Understanding Batch Normalization Layer In PyTorch
Explaining PyTorch's BatchNorm2d: A 4D tensor normalization layer with 7 initialization arguments and a required input tensor of float values.
Buy Me a Coffee☕ *Memos: My post explains Batch Normalization Layer. My post explains BatchNorm1d(). My post explains BatchNorm3d(). My post explains LayerNorm(). My post explains requires_grad. BatchNorm2d() can get the 4D tensor of the zero or more elements computed by 2D Batch Normalization from the 4D tensor of zero or more elements as shown below: *Memos: The 1st argument for initialization is num_features(Required-Type:int). *It must be 1 <= x. The 2nd argument for initialization is eps(Optional-Default:1e-05-Type:float). The 3rd argument for initialization is momentum(Optional-Defau...