start, end = 0, M for train_name in NAMES: for valid_name in ['INIT', 'ALL']: for round_index in range(5): sub_perfs = perfs[start: end] acc, acc_len, stddev = get_acc_and_acc_len(sub_perfs) print_and_log('{} on {} round{}: acc {} stddev {} acc_len {}'.format(train_name, valid_name, round_index, acc, stddev, acc_len)) log_only('{} on {} round {}: {}'.format(train_name, valid_name, round_index, sub_perfs)) start = end end = start + M if __name__ == '__main__': argparser = ParlaiParser(False, False) # ============ below copied from projects/graph_world2/train.py ============ argparser.add_arg('--vocab_size', type=int, default=1000) argparser.add_arg('--terminate', type=bool, default=False) argparser.add_arg('--lr', type=float, default=1e-3) argparser.add_arg('--max_seq_in', type=int, default=30) argparser.add_arg('--embedding_dim', type=int, default=50) argparser.add_arg('--rnn_h', type=int, default=350) argparser.add_arg('--rnn_layers', type=int, default=1) argparser.add_arg('--cuda', type=bool, default=True) argparser.add_arg('--eval_period', type=int, default=200) argparser.add_arg('--max_seq_out', type=int, default=5) argparser.add_arg('--label_ratio', type=float, default=1.0) argparser.add_arg('--max_iter', type=int, default=100000) argparser.add_arg('--exit_iter', type=int, default=3000) argparser.add_arg('--num_runs', type=int, default=10) argparser.add_arg('--train_data_file', type=str, default='')
return additional_validate( opt, cur_model_dict, cur_data_agent, opt['valid_data_file'], constrain_=True, no_hits=True, ) if __name__ == '__main__': if not os.path.exists('tmp'): os.makedirs('tmp') argparser = ParlaiParser() argparser.add_arg('--vocab_size', type=int, default=1000) argparser.add_arg('--terminate', type=bool, default=False) argparser.add_arg('--lr', type=float, default=1e-3) argparser.add_arg('--max_seq_in', type=int, default=30) argparser.add_arg('--embedding_dim', type=int, default=50) argparser.add_arg('--rnn_h', type=int, default=350) argparser.add_arg('--rnn_layers', type=int, default=1) argparser.add_arg('--cuda', type=bool, default=True) argparser.add_arg('--eval_period', type=int, default=200) argparser.add_arg('--max_seq_out', type=int, default=5) argparser.add_arg('--label_ratio', type=float, default=1.0) argparser.add_arg('--max_iter', type=int, default=100000) argparser.add_arg('--exit_iter', type=int, default=3000) argparser.add_arg('--num_runs', type=int, default=10) argparser.add_arg('--train_data_file', type=str, default='')
) best_valid = valid_metric impatience = 0 if 'model_file' in opt: doc_reader.save(opt['model_file']) else: impatience += 1 iteration += 1 if __name__ == '__main__': # Get command line arguments argparser = ParlaiParser() argparser.add_arg( '--train_interval', type=int, default=1000, help='Validate after every N train updates', ) argparser.add_arg( '--patience', type=int, default=10, help='Number of intervals to continue without improvement' ) SimpleDictionaryAgent.add_cmdline_args(argparser) DocReaderAgent.add_cmdline_args(argparser) opt = argparser.parse_args() # Set logging logger = logging.getLogger('DrQA') logger.setLevel(logging.INFO) fmt = logging.Formatter('%(asctime)s: %(message)s', '%m/%d/%Y %I:%M:%S %p') console = logging.StreamHandler() console.setFormatter(fmt)