fullyconnected

A fully connected layer, also known as a dense layer, is a fundamental building block in artificial neural networks. In a fully connected layer, every neuron in the layer is connected to every neuron in the previous layer. This means that each output neuron receives input from all input neurons, with each connection having an associated weight.

The primary function of a fully connected layer is to learn complex relationships and patterns within the data. After preceding layers, such as convolutional layers in image recognition, have extracted hierarchical features, a fully connected layer can then use these features to make predictions or classifications. The weighted sum of the inputs, plus a bias term, is then passed through an activation function to produce the output of each neuron.

The number of neurons in a fully connected layer is a hyperparameter that needs to be set by the network designer. A larger number of neurons allows the layer to learn more complex functions but also increases the number of parameters, potentially leading to overfitting. Fully connected layers are commonly found at the end of many neural network architectures, especially in classification tasks, where they map the high-level features learned by earlier layers to the final output classes.