What is Activation Function in Machine Learning?
Machine Learning activation functions prove to be crucial elements in an ML model comprising all its weights and biases. They are a subject of research that is continuously developing and have played a significant role in making Deep Neural Network training a reality. In essence, they determine the decision to stimulate a neuron. If the information a neuron receives is pertinent to the information already present or if it ought to be disregarded. The non-linear modification we apply to the input signal is called the activation function. The following layer of neurons receives this altered output as input.
Since activation functions conduct non-linear calculations on the input of a Neural Network, they allow it to learn and do more complicated tasks without them, which is essentially a linear regression model in Machine Learning.
It is essential to comprehend the applications of activation functions and weigh the advantages and disadvantages of each activation function to select the appropriate type of activation function that may offer non-linearity and precision in a particular Neural Network model.
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Machine Learning activation function models are basically of two types –
- Hidden Layers
- Output Layers
The activation functions used in the hidden layers of Neural models’ primary role is to supply the non-linearity that neural networks require to simulate non-linear interactions.
The Activation methods employed by Machine Learning models’ output layers have a particular main objective: compress the value within a restricted range, such as 0 to 1.
Let us first understand the different types of Activation Functions in Machine Learning –
1. Binary Step Function
A threshold-based classifier, which determines whether or not the neuron should be engaged, is the first thing that springs to mind when we have an activation function. The neuron is triggered if the value Y is greater than a specified threshold value; else, it is left dormant.
It is often defined as –
f(x) = 1, x>=0
f(x) = 0, x<0
The binary function is straightforward. It is applicable while developing a binary classifier. Assessments are needed, which are the ideal options when we just need to answer yes or no for a single class since they either turn on the neuron or leave it nil.
2. Linear Function
A positive slope may cause a rise in the firing rate as the input rate rises. Linear activation functions are superior at providing a broad range of activations.
The function is precisely proportional to the weighted combination of neurons or input in our straightforward horizontal activation function.
A neuron may be firing or not firing in binary. You might note that the derivative of this function is constant if you are familiar with gradient descent in machine learning.
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3. Non-Linear Function
In terms of activation functions, the Rectified Linear Unit is the best. This is the most popular and default activation function for most issues. When it is negative, it is confined to 0, whereas when it becomes positive, it is unbounded. A deep neural network can benefit from the intrinsic regularization created by this combination of boundedness and unboundedness. The regularization creates a sparse representation that makes training and inference computationally effective.
Positive unboundedness maintains computational simplicity while accelerating the convergence of linear regression. ReLU has just one significant drawback: dead neurons. Some dead neurons switched off early in the training phase and negatively bound to 0 never reactivate. Because the function quickly transitions from unbounded when x > 0 to bounded when x ≤ 0, it cannot be continuously differentiated. However, in practice, this may be overcome with no lasting effects on performance if there is a low learning rate and a significant negative bias.
- ReLU requires fewer mathematical processes than other non-linear functions, making it less computationally costly and linear.
- It prevents and fixes the Vanishing Gradient issue.
- Used in RNN, CNN, and other machine learning models.
Different modifications of ReLU –
A better variant of the ReLU function is the Leaky ReLU function. Since the ReLU function’s gradient is 0, where x<0, the activations in that region led the neurons to die, and leaky ReLU proves to be the most beneficial to solve such issues. We define the ReLU function as a tiny linear component of x rather than as 0, where x<0.
It can be seen as –
- Leaky ReLU, which has a little negative slope, was an attempt to address the “dying ReLU” issue (of 0.01 or so).
- Used in tasks that involve gradients such as GAN.
This is an improvement over Leaky ReLU, where the scalar multiple is trained on the data rather than being selected at random. Because the model was trained using data, it is sensitive to the scaling parameter (a), and it counters differently depending on the value of a.
- When the Leaky ReLU fails, a Parametric ReLU can be utilised to solve the problem of dead neurons.
