how to compute and plot mean square error for two vectors?

i have a dataset to classify, using perceptron learning rule . i've calculated the weight matrix but don't know how to plot MSE .

{𝑝1 = [ 1 1 ],𝑡1 = [ 0 0 ]}, {𝑝2 = [ 1 2 ],𝑡2 = [ 0 0 ]}, {𝑝3 = [ 2 −1 ],𝑡3 = [ 0 1 ]}, {𝑝4 = [ 2 0 ],𝑡4 = [ 0 1 ]}, {𝑝5 = [ −1 2 ],𝑡5 = [ 1 0 ]}, {𝑝6 = [ −2 1 ],𝑡6 = [ 1 0 ]}, {𝑝7 = [ −1 −1 ],𝑡7 = [ 1 1 ]}, {𝑝8 = [ −2 −2 ],𝑡8 = [ 1 1 ]}.

This the dataset and w=[-2 0;0 -2],bias =[-1 0]

Answer 1

[Gaurav Garg]

Hi Deepak,

You can plot MSE/Loss and accuracy for each iteration of your training/testing.

To do this, you can make a network with 'n' number of layers, train your network on it and store the loss returned per iteration in a list. Finally, you can plot this loss on y-axis and number of iterations on x-axis.

For any more information on monitoring metrics, you can look at the documentation here.

[DEEPAK Chekuri]

Thankyou Gaurav,

Storing mse in a new list for every iteration worked out for me and i'm instruucted to use single layer.

I've stored MSE of each iteration in new list and plotted the same.