def main():
# ハイパーパラメータの設定
window_size = 5
hidden_size = 100
batch_size = 100
max_epoch = 10
# データの読み込み
corpus, word_to_id, id_to_word = ptb.load_data('train')
vocab_size = len(word_to_id)
contexts, target = create_contexts_target(corpus, window_size)
# モデルなどの生成
model = CBOW(vocab_size, hidden_size, window_size, corpus)
optimizer = Adam()
trainer = Trainer(model, optimizer)
# 学習開始
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()
# 後ほど利用できるように、必要なデータを保存
word_vecs = model.word_vecs
params = {}
params['word_vecs'] = word_vecs.astype(np.float16)
params['word_to_id'] = word_to_id
params['id_to_word'] = id_to_word
pkl_file = 'cbow_params.pkl'
with open(pkl_file, 'wb') as f:
pickle.dump(params, f, -1)
def test_convert_one_hot_contexts(self):
text = 'you say goodbye and I say hello.'
corpus, w2id, id2w = preprocess(text)
contexts, target = create_contexts_target(corpus, 1)
contexts = convert_one_hot(contexts, len(w2id))
self.assertEqual(contexts.shape, (6, 2, 7))
def test_create_contexts_target(self):
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
contexts, target = create_contexts_target(corpus, window_size=1)
expected_contexts = np.array([[0, 2], [1, 3], [2, 4], [3, 1], [4, 5], [1, 6]])
expected_target = np.array([1, 2, 3, 4, 1, 5])
npt.assert_array_equal(contexts, expected_contexts)
npt.assert_array_equal(target, expected_target)
def main():
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
contexts, target = create_contexts_target(corpus, window_size=1)
vocab_size = len(word_to_id)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
print(vocab_size)
def test_create_contexts_target(self):
text = 'you say goodbye and I say hello.'
corpus, w2id, id2w = preprocess(text)
contexts, target = create_contexts_target(corpus)
expected_contexts = np.array([[0, 2], [1, 3], [2, 4], [3, 1], [4, 5],
[1, 6]])
expected_target = [1, 2, 3, 4, 1, 5]
np.testing.assert_array_almost_equal(contexts, expected_contexts)
np.testing.assert_array_almost_equal(target, expected_target)
def test_convert_one_hot(self):
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
contexts, target = create_contexts_target(corpus, window_size=1)
vocab_size = len(word_to_id)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
expected_target = np.array([
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0]])
expected_contexts = np.array([
[
[1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0]
],
[
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0]
],
[
[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0]
],
[
[0, 0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0]
],
[
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0]
],
[
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1]
]])
npt.assert_array_equal(target, expected_target)
npt.assert_array_equal(contexts, expected_contexts)
def main():
window_size = 1
hidden_size = 5
batch_size = 3
max_epoch = 1000
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
vocab_size = len(word_to_id)
contexts, target = create_contexts_target(corpus, window_size)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
model = SimpleCBOW(vocab_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()
def create_contexts_target(corpus, window_size=1):
target = corpus[window_size:-window_size]
contexts = []
for idx in range(window_size, len(corpus) - window_size):
cs = []
for t in range(-window_size, window_size + 1):
if t == 0:
continue
cs.append(corpus[idx + t])
contexts.append(cs)
return np.array(contexts), np.array(target)
contexts, target = create_contexts_target(corpus, window_size=1)
print(contexts)
print(target)
import sys
sys.path.append('..')
from common.util import preprocess, create_contexts_target, convert_one_hot
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
contexts, target = create_contexts_target(corpus, window_size=1)
vocab_size = len(word_to_id)
target = convert_one_hot(target, vocab_size)
from common.util import create_contexts_target, to_cpu, to_gpu
from dataset import ptb
# 하이퍼파라미터 설정
window_size = 5
hidden_size = 100
batch_size = 100
max_epoch = 10
# 데이터 읽기
corpus, word_to_id, id_to_word = ptb.load_data('train')
print(type(word_to_id))
exit(1)
vocab_size = len(word_to_id) # 10000
contexts, target = create_contexts_target(corpus,
window_size) # 인접한단어, 중심단어 리스트
if config.GPU:
contexts, target = to_gpu(contexts), to_gpu(target)
# 모델 등 생성
model = CBOW(vocab_size, hidden_size, window_size, corpus)
# model = SkipGram(vocab_size, hidden_size, window_size, corpus)
optimizer = Adam()
trainer = Trainer(model, optimizer)
# 학습 시작
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()
# 나중에 사용할 수 있도록 필요한 데이터 저장 (pickle은 파이썬 코드의 객체를 파일로 저장/읽기가 가능함)
word_vecs = model.word_vecs
from common.optimizer import Adam
from simple_cbow import SimpleCBOW
from simple_skip_gram import SimpleSkipGram
from common.util import preprocess, create_contexts_target, convert_one_hot
window_size = 1
hidden_size = 5
batch_size = 3
max_epoch = 1000
text = "You say goodbye and I say hello."
corpus, word_to_id, id_to_word = preprocess(
text) # corpus: 문장에서 단어의 label ('.'포함),
vocab_size = len(word_to_id) # 문장에서 단어 종류의 수, 7
contexts, target = create_contexts_target(
corpus, window_size) # window_size만큼 단어들의 문맥, 단어 label
target = convert_one_hot(target,
vocab_size) #(6,7) target을 vocab_size에 맞게 one_hot
contexts = convert_one_hot(contexts, vocab_size) # (6,2,7)
print(contexts)
exit(1)
#model = SimpleSkipGram(vocab_size, hidden_size)
model = SimpleCBOW(vocab_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()
