When it comes to neural networks, activation functions are a fundamental component. They are responsible for determining whether a neuron should be activated or not based on the input signals. One such activation function is called Mish.
What is Mish?
Mish is a newly proposed activation function that was introduced in a 2019 research paper. It stands for "Mish - A Self-Regularized Non-Monotonic Neural Activation Function" and it is defined by the following formula:
$$ f\left(x\right) = x\cdot\tanh{\text{softplus}\left(x\right)}$$
To better understand this formula, we need to break it down. The first part of the formula, x, is the input signal that comes from the previous neuron. The second part of the formula, $\tanh{\text{softplus}\left(x\right)}$, applies two activation functions: hyperbolic tangent and softplus. Softplus is defined as:
$$\text{softplus}\left(x\right) = \ln\left(1+e^{x}\right)$$
Mish is different than other activation functions because it is non-monotonic. This means that it is not a straight line and can have multiple peaks and valleys. It also has a self-regularization property which helps prevent overfitting and improves generalization. Essentially, this means that it helps the neural network learn more effectively and avoid memorizing patterns instead of truly learning how to classify data.
How Is Mish Different from Other Activation Functions?
Before Mish, there were other activation functions such as ReLU, sigmoid, and tanh, just to name a few. Mish has a few advantages over these other activation functions.
Firstly, ReLU is one of the most commonly used activation functions. However, it has a major drawback called the "dying ReLU" problem. This occurs when the input to a neuron is less than 0, which causes the neuron to die and essentially become useless. Mish can help alleviate this issue because it is always differentiable and does not vanish.
Secondly, the sigmoid and tanh activation functions have been used for a long time but they do have a tendency to saturate. This means that the gradient becomes very small and the network stops learning effectively. Mish helps with this because it never saturates and actually gets steeper as the input gets closer to zero.
Advantages and Disadvantages of Mish
Mish is still relatively new so there are not many empirical studies that have been done to prove its efficacy. However, there are some pros and cons to consider.
Advantages
- Mish helps prevent overfitting and improves generalization.
- It is non-monotonic which enables more efficient learning instead of memorization.
- Mish is differentiable and never vanishes, which means it can improve the learning process.
Disadvantages
- As mentioned before, there have not been many empirical studies done to prove its effectiveness.
- Mish is a more complex function than ReLU, which means it may require more computational resources.
- It could take longer to train a network with Mish compared to ReLU due to the increased complexity.
Mish is a promising new activation function in the world of neural networks. While it is still early days for research and implementation, early studies do show some promising benefits. Since it is non-monotonic, it is believed to allow for better learning patterns, which is a powerful outcome given the rise of AI.
As technology evolves and AI becomes more common, functions like Mish will continue to be developed, tested, and optimized. While there will always be pros and cons to every type of activation function, the most important thing is that we keep pushing forward in the name of innovation and progress. With Mish and other activation functions on the rise, the future of neural networks looks bright.
