def train_eval(x_train, x_test, is_peeky):
if is_peeky:
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
else:
model = Seq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model, question, correct, id_to_char,
verbose)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('val acc %.3f%%' % (acc * 100))
return acc_list
def main():
# データの読み込み
(x_train, t_train), (x_test, t_test) = sequence.load_data('date.txt')
char_to_id, id_to_char = sequence.get_vocab()
# 入力文を反転
x_train, x_test = x_train[:, ::-1], x_test[:, ::-1]
# ハイパーパラメータの設定
vocab_size = len(char_to_id)
wordvec_size = 16
hidden_size = 256
batch_size = 128
max_epoch = 10
max_grad = 5.0
model = AttentionSeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model,
question,
correct,
id_to_char,
verbose,
is_reverse=True)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('val acc %.3f%%' % (acc * 100))
def main():
# データセットの読み込み
(x_train, t_train), (x_test, t_test) = sequence.load_data('addition.txt')
x_train, x_test = x_train[:, ::-1], x_test[:, ::-1]
char_to_id, id_to_char = sequence.get_vocab()
# ハイパーパラメータの設定
vocab_size = len(char_to_id)
wordvec_size = 16
hidden_size = 128
batch_size = 128
max_epoch = 25
max_grad = 5.0
# モデル / オプティマイザ / トレーナーの生成
# model = Seq2seq(vocab_size, wordvec_size, hidden_size)
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model, question, correct, id_to_char,
verbose)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('val acc %.3f%%' % (acc * 100))
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model,
question,
correct,
id_to_char,
verbose,
is_reverse=True)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('val acc %.3f%%' % (acc * 100))
model.save_params()
# グラフの描画
x = np.arange(len(acc_list))
plt.plot(x, acc_list, marker='o')
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.ylim(-0.05, 1.05)
plt.show()
wordvec_size = 16
hidden_size = 128
batch_size = 128
max_epoch = 25
max_grad = 5.0
# 모델 / 옵티마이저 / 트레이너 생성
# model = Seq2Seq(vocab_size, wordvec_size, hidden_size)
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model, question, correct, id_to_char,
verbose)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('검증 정확도 %.3f%%' % (acc * 100))
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
model.save_params('../pkl/myAttentionSeq2seq3.pkl')
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model,
question,
correct,
preprocessing.id_to_word,
verbose,
is_reverse=False)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('정확도 %.3f%%' % (acc * 100))
# 그래프 그리기
x = np.arange(len(acc_list))
plt.plot(x, acc_list, marker='o')
plt.xlabel('에폭')
plt.ylabel('정확도')
plt.ylim(-0.05, 1.05)
plt.show()
