pytorch kaiming

Kaiming Initialization is a weight initialization method for neural networks, named after Kaiming He, who proposed it in his 2015 paper "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification". It is a modification of the Xavier Initialization method that is better suited for deep neural networks.

In PyTorch, Kaiming Initialization can be applied to a layer's weights using the `kaiming_uniform_` or `kaiming_normal_` functions, depending on whether you want to use a uniform or normal distribution for the initialization. For example, to initialize the weights of a linear layer with Kaiming Initialization using a normal distribution, you can do:

import torch.nn as nn

linear_layer = nn.Linear(in_features=10, out_features=20)
nn.init.kaiming_normal_(linear_layer.weight)

This will initialize the weights of the `linear_layer` using Kaiming Initialization with a normal distribution.