Using the model from theano's tutorial, I'm training a 3-layers perceptron with log returns over a very large dataset (~55,000 points). The output's layer contains two neurons, one for each of the classes (either the returns are
-) in which I am trying to cluster my inputs (returns for the past n days).
The machine always predicts the same value, ie the output which has the highest probability over the entire dataset. Has anyone faced a similar problem before? How is this even possible?
Inputs: 100d-array of log returns (quasi normally distributed).
Target Output: sign of consecutive log return (1 or 0).