+parser = argparse.ArgumentParser(
+ description = "Convolutional networks for the SVRT. Written by Francois Fleuret, (C) Idiap research institute.",
+ formatter_class = argparse.ArgumentDefaultsHelpFormatter
+)
+
+parser.add_argument('--nb_train_samples',
+ type = int, default = 100000)
+
+parser.add_argument('--nb_test_samples',
+ type = int, default = 10000)
+
+parser.add_argument('--nb_validation_samples',
+ type = int, default = 10000)
+
+parser.add_argument('--validation_error_threshold',
+ type = float, default = 0.0,
+ help = 'Early training termination criterion')
+
+parser.add_argument('--nb_epochs',
+ type = int, default = 50)
+
+parser.add_argument('--batch_size',
+ type = int, default = 100)
+
+parser.add_argument('--log_file',
+ type = str, default = 'default.log')
+
+parser.add_argument('--nb_exemplar_vignettes',
+ type = int, default = 32)
+
+parser.add_argument('--compress_vignettes',
+ type = distutils.util.strtobool, default = 'True',
+ help = 'Use lossless compression to reduce the memory footprint')
+
+parser.add_argument('--model',
+ type = str, default = 'deepnet',
+ help = 'What model to use')
+
+parser.add_argument('--test_loaded_models',
+ type = distutils.util.strtobool, default = 'False',
+ help = 'Should we compute the test errors of loaded models')
+
+parser.add_argument('--problems',
+ type = str, default = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23',
+ help = 'What problems to process')
+
+args = parser.parse_args()
+
+######################################################################
+
+log_file = open(args.log_file, 'a')
+pred_log_t = None
+last_tag_t = time.time()
+
+print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL)
+
+# Log and prints the string, with a time stamp. Does not log the
+# remark
+
+def log_string(s, remark = ''):
+ global pred_log_t, last_tag_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
+
+ if t > last_tag_t + 3600:
+ last_tag_t = t
+ print(Fore.RED + time.ctime() + Style.RESET_ALL)
+
+ log_file.write(re.sub(' ', '_', time.ctime()) + ' ' + elapsed + ' ' + s + '\n')
+ log_file.flush()
+
+ print(Fore.BLUE + time.ctime() + ' ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s + Fore.CYAN + remark + Style.RESET_ALL)