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Configuration for the "eval" part for the train_and_evaluate
call.
tf.estimator.EvalSpec(
input_fn, steps=100, name=None, hooks=None, exporters=None,
start_delay_secs=120, throttle_secs=600
)
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:
Dataset
object must be a
tuple (features, labels) with same constraints as below.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 Exporter
s, 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.
input_fn
steps
name
hooks
exporters
start_delay_secs
throttle_secs
ValueError
: If any of the input arguments is invalid.TypeError
: If any of the arguments is not of the expected type.