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Checkpoints input pipeline state every N steps or seconds.
tf.contrib.data.CheckpointInputPipelineHook( estimator )
This hook saves the state of the iterators in the
Graph so that when
training is resumed the input pipeline continues from where it left off.
This could potentially avoid overfitting in certain pipelines where the
number of training steps per eval are small compared to the dataset
size or if the training pipeline is pre-empted.
- Saves only the input pipelines in the "iterators" collection and not the global variables or other saveable objects.
- Does not write the
MetaGraphDefto the summary.
Example of checkpointing the training pipeline:
est = tf.estimator.Estimator(model_fn) while True: est.train( train_input_fn, hooks=[tf.data.experimental.CheckpointInputPipelineHook(est)], steps=train_steps_per_eval) # Note: We do not pass the hook here. metrics = est.evaluate(eval_input_fn) if should_stop_the_training(metrics): break
This hook should be used if the input pipeline state needs to be saved separate from the model checkpoint. Doing so may be useful for a few reasons:
- The input pipeline checkpoint may be large, if there are large shuffle or prefetch buffers for instance, and may bloat the checkpoint size.
- If the input pipeline is shared between training and validation, restoring the checkpoint during validation may override the validation input pipeline.
For saving the input pipeline checkpoint alongside the model weights use
tf.data.experimental.make_saveable_from_iterator directly to create a
SaveableObject and add to the
SAVEABLE_OBJECTS collection. Note, however,
that you will need to be careful not to restore the training iterator during
eval. You can do that by not adding the iterator to the SAVEABLE_OBJECTS
collector when building the eval graph.
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.
||A TensorFlow Session that has been created.|
||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.
||A SessionRunValues object.|
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()
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.
end( session )
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.
||A TensorFlow Session that will be soon closed.|