What Weight Initialization Meaning, Applications & Example
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.