#!/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
end
end
--- torch.setnumthreads(params.nbThreads)
-torch.setdefaulttensortype('torch.DoubleTensor')
-torch.manualSeed(2)
-
-a = nn.Linear(10, 10)
-b = nn.ReLU()
-c = nn.Linear(10, 3)
-d = nn.Linear(10, 3)
-e = nn.CMulTable()
-f = nn.Linear(3, 2)
+-- +-- Linear(10, 10) --> ReLU --> d -->
+-- / /
+-- / /
+-- --> a --> b -----------> c ---------------+
+-- \
+-- \
+-- +--------------- e -->
--- a -----> b ---> c ----> e ---
--- \ /
--- \--> d ---/
--- \
--- \---> f ---
+dag = nn.DAG()
-g = nn.DAG()
-
-g:addEdge(c, e)
-g:addEdge(a, b)
-g:addEdge(d, e)
-g:addEdge(b, c)
-g:addEdge(b, d)
-g:addEdge(d, f)
-
-g:setInput({{a}})
-g:setOutput({ e, f })
-
-g:print()
-
-input = torch.Tensor(3, 10):uniform()
-
-output = g:updateOutput({{ input }})
-
-printTensorTable(output)
+a = nn.Linear(50, 10)
+b = nn.ReLU()
+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)
+
+dag:setInput(a)
+dag:setOutput({ d, e })
+
+-- Check the output of the dot file
+print('Writing /tmp/graph.dot')
+dag:saveDot('/tmp/graph.dot')
+
+-- 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())
+
+criterion = nn.MSECriterion()
+
+if cunn then
+ print("Using CUDA")
+ model:cuda()
+ criterion:cuda()
+ torch.setdefaulttensortype('torch.CudaTensor')
+ epsilon = 1e-3
+end
-----------------------------------------------------------------------
+local input = torch.Tensor(30, 50):uniform()
+local output = model:updateOutput(input):clone()
+output:uniform()
-print('******************************************************************')
-print('** updateGradInput ***********************************************')
-print('******************************************************************')
-gradInput = g:updateGradInput({{input}}, output)
+-- Check that DAG:accGradParameters and friends work okay
+print('Gradient estimate error ' .. checkGrad(model, criterion, input, output, epsilon))
-printTensorTable(gradInput)
+-- Check that we can save and reload the model
+model:clearState()
+torch.save('/tmp/test.t7', model)
+local otherModel = torch.load('/tmp/test.t7')
+print('Gradient estimate error ' .. checkGrad(otherModel, criterion, input, output, epsilon))