以下是一种常见的初始化方法: import torch import torch.nn as nn import torch.nn.init as init class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() self.linear = nn.Linear(100, 10) def initialize_weights(self): for m in self.modules(): if isinstance(m,...
definitialize_weights(*models):formodelinmodels:forminmodel.modules():ifisinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu')elifisinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1.) m.bias.data.fill_(1e-4)elifisinstance(m, nn.Linear): m.weight.data...
input_size=10# 输入特征的维度hidden_size=5# 隐藏层的节点数output_size=2# 输出的维度model=SimpleNN(input_size,hidden_size,output_size)# 实例化模型model.apply(init_weights)# 应用权重初始化 1. 2. 3. 4. 5. 6. 5. 训练模型 在权重初始化后,接下来可以进行模型的训练。这里提供一段简单的训练...
#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。 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 not None: torch.nn.i...
self._initialize() def _initialize(self): self.fc1.weight = nn.Parameter( torch.tensor([[2.], [2.]])) self.fc2.weight = nn.Parameter(torch.tensor([2., 2.])) def forward(self, x): return self.fc2(self.fc1(x)) def forward_hook_fn(module, input, output): ...
self._initialize_weights() def forward(self, x): x = self.relu(self.conv1(x)) x = self.relu(self.conv2(x)) x = self.relu(self.conv3(x)) x = self.pixel_shuffle(self.conv4(x)) return x def _initialize_weights(self):
2.1 model.py VGG网络分为卷积层提取特征和全连接层进行分类这两个模块 代码语言:javascript 复制 importtorch.nnasnnimporttorchclassVGG(nn.Module):def__init__(self,features,num_classes=1000,init_weights=False):#super(VGG,self).__init__()self.features=features # 卷积层提取特征 ...
如前所述,每一层具有不同的扩张因子,并且可以为每一层的扩张卷积节点创建传递该因子。 由于跨步为 1,所以填充保持为 0,目的不是上采样或下采样。init_weights_for_test是通过将权重矩阵填充 1 来进行测试的便捷函数。 PyTorch 提供的灵活性使用户可以在线调整参数,这对于调试网络更加有用。forward传递仅调用 PyTor...
net_dict = net.state_dict()取出网络的模型,其中里面的参数是调用:_initialize_weights(self)生成的。 predict_model = torch.load('alexnet_model.pth')是加载预训练模型,并没有将里面的参数加载到网络中。 state_dict = {k: v for k, v in predict_model.items() if k in net_dict.keys()}是寻找...
out = model.forward() loss = some_loss(out,y) loss.backward() scaled_loss += loss.item() # update weights after 8 steps. effective batch = 8*16 optimizer.step() # loss is now scaled up by the number of accumulated batches