GlorotUniform

GlorotUniform is a common initialization method used in deep learning models, particularly in neural networks. It was proposed by Xavier Glorot and Yoshua Bengio in their 2010 paper "Understanding the difficulty of training deep feedforward neural networks." The method is an improvement over the traditional Xavier initialization, also known as Glorot initialization, which is a technique used to initialize the weights of the neural network.

GlorotUniform is a technique that initializes the weights of a neural network using a uniform distribution. The variance of the uniform distribution is determined based on the number of inputs and outputs of the layer. The goal of GlorotUniform is to prevent the exploding or vanishing gradient problem that can occur when training neural networks. This problem occurs when the gradients of the loss function with respect to the weights become either too large or too small, making it difficult to train the model.

When using GlorotUniform, the weights are initialized with a uniform distribution over a range that depends on the number of inputs and outputs. This range is determined using the following formula: np.sqrt(6 / (number of inputs + number of outputs)). The weights are then rescaled to a uniform distribution over the specified range using a probability density function. GlorotUniform is particularly useful when training very deep neural networks, where the traditional Xavier initialization may not be sufficient to prevent the exploding or vanishing gradient problem. However, it can be computationally expensive and is not always necessary for shallower networks.