From 8a133300c260e836a6424ab240c82e8434b3aa68 Mon Sep 17 00:00:00 2001
From: rtalbi <dr_talbi@esi.dz>
Date: Fri, 17 Dec 2021 17:29:26 +0100
Subject: [PATCH] non-privacy presrerving neural networks
---
ML/NN/NN.cpp | 49 +++++++++++++++++++++++++++++++++++++++++--------
ML/NN/NN.h | 6 +++++-
2 files changed, 46 insertions(+), 9 deletions(-)
diff --git a/ML/NN/NN.cpp b/ML/NN/NN.cpp
index 85780c41..248c82f0 100644
--- a/ML/NN/NN.cpp
+++ b/ML/NN/NN.cpp
@@ -6,7 +6,7 @@
NN::NN(double alpha, int epochs, int batchSize, float th, DatasetReader *dt, string logfile,
- bool debug, string mainpath, int sgdWorkers) {
+ bool debug, string mainpath) {
trainTime =0.0;
@@ -97,17 +97,50 @@ int NN::predict(Record *r, bool test ) {
-vector<float> backpropagate_layer(vector<float> XB) {
+vector<vector<float>> NN::backpropagate_layer(vector<neuron*> layer, vector<vector<float>> XB,vector<vector<float>> ytrue) {
-
- vector <float > diff;
- return diff;
+ vector<vector<float>> new_output_layer;
+ for(int i=0; i < ytrue.size(); i++)
+ {
+ layer[i]->train(XB, ytrue[i]);
+ vector <float > new_output_neuron = layer[i]->new_output;
+ new_output_layer.push_back(new_output_neuron);
+ }
+ return new_output_layer;
}
-void NN::train ()
+void NN::backpropagate(vector<Record *> XB){
+
+ forward(XB); //todo define function for forwarding a batch of records
+ vector<vector<float>> R;
+ vector<vector<float>>ytrue;
+ int dim = XB[0]->values.size()-1;
+ for(int i=0; i<XB.size(); i++)
+ {
+
+ vector<float> r = vector<float> (XB[i]->values.begin(), XB[i]->values.end());
+ r.pop_back();
+ R.push_back(r);
+ std::vector<float> hot_label(2); // hard coded the number of classes
+ hot_label[XB[i]->values[dim]] =1 ;
+ hot_label[1-XB[i]->values[dim]] =0;
+ ytrue.push_back(hot_label);
+
+ }
+
+
+
+ for(int j= network.size()-1; j>=0; j-- )
+ {
+ vector<vector<float>> new_output_layer = backpropagate_layer(network[j],ytrue); //todo remove the record from this method and replace it with the previous input resulting from the forward
+ ytrue = new_output_layer;
+ }
+
+}
+void NN::train () //
{
- int counter =0;
+ int counter =0; // use the backprpagation function here
int recordCounter =0;
int sizeBatch=batchSize;
int size = dt->train_size;
@@ -174,7 +207,7 @@ void NN::Test( ){
this->testTime = duration.count(); //- removeTime;
cout << this->testTime << endl;
classOutput.close();
-}
+} //todo use the forward function here
diff --git a/ML/NN/NN.h b/ML/NN/NN.h
index c5cb835f..6b260024 100644
--- a/ML/NN/NN.h
+++ b/ML/NN/NN.h
@@ -52,7 +52,11 @@ public :
int predict (Record *r, bool test);
public :
- vector<float> backpropagate_layer(vector<float> XB);
+ vector<vector<float>> backpropagate_layer(vector<neuron*> layer, vector<vector<float>> XB,vector<vector<float>> ytrue);
+
+
+public :
+ void backpropagate(vector<Record *> XB);
public :
vector<float> forward_layer(vector<neuron*> layer, vector<float> x, bool test);
--
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