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

以上这篇Pytorch 实现自定义参数层的例子就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。