Pytorch 实现自定义参数层的例子

注意，一般官方接口都带有可导功能，如果你实现的层不具有可导功能，就需要自己实现梯度的反向传递。

官方Linear层：

class Linear(Module):
 def __init__(self, in_features, out_features, bias=True):
   super(Linear, self).__init__()
   self.in_features = in_features
   self.out_features = out_features
   self.weight = Parameter(torch.Tensor(out_features, in_features))
   if bias:
     self.bias = Parameter(torch.Tensor(out_features))
   else:
     self.register_parameter('bias', None)
   self.reset_parameters()

def reset_parameters(self):
   stdv = 1. / math.sqrt(self.weight.size(1))
   self.weight.data.uniform_(-stdv, stdv)
   if self.bias is not None:
     self.bias.data.uniform_(-stdv, stdv)

def forward(self, input):
   return F.linear(input, self.weight, self.bias)

def extra_repr(self):
   return 'in_features={}, out_features={}, bias={}'.format(
     self.in_features, self.out_features, self.bias is not None
   )

实现view层

class Reshape(nn.Module):
 def __init__(self, *args):
   super(Reshape, self).__init__()
   self.shape = args

def forward(self, x):
   return x.view((x.size(0),)+self.shape)

实现LinearWise层

class LinearWise(nn.Module):
 def __init__(self, in_features, bias=True):
   super(LinearWise, self).__init__()
   self.in_features = in_features

self.weight = nn.Parameter(torch.Tensor(self.in_features))
   if bias:
     self.bias = nn.Parameter(torch.Tensor(self.in_features))
   else:
     self.register_parameter('bias', None)
   self.reset_parameters()

def reset_parameters(self):
   stdv = 1. / math.sqrt(self.weight.size(0))
   self.weight.data.uniform_(-stdv, stdv)
   if self.bias is not None:
     self.bias.data.uniform_(-stdv, stdv)

def forward(self, input):
   x = input * self.weight
   if self.bias is not None:
     x = x + self.bias
   return x

来源：https://blog.csdn.net/qq_16234613/article/details/81604081