DeepL/RandomUploadByParticipant.lua at master · PnkUncrn/DeepL · GitHub

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--[[RandomUploadByParticipant

Main file that conducts several rounds over all partcipants in which particpants are randomly chosen to interact with the server.

1- has multiple rounds
2- each round iterates over all the participants and randomly chooses which of the participants will interact with the server. The chosen particpants will train, upload and download to and from the server over multiples epoch
3-At the end of the round, we will print the result of the confusion matrix of the Reference User and how many participants interacted with ther Server.

--]]

--[[

Gameplan : 1-
--]]
require 'nn'
require 'torch'
require 'dataset-mnist'
require 'image'
require 'optim'
require 'Helper'

--Basic Parameters
batchSize= 10
learningRate=0.01
weightDecay=1e-7
NumParticipant=20
NumRefUser =10
theta_u_perc=0.1
epoch=20

torch.manualSeed(46)

-- log results to files
trainLogger = optim.Logger(paths.concat("logs", 'train.log'))
testLogger = optim.Logger(paths.concat("logs", 'test.log'))
RefUserLogger=optim.Logger(paths.concat("logs",'RefUserLogger.log'))
ServerLogger=optim.Logger(paths.concat("logs",'ServerLogger.log'))

--model design
classes = {'1','2','3','4','5','6','7','8','9','10'}
geometry = {32,32}

-- log results to files
trainLogger = optim.Logger(paths.concat("logs", 'train.log'))
testLogger = optim.Logger(paths.concat("logs", 'test.log'))


--loss Criterion
criterion=nn.ClassNLLCriterion()

--Dataset- MNIST
nbTrainingPatches = 600
nbTestingPatches = 10000

-- Confusion Matrix
confusion = optim.ConfusionMatrix(classes)


-- create test sets and normalize
testData = mnist.loadTestSet(nbTestingPatches, geometry)
testData:normalizeGlobal(mean, std)

ReferenceUserTrainData = mnist.loadTrainSet(60, geometry, 4550)
ReferenceUserTrainData:normalizeGlobal(mean, std)

--Creating NN table
--creating multiple NN using for loop
Server=CreateNN()
Sparams, SgradParams=Server:getParameters()

NeuralNet={}
ReferenceUser={}
for i = 1,NumRefUser  do
  ReferenceUser[i]=CreateNN()
end

for i = 1,NumParticipant  do
  NeuralNet[i]=CreateNN()
end

for round=1, NumRefUser do
  ParticipantInteractionPerRound=0
  for k=1, NumParticipant do
    randNum=torch.uniform()
    if randNum>0.5 then
    ParticipantInteractionPerRound=ParticipantInteractionPerRound+1
    print("=====Participant: "..k .." Selected. RandNum: "..randNum)
    helper.DriverTrain(NeuralNet[k], k)
  else print("=====Participant: "..k .." Rejected. RandNum: "..randNum)
  end

  end
  print("=======Completed Round: " ..round)
  print("Participant Interaction: "..ParticipantInteractionPerRound)
  helper.ReferenceUserDriver(ReferenceUser[round])
end


--plotting to file

--Initializing everything to zero
epoch=1

Sparams:zero()
SgradParams:zero()