tfestimators: evaluate.tf_estimator – R documentation

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tfestimators

evaluate.tf_estimator

Evaluate an Estimator

Description

Evaluate an estimator on input data provided by an input_fn().

Usage

## S3 method for class 'tf_estimator'
evaluate(object, input_fn, steps = NULL,
  checkpoint_path = NULL, name = NULL, hooks = NULL,
  simplify = TRUE, ...)

Arguments

`object`	A TensorFlow estimator.
`input_fn`	An input function, typically generated by the `input_fn()` helper function.
`steps`	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.
`checkpoint_path`	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`	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.
`hooks`	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.
`simplify`	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.

Details

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.

Value

An R list of evaluation metrics.