Source code for torch.nn.modules.linear

import math

import torch
from torch.nn.parameter import Parameter

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)), stdv) if self.bias is not None:, 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?