浅谈Pytorch torch.optim优化器个性化的使用

 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 <div style="color:transparent">来源gaodai.ma#com搞##代!^码@网</div>= self.features(x) x = x.view(-1, 16*5*5) x = self.classifiter(x) return x net = LeNet()

 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()

 # # 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)

 # # 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) 

 '''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)

 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