A return type for a serving_input_receiver_fn.
This is for use with models that expect a single
as an input feature, as opposed to a dict of features.
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
TensorServingInputReceiver instead. See:
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
SparseTensor, representing the feature
to be passed to the model.
Tensor, or a dict of string to
input nodes where this receiver expects to be fed by default. Typically,
this is a single placeholder expecting serialized
receiver_tensors_alternatives: a dict of string to additional
groups of receiver tensors, each of which may be a
Tensor or a dict of
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.
Alias for field number 0
Alias for field number 1
Alias for field number 2
@staticmethod __new__( cls, features, receiver_tensors, receiver_tensors_alternatives=None )