A fully connected layer, often abbreviated as FC layer, is a type of layer in neural networks where each neuron is connected to every neuron in the previous layer. This means that every input feature influences every output neuron. Essentially, a fully connected layer performs a linear transformation on its inputs, followed by a non-linear activation function, allowing it to learn intricate patterns and representations. This layer is typically found towards the end of convolutional neural networks (CNNs) or as part of multi-layer perceptrons (MLPs).
In practice, when you have a fully connected layer, you start with an input vector, which represents the features extracted from previous layers. For instance, in a classification task, the input might consist of a flattened array of pixel values from an image. This input is then multiplied by a weight matrix that determines how much each feature contributes to each output neuron. After this linear operation, a non-linear activation function, such as ReLU or Sigmoid, is applied. This combination allows the model to capture complex relationships and make more informed predictions.
One important aspect to consider is that fully connected layers tend to have a large number of parameters, especially when the input size is substantial. This can lead to overfitting, particularly if the dataset is small compared to the number of parameters. To mitigate this, techniques such as dropout or weight regularization like L2 regularization can be employed. Overall, fully connected layers are a fundamental component of deep learning architectures, serving as the bridge between the learned feature representations and the final output, such as class scores or regression values.