def test_serving_input_receiver_receiver_tensors_invalid(self): features = { "feature0": constant_op.constant([0]), u"feature1": constant_op.constant([1]), "feature2": sparse_tensor.SparseTensor( indices=[[0, 0]], values=[1], dense_shape=[1, 1]), } with self.assertRaisesRegexp( ValueError, "receiver_tensors must be defined"): export.ServingInputReceiver( features=features, receiver_tensors=None) with self.assertRaisesRegexp( ValueError, "receiver_tensors keys must be strings"): export.ServingInputReceiver( features=features, receiver_tensors={ 1: array_ops.placeholder(dtypes.string, name="example0")}) with self.assertRaisesRegexp( ValueError, "receiver_tensor example1 must be a Tensor"): export.ServingInputReceiver( features=features, receiver_tensors={"example1": [1]})
def test_multi_feature_single_receiver(self): features = { "foo": constant_op.constant(5), "bar": constant_op.constant(6) } receiver_tensor = array_ops.placeholder(dtypes.string) _ = export.ServingInputReceiver(features, receiver_tensor)
def serving_input_receiver_with_asset_fn(): features, receiver_tensor = serving_input_receiver_fn() filename = ops.convert_to_tensor(vocab_file_name, dtypes.string, name='asset_filepath') ops.add_to_collection(ops.GraphKeys.ASSET_FILEPATHS, filename) features['bogus_filename'] = filename return export.ServingInputReceiver(features, receiver_tensor)
def test_multi_feature_multi_receiver(self): features = { "foo": constant_op.constant(5), "bar": constant_op.constant(6) } receiver_tensors = { "baz": array_ops.placeholder(dtypes.int64), "qux": array_ops.placeholder(dtypes.float32) } _ = export.ServingInputReceiver(features, receiver_tensors)
def test_single_feature_single_receiver(self): feature = constant_op.constant(5) receiver_tensor = array_ops.placeholder(dtypes.string) input_receiver = export.ServingInputReceiver(feature, receiver_tensor) # single feature is automatically named feature_key, = input_receiver.features.keys() self.assertEqual("feature", feature_key) # single receiver is automatically named receiver_key, = input_receiver.receiver_tensors.keys() self.assertEqual("input", receiver_key)
def test_serving_input_receiver_features_invalid(self): receiver_tensors = { "example0": array_ops.placeholder(dtypes.string, name="example0"), u"example1": array_ops.placeholder(dtypes.string, name="example1"), } with self.assertRaisesRegexp(ValueError, "features must be defined"): export.ServingInputReceiver( features=None, receiver_tensors=receiver_tensors) with self.assertRaisesRegexp(ValueError, "feature keys must be strings"): export.ServingInputReceiver( features={1: constant_op.constant([1])}, receiver_tensors=receiver_tensors) with self.assertRaisesRegexp( ValueError, "feature feature1 must be a Tensor or SparseTensor"): export.ServingInputReceiver( features={"feature1": [1]}, receiver_tensors=receiver_tensors)
def test_serving_input_receiver_constructor(self): """Tests that no errors are raised when input is expected.""" features = { "feature0": constant_op.constant([0]), u"feature1": constant_op.constant([1]), "feature2": sparse_tensor.SparseTensor( indices=[[0, 0]], values=[1], dense_shape=[1, 1]), } receiver_tensors = { "example0": array_ops.placeholder(dtypes.string, name="example0"), u"example1": array_ops.placeholder(dtypes.string, name="example1"), } export.ServingInputReceiver(features, receiver_tensors)
def serving_input_receiver_fn(): """An input function on serialized SequenceExample protos.""" serialized_sequence_example = array_ops.placeholder( dtype=dtypes.string, shape=[default_batch_size], name="input_sequence_example_tensor") receiver_tensors = {"sequence_example": serialized_sequence_example} features = parse_from_sequence_example( serialized_sequence_example, input_size, context_feature_spec=context_feature_spec, example_feature_spec=example_feature_spec) return export_lib.ServingInputReceiver(features, receiver_tensors)
def test_receiver_wrong_type(self): feature = constant_op.constant(5) receiver_tensor = "not a tensor" with self.assertRaises(ValueError): _ = export.ServingInputReceiver(feature, receiver_tensor)
def test_feature_wrong_type(self): feature = "not a tensor" receiver_tensor = array_ops.placeholder(dtypes.string) with self.assertRaises(ValueError): _ = export.ServingInputReceiver(feature, receiver_tensor)
def test_feature_labeled_tensor(self): feature = LabeledTensorMock() receiver_tensor = array_ops.placeholder(dtypes.string) _ = export.ServingInputReceiver(feature, receiver_tensor)
def serving_input_receiver_fn(): return export.ServingInputReceiver( {'test-features': constant_op.constant([[1], [1]])}, array_ops.placeholder(dtype=dtypes.string))
def serving_input_receiver_fn(): return export.ServingInputReceiver( given_features, array_ops.placeholder(dtype=dtypes.string))