AI领域的“秘密武器”：Mish激活函数
在人工智能，特别是深度学习的世界里，神经网络的每一次计算都离不开一个核心组件——激活函数。它们就像神经元的大脑，决定着信息如何传递以及是否被“激活”。今天，我们要深入浅出地探讨一个近年来备受关注的新型激活函数：Mish。它不仅在性能上超越了许多前辈，更以其独特的“个性”为深度学习模型带来了新的活力。
什么是激活函数？神经网络的“决策者”
想象一下，你正在训练一个机器人识别猫咪。当机器人看到一张图像时，它会通过一层层的“神经元”来分析这张图片。每个神经元接收到一些信息（数字信号），然后需要决定是把这些信息传递给下一个神经元，还是让它们“停止”。这个“决定”的开关，就是激活函数。
早期的激活函数，比如Sigmoid和Tanh，就像是一个简单的“开/关”或“有/无”按钮，它们能让神经网络学习到一些简单的模式。但当网络层数增加，任务变得复杂时，这些简单的按钮就显得力不从心了，很容易出现“梯度消失”（gradient vanishing）的问题，导致学习效率低下，甚至停滞不前。
为了解决这些问题，研究人员推出了ReLU（Rectified Linear Unit）激活函数。 它的操作非常简单：如果输入是正数，就原样输出；如果是负数，就输出0。这就像一个限制器，只让“积极”的信息通过。ReLU的优点是计算速度快，有效地缓解了梯度消失问题。 但它也有一个“死区”，如果输入总是负数，神经元就会“死亡”，不再学习，这被称为“Dying ReLU”问题。
Mish的崛起：一个更“聪明”的决策者
在ReLU及其变体的基础上，研究人员继续探索更强大的激活函数。“Mish：一种自正则化的非单调神经网络激活函数”在2019年由Diganta Misra提出，它的目标是结合现有激活函数的优点，同时避免它们的缺点。
Mish激活函数在数学上的表达是：f(x) = x * tanh(softplus(x))。 第一次看到这个公式可能觉得复杂，但我们可以把它拆解成几个日常生活中的比喻来理解。
- Softplus：平滑的“调光器”
- 首先是
softplus(x)。还记得ReLU的“开关”比喻吗？ReLU就像一个数字门，正数通过，负数直接归零。Softplus则是一个更温柔的“调光器”开关。当输入是负数时，它不会直接归零，而是缓慢地趋近于零，永远不会真的变成零。 当输入是正数时，它则几乎和输入一样大。这就像夜幕降临时，灯光不是“啪”地一下完全关闭，而是柔和地逐渐变暗直到几乎不可见。
- 首先是
- Tanh：信息的“压缩器”
- 接下来是
tanh()函数，它是一个双曲正切函数，可以将输入的任何数值压缩到 -1 到 1 之间。想象你有一大堆各式各样大小的包裹，Tanh的作用就是把它们都规整地压缩，使其体积都在一个可控的范围内。这样，不管原始信息有多大或多小，经过Tanh处理后，都变得更容易管理和传递。
- 接下来是
- x * tanh(softplus(x))：信息的“巧手加工”
- 最后，Mish将原始输入
x乘以tanh(softplus(x))的结果。这就像一个“巧手加工”的过程。softplus(x)提供了平滑的、永不完全关闭的“信号强度”，tanh()对这个信号强度进行了“规范化”处理。这两者相乘，既保留了原始输入x的信息，又引入了一种巧妙的非线性变换。 这种乘法机制与被称为“自门控”（Self-Gating）的特性有关，它允许神经元根据输入自身来调节其输出，从而提高信息流动的效率。
- 最后，Mish将原始输入
综合来看，Mish就像一个精密的信号处理中心。它不是简单地让信号通过或阻断，而是通过平滑的调光器调整信号强度，再用压缩器进行规范，最后巧妙地与原始信号结合，使得传递的信息更加细腻、更富有表现力。
Mish的独特魅力：为什么它更优秀？
Mish激活函数之所以被认为是“下一代”激活函数，得益于其多个关键特性：
- 平滑性（Smoothness）：Mish函数在任何地方都连续可导，没有ReLU那样的“尖角”。 这意味着在神经网络优化过程中，梯度（可以理解为学习的方向和速度）的变化会更平稳，避免了剧烈的震荡，从而使训练过程更稳定、更容易找到最优解。
- 非单调性（Non-monotonicity）：传统激活函数如ReLU是单调递增的。Mish的曲线在某些负值区域会有轻微的下降，然后再上升。 这种非单调性使得Mish能够更好地处理和保留负值信息，避免了“信息损失”，尤其是在面对细微但重要的负面信号时表现出色。
- 无上界但有下界（Unbounded above, Bounded below）：Mish可以接受任意大的正数输入并输出相应的正数，避免了输出值达到上限后饱和的问题（即梯度趋近于零）。 同时，它有一个约-0.31的下界。 这种特性有助于保持梯度流，并具有“自正则化”（Self-regularization）的效果，就像一个聪明的学习者，能够在训练过程中自我调整，提高模型的泛化能力。
应用与展望：Mish带来了什么？
自从Mish被提出以来，它已经在多个深度学习任务中展现出卓越的性能。研究表明，在图像分类（如CIFAR-100、ImageNet-1k数据集）和目标检测（如YOLOv4模型）等任务中，使用Mish激活函数的模型在准确率上能够超过使用ReLU和Swish等其他激活函数的模型1%到2%以上。 尤其是在构建更深层次的神经网络时，Mish能够有效地防止性能下降，使得模型能够学习到更复杂的特征。
例如，在YOLOv4目标检测模型中，Mish被引入作为激活函数，帮助其在MS-COCO目标检测基准测试中将平均精度提高了2.1%。 FastAI团队也通过将Mish与Ranger优化器等结合，在多个排行榜上刷新了记录，证明了Mish在实际应用中的强大潜力。
