Additional layers and functions for the Flux.jl machine learning library.

```
Join(dim::Int64)
Join(dim = dim::Int64)
```

Concatenates a tuple of arrays along a dimension `dim`

. A convenient and type stable way of using `x -> cat(x..., dims = dim)`

.

```
Split(outputs::Int64,dim::Int64)
Split(outputs::Int64, dim = dim::Int64)
```

Breaks an array into a number of arrays which is equal to `output`

along a dimension `dim`

. `dim`

should we divisible by `outputs`

without a remainder.

```
Flatten()
```

Flattens an array. A convenient way of using `x -> Flux.flatten(x)`

.

```
Addition()
```

A convenient way of using `x -> sum(x)`

.

```
Activation(f::Function)
```

A convenient way of using `x -> f(x)`

.

```
Identity()
```

Returns its input without changes. Should be used with a `Parallel`

layer if one wants to have a branch that does not change its input.

```
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.

```
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_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.

```
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_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.

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

Subtracts the mean 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.

```
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).

Makes `Flux.Parallel`

layer type stable when used with tuples.