人工神经网络训练与测试

Question

I'm trying to create an ANN that will solve a simple classification problem the example I am using is a degree classification so the input will be a percentage between 0-100 and the output will be one of five (1st, 2:1, 2:2...).

Currently I have set up a neural network with three layers, 1 input neuron, 3 hidden neurons and 5 output neurons, I have managed to train the network using one input eg 60 and the output (1,0,0,0,0). I am unsure though how i would go about properly training the network for each input and output combination so that after training I would be able to input the percentage and the correct output neuron would be the number closest to 1.

The network uses standard feed forward and back propagation algorithms, random weights and the Sigmoid function.

I have a file which I was thinking would work with inputs 0-100 with the outputs inbetween:

0
1, 0, 0, 0, 0

1
1, 0, 0, 0, 0

.....

40
0, 1, 0, 0, 0

....

100
0, 0, 0, 0, 1

Thanks

Answer 1

I don't quite understand the function you are trying to learn, but it doesn't matter. The usual way to train an ANN is to use SGD (stochastic gradient descent) where backpropagation is used to compute the gradient for each example at a time. You just repeat looping this over all input examples until it has learned those examples.

One thing you didn't mention is that you need a loss function. In your case, a simple mean squared error might be appropriate.

Answer 2

I suggest that you take a look at the classifer.py python script used for classification at this link - http://www.marekrei.com/blog/theano-tutorial/ The complete code for the above tutorial is available at this link - https://github.com/marekrei/theano-tutorial

The classifier script at above link is meant for predicting whether the GDP per capita for a country is more than the average GDP. I, however, used the script for different kind of dataset. I was able to successfully train neural networks in Theano using the above classifier script for classifying a speech sound as the letter "A" or "E".