What are the limitations of sigmoid activation function in deep learning networks?

The Sigmoid activation function is a commonly used nonlinear function used to introduce nonlinear features in neural networks. It maps input values to a range between 0 and 1, so it is often used in binary classification tasks. Although the sigmoid function has some advantages, it also has some disadvantages that can negatively affect network performance. For example, when the input value of the sigmoid function is far away from 0, the gradient is close to 0, causing the gradient disappearance problem and limiting the depth of the network. In addition, the output of the sigmoid function is not centered around 0, which may cause data drift and gradient explosion problems. Therefore, in some cases, other activation functions such as ReLU may be more suitable to use to overcome the shortcomings of the sigmoid function and improve network performance.

The following are some disadvantages of the sigmoid activation function.

1. Gradient disappearance problem

In the backpropagation algorithm, the gradient plays an important role in updating network parameters. However, when the input is close to 0 or 1, the derivative of the sigmoid function is very small. This means that during the training process, the gradient will also become very small in these areas, leading to the problem of gradient disappearance. This makes it difficult for the neural network to learn deeper features because the gradients gradually decrease during backpropagation.

2. The output is not centered on 0

The output of the sigmoid function is not centered on 0, which may cause some problems . For example, in some layers of the network, the average value of the input may become very large or very small. In these cases, the output of the sigmoid function will be close to 1 or 0, which may lead to reduced performance of the network.

3. Time-consuming

The calculation of the sigmoid function takes more time than some other activation functions (such as ReLU). This is because the sigmoid function involves exponential operations, which are a slower operation.

4. Not sparse

Sparse representation is a very useful feature that can reduce computational complexity and storage space use. However, the sigmoid function is not sparse because its output is valuable over the entire range. This means that in a network using a sigmoid function, each neuron produces an output, rather than just a small subset of neurons producing an output. This can result in excessive computational burden on the network and also increases the cost of storing network weights.

5. Negative input is not supported

The input of the sigmoid function must be a non-negative number. This means that if the inputs to the network have negative numerical values, the sigmoid function will not be able to handle them. This may cause the network to degrade in performance or produce erroneous output.

6. Not applicable to multi-category classification tasks

The sigmoid function is most suitable for binary classification tasks because its output range is 0 to 1. However, in multi-category classification tasks, the output needs to represent one of multiple categories, so the softmax function needs to be used to normalize the output. Using the sigmoid function requires training a different classifier for each category, which will lead to increased computational and storage costs.

The above are some shortcomings of the sigmoid function in deep learning networks. Although the sigmoid function is still useful in some cases, in most cases it is more suitable to use other activation functions, such as ReLU, LeakyReLU, ELU, Swish, etc. These functions have better performance, faster calculation speed, and less storage requirements, and therefore are more widely used in practical applications.

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