View source on GitHub
|
Prints the given tensors every N local steps, every N seconds, or at end.
Inherits From: SessionRunHook
tf.estimator.LoggingTensorHook(
tensors, every_n_iter=None, every_n_secs=None, at_end=False, formatter=None
)
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:
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
Note that if at_end is True, tensors should not include any tensor
whose evaluation produces a side effect such as consuming additional inputs.
Args | |
|---|---|
tensors
|
dict that maps string-valued tags to tensors/tensor names, or
iterable of tensors/tensor names.
|
every_n_iter
|
int, print the values of tensors once every N local
steps taken on the current worker.
|
every_n_secs
|
int or float, print the values of tensors once every N
seconds. Exactly one of every_n_iter and every_n_secs should be
provided.
|
at_end
|
bool specifying whether to print the values of tensors at the
end of the run.
|
formatter
|
function, takes dict of tag->Tensor and returns a string.
If None uses default printing all tensors.
|
Raises | |
|---|---|
ValueError
|
if every_n_iter is non-positive.
|
Methods
after_create_session
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.
| Args | |
|---|---|
session
|
A TensorFlow Session that has been created. |
coord
|
A Coordinator object which keeps track of all threads. |
after_run
after_run(
run_context, run_values
)
Called after each call to run().
The run_values argument contains results of requested ops/tensors by
before_run().
The run_context argument is the same one send to before_run call.
run_context.request_stop() can be called to stop the iteration.
If session.run() raises any exceptions then after_run() is not called.
| Args | |
|---|---|
run_context
|
A SessionRunContext object.
|
run_values
|
A SessionRunValues object. |
before_run
before_run(
run_context
)
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.
The 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.
| Args | |
|---|---|
run_context
|
A SessionRunContext object.
|
| Returns | |
|---|---|
None or a SessionRunArgs object.
|
begin
begin()
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.
end
end(
session
)
Called at the end of session.
The session argument can be used in case the hook wants to run final ops,
such as saving a last checkpoint.
If session.run() raises exception other than OutOfRangeError or
StopIteration then end() is not called.
Note the difference between end() and after_run() behavior when
session.run() raises OutOfRangeError or StopIteration. In that case
end() is called but after_run() is not called.
| Args | |
|---|---|
session
|
A TensorFlow Session that will be soon closed. |