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tf.estimator.export.TensorServingInputReceiver

A return type for a serving_input_receiver_fn.

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 Tensors downstream of the tf.parse_example() op. Defaults to None.

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/estimator/export/TensorServingInputReceiver