21) Consider the following MLP network: X1 V2 2 a) b) 1 3 1 8.9 0.8...

Transcribed Image Text:

21) Consider the following MLP network: X1 V2 2 a) b) 1 3 1 8.9 0.8 0.7 0.6 8.5 8.4 0.3 0.2 0.1 8 -10 -5 5 Sigmoid values: s(-5)= 0; s (-4) = .02; s(-3) = .04; s(-2) = .12; s(-1) = .27; s(0) = .5; s(5) = 1; s(1) = .98, s(3) = .96; s(2) = .88; s(1) = .73 18 In order to distribute the error for this net to all the weights, we must not only distribute the blame o directly from the final nodes 2 and 3 to w3 and w4, we must also distribute the blame from node 1 to w1 and w2. The blame for node 1 is calculated from the blame for nodes 2 and 3. Calculate the y values and 6 values. Calculate the new weights. Assume a standard error function and values: w1 = 2, w2 = -2, w3 = 4, w4 = 0; x1 = 2, x2 = 2; n = 1; d2 = 0, d3 = 1. 21) Consider the following MLP network: X1 V2 2 a) b) 1 3 1 8.9 0.8 0.7 0.6 8.5 8.4 0.3 0.2 0.1 8 -10 -5 5 Sigmoid values: s(-5)= 0; s (-4) = .02; s(-3) = .04; s(-2) = .12; s(-1) = .27; s(0) = .5; s(5) = 1; s(1) = .98, s(3) = .96; s(2) = .88; s(1) = .73 18 In order to distribute the error for this net to all the weights, we must not only distribute the blame o directly from the final nodes 2 and 3 to w3 and w4, we must also distribute the blame from node 1 to w1 and w2. The blame for node 1 is calculated from the blame for nodes 2 and 3. Calculate the y values and 6 values. Calculate the new weights. Assume a standard error function and values: w1 = 2, w2 = -2, w3 = 4, w4 = 0; x1 = 2, x2 = 2; n = 1; d2 = 0, d3 = 1.

Answer rating: 100% (QA)

Youve provided an image of a multilayer perceptron MLP network with two input nodes x1 and x2 one hidden node y1 and two output nodes y2 and y3 The network operates with sigmoid activation functions f... View the full answer