Chainer loss(平均二乗誤差)のエラー

Question

chainerでボストン住宅価格予想を行うためにプログラムを書きました。
素人なので見よう見まねで書いたものの、エラーが発生し実行できずにいます。

#Chainerプログラム

import numpy as np
import chainer
import chainer.links as L
import chainer.functions as F
from sklearn.datasets import load_boston
from chainer.datasets import TupleDataset
from chainer.datasets import split_dataset_random
from chainer.iterators import SerialIterator
from chainer import optimizers
from chainer.serializers import save_npz
from chainer.training import extensions
from chainer import training
from chainer.functions.loss.mean_squared_error import mean_squared_error

x,t = load_boston(return_X_y=True)

x = x.astype('float32')
t = t.astype('float32').reshape(len(t),1)



mean = x.mean(axis=0)
x -= mean
std = x.std(axis=0)
x /= std



dataset = TupleDataset(x, t)

class Net(chainer.Chain):
    
    def __init__(self):
        super().__init__()
        with self.init_scope():
            self.l1 = L.Linear(None, 30)
            self.l3 = L.Linear(30,1)
          
            

    def forward(self, x):
        h = F.relu(self.l1(x))
        h = self.l3(h)
        
        return h

net = Net()
t = t.astype('int32')
train ,test = split_dataset_random(dataset, int(len(dataset)*0.7), seed=0)
train_iter = SerialIterator(train, batch_size=1, repeat=True, shuffle=True)
test_iter = SerialIterator(test, batch_size=1, repeat=False, shuffle=False)

      
        
gpu_id = 0  # 使用する GPU 番号
n_epoch = 100# エポック数


net.to_gpu(gpu_id)

net = L.Classifier(net, lossfun=mean_squared_error)
optimizer = optimizers.Adam()
optimizer.setup(net)
updater = training.StandardUpdater(train_iter, optimizer, device = gpu_id)
trainer = training.Trainer(updater, (n_epoch, 'epoch'), out = 'result')

trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(['epoch', 'main/loss', 'main/accuracy','elapsed_time']))
 


trainer.run()

#エラー

Exception in main training loop: 
Invalid operation is performed in: Accuracy (Forward)

Expect: t.dtype.kind == i
Actual: f != i
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/chainer/training/trainer.py", line 315, in run
    update()
  File "/usr/local/lib/python3.6/dist-packages/chainer/training/updaters/standard_updater.py", line 165, in update
    self.update_core()
  File "/usr/local/lib/python3.6/dist-packages/chainer/training/updaters/standard_updater.py", line 177, in update_core
    optimizer.update(loss_func, *in_arrays)
  File "/usr/local/lib/python3.6/dist-packages/chainer/optimizer.py", line 680, in update
    loss = lossfun(*args, **kwds)
  File "/usr/local/lib/python3.6/dist-packages/chainer/link.py", line 242, in __call__
    out = forward(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/chainer/links/model/classifier.py", line 147, in forward
    self.accuracy = self.accfun(self.y, t)
  File "/usr/local/lib/python3.6/dist-packages/chainer/functions/evaluation/accuracy.py", line 98, in accuracy
    return Accuracy(ignore_label=ignore_label)(y, t)
  File "/usr/local/lib/python3.6/dist-packages/chainer/function.py", line 233, in __call__
    ret = node.apply(inputs)
  File "/usr/local/lib/python3.6/dist-packages/chainer/function_node.py", line 245, in apply
    self._check_data_type_forward(in_data)
  File "/usr/local/lib/python3.6/dist-packages/chainer/function_node.py", line 330, in _check_data_type_forward
    self.check_type_forward(in_type)
  File "/usr/local/lib/python3.6/dist-packages/chainer/function.py", line 130, in check_type_forward
    self._function.check_type_forward(in_types)
  File "/usr/local/lib/python3.6/dist-packages/chainer/functions/evaluation/accuracy.py", line 19, in check_type_forward
    t_type.dtype.kind == 'i'
  File "/usr/local/lib/python3.6/dist-packages/chainer/utils/type_check.py", line 546, in expect
    expr.expect()
  File "/usr/local/lib/python3.6/dist-packages/chainer/utils/type_check.py", line 483, in expect
    '{0} {1} {2}'.format(left, self.inv, right))

Will finalize trainer extensions and updater before reraising the exception.

InvalidType                               Traceback (most recent call last)
<ipython-input-24-040b7ee08bef> in <module>()
     71 
     72 
---> 73 trainer.run()
     74 

15 frames
/usr/local/lib/python3.6/dist-packages/chainer/utils/type_check.py in expect(self)
    481             raise InvalidType(
    482                 '{0} {1} {2}'.format(self.lhs, self.exp, self.rhs),
--> 483                 '{0} {1} {2}'.format(left, self.inv, right))
    484 
    485 

InvalidType: 
Invalid operation is performed in: Accuracy (Forward)

Expect: t.dtype.kind == i
Actual: f != i

floatがintではないことはわかります。
しかしながら、int32のままだとエラー(
x[0].shape == x[1].shape
float32 != int32
)
が出るため、
t をastypeでfloat32に変換しています。

ソフトマックス交差エントロピーを損失関数に指定する場合は実行できました。
平均二乗誤差を使うのは初めてで原因がわからずにいます。

Answer 1

> net = Net()
> **t = t.astype('int32')**
> train ,test = split_dataset_random(dataset, int(len(dataset)*0.7), seed=0)

ここで int32 に変換しているから、とか？