Python export.build_parsing_serving_input_receiver_fn函数代码示例

  def _test_complete_flow(self, train_input_fn, eval_input_fn, predict_input_fn,
                          input_dimension, label_dimension, prediction_length):
    feature_columns = [
        feature_column_lib.numeric_column('x', shape=(input_dimension,))
    ]
    est = _baseline_estimator_fn(
        label_dimension=label_dimension,
        model_dir=self._model_dir)

    # TRAIN
    # learn y = x
    est.train(train_input_fn, steps=200)

    # EVALUTE
    scores = est.evaluate(eval_input_fn)
    self.assertEqual(200, scores[ops.GraphKeys.GLOBAL_STEP])
    self.assertIn(metric_keys.MetricKeys.LOSS, six.iterkeys(scores))

    # PREDICT
    predictions = np.array(
        [x['predictions'] for x in est.predict(predict_input_fn)])
    self.assertAllEqual((prediction_length, label_dimension), predictions.shape)

    # EXPORT
    feature_spec = feature_column_lib.make_parse_example_spec(feature_columns)
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                       serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))

  def _test_complete_flow_helper(
      self, linear_feature_columns, dnn_feature_columns, feature_spec,
      train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
      label_dimension, batch_size):
    est = dnn_linear_combined.DNNLinearCombinedRegressor(
        linear_feature_columns=linear_feature_columns,
        dnn_hidden_units=(2, 2),
        dnn_feature_columns=dnn_feature_columns,
        label_dimension=label_dimension,
        model_dir=self._model_dir)

    # TRAIN
    num_steps = 10
    est.train(train_input_fn, steps=num_steps)

    # EVALUTE
    scores = est.evaluate(eval_input_fn)
    self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
    self.assertIn('loss', six.iterkeys(scores))

    # PREDICT
    predictions = np.array([
        x[prediction_keys.PredictionKeys.PREDICTIONS]
        for x in est.predict(predict_input_fn)
    ])
    self.assertAllEqual((batch_size, label_dimension), predictions.shape)

    # EXPORT
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                       serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))

  def _test_complete_flow(self, feature_columns, train_input_fn, eval_input_fn,
                          predict_input_fn, n_classes, batch_size):
    cell_units = [4, 2]
    est = self._create_estimator_fn(feature_columns, n_classes, cell_units,
                                    self._model_dir)

    # TRAIN
    num_steps = 10
    est.train(train_input_fn, steps=num_steps)

    # EVALUATE
    scores = est.evaluate(eval_input_fn)
    self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
    self.assertIn('loss', six.iterkeys(scores))

    # PREDICT
    predicted_proba = np.array([
        x[prediction_keys.PredictionKeys.PROBABILITIES]
        for x in est.predict(predict_input_fn)
    ])
    self.assertAllEqual((batch_size, n_classes), predicted_proba.shape)

    # EXPORT
    feature_spec = parsing_utils.classifier_parse_example_spec(
        feature_columns,
        label_key='label',
        label_dtype=dtypes.int64)
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                       serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))

  def _test_complete_flow(
      self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
      n_classes, batch_size):
    feature_columns = [
        feature_column.numeric_column('x', shape=(input_dimension,))]
    est = dnn.DNNClassifier(
        hidden_units=(2, 2),
        feature_columns=feature_columns,
        n_classes=n_classes,
        model_dir=self._model_dir)

    # TRAIN
    num_steps = 10
    est.train(train_input_fn, steps=num_steps)

    # EVALUTE
    scores = est.evaluate(eval_input_fn)
    self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
    self.assertIn('loss', six.iterkeys(scores))

    # PREDICT
    predicted_proba = np.array([
        x[prediction_keys.PredictionKeys.PROBABILITIES]
        for x in est.predict(predict_input_fn)
    ])
    self.assertAllEqual((batch_size, n_classes), predicted_proba.shape)

    # EXPORT
    feature_spec = feature_column.make_parse_example_spec(feature_columns)
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                       serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))

  def _test_complete_flow(
      self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
      label_dimension, batch_size):
    feature_columns = [
        feature_column.numeric_column('x', shape=(input_dimension,))]
    est = linear.LinearEstimator(
        head=head_lib.regression_head(label_dimension=label_dimension),
        feature_columns=feature_columns,
        model_dir=self._model_dir)

    # TRAIN
    num_steps = 10
    est.train(train_input_fn, steps=num_steps)

    # EVALUTE
    scores = est.evaluate(eval_input_fn)
    self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
    self.assertIn('loss', six.iterkeys(scores))

