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Assume that a system uses a three-layer perceptron neural network to recognize 10 hand-written digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9. Each digit is represented by a 9 x 9 pixels binary image and therefore each sample is represented by an 81-dimensional binary vector. The network uses 10 neurons in the output layer. Each of the output neurons signifies one of the digits. The network uses 120 hidden neurons. Each hidden neuron and output neuron also has a bias input. (i) How many connection weights does the network contain? (ii) For the training samples from each of the 10 digits, write down their possible corresponding desired output vectors. (iii) Describe briefly how the backpropagation algorithm can be applied to train the network. (iv) Describe briefly how a trained network will be applied to recognize an unknown input

Assume that a system uses a three-layer perceptron neural network to recognize 10 hand-written digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9. Each digit is represented by a 9 x 9 pixels binary image and therefore each sample is represented by an 81-dimensional binary vector. The network uses 10 neurons in the output layer. Each of the output neurons signifies one of the digits. The network uses 120 hidden neurons. Each hidden neuron and output neuron also has a bias input. (i) How many connection weights does the network contain? (ii) For the training samples from each of the 10 digits, write down their possible corresponding desired output vectors. (iii) Describe briefly how the backpropagation algorithm can be applied to train the network. (iv) Describe briefly how a trained network will be applied to recognize an unknown input

Computer Networking: A Top-Down Approach (7th Edition)
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
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Assume that a system uses a three-layer perceptron neural network to recognize 10 hand-written digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9. Each digit is represented by a 9 x 9 pixels binary image and therefore each sample is represented by an 81-dimensional binary vector. The network uses 10 neurons in the output layer. Each of the output neurons signifies one of the digits. The network uses 120 hidden neurons. Each hidden neuron and output neuron also has a bias input. (i) How many connection weights does the network contain? (ii) For the training samples from each of the 10 digits, write down their possible corresponding desired output vectors. (iii) Describe briefly how the backpropagation algorithm can be applied to train the network. (iv) Describe briefly how a trained network will be applied to recognize an unknown input
Transcribed Image Text:Assume that a system uses a three-layer perceptron neural network to recognize 10 hand-written digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9. Each digit is represented by a 9 x 9 pixels binary image and therefore each sample is represented by an 81-dimensional binary vector. The network uses 10 neurons in the output layer. Each of the output neurons signifies one of the digits. The network uses 120 hidden neurons. Each hidden neuron and output neuron also has a bias input. (i) How many connection weights does the network contain? (ii) For the training samples from each of the 10 digits, write down their possible corresponding desired output vectors. (iii) Describe briefly how the backpropagation algorithm can be applied to train the network. (iv) Describe briefly how a trained network will be applied to recognize an unknown input
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