Evaluate an estimator on input data provided by an input_fn().
# S3 method for tf_estimator
evaluate(
object,
input_fn,
steps = NULL,
checkpoint_path = NULL,
name = NULL,
hooks = NULL,
simplify = TRUE,
...
)A TensorFlow estimator.
An input function, typically generated by the input_fn()
helper function.
The number of steps for which the model should be evaluated on
this particular evaluate() invocation. If NULL (the default), this function
will either evaluate forever, or until the supplied input_fn() has provided
all available data.
The path to a specific model checkpoint to be used for
prediction. If NULL (the default), the latest checkpoint in model_dir
is used.
Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.
A list of R functions, to be used as callbacks inside the
training loop. By default, hook_history_saver(every_n_step = 10) and
hook_progress_bar() will be attached if not provided to save the metrics
history and create the progress bar.
Whether to simplify evaluation results into a tibble, as
opposed to a list. Defaults to TRUE.
Optional arguments passed on to the estimator's evaluate()
method.
An R list of evaluation metrics.
For each step, this method will call input_fn() to produce a single batch
of data. Evaluation continues until:
steps batches are processed, or
The input_fn() is exhausted of data.
Other custom estimator methods:
estimator_spec(),
estimator(),
export_savedmodel.tf_estimator(),
predict.tf_estimator(),
train.tf_estimator()