Easy TensorFlow Logging
Are you prototyping something and want to be able to magically graph some value without going through all the usual steps to set up TensorFlow logging properly?
easy-tf-log is a simple module to do just that.
from easy_tf_log import tflog
then you can do
for i in range(10):
tflog('really_interesting_variable_name', i)
and you'll find a directory logs that you can point TensorBoard to
$ tensorboard --logdir logs
to get
Based on logging code from OpenAI's baselines.
Installation
pip install easy-tf-log
Note that TensorFlow must be installed separately.
Usage
By default, easy-tf-log saves event files to a directory logs.
To change the directory, call easy_tf_log.set_dir(log_dir).
easy-tf-log also supports writing using an existing EventFileWriter created
by e.g. an instance of tf.summary.FileWriter: call
easy_tf_log.set_writer(file_writer.event_writer). (However, not that because
EventsFileWriter uses a sub-thread to write events, this is not fork-safe. If
you set this in one process and then try to use easy-tf-log a child process,
it will hang.)
To log a value, use tflog(key, value). The step number for each key starts from zero
and increments automatically. To set the step manually, specify the step
argument.
See demo.py for a full demo.