Configuration for the "eval" part for the
EvalSpec combines details of evaluation of the trained model as well as its export. Evaluation consists of computing metrics to judge the performance of the trained model. Export writes out the trained model on to external storage.
Alias for field number 4
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@staticmethod __new__( cls, input_fn, steps=100, name=None, hooks=None, exporters=None, start_delay_secs=120, throttle_secs=600 )
Creates a validated
input_fn: A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following:
Datasetobject must be a tuple (features, labels) with same constraints as below.
Tensoror a dictionary of string feature name to
Tensorand labels is a
Tensoror a dictionary of string label name to
steps: Int. Positive number of steps for which to evaluate model. If
None, evaluates until
input_fn raises an end-of-input exception. See
Estimator.evaluate for details.
name: String. Name of the evaluation if user needs to run multiple evaluations on different data sets. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.
hooks: Iterable of
tf.train.SessionRunHookobjects to run during evaluation.
exporters: Iterable of
Exporters, or a single one, or
exporterswill be invoked after each evaluation.
start_delay_secs: Int. Start evaluating after waiting for this many seconds.
throttle_secs: Int. Do not re-evaluate unless the last evaluation was started at least this many seconds ago. Of course, evaluation does not occur if no new checkpoints are available, hence, this is the minimum.
ValueError: If any of the input arguments is invalid.
TypeError: If any of the arguments is not of the expected type.
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Code samples licensed under the Apache 2.0 License.