tensorflow_estimator/python/estimator/canned/linear_testing_utils.py [585:717]:
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    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_saved_model(tempfile.mkdtemp(),
                                        serving_input_receiver_fn)
    self.assertTrue(tf.compat.v1.gfile.Exists(export_dir))

  def test_numpy_input_fn(self):
    """Tests complete flow with numpy_input_fn."""
    label_dimension = 2
    input_dimension = label_dimension
    batch_size = 10
    prediction_length = batch_size
    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
    data = data.reshape(batch_size, label_dimension)

    train_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        y=data,
        batch_size=batch_size,
        num_epochs=None,
        shuffle=True)
    eval_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        y=data,
        batch_size=batch_size,
        num_epochs=1,
        shuffle=False)
    predict_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        y=None,
        batch_size=batch_size,
        num_epochs=1,
        shuffle=False)

    self._test_complete_flow(
        train_input_fn=train_input_fn,
        eval_input_fn=eval_input_fn,
        predict_input_fn=predict_input_fn,
        input_dimension=input_dimension,
        label_dimension=label_dimension,
        prediction_length=prediction_length)

  def test_pandas_input_fn(self):
    """Tests complete flow with pandas_input_fn."""
    if not HAS_PANDAS:
      return

    # Pandas DataFrame natually supports 1 dim data only.
    label_dimension = 1
    input_dimension = label_dimension
    batch_size = 10
    data = np.array([1., 2., 3., 4.], dtype=np.float32)
    x = pd.DataFrame({'x': data})
    y = pd.Series(data)
    prediction_length = 4

    train_input_fn = pandas_io.pandas_input_fn(
        x=x, y=y, batch_size=batch_size, num_epochs=None, shuffle=True)
    eval_input_fn = pandas_io.pandas_input_fn(
        x=x, y=y, batch_size=batch_size, shuffle=False)
    predict_input_fn = pandas_io.pandas_input_fn(
        x=x, batch_size=batch_size, shuffle=False)

    self._test_complete_flow(
        train_input_fn=train_input_fn,
        eval_input_fn=eval_input_fn,
        predict_input_fn=predict_input_fn,
        input_dimension=input_dimension,
        label_dimension=label_dimension,
        prediction_length=prediction_length)

  def test_input_fn_from_parse_example(self):
    """Tests complete flow with input_fn constructed from parse_example."""
    label_dimension = 2
    input_dimension = label_dimension
    batch_size = 10
    prediction_length = batch_size
    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
    data = data.reshape(batch_size, label_dimension)

    serialized_examples = []
    for datum in data:
      example = example_pb2.Example(
          features=feature_pb2.Features(
              feature={
                  'x':
                      feature_pb2.Feature(
                          float_list=feature_pb2.FloatList(value=datum)),
                  'y':
                      feature_pb2.Feature(
                          float_list=feature_pb2.FloatList(
                              value=datum[:label_dimension])),
              }))
      serialized_examples.append(example.SerializeToString())

    feature_spec = {
        'x': tf.io.FixedLenFeature([input_dimension], tf.dtypes.float32),
        'y': tf.io.FixedLenFeature([label_dimension], tf.dtypes.float32),
    }

    def _train_input_fn():
      feature_map = tf.compat.v1.io.parse_example(serialized_examples,
                                                  feature_spec)
      features = queue_parsed_features(feature_map)
      labels = features.pop('y')
      return features, labels

    def _eval_input_fn():
      feature_map = tf.compat.v1.io.parse_example(
          tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
          feature_spec)
      features = queue_parsed_features(feature_map)
      labels = features.pop('y')
      return features, labels

    def _predict_input_fn():
      feature_map = tf.compat.v1.io.parse_example(
          tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
          feature_spec)
      features = queue_parsed_features(feature_map)
      features.pop('y')
      return features, None

    self._test_complete_flow(
        train_input_fn=_train_input_fn,
        eval_input_fn=_eval_input_fn,
        predict_input_fn=_predict_input_fn,
        input_dimension=input_dimension,
        label_dimension=label_dimension,
        prediction_length=prediction_length)


