mkgrei score 8498
2017/12/10 19:57 投稿
| ytrain.shape = (2, 1) |
| 修正：このコメントは正しくありません。 |
| __（ytrain.shape = (2, 1) |
| と |
| l2 = L.Linear(100,2) |
| が相容れないせいではないでしょうか。 |
| が相容れないせいではないでしょうか。）__ |
| 追記： |
| SoftmaxCrossEntropyはnp.int32を教師データに要求します。 |
| 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。 |
| https://docs.chainer.org/en/stable/reference/generated/chainer.functions.softmax_cross_entropy.html |
| 例を見ると型の変換がありました。 |
| --- |
| ```python |
| import numpy as np |
| from chainer import Link, Chain, ChainList |
| import chainer.functions as F |
| import chainer.links as L |
| from chainer import optimizers, datasets, iterators, training |
| from chainer.training import extensions |
| class MyChain(Chain): |
| def __init__(self): |
| super(MyChain, self).__init__( |
| cn1=L.Convolution2D(3, 8, (2, 3), stride=1, pad=1), |
| cn2=L.Convolution2D(8, 16, (2, 3), stride=1, pad=1), |
| l1=L.Linear(160, 100), |
| l2=L.Linear(100 ,2) |
| ) |
| def __call__(self, x, t): |
| pt = self.fwd(x) |
| return F.softmax_cross_entropy(pt, t) |
| def fwd(self, x): |
| h1 = F.max_pooling_2d(F.relu(self.cn1(x)), 2) #8*3*10 |
| h2 = F.max_pooling_2d(F.relu(self.cn2(h1)), 2) #16*2*5 |
| h3 = F.dropout(F.relu(self.l1(h2))) |
| h4 = self.l2(h3) |
| return h4 |
| xtrain = np.random.random((2, 3, 5, 20)).astype('f') |
| ytrain = np.random.randint(2, size=(2, 1)) |
| ytrain = np.hstack((ytrain, 1-ytrain)) |
| ytrain = np.argmax(ytrain, axis=1).astype('i') |
| model = MyChain() |
| optimizer = optimizers.Adam() |
| optimizer.setup(model) |
| train = datasets.tuple_dataset.TupleDataset(xtrain, ytrain) |
| iterator = iterators.SerialIterator(train, 2) |
| updater = training.StandardUpdater(iterator, optimizer) |
| trainer = training.Trainer(updater, (1000, 'epoch')) |
| trainer.extend(extensions.ProgressBar()) |
| trainer.run() |
| ``` |
