eval_spec: Configuration for the eval component of 'train_and_evaluate'

In tfestimators: Interface to 'TensorFlow' Estimators

Description

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.

Usage

eval_spec(
  input_fn,
  steps = 100,
  name = NULL,
  hooks = NULL,
  exporters = NULL,
  start_delay_secs = 120,
  throttle_secs = 600
)

Arguments

input_fn	Evaluation input function returning a tuple of: features - Tensor or dictionary of string feature name to Tensor. labels - Tensor or dictionary of Tensor with labels.
steps	Positive number of steps for which to evaluate model. If NULL, evaluates until input_fn raises an end-of-input exception.
name	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	List of session run hooks to run during evaluation.
exporters	List of Exporters, or a single one, or NULL. exporters will be invoked after each evaluation.
start_delay_secs	Start evaluating after waiting for this many seconds.
throttle_secs	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.

See Also

Other training methods: train_and_evaluate.tf_estimator(), train_spec()

tfestimators documentation built on Aug. 10, 2021, 1:06 a.m.