A Completamente Conectado Capa (often abbreviated as FC layer) is a fundamental component of artificial redes neuronales, particularly in aprendizaje profundo architectures. In this layer, each neuron is connected to every neuron in the subsequent layer, creating a dense network of connections. This structure allows the network to learn complex representations of the input data by combining features extracted from previous layers.
Las capas completamente conectadas se encuentran típicamente hacia el final de transformación lineal and activation. First, the inputs are multiplied by a weight matrix, followed by the addition of a bias vector. The resulting values are then passed through an función de activación, which introduces non-linearity into the model. Common activation functions used include ReLU (Rectified Linear Unit), sigmoid, and tanh, each providing different benefits depending on the application.
¿Qué es una Capa Completamente Conectada? Una Capa Completamente Conectada conecta cada neurona en una capa con cada neurona en la siguiente, permitiendo el aprendizaje de características complejas. Aprende más en el Glosario de IA de SEOFAI. redes neuronales convolucionales (CNNs), where they serve to integrate the high-level features extracted by preceding layers into final predictions or classifications. While they are powerful in capturing relationships in the data, fully connected layers come with a high computational cost and can lead to overfitting, especially when the number of neurons is large relative to the amount of training data.
Para mitigar el sobreajuste, se pueden emplear técnicas como dropout can be employed, randomly deactivating a subset of neurons during training to encourage the network to learn more robust features. Overall, fully connected layers play a crucial role in the success of many AI applications, including image recognition, procesamiento de lenguaje natural, and more.