5 Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/
6 Written by Francois Fleuret <francois.fleuret@idiap.ch>
8 This file is free software: you can redistribute it and/or modify
9 it under the terms of the GNU General Public License version 3 as
10 published by the Free Software Foundation.
12 It is distributed in the hope that it will be useful, but WITHOUT
13 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
14 or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
15 License for more details.
17 You should have received a copy of the GNU General Public License
18 along with this file. If not, see <http://www.gnu.org/licenses/>.
26 -- torch.setnumthreads(params.nbThreads)
27 torch.setdefaulttensortype('torch.DoubleTensor')
30 function checkGrad(model, criterion, input, target)
31 local params, gradParams = model:getParameters()
35 local output = model:forward(input)
36 local loss = criterion:forward(output, target)
37 local gradOutput = criterion:backward(output, target)
39 model:backward(input, gradOutput)
40 local analyticalGradParam = gradParams:clone()
44 for i = 1, params:size(1) do
47 params[i] = x - epsilon
48 local output0 = model:forward(input)
49 local loss0 = criterion:forward(output0, target)
51 params[i] = x + epsilon
52 local output1 = model:forward(input)
53 local loss1 = criterion:forward(output1, target)
57 local ana = analyticalGradParam[i]
58 local num = (loss1 - loss0) / (2 * epsilon)
61 err = math.max(err, torch.abs(num - ana) / torch.abs(num))
68 function printTensorTable(t)
69 if torch.type(t) == 'table' then
70 for i, t in pairs(t) do
71 print('-- ELEMENT [' .. i .. '] --')
79 -- +-- Linear(10, 10) --> ReLU --> d --+
82 -- --> a --> b -----------> c --------------+ e -->
85 -- +----- Mul(-1) ------+
95 model:connect(a, b, c)
96 model:connect(b, nn.Linear(10, 15), nn.ReLU(), d)
99 model:connect(c, nn.Mul(-1), e)
104 local input = torch.Tensor(30, 50):uniform()
105 local output = model:updateOutput(input):clone()
109 print('Error = ' .. checkGrad(model, nn.MSECriterion(), input, output))
111 print('Writing /tmp/graph.dot')
112 model:saveDot('/tmp/graph.dot')