    # PREDICT
    predictions = np.array([
        x[prediction_keys.PredictionKeys.PREDICTIONS]
        for x in est.predict(predict_input_fn)
    ])
    self.assertAllEqual((batch_size, label_dimension), predictions.shape)

    # EXPORT
    feature_spec = feature_column.make_parse_example_spec(feature_columns)
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                       serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))

  def test_scaffold_is_used_for_saver(self):
    tmpdir = tempfile.mkdtemp()

    def _model_fn_scaffold(features, labels, mode):
      _, _ = features, labels
      variables.Variable(1., name='weight')
      real_saver = saver.Saver()
      self.mock_saver = test.mock.Mock(
          wraps=real_saver, saver_def=real_saver.saver_def)
      scores = constant_op.constant([3.])
      return model_fn_lib.EstimatorSpec(
          mode=mode,
          predictions=constant_op.constant([[1.]]),
          loss=constant_op.constant(0.),
          train_op=constant_op.constant(0.),
          scaffold=training.Scaffold(saver=self.mock_saver),
          export_outputs={'test': export_output.ClassificationOutput(scores)})

    est = estimator.Estimator(model_fn=_model_fn_scaffold)
    est.train(dummy_input_fn, steps=1)
    feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
                    'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)

    # Perform the export.
    export_dir_base = os.path.join(
        compat.as_bytes(tmpdir), compat.as_bytes('export'))
    est.export_savedmodel(export_dir_base, serving_input_receiver_fn)

    self.assertTrue(self.mock_saver.restore.called)

  def test_complete_flow_with_a_simple_linear_model(self):

    def _model_fn(features, labels, mode):
      predictions = layers.dense(
          features['x'], 1, kernel_initializer=init_ops.zeros_initializer())
      export_outputs = {
          'predictions': export_output.RegressionOutput(predictions)
      }

      if mode == model_fn_lib.ModeKeys.PREDICT:
        return model_fn_lib.EstimatorSpec(
            mode, predictions=predictions, export_outputs=export_outputs)

      loss = losses.mean_squared_error(labels, predictions)
      train_op = training.GradientDescentOptimizer(learning_rate=0.5).minimize(
          loss, training.get_global_step())
      eval_metric_ops = {
          'absolute_error': metrics_lib.mean_absolute_error(
              labels, predictions)
      }

      return model_fn_lib.EstimatorSpec(
          mode,
          predictions=predictions,
          loss=loss,
          train_op=train_op,
          eval_metric_ops=eval_metric_ops,
          export_outputs=export_outputs)

    est = estimator.Estimator(model_fn=_model_fn)
    data = np.linspace(0., 1., 100, dtype=np.float32).reshape(-1, 1)

    # TRAIN
    # learn y = x
    train_input_fn = numpy_io.numpy_input_fn(
        x={'x': data}, y=data, batch_size=50, num_epochs=None, shuffle=True)
    est.train(train_input_fn, steps=200)

    # EVALUTE
    eval_input_fn = numpy_io.numpy_input_fn(
        x={'x': data}, y=data, batch_size=50, num_epochs=1, shuffle=True)
    scores = est.evaluate(eval_input_fn)
    self.assertEqual(200, scores['global_step'])
    self.assertGreater(0.1, scores['absolute_error'])

    # PREDICT
    predict_input_fn = numpy_io.numpy_input_fn(
        x={'x': data}, y=None, batch_size=10, num_epochs=1, shuffle=False)
    predictions = list(est.predict(predict_input_fn))
    self.assertAllClose(data, predictions, atol=0.01)

    # EXPORT
    feature_spec = {'x': parsing_ops.FixedLenFeature([1], dtypes.float32)}
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                       serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))

def make_parsing_export_strategy(feature_columns,
                                 default_output_alternative_key=None,
                                 assets_extra=None,
                                 as_text=False,
                                 exports_to_keep=5,
                                 target_core=False,
                                 strip_default_attrs=False):
  # pylint: disable=line-too-long
  """Create an ExportStrategy for use with Experiment, using `FeatureColumn`s.

  Creates a SavedModel export that expects to be fed with a single string
  Tensor containing serialized tf.Examples.  At serving time, incoming
  tf.Examples will be parsed according to the provided `FeatureColumn`s.

