So i have some dataset, which is basically a count dataset. I have my own code for the classification using neural networks. Turns out that the data does not have a lot of correlation so accuracies as high as 55% is acceptable.For some reason the output of my neural network turns out to be the same prediction no matter what the input is on the testing set.I think this has something to do with the data, as the sigmoid function saturates outputs of layers to 1. The code i have written was tried on 5 different UCI classification datasets and it worked perfectly. Any kind of help is appreciated ,i would not mind sharing the code as well. please do help,i need to know how i can transform the data(normalise or something similar) so that i can get the neural network to learn it.
Could you be solving the wrong problem? The underlying neural net may be oscillating or any number of things based on the parameters. If you have many zeros you may be up against against the zero inflated model. In this case you would predict whether you will have zero or non-zero counts then on some the actual number. So your net would first be a binary classification problem. Then you would have another model where you only train on non-zero cases.