TensorFlow 2 version
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View source on GitHub
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A return type for a serving_input_receiver_fn.
tf.estimator.export.TensorServingInputReceiver(
features, receiver_tensors, receiver_tensors_alternatives=None
)
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).
The expected return values are:
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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features
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receiver_tensors
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receiver_tensors_alternatives
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