  Args:
    feature_columns: An iterable of `FeatureColumn`s representing the features
      that must be provided at serving time (excluding labels!).
    default_output_alternative_key: the name of the head to serve when an
      incoming serving request does not explicitly request a specific head.
      Must be `None` if the estimator inherits from ${tf.estimator.Estimator}
      or for single-headed models.
    assets_extra: A dict specifying how to populate the assets.extra directory
      within the exported SavedModel.  Each key should give the destination
      path (including the filename) relative to the assets.extra directory.
      The corresponding value gives the full path of the source file to be
      copied.  For example, the simple case of copying a single file without
      renaming it is specified as
      `{'my_asset_file.txt': '/path/to/my_asset_file.txt'}`.
    as_text: whether to write the SavedModel proto in text format.
    exports_to_keep: Number of exports to keep.  Older exports will be
      garbage-collected.  Defaults to 5.  Set to None to disable garbage
      collection.
    target_core: If True, prepare an ExportStrategy for use with
      tensorflow.python.estimator.*.  If False (default), prepare an
      ExportStrategy for use with tensorflow.contrib.learn.python.learn.*.
    strip_default_attrs: Boolean. If `True`, default-valued attributes will be
      removed from the NodeDefs. For a detailed guide, see
      [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).

  Returns:
    An ExportStrategy that can be passed to the Experiment constructor.
  """
  # pylint: enable=line-too-long
  feature_spec = feature_column.create_feature_spec_for_parsing(feature_columns)
  if target_core:
    serving_input_fn = (
        core_export.build_parsing_serving_input_receiver_fn(feature_spec))
  else:
    serving_input_fn = (
        input_fn_utils.build_parsing_serving_input_fn(feature_spec))
  return make_export_strategy(
      serving_input_fn,
      default_output_alternative_key=default_output_alternative_key,
      assets_extra=assets_extra,
      as_text=as_text,
      exports_to_keep=exports_to_keep,
      strip_default_attrs=strip_default_attrs)

  def test_complete_flow_with_mode(self, distribution):
    label_dimension = 2
    input_dimension = label_dimension
    batch_size = 10
    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
    data = data.reshape(batch_size, label_dimension)
    train_input_fn = self.dataset_input_fn(
        x={'x': data},
        y=data,
        batch_size=batch_size // len(distribution.worker_devices),
        shuffle=True)
    eval_input_fn = self.dataset_input_fn(
        x={'x': data},
        y=data,
        batch_size=batch_size // len(distribution.worker_devices),
        shuffle=False)
    predict_input_fn = numpy_io.numpy_input_fn(
        x={'x': data}, batch_size=batch_size, shuffle=False)

    linear_feature_columns = [
        feature_column.numeric_column('x', shape=(input_dimension,))
    ]
    dnn_feature_columns = [
        feature_column.numeric_column('x', shape=(input_dimension,))
    ]
    feature_columns = linear_feature_columns + dnn_feature_columns
    estimator = dnn_linear_combined.DNNLinearCombinedRegressor(
        linear_feature_columns=linear_feature_columns,
        dnn_hidden_units=(2, 2),
        dnn_feature_columns=dnn_feature_columns,
        label_dimension=label_dimension,
        model_dir=self._model_dir,
        # TODO(isaprykin): Work around the colocate_with error.
        dnn_optimizer=adagrad.AdagradOptimizer(0.001),
        linear_optimizer=adagrad.AdagradOptimizer(0.001),
        config=run_config.RunConfig(
            train_distribute=distribution, eval_distribute=distribution))

    num_steps = 10
    estimator.train(train_input_fn, steps=num_steps)

    scores = estimator.evaluate(eval_input_fn)
    self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
    self.assertIn('loss', six.iterkeys(scores))

    predictions = np.array([
        x[prediction_keys.PredictionKeys.PREDICTIONS]
        for x in estimator.predict(predict_input_fn)
    ])
    self.assertAllEqual((batch_size, label_dimension), predictions.shape)

    feature_spec = feature_column.make_parse_example_spec(feature_columns)
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = estimator.export_savedmodel(tempfile.mkdtemp(),
                                             serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))

  def test_complete_flow(self):
    label_dimension = 2
    batch_size = 10
    feature_columns = [feature_column.numeric_column('x', shape=(2,))]
    est = dnn.DNNRegressor(
        hidden_units=(2, 2),
        feature_columns=feature_columns,
        label_dimension=label_dimension,
        model_dir=self._model_dir)
    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
    data = data.reshape(batch_size, label_dimension)

    # TRAIN
    # learn y = x
    train_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        y=data,
        batch_size=batch_size,
        num_epochs=None,
        shuffle=True)
    num_steps = 200
    est.train(train_input_fn, steps=num_steps)

    # EVALUTE
    eval_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        y=data,
        batch_size=batch_size,
        shuffle=False)
    scores = est.evaluate(eval_input_fn)
    self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
    self.assertIn('loss', six.iterkeys(scores))

    # PREDICT
    predict_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        batch_size=batch_size,
        shuffle=False)
    predictions = np.array([
        x[prediction_keys.PredictionKeys.PREDICTIONS]
        for x in est.predict(predict_input_fn)
    ])
    self.assertAllEqual((batch_size, label_dimension), predictions.shape)
    # TODO(ptucker): Deterministic test for predicted values?

