Package: tensorboard_logger
tensorboard_loggerLog TensorBoard events without TensorFlowTensorBoardis a visualization tool (not this project, it’s a part ofTensorFlowframework)
that makes it easy to check training progress, compare between
different runs, and has lots of other cool features.tensorboard_loggerlibrary allows to write TensorBoard events without TensorFlow:from tensorboard_logger import configure, log_value

configure("runs/run-1234")

for step in range(1000):
    v1, v2 = do_stuff()
    log_value('v1', v1, step)
    log_value('v2', v2, step)Note: if you are already using TensorFlow in your project,
you probably don’t need this library.InstallationTensorFlow is required only for viewing logged events: please check installation guide
on theofficial site(you probably want a CPU-only version).tensorboard_loggercan be installed with pip:pip install tensorboard_loggerUsageYou can either use default logger withtensorboard_logger.configureandtensorboard_logger.log_valuefunctions, or usetensorboard_logger.Loggerclass.This library can be used to log numerical values of some variables in TensorBoard format, so you can
use TensorBoard to visualize how they changed, and compare same variables between different runs.
Log file is written into a directory, so you need a separate directory for each run
(you can place other logs or output files you use in the same directory).
Directories from different runs you wish to compare should have the same parent
(there can be other files or directories with the same parent, TensorBoard will figure out
which directories contain logs).Apart from variable names and their values, another important thing is thestep: this must
be an integer that represents some increasing step - it can be a step in training or some
other number. The values are ordered by step in TensorBoard, although you can view them
ordered by time or relative step too.A simple usage example:from tensorboard_logger import configure, log_value

configure("runs/run-1234", flush_secs=5)

for step in range(1000):
    v1, v2 = do_stuff()
    log_value('v1', v1, step)
    log_value('v2', v2, step)You can start TensorBoard right away:tensorboard --logdir runsAnd go check the metrics to TensorBoard UI athttp://localhost:6006(note that it binds to 0.0.0.0 by default).
Metrics are refreshed on switch to browser tab, and there is also a refresh button
at the top right.Runtime overhead rather large, about 0.1 - 0.2 ms for a single value logged
(so about 5,000 - 10,000 operations per second).APItensorboard_logger.configure(logdir, flush_secs=2)Configure logging: a file will be written tologdir, and flushed everyflush_secs.NOTE:right now file is flushed after each event written.tensorboard_logger.log_value(name, value, step=None)Log newvaluefor givennameon givenstep.valueshould be a real number (it will be converted to float),
andnameshould be a string (it will be converted to a valid
TensorFlow summary name).stepshould be an non-negative integer,
and is used for visualization: you can log several different
variables on one step, but should not log different values
of the same variable on the same step (this is not checked).
You can also omit step entirely.tensorboard_logger.LoggerA class for writing logs in a directory.
Use it if default logger used in two above functions is not enough for you
(e.g. you want to log into several different directories, or don’t like
global variables).
Constructor has the same signature astensorboard_logger.configure,
and it has a singlelog_valuemethod with the same signature astensorboard_logger.log_value.DevelopmentCompiling python protobuf files:protoc --python_out . tensorboard_logger/tf_protobuf/summary.proto
protoc --python_out . tensorboard_logger/tf_protobuf/event.protoLicenseMIT licenseHistory0.1.0 (2018-02-08)Ability to log histograms and images (#18)0.0.4 (2017-07-31)Fix working with TensorFlow installed (#11)0.0.3 (2016-11-17)Fix PyPI packaging0.0.2 (2016-11-03)TensorFlow is no longer required (#1)Travis build is fixed (#2)0.0.1 (2016-11-01)First release on PyPI.