Weights not converging while cost function has converged in neural networks My cost/loss function drops drastically and approaches 0, which looks a sign of convergence. But the weights are still changing in a visible way, a lot faster than the cost function. Should I ensure the weights converge too?

Some details: I just calculated for 1 epoch. My loss function is mean square difference. I use no optimizer. I tried several experiments and different initial weights but all got converged loss function with ever-changing weights.
The weights in a model do not need to converge to stop training.

One possible explanation is that the model error surface has a big, wide valley. If that is the case, the loss function would be low throughout the valley but there would be many weight combinations that would all yield similar performance on the training dataset. Once a model has reached an acceptable loss function value there is no reason to continue training, just take any set of weight values.