A Denso Capa, also known as a capa totalmente conectada, is a fundamental component in artificial neural networks, particularly in deep learning models. In a Dense Layer, each neuron is connected to every neuron in the previous layer, which enables the model to learn complex patterns and relationships in the input data.
La función principal de una Capa Densa es realizar una transformación lineal of the input data followed by a non-linear activation function. Mathematically, this can be expressed as:
output = activation_function(weights * input + bias)Donde:
- weights are the learned parameters que determinan la fuerza de la conexión entre neuronas.
- input es los datos que se introducen en la capa.
- bias is an additional parameter that allows the model to fit the datos de entrenamiento mejor.
- activation_function is a función no lineal, such as ReLU (Rectified Linear Unit) or Sigmoid, that introduces non-linearity into the model.
Dense Layers are commonly used in the hidden layers of neural networks and can also be the final layer for classification tasks. In the latter case, a softmax activation function is often applied to output a probability distribution over the predicted classes.
Overall, Dense Layers play a crucial role in enabling neural networks to capture intricate patterns in high-dimensional data, making them integral to various applications, including image recognition, procesamiento de lenguaje natural, and more.