I am trying to create a neural network from scratch using numpy. I have created a network that can classify iris data base to a high degree of accuracy.
I am facing the problem that sometimes instead of predicting a class given input, the network just trains to return the probability distribution of the training sample irrespective of the input.
So, after training the network on 150 samples (50 sample from each class), then on testing on a sample I would get (0.333 0.333 0.333) for each of the three classes, where the expected output was supposed to be, say, (1,0,0).
I had solved that problem by tweaking the Hyperparameters, but I am facing a similar problem with wine data set, for which I cannot do the same.
Has anybody faced this problem before? How did you solve it?