From e6516772e13dd5424f0a1b7e2063a7417614844c Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Thu, 12 Jan 2017 17:13:19 +0100 Subject: [PATCH] Updated the headers. --- dagnn.lua | 21 +++++++++++++++++++-- test-dagnn.lua | 33 +++++++++++++++++++++++++++++---- 2 files changed, 48 insertions(+), 6 deletions(-) diff --git a/dagnn.lua b/dagnn.lua index 0f93d95..158ef78 100755 --- a/dagnn.lua +++ b/dagnn.lua @@ -1,4 +1,23 @@ +--[[ + + Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/ + Written by Francois Fleuret + + This file is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License version 3 as + published by the Free Software Foundation. + + It is distributed in the hope that it will be useful, but WITHOUT + ANY WARRANTY; without even the implied warranty of MERCHANTABILITY + or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public + License for more details. + + You should have received a copy of the GNU General Public License + along with this file. If not, see . + +]]-- + require 'torch' require 'nn' @@ -235,5 +254,3 @@ function DAG:accGradParameters(input, gradOutput, scale) nnm:accGradParameters(node.input, self:computeGradInput(node.gradInputSucc), scale) end end - -return DAG diff --git a/test-dagnn.lua b/test-dagnn.lua index cac5a94..5b266da 100755 --- a/test-dagnn.lua +++ b/test-dagnn.lua @@ -1,5 +1,24 @@ #!/usr/bin/env luajit +--[[ + + Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/ + Written by Francois Fleuret + + This file is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License version 3 as + published by the Free Software Foundation. + + It is distributed in the hope that it will be useful, but WITHOUT + ANY WARRANTY; without even the implied warranty of MERCHANTABILITY + or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public + License for more details. + + You should have received a copy of the GNU General Public License + along with this file. If not, see . + +]]-- + require 'torch' require 'nn' @@ -32,10 +51,18 @@ function checkGrad(model, criterion, input, target) local ana = analyticalGradParam[i] local num = (loss1 - loss0) / (2 * epsilon) - local err = torch.abs(num - ana) / torch.abs(num) + local err + + if num == ana then + err = 0 + else + err = torch.abs(num - ana) / torch.abs(num) + end print( - err .. ' checkGrad ' .. i + 'CHECK ' + .. err + .. ' checkGrad ' .. i .. ' analytical ' .. ana .. ' numerical ' .. num ) @@ -74,8 +101,6 @@ e = nn.CMulTable() f = nn.Linear(3, 3) g = nn.CAddTable() ----------------------------------------------------------------------- - model = nn.DAG() model:addEdge(a, b) -- 2.20.1