## Normalizations

`FluxExtra.Normalizations.norm_01!`

— Function`norm_01!(data::T,min_vals::T, max_vals::T) where {F<:AbstractFloat,N,T<:Array{F,N}}`

Rescales each feature (last dimension) to be in the range [0,1].

`norm_01!(data::Vector{T}) where {F<:AbstractFloat,N,T<:Array{F,N}}`

Rescales each feature (last dimension) to be in the range [0,1]. Returns min and max values for each feature.

`FluxExtra.Normalizations.norm_negpos1!`

— Function`norm_negpos1(data::T,min_vals::T,max_vals::T) where {F<:AbstractFloat,N,T<:Array{F,N}}`

Rescales each feature (last dimension) to be in the range [-1,1].

`norm_negpos1(data::Vector{T}) where {F<:AbstractFloat,N,T<:Array{F,N}}`

Rescales each feature (last dimension) to be in the range [-1,1]. Returns min and max values for each feature.

`FluxExtra.Normalizations.norm_zerocenter!`

— Function`norm_zerocenter!(data::T,mean_vals::T) where {F<:AbstractFloat,N,T<:Array{F,N}}`

Subtracts the mean of each feature (last dimension).

`norm_zerocenter!(data::Vector{T}) where {F<:AbstractFloat,N,T<:Array{F,N}}`

Subtracts the mean of each feature (last dimension). Returns a mean value for each feature.

`FluxExtra.Normalizations.norm_zscore!`

— Function`norm_zscore!(data::T,mean_vals::T,std_vals::T) where {F<:AbstractFloat,N,T<:Array{F,N}}`

Subtracts the mean and divides by the standard deviation of each feature (last dimension).

`norm_zscore!(data::Vector{T}) where {F<:AbstractFloat,N,T<:Array{F,N}}`

Subtracts the mean and divides by the standard deviation of each feature (last dimension). Returns mean and standard deviation values for each feature.

## Other

Makes `Flux.Parallel`

layer type stable when used with tuples.