Q5 Backpropagation Consider the following neural network for regression:
Q5 Backpropagation Consider the following neural network for regression:
Computer Networking: A Top-Down Approach (7th Edition)
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![Q5 Backpropagation
Consider the following neural network for regression:](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F05462cee-8357-4e37-b788-93d6559fda7d%2F989b21ce-a62b-4d9f-bedd-3e95bc6b1e05%2Faurhgtn_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Q5 Backpropagation
Consider the following neural network for regression:
![1
Vo
Wo
W1
V1
ni
n2
where all weights have value 1. Both neurons have a linear function
f (x) = x , so that, the output of neuron n1 is Z1 = Wot WX,
and the output of neuron n2 is y = Z 2 = Vot VIZ 1.
Compute the value of the weights V1 and W1 after one step of
gradient descent with learning rate n = 0.1 on the mean squared
%3D
t)2, where y(x) is the output of the network
on point X and t is the desired value for X, on the input point (1, 2)
error E =
(where the first value of the vector is the input, and the second
value the corresponding target).
Q5.1
V1 =
=0.8+-0.001
Q5.2
W1=
=0.9+-0.001](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F05462cee-8357-4e37-b788-93d6559fda7d%2F989b21ce-a62b-4d9f-bedd-3e95bc6b1e05%2Fa5n6v52_processed.jpeg&w=3840&q=75)
Transcribed Image Text:1
Vo
Wo
W1
V1
ni
n2
where all weights have value 1. Both neurons have a linear function
f (x) = x , so that, the output of neuron n1 is Z1 = Wot WX,
and the output of neuron n2 is y = Z 2 = Vot VIZ 1.
Compute the value of the weights V1 and W1 after one step of
gradient descent with learning rate n = 0.1 on the mean squared
%3D
t)2, where y(x) is the output of the network
on point X and t is the desired value for X, on the input point (1, 2)
error E =
(where the first value of the vector is the input, and the second
value the corresponding target).
Q5.1
V1 =
=0.8+-0.001
Q5.2
W1=
=0.9+-0.001
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