Contents
How does Adam Optimizer update weights?
Adam is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure to update network weights iterative based in training data.
How does Adam Update learning rate?
It uses the squared gradients to scale the learning rate like RMSprop and it takes advantage of momentum by using moving average of the gradient instead of gradient itself like SGD with momentum. Adam is an adaptive learning rate method, which means, it computes individual learning rates for different parameters.
Does Adam have weight decay?
In this paper, we show that a major factor in the poor generalization of the most popular adaptive gradient method, Adam, lies in its dysfunctional implementation of weight decay; the issue we identify in Adam also pertains to other adaptive gradient methods.
What is Adam weight decay?
Optimal weight decay is a function (among other things) of the total number of batch passes/weight updates. Our empirical analysis of Adam suggests that the longer the runtime/number of batch passes to be performed, the smaller the optimal weight decay.
How is the weight update done in Adam?
The way it’s done in Adam is very simple, to perform weight update we do the following: Where w is model weights, eta (look like the letter n) is the step size (it can depend on iteration). And that’s it, that’s the update rule for Adam.
Why do we need an improved version of Adam?
The authors, therefore, suggest an improved version of Adam called AdamW where the weight decay is performed only after controlling the parameter-wise step size (see the green term in line 12). The weight decay or regularization term does not end up in the moving averages and is thus only proportional to the weight itself.
How is Adam optimizer different from weight decay?
When using the Adam optimizer, it gets even more different: in the case of L2 regularization we add this wd*w to the gradients then compute a moving average of the gradients and their squares before using both of them for the update. Whereas the weight decay method simply consists in doing the update, then subtract to each weight.
Why do adaptive optimizers like Adam have matters?
The rate of the weight decay per step w defines the relative importance of minimizing the original loss function (more important if small w is chosen) and finding small weights (more important if large w is chosen). If you compare the update of the weights as explained before (new weight is equal to old weight minus learning rate times gradient)