    # EXPORT
    feature_spec = feature_column.make_parse_example_spec(feature_columns)
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                       serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))

  def test_build_parsing_serving_input_receiver_fn(self):
    feature_spec = {"int_feature": parsing_ops.VarLenFeature(dtypes.int64),
                    "float_feature": parsing_ops.VarLenFeature(dtypes.float32)}
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    with ops.Graph().as_default():
      serving_input_receiver = serving_input_receiver_fn()
      self.assertEqual(set(["int_feature", "float_feature"]),
                       set(serving_input_receiver.features.keys()))
      self.assertEqual(set(["examples"]),
                       set(serving_input_receiver.receiver_tensors.keys()))

      example = example_pb2.Example()
      text_format.Parse("features: { "
                        "  feature: { "
                        "    key: 'int_feature' "
                        "    value: { "
                        "      int64_list: { "
                        "        value: [ 21, 2, 5 ] "
                        "      } "
                        "    } "
                        "  } "
                        "  feature: { "
                        "    key: 'float_feature' "
                        "    value: { "
                        "      float_list: { "
                        "        value: [ 525.25 ] "
                        "      } "
                        "    } "
                        "  } "
                        "} ", example)

      with self.test_session() as sess:
        sparse_result = sess.run(
            serving_input_receiver.features,
            feed_dict={
                serving_input_receiver.receiver_tensors["examples"].name:
                [example.SerializeToString()]})
        self.assertAllEqual([[0, 0], [0, 1], [0, 2]],
                            sparse_result["int_feature"].indices)
        self.assertAllEqual([21, 2, 5],
                            sparse_result["int_feature"].values)
        self.assertAllEqual([[0, 0]],
                            sparse_result["float_feature"].indices)
        self.assertAllEqual([525.25],
                            sparse_result["float_feature"].values)

  def testTrainEvaluateWithDnnForInputAndTreeForPredict(self):
    head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss(
        loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS)

    learner_config = learner_pb2.LearnerConfig()
    learner_config.num_classes = 2
    learner_config.constraints.max_tree_depth = 3
    model_dir = tempfile.mkdtemp()
    config = run_config.RunConfig()

    est = estimator.CoreDNNBoostedTreeCombinedEstimator(
        head=head_fn,
        dnn_hidden_units=[1],
        dnn_feature_columns=[core_feature_column.numeric_column("x")],
        tree_learner_config=learner_config,
        num_trees=1,
        tree_examples_per_layer=3,
        model_dir=model_dir,
        config=config,
        dnn_steps_to_train=10,
        dnn_input_layer_to_tree=True,
        predict_with_tree_only=True,
        dnn_to_tree_distillation_param=(0.5, None),
        tree_feature_columns=[])

    # Train for a few steps.
    est.train(input_fn=_train_input_fn, steps=1000)
    res = est.evaluate(input_fn=_eval_input_fn, steps=1)
    self.assertLess(0.5, res["auc"])
    est.predict(input_fn=_eval_input_fn)
    serving_input_fn = (
        export.build_parsing_serving_input_receiver_fn(
            feature_spec={"x": parsing_ops.FixedLenFeature(
                [1], dtype=dtypes.float32)}))
    base_exporter = exporter.FinalExporter(
        name="Servo",
        serving_input_receiver_fn=serving_input_fn,
        assets_extra=None)
    export_path = os.path.join(model_dir, "export")
    base_exporter.export(
        est,
        export_path=export_path,
        checkpoint_path=None,
        eval_result={},
        is_the_final_export=True)

  def test_scaffold_is_used_for_local_init(self):
    tmpdir = tempfile.mkdtemp()

    def _model_fn_scaffold(features, labels, mode):
      _, _ = features, labels
      my_int = variables.Variable(1, name='my_int',
                                  collections=[ops.GraphKeys.LOCAL_VARIABLES])
      scores = constant_op.constant([3.])
      with ops.control_dependencies(
          [variables.local_variables_initializer(),
           data_flow_ops.tables_initializer()]):
        assign_op = state_ops.assign(my_int, 12345)

