As @dga mentioned this is not implemented yet. Here is some code that uses EventAccumulator
to combine scalar tensorflow summary values. This can be extended to accommodate the other summary types.
import os
from collections import defaultdict
import numpy as np
import tensorflow as tf
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
def tabulate_events(dpath):
summary_iterators = [EventAccumulator(os.path.join(dpath, dname)).Reload() for dname in os.listdir(dpath)]
tags = summary_iterators[0].Tags()['scalars']
for it in summary_iterators:
assert it.Tags()['scalars'] == tags
out = defaultdict(list)
for tag in tags:
for events in zip(*[acc.Scalars(tag) for acc in summary_iterators]):
assert len(set(e.step for e in events)) == 1
out[tag].append([e.value for e in events])
return out
def write_combined_events(dpath, d_combined, dname='combined'):
fpath = os.path.join(dpath, dname)
writer = tf.summary.FileWriter(fpath)
tags, values = zip(*d_combined.items())
timestep_mean = np.array(values).mean(axis=-1)
for tag, means in zip(tags, timestep_mean):
for i, mean in enumerate(means):
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=mean)])
writer.add_summary(summary, global_step=i)
writer.flush()
dpath = '/path/to/root/directory'
d = tabulate_events(dpath)
write_combined_events(dpath, d)
This solution assumes a directory structure like the following:
dpath
├── 1
│?? └── events.out.tfevents.1518552132.Alexs-MacBook-Pro-2.local
├── 11
│?? └── events.out.tfevents.1518552180.Alexs-MacBook-Pro-2.local
├── 21
│?? └── events.out.tfevents.1518552224.Alexs-MacBook-Pro-2.local
├── 31
│?? └── events.out.tfevents.1518552264.Alexs-MacBook-Pro-2.local
└── 41
?? └── events.out.tfevents.1518552304.Alexs-MacBook-Pro-2.local
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