#!/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'
-
require 'dagnn'
+-- torch.setnumthreads(params.nbThreads)
+torch.setdefaulttensortype('torch.DoubleTensor')
+torch.manualSeed(2)
+
function checkGrad(model, criterion, input, target)
local params, gradParams = model:getParameters()
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
)
end
end
--- torch.setnumthreads(params.nbThreads)
-torch.setdefaulttensortype('torch.DoubleTensor')
-torch.manualSeed(2)
-
-- +--> c ----> e --+
-- / / \
-- / / \
-- \ /
-- +--> f ---+
-a = nn.Linear(10, 10)
+a = nn.Linear(50, 10)
b = nn.ReLU()
-c = nn.Linear(10, 3)
-d = nn.Linear(10, 3)
+c = nn.Linear(10, 15)
+d = nn.Linear(10, 15)
e = nn.CMulTable()
-f = nn.Linear(3, 3)
+f = nn.Linear(15, 15)
g = nn.CAddTable()
-----------------------------------------------------------------------
-
model = nn.DAG()
model:addEdge(a, b)
model:setInput(a)
model:setOutput(g)
-input = torch.Tensor(3, 10):uniform()
-
-print('******************************************************************')
-print('** updateOutput **************************************************')
-print('******************************************************************')
-
-output = model:updateOutput(input):clone()
-
-printTensorTable(output)
-
-print('******************************************************************')
-print('** updateGradInput ***********************************************')
-print('******************************************************************')
-
-gradInput = model:updateGradInput(input, output)
-
-printTensorTable(gradInput)
-
-print('******************************************************************')
-print('** checkGrad *****************************************************')
-print('******************************************************************')
+local input = torch.Tensor(30, 50):uniform()
+local output = model:updateOutput(input):clone()
output:uniform()