tf.estimator.export.TensorServingInputReceiver

View source on GitHub

A return type for a serving_input_receiver_fn.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.estimator.export.TensorServingInputReceiver, `tf.compat.v2.estimator.export.TensorServingInputReceiver`

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).

The expected return values are: 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.

Attributes
features
receiver_tensors
receiver_tensors_alternatives