Ops and objects returned from a model_fn
and passed to an Estimator
.
tf.estimator.EstimatorSpec(
mode, predictions=None, loss=None, train_op=None, eval_metric_ops=None,
export_outputs=None, training_chief_hooks=None, training_hooks=None,
scaffold=None, evaluation_hooks=None, prediction_hooks=None
)
EstimatorSpec
fully defines the model to be run by an Estimator
.
Args |
mode
|
A ModeKeys . Specifies if this is training, evaluation or
prediction.
|
predictions
|
Predictions Tensor or dict of Tensor .
|
loss
|
Training loss Tensor . Must be either scalar, or with shape [1] .
|
train_op
|
Op for the training step.
|
eval_metric_ops
|
Dict of metric results keyed by name.
The values of the dict can be one of the following:
(1) instance of Metric class.
(2) Results of calling a metric function, namely a
(metric_tensor, update_op) tuple. metric_tensor should be
evaluated without any impact on state (typically is a pure computation
results based on variables.). For example, it should not trigger the
update_op or requires any input fetching.
|
export_outputs
|
Describes the output signatures to be exported to
SavedModel and used during serving.
A dict {name: output} where:
- name: An arbitrary name for this output.
- output: an
ExportOutput object such as ClassificationOutput ,
RegressionOutput , or PredictOutput .
Single-headed models only need to specify one entry in this dictionary.
Multi-headed models should specify one entry for each head, one of
which must be named using
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY .
If no entry is provided, a default PredictOutput mapping to
predictions will be created.
|
training_chief_hooks
|
Iterable of tf.train.SessionRunHook objects to
run on the chief worker during training.
|
training_hooks
|
Iterable of tf.train.SessionRunHook objects to 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.
|
evaluation_hooks
|
Iterable of tf.train.SessionRunHook objects to
run during evaluation.
|
prediction_hooks
|
Iterable of tf.train.SessionRunHook objects to
run during predictions.
|
Raises |
ValueError
|
If validation fails.
|
TypeError
|
If any of the arguments is not the expected type.
|
Attributes |
mode
|
|
predictions
|
|
loss
|
|
train_op
|
|
eval_metric_ops
|
|
export_outputs
|
|
training_chief_hooks
|
|
training_hooks
|
|
scaffold
|
|
evaluation_hooks
|
|
prediction_hooks
|
|