Updated the headers.

diff --git a/dagnn.lua b/dagnn.lua
index 0f93d95..158ef78 100755 (executable)
--- a/dagnn.lua
+++ b/dagnn.lua
@@ -1,4 +1,23 @@
 
+--[[
+
+   Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/
+   Written by Francois Fleuret <francois.fleuret@idiap.ch>
+
+   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 <http://www.gnu.org/licenses/>.
+
+]]--
+
 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 (executable)
--- 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 <francois.fleuret@idiap.ch>
+
+   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 <http://www.gnu.org/licenses/>.
+
+]]--
+
 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)