A Densa Camada, also known as a camada 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.
A função principal de uma Camada Densa é realizar uma transformação linear of the input data followed by a non-linear activation function. Mathematically, this can be expressed as:
output = activation_function(weights * input + bias)Onde:
- weights are the learned parameters que determinam a força da conexão entre os neurônios.
- input é o dado fornecido à camada.
- bias is an additional parameter that allows the model to fit the dados de treinamento melhor.
- activation_function is a função não linear, 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, processamento de linguagem natural, and more.