andreaskoepf

using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Linq;
using System.Reactive;
using System.Reactive.Concurrency;
using System.Reactive.Disposables;
using System.Reactive.Linq;
using System.Reactive.Subjects;
using System.Threading;

andreaskoepf

local RotationDistance, parent = torch.class('RotationDistance', 'nn.Criterion')

function RotationDistance:__init(weights)
   parent.__init(self)
end

function RotationDistance:updateOutput(input, target)
  -- acos(abs(<a,b> / norm(a) / norm(b))) + abs(1 - norm(a))
  local one = torch.ones(input:size(1)):cuda() 
  local a = torch.cmul(input, target):sum(2):cdiv(torch.cmul(input:norm(2, 2), target:norm(2, 2))):abs():clamp(-1, 1):acos()

andreaskoepf

require 'nn'

-- mini bottleneck auto-encoder weight tying demo
net = nn.Sequential()
net:add(nn.Linear(8, 8))
net:add(nn.PReLU())
net:add(nn.Linear(8, 3))
net:add(nn.PReLU())
net:add(nn.Linear(3, 8))

andreaskoepf

require 'image'
require 'nngraph'
require 'optim'

-- mini single layer denoising auto-encoder experiment 
-- (v1: no weight tying yet)
-- Andreas Köpf 2015-09-16 

input = image.lena() -- 3x512x512
input_size = input:size()