tf.estimator.EstimatorSpec

TensorFlow 1 version View source on GitHub

Ops and objects returned from a model_fn and passed to an Estimator.

@staticmethod
tf.estimator.EstimatorSpec(
    cls, 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
)

Used in the notebooks

Used in the guide Used in the tutorials

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.

Attributes:

  • mode
  • predictions
  • loss
  • train_op
  • eval_metric_ops
  • export_outputs
  • training_chief_hooks
  • training_hooks
  • scaffold
  • evaluation_hooks
  • prediction_hooks

Raises:

  • ValueError: If validation fails.
  • TypeError: If any of the arguments is not the expected type.