tf.estimator.EstimatorSpec

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

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tf.compat.v1.estimator.EstimatorSpec

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`	A `namedtuple` alias for field number 0
`predictions`	A `namedtuple` alias for field number 1
`loss`	A `namedtuple` alias for field number 2
`train_op`	A `namedtuple` alias for field number 3
`eval_metric_ops`	A `namedtuple` alias for field number 4
`export_outputs`	A `namedtuple` alias for field number 5
`training_chief_hooks`	A `namedtuple` alias for field number 6
`training_hooks`	A `namedtuple` alias for field number 7
`scaffold`	A `namedtuple` alias for field number 8
`evaluation_hooks`	A `namedtuple` alias for field number 9
`prediction_hooks`	A `namedtuple` alias for field number 10

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/estimator/EstimatorSpec

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tf.estimator.EstimatorSpec

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