Täysliitännäiskerros

Täysliitännäiskerros, often translated as a fully connected layer or dense layer, is a fundamental component in artificial neural networks. In a fully connected layer, every neuron in the layer is connected to every neuron in the preceding layer. This means that each output of the previous layer is an input to every neuron in the current layer.

The primary function of a fully connected layer is to learn global patterns or high-level combinations of features extracted by earlier layers. After convolutional or recurrent layers have processed local features or sequential information, a fully connected layer can combine these learned representations to make classifications or predictions. Each connection between neurons has an associated weight, and during the training process, these weights are adjusted to minimize the error of the network. Additionally, each neuron in a fully connected layer typically has a bias term.

The output of a neuron in a fully connected layer is calculated by taking the weighted sum of all inputs from the previous layer, adding the bias, and then passing this result through an activation function. Common activation functions include ReLU, sigmoid, and tanh. The number of neurons in a fully connected layer is a hyperparameter that needs to be chosen based on the specific task and dataset. While powerful for learning complex relationships, fully connected layers can also lead to a large number of parameters, increasing the risk of overfitting, especially with high-dimensional input data. Therefore, they are often placed towards the end of a neural network architecture.