xs = sum(corpus_train, [])[:-1]
ts = sum(corpus_train, [])[1:]
corpus_val = sum(corpus_val, [])
corpus_test = sum(corpus_test, [])
model = BetterRnnlm(vocab_size, wordvec_size, hidden_size, dropout)
# optimizer = SGD(lr)
optimizer = Adam(lr=lr)
trainer = RnnlmTrainer(model, optimizer)
best_ppl = float('inf')
for epoch in range(max_epoch):
trainer.fit(xs,
ts,
max_epoch=1,
batch_size=batch_size,
time_size=time_size,
max_grad=max_grad)
model.reset_state()
ppl = eval_perplexity(model, corpus_val)
print('검증 퍼플렉서티: ', ppl)
if best_ppl > ppl:
best_ppl = ppl
model.save_params()
else:
lr /= 4.0
optimizer.lr = lr
model.reset_state()
xs = corpus[:-1]
ts = corpus[1:]
# モデルの生成
model = Rnnlm(vocab_size, wordvec_size, hidden_size)
optimizer = SGD(lr)
trainer = RnnlmTrainer(model, optimizer)
# パラメータの読み込み
#model.load_params()
# 勾配クリッピングを適用して学習
trainer.fit(xs,
ts,
max_epoch,
batch_size,
time_size,
max_grad,
eval_interval=20)
trainer.plot(ylim=(0, 500))
# テストデータで評価
model.reset_state()
ppl_test = eval_perplexity(model, corpus_test)
print('test perplexity: ', ppl_test)
# パラメータの保存
model.save_params()
# | epoch 4 | iter 1321 / 1327 | time 2590[s] | perplexity 110.01
# evaluating perplexity ...
max_epoch = 25
max_grad = 5.0
# Normal or Peeky? ==============================================
#model = Seq2seq(vocab_size, wordvec_size, hidden_size)
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
# ================================================================
optimizer = Adam()
trainer = RnnlmTrainer(model, optimizer)
best_ppl = float('inf')
t1 = time.time()
for epoch in range(max_epoch):
flag = trainer.fit(xs,
ts,
mode,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
model.reset_state()
ppl = eval_perplexity(model, corpus)
print('valid perplexity: ', ppl)
if best_ppl > ppl:
best_ppl = ppl
model.save_params()
else:
lr /= 4.0
optimizer.lr = lr
model.reset_state()
@author: ghqls
"""
import sys
sys.path.append('..')
from common.optimizer import SGD
from common.trainer import RnnlmTrainer
from dataset import ptb
from simple_rnnlm import SimpleRnnlm
batch_size = 10
wordvec_size = 100
hidden_size = 100
time_size = 5
lr = 0.1
max_epoch = 100
corpus, word_to_id, id_to_word = ptb.load_data('train')
corpus_size = 1000
corpus = corpus[:corpus_size]
vocab_size = int(max(corpus) + 1)
xs = corpus[:-1]
ts = corpus[1:]
model = SimpleRnnlm(vocab_size, wordvec_size, hidden_size)
optimizer = SGD(lr)
trainer = RnnlmTrainer(model, optimizer)
trainer.fit(xs, ts, max_epoch, batch_size, time_size)
trainer.plot()
sys.path.append('..')
from common.optimizer import SGD
from common.trainer import RnnlmTrainer # RnnlmTrainer 는 RNNLM 의 학습을 클래스 안으로 숨겨준다.
from dataset import ptb
from simple_rnnlm import SimpleRnnlm
# 하이퍼파라미터 설정
batch_size = 10
wordvec_size = 100
hidden_size = 100 # RNN의 은닉 상태 벡터의 원소 수
time_size = 5 # RNN을 펼치는 크기
lr = 0.1
max_epoch = 100
# 학습 데이터 읽기
corpus, word_to_id, id_to_word = ptb.load_data('train')
corpus_size = 1000 # 테스트 데이터셋을 작게 설정
corpus = corpus[:corpus_size]
vocab_size = int(max(corpus) + 1)
xs = corpus[:-1] # 입력
ts = corpus[1:] # 출력（정답 레이블）
# 모델 생성
model = SimpleRnnlm(vocab_size, wordvec_size, hidden_size)
optimizer = SGD(lr)
trainer = RnnlmTrainer(model, optimizer) # 먼저 model, optimzer 을 주어 초기화
trainer.fit(xs, ts, max_epoch, batch_size, time_size) # fit 메서드를 통해 학습을 수행한다.
trainer.plot()
