The most important component of every deep learning neural network is its activation functions. Image categorization, language transformation, object detection, and other extremely challenging problems in deep learning must be handled with the help of neural networks and activation functions. Therefore, handling these jobs without it would be not easy.
The most important part of deep learning, generally speaking, is the neural network activation functions. These essentially determine the output of deep learning models, their correctness, and the performance effectiveness of the training model that can create or split a massive-scale neural network.
What is Activation Function?
In other words, the activation function defines the node of the output of the node that is provided in the inputs or the output of an input or collection of inputs.
The activation function aids in normalising any output with an input value between 1 and -1. Because the neural network is occasionally trained on millions of data points, the activation function must be effective and shorten computation time.
In a nutshell, the activation function determines whether a specific input or information received is meaningful or irrelevant in a neural network. Let’s look at an example to comprehend better what a neuron is and how the activation function restricts the output value to a certain level.
The neuron consists of an average of the input weights, which is then placed through an activation function to produce an output.
Y = ∑ (weights*input + bias)
Y can be any value between -infinity and +infinity for a neuron in this situation. Therefore, we must bind our output to obtain the desired forecast or generalised outcomes.
Y = Activation function(∑ (weights*input + bias))
We pass that neuron to the activation function to bind the output values.
Why do we need activation functions?
A linear equation is a polynomial of one degree only, which is easy to solve but has limitations in terms of solving complex problems or higher-degree polynomials. Without activation function, weight and bias would only have a linear transformation, or the neural network would just be a linear regression model.
Types of Activation functions
A. Binary Step Function
1. Binary Step Function
This extremely basic activation function always comes to mind when we attempt to bind output. It functions as a threshold-based classifier, where we choose a threshold value to determine whether a neuron should be activated or deactivated at the output.
f(x) = 1 if x > 0 else 0 if x < 0
Here, we choose to set the threshold value to 0. Binary problem classification or classifier is very straightforward and helpful.
B. Linear Neural Network Activation Function
2. Linear Function
The activation function is a straightforward straight line directly proportional to the input’s weighted neurons sum. A line with a positive slope may cause the firing rate to increase as the input rate increases. Linear activation functions are superior at providing a wide range of activations.
In binary, a neuron is either firing or not firing. If you are familiar with gradient descent in deep learning, you will observe that the derivative in this function is constant.
Y = mZ
Where m is a constant and the derivative concerning Z. Additionally, constant and unrelated to Z is the meaning gradient. If the backpropagation changes in this are consistent and independent of Z, learning will not be improved.
Our second layer is the result of a linear function of the inputs to the layers before it. What have we learned from this, huh? We can only obtain an output that is a linear function of the first layer if we compare all of our layers and delete all but the first and last layers.
C. Non-Linear Neural Network Activation Function
3. ReLU (Rectified Linear unit) Activation function
Rectified linear unit, or ReLU, is now the most popular activation function, ranging from 0 to infinity. Because of its rapid conversion rate, neither can it map nor fit into data adequately, which poses a challenge. However, where there is a problem, there is usually a solution.
To avoid this misfitting, we utilise the Leaky ReLU function instead of ReLU. This function’s wider range improves performance.
4. Leaky ReLU Activation Function
To tackle the “Dying ReLU” problem, which was discussed in ReLU, we needed the Leaky ReLU activation function. With Leaky ReLU, the fundamental difficulty with the ReLU activation function is resolved by not making all negative input values zero but rather by making them close to zero.
5. Sigmoid Activation Function
The sigmoid activation function is most frequently used because it performs its task very effectively; it essentially takes a probabilistic approach to decision-making and ranges from 0 to 1. Because this activation function’s range is the smallest, predictions are more accurate when we use it to make decisions or predict outcomes.
The equation for the sigmoid function is
f(x) = 1/(1+e(-x) )
The sigmoid function has an issue known as the “vanishing gradient problem,” which happens when we convert big input values between 0 and 1; as a result, their derivatives get considerably smaller, and the results are unsuitable. Another activation function, such as ReLU, is utilised to solve this issue without a tiny derivative problem.
6. Hyperbolic Tangent Activation Function(Tanh)
Like the sigmoid function, this activation function is used 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.
7. Softmax Activation Function
Like sigmoid activation functions, softmax is primarily utilised for decision-making at the last layer or output layer. The softmax assigns values to the input variables based on their weights, and the sum of these weights eventually equals one.
The important 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 any limitations that may apply; these activation functions provide the same function but are applied under various circumstances.