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.
session_run_hook(begin = function() { },
after_create_session = function(session, coord) { },
before_run = function(context) { }, after_run = function(context,
values) { }, end = function(session) { })function(): An R function, to be called once before using the session.
function(session, coord): An R function, to be called
once the new TensorFlow session has been created.
function(run_context): An R function to be called before a run.
function(run_context, run_values): An R function to be called
after a run.
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).
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