回答編集履歴
1
edit
answer
CHANGED
|
@@ -1,4 +1,61 @@
|
|
|
1
|
+
修正：このコメントは正しくありません。
|
|
1
|
-
ytrain.shape = (2, 1)
|
|
2
|
+
__（ytrain.shape = (2, 1)
|
|
2
3
|
と
|
|
3
4
|
l2 = L.Linear(100,2)
|
|
4
|
-
が相容れないせいではないでしょうか。
|
|
5
|
+
が相容れないせいではないでしょうか。）__
|
|
6
|
+
|
|
7
|
+
追記：
|
|
8
|
+
SoftmaxCrossEntropyはnp.int32を教師データに要求します。
|
|
9
|
+
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。
|
|
10
|
+
https://docs.chainer.org/en/stable/reference/generated/chainer.functions.softmax_cross_entropy.html
|
|
11
|
+
例を見ると型の変換がありました。
|
|
12
|
+
|
|
13
|
+
---
|
|
14
|
+
|
|
15
|
+
```python
|
|
16
|
+
import numpy as np
|
|
17
|
+
|
|
18
|
+
from chainer import Link, Chain, ChainList
|
|
19
|
+
import chainer.functions as F
|
|
20
|
+
import chainer.links as L
|
|
21
|
+
from chainer import optimizers, datasets, iterators, training
|
|
22
|
+
from chainer.training import extensions
|
|
23
|
+
|
|
24
|
+
class MyChain(Chain):
|
|
25
|
+
def __init__(self):
|
|
26
|
+
super(MyChain, self).__init__(
|
|
27
|
+
cn1=L.Convolution2D(3, 8, (2, 3), stride=1, pad=1),
|
|
28
|
+
cn2=L.Convolution2D(8, 16, (2, 3), stride=1, pad=1),
|
|
29
|
+
l1=L.Linear(160, 100),
|
|
30
|
+
l2=L.Linear(100 ,2)
|
|
31
|
+
)
|
|
32
|
+
|
|
33
|
+
def __call__(self, x, t):
|
|
34
|
+
pt = self.fwd(x)
|
|
35
|
+
return F.softmax_cross_entropy(pt, t)
|
|
36
|
+
|
|
37
|
+
def fwd(self, x):
|
|
38
|
+
h1 = F.max_pooling_2d(F.relu(self.cn1(x)), 2) #8*3*10
|
|
39
|
+
h2 = F.max_pooling_2d(F.relu(self.cn2(h1)), 2) #16*2*5
|
|
40
|
+
h3 = F.dropout(F.relu(self.l1(h2)))
|
|
41
|
+
h4 = self.l2(h3)
|
|
42
|
+
return h4
|
|
43
|
+
|
|
44
|
+
xtrain = np.random.random((2, 3, 5, 20)).astype('f')
|
|
45
|
+
ytrain = np.random.randint(2, size=(2, 1))
|
|
46
|
+
ytrain = np.hstack((ytrain, 1-ytrain))
|
|
47
|
+
ytrain = np.argmax(ytrain, axis=1).astype('i')
|
|
48
|
+
|
|
49
|
+
model = MyChain()
|
|
50
|
+
optimizer = optimizers.Adam()
|
|
51
|
+
optimizer.setup(model)
|
|
52
|
+
|
|
53
|
+
train = datasets.tuple_dataset.TupleDataset(xtrain, ytrain)
|
|
54
|
+
|
|
55
|
+
iterator = iterators.SerialIterator(train, 2)
|
|
56
|
+
updater = training.StandardUpdater(iterator, optimizer)
|
|
57
|
+
trainer = training.Trainer(updater, (1000, 'epoch'))
|
|
58
|
+
|
|
59
|
+
trainer.extend(extensions.ProgressBar())
|
|
60
|
+
trainer.run()
|
|
61
|
+
```
|