init_weights函数用于初始化PyTorch模型的参数 。 它在模型训练前期对参数赋值,影响模型收敛和性能 。常见的初始化方法包括随机初始化 。随机初始化能让参数在一定范围内取值 。均匀分布初始化是将参数在指定区间均匀赋值 。比如torch.nn.init.uniform_可实现均匀分布初始化 。正态分布初始化是让参数符合正态分布 。to...
# coding:utf-8fromtorch import nn def weights_init(mod):"""设计初始化函数"""classname=mod.__class__.__name__ # 返回传入的module类型 print(classname)ifclassname.find('Conv')!= -1: #这里的Conv和BatchNnorm是torc.nn里的形式 mod.weight.data.normal_(0.0,0.02) elif classname.find('Batch...
pytorch中的权值初始化 torch.nn.Module.apply(fn) #递归的调用weights_init函数,遍历nn.Module的submodule作为参数#常用来对模型的参数进行初始化#fn是对参数进行初始化的函数的句柄,fn以nn.Module或者自己定义的nn.Module的子类作为参数#fn (Module -> None) – function to be applied to each submodule#Returns...
批量初始化方法,注意net里面的apply函数,可以作用网络的所有module def weights_init(m): # 1 classname = m.__class__.__name__ # 2 if classname.find('Conv') != -1: # 3 nn.init.kaiming_normal_(m.weight.data) # 4 elif classname.find('BatchNorm') != -1: # 5 nn.init.normal_(m....
init_weights() def init_weights(self): self.conv1.weight.data.normal_(0, 0.01) self.conv2.weight.data.normal_(0, 0.01) if self.downsample is not None: self.downsample.weight.data.normal_(0, 0.01) def forward(self, x): out = self.net(x) res = x if self.downsample is None ...
def weights_init(m): if isinstance(m, nn.Conv2d): nn.init.xavier_normal_(m.weight.data) nn.init.xavier_normal_(m.bias.data) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight,1) nn.init.constant_(m.bias, 0)
importtorchimporttorch.nnasnnimporttorch.nn.functionalasFclassMyNet(nn.Module):def__init__(self):super(MyNet, self).__init__() self.layer1 = nn.Linear(4,5) self.layer2 = nn.Linear(5,5) self.layer3 = nn.Linear(5,3)defforward(self, x):layer1_output = torch.relu(self.layer1(...
编写好weights_init函数后,可以使用模型的apply方法对模型进行权重初始化。 代码运行次数:0 代码运行 net=Residual()# generate an instance network from the Netclassnet.apply(weights_init)# apply weight init
nn.Module.__init__(self) self.weight = weight self.gamma = gamma self.reduction = reduction def forward(self, input_tensor, target_tensor): log_prob = F.log_softmax(input_tensor, dim=-1) prob = torch.exp(log_prob) return F.nll_loss( ...
Weights权重是存在于神经网络各层中的Learnable Parameters Where are the learnable parameters —— 获取Network实例 network = Network() 执行此代码后,将运行__init__中代码 当我们扩展(extend)一个类时,可以获得它的所有功能,作为补充,可以添加其它功能。但同时也可以覆盖(override)现有功能以实现其它功能。可以使...