These 2048 parameters are in fact [gamma weights, beta weights, moving_mean(non-trainable), moving_variance(non-trainable)], each having 512 elements (the size of the input layer).
You can do it. But the nice thing about batchnorm, in addition to activation distribution stabilization, is that the mean and std deviation are likely migrate as the network learns. Effectively, setting the batchnorm right after the input layer is a fancy data pre-processing step. It helps, sometimes a lot (e.g. in linear regression). But … Read more
Definition Let’s begin with the strict definition of both: Batch normalization Instance normalization As you can notice, they are doing the same thing, except for the number of input tensors that are normalized jointly. Batch version normalizes all images across the batch and spatial locations (in the CNN case, in the ordinary case it’s different); … Read more
Let’s start with the terms. Remember that the output of the convolutional layer is a 4-rank tensor [B, H, W, C], where B is the batch size, (H, W) is the feature map size, C is the number of channels. An index (x, y) where 0 <= x < H and 0 <= y < … Read more
Just to answer this question in a little more detail, and as Pavel said, Batch Normalization is just another layer, so you can use it as such to create your desired network architecture. The general use case is to use BN between the linear and non-linear layers in your network, because it normalizes the input … Read more