BatchNorm(channels::Integer, λ=identity;
          initβ=zeros32, initγ=ones32,
          affine=true, track_stats=true, active=nothing,
          eps=1f-5, momentum= 0.1f0)

Batch Normalization layer. channels should be the size of the channel dimension in your data (see below).

Given an array with N dimensions, call the N-1th the channel dimension. For a batch of feature vectors this is just the data dimension, for WHCN images it's the usual channel dimension.

BatchNorm computes the mean and variance for each D_1×...×D_{N-2}×1×D_N input slice and normalises the input accordingly.

If affine=true, it also applies a shift and a rescale to the input through to learnable per-channel bias β and scale γ parameters.

After normalisation, elementwise activation λ is applied.

If track_stats=true, accumulates mean and var statistics in training phase that will be used to renormalize the input in test phase.

Use testmode! during inference.

Examples

			
			
			
			julia
			>
			 
			
			using
			
			 
			Statistics
			
			

			

			
			
			julia
			>
			 
			xs
			 
			=
			 
			
			rand
			(
			3
			,
			 
			3
			,
			 
			3
			,
			 
			2
			)
			;
			
			  
			# a batch of 2 images, each having 3 channels
			

			

			
			
			julia
			>
			 
			m
			 
			=
			 
			

			BatchNorm
			(
			3
			)
			;
			
			

			

			
			julia
			>
			 
			
			

	
			Flux
			.
			

	
			trainmode!
			(
			m
			)
			;
			

			

			
			
			julia
			>
			 
			
			isapprox
			(
			
			std
			(
			
			m
			(
			xs
			)
			)
			,
			 
			1
			,
			 
			
			atol
			=
			0.1
			)
			 
			&&
			
			 
			
			std
			(
			xs
			)
			 
			!=
			 
			
			std
			(
			
			m
			(
			xs
			)
			)
			

			true