A Dense Couche, also known as a couche entièrement connectée, 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 fonction principale d'une couche Dense est d'effectuer une transformation linéaire of the input data followed by a non-linear activation function. Mathematically, this can be expressed as:
output = activation_function(weights * input + bias)Où :
- weights are the learned parameters qui déterminent la force de la connexion entre les neurones.
- input est les données fournies à la couche.
- bias is an additional parameter that allows the model to fit the données d'entraînement mieux.
- activation_function is a fonction non linéaire, 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, traitement du langage naturel, and more.