Contents
What does gradient norm mean?
Gradient norm scaling involves changing the derivatives of the loss function to have a given vector norm when the L2 vector norm (sum of the squared values) of the gradient vector exceeds a threshold value.
How do you deal with an exploding gradient?
How to Fix Exploding Gradients?
- Re-Design the Network Model. In deep neural networks, exploding gradients may be addressed by redesigning the network to have fewer layers.
- Use Long Short-Term Memory Networks.
- Use Gradient Clipping.
- Use Weight Regularization.
Why do we vanish gradient?
The term vanishing gradient refers to the fact that in a feedforward network (FFN) the backpropagated error signal typically decreases (or increases) exponentially as a function of the distance from the final layer.
When to change loss function to gradient norm?
Gradient Norm Scaling Gradient norm scaling involves changing the derivatives of the loss function to have a given vector norm when the L2 vector norm (sum of the squared values) of the gradient vector exceeds a threshold value.
When to use gradient clipping in loss function?
Gradient value clipping involves clipping the derivatives of the loss function to have a given value if a gradient value is less than a negative threshold or more than the positive threshold.
When does gradient descent always converge to a critical point?
We showed that backtracking gradient descent, when applied to an arbitrary C^1 function f, with only a countable number of critical points, will always either converge to a critical point or diverge to infinity. This condition is satisfied for a generic function, for example for all Morse functions.
When do you rescale gradients in gradient scaling?
Gradient scaling involves normalizing the error gradient vector such that vector norm (magnitude) equals a defined value, such as 1.0. … one simple mechanism to deal with a sudden increase in the norm of the gradients is to rescale them whenever they go over a threshold