Generate Predictions with an Estimator

Generate predicted labels / values for input data provided by input_fn().

# S3 method for tf_estimator
predict(object, input_fn, checkpoint_path = NULL,
  predict_keys = c("predictions", "classes", "class_ids", "logistic",
  "logits", "probabilities"), hooks = NULL, as_iterable = FALSE,
  simplify = TRUE, ...)

Arguments

object	A TensorFlow estimator.
input_fn	An input function, typically generated by the `input_fn()` helper function.
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.
predict_keys	The types of predictions that should be produced, as an R list. When this argument is not specified (the default), all possible predicted values will be returned.
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.
as_iterable	Boolean; should a raw Python generator be returned? When `FALSE` (the default), the predicted values will be consumed from the generator and returned as an R object.
simplify	Whether to simplify prediction results into a `tibble`, as opposed to a list. Defaults to `TRUE`.
...	Optional arguments passed on to the estimator's `predict()` method.

Yields

Evaluated values of predictions tensors.

Raises

ValueError: Could not find a trained model in model_dir. ValueError: if batch length of predictions are not same. ValueError: If there is a conflict between predict_keys and predictions. For example if predict_keys is not NULL but EstimatorSpec.predictions is not a dict.

Generate Predictions with an Estimator

Arguments

Yields

Raises

See also