Contents
What is momentum and learning rate?
The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. Momentum can accelerate training and learning rate schedules can help to converge the optimization process.
How does momentum help training?
Momentum [1] or SGD with momentum is method which helps accelerate gradients vectors in the right directions, thus leading to faster converging. It is one of the most popular optimization algorithms and many state-of-the-art models are trained using it.
What is momentum rate?
Momentum simply adds a fraction m of the previous weight update to the current one. When the gradient keeps pointing in the same direction, this will increase the size of the steps taken towards the minimum. If you combine a high learning rate with a lot of momentum, you will rush past the minimum with huge steps!
Why does momentum do not enable learning rate?
The momentum term does not explicitly include the error gradient in its formula. Therefore, momentum by itself does not enable learning. If you were to only use momentum after establishing an initial weight delta, the weight update equation would look as such:
How does momentum help in the optimization process?
Momentum can accelerate training and learning rate schedules can help to converge the optimization process. Adaptive learning rates can accelerate training and alleviate some of the pressure of choosing a learning rate and learning rate schedule.
Why does momentum need the rate of convergence?
Momentum, on the other hand, aims to improve the rate of convergence and to avoid local minimas. The momentum term does not explicitly include the error gradient in its formula. Therefore, momentum by itself does not enable learning.
How can momentum help network out of local minima?
Another technique that can help the network out of local minima is the use of a momentumterm. This is probably the most popular extension of the backprop algorithm; it is hard to find cases where this is not used.