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)
