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A return type for a serving_input_receiver_fn.
tf.estimator.export.TensorServingInputReceiver( features, receiver_tensors, receiver_tensors_alternatives=None )
This is for use with models that expect a single Tensor
or SparseTensor
as an input feature, as opposed to a dict of features.
The normal 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 Estimator.export_saved_model()
should return this TensorServingInputReceiver
instead. See: https://github.com/tensorflow/tensorflow/issues/11674
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).
Attributes | |
---|---|
features | A single Tensor or SparseTensor , representing the feature to be passed to the model. |
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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/estimator/export/TensorServingInputReceiver