def main():
batch_size = 20
wordvec_size = 100
hidden_size = 100
time_size = 35
lr = 20.0
#max_epoch = 4
max_epoch = 1
max_grad = 0.25
corpus, word_to_id, _ = ptb.load_data('train')
corpus_test, _, _ = ptb.load_data('test')
vocab_size = len(word_to_id)
xs = corpus[:-1]
ts = corpus[1:]
model = Rnnlm(vocab_size, wordvec_size, hidden_size)
optimizer = SGD(lr)
trainer = RnnlmTrainer(model, optimizer)
trainer.fit(xs,
ts,
max_epoch,
batch_size,
time_size,
max_grad,
eval_interval=20)
model.reset_state()
ppl_test = eval_perplexity(model, corpus_test)
print(f'test perplexity: {ppl_test}')
model.save_params()
print('DONE')
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()
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()