Class ServingInputReceiver
A return type for a serving_input_receiver_fn.
The expected return values are:
features: A Tensor
, SparseTensor
, or dict of string to Tensor
or
SparseTensor
, specifying the features to be passed to the model. Note:
if features
passed is not a dict, it will be wrapped in a dict with a
single entry, using 'feature' as the key. Consequently, the model must
accept a feature dict of the form {'feature': tensor}. You may use
TensorServingInputReceiver
if you want the tensor to be passed as is.
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.
__new__
@staticmethod
__new__(
cls,
features,
receiver_tensors,
receiver_tensors_alternatives=None
)
Create new instance of ServingInputReceiver(features, receiver_tensors, receiver_tensors_alternatives)