Spaces:
Running
Running
File size: 1,870 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
r"""After running tuning, use this script to aggregate the results.
Usage:
OUT_DIR="<my_tuning_dir>"
bazel run -c opt single_task:aggregate_tuning_results -- \
--alsologtostderr \
--tuning_dir="$OUT_DIR"
"""
import ast
import os
from absl import app
from absl import flags
import tensorflow as tf
FLAGS = flags.FLAGS
flags.DEFINE_string(
'tuning_dir', '',
'Absolute path where results tuning trial folders are found.')
def main(argv):
del argv # Unused.
try:
trial_dirs = tf.gfile.ListDirectory(FLAGS.tuning_dir)
except tf.errors.NotFoundError:
print('Tuning directory %s does not exist.' % (FLAGS.tuning_dir,))
return
metrics = []
for trial_dir in trial_dirs:
tuning_results_file = os.path.join(
FLAGS.tuning_dir, trial_dir, 'tuning_results.txt')
if tf.gfile.Exists(tuning_results_file):
with tf.gfile.FastGFile(tuning_results_file, 'r') as reader:
for line in reader:
metrics.append(ast.literal_eval(line.replace(': nan,', ': 0.0,')))
if not metrics:
print('No trials found.')
return
num_trials = [m['num_trials'] for m in metrics]
assert all(n == num_trials[0] for n in num_trials)
num_trials = num_trials[0]
print('Found %d completed trials out of %d' % (len(metrics), num_trials))
# Sort by objective descending.
sorted_trials = sorted(metrics, key=lambda m: -m['objective'])
for i, metrics in enumerate(sorted_trials):
hparams = metrics['hparams']
keys = sorted(hparams.keys())
print(
str(i).ljust(4) + ': '
+ '{0:.2f}'.format(metrics['objective']).ljust(10)
+ '['
+ ','.join(['{}={}'.format(k, hparams[k]).ljust(24) for k in keys])
+ ']')
if __name__ == '__main__':
app.run(main)
|