googlecolab上でChainerを用いて224枚のシカの画像を学習させ、その学習データを生成するプログラムを作成しているのですが、おそらく、L.Convolution2DとL.linearの使い方がいまいちわからず、不適切な値を入れているせいで以下のエラーが出てしまいます。どうしたらよろしいでしょうか。
エラー文
Exception in main training loop: index 218 is out of bounds for axis 0 with size 3
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/chainer/training/trainer.py", line 343, in run
update()
File "/usr/local/lib/python3.6/dist-packages/chainer/training/updaters/standard_updater.py", line 240, in update
self.update_core()
File "/usr/local/lib/python3.6/dist-packages/chainer/training/updaters/standard_updater.py", line 253, in update_core
optimizer.update(loss_func, *in_arrays)
File "/usr/local/lib/python3.6/dist-packages/chainer/optimizer.py", line 874, in update
loss = lossfun(*args, **kwds)
File "/usr/local/lib/python3.6/dist-packages/chainer/link.py", line 287, in call
out = forward(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/chainer/links/model/classifier.py", line 144, in forward
self.loss = self.lossfun(self.y, t)
File "/usr/local/lib/python3.6/dist-packages/chainer/functions/loss/softmax_cross_entropy.py", line 556, in softmax_cross_entropy
loss, = func.apply((x, t))
File "/usr/local/lib/python3.6/dist-packages/chainer/function_node.py", line 334, in apply
outputs = self.forward(in_data)
File "/usr/local/lib/python3.6/dist-packages/chainer/function_node.py", line 592, in forward
return self.forward_cpu(inputs)
File "/usr/local/lib/python3.6/dist-packages/chainer/functions/loss/softmax_cross_entropy.py", line 162, in forward_cpu
log_p = log_yd[t.ravel(), numpy.arange(t.size)]
Will finalize trainer extensions and updater before reraising the exception.
IndexError Traceback (most recent call last)
<ipython-input-1-3bf3422f7400> 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)
/usr/local/lib/python3.6/dist-packages/chainer/functions/loss/softmax_cross_entropy.py in forward_cpu(self, inputs)
160 t_valid = t != self.ignore_label
161 t = t * t_valid
--> 162 log_p = log_yd[t.ravel(), numpy.arange(t.size)]
163
164 log_p *= t_valid.ravel()
IndexError: index 218 is out of bounds for axis 0 with size 3
コード
python
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.functions 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,16,3,pad=2) self.conv2 = L.Convolution2D(None,32,3,pad=2) self.l3 = L.Linear(None,256) self.l4 = L.Linear(None,3) def __call__(self,x): h = F.max_pooling_2d(F.relu(self.conv1(x)),ksize=3,stride=2,pad=2) h = F.max_pooling_2d(F.relu(self.conv2(x)),ksize=3,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)
まだ回答がついていません
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