def __init__(self, **kwargs) :
dataset_folder = Path(kwargs["dataset_folder"]).resolve()
check_valid_path(dataset_folder)
result_folder = kwargs["result_folder"]
self.initial_epoch = 1
self.test_mode = kwargs["test"]
self.epochs = kwargs["epochs"]
self.hidden_size = kwargs["hidden_size"]
self.num_heads = kwargs["heads"]
self.use_label_smoothing = kwargs["label_smoothing"]
self.ckpt_path = kwargs["ckpt_path"]
self.ckpt_epoch = kwargs["ckpt_epoch"]
# model에 필요한 폴더 및 파일 생성
self.log_folder, self.ckpt_folder, self.image_folder = create_folder(result_folder)
if not self.test_mode :
self.training_result_file = self.log_folder / "training_result.txt"
self.test_result_file = None
# kwargs 값 저장
msg = ""
for k, v in list(kwargs.items()) :
msg += "{} = {}\n".format(k, v)
msg += "new model checkpoint path = {}\n".format(self.ckpt_folder)
with (self.log_folder / "model_settings.txt").open("w", encoding = "utf-8") as fp :
fp.write(msg)
# 필요한 data를 불러옴
self.src_word2id, self.src_id2word, self.src_vocab_size = load_word_dic(dataset_folder / "src_word2id.pkl")
self.tar_word2id, self.tar_id2word, self.tar_vocab_size = load_word_dic(dataset_folder / "tar_word2id.pkl")
if not self.test_mode :
# encoder data : <END> tag 추가
# decoder data : 1) input = <START> tag만 추가 2)output = <END> tag만 추가
train_src, num_train_src = get_dataset(self.src_word2id, dataset_folder / "train_src.txt", False, True, True)
train_tar, num_train_tar = get_dataset(self.tar_word2id, dataset_folder / "train_tar.txt", True, True, True)
if num_train_src != num_train_tar :
raise Exception("한글 데이터셋({})과 영어 데이터셋({})의 크기가 다릅니다.".format(
num_train_src, num_train_tar))
self.num_train = num_train_src
self.train_dataset = tf.data.Dataset.from_generator(lambda: zip(train_src, train_tar), (tf.int32, tf.int32))
self.train_dataset = self.train_dataset.cache().shuffle(self.num_train + 1).padded_batch(
batch_size = kwargs["batch_size"], padded_shapes = (tf.TensorShape([None]), tf.TensorShape([None])),
padding_values = (self.src_word2id["<PAD>"], self.tar_word2id["<PAD>"])).prefetch(1)
test_src_path = dataset_folder / "test.txt"
if test_src_path.exists() :
test_src, self.num_test = get_dataset(self.src_word2id, test_src_path, False, True, False)
self.test_dataset = tf.data.Dataset.from_generator(lambda: test_src, tf.int32)
self.test_dataset = self.test_dataset.cache().batch(1).prefetch(1)
self.test_result_file = self.log_folder / "test_result.txt"
elif self.test_mode :
raise FileNotFoundError("[ {} ] 경로가 존재하지 않습니다.".format(test_src_path))
self.transformer = Transformer(self.src_vocab_size, self.tar_vocab_size, self.src_word2id["<PAD>"],
kwargs["num_layers"], kwargs["heads"], kwargs["embedding_size"], kwargs["hidden_size"],
kwargs["dropout_rate"], kwargs["use_conv"])
from Dataset import get_dataset
from model import LSTMClassifier, train_model
from utils import make_submit
import torch
from tqdm import tqdm
import numpy as np
TEXT, vocab_size, word_embeddings, train_iter, val_iter, test_iter = get_dataset(
)
lr = 1e-4
batch_size = 32
output_size = 2
hidden_size = 228
embedding_length = 300
num_epochs = 20
model = LSTMClassifier(vocab_size=vocab_size,
output_size=output_size,
embedding_dim=embedding_length,
hidden_dim=hidden_size,
n_layers=2,
weights=word_embeddings)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model.to(device)
optim = torch.optim.Adam(model.parameters(), lr=lr)
