def main():
# ハイパーパラメータの設定
batch_size = 20
wordvec_size = 650
hidden_size = 650
time_size = 35
lr = 20.0
max_epoch = 40
max_grad = 0.25
dropout = 0.5
# 学習データの読み込み
corpus, word_to_id, id_to_word = ptb.load_data('train')
corpus_val, _, _ = ptb.load_data('val')
corpus_test, _, _ = ptb.load_data('test')
vocab_size = len(word_to_id)
xs = corpus[:-1]
ts = corpus[1:]
model = BetterRnnlm(vocab_size, wordvec_size, hidden_size, dropout)
optimizer = SGD(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('valid perplexity: ', ppl)
if best_ppl > ppl:
best_ppl = ppl
model.save_params()
else:
lr /= 4.0
optimizer.lr = lr
model.reset_state()
print('-' * 50)
def main():
# ハイパーパラメータの設定
batch_size = 20
wordvec_size = 100
hidden_size = 100 # RNNの隠れ状態ベクトルの要素数
time_size = 35 # RNNを展開するサイズ
lr = 20.0
max_epoch = 4
max_grad = 0.25
# 学習データの読み込み
corpus, word_to_id, id_to_word = ptb.load_data('train')
corpus_test, _, _ = ptb.load_data('test')
vocab_size = len(word_to_id)
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)
# 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)
'''
eval_interval=20
20イテレーションごとにパープレキシティを評価
'''
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()
lr = 0.001
time_size = 35
#モデルの生成
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = RnnlmTrainer(model, optimizer)
#学習
best_ppl = float('inf')
t1 = time.time()
for epoch in range(max_epoch):
trainer.fit(xs, ts, 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()
print('-' * 50)
'''
flag = trainer.fit(xs, ts, lr,max_epoch=1, batch_size=batch_size, max_grad=max_grad)
model.reset_state()
ppl = eval_perplexity(model, corpus)
hidden_size = 100 # RNNの隠れ状態ベクトルの要素数
time_size = 35 # RNNを展開するサイズ
lr = 20.0
max_epoch = 4
max_grad = 0.25
# 学習データの読み込み
corpus, word_to_id, id_to_word = ptb.load_data('train')
corpus_test, _, _ = ptb.load_data('test')
vocab_size = len(word_to_id)
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)
# 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()
def get_perplexity(model):
from common.util import eval_perplexity
corpus_val, _, _ = ptb.load_data('val')
ppl = eval_perplexity(model, corpus_val)
return ppl
