chainerのモデルに入力する変数について

TypeError: numpy.ndarray or cuda.ndarray are expected.

chainerにおいて自分でトレーニングループを書いて実行したのですが、
modelに代入する段階でエラーがおきます。

以下にエラーを示します。

TypeError                                 Traceback (most recent call last)
<ipython-input-131-22fce9e330d5> in <module>
     32         target_movie = target_movie
     33 
---> 34         prediction_train = model(input_movie)
     35         loss = F.mean_squared_error(prediction_train,target_movie)
     36 

<ipython-input-125-12b6f13f8eed> in __call__(self, x)
      5             self.conv1 = L.Convolution3D(None,out_channels=3, ksize=3, stride=1, pad=1)
      6     def __call__(self,x):
----> 7         return F.relu(self.conv1)

~/.local/lib/python3.6/site-packages/chainer/functions/activation/relu.py in relu(x)
    174 
    175     """
--> 176     y, = ReLU().apply((x,))
    177     return y

~/.local/lib/python3.6/site-packages/chainer/function_node.py in apply(self, inputs)
    224 
    225         """
--> 226         input_vars = [chainer.as_variable(x) for x in inputs]
    227         in_data = tuple([x.data for x in input_vars])
    228         requires_grad = any([x.requires_grad for x in input_vars])

~/.local/lib/python3.6/site-packages/chainer/function_node.py in <listcomp>(.0)
    224 
    225         """
--> 226         input_vars = [chainer.as_variable(x) for x in inputs]
    227         in_data = tuple([x.data for x in input_vars])
    228         requires_grad = any([x.requires_grad for x in input_vars])

~/.local/lib/python3.6/site-packages/chainer/variable.py in as_variable(obj)
   1373     if isinstance(obj, Variable):
   1374         return obj
-> 1375     return Variable(obj, requires_grad=False)
   1376 
   1377 

~/.local/lib/python3.6/site-packages/chainer/variable.py in __init__(self, data, **kwargs)
    503             msg = '''numpy.ndarray or cuda.ndarray are expected.
    504 Actual: {0}'''.format(type(data))
--> 505             raise TypeError(msg)
    506 
    507         # Use a list as a data structure to hold the data array indirectly to

TypeError: numpy.ndarray or cuda.ndarray are expected.
Actual: <class 'chainer.links.connection.convolution_nd.Convolution3D'>

該当部分のコードを示します。

model = conv_3d()
model.to_gpu(0)


optimizer = optimizers.MomentumSGD(lr=0.01, momentum=0.9)

optimizer.setup(model)

max_epoch = 100
batch_size = 50

epoch_idx = 0

while epoch_idx < max_epoch:

    train_path = random.sample(train_path, len(train_path))


    train_losses = []
    for i in range(int(len(train_path) // batch_size)):



        batch = train_path[i * batch_size: (i+1) * batch_size]
        input_movie, target_movie = loader(batch)

        prediction_train = model(input_movie)
        loss = F.mean_squared_error(prediction_train,target_movie)

        train_losses.append(to_cpu(loss.array))

        model.cleargrads()
        loss.backward()

        optimizer.update()

    print('epoch:{:03d} train_loss:{:.04f} '.format(epoch_idx + 1, np.mean(train_losses)), end='')

    test_losses = []
    for test_batch in range(len(validation)//batch_size):

        batch = validation[test_batch * batch_size:(test_batch + 1) * 50]
        validation_input_movie, validation_target_movie = loader(batch)

        prediction_validation = model(validation_input_movie)

        loss_validation = F.mean_squared_error(prediction_validation,validation_target_movie)
        test_losses.append(to_cpu(loss_test.array))


    print('val_loss:{:.04f}'.format(
            np.mean(test_losses)))

    epoch_idx += 1

また、loader関数は

def loader(path_list):

    input_movie = [i[0] for i in path_list]
    target_movie = [i[1] for i in path_list]

    input_movie = np.asarray([[np.asarray(cv2.resize(cv2.imread("../image/" + img),(1024//10,780//10))) for img in img_path] for img_path in input_movie])
    target_movie = np.asarray([[np.asarray(cv2.resize(cv2.imread("../image/" + img),(1024//10,780//10))) for img in img_path] for img_path in target_movie])
    return tuple([input_movie,target_movie])

です。
エラーが出てから、numpyの配列にしてから入力しているのですが、うまく行きません。

ご教授のほどよろしくお願いいたします。