Contents
When to do cross validated with no convergence of loss?
And while doing that when is no convergence of loss ( Loss is not decreasing much or stabilizing), that means we are getting to the correct number of the epoch. Thanks for contributing an answer to Cross Validated!
How can Super convergence be used in training?
An insight that allows “Super Convergence” in training is the use of large learning rates that regularizes the network, hence requiring a reduction of all other forms of regularization to preserve a balance between underfitting and overfitting. There was a recent competition named DAWNBench organized by Stanford University.
How to choose loss functions when training deep?
This requires the choice of an error function, conventionally called a loss function, that can be used to estimate the loss of the model so that the weights can Navigation Machine Learning MasteryMaking developers awesome at machine learning Click to Take the FREE Deep Learning Performane Crash-Course Home Main Menu Get Started Blog Topics
Which is better convergence stability or learning rate?
Greater convergence stability over a larger range of learning rate. One more thing to note that decreasing the momentum and then increasing it is giving much better results compared to another way around. This is the last important hyper-parameter that is worth discussing.
When does the training of a neural network stop?
Yes you are correct. The usual way to train a neural network is to train the same network on several epochs. The training stops when a certain number of epochs is attained or when an early stopping criterion is attained: when the loss on the validation set stops decreasing. Putting it in more simple terms.
When does the training stop in the Black Box?
The training stops when a certain number of epochs is attained or when an early stopping criterion is attained: when the loss on the validation set stops decreasing. Putting it in more simple terms. In the black box of the neural network, we don’t know the optimum value of the number of epochs.
Why does my loss not show up as Nan?
This is especially nasty since normally if this happens on CPU your loss will simply read “NaN”, alerting to you that something is wrong. However, if you are training on the GPU, this will not show up as NaN. Thanks for contributing an answer to Data Science Stack Exchange!