We consider the following Fully Connecte NN to solve a 4-class Classification Problem: - We have 5 units in the Input Layer; - We have 10 Neurons in the first Hidden Layer, and 20 Neurons on the second Hidden Layer; – We are applying ReLU Activation Functions to Hidden Layers; - We are using the SoftMax Activation Function on the Output Layer. How many Parameters (weights and biases) does this NN have?

Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
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We consider the following Fully Connected
NN to solve a 4-class Classification
Problem:
- We have 5 units in the Input Layer;
-
- We have 10 Neurons in the first Hidden
Layer, and 20 Neurons on the second
Hidden Layer;
- We are applying ReLU Activation
Functions to Hidden Layers;
- We are using the SoftMax Activation
Function on the Output Layer. How many
Parameters (weights and biases) does this
NN have?
Transcribed Image Text:We consider the following Fully Connected NN to solve a 4-class Classification Problem: - We have 5 units in the Input Layer; - - We have 10 Neurons in the first Hidden Layer, and 20 Neurons on the second Hidden Layer; - We are applying ReLU Activation Functions to Hidden Layers; - We are using the SoftMax Activation Function on the Output Layer. How many Parameters (weights and biases) does this NN have?
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