I read an idea for a project in the book “Introduction to Machine Learning” by Tom Mitchell. The project is about determining the direction of the face (left, right, down, straight). I use my own developed neural network, which works (tested with XOR, parabola function…), but can’t train it enough well to determine them correctly. The best case I got is 43% correct, which is pretty low.
Here is a description of the project:
Images 32 x 30, grey-scale (I use 13 people x 32 images for training examples and 4 poeple x 32 images for tests).
Neural Network: 3 layers – input, hidden, output
32 x 30 input units
3 hidden units, using Sigmoid as a transfer function
1 output unit, using linear as a transfer function.
OUT: 0.2 = left ; 0.4 = down; 0.6 = right; 0.8 straight
Learning rate = Momentum = 0.3
Weights and biases are set to random small values.
After 25000 iterations, still, I have just ~40% correct. In the book they managed to get
90% accuracy!
Any ideas?
After the comment of @ffriend everything worked like a charm. I used 4 output neurons and got more than 90% accuracy. If I use more neurons in the hidden layers, the errors get smaller, but the program needs more time to run through the network and back-propagate.