edflow.hooks.checkpoint_hooks.tf_checkpoint_hook module

Summary

Classes:

CheckpointHook	Does that checkpoint thingy where it stores everything in a checkpoint.
RestoreCurrentCheckpointHook	Restores a TensorFlow model from a checkpoint at each epoch.
RestoreModelHook	Restores a TensorFlow model from a checkpoint at each epoch.
RestoreTFModelHook	alias of edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.RestoreModelHook
RetrainHook	Restes the global step at the beginning of training.
WaitForManager	Wait to make sure checkpoints are not overflowing.

Reference

class edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.RestoreModelHook(variables, checkpoint_path, filter_cond=<function RestoreModelHook.<lambda>>, global_step_setter=None)

Bases: edflow.hooks.hook.Hook

Restores a TensorFlow model from a checkpoint at each epoch. Can also be used as a functor.

__init__(variables, checkpoint_path, filter_cond=<function RestoreModelHook.<lambda>>, global_step_setter=None)

Parameters

• variables (list) – tf.Variable to be loaded from the checkpoint.

• checkpoint_path (str) – Directory in which the checkpoints are stored or explicit checkpoint. Ignored if used as functor.

• filter_cond (Callable) – A function used to filter files, to only get the checkpoints that are wanted. Ignored if used as functor.

• global_step_setter (Callable) – Callback to set global_step.

property session

before_epoch(ep)

Parameters

ep –

static parse_global_step(checkpoint)

Parameters

checkpoint –

edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.RestoreTFModelHook

alias of edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.RestoreModelHook

class edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.CheckpointHook(root_path, variables, modelname='model', session=None, step=None, interval=None, max_to_keep=5)

Bases: edflow.hooks.hook.Hook

Does that checkpoint thingy where it stores everything in a checkpoint.

__init__(root_path, variables, modelname='model', session=None, step=None, interval=None, max_to_keep=5)

Parameters

• root_path (str) – Path to where the checkpoints are stored.

• variables (list) – List of all variables to keep track of.

• session (tf.Session) – Session instance for saver.

• modelname (str) – Used to name the checkpoint.

• step (tf.Tensor or callable) – Step op, that can be evaluated: i,.e. a tf.Tensor or a python callable returning the step as an integer).

• interval (int) – Number of iterations after which a checkpoint is saved. If None, a checkpoint is saved after each epoch.

• max_to_keep (int) – Maximum number of checkpoints to keep on disk. Use 0 or None to never delete any checkpoints.

before_epoch(ep)

Parameters

ep –

after_epoch(epoch)

Parameters

epoch –

after_step(step, last_results)

Parameters

• step –

• last_results –

at_exception(*args, **kwargs)

Parameters

• *args –

• **kwargs –

save()

global_step()

class edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.RetrainHook(global_step=None)

Bases: edflow.hooks.hook.Hook

Restes the global step at the beginning of training.

__init__(global_step=None)

Parameters

global_step (tf.Variable) – Variable tracking the training step.

before_epoch(epoch)

Parameters

epoch –

before_step(batch_index, fetches, feeds, batch)

Parameters

• batch_index –

• fetches –

• feeds –

• batch –

after_step(step, *args, **kwargs)

Parameters

• step –

• *args –

• **kwargs –

class edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.WaitForManager(checkpoint_root, max_n, interval=5)

Bases: edflow.hooks.hook.Hook

Wait to make sure checkpoints are not overflowing.

__init__(checkpoint_root, max_n, interval=5)

Parameters

• checkpoint_root (str) – Path to look for checkpoints.

• max_n (int) – Wait as long as there are more than max_n ckpts.

• interval (float) – Number of seconds after which to check for number of checkpoints again.

wait()

Loop until the number of checkpoints got reduced.

before_epoch(ep)

Parameters

ep –

class edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.RestoreCurrentCheckpointHook(variables, checkpoint_path, filter_cond=<function RestoreModelHook.<lambda>>, global_step_setter=None)

Bases: edflow.hooks.checkpoint_hooks.tf_checkpoint_hook.RestoreModelHook

Restores a TensorFlow model from a checkpoint at each epoch. Can also be used as a functor.

before_epoch(ep)

Parameters

ep –