CODEWITHSUNDEEP
neural-network
Plasty Grove
Plasty Grove

Reputation: 2877

Neural network doesn't converge - using Multilayer Perceptron

I've developed a "Pong" style game which effectively has a ball at the bottom of the screen and bouncy walls on the left and right and a sticky wall on the top. It randomly chooses a point on the bottom (on a straight horizontal line) and a random angle, bounces off the side walls, and hits the top wall. This is repeated a 1000 times and each time, the x-value of the launch position, the launch angle and the final x-value of the position it collides with on the top wall.

This gives me 2 inputs - x-value of launch and launch angle and 1 output - x-value of final position. I tried using a multilayer perceptron with 2 input nodes, 2 hidden nodes (1 layer) and 1 output node. However it converges upto a point ~20 and then tapers off. Here's what I've tried and none of them helped, either the error never converges or it starts diverging:

  1. Transform inputs and output to be between 0 and 1
  2. Transform inputs and output to be between -1 and 1
  3. Increase number of hidden layers
  4. Increase number of nodes in hidden layer
  5. Convert the launch position, launch angle and final position into 0s and 1s resulting in ~750+175 inputs and ~750 outputs - no convergence

So, after spending all night and morning and making my brain and body revolt against me, I'm hoping someone can help me identify the problem here. Is this a task that's just not solvable by a neural network or am I doing something wrong?

PS: I'm using the online version of Neuroph and not coding my own procedure. At least this will help me avoid issues in implementation

Upvotes: 4

Views: 8070

Answers (2)

Sean Barbeau
Sean Barbeau

Reputation: 11756

To eliminate potential implementation issues in Neuroph, I'd suggest trying the exact same process (Multi-Layer Perceptron, same parameters, same data, etc.) but use Weka instead.

I've used the MLP in Weka before with success, so I can verify that this implementation works correctly. I know Weka has a fairly high-penetration in the academic community and its fairly well vetted, but I'm not sure about Neuroph since its newer. If you get the same results as Neuroph, then you know the issue is in your data or neural net topology or configuration.

Qnan brings up a good point - what exactly is the error you are measuring? To really determine why the training error isn't converging towards zero, you need to determine what exactly it is that the error represents.

Also, how many epochs (i.e., number of iterations) is the neural net running in training before it stops converging?

In Weka, if I recall correctly you can set the training to execute either until the error reaches a certain value or for a certain number of epochs. Looks like Neuroph is the same way, from a quick look.

If you're limiting the number of epochs, try bumping up the number to something significantly higher to give the network more iterations to converge.

Upvotes: 1

Qnan
Qnan

Reputation: 3744

If it doesn't minimize the training error, that's most likely a bug in the implementation. If you're measuring the accuracy on a held-out test set, on the other hand, there's nothing surprising about the error going up after a while.

As to the formulation, I think with sufficient amount of training data and sufficiently long training time, a sufficiently complex NN can learn the mapping whether you binarize the input or not (provided the implementation you use supports non-binary input and output). I have only a vague idea of what "sufficient" means in the above sentence, but I'd venture a guess that 1000 samples won't do. Note also that the more complex the network, the more data it will generally need to estimate the parameters.

Upvotes: 3

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