contrib.learn.ModelFnOps
tf.contrib.learn.ModelFnOps
class tf.contrib.learn.ModelFnOps
Defined in tensorflow/contrib/learn/python/learn/estimators/model_fn.py
.
See the guide: Learn (contrib) > Estimators
Ops returned from a model_fn.
Properties
eval_metric_ops
Alias for field number 3
loss
Alias for field number 1
mode
Alias for field number 8
output_alternatives
Alias for field number 4
predictions
Alias for field number 0
scaffold
Alias for field number 7
train_op
Alias for field number 2
training_chief_hooks
Alias for field number 5
training_hooks
Alias for field number 6
Methods
__new__
__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.
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 SignatureDef
s 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 ofModeKeys
. Specifies if this training, evaluation or prediction. -
predictions
: PredictionsTensor
or dict ofTensor
. -
loss
: Training lossTensor
. -
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 asTensor
. -
output_alternatives
: a dict of{submodel_name: (problem_type, {tensor_name: Tensor})}
, wheresubmodel_name
is a submodel identifier that should be consistent across the pipeline (here likely taken from the name of eachHead
, for models that use them),problem_type
is aProblemType
,tensor_name
is a symbolic name for an output Tensor possibly but not necessarily taken fromPredictionKey
, andTensor
is the corresponding output Tensor itself. -
training_chief_hooks
: A list ofSessionRunHook
objects that will be run on the chief worker during training. -
training_hooks
: A list ofSessionRunHook
objects that will be run on all workers during training. -
scaffold
: Atf.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.
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 inoutput_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.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/learn/ModelFnOps