tf.estimator.export.TensorServingInputReceiver

Class TensorServingInputReceiver

Defined in tensorflow/python/estimator/export/export.py.

A return type for a serving_input_receiver_fn.

This is for use with models that expect a single Tensor or SparseTensor as an input feature, as opposed to a dict of features.

The normal ServingInputReceiver always returns a feature dict, even if it contains only one entry, and so can be used only with models that accept such a dict. For models that accept only a single raw feature, the serving_input_receiver_fn provided to Estimator.export_savedmodel() should return this TensorServingInputReceiver instead. See: https://github.com/tensorflow/tensorflow/issues/11674

Note that the receiver_tensors and receiver_tensor_alternatives arguments will be automatically converted to the dict representation in either case, because the SavedModel format requires each input Tensor to have a name (provided by the dict key).

The expected return values are: features: A single Tensor or SparseTensor, representing the feature to be passed to the model. receiver_tensors: a Tensor, or a dict of string to Tensor, specifying input nodes where this receiver expects to be fed by default. Typically, this is a single placeholder expecting serialized tf.Example protos. receiver_tensors_alternatives: a dict of string to additional groups of receiver tensors, each of which may be a Tensor or a dict of string to Tensor. These named receiver tensor alternatives generate additional serving signatures, which may be used to feed inputs at different points within the input receiver subgraph. A typical usage is to allow feeding raw feature Tensors downstream of the tf.parse_example() op. Defaults to None.

Properties

features

Alias for field number 0

receiver_tensors

Alias for field number 1

receiver_tensors_alternatives

Alias for field number 2

Methods

__new__

@staticmethod
__new__(
    cls,
    features,
    receiver_tensors,
    receiver_tensors_alternatives=None
)

Create new instance of TensorServingInputReceiver(features, receiver_tensors, receiver_tensors_alternatives)