Prove the updating equation for the Backpropagation:

Computer Networking: A Top-Down Approach (7th Edition)
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Author:James Kurose, Keith Ross
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Prove the updating equation for the Backpropagation:

 

Step 1. Show that for the Logistic Activation function for the neurons as below:
1
1+ exp(-v)
o(v) =
Then the derivative is as following:
q'(t) = y(t)(1- q(t))
Step 2. Using the model of a single neuron, calculate the input of the neuron j, netj,
as:
Σ(WX+Threshold)
where X, is the activation of previous layer neuron i
W₁ is the weight of going from node i to node j
☐p is the number of neurons in the previous layer
Step 3. Using the error function below,
= 1/2
k=1
jk
E(x)=
(yk (x)-1(x))²
Take the partial derivative with respect to the weight as:
JE (x)
owjk
Use chain rule of calculus and simplify it using results of steps 1 and 2, show that:
dE (x)
=-y; 9(net) (1-9 (net;)) (t; − y;)
dw
Transcribed Image Text:Step 1. Show that for the Logistic Activation function for the neurons as below: 1 1+ exp(-v) o(v) = Then the derivative is as following: q'(t) = y(t)(1- q(t)) Step 2. Using the model of a single neuron, calculate the input of the neuron j, netj, as: Σ(WX+Threshold) where X, is the activation of previous layer neuron i W₁ is the weight of going from node i to node j ☐p is the number of neurons in the previous layer Step 3. Using the error function below, = 1/2 k=1 jk E(x)= (yk (x)-1(x))² Take the partial derivative with respect to the weight as: JE (x) owjk Use chain rule of calculus and simplify it using results of steps 1 and 2, show that: dE (x) =-y; 9(net) (1-9 (net;)) (t; − y;) dw
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