tf.summary.FileWriter
tf.summary.FileWriter
class tf.summary.FileWriter
Defined in tensorflow/python/summary/writer/writer.py
.
See the guide: Summary Operations > Generation of Summaries
Writes Summary
protocol buffers to event files.
The FileWriter
class provides a mechanism to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training.
Methods
__init__
__init__( logdir, graph=None, max_queue=10, flush_secs=120, graph_def=None, filename_suffix=None )
Creates a FileWriter
and an event file.
On construction the summary writer creates a new event file in logdir
. This event file will contain Event
protocol buffers constructed when you call one of the following functions: add_summary()
, add_session_log()
, add_event()
, or add_graph()
.
If you pass a Graph
to the constructor it is added to the event file. (This is equivalent to calling add_graph()
later).
TensorBoard will pick the graph from the file and display it graphically so you can interactively explore the graph you built. You will usually pass the graph from the session in which you launched it:
...create a graph... # Launch the graph in a session. sess = tf.Session() # Create a summary writer, add the 'graph' to the event file. writer = tf.summary.FileWriter(<some-directory>, sess.graph)
The other arguments to the constructor control the asynchronous writes to the event file:
-
flush_secs
: How often, in seconds, to flush the added summaries and events to disk. -
max_queue
: Maximum number of summaries or events pending to be written to disk before one of the 'add' calls block.
Args:
-
logdir
: A string. Directory where event file will be written. -
graph
: AGraph
object, such assess.graph
. -
max_queue
: Integer. Size of the queue for pending events and summaries. -
flush_secs
: Number. How often, in seconds, to flush the pending events and summaries to disk. -
graph_def
: DEPRECATED: Use thegraph
argument instead. -
filename_suffix
: A string. Every event file's name is suffixed withsuffix
.
add_event
add_event(event)
Adds an event to the event file.
Args:
-
event
: AnEvent
protocol buffer.
add_graph
add_graph( graph, global_step=None, graph_def=None )
Adds a Graph
to the event file.
The graph described by the protocol buffer will be displayed by TensorBoard. Most users pass a graph in the constructor instead.
Args:
-
graph
: AGraph
object, such assess.graph
. -
global_step
: Number. Optional global step counter to record with the graph. -
graph_def
: DEPRECATED. Use thegraph
parameter instead.
Raises:
-
ValueError
: If both graph and graph_def are passed to the method.
add_meta_graph
add_meta_graph( 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
: AMetaGraphDef
object, often as returned bysaver.export_meta_graph()
. -
global_step
: Number. Optional global step counter to record with the graph.
Raises:
-
TypeError
: If bothmeta_graph_def
is not an instance ofMetaGraphDef
.
add_run_metadata
add_run_metadata( run_metadata, tag, global_step=None )
Adds a metadata information for a single session.run() call.
Args:
-
run_metadata
: ARunMetadata
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.
add_session_log
add_session_log( session_log, global_step=None )
Adds a SessionLog
protocol buffer to the event file.
This method wraps the provided session in an Event
protocol buffer and adds it to the event file.
Args:
-
session_log
: ASessionLog
protocol buffer. -
global_step
: Number. Optional global step value to record with the summary.
add_summary
add_summary( 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
: ASummary
protocol buffer, optionally serialized as a string. -
global_step
: Number. Optional global step value to record with the summary.
close
close()
Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore.
flush
flush()
Flushes the event file to disk.
Call this method to make sure that all pending events have been written to disk.
get_logdir
get_logdir()
Returns the directory where event file will be written.
reopen
reopen()
Reopens the EventFileWriter.
Can be called after close()
to add more events in the same directory. The events will go into a new events file.
Does nothing if the EventFileWriter was not closed.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/summary/FileWriter