What does weight decay do?

Why do we use weight decay? To prevent overfitting. To keep the weights small and avoid exploding gradient. This will help keep the weights as small as possible, preventing the weights to grow out of control, and thus avoid exploding gradient.

What is a good weight decay?

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.

Why is it called weight decay?

L2 regularization is often referred to as weight decay since it makes the weights smaller. It is also known as Ridge regression and it is a technique where the sum of squared parameters, or weights of a model (multiplied by some coefficient) is added into the loss function as a penalty term to be minimized.

What is a good weight decay for Adam?

We consistently reached values between 94\% and 94.25\% with Adam and weight decay. In the tests we ran, the best learning rate with L2 regularization was 1e-6 (with a maximum learning rate of 1e-3) while 0.3 was the best value for weight decay (with a learning rate of 3e-3).

Is AdamW better than Adam?

The authors show experimentally that AdamW yields better training loss and that the models generalize much better than models trained with Adam allowing the new version to compete with stochastic gradient descent with momentum.

Does weight decay affect learning rate?

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 do you calculate weight decay?

To prevent that from happening, we multiply the sum of squares with another smaller number. This number is called weight decay or wd. That is from now on, we would not only subtract the learning rate * gradient from the weights but also 2 * wd * w .

Is AdamW always better than Adam?

The empirical results show that AdamW can have better generalization performance than Adam (closing the gap to SGD with momentum) and that the basin of optimal hyperparameters is broader for AdamW. Update Rule for LARS [6]. LARS is an extension of SGD with momentum which adapts a learning rate per layer.

Is AdamW better?

Is Adam faster than SGD?

Adam is great, it’s much faster than SGD, the default hyperparameters usually works fine, but it has its own pitfall too. Many accused Adam has convergence problems that often SGD + momentum can converge better with longer training time. We often see a lot of papers in 2018 and 2019 were still using SGD.

What is weight decay in CNN?

Weight Decay, or Regularization, is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the Norm of the weights: L n e w ( w ) = L o r i g i n a l ( w ) + λ w T w.

What is the difference between weight decay and learning rate?