torch.nn.init.calculate_gain() torch.nn.init.uniform_(tensor, a=0.0, b=1.0) torch.nn.init.constant_(tensor, val) torch.nn.init.sparse_(tensor, sparsity, std=0.01) 等 torch.onnx 这个也有好多内容。Open Neural Network Exchange(ONNX,开放神经网络交换)格式,是一个用于表示深度学习模型的标准,...
torch.nn.init.zeros_(tensor) 用标量0来填充输入张量 参数 tensor - n维的torch.Tensor torch.nn.init.eye_(tensor) 用单位矩阵来填充2维的输入张量。在线性层中保持输入的一致性(尽可能多的输入被保存下来) 参数 tensor - 一个3/4/5维的torch.Tensor torch.nn.init.xavier_uniform_(tensor,gain=1.0) 根...
AI代码解释 classMLP(nn.Module):def__init__(self,neural_num,layers):super(MLP,self).__init__()self.linears=nn.ModuleList([nn.Linear(neural_num,neural_num,bias=False)for_inrange(layers)])self.neural_num=neural_num defforward(self,x):for(i,linear)inenumerate(self.linears):x=linear(x...
>>> w = torch.Tensor(3, 5) >>> nn.init.orthogonal(w) torch.nn.init.sparse(tensor, sparsity, std=0.01) 将2维的输入张量或变量当做稀疏矩阵填充,其中非零元素根据一个均值为0,标准差为std的正态分布生成。参考Martens, J.(2010)的“Deep learning via Hessian-free optimization”. 参数: tensor...
w = torch.Tensor(3, 5) print torch.nn.init.normal(w)torch.nn.init.constant(tensor, val)使用值val填充输入Tensor或Variable 。参数:tensor – n维的torch.Tensor或autograd.Variable val – 填充张量的值例子:w = torch.Tensor(3, 5) print torch.nn.init.constant(w)...
torch.nn.init.calculate_gain(nonlinearity,param=None)[source] Return the recommended gain value for the given nonlinearity function. The values are as follows: Parameters nonlinearity– the non-linear function (nn.functional name) param– optional parameter for the non-linear function ...
torch.nn.init.constant(tensor, val) 使用值val填充输入Tensor或Variable 。 参数: tensor – n维的torch.Tensor或autograd.Variable val – 填充张量的值 例子: w = torch.Tensor(3,5)printtorch.nn.init.constant(w) torch.nn.init.eye(tensor)
#torch.nn.init.uniform_(tensor, a=0.0, b= 1.0)print(nn.init.uniform_(w))# === # tensor([[0.9160, 0.1832, 0.5278, 0.5480, 0.6754],# [0.9509, 0.8325, 0.9149, 0.8192, 0.9950],# [0.4847, 0.4148, 0.8161, 0.0948, 0.3787]])# ===...
nninit.sparse(module, tensor, sparsity) Sets(1 - sparsity)percent of the tensor to 0, wheresparsityis between 0 and 1. For example, asparsityof 0.2 drops out 80% of the tensor. Martens, J. (2010). Deep learning via Hessian-free optimization. InProceedings of the 27th International Confer...
常数初始化:使用torch.nn.init.constant_函数。 单位矩阵初始化:使用torch.nn.init.eye_函数。 Xavier初始化:包括xavier_uniform_和xavier_normal_两种。 Kaiming初始化:包括kaiming_uniform_和kaiming_normal_两种。 正交矩阵初始化:使用torch.nn.init.orthogonal_函数。 稀疏矩阵初始化:使用torch.nn.init.sparse_函数...