tfestimators: predict.tf_estimator – R documentation

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tfestimators

predict.tf_estimator

Generate Predictions with an Estimator

Description

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

Usage

## S3 method for class '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, yield_single_examples = 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`.
`yield_single_examples`	(Available since TensorFlow v1.7) If `FALSE`, yields the whole batch as returned by the `model_fn` instead of decomposing the batch into individual elements. This is useful if `model_fn` returns some tensors with first dimension not equal to the batch size.
`...`	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.