you can always normalize the weights to be in 0-1 range. At the end what you do with the weight is give a relative importance to each class so it does not matter how big is the weight as long as that relative importance is preserved. You can also  reduce your learning rate if your weights are big so your stochastic optimization algorithm does not oscillate.
Hope it helps.