A return type for a serving_input_receiver_fn.
tf.estimator.export.ServingInputReceiver(
features, receiver_tensors, receiver_tensors_alternatives=None
)
Used in the notebooks
Attributes |
|---|
features
|
A Tensor, SparseTensor, or dict of string or int 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 Tensors downstream of
the tf.parse_example() op. Defaults to None.
|
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TensorFlow 1 version
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