Source code for torch.nn.modules.linear

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)` - 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.apply(input, self.weight) else: return self._backend.Linear.apply(input, self.weight, self.bias) def __repr__(self): return self.__class__.__name__ + ' (' \ + str(self.in_features) + ' -> ' \ + str(self.out_features) + ')'
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)) >>> input1 = 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) + ')' # TODO: PartialLinear - maybe in sparse?