def __init__(
self,
out_dir,
export_tensorboard=False,
tensorboard_dir=None,
dry_run=False,
reduction_config=None,
save_config=None,
include_regex=None,
include_collections=None,
save_all=False,
include_workers="one",
):
TensorflowBaseHook.__init__(
self,
out_dir=out_dir,
export_tensorboard=export_tensorboard,
tensorboard_dir=tensorboard_dir,
init_step=-1,
dry_run=dry_run,
reduction_config=reduction_config,
save_config=save_config,
include_regex=include_regex,
include_collections=include_collections,
save_all=save_all,
include_workers=include_workers,
)
tf.keras.callbacks.Callback.__init__(self)
self.tensor_refs_to_save_this_step = set()
self._fetches_added = set()
self.callable_cache = CallableCache()
self.custom_tensors_to_save = (
dict()) # stores tensors custom tensors saved by users every step
self.saved_layers = dict()
self.has_registered_model = False
def __init__(
self,
out_dir,
export_tensorboard=False,
tensorboard_dir=None,
dry_run=False,
reduction_config=None,
save_config=None,
include_regex=None,
include_collections=None,
save_all=False,
include_workers="one",
):
TensorflowBaseHook.__init__(
self,
out_dir=out_dir,
export_tensorboard=export_tensorboard,
tensorboard_dir=tensorboard_dir,
init_step=-1,
dry_run=dry_run,
reduction_config=reduction_config,
save_config=save_config,
include_regex=include_regex,
include_collections=include_collections,
save_all=save_all,
include_workers=include_workers,
)
tf.keras.callbacks.Callback.__init__(self)
self.tensor_refs_to_save_this_step = set()
self._fetches_added = set()
self.callable_cache = CallableCache()
def __init__(
self,
out_dir,
export_tensorboard=False,
tensorboard_dir=None,
dry_run=False,
reduction_config=None,
save_config=None,
include_regex=None,
include_collections=None,
save_all=False,
include_workers="one",
):
TensorflowBaseHook.__init__(
self,
out_dir=out_dir,
export_tensorboard=export_tensorboard,
tensorboard_dir=tensorboard_dir,
init_step=-1,
dry_run=dry_run,
reduction_config=reduction_config,
save_config=save_config,
include_regex=include_regex,
include_collections=include_collections,
save_all=save_all,
include_workers=include_workers,
)
tf.keras.callbacks.Callback.__init__(self)
self.tensor_refs_to_save_this_step = set()
self._fetches_added = set()
self.callable_cache = CallableCache()
self.custom_tensors_to_save = (
dict()
) # stores tensors custom tensors saved by users every step
self.saved_layers = dict()
self.has_registered_model = False
# supports_tf_logs property was introduced in TF 2.3.0
# it indicates to the framework that the callback is not
# limited to reading only numpy logs
self._supports_tf_logs = True
# TF 2.3.0 has a callback ordering bug
# this flag indicated to the train_batch_begin callback
# the the step was already incremented in the on_train_begin callback
self.step_incremented_in_on_train_begin = False
def __init__(
self,
out_dir,
export_tensorboard=False,
tensorboard_dir=None,
dry_run=False,
reduction_config=None,
save_config=None,
include_regex=None,
include_collections=None,
save_all=False,
include_workers="one",
):
super().__init__(
out_dir=out_dir,
export_tensorboard=export_tensorboard,
tensorboard_dir=tensorboard_dir,
init_step=-1,
dry_run=dry_run,
reduction_config=reduction_config,
save_config=save_config,
include_regex=include_regex,
include_collections=include_collections,
save_all=save_all,
include_workers=include_workers,
)
self._exported_collections = False
self._exported_model = {
ModeKeys.TRAIN: False,
ModeKeys.EVAL: False,
ModeKeys.PREDICT: False,
}
self.tensor_refs_to_save_this_step = set()
self._fetches_added = set()
self._prepared_tensors = {
ModeKeys.TRAIN: False,
ModeKeys.EVAL: False,
ModeKeys.PREDICT: False,
}
self.callable_cache = CallableCache()