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#!/usr/bin/env python3
import tensorflow as tf
import re
import glob
import os
# what --opts columns to use?
OPTION_KEYS = ("embedding_dim learning_rate margin negative_frame_range"
" negative_selection_mode".split(" "))
def options_for_run(run_id):
for line in open("logs/%s.out" % run_id).readlines():
m = re.match("^Namespace\((.*)\)$", line.strip())
assert m
opts = {}
for opt_str in","):
key, value = opt_str.strip().split("=")
if key in OPTION_KEYS:
opts[key] = value
# return in deterministic order
return [opts[k] for k in OPTION_KEYS]
def event_files_for_run(run_id):
return glob.glob("tb/%s/events*" % run_id)
# header
print("\t".join(['exp', 'sample'] + OPTION_KEYS + ['step', 'k', 'v']))
for run in os.listdir('tb'):
m = re.match("^(.*)_(\d)$", run)
exp, sample = m.groups()
opts = options_for_run(run)
for event_file in event_files_for_run(run):
for e in tf.train.summary_iterator(event_file):
# all the meta events have an (implied) step value
# of zero so ignoring any event with 0 skips these.
# TODO: dangerous!!
if e.step == 0: continue
step = e.step
key = e.summary.value[0].tag
value = e.summary.value[0].simple_value
print("\t".join(map(str, [exp, sample] + opts + [step, key, value])))
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