本文整理汇总了Python中tensorflow.core.util.event_pb2.Event方法的典型用法代码示例。如果您正苦于以下问题:Python event_pb2.Event方法的具体用法?Python event_pb2.Event怎么用?Python event_pb2.Event使用的例子?那么, 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.core.util.event_pb2
的用法示例。
在下文中一共展示了event_pb2.Event方法的15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: setup_logger
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def setup_logger(out_dir="results", exp_name="test", output_formats=None):
timestamp = utils.make_timestamp()
exp_name = exp_name.replace("/", "_") # environment names can contain /'s
filename = "{}-{}".format(timestamp, exp_name)[0:255] # Linux has filename limit of 255
out_dir = osp.join(out_dir, filename)
os.makedirs(out_dir, exist_ok=True)
logger.configure(folder=osp.join(out_dir, "rl"), format_strs=["tensorboard", "stdout"])
logger_instance = logger.Logger.CURRENT
if output_formats is not None:
logger_instance.output_formats += output_formats
for fmt in logger_instance.output_formats:
if isinstance(fmt, logger.TensorBoardOutputFormat):
writer = fmt.writer
layout = tb_layout()
event = event_pb2.Event(summary=layout)
writer.WriteEvent(event)
writer.Flush()
return out_dir, logger_instance
示例2: load_tensor_from_event_file
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def load_tensor_from_event_file(event_file_path):
"""Load a tensor from an event file.
Assumes that the event file contains a `Event` protobuf and the `Event`
protobuf contains a `Tensor` value.
Args:
event_file_path: (`str`) path to the event file.
Returns:
The tensor value loaded from the event file, as a `numpy.ndarray`. For
uninitialized Tensors, returns `None`. For Tensors of data types that
cannot be converted to `numpy.ndarray` (e.g., `tf.resource`), return
`None`.
"""
event = event_pb2.Event()
with gfile.Open(event_file_path, "rb") as f:
event.ParseFromString(f.read())
return load_tensor_from_event(event)
示例3: add_summary
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def add_summary(self, summary, global_step=None):
"""Adds a `Summary` protocol buffer to the event file.
This method wraps the provided summary in an `Event` protocol buffer
and adds it to the event file.
You can pass the result of evaluating any summary op, using
@{tf.Session.run} or
@{tf.Tensor.eval}, to this
function. Alternatively, you can pass a `tf.Summary` protocol
buffer that you populate with your own data. The latter is
commonly done to report evaluation results in event files.
Args:
summary: A `Summary` protocol buffer, optionally serialized as a string.
global_step: Number. Optional global step value to record with the
summary.
"""
if isinstance(summary, bytes):
summ = summary_pb2.Summary()
summ.ParseFromString(summary)
summary = summ
event = event_pb2.Event(summary=summary)
self._add_event(event, global_step)
示例4: add_meta_graph
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def add_meta_graph(self, meta_graph_def, global_step=None):
"""Adds a `MetaGraphDef` to the event file.
The `MetaGraphDef` allows running the given graph via
`saver.import_meta_graph()`.
Args:
meta_graph_def: A `MetaGraphDef` object, often as returned by
`saver.export_meta_graph()`.
global_step: Number. Optional global step counter to record with the
graph.
Raises:
TypeError: If both `meta_graph_def` is not an instance of `MetaGraphDef`.
"""
if not isinstance(meta_graph_def, meta_graph_pb2.MetaGraphDef):
raise TypeError("meta_graph_def must be type MetaGraphDef, saw type: %s" %
type(meta_graph_def))
meta_graph_bytes = meta_graph_def.SerializeToString()
event = event_pb2.Event(meta_graph_def=meta_graph_bytes)
self._add_event(event, global_step)
示例5: Load
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def Load(self):
"""Loads all new values from disk.
Calling Load multiple times in a row will not 'drop' events as long as the
return value is not iterated over.
Yields:
All values that were written to disk that have not been yielded yet.
"""
while True:
try:
with errors.raise_exception_on_not_ok_status() as status:
self._reader.GetNext(status)
except (errors.DataLossError, errors.OutOfRangeError):
# We ignore partial read exceptions, because a record may be truncated.
# PyRecordReader holds the offset prior to the failed read, so retrying
# will succeed.
break
event = event_pb2.Event()
event.ParseFromString(self._reader.record())
yield event
logging.debug('No more events in %s', self._file_path)
示例6: add_summary
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def add_summary(self, summary, global_step=None):
"""Adds a `Summary` protocol buffer to the event file.
This method wraps the provided summary in an `Event` protocol buffer
and adds it to the event file.
You can pass the result of evaluating any summary op, using
[`Session.run()`](client.md#Session.run) or
[`Tensor.eval()`](framework.md#Tensor.eval), to this
function. Alternatively, you can pass a `tf.Summary` protocol
buffer that you populate with your own data. The latter is
commonly done to report evaluation results in event files.
Args:
summary: A `Summary` protocol buffer, optionally serialized as a string.
global_step: Number. Optional global step value to record with the
summary.
"""
if isinstance(summary, bytes):
summ = summary_pb2.Summary()
summ.ParseFromString(summary)
summary = summ
event = event_pb2.Event(summary=summary)
self._add_event(event, global_step)
示例7: add_run_metadata
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def add_run_metadata(self, run_metadata, tag, global_step=None):
"""Adds a metadata information for a single session.run() call.
Args:
run_metadata: A `RunMetadata` protobuf object.
tag: The tag name for this metadata.
global_step: Number. Optional global step counter to record with the
StepStats.
Raises:
ValueError: If the provided tag was already used for this type of event.
