TensorFlow 2 version
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View source on GitHub
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A return type for a serving_input_receiver_fn.
tf.estimator.export.ServingInputReceiver(
features, receiver_tensors, receiver_tensors_alternatives=None
)
The expected return values are:
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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features
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receiver_tensors
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receiver_tensors_alternatives
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