回答編集履歴
3
fixed xp to np on code
test
CHANGED
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@@ -2,7 +2,7 @@
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2
2
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```Python
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3
3
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if model.train:
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4
4
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x = chainer.Variable(np.asarray(inp[0]))
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5
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-
t = chainer.Variable(
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5
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+
t = chainer.Variable(np.asarray(inp[1]))
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6
6
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optimizer.update(model, x, t)
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7
7
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if not graph_generated:
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8
8
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with open('graph.dot', 'w') as o:
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@@ -13,7 +13,7 @@
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13
13
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else:
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14
14
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with chainer.no_backprop_mode():
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15
15
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x = chainer.Variable(np.asarray(inp[0]))
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16
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-
t = chainer.Variable(
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16
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+
t = chainer.Variable(np.asarray(inp[1]))
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17
17
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model(x, t)
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18
18
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res_q.put((float(model.loss.data), float(model.accuracy.data)))
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19
19
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del x, t
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2
fixed code
test
CHANGED
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@@ -1,6 +1,6 @@
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1
1
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やはり，`optimizer.update()`が`with chainer.no_backprop_mode():`の中にあるのは望ましくないと思われます．
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2
2
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```Python
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3
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-
if model.train:
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3
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+
if model.train:
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4
4
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x = chainer.Variable(np.asarray(inp[0]))
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5
5
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t = chainer.Variable(xp.asarray(inp[1]))
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6
6
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optimizer.update(model, x, t)
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1
fix code
test
CHANGED
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@@ -1,9 +1,8 @@
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1
1
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やはり，`optimizer.update()`が`with chainer.no_backprop_mode():`の中にあるのは望ましくないと思われます．
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2
2
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```Python
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3
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+
if model.train:
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3
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-
x = chainer.Variable(np.asarray(inp[0]))
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4
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+
x = chainer.Variable(np.asarray(inp[0]))
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4
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-
with chainer.no_backprop_mode():
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5
5
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t = chainer.Variable(xp.asarray(inp[1]))
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6
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-
if model.train:
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7
6
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optimizer.update(model, x, t)
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8
7
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if not graph_generated:
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9
8
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with open('graph.dot', 'w') as o:
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@@ -12,24 +11,10 @@
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12
11
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print('generated graph', file=sys.stderr)
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13
12
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graph_generated = True
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14
13
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else:
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14
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+
with chainer.no_backprop_mode():
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15
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+
x = chainer.Variable(np.asarray(inp[0]))
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16
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+
t = chainer.Variable(xp.asarray(inp[1]))
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15
17
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model(x, t)
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16
18
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res_q.put((float(model.loss.data), float(model.accuracy.data)))
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17
19
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del x, t
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18
20
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```
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19
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-
もしかしたら，`t`を定義するときもここから外す必要があるかも．そのときはこう．
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20
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-
```Python
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21
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-
x = chainer.Variable(np.asarray(inp[0]), volatile = not model.train)
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22
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-
t = chainer.Variable(xp.asarray(inp[1]), volatile = not model.train)
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23
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-
if model.train:
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24
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-
optimizer.update(model, x, t)
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25
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-
if not graph_generated:
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26
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-
with open('graph.dot', 'w') as o:
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27
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-
o.write(computational_graph.build_computational_graph(
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28
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-
(model.loss,)).dump())
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29
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-
print('generated graph', file=sys.stderr)
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30
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-
graph_generated = True
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31
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-
else:
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32
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-
model(x, t)
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33
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-
res_q.put((float(model.loss.data), float(model.accuracy.data)))
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34
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-
del x, t
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35
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-
```
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