Weight Initialization
2024 | AI Dictionary
Process of setting initial neural network parameters.
What is Weight Initialization?
Weight Initialization refers to the process of setting the initial values for the weights in a neural network before training begins. Proper initialization is crucial because it can significantly affect the performance and speed of the training process, as well as the convergence of the model .
Common Weight Initialization Methods
Zero Initialization: All weights are set to zero. However, this method leads to poor performance, as all neurons will learn the same features, making them redundant.
Random Initialization: Weights are initialized with small random values. This helps break symmetry, but may still cause issues with vanishing or exploding gradients if not done carefully.
Xavier/Glorot Initialization: Used for sigmoid or tanh activation functions. Weights are initialized randomly with a distribution that depends on the number of input and output units in a layer to keep the variance of the gradients roughly constant across layers.
He Initialization: Similar to Xavier but used for ReLU activation functions. It scales the weights by a factor that helps mitigate the vanishing gradient problem when using ReLU.
Example of He Initialization
For a layer with n_in input units, the weights can be initialized as:
W = np.random.randn(n_in, n_out) * sqrt(2 / n_in)
Where:
n_inis the number of input units to the layer,n_outis the number of output units.
This method helps maintain the gradient magnitude and allows for faster and more stable training.
Did you liked the Weight Initialization gist?
Learn about 250+ need-to-know artificial intelligence terms in the AI Dictionary.