Is Nesterov momentum better?

Is Nesterov momentum better?

4 Answers. Arech’s answer about Nesterov momentum is correct, but the code essentially does the same thing. So in this regard the Nesterov method does give more weight to the lr⋅g term, and less weight to the v term.

Why momentum is used in neural network?

When training a neural network, you must experiment with different momentum factor values. In some situations, using no momentum (or equivalently, a momentum factor of 0.0) leads to better results than using momentum. However, in most scenarios, using momentum gives you faster training and better predictive accuracy.

What is Nesterov momentum?

Nesterov Momentum is an extension to the gradient descent optimization algorithm. The approach was described by (and named for) Yurii Nesterov in his 1983 paper titled “A Method For Solving The Convex Programming Problem With Convergence Rate O(1/k^2).”

What is a good momentum for SGD?

I used beta = 0.9 above. It is a good value and most often used in SGD with momentum.

Is Nadam better than Adam?

With the Fashion MNIST dataset, Adam/Nadam eventually performs better than RMSProp and Momentum/Nesterov Accelerated Gradient. This depends on the model, usually, Nadam outperforms Adam but sometimes RMSProp gives the best performance.

What is momentum gradient?

Momentum is an extension to the gradient descent optimization algorithm that allows the search to build inertia in a direction in the search space and overcome the oscillations of noisy gradients and coast across flat spots of the search space.

What is the difference between momentum and learning rate?

In summary: when performing gradient descent, learning rate measures how much the current situation affects the next step, while momentum measures how much past steps affect the next step.

What are the disadvantages of deep neural networks?

Following are the drawbacks or disadvantages of Deep Learning: ➨It requires very large amount of data in order to perform better than other techniques. ➨It is extremely expensive to train due to complex data models. Moreover deep learning requires expensive GPUs and hundreds of machines.

Which is better SGD or Adam?

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 momentum in learning rate?

Momentum simply adds a fraction m of the previous weight update to the current one. If you combine a high learning rate with a lot of momentum, you will rush past the minimum with huge steps! When the gradient keeps changing direction, momentum will smooth out the variations.

Is SGD faster than Adam?

Which Optimizer is better than Adam?

One interesting and dominant argument about optimizers is that SGD better generalizes than Adam. These papers argue that although Adam converges faster, SGD generalizes better than Adam and thus results in improved final performance.

Which is the best description of Nesterov momentum?

Momentum and Nesterov Momentum (also called Nesterov Accelerated Gradient/NAG) are slight variations of normal gradient descent that can speed up training and improve convergence significantly.

Is there a way to express Nesterov accelerated gradient?

A way to express Nesterov Accelerated Gradient in terms of a regular momentum update was noted by Sutskever and co-workers, and perhaps more importantly, when it came to training neural networks, it seemed to work better than classical momentum schemes.

What is the idea of Sutskever momentum derivation?

The key idea behind the Sutskever momentum derivation is to shift the perspective about which of the parameters we want as the result of the iteration, from \\ (y\\) to \\ ( heta\\).

Which is the best way to write momentum update rules?

The other way, which is most popular way to write momentum update rules, is less intuitive and just omits (1 – beta) term. This is pretty much identical to the first pair of equation, the only difference is that you need to scale learning rate by (1 – beta) factor.