When should you stop losing training?
Underfitting means that model is not able to classify any of the samples even after learning them. The model should stop its training when the accuracy and loss seem to be constant or they only revolve around a certain value. In your case : The loss for the train as well as test seem to decreasing simultaneously.
Is Early Stopping good?
In machine learning, early stopping is a form of regularization used to avoid overfitting when training a learner with an iterative method, such as gradient descent. Such methods update the learner so as to make it better fit the training data with each iteration.
Why does the training loss increase with time?
However a couple of epochs later I notice that the training loss increases and that my accuracy drops. This seems weird to me as I would expect that on the training set the performance should improve with time not deteriorate. I am using cross entropy loss and my learning rate is 0.0002. Update: It turned out that the learning rate was too high.
Why is my validation loss lower than my training loss?
Regularization methods often sacrifice training accuracy to improve validation/testing accuracy — in some cases that can lead to your validation loss being lower than your training loss. Secondly, keep in mind that regularization methods such as dropout are not applied at validation/testing time.
How to minimise training loss in machine learning?
Set up a very small step and train it. The second one is to decrease your learning rate monotonically. Here is a simple formula: Where a is your learning rate, t is your iteration number and m is a coefficient that identifies learning rate decreasing speed. It means that your step will minimise by a factor of two when t is equal to m.
What to do when your running performance decreases?
Often taking 2-3 weeks off from training will resolve the condition and resting, rather than ramping up, is the correct response to decreased performance. The problem is often cured with a short rest.