dense_shape=[3, 2])
    features = {"ids": ids_tensor, "weights": weights_tensor}
    embeded_sparse = feature_column.embedding_column(
        weighted_ids,
        1,
        combiner="sum",
        initializer=init_ops.ones_initializer())
    output = feature_column_ops.input_from_feature_columns(features,
                                                           [embeded_sparse])
    with self.test_session():
      variables_lib.global_variables_initializer().run()
      data_flow_ops.tables_initializer().run()
      # score: (sum of weights)
      self.assertAllEqual(output.eval(), [[10.], [50.], [0.]])