tf.estimator.export.ServingInputReceiver
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A return type for a serving_input_receiver_fn.
tf.estimator.export.ServingInputReceiver(
features, receiver_tensors, receiver_tensors_alternatives=None
)
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 Tensor s downstream of
the tf.parse_example() op. Defaults to None.
|
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Last updated 2021-02-18 UTC.
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