一、简化前馈网络LeNet
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import
torch as t
class
LeNet(t.nn.Module):
def
__init__(
self
):
super
(LeNet,
self
).__init__()
self
.features
=
t.nn.Sequential(
t.nn.Conv2d(
3
,
6
,
5
),
t.nn.ReLU(),
t.nn.MaxPool2d(
2
,
2
),
t.nn.Conv2d(
6
,
16
,
5
),
t.nn.ReLU(),
t.nn.MaxPool2d(
2
,
2
)
)
# 由于调整shape并不是一个class层，
# 所以在涉及这种操作（非nn.Module操作）需要拆分为多个模型
self
.classifiter
=
t.nn.Sequential(
t.nn.Linear(
16
*
5
*
5
,
120
),
t.nn.ReLU(),
t.nn.Linear(
120
,
84
),
t.nn.ReLU(),
t.nn.Linear(
84
,
10
)
)
def
forward(
self
, x):
x
=
self
.features(x)
x
=
x.view(
-
1
,
16
*
5
*
5
)
x
=
self
.classifiter(x)
return
x
net
=
LeNet()
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二、优化器基本使用方法
- 建立优化器实例
- 循环：
- 清空梯度
- 向前传播
- 计算Loss
- 反向传播
- 更新参数
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from
torch
import
optim
# 通常的step优化过程
optimizer
=
optim.SGD(params
=
net.parameters(), lr
=
1
)
optimizer.zero_grad()
# net.zero_grad()
input_
=
t.autograd.Variable(t.randn(
1
,
3
,
32
,
32
))
output
=
net(input_)
output.backward(output)
optimizer.step()
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三、网络模块参数定制
为不同的子网络参数不同的学习率，finetune常用，使分类器学习率参数更高，学习速度更快（理论上）。
1.经由构建网络时划分好的模组进行学习率设定，
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# # 直接对不同的网络模块制定不同学习率
optimizer
=
optim.SGD([{
'params'
: net.features.parameters()},
# 默认lr是1e-5
{
'params'
: net.classifiter.parameters(),
'lr'
:
1e
-
2
}], lr
=
1e
-
5
)
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2.以网络层对象为单位进行分组，并设定学习率
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# # 以层为单位，为不同层指定不同的学习率
# ## 提取指定层对象
special_layers
=
t.nn.ModuleList([net.classifiter[
0
], net.classifiter[
3
]])
# ## 获取指定层参数id
special_layers_params
=
list
(
map
(
id
, special_layers.parameters()))
print
(special_layers_params)
# ## 获取非指定层的参数id
base_params
=
filter
(
lambda
p:
id
(p)
not
in
special_layers_params, net.parameters())
optimizer
=
t.optim.SGD([{
'params'
: base_params},
{
'params'
: special_layers.parameters(),
'lr'
:
0.01
}], lr
=
0.001
)
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四、在训练中动态的调整学习率
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'''调整学习率'''
# 新建optimizer或者修改optimizer.params_groups对应的学习率
# # 新建optimizer更简单也更推荐，optimizer十分轻量级，所以开销很小
# # 但是新的优化器会初始化动量等状态信息，这对于使用动量的优化器（momentum参数的sgd）可能会造成收敛中的震荡
# ## optimizer.param_groups:长度2的list，optimizer.param_groups[0]：长度6的字典
print
(optimizer.param_groups[
0
][
'lr'
])
old_lr
=
0.1
optimizer
=
optim.SGD([{
'params'
: net.features.parameters()},
{
'params'
: net.classifiter.parameters(),
'lr'
: old_lr
*
0.1
}], lr
=
1e
-
5
)
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可以看到optimizer.param_groups结构，[{'params','lr', 'momentum', 'dampening', 'weight_decay', 'nesterov'},{……}]，集合了优化器的各项参数。
-
torch.optim的灵活使用
- 重写sgd优化器
import torch from torch.optim.optimizer import Optimizer, required class SGD(Optimizer): def __init__(self, params, lr=required, momentum=0, dampening=0, weight_decay1=0, weight_decay2=0, nesterov=False): defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay1=weight_decay1, weight_decay2=weight_decay2, nesterov=nesterov) if nesterov and (momentum <= 0 or dampening != 0): raise ValueError("Nesterov momentum requires a momentum and zero dampening") super(SGD, self).__init__(params, defaults) def __setstate__(self, state): super(SGD, self).__setstate__(state) for group in self.param_groups: group.setdefault('nesterov', False) def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: weight_decay1 = group['weight_decay1'] weight_decay2 = group['weight_decay2'] momentum = group['momentum'] dampening = group['dampening'] nesterov = group['nesterov'] for p in group['params']: if p.grad is None: continue d_p = p.grad.data if weight_decay1 != 0: d_p.add_(weight_decay1, torch.sign(p.data)) if weight_decay2 != 0: d_p.add_(weight_decay2, p.data) if momentum != 0: param_state = self.state[p] if 'momentum_buffer' not in param_state: buf = param_state['momentum_buffer'] = torch.zeros_like(p.data) buf.mul_(momentum).add_(d_p) else: buf = param_state['momentum_buffer'] buf.mul_(momentum).add_(1 - dampening, d_p) if nesterov: d_p = d_p.add(momentum, buf) else: d_p = buf p.data.add_(-group['lr'], d_p) return loss