學習率迭代策略

base_lr =

0.01
lr_policy =
"fixed"

lr×g

amma

lr=lr×gamma

lr=lr×

gamm

a

base_lr =

0.01
lr_policy =
"step"
gamma =
0.1stepsize=
10000

t)=b

ase_

lr×(

tt)p

ower

lr(t)=base\_lr\times(\frac)^

lr(t)=

base

_lr×

(tt​

)pow

er

base_lr =

0.01
lr_policy =
"step"
gamma =
0.1stepsize=
10000

t)=b

ase_

lr×(

1+ga

mma×

iter

)pow

er

lr(t)=base\_lr\times(1+gamma\times iter)^

lr(t)=

base

_lr×

(1+g

amma

×ite

r)po

wer

nit_lr =5e-

4max_iter =
15000
optimizer = sgd(params=net.parameters(
), lr=init_lr, momentum=
0.9, weight_decay=
0.0005
)lambda_func =
lambda step:(1
-step/max_iter)
**0.9
scheduler = torch.optim.lr_scheduler.lambdalr(optimizer, lr_lambda=lambda_func)
for epoch in
range(50
):for i in
range
(1000):
... optimizer.step(
) scheduler.step(
)

需要繼承torch.optim.lr_scheduler._lrscheduler類:

from torch.optim.lr_scheduler import _lrscheduler

class
polylrdecay
(_lrscheduler)
:def
__init__
(self, optimizer, max_decay_steps, end_learning_rate=
0.0001
, power=
1)

def

get_lr
(self)
:def
step
(self, step=
none
):

l r=

lr×(

1−it

erma

x_it

er)p

ower

lr=lr\times (1-\frac)^

lr=lr×

(1−m

ax_i

teri

ter​

)pow

er

init_lr =5e-

4max_iter =
15000
optimizer = sgd(params=net.parameters(
), lr=init_lr, momentum=
0.9, weight_decay=
0.0005
)lambda_func =
lambda step:(1
-step/max_iter)
**0.9
scheduler = torch.optim.lr_scheduler.lambdalr(optimizer, lr_lambda=lambda_func)
lrs =
for epoch in
range(15
):for i in
range
(1000):
... optimizer.step()0
]["lr"])
scheduler.step(
)plt.plot(
range
(15000
), lrs)
plt.show(
)

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