(c) A fully connected neural network is given below. ● x1 x2 1 Note that: i. ii. iii. w11 w12 w21, w31 w22 w32 al กาอย 1 h12 h21 a2h22 h11 h31 y1 81: y2 The ReLU activation function is defined as follows: h32 For an input x, ReLU(x) = max(0, x) The Softmax function is defined as follows: S Given the inputs, xi, i=1, ..., n; the outputs are as follows: exi = j=1 Calculate the net inputs a1 and a2. Calculate the ReLU outputs. Calculate y1 and y2. Softmax exj Suppose that the weights of the network are w11=-1.5; w12=-1.0, w21-1.2; w22=1.0; w31=-0.6; w32=1.0 h11=-0.5; h12=-1.0; h21=1.0, h22=1.5; h31= 0.4; h32=0.3 For an input of x1= 1.0 and x2=2.0 z1 z2

(c) A fully connected neural network is given below. ● x1 x2 1 Note that: i. ii. iii. w11 w12 w21, w31 w22 w32 al กาอย 1 h12 h21 a2h22 h11 h31 y1 81: y2 The ReLU activation function is defined as follows: h32 For an input x, ReLU(x) = max(0, x) The Softmax function is defined as follows: S Given the inputs, xi, i=1, ..., n; the outputs are as follows: exi = j=1 Calculate the net inputs a1 and a2. Calculate the ReLU outputs. Calculate y1 and y2. Softmax exj Suppose that the weights of the network are w11=-1.5; w12=-1.0, w21-1.2; w22=1.0; w31=-0.6; w32=1.0 h11=-0.5; h12=-1.0; h21=1.0, h22=1.5; h31= 0.4; h32=0.3 For an input of x1= 1.0 and x2=2.0 z1 z2

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter11: Nonlinear Programming

Section11.10: Quadratic Programming

Problem 7P

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