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
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
def __init__(self,
checkpoint_dir,
save_secs=None,
save_steps=None,
saver=None,
checkpoint_basename="model.ckpt",
scaffold=None):
"""Initialize CheckpointSaverHook monitor.
Args:
checkpoint_dir: `str`, base directory for the checkpoint files.
save_secs: `int`, save every N secs.
save_steps: `int`, save every N steps.
saver: `Saver` object, used for saving.
checkpoint_basename: `str`, base name for the checkpoint files.
scaffold: `Scaffold`, use to get saver object.
Raises:
ValueError: One of `save_steps` or `save_secs` should be set.
"""
logging.info("Create CheckpointSaverHook.")
self._saver = saver
self._checkpoint_dir = checkpoint_dir
self._summary_writer = SummaryWriterCache.get(checkpoint_dir)
self._save_path = os.path.join(checkpoint_dir, checkpoint_basename)
self._scaffold = scaffold
self._save_secs = save_secs
self._save_steps = save_steps
self._last_saved_time = None
self._last_saved_step = None
if save_steps is None and save_secs is None:
raise ValueError("Either save_steps or save_secs should be provided")
if (save_steps is not None) and (save_secs is not None):
raise ValueError("Can not provide both save_steps and save_secs.")
def __init__(self,
checkpoint_dir,
save_secs=None,
save_steps=None,
saver=None,
checkpoint_basename="model.ckpt",
scaffold=None):
"""Initialize CheckpointSaverHook monitor.
Args:
checkpoint_dir: `str`, base directory for the checkpoint files.
save_secs: `int`, save every N secs.
save_steps: `int`, save every N steps.
saver: `Saver` object, used for saving.
checkpoint_basename: `str`, base name for the checkpoint files.
scaffold: `Scaffold`, use to get saver object.
Raises:
ValueError: One of `save_steps` or `save_secs` should be set.
"""
logging.info("Create CheckpointSaverHook")
self._saver = saver
self._checkpoint_dir = checkpoint_dir
self._summary_writer = SummaryWriterCache.get(checkpoint_dir)
self._save_path = os.path.join(checkpoint_dir, checkpoint_basename)
self._scaffold = scaffold
self._save_secs = save_secs
self._save_steps = save_steps
self._last_saved_time = None
self._last_saved_step = None
if save_steps is None and save_secs is None:
raise ValueError("Either save_steps or save_secs should be provided")
if (save_steps is not None) and (save_secs is not None):
raise ValueError("Can not provide both save_steps and save_secs.")
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)
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)
def __init__(self,
every_n_steps=100,
output_dir=None,
summary_writer=None):
self._summary_tag = "global_step/sec"
self._every_n_steps = every_n_steps
self._summary_writer = summary_writer
if summary_writer is None and output_dir:
self._summary_writer = SummaryWriterCache.get(output_dir)
def begin(self):
if self.summary_writer is None and self.output_dir:
self.summary_writer = SummaryWriterCache.get(self.output_dir)
graph = ops.get_default_graph()
self.fake_seq = graph.get_tensor_by_name("model/" + FAKE_PROTEINS +
":0")
self.labels = graph.get_tensor_by_name("model/" + LABELS + ":0")
self.d_score = graph.get_tensor_by_name("model/d_score:0")
self.global_step_tensor = training_util._get_or_create_global_step_read(
)
if self.global_step_tensor is None:
raise RuntimeError("Could not global step tensor")
if self.fake_seq is None:
raise RuntimeError("Could not get fake seq tensor")
def __init__(self, every_n_steps, saver, checkpoint_dir,
checkpoint_basename="model3124.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)
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)
def add_custom_scalar(logdir):
summary_writer = SummaryWriterCache.get(logdir)
layout_summary = summary.custom_scalar_pb(
layout_pb2.Layout(category=[
layout_pb2.Category(
title='Loss',
chart=[
layout_pb2.Chart(
title='Loss',
multiline=layout_pb2.MultilineChartContent(
tag=[r'1_loss/*'], )),
layout_pb2.Chart(
title='Loss Component',
multiline=layout_pb2.MultilineChartContent(
tag=[r'2_loss_component/*'], )),
layout_pb2.Chart(
title='Discriminator Values',
multiline=layout_pb2.MultilineChartContent(
tag=[r'3_discriminator_values/*'], )),
layout_pb2.Chart(
title='Variation of sequences',
multiline=layout_pb2.MultilineChartContent(
tag=[r'Stddev/*'], )),
layout_pb2.Chart(
title='BLOMSUM45',
multiline=layout_pb2.MultilineChartContent(
tag=[r'Blast/*/BLOMSUM45'], )),
layout_pb2.Chart(
title='Evalue',
multiline=layout_pb2.MultilineChartContent(
tag=[r'Blast/*/Evalue'], )),
layout_pb2.Chart(
title='Identity',
multiline=layout_pb2.MultilineChartContent(
tag=[r'Blast/*/Identity'], )),
]),
]))
summary_writer.add_summary(layout_summary)
def __init__(self, every_n_steps=100, output_dir=None, summary_writer=None):
self._summary_tag = "global_step/sec"
self._every_n_steps = every_n_steps
self._summary_writer = summary_writer
if summary_writer is None and output_dir:
self._summary_writer = SummaryWriterCache.get(output_dir)
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)
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)
def after_create_session(self, session, coord):
self._global_step_tensor = tf.train.get_global_step()
self._writer = SummaryWriterCache.get(self.summary_dir)
