msg = '** Epoch : {0:>2} finished, Train Loss : {1:>6.2}, Train Acc : {2:6.2%}, Time : {3}'
print_log(msg.format(epoch + 1, total_loss, total_acc, time_diff), file = log)
if __name__ == '__main__':
# read config
config = Config.ModelConfig()
arg = config.arg
vocab_dict = load_vocab(arg.vocab_path)
arg.vocab_dict_size = len(vocab_dict)
if arg.embedding_path:
embeddings = load_embeddings(arg.embedding_path, vocab_dict)
else:
embeddings = init_embeddings(vocab_dict, arg.embedding_size)
arg.n_vocab, arg.embedding_size = embeddings.shape
if arg.embedding_normalize:
embeddings = normalize_embeddings(embeddings)
arg.n_classes = len(CATEGORIE_ID)
dt = datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
arg.log_path = 'config/log/log.{}'.format(dt)
log = open(arg.log_path, 'w')
print_log('CMD : python3 {0}'.format(' '.join(sys.argv)), file = log)
print_log('Training with following options :', file = log)
print_args(arg, log)
model = Decomposable(arg, export=False)
train()
log.close()
# read config
config = Config.ModelConfig()
arg = config.arg
vocab_dict = load_vocab(arg.vocab_path)
arg.vocab_dict_size = len(vocab_dict)
index2word = {index : word for word, index in vocab_dict.items()}
if arg.embedding_path:
embeddings = load_embeddings(arg.embedding_path, vocab_dict)
else:
embeddings = init_embeddings(vocab_dict, arg.embedding_size)
arg.n_vocab, arg.embedding_size = embeddings.shape
if arg.embedding_normalize:
embeddings = normalize_embeddings(embeddings)
arg.n_classes = len(CATEGORIE_ID)
dt = datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
arg.log_path = 'config/log/log.{}'.format(dt)
log = open(arg.log_path, 'w')
print_log('CMD : python3 {0}'.format(' '.join(sys.argv)), file=log)
print_log('Testing with following options :', file=log)
print_args(arg, log)
model = Decomposable(arg.seq_length, arg.n_vocab, arg.embedding_size, arg.hidden_size, arg.attention_size, arg.n_classes, \
arg.batch_size, arg.learning_rate, arg.optimizer, arg.l2, arg.clip_value)
predict()
log.close()