mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
import math
|
|
|
|
import torch
|
|
from torch.autograd import Variable
|
|
|
|
from .module import Module
|
|
|
|
|
|
class Linear(Module):
|
|
"""Applies a linear transformation to the incoming data, y = Ax + b
|
|
The input is a 2D mini-batch of samples, each of size in_features
|
|
The output will be a 2D Tensor of size mini-batch x out_features
|
|
|
|
Args:
|
|
in_features: size of each input sample
|
|
out_features: size of each output sample
|
|
Input Shape: [*, in_features] : Input can be of shape minibatch x in_features
|
|
Output Shape:[*, out_features] : Output is of shape minibatch x out_features
|
|
Members:
|
|
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.forward(input)
|
|
>>> print(output.size())
|
|
"""
|
|
def __init__(self, in_features, out_features):
|
|
super(Linear, self).__init__()
|
|
self.in_features = in_features
|
|
self.out_features = out_features
|
|
|
|
self.weight = Variable(torch.DoubleTensor(out_features, in_features))
|
|
self.bias = Variable(torch.DoubleTensor(out_features))
|
|
|
|
self.reset_parameters()
|
|
|
|
def reset_parameters(self):
|
|
stdv = 1./math.sqrt(self.weight.size(1))
|
|
self.weight.data.uniform_(-stdv, stdv)
|
|
self.bias.data.uniform_(-stdv, stdv)
|
|
|
|
def forward(self, input):
|
|
return self._backend.Linear()(input, self.weight, self.bias)
|
|
|
|
|
|
# TODO: Bilinear
|
|
# TODO: PartialLinear - maybe in sparse?
|