dnn_test.py 文件源码

def testMultipliesGradient(self):
    embedding_language = feature_column.embedding_column(
        feature_column.sparse_column_with_hash_bucket('language', 10),
        dimension=1,
        initializer=init_ops.constant_initializer(0.1))
    embedding_wire = feature_column.embedding_column(
        feature_column.sparse_column_with_hash_bucket('wire', 10),
        dimension=1,
        initializer=init_ops.constant_initializer(0.1))

    params = {
        'feature_columns': [embedding_language, embedding_wire],
        'head': head_lib._multi_class_head(2),
        'hidden_units': [1],
        # Set lr mult to 0. to keep embeddings constant.
        'embedding_lr_multipliers': {
            embedding_language: 0.0
        },
    }
    features = {
        'language':
            sparse_tensor.SparseTensor(
                values=['en', 'fr', 'zh'],
                indices=[[0, 0], [1, 0], [2, 0]],
                dense_shape=[3, 1]),
        'wire':
            sparse_tensor.SparseTensor(
                values=['omar', 'stringer', 'marlo'],
                indices=[[0, 0], [1, 0], [2, 0]],
                dense_shape=[3, 1]),
    }
    labels = constant_op.constant([[0], [0], [0]], dtype=dtypes.int32)
    model_ops = dnn._dnn_model_fn(features, labels, model_fn.ModeKeys.TRAIN,
                                  params)
    with monitored_session.MonitoredSession() as sess:
      language_var = dnn_linear_combined._get_embedding_variable(
          embedding_language, 'dnn', 'dnn/input_from_feature_columns')
      wire_var = dnn_linear_combined._get_embedding_variable(
          embedding_wire, 'dnn', 'dnn/input_from_feature_columns')
      for _ in range(2):
        _, language_value, wire_value = sess.run(
            [model_ops.train_op, language_var, wire_var])
      initial_value = np.full_like(language_value, 0.1)
      self.assertTrue(np.all(np.isclose(language_value, initial_value)))
      self.assertFalse(np.all(np.isclose(wire_value, initial_value)))