Inicialização de pesos refers to the method of assigning initial values to the weights of a modelo de rede neural before the training process begins. Proper weight initialization is crucial because it can significantly impact the efficiency and effectiveness of the training phase, influencing how quickly and accurately the network converges to a solution.
In redes neurais, weights are the parameters that the model learns during training. If these weights are initialized poorly, it can lead to issues such as slow convergence, getting stuck in local minima, or even divergence of the learning process altogether. Common strategies for weight initialization include:
- Zero Inicialização: Setting all weights to zero, which is generally not recommended because it makes neurons learn the same features during training.
- Inicialização Aleatória: Assigning random values to weights, typically drawn from a Gaussian or uniform distribution. This helps to break symmetry but can still lead to problems if the variance is not appropriately scaled.
- Inicialização Xavier/Glorot: This method scales the initial weights based on the number of input and output neurons, promoting better flow of gradients during training.
- Inicialização He: Similar to Xavier, but specifically designed for funções de ativação como ReLU. Ele ajusta os pesos com base no número de neurônios de entrada.
Escolher uma estratégia adequada de inicialização de pesos é uma etapa importante em otimizar o desempenho de redes neurais, as it can enhance learning speed and improve the model’s predictive accuracy.