I'm working on a project wherein we train a neural network for parameter regression. We've noticed our model's predictions vary a fair amount each time we retrain the same model architecture, presumably from the stochastic gradient descent. E.g.

• Training session 1: The model predicts 10.26 for output variable Y
• Training session 2: The model predicts 13.61 for output variable Y
• Training session 3: The model predicts 8.14 for output variable Y

Is there some de facto way to build a statistical conclusion of our results?

I suppose the simplest method would be training the model say 10 times across different random seeds and presenting the mean and std for each output variable. But I'm not sure if there is a better or more standard way of doing this.

It seems like this would be a common thing to do but I'm struggling to find information - maybe I am searching the wrong keywords.