Väljundikiht

Väljundikiht, often translated as output layer, is a fundamental concept in neural networks. It represents the final layer of a neural network that produces the prediction or classification result. The number of neurons in the output layer and the activation function used depend on the specific task the neural network is designed for.

For regression tasks, where the network predicts a continuous value, the output layer typically has a single neuron with a linear activation function. This allows the neuron to output any real number. In contrast, for classification tasks, the number of neurons in the output layer usually corresponds to the number of classes. For binary classification, a single neuron with a sigmoid activation function is common, outputting a probability between 0 and 1. For multi-class classification, multiple neurons, each with a softmax activation function, are used. The softmax function ensures that the output of each neuron represents the probability of belonging to a specific class, and the sum of all output probabilities equals 1.

The values produced by the output layer are the direct result of the network's computations, transforming the input data through its hidden layers. The training process of a neural network aims to adjust the weights and biases of all layers, including the output layer, so that the output layer produces accurate predictions based on the input data and the desired outcome.