The ANN cannot stop at the lowest point, because at the time of training there is no lowest point. Ideally, the lowest loss that a model can achieve is 0. However, during training, the model has no idea of whether the loss will increase or decrease further down the line.
A model training works in the following way:
- The model starts off with initial weights.
- The model makes a prediction and assesses the loss attained for a data point.
- The model corrects the weight according to the loss attained.
- The model makes a prediction using the corrected weight and assesses the loss for next data point.
- The process continues until all data points are exhausted.
- The performance plot shows the loss attained at each step.
The model thus, has no way of knowing it’s performance on data it has not seen.
It is to be noted that the lowest mean squared error does not mean that the model is performing the best at that point. It means that the model performs well for the data it has already seen. Allowing it to train over more data would ensure that the model makes accurate predictions for unseen data.
Hope this answer helps!
