tf.contrib.estimator.build_supervised_input_receiver_fn_from_input_fn
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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.
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
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