tf.estimator.EstimatorSpec

Class EstimatorSpec

Defined in tensorflow/python/estimator/model_fn.py.

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

EstimatorSpec fully defines the model to be run by an Estimator.

Properties

eval_metric_ops

Alias for field number 4

evaluation_hooks

Alias for field number 9

export_outputs

Alias for field number 5

loss

Alias for field number 2

mode

Alias for field number 0

prediction_hooks

Alias for field number 10

predictions

Alias for field number 1

scaffold

Alias for field number 8

train_op

Alias for field number 3

training_chief_hooks

Alias for field number 6

training_hooks

Alias for field number 7

Methods

__new__

@staticmethod
__new__(
    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
)

Creates a validated EstimatorSpec instance.

Depending on the value of mode, different arguments are required. Namely

• For mode == ModeKeys.TRAIN: required fields are loss and train_op.
• For mode == ModeKeys.EVAL: required field is loss.
• For mode == ModeKeys.PREDICT: required fields are predictions.

model_fn can populate all arguments independent of mode. In this case, some arguments will be ignored by an Estimator. E.g. train_op will be ignored in eval and infer modes. Example:

def my_model_fn(mode, features, labels):
  predictions = ...
  loss = ...
  train_op = ...
  return tf.estimator.EstimatorSpec(
      mode=mode,
      predictions=predictions,
      loss=loss,
      train_op=train_op)

Alternatively, model_fn can just populate the arguments appropriate to the given mode. Example:

def my_model_fn(mode, features, labels):
  if (mode == tf.estimator.ModeKeys.TRAIN or
      mode == tf.estimator.ModeKeys.EVAL):
    loss = ...
  else:
    loss = None
  if mode == tf.estimator.ModeKeys.TRAIN:
    train_op = ...
  else:
    train_op = None
  if mode == tf.estimator.ModeKeys.PREDICT:
    predictions = ...
  else:
    predictions = None

  return tf.estimator.EstimatorSpec(
      mode=mode,
      predictions=predictions,
      loss=loss,
      train_op=train_op)

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 are the 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 signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY.
• 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.

Returns:

A validated EstimatorSpec object.

Raises:

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