Mish的出现，再次证明了激活函数在深度学习中不可或缺的地位及其对模型性能的深远影响。它提供了一个更平滑、更灵活、更具自适应能力的“神经元决策机制”，帮助AI模型更好地理解和学习复杂数据。虽然计算量可能略高于ReLU，但其带来的性能提升往往是值得的。 随着深度学习技术不断发展，Mish很可能成为未来AI模型设计中的一个重要选择，持续推动人工智能走向更智能、更高效的未来。
Mish Activation
In the world of Artificial Intelligence, especially Deep Learning, every calculation in a neural network relies on a core component — the activation function. They are like the brain of a neuron, deciding how information is transmitted and whether it is “activated.” Today, we will explore in simple terms a novel activation function that has received much attention in recent years: Mish. It not only outperforms many predecessors in performance but also brings new vitality to deep learning models with its unique “personality.”
What is an Activation Function? The “Decision Maker” of Neural Networks
Imagine you are training a robot to recognize cats. When the robot sees an image, it analyzes the picture through layers of “neurons.” Each neuron receives some information (digital signals) and then needs to decide whether to pass this information to the next neuron or simply “stop” it. The switch for this “decision” is the activation function.
Early activation functions, such as Sigmoid and Tanh, were like simple “on/off” or “yes/no” buttons, allowing neural networks to learn some simple patterns. But as the number of network layers increases and tasks become complex, these simple buttons become powerless, easily leading to the “gradient vanishing” problem, resulting in low learning efficiency or even stagnation.
To solve these problems, researchers introduced the ReLU (Rectified Linear Unit) activation function. Its operation is very simple: if the input is positive, output it as is; if it is negative, output 0. This is like a limiter, only letting “positive” information pass. ReLU’s advantage is fast calculation speed, effectively mitigating the gradient vanishing problem. But it also has a “dead zone”: if the input is always negative, the neuron will “die” and stop learning, which is called the “Dying ReLU” problem.
The Rise of Mish: A Smarter Decision Maker
On the basis of ReLU and its variants, researchers continued to explore more powerful activation functions. “Mish: A Self Regularized Non-Monotonic Neural Activation Function” was proposed by Diganta Misra in 2019, aiming to combine the advantages of existing activation functions while avoiding their disadvantages.
