Pytorch 实现自定义参数层的例子

Python  /  管理员 发布于 5年前   301

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。