chainerでのStopiterationを回避するには？

Question

現在CNNを用いた株価騰落の分類を行うモデルを作成しています。
以前まできちんと動作していたのですがある時から急に
StopIterationのエラーが出て２回目以降の学習を繰り返してくれなくなりました。
調べてみても原因が全く分からず困っております。

以下エラー全文です
epoch main/loss main/accuracy validation/main/loss validation/main/accuracy elapsed_time
1 20.1328 0.509596 0.692708 0.526667 605.443
Exception in main training loop:
Traceback (most recent call last):
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 316, in run
update()
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 175, in update
self.update_core()
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 180, in update_core
batch = iterator.next()
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/iterators/serial_iterator.py", line 75, in next
raise StopIteration
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "cnn.py", line 67, in <module>
trainer.run()
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 349, in run
six.reraise(*exc_info)
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/six.py", line 693, in reraise
raise value
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 316, in run
update()
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 175, in update
self.update_core()
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 180, in update_core
batch = iterator.next()
File "/Users/kojimakazuya/anaconda3/lib/python3.7/site-packages/chainer/iterators/serial_iterator.py", line 75, in next
raise StopIteration
StopIteration

import matplotlib.pyplot as plt
import load_data
import stock_data
import img_to_vector
import load_vector
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import chainer
import chainer.links as L
import chainer.functions as F
from chainer import Chain, Variable, datasets, optimizers
from chainer import report, training
from chainer.training import extensions
from chainer import iterators
from chainer import serializers
import chainer.cuda

#データの準備
sp500, topix500, usd_jpy = load_vector.load_vector()
x_train, x_test = topix500[:2000], topix500[2000:]

t_train, t_test = load_data.label(stock_data.teacher_dataset()[:2000]), load_data.label(stock_data.teacher_dataset()[2000:])

train = list(zip(x_train, t_train))
test = list(zip(x_test, t_test))

class CNN(Chain):
    def __init__(self):
        super(CNN, self).__init__()
        with self.init_scope():
            self.cn1 = L.Convolution2D(None, 32, ksize=3, pad=1)
            self.cn2 = L.Convolution2D(None, 32, ksize=3, pad=1)
            self.fc1 = L.Linear(None, 100)
            self.fc2 = L.Linear(100, 2)

    def __call__(self, x, t=None):
        h1 = F.max_pooling_2d(F.relu(self.cn1(x)), ksize=2, stride=2)
        h2 = F.max_pooling_2d(F.relu(self.cn2(h1)), ksize=2, stride=2)
        h3 = F.dropout(F.relu(self.fc1(h2)))
        return self.fc2(h3)


model = CNN()
net = L.Classifier(model)
optimizer = optimizers.Adam().setup(net)

batchsize = 20
train_iter = chainer.iterators.SerialIterator(train, batchsize, repeat=False, shuffle=True)
test_iter = chainer.iterators.SerialIterator(test, batchsize, repeat=False, shuffle=True)

updater = training.StandardUpdater(train_iter, optimizer, device=-1) # device=-1でCPUでの計算実行を指定

epoch = 100
trainer = training.Trainer(updater, (epoch, 'epoch'), out='result')

# テストデータで評価
trainer.extend(extensions.Evaluator(test_iter, net, device = -1))

# 学習を記録
trainer.extend(extensions.LogReport(trigger=(1, 'epoch')))

# グラフに描画、保存
trainer.extend(extensions.PlotReport(['main/loss', 'validation/main/loss'], x_key='epoch', file_name='cnn2_loss.png'))
trainer.extend(extensions.PlotReport(['main/accuracy', 'validation/main/accuracy'], x_key='epoch', file_name='cnn2_accuracy.png'))
trainer.extend(extensions.PrintReport(['epoch', 'main/loss', 'main/accuracy','validation/main/loss', 'validation/main/accuracy','elapsed_time']), trigger=(1, 'epoch'))

trainer.run()

Answer 1

iterの箇所のrepeat引数をTrueに変えると治りました。
そういえばshuffleをtrueに変えた時にrepeatの意味を理解せずにtest同様にfalseにしてしまっていた。
repeatは繰り返しをするかどうか、ですね。