As many of us know, deep learning is a booming field in technology and innovations. Understanding it requires a substantial amount of information on many different concepts. It is important to know the basics of any technology for better understanding and avoid failures while its implementation. Let’s explore one of the key ideas of deep learning in this post, the vanishing gradient problem.
We will discover the precise nature of the vanishing gradient issue, its causes, and any potential solutions. Before discussing the vanishing gradient descent problem, it is essential that we comprehend a few fundamental terms and also how they are used in deep learning. To get started, here’s an article we covered about neural networks that gives a basic background of the concepts.
In order to comprehend the problems with gradient descent, one must also understand what it means. A gradient descent determines how much a function’s output will vary if its inputs are slightly changed, to explain it simply. We’ve already covered more on gradient descent and its fundamental application.
What is Vanishing Gradient Descent Problem?
When employing gradient-based training techniques like backpropagation, one might encounter an issue known as the vanishing gradient problem. The gradients of the loss function touch 0 when more neural layers with specific activation functions are added to neural networks, making the network challenging to train. The parameters of the network’s earlier levels are more difficult to comprehend and adjust as a consequence of this problem.
Causes of the Vanishing Gradient Problem
The vanishing gradient problem occurs when gradients of the loss function approach zero in deep neural networks, making them difficult to train. This issue can be mitigated by using activation functions like ReLU or ELU, LSTM models, or batch normalization techniques.
While performing backpropagation, we update the weights in each layer, sometimes the gradient we are using for this updation of the network starts gradually decreasing, this very little change in the gradient descent provides very little almost negligible changes in the weights.
The value of gradient descent reaches zero and the updation of weights stops midway. The initial layers of the neural network remain unmodified and this way causes the Vanishing Gradient Descent problem for the neural network.
This problem usually occurs when the neural network is very deep with numerous layers. In situations like this, it becomes challenging for the gradient descent to reach the first layer without turning zero.
Also, using activation functions like the sigmoid activation function which generates small changes in output for training multi-layered neural networks causes the Vanishing gradient descent problem. (To learn more about the sigmoid function and its representation on a graph, please refer to this article by clicking here.)
Solutions to the Vanishing Gradient Problem
- An easy solution to avoid the vanishing gradient problem is by selecting the activation function wisely, taking into account factors such as the number of layers in the neural network. Prefer using activation functions like ReLU, ELU, etc.
- Use LSTM models (Long Short-Term Memory). It has an additional forget gate that helps to solve the problems like vanishing Gradient Descent.
- Perform Batch Normalization, as it makes sure that the gradient descent does not too small causing problems for the network. To have a better understanding and to know its implementation in Python programming language, please click here.
Neural Network is a vast and challenging to implement topic, but it is harder to tune and train these networks. Many problems occur while readjusting the weights, in this article we have thoroughly understood one such problem faced in the updation of neural networks which is the vanishing gradient problem. We have discussed the nature of the vanishing gradient problem, what causes the vanishing gradient problem as well as what are the solutions for this problem.
To learn from more such detailed and easy-to-understand articles on various topics related to deep learning and Python programming language, visit here.