ptschandl/tensorboard_events_to_csv.py

## tensorboard_events_to_csv.py
#!/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)
	#!/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)