回答編集履歴
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test
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@@ -1,7 +1,121 @@
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修正:このコメントは正しくありません。
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ytrain.shape = (2, 1)
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__(ytrain.shape = (2, 1)
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と
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l2 = L.Linear(100,2)
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が相容れないせいではないでしょうか。
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が相容れないせいではないでしょうか。)__
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追記:
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SoftmaxCrossEntropyはnp.int32を教師データに要求します。
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tに代入される部分を.astype('i')に変換しておく必要があります。
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https://docs.chainer.org/en/stable/reference/generated/chainer.functions.softmax_cross_entropy.html
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例を見ると型の変換がありました。
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---
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```python
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import numpy as np
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from chainer import Link, Chain, ChainList
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import chainer.functions as F
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import chainer.links as L
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from chainer import optimizers, datasets, iterators, training
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from chainer.training import extensions
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class MyChain(Chain):
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def __init__(self):
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super(MyChain, self).__init__(
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cn1=L.Convolution2D(3, 8, (2, 3), stride=1, pad=1),
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cn2=L.Convolution2D(8, 16, (2, 3), stride=1, pad=1),
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l1=L.Linear(160, 100),
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l2=L.Linear(100 ,2)
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)
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def __call__(self, x, t):
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pt = self.fwd(x)
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return F.softmax_cross_entropy(pt, t)
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def fwd(self, x):
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h1 = F.max_pooling_2d(F.relu(self.cn1(x)), 2) #8*3*10
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h2 = F.max_pooling_2d(F.relu(self.cn2(h1)), 2) #16*2*5
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h3 = F.dropout(F.relu(self.l1(h2)))
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h4 = self.l2(h3)
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return h4
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xtrain = np.random.random((2, 3, 5, 20)).astype('f')
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ytrain = np.random.randint(2, size=(2, 1))
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ytrain = np.hstack((ytrain, 1-ytrain))
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ytrain = np.argmax(ytrain, axis=1).astype('i')
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model = MyChain()
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optimizer = optimizers.Adam()
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optimizer.setup(model)
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train = datasets.tuple_dataset.TupleDataset(xtrain, ytrain)
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iterator = iterators.SerialIterator(train, 2)
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updater = training.StandardUpdater(iterator, optimizer)
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trainer = training.Trainer(updater, (1000, 'epoch'))
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trainer.extend(extensions.ProgressBar())
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trainer.run()
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```
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