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@ -38,6 +38,13 @@ class BeginEpochEvent(object):
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class EndEpochEvent(object):
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class EndEpochEvent(object):
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"""
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The end of a training epoch.
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Args:
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epoch_id(int): The current epoch ID.
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"""
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def __init__(self, epoch_id):
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def __init__(self, epoch_id):
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self.epoch = epoch_id
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self.epoch = epoch_id
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@ -50,6 +57,16 @@ class BeginStepEvent(object):
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class EndStepEvent(object):
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class EndStepEvent(object):
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"""
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The end of a training step.
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Args:
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epoch_id(int): The current epoch ID.
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step_id(int): The current step ID.
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metrics(list): A list of fetched tensor. The order of this list is same
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as the :code:`train_func` returns.
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"""
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def __init__(self, epoch_id, step_id, metrics):
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def __init__(self, epoch_id, step_id, metrics):
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self.epoch = epoch_id
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self.epoch = epoch_id
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self.step = step_id
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self.step = step_id
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@ -57,6 +74,27 @@ class EndStepEvent(object):
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class CheckpointConfig(object):
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class CheckpointConfig(object):
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"""
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Parameter object for :code:`fluid.io.save_checkpoint` and
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:code:`fluid.Trainer`. Used to configuration how to save checkpoint.
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Args:
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checkpoint_dir(str): Directory path to save check point. Default is the
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current directory.
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max_num_checkpoints(int): The max number of local check points.
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epoch_interval(int): Every number of epoch to save check point.
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step_interval(int): Every number of step to save check point.
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Examples:
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>>> config = fluid.CheckpointConfig("./checkpoints")
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>>> trainer = fluid.Trainer(train_func=train_program,
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>>> place=place,
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>>> optimizer_func=optimizer_func,
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>>> checkpoint_config=config)
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>>> trainer.train(...)
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"""
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def __init__(self,
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def __init__(self,
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checkpoint_dir=None,
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checkpoint_dir=None,
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max_num_checkpoints=3,
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max_num_checkpoints=3,
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