Machine Learning : Design a Multi-Layer Perceptron neural network and have it learn the function y = x^2 + 1. Use the interval -1 to 1 in increments of .1 (this gives you 21 total data points.)Use the generalized delta rule (backpropagation) weight update algorithm. Run the simulation twice: once with 2 hidden neurons and once with 5 hidden neurons.Generate a learning curve (MSE vs iterations) for each case.Create a learning graph as well. Here is what I have in python so far.

import random import math #li - iteration layer

e = 2.72 inputs[-1.0,-0.9,-0.8,.-0.7,-0.6,-0.5,-0.4,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9.1.0]

def f(x): return x

def fderiv(x): return 1

def forward_pass(x, w): y = [x] for l in range(len(w)): yl = [] n_inputs = len(w[l]) n_outputs = len(w[l][0]) for i in range(n_inputs): netli = 0 for j in range(n_outputs): print(y[-1], i, j) netli += w[l][i][j] * y[-1][j] yli = f(netli) yl.append(yli) y.append(yl) return y

def create_weights(structure): w = [] n_layers = len(structure) for l in range(n_layers - 1): n_rows = structure[l] n_cols = structure[l + 1] matrix = [] for i in range(n_rows): matrix.append([0] * n_cols) w.append(matrix) return w def sigmoidfunction(f,x): f= 1/(1 + (math.e) ^(-x))

print(" ".join(map(str, create_weights([5, 6, 7])))) x = [1, 2, 3, 4, 5] w = create_weights([5, 6, 7])

def error(yi): ei di ei= di-y return ei def back_pass(d, y, net, w): sigma = [] sigmal = [] #local error of a layer for i in range(len(y[-1])): ei = d[i] - y[-1][i] sigmali = ei * fderiv(net[l][i]) sigmal.append(sigmali) sigma.append(sigmal) for l in range(len(y) - 2, -1, -1): sigmal = [] #local error of a layer for i in range(len(y[l])): # ei = d[i] - y[-1][i] sigmali = ei * fderiv(net[l][i]) sigmali sigmal.append(sigmali) sigma.append(sigmal) sigmli = fderive(net[i][l]) * sigma * w[l + 1][i][k] def weightupdate():

def hiddenlayer(): def outputlayer():

def deltarule():

print(forward_pass(x, w)) I need help with weight update for hidden layer, backpropagation, and my graphs.