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() -> None:
(x_train, t_train), (x_test, t_test) = sequence.load_data('data.txt')
char_to_id, id_to_char = sequence.get_vocab()
x_train, x_test = x_train[:, ::-1], x_test[:, ::-1]
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(f"val acc {acc*100}%")
model.save_params()
print("DONE")
# 載入資料
(x_train, t_train), (x_test, t_test) = sequence.load_data('train_33839.txt')
char_to_id, id_to_char = sequence.get_vocab()
# 反轉輸入內容
x_train, x_test = x_train[:, ::-1], x_test[:, ::-1]
# 設定超參數
vocab_size = len(char_to_id)
wordvec_size = int(16 / 1)
hidden_size = int(256 * 2)
batch_size = int(128 * 2)
max_epoch = int(len(x_train) / 2000)
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):
print("Training epoch %d / %d" % (epoch, max_epoch))
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
total = len(x_test)
print("Evaluating epoch %d, Total: %d" % (epoch, total))