tf.estimator.export.ServingInputReceiver

A return type for a serving_input_receiver_fn.

Compat aliases for migration

See Migration guide for more details.

tf.estimator.export.ServingInputReceiver(
    features, receiver_tensors, receiver_tensors_alternatives=None
)

Attributes
`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` or`SparseTensor`. 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.

Attributes