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Extract all tensorboard events files to pandas dataframe
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#!/usr/bin/env python3 | |
''' | |
This script exctracts training variables from all logs from | |
tensorflow event files ("event*"), writes them to Pandas | |
and finally stores in long-format to a CSV-file including | |
all (readable) runs of the logging directory. | |
The magic "5" infers there are only the following v.tags: | |
[lr, loss, acc, val_loss, val_acc] | |
''' | |
import tensorflow as tf | |
import glob | |
import os | |
import pandas as pd | |
# Get all event* runs from logging_dir subdirectories | |
logging_dir = './logs' | |
event_paths = glob.glob(os.path.join(logging_dir, "*","event*")) | |
# Extraction function | |
def sum_log(path): | |
runlog = pd.DataFrame(columns=['metric', 'value']) | |
try: | |
for e in tf.train.summary_iterator(path): | |
for v in e.summary.value: | |
r = {'metric': v.tag, 'value':v.simple_value} | |
runlog = runlog.append(r, ignore_index=True) | |
# Dirty catch of DataLossError | |
except: | |
print('Event file possibly corrupt: {}'.format(path)) | |
return None | |
runlog['epoch'] = [item for sublist in [[i]*5 for i in range(0, len(runlog)//5)] for item in sublist] | |
return runlog | |
# Call & append | |
all_log = pd.DataFrame() | |
for path in event_paths: | |
log = sum_log(path) | |
if log is not None: | |
if all_log.shape[0] == 0: | |
all_log = log | |
else: | |
all_log = all_log.append(log) | |
# Inspect | |
print(all_log.shape) | |
all_log.head() | |
# Store | |
all_log.to_csv('all_training_logs_in_one_file.csv', index=None) |
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unfortunately I am not involved in TF and PT related projects anymore, so do not expect any updates on this. Sorry guys.
Feel free to modify the code in every way it becomes useful.