Gradient clipping is a technique used in deep learning to help optimize the performance of neural networks. The problem that arises with optimization is that the large gradients can lead an optimizer to wrongly update the parameters to a point where the loss function becomes much greater. This makes the solution ineffective, undoing much of the important work.
What is Gradient Clipping?
Gradient Clipping is a technique that ensures optimization runs more reasonably around the sharp areas of the loss surface. This technique can be applied in different ways. One way is to clip an element in the parameter gradient before updating. Another way is to clip the norm of the gradient before a parameter update is executed.
Types of Gradient Clipping
There are two main types of Gradient Clipping: Element-wise Clipping and Norm Clipping
Element-Wise Clipping
The parameter gradients can be clipped element-wise. This means that the gradient is clipped after it has been computed but before it starts to change the weights. Element-Wise Clipping involves fixing a maximum and minimum value for the weights so that the gradient for each individual weight is clipped to a range between the two values.
Norm Clipping
The norm of the gradient is clipped in Norm clipping. Norm Clip clipping checks if the norm of the parameter gradient is greater than some maximum value. If it is, the gradient is normalized by its norm before the parameter update.
The formula for norm clipping is as follows: $$\text{if } ||\textbf{g}|| > v \text{ then } \textbf{g} \leftarrow \frac{\textbf{g}{v}}{||\textbf{g}||}$$
Where $v$ is the maximum norm value. If the norm of the gradient is higher than the maximum value, the gradient is divide by the norm and multiplied by the maximum value.
Why Use Gradient Clipping?
The reason to use Gradient Clipping is to avoid exploding gradients of the loss function. When there is a steep gradient of the loss function, it can cause the weights to update themselves in a large way, making the optimization difficult to perform optimally. A large parameter gradient can lead an optimizer to update the parameters strongly into a region where the loss function is much greater, essentially undoing much of the work that was needed to get to the current solution.
Therefore, Gradient Clipping is employed to ensure that optimization is performed with more accuracy, making it less likely to change optimization surface and making the loss function more centralized. Since centralization is crucial in gradient optimization, it should ensure that the parameters are updated within ranges that are not too high or too low. Gradient clipping is performing well in machine translation, and language modelling.
Advantages of Gradient Clipping
Improved Optimization
Gradient Clipping ensures optimization is performed with more accuracy. Since centralization is crucial in gradient optimization, it should ensure that the parameters are updated within ranges that are not too high or too low, thus improving optimization overall.
Improved Performance
The use of Gradient Clipping leads to an overall improvement in the performance of the neural network. The network trains better, is more stable and is able to generalize better when tested on new data.
Preventing Exploding Gradients and Vanishing Gradients
Gradient Clipping also has the benefit of preventing exploding gradients and vanishing gradients. When the gradients are too large, they can cause the optimization procedure to behave in a way that is not optimal. Vanishing gradients, on the other hand, make it difficult to update the parameters of the neural network. Gradient Clipping helps to prevent these issues, thus leading to better performance.
Gradient Clipping is a technique used to ensure optimization is performed with more accuracy in deep learning. There are two types of Gradient Clipping: Element-Wise Clipping and Norm Clipping. Gradient Clipping helps prevent exploding gradients and vanishing gradients, and ultimately leads to an overall improvement in the performance of the neural network. Gradient Clipping is especially useful for optimizing neural networks when dealing with large datasets or complex architectures, and can be used in various deep learning tasks including machine translation, natural language processing, and speech recognition, just to name a few.
