Question # 3 (55 pts) Given the following neural network with initialized weights as in the picture below, we are trying to distinguish between apples and bananas. An example of training set is as follows: Apple: Input (0.2, 0.3) and desired output (1,0) Banana: Input (0.1, 0.4) and desired output (0,1) Assume that all the neurons have a sigmoid activation function. Let the learning rate be 0.2 and the weights are as indicated in the figure below. 2. Apply the forward propagation of the signals in the network using (0.2,03) as input then perform the back propagation of the error. Show the changes of the weights (25 pts) b. Apply the forward propagation of the signals in the network using (0.1, 0.4) as input, then perform the back propagation of the error. Show the changes of the weights. (25 pts) c. Calculate the average error energy E (5 pts) P.S: You can use the equations below as our OA 02 0.1 Holdidas 06 OU 2 03 0.1 04 05 bnpur PS. The inputs are denoted a and b, the neurons are numbered from 1 to 4 and the blases are represented at the right side of each neuron. (n) N Um) - 1 cm.- 5,m) = e(n);(v(n)) neuronj is an output node ald, (n) - 0,(n),()|1 - 0,(r)]. 8/(m) = jwy(n)o(n)w, (n) =ay,(n) [1 yeni mwa (m) neuron is hidden