"""
if tag in self._session_run_tags:
raise ValueError("The provided tag was already used for this event type")
self._session_run_tags[tag] = True
tagged_metadata = event_pb2.TaggedRunMetadata()
tagged_metadata.tag = tag
# Store the `RunMetadata` object as bytes in order to have postponed
# (lazy) deserialization when used later.
tagged_metadata.run_metadata = run_metadata.SerializeToString()
event = event_pb2.Event(tagged_run_metadata=tagged_metadata)
self._add_event(event, global_step)
示例8: event_to_record
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def event_to_record(event):
"""
Convert an event protobuf to a TFRecord
Arguments:
event (Event): Event Protobuf to write in TFRecord format
Returns:
TFRecord formatted bytestring
"""
event_str = serialize_protobuf(event)
header = struct.pack('Q', len(event_str))
record = [header,
struct.pack('I', masked_crc32c(header)),
event_str,
struct.pack('I', masked_crc32c(event_str))]
return b"".join(record)
示例9: add_meta_graph
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def add_meta_graph(self, meta_graph_def, global_step=None):
"""Adds a `MetaGraphDef` to the event file.
The `MetaGraphDef` allows running the given graph via
`saver.import_meta_graph()`.
Args:
meta_graph_def: A `MetaGraphDef` object, often as retured by
`saver.export_meta_graph()`.
global_step: Number. Optional global step counter to record with the
graph.
Raises:
TypeError: If both `meta_graph_def` is not an instance of `MetaGraphDef`.
"""
if not isinstance(meta_graph_def, meta_graph_pb2.MetaGraphDef):
raise TypeError("meta_graph_def must be type MetaGraphDef, saw type: %s"
% type(meta_graph_def))
meta_graph_bytes = meta_graph_def.SerializeToString()
event = event_pb2.Event(meta_graph_def=meta_graph_bytes)
self._add_event(event, global_step)
示例10: log_key_value
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def log_key_value(self, key, value, step=None):
def summary_val(k, v):
kwargs = {'tag': k, 'simple_value': float(v)}
return tf.Summary.Value(**kwargs)
summary = tf.Summary(value=[summary_val(key, value)])
event = event_pb2.Event(wall_time=time.time(), summary=summary)
# Use a separate step counter for each key
if key not in self.key_steps:
self.key_steps[key] = 0
if step is not None:
self.key_steps[key] = step
event.step = self.key_steps[key]
self.writer.WriteEvent(event)
self.writer.Flush()
self.key_steps[key] += 1
示例11: __init__
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def __init__(self, logdir, flush_secs=2, is_dummy=False, dummy_time=None):
self._name_to_tf_name = {}
self._tf_names = set()
self.is_dummy = is_dummy
self.logdir = logdir
self.flush_secs = flush_secs # TODO
self._writer = None
self._dummy_time = dummy_time
if is_dummy:
self.dummy_log = defaultdict(list)
else:
if not os.path.exists(self.logdir):
os.makedirs(self.logdir)
hostname = socket.gethostname()
filename = os.path.join(
self.logdir, 'events.out.tfevents.{}.{}'.format(
int(self._time()), hostname))
self._writer = open(filename, 'wb')
self._write_event(event_pb2.Event(
wall_time=self._time(), step=0, file_version='brain.Event:2'))
示例12: write_values
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def write_values(self, key2val):
summary = tf.Summary(value=[tf.Summary.Value(tag=k, simple_value=float(v))
for (k, v) in key2val.items()])
event = event_pb2.Event(wall_time=time.time(), summary=summary)
event.step = self.step # is there any reason why you'd want to specify the step?
self.evwriter.WriteEvent(event)
self.evwriter.Flush()
self.step += 1
示例13: load_tensor_from_event
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def load_tensor_from_event(event):
"""Load a tensor from an Event proto.
Args:
event: The Event proto, assumed to hold a tensor value in its
summary.value[0] field.
Returns:
The tensor value loaded from the event file, as a `numpy.ndarray`, if
representation of the tensor value by a `numpy.ndarray` is possible.
For uninitialized Tensors, returns `None`. For Tensors of data types that
cannot be represented as `numpy.ndarray` (e.g., `tf.resource`), return
the `TensorProto` protobuf object without converting it to a
`numpy.ndarray`.
"""
tensor_proto = event.summary.value[0].tensor
if tensor_proto.tensor_content or tensor_proto.string_val:
# Initialized tensor.
if tensor_proto.dtype == types_pb2.DT_RESOURCE:
tensor_value = InconvertibleTensorProto(tensor_proto)
else:
try:
tensor_value = tensor_util.MakeNdarray(tensor_proto)
except KeyError:
tensor_value = InconvertibleTensorProto(tensor_proto)
else:
# Uninitialized tensor or tensor of unconvertible data type.
tensor_value = InconvertibleTensorProto(tensor_proto, False)
return tensor_value
示例14: _load_graph_def_from_event_file
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def _load_graph_def_from_event_file(event_file_path):
event = event_pb2.Event()
with gfile.Open(event_file_path, "rb") as f:
event.ParseFromString(f.read())
return graph_pb2.GraphDef.FromString(event.graph_def)
示例15: _load_log_message_from_event_file
# 需要导入模块: from tensorflow.core.util import event_pb2 [as 别名]
# 或者: from tensorflow.core.util.event_pb2 import Event [as 别名]
def _load_log_message_from_event_file(event_file_path):
event = event_pb2.Event()
with gfile.Open(event_file_path, "rb") as f:
event.ParseFromString(f.read())
return event.log_message.message