Hi, I'm trying to use a neural network for a classification problem, but the result of the training produce very bad performance. The classification problem:
- I have more than 300,000 training samples
- Each input is a vector of 32 values (real values)
- Each output is a vector of 32 values (0 or 1)
This is how I train the network:
DNN_SIZE = [1000, 1000];
net = feedforwardnet(DNN_SIZE, 'traingda');
net.performParam.regularization = 0.2;
for i=1:length(DNN_SIZE)
net.layers{i}.transferFcn = 'poslin';
end
net.layers{end}.transferFcn = 'logsig';
net = train(net, train_inputs, train_outputs);
- Note: I have tried different values for DNN_SIZE including larger and smaller values, for hidden layers and less, but it didn't make a difference.
- Note 2: I have tried training the same network using a data set from Matlab's examples (simpleclass_dataset) and I still got bad performance.
The performance of the trained network is very bad- Its output is basically 0.5 in every output for every input vector (when the target outputs during training are always 0 or 1). What am I doing wrong, and how can I fix it?
Thanks.
