一、简化前馈网络LeNet

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()

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

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()

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，

# # 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)

2.以网络层对象为单位进行分组，并设定学习率

# # 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idspecial_layers_params = list(map(id, special_layers.parameters()))print(special_layers_params)# ## 获取非指定层的参数idbase_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)

四、在训练中动态的调整学习率

'''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 = 0.1optimizer = optim.SGD([{'params': net.features.parameters()}, {'params': net.classifiter.parameters(), 'lr': old_lr*0.1}], lr=1e-5)

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 torchfrom torch.optim.optimizer import Optimizer, requiredclass 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

以上这篇浅谈Pytorch torch.optim优化器个性化的使用就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。