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