# 其中fan_in是指第i层神经元的个数,fan_out是指第i + 1层神经元的个数forminnet.modules():ifisinstance(m,(torch.nn.Linear,torch.nn.Conv1d,torch.nn.Conv2d)):torch.nn.init.xavier_uniform_(m.weight)forminnet.modules():ifisinstance(m,to
out_features): # 必须初始化构造函数 super(Linear, self).__init__() # 等价于nn.Module.__init__(self) self.w = nn.Parameter(t.randn(in_features, out_features)) # 自封装学习参数 self.b = nn.Parameter(t.randn(out_features)) def forward(self, x): x = (self.w) return x + sel...
fc4 = nn.Linear(40, 10) # 输出层 # 使用He初始化来初始化权重 nn.init.kaiming_uniform_(self.fc1.weight, mode='fan_in', nonlinearity='relu') nn.init.kaiming_uniform_(self.fc2.weight, mode='fan_in', nonlinearity='relu') nn.init.kaiming_uniform_(self.fc3.weight, mode='fan_in', ...
1class ValueNet(nn.Module):2 def __init__(self, inplanes, outplanes): 3 super(ValueNet, self).__init__() 4 self.outplanes = outplanes 5 self.conv = nn.Conv2d(inplanes, 1, kernel_size=1) 6 self.bn = nn.BatchNorm2d(1) 7 self.fc1 = nn.Linear(outplanes...
classMyDataset(Dataset):def__init__(self,x,y):super().__init__()…… def__getitem__(self):return…… def__len__(self):return…… 2.TensorDataset 上述通过Dataset的方式可以实现一个标准自定义数据集的构建,但如果对于比较简单的数据集仍需八股文似的重载__getitem__和__len__两个方法,则难免...
torch.nn.init.constant_(tensor, val) tensor——一个n维的torch.Tensor val – 用来填充张量的值 1.4 一值初始化(ones_) 用1来填充tensor torch.nn.init.ones_(tensor) 1.5 零值初始化(zeros_) 用0来填充tensor torch.nn.init.zeros_(tensor) ...
__init__() self.fc1 = nn.Linear(10, 5) self.fc2 = nn.Linear(5, 1) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return x # 加载数据集 train_data = torch.randn(1000, 10) # 假设有1000个样本,每个样本有10个特征 train_labels = torch.randn(1000, ...
self.lstm.bias_hh_l0.data.zero_() 1. 2. 3. 4. 5. 6. 7. 8. Embedding进行初始化 self.embedding = nn.Embedding(embedding_tokens, embedding_features, padding_idx=0) init.xavier_uniform(self.embedding.weight) 1. 2. 其他通用初始化方法 ...
def__init__(self,target,weight):super(Content_Loss,self).__init__()#继承父类的初始化 self.weight=weight self.target=target.detach()*self.weight # 必须要用detach来分离出target,这时候target不再是一个Variable,这是为了动态计算梯度,否则forward会出错,不能向前传播 ...
def __init__(self): """ In the constructor we instantiate nn.Linear module """ super(Model, self).__init__() self.linear = torch.nn.Linear(1, 1) # One in and one out def forward(self, x): """ In the forward function we accept a Variable of input data and we must return...