Project $1$ Assignment This assignment involves using backpropagation $($BP$)$ learning procedure to train a multi$-$layer feedforward network to compute the $4-$bit parity function. The bipolar representation is recommended. There are total of sixteen training samples $($x: class$($x$))$ where vector x is a unique $4-$bit input pattern and class$($x$)$ is its corresponding target function value. It is well known that class$($x$)=1$ if x contains odd number of $1\text{'}$s$,$ and class$($x$)=-1,$ otherwise. You are asked to implement two versions of BP learning, one using the standard BP procedure and the other with a momentum term, and compare their respective learning performance. Besides the source code of your program, you should submit a written report of the following: $1.$ The architecture of your network $($number of hidden layers and their sizes, the sigmoid function you choose, etc.$)2.$ The parameters you selected for the network, such as the learning rate, the initial weight matrix. $3.$ Criteria used for termination $($e$.$g$.,$ acceptable error$).4.$ Whether learning is conducted in sequential or batch mode. $5.$ For each of the two versions, report $1)$ The number of iterations $($or the number of epochs if you choose batch mode$)$ the learning takes to its termination. $2)$ The learned weights $($including weights for the dummy$/$bias nodes$).3)$ The output value of the output node when each of the sixteen input patterns is applied to the learned network. $6.$ Any other issues you wish to report and discuss. To highlight the linear inseparability nature of this problem, you are also asked to apply Adaline learning procedure to the same parity function. After the training is terminated, report the decision boundary $($i$.$e$.,$ the linear function$)$ found by the learning process, and which of the sixteen inputs are correctly classified and which are not.