可以根据网络结构和任务需求,设计自己的权重初始化策略。 def initialize_weights(model): for m in model.modules(): if isinstance(m, nn.Linear): # 自定义线性层权重初始化 init.uniform_(m.weight, -1, 1) init.zeros_(m.bias) elif isinstance(m, nn.Conv2d): # 自定义卷积层权重初始化 init.ka...
人们常常将各种初始化方法定义为一个initialize_weights()的函数并在模型初始后进行使用。 123456789101112131415 def initialize_weights(self): for m in self.modules(): # 判断是否属于Conv2d if isinstance(m, nn.Conv2d): torch.nn.init.xavier_normal_(m.weight.data) # 判断是否有偏置 if m.bias is no...
definitialize_weights(self): forminself.modules(): ifisinstance(m,nn.Conv2d): torch.nn.init.xavier_normal_(m.weight.data) ifm.biasisnotNone: m.bias.data.zero_() elifisinstance(m,nn.BatchNorm2d): m.weight.data.fill_(1) m.bia...
} class init_weights { +apply() } SimpleNN ||--o{ init_weights : initializes 类图 接下来是该模型的类图表示: 结论 通过以上步骤,你已经掌握了如何在PyTorch中进行权重初始化的基本方法。权重初始化对于模型的训练和最终性能至关重要,因此选择合适的初始化方法是非常重要的。你可以尝试不同的初始化方式,例...
defweights_init(m):classname=m.__class__.__name__ifclassname.find('Conv')!=-1:m.weight.data.normal_(0.0,0.02)elif classname.find('BatchNorm')!=-1:m.weight.data.normal_(1.0,0.02)m.bias.data.fill_(0)# recommend definitialize_weights(m):ifisinstance(m,nn.Conv2d):m.weight.data.no...
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...
range(num_epochs): # Initialize weights before each epoch linear.apply(init_weights) ...
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): ...
# 定义权值初始化definitialize_weights(self):forminself.modules():ifisinstance(m,nn.Conv2d):torch.nn.init.xavier_normal_(m.weight.data)ifm.biasisnotNone:m.bias.data.zero_()elifisinstance(m,nn.BatchNorm2d):m.weight.data.fill_(1)m.bias.data.zero_()elifisinstance(m,nn.Linear):torch.nn...
# 定义权值初始化definitialize_weights(self):forminself.modules():ifisinstance(m,nn.Conv2d): torch.nn.init.xavier_normal_(m.weight.data)ifm.biasisnotNone: m.bias.data.zero_()elifisinstance(m,nn.BatchNorm2d): m.weight.data.fill_(1) ...