1.使用apply() 举例说明: Encoder :设计的编码其模型 weights_init(): 用来初始化模型 model.apply():实现初始化 返回: 2.直接在定义网络时定义 然后调用即可
列表11.9 model.py:30,LunaModel._init_weights def _init_weights(self): for m in self.modules(): if type(m) in { nn.Linear, nn.Conv3d, }: nn.init.kaiming_normal_( m.weight.data, a=0, mode='fan_out', nonlinearity='relu', ) if m.bias is not None: fan_in, fan_out = \ ...
state_dict(), 'model_weights.pth') # 创建一个新的模型实例 new_model = SimpleNet() # 加载权重 new_model.load_state_dict(torch.load('model_weights.pth')) # 将新模型移动到选定的设备上 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") new_model.to(device) # ...
definit_weights(m):iftype(m)==nn.Linear:nn.init.xavier_normal_(m.weight)# 使用Xavier正态分布初始化权重nn.init.zeros_(m.bias)# 将偏置初始化为0model=MyModel()model.apply(init_weights) 1. 2. 3. 4. 5. 6. 7. 在上述代码中,我们定义了一个init_weights函数,该函数会初始化线性层的权重...
列表11.9 model.py:30,LunaModel._init_weights def _init_weights(self):for m in self.modules():if type(m) in {nn.Linear,nn.Conv3d,}:nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_out', nonlinearity='relu',)if m.bias is not None:fan_in, fan_out = \nn.init._calculat...
importtorchimporttorch.nnasnndefweights_init(m):classname=m.__class__.__name__ifclassname.find('Conv')!=-1:nn.init.normal_(m.weight.data,0.0,0.02)elifclassname.find('BatchNorm')!=-1:nn.init.normal_(m.weight.data,1.0,0.02)nn.init.constant_(m.bias.data,0)model=nn.Sequential(nn.Con...
model = LeNet()# first synchronization of initial weightssync_initial_weights(model, rank, world_size)optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.85)model.train()for epoch in range(1, epochs + 1):for data, target in train_loader: optimizer.zero_grad() output = model...
super(myModel, self).__init__() self.conv1= nn.Sequential(nn.Conv2d(3,64, kernel_size=3, padding=1), nn.BatchNorm2d(64), nn.ReLU(True)) self.conv2= nn.Sequential(nn.Conv2d(64,128, kernel_size=3, padding=1), nn.BatchNorm2d(128), ...
super(MyModel, self).__init__() self.linear = nn.Linear(100, 10) def initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Linear): init.xavier_uniform_(m.weight) if m.bias is not None: init.constant_(m.bias, 0) model = MyModel() model.initialize_weights(...
feature.13 不存在,也就是说,我们新加的卷积层,在预训练模型alexnet_model.pth中是不存在的。 应该使用以下方法加载: net = AlexNet(num_classes=5, init_weights=True) net.to(device) # net.load_state_dict(torch.load('alexnet_model.pth')) ...