tf.contrib.learn.ModelFnOps

Class ModelFnOps

Defined in tensorflow/contrib/learn/python/learn/estimators/model_fn.py.

Ops returned from a model_fn.

THIS CLASS IS DEPRECATED. See contrib/learn/README.md for general migration instructions.

__new__

@staticmethod
__new__(
    cls,
    mode,
    predictions=None,
    loss=None,
    train_op=None,
    eval_metric_ops=None,
    output_alternatives=None,
    training_chief_hooks=None,
    training_hooks=None,
    scaffold=None
)

Creates a validated ModelFnOps instance. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the estimator_spec method to create an equivalent one.

For a multi-headed model, the predictions dict here will contain the outputs of all of the heads. However: at serving time, requests will be made specifically for one or more heads, and the RPCs used for these requests may differ by problem type (i.e., regression, classification, other). The purpose of the output_alternatives dict is to aid in exporting a SavedModel from which such head-specific queries can be served. These output_alternatives will be combined with input_alternatives (see saved_model_export_utils) to produce a set of SignatureDefs specifying the valid requests that can be served from this model.

For a single-headed model, it is still adviseable to provide output_alternatives with a single entry, because this is how the problem type is communicated for export and serving. If output_alternatives is not given, the resulting SavedModel will support only one head of unspecified type.

Args:

  • mode: One of ModeKeys. Specifies if this training, evaluation or prediction.
  • predictions: Predictions Tensor or dict of Tensor.
  • loss: Training loss Tensor.
  • train_op: Op for the training step.
  • eval_metric_ops: Dict of metric results keyed by name. The values of the dict are the results of calling a metric function, such as Tensor.
  • output_alternatives: a dict of {submodel_name: (problem_type, {tensor_name: Tensor})}, where submodel_name is a submodel identifier that should be consistent across the pipeline (here likely taken from the name of each Head, for models that use them), problem_type is a ProblemType, tensor_name is a symbolic name for an output Tensor possibly but not necessarily taken from PredictionKey, and Tensor is the corresponding output Tensor itself.
  • training_chief_hooks: A list of SessionRunHook objects that will be run on the chief worker during training.
  • training_hooks: A list of SessionRunHook objects that will be run on all workers during training.
  • scaffold: A tf.train.Scaffold object that can be used to set initialization, saver, and more to be used in training.

Returns:

A validated ModelFnOps object.

Raises:

  • ValueError: If validation fails.

Properties

predictions

loss

train_op

eval_metric_ops

output_alternatives

training_chief_hooks

training_hooks

scaffold

mode

Methods

estimator_spec

estimator_spec(default_serving_output_alternative_key=None)

Creates an equivalent EstimatorSpec.

Args:

  • default_serving_output_alternative_key: Required for multiple heads. If you have multiple entries in output_alternatives dict (comparable to multiple heads), EstimatorSpec requires a default head that will be used if a Servo request does not explicitly mention which head to infer on. Pass the key of the output alternative here that you want to designate as default. A separate ExportOutpout for this default head wil be added to the export_outputs dict with the special key signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, unless there is already an enry in output_alternatives with this special key.

Returns:

Instance of EstimatorSpec that is equivalent to this ModelFnOps

Raises:

  • ValueError: If problem type is unknown.