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.

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.

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.