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/...
for epoch in range(num_epochs): # Initialize weights before each epoch linear.apply(init...
self.fc3 = nn.Linear(84,10) defforward(self, x): x = self.pool1(F.relu(self.conv1(x))) x = self.pool2(F.relu(self.conv2(x))) x = x.view(-1,16*5*5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) ...
=-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.normal_(0,0.02)m.bias.data.zero_()elifisinstance(m,nn.Linear):m.weight.data....
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...
self.avgpool=nn.AdaptiveAvgPool2d((7,7))self.classifier=nn.Sequential(nn.Linear(512*7*7,4096),nn.ReLU(True),nn.Dropout(),nn.Linear(4096,4096),nn.ReLU(True),nn.Dropout(),nn.Linear(4096,num_classes),)self._initialize_weights()defforward(self,x):x=self.features(x)x=self.avgpool(x...
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): ...
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) 1. 2.
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) ...