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_saved_model()
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).
Attributes
features
A single Tensor or SparseTensor, representing the feature to
be passed to the model.
receiver_tensors
A Tensor, SparseTensor, or dict of string to Tensor
or SparseTensor, 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, SparseTensor, or dict
of string to Tensor orSparseTensor. 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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]