eval_spec: Configuration for the eval component of 'train_and_evaluate'
In rstudio/tfestimators: Interface to 'TensorFlow' Estimators

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.

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

`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 `Exporter`s, 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.