零初始化(Zero Initialization):将所有的权重和偏置初始化为零。这种方法简单直接,但在实践中这种方法很少使用,因为它会导致所有的神经元具有相同的更新,并且会带来梯度消失问题等等。 随机初始化(Random Initialization):将权重和偏置随机地初始化为较小的随机值。这可以打破对称性,并为神经元提供不同的
accelerator = Accelerator()除了提供要使用的主要对象之外，此行还将从环境中分析分布式训练运行的类型并执行必要的初始化。用户可以通过将 cpu = True 或 fp16 = True 传递给此 init 来强制进行 CPU 训练或混合精度训练。这两个选项都可以使用脚本的启动器进行设置。model, optim, data = accelerator.prepare(mod...
self).__init__() 4 self.conv1 = nn.Conv2d(inplanes, outplanes, stride=1, 5 kernel_size=3, padding=1, bias=False) 6 self.bn1 = nn.BatchNorm2d(outplanes) 7 8 for block in range(BLOCKS): 9 setattr(self, "res{}".format(block), \10 Basic...
class ResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None): super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._n...
flatten(x) logits = self.linear_relu_stack(x) return logits然后定义loss函数、计算输出和losses。loss_fn = CrossEntropyLoss()# Calculate lossesout = nn(t)loss = loss_fn(out, label)# Backward passnn.zero_grad()loss.backward()好了，如何使用koila来防止内存溢出？超级简单！只需在第一行...
class Optimizer(object):def zero_grad(self):for group in self.param_groups:for p in group['params']:if p.grad is not None:p.grad.detach_()p.grad.zero_() class Optimizer(object):def __init__(self, params, defaults):self.defaults = defaultsself.state = defaultdict(dict)self.param_...
def__init__(self,target,weight):super(Content_Loss,self).__init__()#继承父类的初始化 self.weight=weight self.target=target.detach()*self.weight # 必须要用detach来分离出target,这时候target不再是一个Variable,这是为了动态计算梯度,否则forward会出错,不能向前传播 ...
()for _, batch in enumerate(tqdm(train_dataloader, desc=f"Epoch {epoch}")):batch = batch.to(device)optimizer.zero_grad()y_pred = torch.squeeze(model(batch, device))loss= criterion(y_pred.float(), torch.squeeze(batch.y).flo...
optimizer.zero_grad()# 待优化参数梯度清空 prob=model(data)# 执行一次前向传播,计算预测结果 loss=creterion(prob,label)# 评估模型损失 loss.backward()# 损失反向传播,完成对待优化参数的梯度求解 optimizer.step()# 参数更新if(epoch+1)%5==0:# 每隔5个epoch打印当前模型训练效果withtorch.no_grad():tr...
class SinusoidalPosEmb(nn.Module):def __init__(self, dim, theta=10000):super().__init__()self.dim = dimself.theta = theta def forward(self, x):device = x.devicehalf_dim = self.dim // 2emb = math.log(self.theta) / (half_dim - 1)em...