一、简化前馈网络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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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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2.以网络层对象为单位进行分组,并设定学习率
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四、在训练中动态的调整学习率
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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。
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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
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