void NN::BackProp(){
    ForwardProp();
    for(int i = 0; i < nOutputs; i++){//same size as nOutputs
        deltaOutput[i] = d_activation(netOutput[i]) * currentError[i];//δ = σ(z) · (1 − σ(z)) · (y − t)
        d_biasOutput[i] = deltaOutput[i];//biases are changed
		//derroe/dbj = δj derivative of error in respect to output is delta output slide 67

        for(int j = 0; j < nHiddenNeurons;j++){
			d_weightsOutput[i*nHiddenNeurons+j] = deltaOutput[i] * outHidden[j]; //changing the direvatives of the weights vias biases * hidden outputs
		}

    }
        //check code with arthur

        //working on the nhidden
    for(int x = 0; x < nHiddenNeurons; x++){//x is all hidden neutrons

		deltaHidden[x] = 0.0;
		double sumOutput =0; //suming up (Xiδi· wouti,j)
        for(int i = 0; i < nOutputs; i++){
            //getting all z = netHidden all in respect to nHiddenN dw
           sumOutput += deltaOutput[i] * weightsOutput[x*nOutputs+i];
            //deltaHidden[x] += currentInputs[x] * deltaOutput[i] * weightsOutput[x*nOutputs + i] * d_activation(netOutput[x]);//delta outputs kinda a constant sums itup δ · xi
           //de/wi

                                        //need += because it adds up other wise it'll be useless
            //deltaHidden[nHiddenNeurons] = d_activation(netHidden[nHiddenNeurons]);//direvative of the error in respect to the hidden weights

			}
        double z = netHidden[x];//z is hidden net for that formula
        deltaHidden[x] = sumOutput * d_activation(z); // (1 − σ(zj))
        d_biasHidden[x] = deltaHidden[x]; //tweaking biases of hidden neurons, propagating backwards

        for(int i = 0; i < nInputs; i++){
			d_weightsHidden[x*nInputs+i] = deltaHidden[x] * currentInputs[i];//changing hiddens weighhts for the inputs (last step of propagation)
		}

	}
}