from tensorflow.contrib.layers.python.layers import feature_column

        dnn_feature_columns=(cont_feature,),
        dnn_hidden_units=(3, 3),
        dnn_optimizer=adagrad.AdagradOptimizer(learning_rate=0.1))

    input_fn = test_data.iris_input_logistic_fn
    metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
        input_fn=input_fn, steps=100)
    self._assertSingleClassMetrics(metrics)

  def benchmarkMultiClass(self):
    iris = base.load_iris()
    cont_feature = feature_column.real_valued_column('feature', dimension=4)
    bucketized_feature = feature_column.bucketized_column(
        cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))

    classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
        n_classes=3,
        linear_feature_columns=(bucketized_feature,),
        dnn_feature_columns=(cont_feature,),
        dnn_hidden_units=(3, 3))

    input_fn = test_data.iris_input_multiclass_fn
