Smaller network width than output size?
In high dimensional spaces like the one-dimensional space, you have to employ a very simple network, maybe just a single neuron and investigate whether your data is linearly separable in that feature space which can not be visualized. If you observe that you do not have a good performance, you can increase the size or the layers of your network step by step. You can generalise what I've just referred to other networks like RNNs. 

What I've referred means that your data may be in a way that is separable though it's in 1000 dimensional space and you just need one neuron to classify it.