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TensorFlow 1 version View source on GitHub

Configuration for the "eval" part for the train_and_evaluate call.

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.

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:

  • A '' object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
  • A tuple (features, labels): Where features is a Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor.
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.SessionRunHook objects to run during evaluation.
exporters Iterable of Exporters, or a single one, or None. exporters will 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.