An activation function is used in artificial neural networks that determine whether a neuron should be activated or not by calculating its output to the next hidden layer (or output layer) based on the input from the previous layer (or input layer).
What it does ?
The activation function performs a mathematical operation on the input to match with the node output. The activation function is responsible for the non-linear transformation of a neural network. An Activation Function is a mathematical operation that determines the output of a neural network node. It’s used to determine whether or not a neuron should be activated, based on a certain input.
Activation Functions in Neural Networks
Activation functions are important components in neural networks because they can add non-linearity to the model, allowing it to learn more complex data patterns and relationships. The most common activation functions include sigmoid, tanh, ReLU (Rectified Linear Unit), Softmax, and LeakyReLU. Each of these activation functions has its own unique properties, benefits, and drawbacks which make them suitable for different types of tasks. The Sigmoid activation function is one of the oldest and most widely used activation functions. It works by compressing the input values into a range between 0 and 1. This allows for efficient training, as well as easy interpretation of results since all outputs are in the same range. However, due to its saturated behaviour at large input values, it can suffer from vanishing gradients when used in deep neural networks.
The Tanh (Hyperbolic Tangent) activation function is similar to sigmoid but has improved performance in deep learning networks due to its zero-centred output range (-1 to 1). Since this range avoids negative inputs being ignored or diminished during training (like in sigmoid), it helps prevent vanishing gradient problems in deeper layers of the network while still allowing for efficient learning.
The Rectified Linear Unit (ReLU) activation function is another widely used one due to its simplicity and efficiency. ReLU (Rectified Linear Unit) is the most popular activation function for deep neural networks. It has a simple mathematical definition and runs faster than other activation functions, making it suitable for large datasets. The ReLU function sets all negative inputs to zero while keeping all positive values unchanged. This makes it much faster than other traditional sigmoid-like activation functions since it cuts down on the time needed for calculations by quickly filtering out undesirable values without any calculation at all.
The Softmax activation function is mostly used in classification tasks like image recognition where there are multiple classes involved with each class having an associated probability value assigned to it when predicting which class an image belongs to. This method uses the exponential transformation on each input so that outputs sum up to 1 over all classes which makes them comparable with each other for prediction purposes.
Finally, we have Leaky ReLU which is an extension of standard ReLU that solves the ‘dying neuron’ problem by introducing small non-zero slopes for negative inputs instead of ignoring them altogether as standard ReLU does. This lets some information pass through even if the neuron has been deactivated before making sure neurons don’t stay inactive forever thus helping preserve information throughout network layers during the training phase!