Add all steps, thank you Consider the following feedforward neural network: - Nodes x1 and x2
are input nodes. - Nodes h1,h2, and yp are rectified linear units; i.e., they use the activation
g(t)=max{0,t}. - The inputs to h1 are weighted by w1,1(1) and w1,2(1);h2 's inputs have weights
w2,1(1) and w2,2(1) - The inputs to yp are weighted by w1(2) and w1(2). (Note that the output
node is called yp to distinguish it from the "true" value of y in a training example) - There are no
offsets in this network; the weights are the only parameters (a) [5 points] Let
yp=f(x1,x2;w1,1(1),w1,2(1),w2,1(1),w2,2(1),w1(2),w1(2)) be the prediction of the neural
network on an input x1,x2. Give an expression for f. (b) [3 points] Let
L(w1,1(1),w1,2(1),w2,1(1),w2,2(1),w1(2),w1(2)) be the mean-squared error of f on a single
example (x1,x2,y). Give an expression for L. (c) [10 points] Give a finite numerical algorithm
for computing L..
Add all steps, thank you Consider the following feedforward neural n.pdf
1. Add all steps, thank you Consider the following feedforward neural network: - Nodes x1 and x2
are input nodes. - Nodes h1,h2, and yp are rectified linear units; i.e., they use the activation
g(t)=max{0,t}. - The inputs to h1 are weighted by w1,1(1) and w1,2(1);h2 's inputs have weights
w2,1(1) and w2,2(1) - The inputs to yp are weighted by w1(2) and w1(2). (Note that the output
node is called yp to distinguish it from the "true" value of y in a training example) - There are no
offsets in this network; the weights are the only parameters (a) [5 points] Let
yp=f(x1,x2;w1,1(1),w1,2(1),w2,1(1),w2,2(1),w1(2),w1(2)) be the prediction of the neural
network on an input x1,x2. Give an expression for f. (b) [3 points] Let
L(w1,1(1),w1,2(1),w2,1(1),w2,2(1),w1(2),w1(2)) be the mean-squared error of f on a single
example (x1,x2,y). Give an expression for L. (c) [10 points] Give a finite numerical algorithm
for computing L.
![Add all steps, thank you Consider the following feedforward neural network: - Nodes x1 and x2
are input nodes. - Nodes h1,h2, and yp are rectified linear units; i.e., they use the activation
g(t)=max{0,t}. - The inputs to h1 are weighted by w1,1(1) and w1,2(1);h2 's inputs have weights
w2,1(1) and w2,2(1) - The inputs to yp are weighted by w1(2) and w1(2). (Note that the output
node is called yp to distinguish it from the "true" value of y in a training example) - There are no
offsets in this network; the weights are the only parameters (a) [5 points] Let
yp=f(x1,x2;w1,1(1),w1,2(1),w2,1(1),w2,2(1),w1(2),w1(2)) be the prediction of the neural
network on an input x1,x2. Give an expression for f. (b) [3 points] Let
L(w1,1(1),w1,2(1),w2,1(1),w2,2(1),w1(2),w1(2)) be the mean-squared error of f on a single
example (x1,x2,y). Give an expression for L. (c) [10 points] Give a finite numerical algorithm
for computing L.](https://image.slidesharecdn.com/addallstepsthankyouconsiderthefollowingfeedforwardneuraln-230401025733-05aaf276/85/Add-all-steps-thank-you-Consider-the-following-feedforward-neural-n-pdf-1-320.jpg)
