機械学習初学者です。Chainerを用いて固定サイズの画像を学習させるため、サンプルのimagenet内のtrain_imagenet.pyとalex.pyをもとにコードを書いたのですがエラーを吐きました。  
以下、ソースコードとエラー文です。

```

    #!/usr/bin/env python
    """Example code of learning a large scale convnet from ILSVRC2012 dataset.
    
    Prerequisite: To run this example, crop the center of ILSVRC2012 training and
    validation images, scale them to 256x256 and convert them to RGB, and make
    two lists of space-separated CSV whose first column is full path to image and
    second column is zero-origin label (this format is same as that used by Caffe's
    ImageDataLayer).
    
    """
    import argparse
    import random
    
    import numpy as np
    
    import chainer
    from chainer import training
    from chainer.training import extensions
    
    import alex
    import googlenet
    import googlenetbn
    import nin
    import resnet50
    
    count = 0
    
    class PreprocessedDataset(chainer.dataset.DatasetMixin):
    
        def __init__(self, path, root, mean, crop_size_x, crop_size_y, random=True):
            self.base = chainer.datasets.LabeledImageDataset(path, root)
            self.mean = mean.astype(np.float32)
            self.crop_size_x = crop_size_x
            self.crop_size_y = crop_size_y
            self.random = random
        def __len__(self):
            return len(self.base)
    
        def get_example(self, i):
            global count
            #print(count)
            count = count+1
            # It reads the i-th image/label pair and return a preprocessed image.
            # It applies following preprocesses:
            #     - Cropping (random or center rectangular)
            #     - Random flip
            #     - Scaling to [0, 1] value
            #crop_size = self.crop_size
            crop_size_x = self.crop_size_x
            crop_size_y = self.crop_size_y
    
            image, label = self.base[i]
            _, h, w = image.shape
            print(_,h,w)
    
            #if h != crop_size_y:
            #image = image[:, 0:h, :]
            #if w != crop_size_x:
            image -= self.mean[:, 0:crop_size_y, 0:crop_size_x]
            image *= (1.0 / 255.0)  # Scale to [0, 1]
            return image, label                
            """
            if self.random:
                # Randomly crop a region and flip the image
                top = random.randint(0, h - crop_size_y - 1)
                left = random.randint(0, w - crop_size_x - 1)
                if random.randint(0, 1):
                    image = image[:, :, ::-1]
            else:
                # Crop the center
                top = (h - crop_size_y) // 2
                left = (w - crop_size_x) // 2
    
            bottom = top + crop_size_y
            right = left + crop_size_x
    
            image = image[:, top:bottom, left:right]
            image -= self.mean[:, top:bottom, left:right]
            image *= (1.0 / 255.0)  # Scale to [0, 1]
            return image, label
            """
    
    def main():
        archs = {
            'alex': alex.Alex,
            'alex_fp16': alex.AlexFp16,
            'googlenet': googlenet.GoogLeNet,
            'googlenetbn': googlenetbn.GoogLeNetBN,
            'googlenetbn_fp16': googlenetbn.GoogLeNetBNFp16,
            'nin': nin.NIN,
            'resnet50': resnet50.ResNet50,
            'resnext50': resnet50.ResNeXt50,
        }
    
        parser = argparse.ArgumentParser(
            description='Learning convnet from ILSVRC2012 dataset')
        parser.add_argument('train', help='Path to training image-label list file')
        parser.add_argument('val', help='Path to validation image-label list file')
        parser.add_argument('--arch', '-a', choices=archs.keys(), default='nin',
                            help='Convnet architecture')
        parser.add_argument('--batchsize', '-B', type=int, default=32,
                            help='Learning minibatch size')
        parser.add_argument('--epoch', '-E', type=int, default=5,
                            help='Number of epochs to train')
        parser.add_argument('--gpu', '-g', type=int, default=0,
                            help='GPU ID (negative value indicates CPU')
        parser.add_argument('--initmodel',
                            help='Initialize the model from given file')
        parser.add_argument('--loaderjob', '-j', type=int,
                            help='Number of parallel data loading processes')
        parser.add_argument('--mean', '-m', default='mean.npy',
                            help='Mean file (computed by compute_mean.py)')
        parser.add_argument('--resume', '-r', default='',
                            help='Initialize the trainer from given file')
        parser.add_argument('--out', '-o', default='result',
                            help='Output directory')
        parser.add_argument('--root', '-R', default='images/',
                            help='Root directory path of image files')
        parser.add_argument('--val_batchsize', '-b', type=int, default=250,
                            help='Validation minibatch size')
        parser.add_argument('--test', action='store_true')
        parser.set_defaults(test=False)
        args = parser.parse_args()
    
        # Initialize the model to train
        model = archs[args.arch]()
        if args.initmodel:
            print('Load model from {}'.format(args.initmodel))
            chainer.serializers.load_npz(args.initmodel, model)
        if args.gpu >= 0:
            chainer.backends.cuda.get_device_from_id(
                args.gpu).use()  # Make the GPU current
            model.to_gpu()
    
        # Load the datasets and mean file
        mean = np.load(args.mean)
        train = PreprocessedDataset(args.train, args.root, mean, model.insize_x, model.insize_y)
        val = PreprocessedDataset(args.val, args.root, mean, model.insize_x,model.insize_y, False)
        # These iterators load the images with subprocesses running in parallel to
        # the training/validation.
        train_iter = chainer.iterators.MultiprocessIterator(
            train, args.batchsize, n_processes=args.loaderjob)
        val_iter = chainer.iterators.MultiprocessIterator(
            val, args.val_batchsize, repeat=False, n_processes=args.loaderjob)
    
        # Set up an optimizer
        optimizer = chainer.optimizers.MomentumSGD(lr=0.01, momentum=0.9)
        optimizer.setup(model)
    
        # Set up a trainer
        updater = training.StandardUpdater(
            train_iter, optimizer, device=args.gpu)
        trainer = training.Trainer(updater, (args.epoch, 'epoch'), args.out)
    
        val_interval = (1 if args.test else 100000), 'iteration'
        log_interval = (1 if args.test else 1000), 'iteration'
    
        trainer.extend(extensions.Evaluator(val_iter, model, device=args.gpu),
                       trigger=val_interval)
        trainer.extend(extensions.dump_graph('main/loss'))
        trainer.extend(extensions.snapshot(), trigger=val_interval)
        trainer.extend(extensions.snapshot_object(
            model, 'model_iter_{.updater.iteration}'), trigger=val_interval)
        # Be careful to pass the interval directly to LogReport
        # (it determines when to emit log rather than when to read observations)
        trainer.extend(extensions.LogReport(trigger=log_interval))
        trainer.extend(extensions.observe_lr(), trigger=log_interval)
        trainer.extend(extensions.PlotReport(['main/accuracy', 'main/loss'], x_key='epoch', file_name='main.png'))
        trainer.extend(extensions.PlotReport(['validation/main/accuracy', 'validation/main/loss'], x_key='epoch', file_name='valid.png'))    
        trainer.extend(extensions.PrintReport([
            'epoch', 'iteration', 'main/loss', 'validation/main/loss',
            'main/accuracy', 'validation/main/accuracy', 'lr'
        ]), trigger=log_interval)
        trainer.extend(extensions.ProgressBar(update_interval=10))
    
        if args.resume:
            chainer.serializers.load_npz(args.resume, trainer)
    
        trainer.run()
    
        chainer.serializers.save_npz('result/model_final', model)
        chainer.serializers.save_npz('result/optimizer_final', optimizer)
    
    
    
    if __name__ == '__main__':
        main()
    
```

