TensorFlow 2 version | View source on GitHub |
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 Tensor
s downstream of the tf.parse_example() op.
Defaults to None.
Attributes | |
---|---|
features
|
|
receiver_tensors
|
|
receiver_tensors_alternatives
|