Command-line-interface debugger hook.
__init__( ui_type='curses', dump_root=None, thread_name_filter=None )
Create a local debugger command-line interface (CLI) hook.
ui_type: (str) user-interface type.
str) optional path to the dump root directory. Must be a directory that does not exist or an empty directory. If the directory does not exist, it will be created by the debugger core during debug
run()calls and removed afterwards.
thread_name_filter: Regular-expression white list for threads on which the wrapper session will be active. See doc of
BaseDebugWrapperSessionfor more details.
add_tensor_filter( filter_name, tensor_filter )
Add a tensor filter.
See doc of
LocalCLIDebugWrapperSession.add_tensor_filter() for details. Override default behavior to accommodate the possibility of this method being called prior to the initialization of the underlying
filter_name: See doc of
tensor_filter: See doc of
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:
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() call.
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. After the
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 StopIteration then
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.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.