How to Choose the Right Activation Function for Neural Networks

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Every neural network needs at least one activation function to make accurate predictions. It’s at the heart of the processing capabilities....

Every neural network needs at least one activation function to make accurate predictions. It’s at the heart of the processing capabilities. Choosing the right one can result in a precise, high-performance network consistently delivering the desired results. Data scientists can use an easy process to determine what activation function is the right fit for their systems. 

The activation function usuallyacts as a filter for input data. It determines what a node’s output should be based on the relevancy of the input value to the prediction at hand. Information enters the node, which processes it using any programmed weights and biases and passes it through the activation function. This function will determine the output from the node that’s sent to the next layer.

Hidden layers typically use the same activation function, regardless of how many are in the neural network. However, the output layer typically uses a different function.

Three basic activation functions are used in neural networks: linear, nonlinear, and binary. Each type has different use cases, and a few specific ones tend to dominate the neural network field. The easiest way to determine what to use is by breaking it down by hidden layers and the output layer.

Neural networks tend to have the most activation function variety in the output layer. The right one mainly depends on the kind of output a data scientist wants.

A Softmax activation function could be a good choice if a data scientist wants output sorted into various classifications. For example, a neural network could use it to predict what a teacher shouldadd at the end of their classbased on earlier activities (the input values). The Softmax function would be the last stage of the neural network’s processing, leading to predictions that sort class sessions into categories for each activity. A neural network like this could be used to automate lesson planning for teachers at various grade levels.

The Sigmoid activation function is a good choice if a data scientist wants output that only goes into two binary classifications rather than several. It’s a type of logistic function with a range between zero and one. It is a simpler function and is not used in hidden layers much in modern neural networks, but it can be the perfect fit when binary classification is needed in the output layer.

The hidden layers are where neural networks do the bulk of their processing through a web of interconnected nodes. They usually use a nonlinear activation function. A linear function limits the range of calculations or predictions that the model is capable of. It can only do linear regression, so nonlinear functions are used instead, typically the popular ReLU or Rectified Linear Unit.

The ReLU function has become the industry standard over recent years and is usually the best choice for hidden layer activation functions. It isefficient and less computationally intensivethan others because it limits the number of nodes it activates. It only activates nodes with an output greater than or equal to zero, meaning negative nodes are deactivated. This is perfect for data scientists creating a convolutional or deep learning neural network.

Unfortunately, the ReLU function can run into issues when data scientists use backpropagation to correct weights and biases, often as part of the algorithm training process. The lack of room for negative values in a standard ReLU can be corrected using a “leaky” function, which allows for a slight positive slope in the negative direction. This means negative nodes aren’t immediately deactivated, enabling backpropagation to work correctly.

If backpropagation is needed, the neural network should use a Leaky ReLU activation function for the hidden layers. Otherwise, a standard ReLU activation function is often the best choice.

Activation functions are key parts of a successful neural network model. The right one will ensure it can carry out the predictions and calculations it was designed to process. Today, most neural networks use a ReLU activation function for the hidden layers and some type of classification function for the output layer, such as a Sigmoid or Softmax function.

Data scientists can pinpoint the right activation function for their specific network by testing hidden layers and the output layer separately until they find one that delivers the best possible results.