class BaseLinearRegressorTrainingTest(object):
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tensorflow_estimator/python/estimator/canned/v1/linear_testing_utils_v1.py [663:795]:
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    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
        feature_spec)
    export_dir = est.export_saved_model(tempfile.mkdtemp(),
                                        serving_input_receiver_fn)
    self.assertTrue(tf.compat.v1.gfile.Exists(export_dir))

  def test_numpy_input_fn(self):
    """Tests complete flow with numpy_input_fn."""
    label_dimension = 2
    input_dimension = label_dimension
    batch_size = 10
    prediction_length = batch_size
    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
    data = data.reshape(batch_size, label_dimension)

    train_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        y=data,
        batch_size=batch_size,
        num_epochs=None,
        shuffle=True)
    eval_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        y=data,
        batch_size=batch_size,
        num_epochs=1,
        shuffle=False)
    predict_input_fn = numpy_io.numpy_input_fn(
        x={'x': data},
        y=None,
        batch_size=batch_size,
        num_epochs=1,
        shuffle=False)

    self._test_complete_flow(
        train_input_fn=train_input_fn,
        eval_input_fn=eval_input_fn,
        predict_input_fn=predict_input_fn,
        input_dimension=input_dimension,
        label_dimension=label_dimension,
        prediction_length=prediction_length)

  def test_pandas_input_fn(self):
    """Tests complete flow with pandas_input_fn."""
    if not HAS_PANDAS:
      return

    # Pandas DataFrame natually supports 1 dim data only.
    label_dimension = 1
    input_dimension = label_dimension
    batch_size = 10
    data = np.array([1., 2., 3., 4.], dtype=np.float32)
    x = pd.DataFrame({'x': data})
    y = pd.Series(data)
    prediction_length = 4

    train_input_fn = pandas_io.pandas_input_fn(
        x=x, y=y, batch_size=batch_size, num_epochs=None, shuffle=True)
    eval_input_fn = pandas_io.pandas_input_fn(
        x=x, y=y, batch_size=batch_size, shuffle=False)
    predict_input_fn = pandas_io.pandas_input_fn(
        x=x, batch_size=batch_size, shuffle=False)

    self._test_complete_flow(
        train_input_fn=train_input_fn,
        eval_input_fn=eval_input_fn,
        predict_input_fn=predict_input_fn,
        input_dimension=input_dimension,
        label_dimension=label_dimension,
        prediction_length=prediction_length)

  def test_input_fn_from_parse_example(self):
    """Tests complete flow with input_fn constructed from parse_example."""
    label_dimension = 2
    input_dimension = label_dimension
    batch_size = 10
    prediction_length = batch_size
    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
    data = data.reshape(batch_size, label_dimension)

    serialized_examples = []
    for datum in data:
      example = example_pb2.Example(
          features=feature_pb2.Features(
              feature={
                  'x':
                      feature_pb2.Feature(
                          float_list=feature_pb2.FloatList(value=datum)),
                  'y':
                      feature_pb2.Feature(
                          float_list=feature_pb2.FloatList(
                              value=datum[:label_dimension])),
              }))
      serialized_examples.append(example.SerializeToString())

    feature_spec = {
        'x': tf.io.FixedLenFeature([input_dimension], tf.dtypes.float32),
        'y': tf.io.FixedLenFeature([label_dimension], tf.dtypes.float32),
    }

    def _train_input_fn():
      feature_map = tf.compat.v1.io.parse_example(serialized_examples,
                                                  feature_spec)
      features = queue_parsed_features(feature_map)
      labels = features.pop('y')
      return features, labels

    def _eval_input_fn():
      feature_map = tf.compat.v1.io.parse_example(
          tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
          feature_spec)
      features = queue_parsed_features(feature_map)
      labels = features.pop('y')
      return features, labels

    def _predict_input_fn():
      feature_map = tf.compat.v1.io.parse_example(
          tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
          feature_spec)
      features = queue_parsed_features(feature_map)
      features.pop('y')
      return features, None

    self._test_complete_flow(
        train_input_fn=_train_input_fn,
        eval_input_fn=_eval_input_fn,
        predict_input_fn=_predict_input_fn,
        input_dimension=input_dimension,
        label_dimension=label_dimension,
        prediction_length=prediction_length)


class BaseLinearRegressorTrainingTest(object):
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