ChainerでLSTMの学習をすすめると1epochあたりにかかる計算時間が増えていく

chainer初心者です。LSTMでsin波を学習させようと思って、次のようなコードを書いたのですが、学習が進むに連れて(epoch数がますに連れて)、1エポックの学習にかかる時間が増えているのがわかります。これはなぜでしょうか？

python
1import numpy as np
2import chainer
3import chainer.functions as F
4import chainer.links as L
5from chainer import Chain, optimizers, Variable
6import matplotlib.pyplot as plt
7import time
8
9class MLP(Chain):
10	def __init__(self):
11		super().__init__()
12		with self.init_scope():
13			self.l1 = L.Linear(1, 5)
14			self.l2 = L.LSTM(5, 5)
15			self.l3 = L.Linear(5, 1)
16
17	def reset_state(self):
18		self.l2.reset_state()
19
20	def __call__(self, x):
21		h = self.l1(x)
22		h = self.l2(h)
23		h = self.l3(h)
24
25		return h
26
27def generate_data(number_of_data, number_of_loop):
28	x = np.linspace(0, 2*np.pi*number_of_loop, number_of_data)
29
30	return x, np.sin(x)
31
32def main():
33	number_of_data = 100
34	number_of_loop = 3
35
36	x, input_data = generate_data(number_of_data, number_of_loop)
37	input_data = np.reshape(input_data, (number_of_data, 1)).astype(np.float32)
38
39	model = MLP()
40	optimizer = optimizers.Adam()
41	optimizer.setup(model)
42	
43	for epoch in range(200):
44		start = time.time()
45		model.cleargrads()
46		t = model(input_data)
47		loss = F.mean_squared_error(t, input_data)
48		loss.backward()
49		optimizer.update()
50		elapsed_time = time.time() - start
51		print("time{}[sec]".format(elapsed_time))
52
53	inp = model(input_data)
54	plt.plot(x, np.sin(x))
55	plt.plot(x, inp.array)
56	plt.show()
57
58if __name__ == "__main__":
59	main()