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Project 3 Neural Networks CS 539

Project 3 Neural Networks CS 539. Skyler Whorton September 28, 2012. Objectives. Faces dataset Direction classification Which specific directions are easier/harder to identify? Name classification Who gets mistaken for whom? Spambase dataset

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Project 3 Neural Networks CS 539

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  1. Project 3Neural NetworksCS 539 SkylerWhortonSeptember 28, 2012

  2. Objectives • Faces dataset • Direction classification • Which specific directions are easier/harder to identify? • Name classification • Who gets mistaken for whom? • Spambase dataset • Are there relationships in the data on than entropy measures can’t detect?

  3. Faces - Preprocessing • 30x32 pixel images = 960 predicting attributes • Convert ‘binary’ PGMB to ASCII-readable PGMA • Transform irregular text file to comma-separated pixel data • Two variations: each instance labeled with Name or Direction

  4. Neural Net Topology • Network topology • MultilayerPerceptron: suggested defaults of: • ‘a’ = (instaces + classes) / 2 • ‘o’ = classes. Put into two hidden layers: ‘o, o’ • Nprtool: searched for least-error single hidden layer topology:

  5. Faces - Direction • 960 predicting attributes • 4 classes • 624 instances • Nprtool ‘Optimal’ network topology: 15 nodes

  6. Faces - Name • 960 predicting attributes • 20 classes • 624 instances • Nprtool: 10 nodes

  7. Faces – Name, cropped • 538 predicting attributes (Trimmed 4 pixels from each edge) • 20 classes • 624 instances • Nprtool: 10 nodes

  8. Confusion • Direction • ‘straight’ sometimes seems like ‘up’ • ‘left’ and ‘right’ seldom confused • Name • The models are too good! • Only misclassify ~2-4 instances a b c d <-- classified as 136 18 0 2 | a = straight 8 144 4 0 | b = up 3 3 151 0 | c = left 2 2 1 150 | d = right

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