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.
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