See the guide: Training > Training Hooks
Prints the given tensors every N local steps, every N seconds, or at end.
The tensors will be printed to the log, with
INFO severity. If you are not
seeing the logs, you might want to add the following line after your imports:
Note that if
at_end is True,
tensors should not include any tensor
whose evaluation produces a side effect such as consuming additional inputs.
__init__( tensors, every_n_iter=None, every_n_secs=None, at_end=False, formatter=None )
dictthat maps string-valued tags to tensors/tensor names, or
iterableof tensors/tensor names.
int, print the values of
tensorsonce every N local steps taken on the current worker.
float, print the values of
tensorsonce every N seconds. Exactly one of
every_n_secsshould be provided.
boolspecifying whether to print the values of
tensorsat the end of the run.
formatter: function, takes dict of
Tensorand returns a string. If
Noneuses default printing all tensors.
after_create_session( session, coord )
Called when new TensorFlow session is created.
This is called to signal the hooks that a new session has been created. This
has two essential differences with the situation in which
begin is called:
- When this is called, the graph is finalized and ops can no longer be added to the graph.
- This method will also be called as a result of recovering a wrapped session, not only at the beginning of the overall session.
session: A TensorFlow Session that has been created.
coord: A Coordinator object which keeps track of all threads.
after_run( run_context, run_values )
Called after each call to run().
run_values argument contains results of requested ops/tensors by
run_context argument is the same one send to
run_context.request_stop() can be called to stop the iteration.
session.run() raises any exceptions then
after_run() is not called.
run_values: A SessionRunValues object.
Called before each call to run().
You can return from this call a
SessionRunArgs object indicating ops or
tensors to add to the upcoming
run() call. These ops/tensors will be run
together with the ops/tensors originally passed to the original run() call.
The run args you return can also contain feeds to be added to the run()
run_context argument is a
SessionRunContext that provides
information about the upcoming
run() call: the originally requested
op/tensors, the TensorFlow Session.
At this point graph is finalized and you can not add ops.
None or a
Called once before using the session.
When called, the default graph is the one that will be launched in the
session. The hook can modify the graph by adding new operations to it.
begin() call the graph will be finalized and the other callbacks
can not modify the graph anymore. Second call of
begin() on the same
graph, should not change the graph.
Called at the end of session.
session argument can be used in case the hook wants to run final ops,
such as saving a last checkpoint.
session.run() raises exception other than OutOfRangeError or
end() is not called.
Note the difference between
after_run() behavior when
session.run() raises OutOfRangeError or StopIteration. In that case
end() is called but
after_run() is not called.
session: A TensorFlow Session that will be soon closed.