loss = torch.nn.CrossEntropyLoss()
train_loss, train_acc, val_loss, val_acc = train_model(model=model,
def __init__(self, **kwargs):
dataset_folder = Path(kwargs["dataset_folder"]).resolve()
check_valid_path(dataset_folder)
result_folder = kwargs["result_folder"]
self.initial_epoch = 1
self.test_mode = kwargs["test"]
self.epochs = kwargs["epochs"]
self.use_label_smoothing = kwargs["label_smoothing"]
self.ckpt_path = kwargs["ckpt_path"]
self.ckpt_epoch = kwargs["ckpt_epoch"]
# model에 필요한 폴더 및 파일 생성
self.log_folder, self.ckpt_folder, self.image_folder = create_folder(
result_folder)
if not self.test_mode:
self.training_result_file = self.log_folder / "training_result.txt"
self.test_result_file = None
# kwargs 값 저장
msg = ""
for k, v in list(kwargs.items()):
msg += "{} = {}\n".format(k, v)
msg += "new model checkpoint path = {}\n".format(self.ckpt_folder)
with (self.log_folder / "model_settings.txt").open(
"w", encoding="utf-8") as fp:
fp.write(msg)
# 필요한 data를 불러옴
self.src_word2id, self.src_id2word, self.src_vocab_size = load_word_dic(
dataset_folder / "src_word2id.pkl")
self.tar_word2id, self.tar_id2word, self.tar_vocab_size = load_word_dic(
dataset_folder / "tar_word2id.pkl")
if not self.test_mode:
train_src, num_train_src = get_dataset(
self.src_word2id, dataset_folder / "train_src.txt", False,
True, True)
train_tar, num_train_tar = get_dataset(
self.tar_word2id, dataset_folder / "train_tar.txt", True, True,
True)
if num_train_src != num_train_tar:
raise Exception(
"source 데이터셋({})과 target 데이터셋({})의 크기가 다릅니다.".format(
num_train_src, num_train_tar))
self.num_train = num_train_src
self.train_dataset = tf.data.Dataset.from_generator(
lambda: zip(train_src, train_tar), (tf.int32, tf.int32))
self.train_dataset = self.train_dataset.cache().shuffle(
self.num_train + 1).padded_batch(
batch_size=kwargs["batch_size"],
padded_shapes=(tf.TensorShape([None]),
tf.TensorShape([None])),
padding_values=(self.src_word2id["<PAD>"],
self.tar_word2id["<PAD>"])).prefetch(1)
test_src_path = dataset_folder / "test.txt"
if test_src_path.exists():
test_src, self.num_test = get_dataset(self.src_word2id,
test_src_path, False, True,
False)
# self.test_src_max_len = max([len(sentence) for sentence in test_src])
# padded_test_src = tf.keras.preprocessing.sequence.pad_sequences(
# test_src, maxlen = self.test_src_max_len, padding = 'post',
# dtype = 'int32', value = self.src_word2id["<PAD>"])
self.test_dataset = tf.data.Dataset.from_generator(
lambda: test_src, tf.int32)
self.test_dataset = self.test_dataset.cache().batch(1).prefetch(1)
self.test_result_file = self.log_folder / "test_result.txt"
elif self.test_mode:
raise FileNotFoundError(
"[ {} ] 경로가 존재하지 않습니다.".format(test_src_path))
self.encoder = Encoder(self.src_vocab_size, kwargs["embedding_size"],
kwargs["hidden_size"], kwargs["dropout_rate"],
kwargs["gru"], kwargs["bi"])
self.decoder = Decoder(self.tar_vocab_size, kwargs["embedding_size"],
kwargs["hidden_size"], kwargs["attention_size"],
kwargs["dropout_rate"], kwargs["gru"],
kwargs["bi"])
# 아래 line 6줄은 colab에서 한글 깨짐을 방지하기 위한 부분으로 생략해도 됩니다.
# %config InlineBackend.figure_format = 'retina'
# !apt -qq -y install fonts-nanum
fontpath = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'
font = fm.FontProperties(fname=fontpath, size=9)
plt.rc('font', family='NanumBarunGothic')
mpl.font_manager._rebuild()