我们永远不能怪罪任何帮了倒忙的志愿者。他们是一心对你好,别让他们心凉。相信自已。不要妄加评判自已,也不会把自已交给别人评判,更不会贬低自已。
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。