AttributeError: module 'chainer.function' has no attribute 'max_pooling_2d'の解決法がわかりません

Question

Google colaboratory上で以下のコードを実行したのですが、下記のエラーが出てつまづいています。
解決方法を教えてください。

AttributeError Traceback (most recent call last)
<ipython-input-7-ab72e94bd54e> in <module>()
81 trainer.extend(extensions.PlotReport(['main/loss'],'epoch',file_name='loss.png'))
82 trainer.extend(extensions.PlotReport(['main/accuracy'],'epoch',file_name='accuracy.png'))
---> 83 trainer.run()
84
85 serializer.save_npz("mymodel.npz",model)

8 frames
<ipython-input-7-ab72e94bd54e> in call(self, x)
22
23 def call(self,x):
---> 24 h = F.max_pooling_2d(F.relu(self.conv1(x)),ksize=5,stride=2,pad=2)
25 h = F.max_pooling_2d(F.relu(self.conv2(x)),ksize=5,stride=2,pad=2)
26 h = F.dropout(F.relu(self.l3(h)))

AttributeError: module 'chainer.function' has no attribute 'max_pooling_2d'

import chainer
import glob
from itertools import chain
from chainer.datasets import LabeledImageDataset
from chainer import iterators,training,optimizers,datasets,serializers
from chainer.training import extensions,triggers
from chainer.dataset import concat_examples
import chainer.function as F
import chainer.links as L

#-----MyChain-----

class MyChain(chainer.Chain):

    def __init__(self):
        super(MyChain,self).__init__()
        with self.init_scope():
            self.conv1 = L.Convolution2D(None,16,5,pad=2)
            self.conv2 = L.Convolution2D(None,32,5,pad=2)
            self.l3 = L.Linear(None,256)
            self.l4 = L.Linear(None,5)

    def __call__(self,x):
        h = F.max_pooling_2d(F.relu(self.conv1(x)),ksize=5,stride=2,pad=2)
        h = F.max_pooling_2d(F.relu(self.conv2(x)),ksize=5,stride=2,pad=2)
        h = F.dropout(F.relu(self.l3(h)))
        y = self.l4(h)
        return y


#-----img-----

train_path = glob.glob('img/Deers/Deers_train/*')

tr = 0
label = []
for index,item in enumerate(train_path):
    label.append(tr)
    tr = tr+1

pa_tuple = tuple(train_path)
la_tuple = tuple(label)

train_data = [(0,0)] * len(label)

tr = 0
for index,item in enumerate(train_path):
    train_data[tr] = [train_path[tr],label[tr]]
    tr = tr + 1
    
dataset_train = chainer.datasets.LabeledImageDataset(train_data)

from chainercv.transforms import resize
from chainer.datasets import TransformDataset

def transform(data):
    img,label = data
    img = resize(img,(500,500))
    return img,label

deerset_train = chainer.datasets.TransformDataset(dataset_train,transform)

#-----train-----

epoch = 10
batch = 5

model = L.Classifier(MyChain())
optimizer = optimizers.Adam()
optimizer.setup(model)

train_iter = iterators.SerialIterator(deerset_train,batch)
updater = training.StandardUpdater(train_iter,optimizer)
trainer = training.Trainer(updater,(epoch,'epoch'),out='result')

trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.snapshot(),trigger=(epoch,'epoch'))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(['epoch','main/loss','main/accuracy']))
trainer.extend(extensions.ProgressBar())
trainer.extend(extensions.PlotReport(['main/loss'],'epoch',file_name='loss.png'))
trainer.extend(extensions.PlotReport(['main/accuracy'],'epoch',file_name='accuracy.png'))
trainer.run()

serializer.save_npz("mymodel.npz",model)

Answer 1

普通にinportのミスでした