Väljundkihti

Väljundkihti, often translated as output layer, is a fundamental concept in artificial neural networks. It represents the final layer of a neural network where the model produces its prediction or classification. The structure and activation function of the output layer are highly dependent on the specific task the neural network is designed for.

For regression tasks, where the goal is to predict a continuous numerical value, the output layer typically consists of a single neuron. This neuron usually employs a linear activation function, allowing it to output any real number. The output directly represents the predicted value.

In contrast, for classification tasks, the output layer's configuration varies based on the number of classes. For binary classification, where there are only two possible outcomes, a single output neuron with a sigmoid activation function is common. The sigmoid function squashes the output to a range between 0 and 1, which can be interpreted as the probability of belonging to the positive class. For multi-class classification, where there are more than two classes, the output layer will have a number of neurons equal to the number of classes. Each neuron typically uses a softmax activation function. Softmax converts the raw outputs into a probability distribution across all classes, ensuring that the probabilities sum up to 1. The class with the highest probability is then selected as the model's prediction. The design of the väljundkihti is therefore crucial for enabling a neural network to effectively communicate its learned insights.