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。
