View source on GitHub
|
View source on GitHub
|
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.
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:
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.modepredictionslosstrain_opeval_metric_opsexport_outputstraining_chief_hookstraining_hooksscaffoldevaluation_hooksprediction_hooksValueError: If validation fails.TypeError: If any of the arguments is not the expected type.