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Questions to be answered by the end of the session
(i) Does one hidden layer with the same number of neurons in it (say 8) work better or worse than a network with 2 hidden layers with four in each?
(ii) Does varying the neurons in hidden layer effect the result?
(iii) How noisy can the input be and the network still acceptably classify?
(iv) Do all input patterns (see A in second figure) work? Are some easier to classify?
1. Click on http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle®Dataset=reg-plane&learningRate=0.03®ularizationRate=0&noise=0&networkShape=4,2&seed=0.11478&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false
You should see something like this.
2. Now play with different settings and see what they do.
Change A to try about different patterns.
Change B to increase or decrease the numbers of hidden layers
Change C to change the number of Neurons
Questions to be answered by the end of the session
(v) Does one hidden layer with the same number of neurons in it (say 10) work better or worse than a network with 2 hidden layers with five in each?
(vi) Does varying the neurons in hidden layer effect the result?
(vii) How noisy can the input be and the network still acceptably classify?
(viii) Do all input patterns (see A in second figure) work? Are some easier to classify?
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