Get a function that returns a SupervisedInputReceiver matching an input_fn.
tf.contrib.estimator.build_supervised_input_receiver_fn_from_input_fn(
input_fn, **input_fn_args
)
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 a Tensor or a
dictionary of string feature name to Tensor and labels is a
Tensor or a dictionary of string label name to Tensor. Both
features and labels are consumed by model_fn. They should
satisfy the expectation of model_fn from inputs.
|
**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.
|
View source on GitHub