Imagine you have a network that is trying to predict a categorical response with three levels (categories) and uses a softmax activation function on the final layer. If there is just one node in the L-1 layer and that node has an activation value of 5 for a particular observation, use the parameters given below to calculate the weighted input that goes into the 1st node in the final layer. The value is: e: (W₁ = -0.2; W=0.4: W=0.1, b = 0.3, b = -0.2, b=0.8 O A-1 OB. 0.3 OC -0.7 OD. 1.8
Imagine you have a network that is trying to predict a categorical response with three levels (categories) and uses a softmax activation function on the final layer. If there is just one node in the L-1 layer and that node has an activation value of 5 for a particular observation, use the parameters given below to calculate the weighted input that goes into the 1st node in the final layer. The value is: e: (W₁ = -0.2; W=0.4: W=0.1, b = 0.3, b = -0.2, b=0.8 O A-1 OB. 0.3 OC -0.7 OD. 1.8
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Transcribed Image Text:Imagine you have a network that is trying to predict a categorical response with three levels (categories) and uses a softmax activation function on the
final layer. If there is just one node in the L-1 layer and that node has an activation value of 5 for a particular observation, use the parameters given
below to calculate the weighted input that goes into the 1st node in the final layer. The value is:
e : (Wf = -0.2; W = 0.4; W = 0.1, 6f = 0.3, b = -0.2, b = 0.8
O A.-1
O B. 0.3
OC-.0.7
O D. 1.8
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