import math
import torch
from torch.nn.parameter import Parameter
from .. import functional as F
from .module import Module
[docs]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)` where `*` means any number of
additional dimensions
- Output: :math:`(N, *, out\_features)` where all but the last dimension
are the same shape as the input.
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):
return F.linear(input, self.weight, self.bias)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'in_features=' + str(self.in_features) \
+ ', out_features=' + str(self.out_features) \
+ ', bias=' + str(self.bias is not None) + ')'
[docs]class Bilinear(Module):
r"""Applies a bilinear transformation to the incoming data:
:math:`y = x_1 * A * x_2 + b`
Args:
in1_features: size of each first input sample
in2_features: size of each second 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, in1\_features)`, :math:`(N, in2\_features)`
- Output: :math:`(N, out\_features)`
Attributes:
weight: the learnable weights of the module of shape
(out_features x in1_features x in2_features)
bias: the learnable bias of the module of shape (out_features)
Examples::
>>> m = nn.Bilinear(20, 30, 40)
>>> input1 = autograd.Variable(torch.randn(128, 20))
>>> input2 = autograd.Variable(torch.randn(128, 30))
>>> output = m(input1, input2)
>>> print(output.size())
"""
def __init__(self, in1_features, in2_features, out_features, bias=True):
super(Bilinear, self).__init__()
self.in1_features = in1_features
self.in2_features = in2_features
self.out_features = out_features
self.weight = Parameter(torch.Tensor(out_features, in1_features, in2_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, input1, input2):
return F.bilinear(input1, input2, self.weight, self.bias)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'in1_features=' + str(self.in1_features) \
+ ', in2_features=' + str(self.in2_features) \
+ ', out_features=' + str(self.out_features) \
+ ', bias=' + str(self.bias is not None) + ')'
# TODO: PartialLinear - maybe in sparse?