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Build a minimal two hidden layer network with step activation that realizes the following decision  boundary and specify all the weights and bias. For step activation function, output is +1 if total input  >= bias T else output is -1 A. Draw the network architecture. What is the minimum number of hidden nodes required  at hidden layer 1 and hidden layer 2? B. Specify all the weights and biases. Weights can be only -1, 1 or 0 only. C. Can this decision boundary be realized with one hidden layer? If yes, how many hidden  nodes will be required?

Build a minimal two hidden layer network with step activation that realizes the following decision  boundary and specify all the weights and bias. For step activation function, output is +1 if total input  >= bias T else output is -1 A. Draw the network architecture. What is the minimum number of hidden nodes required  at hidden layer 1 and hidden layer 2? B. Specify all the weights and biases. Weights can be only -1, 1 or 0 only. C. Can this decision boundary be realized with one hidden layer? If yes, how many hidden  nodes will be required?

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Build a minimal two hidden layer network with step activation that realizes the following decision 
boundary and specify all the weights and bias. For step activation function, output is +1 if total input 
>= bias T else output is -1

A. Draw the network architecture. What is the minimum number of hidden nodes required 
at hidden layer 1 and hidden layer 2?

B. Specify all the weights and biases. Weights can be only -1, 1 or 0 only.
C. Can this decision boundary be realized with one hidden layer? If yes, how many hidden 
nodes will be required? 

x2 (2,9) + + + (2,3) + + I I x1 (8,9) (8,3)
Transcribed Image Text:x2 (2,9) + + + (2,3) + + I I x1 (8,9) (8,3)
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