######################################################################
log_file = open(args.log_file, 'w')
+pred_log_t = None
print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL)
def log_string(s):
- s = Fore.GREEN + time.ctime() + Style.RESET_ALL + ' ' + s
+ global pred_log_t
+ t = time.time()
+
+ if pred_log_t is None:
+ elapsed = 'start'
+ else:
+ elapsed = '+{:.02f}s'.format(t - pred_log_t)
+ pred_log_t = t
+ s = Fore.BLUE + time.ctime() + ' ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s
log_file.write(s + '\n')
log_file.flush()
print(s)
for problem_number in range(1, 24):
+ log_string('**** problem ' + str(problem_number) + ' ****')
+
model = AfrozeShallowNet()
if torch.cuda.is_available():
train_set = CompressedVignetteSet(problem_number,
args.nb_train_batches, args.batch_size,
cuda=torch.cuda.is_available())
- test_set = CompressedVignetteSet(problem_number,
- args.nb_test_batches, args.batch_size,
- cuda=torch.cuda.is_available())
else:
train_set = VignetteSet(problem_number,
args.nb_train_batches, args.batch_size,
cuda=torch.cuda.is_available())
- test_set = VignetteSet(problem_number,
- args.nb_test_batches, args.batch_size,
- cuda=torch.cuda.is_available())
log_string('data_generation {:0.2f} samples / s'.format(
(train_set.nb_samples + test_set.nb_samples) / (time.time() - t))
train_set.nb_samples)
)
- nb_test_errors = nb_errors(model, test_set)
+ if args.compress_vignettes:
+ test_set = CompressedVignetteSet(problem_number,
+ args.nb_test_batches, args.batch_size,
+ cuda=torch.cuda.is_available())
+ else:
+ test_set = VignetteSet(problem_number,
+ args.nb_test_batches, args.batch_size,
+ cuda=torch.cuda.is_available())
+
+ nb_test_errors = nb_errors(model, test_set)
+
+ log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(
+ problem_number,
+ 100 * nb_test_errors / test_set.nb_samples,
+ nb_test_errors,
+ test_set.nb_samples)
+ )
- log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(
- problem_number,
- 100 * nb_test_errors / test_set.nb_samples,
- nb_test_errors,
- test_set.nb_samples)
- )
######################################################################