是的，PyTorch 会默认初始化权重。当定义神经网络的模型时，PyTorch 会自动初始化权重。具体来说，PyTorch...
def initialize_weights(m): if isinstance(m, nn.Conv2d): m.weight.data.normal_(0, 0.02) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.02) m.bias.data.zero_() # recommend def weights_init(m): if isinstance(m, nn.Conv2d): nn.init.xavier_normal...
IMHO there is a discrepancy between the docs and code of nn.Linear, when it comes to initialization. documentation says that the weights are initialized from uniform ( 1/sqrt(in_ feaures) , 1/sqrt(in_ feaures)): pytorch/torch/nn/modules/...
def initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.xavier_normal_(m.weight.data) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Line...
复制代码 在上面的代码中,我们定义了一个MyModel类,其中包含一个线性层nn.Linear(100, 10)。使用initialize_weights函数对模型的参数进行初始化,其中我们使用了Xavier初始化方法对权重进行初始化,并将偏置初始化为0。您也可以根据需要选择其他初始化方法。 0 赞 0 踩...
m.bias.data.zero_()elifisinstance(m, nn.Linear): torch.nn.init.normal_(m.weight.data,0,0.01)# m.weight.data.normal_(0,0.01)m.bias.data.zero_() net = Net() net.initialize_weights()print(net.modules())forminnet.modules():print(m) ...
definitialize_weights(m):ifisinstance(m,nn.Conv2d):m.weight.data.normal_(0,0.02)m.bias.data.zero_()elifisinstance(m,nn.Linear):m.weight.data.normal_(0,0.02)m.bias.data.zero_()# recommend defweights_init(m):ifisinstance(m,nn.Conv2d):nn.init.xavier_normal_(m.weight.data)nn.init....
elifisinstance(m, nn.Linear): torch.nn.init.normal_(m.weight.data,0,0.01) # m.weight.data.normal_(0,0.01) m.bias.data.zero_() net = Net() net.initialize_weights() print(net.modules()) forminnet.modules(): print(m) 运行...
nn.Linear(1024, n_out), nn.Tanh() )defforward(self, x): x = self.hidden0(x) x = self.hidden1(x) x = self.hidden2(x) x = self.out(x)returnx 生成器训练器函数比判别器训练器函数简单得多,因为它不需要从两个来源获取输入,也不必针对不同的目的进行训练,而判别器则必须最大化将真实图...
基本过程为:先从 self.modules()中遍历每一层,然后判断各层属于什么类型,例如,是否是 nn.Conv2d、nn.BatchNorm2d、nn.Linear 等,然后根据不同类型的层,设定不同的权值初始化方法,例如,Xavier,kaiming,normal_,uniform_等。 2.2 常用初始化例子2 # 定义权值初始化def initialize_weights(self):# 其中self.modul...