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Problem- 1 Create a backpropagation based neural network that learns the XOR function: Train the network using the 4 examples that correspond to the correct outputs. After the learning process is over, generate several points (use at least 0.1 increments) in the Ixl box and show the shape of the function leamed by plotting the positive points. You may use your own plotting/displaying methods or use a spreadsheet after creating the data points with your program.

Problem- 1 Create a backpropagation based neural network that learns the XOR function: Train the network using the 4 examples that correspond to the correct outputs. After the learning process is over, generate several points (use at least 0.1 increments) in the Ixl box and show the shape of the function leamed by plotting the positive points. You may use your own plotting/displaying methods or use a spreadsheet after creating the data points with your program.

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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Problem- 1 Create a backpropagation based neural network that learns the XOR function: Train the network using the 4 examples that correspond to the correct outputs. After the learning process is over, generate several points (use at least 0.1 increments) in the 1x1 box and show the shape of the function learned by plotting the positive points. You may use your own plotting/displaying methods or use a spreadsheet after creating the data points with your program. Problem- 2 Create a backpropagation based neural network that learns the y = x2 function. The program should learn from data that is classified as negative or positive, not from x,y pairs given as input. To do this, generate several examples such that y2x2 are positive and y<x2 are negative. Use these examples to train your network. After the learning process is over, generate several more points and show the shape of the function learned by plotting the positive points. Provide answers to the folowing six questions: 1. Did you implement the basic framework of a feedforward multilayer neural network? 2. Did you test whether feedforward and backpropagation work? Which data sets did you use? 3. Did you implement or think about how to change the number of layers or nodes? 4. Did you prepare the data points for the training examples for the XOR function and the x2 function? Did you plan how to input those points? 5. Did you implement or think about how to graph the final neural network? 6. What problems or obstacles did you encounter so far? How do you plan to deal with them?
Transcribed Image Text:Problem- 1 Create a backpropagation based neural network that learns the XOR function: Train the network using the 4 examples that correspond to the correct outputs. After the learning process is over, generate several points (use at least 0.1 increments) in the 1x1 box and show the shape of the function learned by plotting the positive points. You may use your own plotting/displaying methods or use a spreadsheet after creating the data points with your program. Problem- 2 Create a backpropagation based neural network that learns the y = x2 function. The program should learn from data that is classified as negative or positive, not from x,y pairs given as input. To do this, generate several examples such that y2x2 are positive and y<x2 are negative. Use these examples to train your network. After the learning process is over, generate several more points and show the shape of the function learned by plotting the positive points. Provide answers to the folowing six questions: 1. Did you implement the basic framework of a feedforward multilayer neural network? 2. Did you test whether feedforward and backpropagation work? Which data sets did you use? 3. Did you implement or think about how to change the number of layers or nodes? 4. Did you prepare the data points for the training examples for the XOR function and the x2 function? Did you plan how to input those points? 5. Did you implement or think about how to graph the final neural network? 6. What problems or obstacles did you encounter so far? How do you plan to deal with them?
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