tfestimators: train.tf_estimator – R documentation

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train.tf_estimator

Train an Estimator

Train an estimator on a set of input data provides by the input_fn().

## S3 method for class 'tf_estimator'
train(object, input_fn, steps = NULL,
  hooks = NULL, max_steps = NULL, saving_listeners = NULL, ...)

`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 trained on this particular `train()` invocation. If `NULL` (the default), this function will either train forever, or until the supplied `input_fn()` has provided all available data.
`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.
`max_steps`	The total number of steps for which the model should be trained. If set, `steps` must be `NULL`. If the estimator has already been trained a total of `max_steps` times, then no training will be performed.
`saving_listeners`	(Available since TensorFlow v1.4) A list of `CheckpointSaverListener` objects used for callbacks that run immediately before or after checkpoint savings.
`...`	Optional arguments, passed on to the estimator's `train()` method.

A data.frame of the training loss history.