GeLU (Gaussian Error Linear Unit)
The newest kid on the block and unquestionably the victor for NLP (Natural Language Processing) – related tasks is the Gaussian Error Linear Unit, which is utilised in transformer-based systems and SOTA algorithms such as GPT-3 and BERT. GeLU combines ReLU, Zone Out, and Dropout (which randomly zeros off neurons for a sparse network). ReLU is made smoother with the GeLU since it weights inputs by percentile rather than gates.
- Computer Vision, NLP, Speech Recognition
ELU (Exponential Linear Unit)
The 2015-introduced ELU is positively unbounded and employs a log curve for negative values. Compared to Leaky and Parameter ReLU, this strategy for solving the dead neuron problem is slightly different. In contrast to ReLU, the negative values gradually smooth out and become constrained to prevent dead neurons. However, it is expensive since an exponential function is used to describe the negative slope. When using a less-than-ideal starting technique, the exponential function occasionally results in an expanding gradient.
The small negative values of Swish, which were first introduced in 2017, are still helpful in capturing underlying patterns, whereas large negative values will have a derivative of 0. Swish may be used to replace ReLU with ease because of its intriguing form.
- The result is a workaround between the Sigmoid function and RELU that helps to normalise the result.
- Has the ability to deal with the Vanishing Gradient Problem.
- In terms of picture categorisation and machine translation, it is on par with or even superior to ReLU.
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4. Softmax Activation Function
Like sigmoid activation functions, softmax is mainly utilised in the final layer, or output layer, for making decisions. The softmax simply assigns values to the input variables based on their weights, and the total of these weights eventually equals one.
- When compared to the RELU function, gradient convergence is smoother in Softmax.
- It has the ability to handle the Vanishing Gradient issue.
- Multiclass and Multinomina classification.
Sigmoid Function in Machine Learning is one of the most popular activation functions. The equation is –
These activation functions have the benefit of reducing the inputs to a value ranging from 0 and 1, which makes them ideal for modelling probability. When applied to a deep neural network, the function becomes differentiable but rapidly saturates due to the boundedness, resulting in a diminishing gradient. The cost of exponential computing increases when a model with hundreds of layers and neurons needs to be trained.
The derivative is constrained between -3 and 3, whereas the function is constrained between 0 and 1. It is not ideal for training hidden layers since the output is not symmetric around zero, which would cause all the neurons to adopt the same sign during training.
- Provides a smooth gradient during converging.
- It often gives a precise classification of prediction with 0 and 1.
- The Sigmoid function in Machine Learning is typically utilised in binary classification and logistic regression models in the output layer.
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6. Tanh – Hyperbolic Tangent Activation Function
Similar to the Sigmoid Function in Machine Learning, this activation function is utilised to forecast or distinguish between two classes, except it exclusively transfers the negative input into negative quantities and has a range of -1 to 1.
It essentially resolves our issue with the values having the same sign. Other characteristics are identical to those of the sigmoid function. At any point, it is continuous and distinct.
- Unlike sigmoid, it has a zero-centric function.
- This function also has a smooth gradient.
Although Tahn and Sigmoid functions in Machine Learning may be used in hidden layers because of their positive boundedness, deep neural networks cannot employ them due to training saturation and vanishing gradients.
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The critical operations known as activation functions alter the input in a non-linear way, enabling it to comprehend and carry out more complicated tasks. We addressed the most popular activation functions and their uses that may apply; these activation functions provide the same function but are applied under various circumstances.
How can you decide which activation function is best?
Choosing an activation function is a complex decision entirely dependent on the issue at hand. However, you may want to start with the sigmoid function if you're new to machine learning before continuing to others.
Should the activation function be linear or non-linear?
No matter how complicated the design is, a linear activation function is only effective up to one layer deep. Hence the activation layer cannot be linear. Additionally, the world today and its challenges are very non-linear.
Which activation function can be learnt easily?
Tanh. By widening the range to cover -1 to 1, it addresses the drawback of the sigmoid activation function. This results in zero-centeredness, which causes the concealed layer's weights' mean to go close to 0. Learning becomes quicker and easier as a result.