X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=test-dagnn.lua;h=b390a29a5b7b412c6ff019e44f51ef5ef5e2a6de;hp=3b1e66a2f2ef13d602c24e20e1f5f6841e01cf39;hb=HEAD;hpb=60568def49e4c624e54f53b4be5783d6cfbe1ea9

diff --git a/test-dagnn.lua b/test-dagnn.lua
index 3b1e66a..b390a29 100755
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -1,10 +1,72 @@
 #!/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 'cunn'
+
 require 'dagnn'
 
+torch.setdefaulttensortype('torch.DoubleTensor')
+torch.manualSeed(1)
+
+function checkGrad(model, criterion, input, target, epsilon)
+   local params, gradParams = model:getParameters()
+
+   local epsilon = epsilon or 1e-5
+
+   local output = model:forward(input)
+   local loss = criterion:forward(output, target)
+   local gradOutput = criterion:backward(output, target)
+   gradParams:zero()
+   model:backward(input, gradOutput)
+   local analyticalGradParam = gradParams:clone()
+
+   local err = 0
+
+   for i = 1, params:size(1) do
+      local x = params[i]
+
+      params[i] = x - epsilon
+      local output0 = model:forward(input)
+      local loss0 = criterion:forward(output0, target)
+
+      params[i] = x + epsilon
+      local output1 = model:forward(input)
+      local loss1 = criterion:forward(output1, target)
+
+      params[i] = x
+
+      local ana = analyticalGradParam[i]
+      local num = (loss1 - loss0) / (2 * epsilon)
+
+      if num ~= ana then
+         err = math.max(err, math.abs(num - ana) / math.max(epsilon, math.abs(num)))
+      end
+   end
+
+   return err
+end
+
 function printTensorTable(t)
    if torch.type(t) == 'table' then
       for i, t in pairs(t) do
@@ -16,43 +78,66 @@ function printTensorTable(t)
    end
 end
 
--- torch.setnumthreads(params.nbThreads)
-torch.setdefaulttensortype('torch.DoubleTensor')
-torch.manualSeed(2)
+--               +-- Linear(10, 10) --> ReLU --> d -->
+--              /                               /
+--             /                               /
+--  --> a --> b -----------> c ---------------+
+--                            \
+--                             \
+--                              +--------------- e -->
 
-a = nn.Linear(10, 10)
+dag = nn.DAG()
+
+a = nn.Linear(50, 10)
 b = nn.ReLU()
-c = nn.Linear(10, 3)
-d = nn.Linear(10, 3)
-e = nn.CMulTable()
-f = nn.Linear(3, 2)
+c = nn.Linear(10, 15)
+d = nn.CMulTable()
+e = nn.Mul(-1)
+
+dag:connect(a, b, c)
+dag:connect(b, nn.Linear(10, 15), nn.ReLU(), d)
+dag:connect(c, d)
+dag:connect(c, e)
 
---   a -----> b ---> c ----> e ---
---             \           /
---              \--> d ---/
---                    \
---                     \---> f ---
+dag:setLabel(a, 'first module')
 
-g = nn.DAG()
+dag:setInput(a)
+dag:setOutput({ d, e })
 
-g:addEdge(c, e)
-g:addEdge(a, b)
-g:addEdge(d, e)
-g:addEdge(b, c)
-g:addEdge(b, d)
-g:addEdge(d, f)
+-- Check the output of the dot file. Generate a pdf with:
+--
+-- dot ./graph.dot -Lg -T pdf -o ./graph.pdf
+--
+print('Writing ./graph.dot')
+dag:saveDot('./graph.dot')
 
-g:setInput({a})
-g:setOutput({e, f})
+-- Let's make a model where the dag is inside another nn.Container.
+model = nn.Sequential()
+   :add(nn.Linear(50, 50))
+   :add(dag)
+   :add(nn.CAddTable())
 
-g:print()
+criterion = nn.MSECriterion()
 
-input = torch.Tensor(3, 10):uniform()
+if cunn then
+   print("Using CUDA")
+   model:cuda()
+   criterion:cuda()
+   torch.setdefaulttensortype('torch.CudaTensor')
+   epsilon = 1e-3
+end
 
-output = g:updateOutput({input})
+local input = torch.Tensor(30, 50):uniform()
+local output = model:updateOutput(input):clone()
+output:uniform()
 
-printTensorTable(output)
+-- Check that DAG:accGradParameters and friends work okay
+print('Gradient estimate error ' .. checkGrad(model, criterion, input, output, epsilon))
 
-----------------------------------------------------------------------
+-- Check that we can save and reload the model
+model:clearState()
+torch.save('./test.t7', model)
+local otherModel = torch.load('./test.t7')
+print('Gradient estimate error ' .. checkGrad(otherModel, criterion, input, output, epsilon))
 
--- gradInput = g:updateGradInput({ input }, output)
+dag:print()