feature_column_ops_test.py 文件源码

def testEmbeddingColumnWithMaxNormForDNN(self):
    hashed_sparse = feature_column.sparse_column_with_hash_bucket("wire", 10)
    wire_tensor = sparse_tensor.SparseTensor(
        values=["omar", "stringer", "marlo"],
        indices=[[0, 0], [1, 0], [1, 1]],
        dense_shape=[3, 2])
    features = {"wire": wire_tensor}
    embedded_sparse = feature_column.embedding_column(
        hashed_sparse,
        1,
        combiner="sum",
        initializer=init_ops.ones_initializer(),
        max_norm=0.5)
    output = feature_column_ops.input_from_feature_columns(features,
                                                           [embedded_sparse])
    with self.test_session():
      variables_lib.global_variables_initializer().run()
      # score: (number of values * 0.5)
      self.assertAllClose(output.eval(), [[0.5], [1.], [0.]])