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Exercise 17: Classification with Neural Networks Summer Term 2019

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Exercise 17: Classification with Neural Networks

Summer Term 2019

Patternclassificationis a typical application area in which neural networks can yield good results.

For educational purposes, this exercise resorts to the very simpleencoderproblems. Generally, the classical encoders have n input units, m = log2n hidden units, and n output units. An encoder maps its input patterns onto the same output pattern. Furthermore, any input pattern consists of ’0’s only, except one single ’1’.

Review: What is an appropriate stopping criterion for this task?

To Do: Implement a simple 4-2-4 encode network and explore the influence of both the learning rateηand the momentumαon the speed of the learning process.

Tasks:

1. Please, implement a 4-2-4 encoder that maps the four possible input patterns ‘1000’,

‘0100’, ‘0010’, and ‘0001’ onto identical outputs. Before doing so, discuss the fol- lowing questions:

(a) How many training patterns do you have in this application?

(b) What is a reasonable stopping criterion?

2. Explore how the learning speed depends on the parameter settings forηandα.

α η

10 3 1 0.3 0.1 0.001

0.0 0.2 0.4 0.6 0.8 1.0

Have fun, Theo and Ralf.

1

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