エラー文

```

    Exception in main training loop: 'NoneType' object has no attribute 'dot'
    Traceback (most recent call last):
      File "/lib/python3.5/site-packages/chainer/training/trainer.py", line 306, in run
        update()
      File "/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
        self.update_core()
      File "/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 160, in update_core
        optimizer.update(loss_func, *in_arrays)
      File "/lib/python3.5/site-packages/chainer/optimizer.py", line 645, in update
        loss.backward(loss_scale=self._loss_scale)
      File "/lib/python3.5/site-packages/chainer/variable.py", line 963, in backward
        self._backward_main(retain_grad, loss_scale)
      File "/lib/python3.5/site-packages/chainer/variable.py", line 1092, in _backward_main
        target_input_indexes, out_grad, in_grad)
      File "/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
        gxs = self.backward(target_input_indexes, grad_outputs)
      File "/lib/python3.5/site-packages/chainer/functions/connection/linear.py", line 80, in backward
        gx, = LinearGradData().apply((W, gy))
      File "/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
        outputs = self.forward(in_data)
      File "/lib/python3.5/site-packages/chainer/functions/connection/linear.py", line 112, in forward
        gx = gy.dot(W).astype(gy.dtype, copy=False)
    Will finalize trainer extensions and updater before reraising the exception.
    Traceback (most recent call last):
      File "train_imagenet.py", line 187, in <module>
        main()
      File "train_imagenet.py", line 179, in main
        trainer.run()
      File "/lib/python3.5/site-packages/chainer/training/trainer.py", line 320, in run
        six.reraise(*sys.exc_info())
      File "/lib/python3.5/site-packages/six.py", line 693, in reraise
        raise value
      File "/lib/python3.5/site-packages/chainer/training/trainer.py", line 306, in run
        update()
      File "/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
        self.update_core()
      File "/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 160, in update_core
        optimizer.update(loss_func, *in_arrays)
      File "/lib/python3.5/site-packages/chainer/optimizer.py", line 645, in update
        loss.backward(loss_scale=self._loss_scale)
      File "/lib/python3.5/site-packages/chainer/variable.py", line 963, in backward
        self._backward_main(retain_grad, loss_scale)
      File "/lib/python3.5/site-packages/chainer/variable.py", line 1092, in _backward_main
        target_input_indexes, out_grad, in_grad)
      File "/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
        gxs = self.backward(target_input_indexes, grad_outputs)
      File "/lib/python3.5/site-packages/chainer/functions/connection/linear.py", line 80, in backward
        gx, = LinearGradData().apply((W, gy))
      File "/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
        outputs = self.forward(in_data)
      File "/lib/python3.5/site-packages/chainer/functions/connection/linear.py", line 112, in forward
        gx = gy.dot(W).astype(gy.dtype, copy=False)
    AttributeError: 'NoneType' object has no attribute 'dot'
    
```

ソースコードはtrain_imagenet.pyを、画像をクリッピングせずにそのまま学習に入力できるように書き換えました。  
エラーの原因がわかるかたがいましたらよろしくお願い致します。
CC BY-SA 4.0
2018-05-17T09:36:32.250
44040
2022-04-22T11:04:25.393
2018-05-17T11:14:28.607
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question
python chainer
chainerで画像を学習させようとすると、 AttributeError: 'NoneType' object has no attribute 'dot'がでます。
入力部分になんらかの誤りがあるものかと思います。  
エラーメッセージの意味は、NoneType(空のオブジェクト)に対して.dot (おそらくnumpy.dot) を呼び出せないということです

どのようなargument でコマンドを実行したのか記述して頂けると参考になります
CC BY-SA 4.0
2018-05-21T11:43:55.683
44134
2018-05-21T11:43:55.683
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answer