Output of neural network holds same values everytime

Question

I am working on a very simple feed forward neural network to practice my programming skills. There are 3 classes :

• Neural::Net ; builds the network, feeds forward input values (no backpropagation for the moment)
• Neural::Neuron ; has characteristics of the neuron (index, output, weight etc)
• Neural::Connection ; a structure-like class that randomizes the weights and hold the output, delta weight etc..

The program is very basic: I build the network with 2 hidden layers and randomized weights, then ask it to feed forward the same input values.

My problem is: It is expected that the program ends up with different output values after every run, yet the output are always the same. I tried placing markers everywhere to understand why it is calculating the same thing over and over again but I can't put my finger on the error.

Here is the code:

#include <iostream>
#include <cassert>
#include <cstdlib>
#include <vector>
#include "ConsoleColor.hpp"

using namespace std;

namespace Neural {
    class Neuron;
    typedef vector<Neuron> Layer;

    // ******************** Class: Connection ******************** //
    class Connection {
    public:
        Connection();
        void setOutput(const double& outputVal) { myOutputVal = outputVal; }
        void setWeight(const double& weight) { myDeltaWeight = myWeight - weight; myWeight = weight; }
        double getOutput(void) const { return myOutputVal; }
        double getWeight(void) const { return myWeight; }
    private:
        static double randomizeWeight(void) { return rand() / double(RAND_MAX); }
        double myOutputVal;
        double myWeight;
        double myDeltaWeight;
    };

    Connection::Connection() { 
        myOutputVal = 0;
        myWeight = Connection::randomizeWeight();
        myDeltaWeight = myWeight;
        cout << "Weight: " << myWeight << endl;
    }

    // ******************** Class: Neuron ************************ //
    class Neuron {
    public:
        Neuron();
        void setIndex(const unsigned int& index) { myIndex = index; }
        void setOutput(const double& output) { myConnection.setOutput(output); }
        unsigned int getIndex(void) const { return myIndex; }
        double getOutput(void) const { return myConnection.getOutput(); }
        void feedForward(const Layer& prevLayer);
        void printOutput(void) const;

    private:
        inline static double transfer(const double& weightedSum);
        Connection myConnection;
        unsigned int myIndex;
    };

    Neuron::Neuron() : myIndex(0), myConnection() { } 
    double Neuron::transfer(const double& weightedSum) { return 1 / double((1 + exp(-weightedSum))); }
    void Neuron::printOutput(void) const { cout << "Neuron " << myIndex << ':' << myConnection.getOutput() << endl; }
    void Neuron::feedForward(const Layer& prevLayer) {
        // Weight sum of the previous layer's output values
        double weightedSum = 0;
        for (unsigned int i = 0; i < prevLayer.size(); ++i) {
            weightedSum += prevLayer[i].getOutput()*myConnection.getWeight();
            cout << "Neuron " << i << " from prevLayer has output: " << prevLayer[i].getOutput() << endl;
            cout << "Weighted sum: " << weightedSum << endl;
        }
        // Transfer function
        myConnection.setOutput(Neuron::transfer(weightedSum));
        cout << "Transfer: " << myConnection.getOutput() << endl;
    }

    // ******************** Class: Net *************************** //
    class Net {
    public:
        Net(const vector<unsigned int>& topology);
        void setTarget(const vector<double>& targetVals);
        void feedForward(const vector<double>& inputVals);
        void backPropagate(void);
        void printOutput(void) const;
    private:
        vector<Layer> myLayers;
        vector<double> myTargetVals;

    };
    Net::Net(const vector<unsigned int>& topology) : myTargetVals() {
        assert(topology.size() > 0);
        for (unsigned int i = 0; i < topology.size(); ++i) { // Creating the layers
            myLayers.push_back(Layer(((i + 1) == topology.size()) ? topology[i] : topology[i] + 1)); // +1 is for bias neuron
            // Setting each neurons index inside layer
            for (unsigned int j = 0; j < myLayers[i].size(); ++j) {
                myLayers[i][j].setIndex(j); 
            }
            // Console log
            cout << red;
            if (i == 0) {
                cout << "Input layer (" << myLayers[i].size() << " neurons including bias neuron) created." << endl;
                myLayers[i].back().setOutput(1);
            }
            else if (i < topology.size() - 1) { 
                cout << "Hidden layer " << i << " (" << myLayers[i].size() << " neurons including bias neuron) created." << endl; 
                myLayers[i].back().setOutput(1);
            }
            else { cout << "Output layer (" << myLayers[i].size() << " neurons) created." << endl; }
            cout << white;
        }
    }
    void Net::setTarget(const vector<double>& targetVals) { assert(targetVals.size() == myLayers.back().size()); myTargetVals = targetVals; }
    void Net::feedForward(const vector<double>& inputVals) {
        assert(myLayers[0].size() - 1 == inputVals.size());
        for (unsigned int i = 0; i < inputVals.size(); ++i) { // Setting input vals to input layer
            cout << yellow << "Setting input vals...";
            myLayers[0][i].setOutput(inputVals[i]); // myLayers[0] is the input layer
            cout << "myLayer[0][" << i << "].getOutput()==" << myLayers[0][i].getOutput() << white << endl;
        }
        for (unsigned int i = 1; i < myLayers.size() - 1; ++i) { // Updating hidden layers
            for (unsigned int j = 0; j < myLayers[i].size() - 1; ++j) { // - 1 because bias neurons do not have input
                cout << "myLayers[" << i << "].size()==" << myLayers[i].size() << endl;
                cout << green << "Updating neuron " << j << " inside layer " << i << white << endl;
                myLayers[i][j].feedForward(myLayers[i - 1]); // Updating the neurons output based on the neurons of the previous layer
            }
        }
        for (unsigned int i = 0; i < myLayers.back().size(); ++i) { // Updating output layer
            cout << green << "Updating output neuron " << i << ": " << white << endl;
            const Layer& prevLayer = myLayers[myLayers.size() - 2];
            myLayers.back()[i].feedForward(prevLayer); // Updating the neurons output based on the neurons of the previous layer
        }
    }
    void Net::printOutput(void) const {
        for (unsigned int i = 0; i < myLayers.back().size(); ++i) {
            cout << blue;  myLayers.back()[i].printOutput(); cout << white;
        }
    }
    void Net::backPropagate(void) {

    }
}

int main(int argc, char* argv[]) {
    vector<unsigned int> myTopology;
    myTopology.push_back(3);
    myTopology.push_back(4);
    myTopology.push_back(2);
    myTopology.push_back(2);

    cout << myTopology.size() << endl << endl; // myTopology == {3, 4, 2 ,1}

    vector<double> myTargetVals= {0.5,1};
    vector<double> myInputVals= {1, 0.5, 1};

    Neural::Net myNet(myTopology);
    myNet.feedForward(myInputVals);
    myNet.printOutput();

    return 0;
}

Edit: I figured that the bias neuron in the input layer was correctly set to output 1 while the ones in the hidden layers are set to 0 and I fixed that. But the outputs are still the same every run. I did the math on a sheet of paper and it works out. Here is the output (Color coded for clarity) :

I have expected the values to be random just like the weights. Shouldn't that be the case ? I am confused.

Answer 1

I found my mistake. I thought that rand() initialized its seed automatically. I knew it was a dumb thing. I added srand(time(NULL)); at the beginning of the program and now it works as it should.