一種可能的方式:
from tensorflow.python.summary import event_accumulator
import numpy as np
import pandas as pd
import sys
def create_csv(inpath, outpath):
sg = {event_accumulator.COMPRESSED_HISTOGRAMS: 1,
event_accumulator.IMAGES: 1,
event_accumulator.AUDIO: 1,
event_accumulator.SCALARS: 0,
event_accumulator.HISTOGRAMS: 1}
ea = event_accumulator.EventAccumulator(inpath, size_guidance=sg)
ea.Reload()
scalar_tags = ea.Tags()['scalars']
df = pd.DataFrame(columns=scalar_tags)
for tag in scalar_tags:
events = ea.Scalars(tag)
scalars = np.array(map(lambda x: x.value, events))
df.loc[:, tag] = scalars
df.to_csv(outpath)
if __name__ == '__main__':
args = sys.argv
inpath = args[1]
outpath = args[2]
create_csv(inpath, outpath)
請注意,此代碼將加載整個事件文件到內存中,所以最好在集羣上運行此。有關EventAccumulator
的參數sg
的信息,請參閱this SO question。
一個額外的改進可能不僅是存儲每個標量的value
,而且還存儲step
。