The mathematical expression of the Mish activation function is: f(x) = x * tanh(softplus(x)). Seeing this formula for the first time might seem complicated, but we can break it down into a few metaphors from daily life to understand.
- Softplus: The Smooth “Dimmer”
- First is
softplus(x). Remember the “switch” metaphor of ReLU? ReLU is like a digital gate, pass if positive, zero if negative. Softplus is a gentler “dimmer” switch. When the input is negative, it doesn’t drop to zero directly but slowly approaches zero, never truly becoming zero. When the input is positive, it is almost as large as the input. It’s like when night falls, the light doesn’t turn off completely with a “click,” but gently dims until it is almost invisible.
- First is
- Tanh: The “Compressor” of Information
- Next is the
tanh()function, which is a hyperbolic tangent function capable of compressing any input value to between -1 and 1. Imagine you have a pile of packages of various sizes. Tanh’s job is to neatly compress them so their volume is within a controllable range. In this way, no matter how large or small the original information is, it becomes easier to manage and transmit after being processed by Tanh.
- Next is the
- x * tanh(softplus(x)): The “Skillful Processing” of Information
- Finally, Mish multiplies the original input
xby the result oftanh(softplus(x)). This is like a “skillful processing” procedure.softplus(x)provides a smooth, never fully closed “signal strength,” andtanh()“normalizes” this signal strength. Multiplying these two not only retains the information of the original inputxbut also introduces a clever non-linear transformation. This multiplication mechanism is related to a property called “Self-Gating,” allowing the neuron to adjust its output based on the input itself, thereby improving the efficiency of information flow.
- Finally, Mish multiplies the original input
Taken together, Mish is like a sophisticated signal processing center. It doesn’t simply let the signal pass or block it, but adjusts the signal strength through a smooth dimmer, then normalizes it with a compressor, and finally cleverly combines it with the original signal, making the transmitted information more detailed and expressive.
The Unique Charm of Mish: Why is it Better?
The reason why Mish activation function is considered the “next generation” activation function is due to its multiple key features:
- Smoothness: The Mish function is continuously differentiable everywhere, without the “sharp corners” like ReLU. This means that during the neural network optimization process, the gradient (can be understood as the direction and speed of learning) changes more smoothly, avoiding drastic oscillations, thereby making the training process more stable and easier to find the optimal solution.
- Non-monotonicity: Traditional activation functions like ReLU are monotonically increasing. Mish’s curve has a slight dip in some negative value areas before rising again. This non-monotonicity allows Mish to better handle and retain negative value information, avoiding “information loss,” especially performing well when facing subtle but important negative signals.
- Unbounded above, Bounded below: Mish can accept arbitrarily large positive inputs and output corresponding positive numbers, avoiding the problem of saturation after the output value reaches an upper limit (i.e., gradient approaching zero). At the same time, it has a lower bound of about -0.31. This feature helps maintain gradient flow and has a “Self-regularization” effect, just like a smart learner who can self-adjust during training to improve the model’s generalization ability.
Applications and Outlook: What Has Mish Brought?
Since Mish was proposed, it has demonstrated excellent performance in multiple deep learning tasks. Research shows that in tasks such as image classification (e.g., CIFAR-100, ImageNet-1k datasets) and object detection (e.g., YOLOv4 model), models using Mish activation function can surpass models using other activation functions like ReLU and Swish by more than 1% to 2% in accuracy. Especially when building deeper neural networks, Mish effectively prevents performance degradation, enabling models to learn more complex features.
For example, in the YOLOv4 object detection model, Mish was introduced as the activation function, helping it increase the average precision by 2.1% on the MS-COCO object detection benchmark. The FastAI team also set records on multiple leaderboards by combining Mish with the Ranger optimizer, proving the powerful potential of Mish in practical applications.
The emergence of Mish once again proves the indispensable status of activation functions in deep learning and their profound impact on model performance. It provides a smoother, more flexible, and more adaptive “neuron decision mechanism,” helping AI models better understand and learn complex data. Although the computational cost may be slightly higher than ReLU, the performance improvement it brings is often worth it. With the continuous development of deep learning technology, Mish is likely to become an important choice in future AI model design, continuing to drive artificial intelligence towards a smarter and more efficient future.