Convergence of neural network weights I came to a situation where the weights of my Neural Network are not converging even after 500 iterations. My neural network contains 1 Input layer, 1 Hidden layer and 1 Output Layer. They are around 230 nodes in the input layer, 9 nodes in the hidden layer and 1 output node in the output layer. I wanted to know, if I do early stopping condition (say stopping my neural network training after 100 iteration). What effect will have this on the model?
Also wanted to know what is the industry standard of work around if the weights in the neural network are not converging?
 A: To me is hard to say what your problem might be. One point to consider is the  concrete implementation you use. Concretely, what optimization algorithm. If your network takes really long to converge, and you are using some form of stochastic gradient descent (or mini-batch) then it could be the case that your network is in a plateau (a region where the energy/error function is very flat so that gradients are very low and thus convergence).
If so, please check the magnitude of the gradients to see if this is the case. There are a number of different techniques to deal with this problem, like adding a momentum to the gradient.
For a detail overview of techniques and tricks of the trade, take a look at this (must read) paper by Yann LeCun.
A: There are a number of questions to ask:


*

*do you have the appropriate number of neurons in each layer

*are you using the appropriate types of transfer functions?

*are you using the appropriate type of learning algorithm

*do you have a large enough sample size 

*can you confirm that your samples have the right sorts of relationship with each other to be informative?  (not redundant, of relevant dimension, etc...)


What can you give in the way of ephemeris?  Can you tell us something about the nature of the data?
You could make a gradient boosted tree of neural networks.  
You asked what happens if you stop early.
You can try yourself.  Run 300x where you start with random initialized weights, and then stop at a specified number of iterations, lets say 100.  At that point compute your ensemble error, your training-subset error, and your test-set error.  Repeat.  After you have 300 values to tell you what the error is, you can get an idea of your error distribution given 100 learning iterations.  If you like, you can then sample that distribution at several other values of learning.  I suggest 200, 500, and 1000 iterations.  This will give you an idea how your SNR changes over time.  A plot of the SNR vs iteration count can give you an idea about "cliffs" or "good enough".  Sometimes there are cliffs where error collapses.  Sometimes the error is acceptable at that point.  
It takes "relatively simple" data or "pretty good" luck for your system to consistently converge in under 100 iterations.  Both of which are not about repeatability nor are they generalizable.  
Why are you thinking in terms of weights converging and not error being below a particular threshold.  Have you ever heard of a voting paradox?  (link)  When you have cyclic interactions in your system (like feedback in Neural Networks) then you can have voting paradoxes - coupled changes.  I don't know if weights alone is a sufficient indicator for convergence of the network.
You can think of the weights as a space.  It has more than 3 dimensions, but it is still a space.  In the "centroid" of that space is your "best fit" region.  Far from the centroid is a less good fit.  You can think of the current setting of your weights as a single point in that space. 
Now you don't know where the "good" actually is.  What you do have is a local "slope".  You can perform gradient descent toward local "better" given where your point is right now.  It doesn't tell you the "universal" better, but local is better than nothing.  
So you start iterating, walking downhill toward that valley of betterness.  You iterate until you think you are done.  Maybe the value of your weights are large.  Maybe they are bouncing all over the place.  Maybe the compute is "taking too long".  You want to be done.  
So how do you know whether where you are is "good enough"?  
Here is a quick test that you could do:  
Take 30 uniform random subsets of the data (like a few percent of the data each) and retrain the network on them.  It should be much faster.  Observe how long it takes them to converge and compare it with the convergence history of the big set.  Test the error of the network for the entire data on these subsets and see how that distribution of errors compares to your big error.  Now bump the subset sizes up to maybe 5% of your data and repeat.  See what this teaches you.  
This is a variation on particle swarm optimization(see reference) modeled on how honeybees make decisions based on scouting.  
You asked what happens if weights do not converge.
Neural Networks are one tool.  They are not the only tool.  There are others.  I would look at using one of them.
I work in terms of information criteria, so I look at both the weights (parameter count) and the error.  You might try one of those.
There are some types of preprocessing that can be useful.  Center and Scale.  Rotate using principal components.  If you look at the eigenvalues in your principal components you can use skree plot rules to estimate the dimension of your data.  Reducing the dimension can improve convergence.  If you know something about the 'underlying physics' then you can smooth or filter the data to remove noise.  Sometimes convergence is about noise in the system.
I find the idea of Compressed sensing to be interesting.  It can allow radical sub-sampling of some systems without loss of generalization.  I would look at some bootstrap re-sampled statistics and distributions of your data to determine if and at what level of sub-sampling the training set becomes representative.  This gives you some measure of the "health" of your data.
Sometimes it is a good thing that they not converge
Have you ever heard of a voting paradox?  You might think of it as a higher-count cousin to a two-way impasse.  It is a loop.  In a 2-person voting paradox the first person wants candidate "A" while the second wants candidate "B" (or not-A or such).  The important part is that you can think of it as a loop.
Loops are important in neural networks.  Feedback.  Recursion.  It made the perceptron able to resolve XOR-like problems.  It makes loops, and sometimes the loops can act like the voting paradox, where they will keep changing weights if you had infinite iterations. They aren't meant to converge because it isn't the individual weight that matters but the interaction of the weights in the loop.
Note:
Using only 500 iterations can be a problem.  I have had NN's where 10,000 iterations was barely enough.  The number of iterations to be "enough" is dependent, as I have already indicated, on data, NN-topology, node-transfer functions, learning/training function, and even computer hardware.  You have to have a good understanding of how they all interact with your iteration count before saying that there have been "enough" or "too many" iterations.  Other considerations like time, budget, and what you want to do with the NN when you are done training it should also be considered.
Chen, R. B., Chang, S. P., Wang, W., & Wong, W. K., (2011, September). Optimal Experimental Designs via Particle Swarm Optimization Methods (preprint), Retrieved March 25, 2012, from http://www.math.ntu.edu.tw/~mathlib/preprint/2011-03.pdf
A: Make sure that your gradients are not going beyond limits or it is also possible that the gradients are becoming zero. This is popularly known as exploding gradients and vanishing gradients problems. 
One possible solution is to use a adaptive optimizer such as AdaGrad or Adam.
I had faced a similar problem while training a simple neural network when I was getting started with neural networks. 
Few references: https://en.wikipedia.org/wiki/Vanishing_gradient_problem
https://www.youtube.com/watch?v=VuamhbEWEWA
A: I've had many data sets that converged slowly - probably because the inputs were highly correlated.
I wrote my own C++ NN analyzer, and with it, I can vary the learning rate for each weight.  For each weight at each edge I do two things that help some.
First, I multiply each learning rate by a uniformly distributed random number from [0,1].  I'm guessing that that helps with the correlation problem.
The other trick is that I compare the current gradient with the previous gradient at each edge.  If the gradient barely lowered percentagewise, then I multiply the learning rate for that edge by up to 5.
I don't have any particular justification for either of those tricks, but they seem to work pretty well.
Hope this helps.