      # local_initSop must be an Operation, not a Tensor.
      custom_local_init_op = control_flow_ops.group(assign_op)
      return model_fn_lib.EstimatorSpec(
          mode=mode,
          predictions=constant_op.constant([[1.]]),
          loss=constant_op.constant(0.),
          train_op=constant_op.constant(0.),
          scaffold=training.Scaffold(local_init_op=custom_local_init_op),
          export_outputs={'test': export_output.ClassificationOutput(scores)})

    est = estimator.Estimator(model_fn=_model_fn_scaffold)
    est.train(dummy_input_fn, steps=1)
    feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
                    'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)

    # Perform the export.
    export_dir_base = os.path.join(
        compat.as_bytes(tmpdir), compat.as_bytes('export'))
    export_dir = est.export_savedmodel(export_dir_base,
                                       serving_input_receiver_fn)

    # Restore, to validate that the custom local_init_op runs.
    with ops.Graph().as_default() as graph:
      with session.Session(graph=graph) as sess:
        loader.load(sess, [tag_constants.SERVING], export_dir)
        my_int = graph.get_tensor_by_name('my_int:0')
        my_int_value = sess.run(my_int)
        self.assertEqual(12345, my_int_value)

  def test_export_savedmodel_with_saveables_proto_roundtrip(self):
    tmpdir = tempfile.mkdtemp()
    est = estimator.Estimator(
        model_fn=_model_fn_with_saveables_for_export_tests)
    est.train(input_fn=dummy_input_fn, steps=1)
    feature_spec = {'x': parsing_ops.VarLenFeature(dtype=dtypes.int64),
                    'y': parsing_ops.VarLenFeature(dtype=dtypes.int64)}
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)

    # Perform the export.
    export_dir_base = os.path.join(
        compat.as_bytes(tmpdir), compat.as_bytes('export'))
    export_dir = est.export_savedmodel(
        export_dir_base, serving_input_receiver_fn)

    # Check that all the files are in the right places.
    self.assertTrue(gfile.Exists(export_dir_base))
    self.assertTrue(gfile.Exists(export_dir))
    self.assertTrue(gfile.Exists(os.path.join(
        compat.as_bytes(export_dir),
        compat.as_bytes('saved_model.pb'))))
    self.assertTrue(gfile.Exists(os.path.join(
        compat.as_bytes(export_dir),
        compat.as_bytes('variables'))))
    self.assertTrue(gfile.Exists(os.path.join(
        compat.as_bytes(export_dir),
        compat.as_bytes('variables/variables.index'))))
    self.assertTrue(gfile.Exists(os.path.join(
        compat.as_bytes(export_dir),
        compat.as_bytes('variables/variables.data-00000-of-00001'))))

    # Restore, to validate that the export was well-formed.
    with ops.Graph().as_default() as graph:
      with session.Session(graph=graph) as sess:
        loader.load(sess, [tag_constants.SERVING], export_dir)
        graph_ops = [x.name for x in graph.get_operations()]
        self.assertTrue('input_example_tensor' in graph_ops)
        self.assertTrue('ParseExample/ParseExample' in graph_ops)
        self.assertTrue('save/LookupTableImport' in graph_ops)

    # Clean up.
    gfile.DeleteRecursively(tmpdir)

  def test_complete_flow(self):
    label_dimension = 2
    batch_size = 10
    feature_columns = [
        feature_column_lib.numeric_column('x', shape=(2,))
    ]
    est = linear.LinearRegressor(
        feature_columns=feature_columns, label_dimension=label_dimension,
        model_dir=self._model_dir)
    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
    data = data.reshape(batch_size, label_dimension)

    # TRAIN
    # learn y = x
    train_input_fn = numpy_io.numpy_input_fn(
        x={'x': data}, y=data, batch_size=batch_size, num_epochs=None,
        shuffle=True)
    est.train(train_input_fn, steps=200)

    # EVALUTE
    eval_input_fn = numpy_io.numpy_input_fn(
        x={'x': data}, y=data, batch_size=batch_size, num_epochs=1,
        shuffle=False)
    scores = est.evaluate(eval_input_fn)
    self.assertEqual(200, scores[ops.GraphKeys.GLOBAL_STEP])
    self.assertIn(metric_keys.MetricKeys.LOSS, six.iterkeys(scores))

    # PREDICT
    predict_input_fn = numpy_io.numpy_input_fn(
        x={'x': data}, y=None, batch_size=batch_size, num_epochs=1,
        shuffle=False)
    predictions = list(
        [x['predictions'] for x in est.predict(predict_input_fn)])
    self.assertAllClose(data, predictions, atol=0.01)

    # EXPORT
    feature_spec = feature_column_lib.make_parse_example_spec(
        feature_columns)
    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                       serving_input_receiver_fn)
    self.assertTrue(gfile.Exists(export_dir))