multiple_optimizers.py

import chainer
import chainer.links as L
import chainer.functions as F
import numpy as np


class Wrapper(chainer.Chain):
    pass


x = chainer.Variable(np.random.uniform(-1, 1, (1, 3)).astype(np.float32))
l = L.Linear(3, 2)
l2 = Wrapper(l=l)

o = chainer.optimizers.SGD(0.01)
o.setup(l)
p = chainer.optimizers.SGD(0.01)
p.setup(l2)

for i in range(10):
    l.cleargrads()
    y = l(x)
    w = F.sum(y * y)
    w.backward()
    if i % 2 == 0:
        o.update()
    else:
        p.update()
    print(np.linalg.norm(l.W.data))

'''
1.66004
1.62767
1.5969
1.56765
1.53986
1.51344
1.48833
1.46447
1.44179
1.42025
'''