本文整理汇总了Python中tensorflow.contrib.learn.python.learn.summary_writer_cache.SummaryWriterCache.get方法的典型用法代码示例。如果您正苦于以下问题：Python SummaryWriterCache.get方法的具体用法？Python SummaryWriterCache.get怎么用？Python SummaryWriterCache.get使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.learn.python.learn.summary_writer_cache.SummaryWriterCache的用法示例。
在下文中一共展示了SummaryWriterCache.get方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from tensorflow.contrib.learn.python.learn.summary_writer_cache import SummaryWriterCache [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.summary_writer_cache.SummaryWriterCache import get [as 别名]
def __init__(self,
save_steps=100,
output_dir=None,
summary_writer=None,
scaffold=None,
summary_op=None):
"""Initializes a `SummarySaver` monitor.
Args:
save_steps: `int`, save summaries every N steps. See `EveryN`.
output_dir: `string`, the directory to save the summaries to. Only used
if no `summary_writer` is supplied.
summary_writer: `SummaryWriter`. If `None` and an `output_dir` was passed,
one will be created accordingly.
scaffold: `Scaffold` to get summary_op if it's not provided.
summary_op: `Tensor` of type `string`. A serialized `Summary` protocol
buffer, as output by TF summary methods like `scalar_summary` or
`merge_all_summaries`.
"""
# TODO(ipolosukhin): Implement every N seconds.
self._summary_op = summary_op
self._summary_writer = summary_writer
if summary_writer is None and output_dir:
self._summary_writer = SummaryWriterCache.get(output_dir)
self._scaffold = scaffold
self._save_steps = save_steps示例2: __init__
# 需要导入模块: from tensorflow.contrib.learn.python.learn.summary_writer_cache import SummaryWriterCache [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.summary_writer_cache.SummaryWriterCache import get [as 别名]
def __init__(self, every_n_steps=100, output_dir=None, summary_writer=None):
super(StepCounter, self).__init__(every_n_steps=every_n_steps)
self._summary_tag = "global_step/sec"
self._last_reported_step = None
self._last_reported_time = None
self._summary_writer = summary_writer
if summary_writer is None and output_dir:
self._summary_writer = SummaryWriterCache.get(output_dir)示例3: __init__
# 需要导入模块: from tensorflow.contrib.learn.python.learn.summary_writer_cache import SummaryWriterCache [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.summary_writer_cache.SummaryWriterCache import get [as 别名]
def __init__(self, every_n_steps, saver, checkpoint_dir,
checkpoint_basename="model.ckpt",
first_n_steps=-1):
"""Initialize CheckpointSaver monitor.
Args:
every_n_steps: `int`, save every N steps.
saver: `Saver` object, used for saving.
checkpoint_dir: `str`, base directory for the checkpoint files.
checkpoint_basename: `str`, base name for the checkpoint files.
first_n_steps: `int`, if positive, save every step during the
first `first_n_steps` steps.
"""
logging.info("Create CheckpointSaver")
super(CheckpointSaver, self).__init__(every_n_steps=every_n_steps,
first_n_steps=first_n_steps)
self._saver = saver
self._summary_writer = SummaryWriterCache.get(checkpoint_dir)
self._save_path = os.path.join(checkpoint_dir, checkpoint_basename)示例4: set_estimator
# 需要导入模块: from tensorflow.contrib.learn.python.learn.summary_writer_cache import SummaryWriterCache [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.summary_writer_cache.SummaryWriterCache import get [as 别名]
def set_estimator(self, estimator):
super(StepCounter, self).set_estimator(estimator)
if self._summary_writer is None:
self._summary_writer = SummaryWriterCache.get(estimator.model_dir)