So I was working on a classification task with the help of a NN. The data-set was normalised, weights random between
0-1, and all the activations were sigmoid function.
Now, when I used a 2 hidden layer model the accuracy was 50%, whereas, when I used 1 hidden layer model the accuracy was 99%. Isn't this contrary to intuitive understanding about NN's. I knew more layers means better fitting even over-fitting, but apparently something different is happening in this case (maybe the values outputted by the second hidden layer is too small for the output layer to discern). So what exactly am I missing?