session_run_hook: Create Custom Session Run Hooks

In rstudio/tflearn: Interface to 'TensorFlow' Estimators

Description

Create a set of session run hooks, used to record information during training of an estimator. See Details for more information on the various hooks that can be defined.

Usage

session_run_hook(
  begin = function() { },
  after_create_session = function(session, coord) { },
  before_run = function(context) { },
  after_run = function(context, values) { },
  end = function(session) { }
)

Arguments

begin	function(): An R function, to be called once before using the session.
after_create_session	function(session, coord): An R function, to be called once the new TensorFlow session has been created.
before_run	function(run_context): An R function to be called before a run.
after_run	function(run_context, run_values): An R function to be called after a run.
end	function(session): An R function to be called at the end of the session. Typically, you'll want to define a before_run() hook that defines the set of tensors you're interested in for a particular run, and then you'll use the resulting values of those tensors in your after_run() hook. The tensors requested in your before_run() hook will be made available as part of the second argument in the after_run() hook (the values argument).

See Also

session_run_args()

Other session_run_hook wrappers: hook_checkpoint_saver(), hook_global_step_waiter(), hook_history_saver(), hook_logging_tensor(), hook_nan_tensor(), hook_progress_bar(), hook_step_counter(), hook_stop_at_step(), hook_summary_saver()

rstudio/tflearn documentation built on Nov. 25, 2021, 2:45 a.m.