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What is the difference between weight decay and learning rate?
4 Answers. The learning rate is a parameter that determines how much an updating step influences the current value of the weights. While weight decay is an additional term in the weight update rule that causes the weights to exponentially decay to zero, if no other update is scheduled.
How large should weight decay be?
The most common type of regularization is L2, also called simply “weight decay,” with values often on a logarithmic scale between 0 and 0.1, such as 0.1, 0.001, 0.0001, etc. Reasonable values of lambda [regularization hyperparameter] range between 0 and 0.1.
What is the difference between the learning rate and weight decay?
In the context of neural networks, what is the difference between the learning rate and weight decay? The learning rate is a parameter that determines how much an updating step influences the current value of the weights.
How does weight decay affect the regularization effect?
Since a higher weight decay value provides a greater regularization effect, a balance in the amount of regularization is disturbed when used with a large learning rate which also has a regularization effect. The batch size can also affect the underfitting and overfitting balance. Smaller batch sizes provide a regularization effect.
Is it worthwhile to use cyclic weight decay?
Cyclic weight decay is not helpful. Experiments show that weight decay is not like learning rates or momentum and the best value should remain constant throughout the training. A grid search is worthwhile and any difference due to the weight decay value is visible early in the training.
Why is the loss function called weight decay?
Now since our loss function has 2 terms in it, the derivative of the 2nd term w.r.t w would be: That is from now on, we would not only subtract the learning rate * gradient from the weights but also 2 * wd * w . We are subtracting a constant times the weight from the original weight. This is why it is called weight decay.