初めて利用します。
私は、chainerを利用してConvolutionalLSTMという機構を用いた機械学習の研究を行っております。

MovingMnistを利用した実験ではプログラムが動いたのですが、手持ちのデータを利用しようとした所エラーが出てしまい、困っております。
入力データは、320x256のbmpデータ(1チャンネル)になります。

エラー内容

/home/denko/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

Exception in main training loop: Unsupported dtype object
Traceback (most recent call last):
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/training/trainer.py", line 299, in run
    update()
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/training/updater.py", line 223, in update
    self.update_core()
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/training/updater.py", line 228, in update_core
    in_arrays = self.converter(batch, self.device)
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/dataset/convert.py", line 93, in concat_examples
    [example[i] for example in batch], padding[i])))
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/dataset/convert.py", line 35, in to_device
    return cuda.to_gpu(x, device)
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/backends/cuda.py", line 275, in to_gpu
    return _array_to_gpu(array, device_, stream)
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/backends/cuda.py", line 322, in _array_to_gpu
    return cupy.asarray(array)
  File "/home/denko/anaconda3/lib/python3.6/site-packages/cupy/creation/from_data.py", line 61, in asarray
    return core.array(a, dtype, False)
  File "cupy/core/core.pyx", line 2070, in cupy.core.core.array
  File "cupy/core/core.pyx", line 2101, in cupy.core.core.array
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
  File "train.py", line 81, in <module>
    train()
  File "train.py", line 69, in train
    trainer.run()
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/training/trainer.py", line 313, in run
    six.reraise(*sys.exc_info())
  File "/home/denko/anaconda3/lib/python3.6/site-packages/six.py", line 693, in reraise
    raise value
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/training/trainer.py", line 299, in run
    update()
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/training/updater.py", line 223, in update
    self.update_core()
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/training/updater.py", line 228, in update_core
    in_arrays = self.converter(batch, self.device)
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/dataset/convert.py", line 93, in concat_examples
    [example[i] for example in batch], padding[i])))
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/dataset/convert.py", line 35, in to_device
    return cuda.to_gpu(x, device)
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/backends/cuda.py", line 275, in to_gpu
    return _array_to_gpu(array, device_, stream)
  File "/home/denko/anaconda3/lib/python3.6/site-packages/chainer/backends/cuda.py", line 322, in _array_to_gpu
    return cupy.asarray(array)
  File "/home/denko/anaconda3/lib/python3.6/site-packages/cupy/creation/from_data.py", line 61, in asarray
    return core.array(a, dtype, False)
  File "cupy/core/core.pyx", line 2070, in cupy.core.core.array
  File "cupy/core/core.pyx", line 2101, in cupy.core.core.array
ValueError: Unsupported dtype object

コード

--dataset_for_rr--

import numpy as np
import chainer
from PIL import Image
from chainer import cuda, Variable

class rrDataset(chainer.dataset.DatasetMixin):
    def __init__(self,l,r,inf,outf):
        self.rr_list = np.load('/media/denko/OS/data/rr_list/test.npy')
        self.l = l
        self.r = r
        self.inf = inf
        self.outf = outf
        self.flames = self.inf + self.outf
        self.num = self.r - self.l
        self.xp = cuda.cupy

    def __len__(self):
        return self.num

    def get_example(self,i):
        self.ind = self.l + i
        self.data = np.empty((self.flames,1,256,320),dtype=np.float32)

        for j in range(self.flames):
            self.bmp_name = str(int(self.rr_list[self.ind][j]))
            self.year = self.bmp_name[0:4]
            self.month = self.bmp_name[4:6]
            self.path = '/media/denko/OS/data/bmp/' + self.year + '/' + self.month + '/' + self.bmp_name + '.bmp'
            self.bmp_data = Image.open(self.path)
            self.bmp_data = np.array(self.bmp_data).astype(dtype=np.float32)
            self.bmp_data = self.bmp_data / 255
            self.data[j,0,:,:] = self.bmp_data

        self.data = Variable(np.array(self.data,dtype=np.float32))

        return self.data[:self.inf,0,:,:], self.data[self.inf:self.flames,0,:,:]