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import math
import torch
from torch.nn.parameter import Parameter
from .module import Module
class Linear(Module):
r"""Applies a linear transformation to the incoming data: :math:`y = Ax + b`
Args:
in_features: size of each input sample
out_features: size of each output sample
bias: If set to False, the layer will not learn an additive bias. Default: True
Shape:
- Input: :math:`(N, in\_features)`
- Output: :math:`(N, out\_features)`
Attributes:
weight: the learnable weights of the module of shape (out_features x in_features)
bias: the learnable bias of the module of shape (out_features)
Examples::
>>> m = nn.Linear(20, 30)
>>> input = autograd.Variable(torch.randn(128, 20))
>>> output = m(input)
>>> print(output.size())
"""
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):
if self.bias is None:
return self._backend.Linear()(input, self.weight)
else:
return self._backend.Linear()(input, self.weight, self.bias)
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ str(self.in_features) + ' -> ' \
+ str(self.out_features) + ')'
# TODO: Bilinear
# TODO: PartialLinear - maybe in sparse?
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