tf.contrib.estimator.build_supervised_input_receiver_fn_from_input_fn(
input_fn,
**input_fn_args
)
Get a function that returns a SupervisedInputReceiver matching an input_fn.
Note that this function calls the input_fn in a local graph in order to extract features and labels. Placeholders are then created from those features and labels in the default graph.
Args:
input_fn
: An Estimator input_fn, which is a function that returns one of:- A 'tf.data.Dataset' object: Outputs of
Dataset
object must be a tuple (features, labels) with same constraints as below. - A tuple (features, labels): Where
features
is aTensor
or a dictionary of string feature name toTensor
andlabels
is aTensor
or a dictionary of string label name toTensor
. Bothfeatures
andlabels
are consumed bymodel_fn
. They should satisfy the expectation ofmodel_fn
from inputs.
- A 'tf.data.Dataset' object: Outputs of
**input_fn_args
: set of kwargs to be passed to the input_fn. Note that these will not be checked or validated here, and any errors raised by the input_fn will be thrown to the top.
Returns:
A function taking no arguments that, when called, returns a SupervisedInputReceiver. This function can be passed in as part of the input_receiver_map when exporting SavedModels from Estimator with multiple modes.