tf.estimator.export.ServingInputReceiver

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A return type for a serving_input_receiver_fn.

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Compat aliases for migration

See Migration guide for more details.

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

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.

Attributes
features
receiver_tensors
receiver_tensors_alternatives