# Neural Network primitives from NNlib.jl

Flux re-exports all of the functions exported by the NNlib package. This includes activation functions, described on the next page. Many of the functions on this page exist primarily as the internal implementation of Flux layer, but can also be used independently.

## Softmax

`Flux`

's `logitcrossentropy`

uses `NNlib.softmax`

internally.

`NNlib.softmax`

— Function`softmax(x; dims = 1)`

Softmax turns input array `x`

into probability distributions that sum to 1 along the dimensions specified by `dims`

. It is semantically equivalent to the following:

`softmax(x; dims = 1) = exp.(x) ./ sum(exp.(x), dims = dims)`

with additional manipulations enhancing numerical stability.

For a matrix input `x`

it will by default (`dims = 1`

) treat it as a batch of vectors, with each column independent. Keyword `dims = 2`

will instead treat rows independently, and so on.

See also `logsoftmax`

.

**Examples**

```
julia> softmax([1, 2, 3])
3-element Vector{Float64}:
0.09003057317038046
0.24472847105479764
0.6652409557748218
julia> softmax([1 2 3; 2 2 2]) # dims=1
2×3 Matrix{Float64}:
0.268941 0.5 0.731059
0.731059 0.5 0.268941
julia> softmax([1 2 3; 2 2 2]; dims=2)
2×3 Matrix{Float64}:
0.0900306 0.244728 0.665241
0.333333 0.333333 0.333333
```

Note that, when used with Flux.jl, `softmax`

must not be passed to layers like `Dense`

which accept an activation function. The activation is broadcasted over the result, thus applies to individual numbers. But `softmax`

always needs to see the whole column.

```
julia> using Flux
julia> x = randn(Float32, 4, 4, 3, 13);
julia> model = Chain(Conv((4, 4), 3 => 8, tanh), Flux.flatten, Dense(8 => 7), softmax);
julia> model(x) |> size
(7, 13)
julia> Dense(4 => 7, softmax)(x)
ERROR: `softmax(x)` called with a number, but it expects an array.
```

`NNlib.logsoftmax`

— Function`logsoftmax(x; dims = 1)`

Computes the log of softmax in a more numerically stable way than directly taking `log.(softmax(xs))`

. Commonly used in computing cross entropy loss.

It is semantically equivalent to the following:

`logsoftmax(x; dims = 1) = x .- log.(sum(exp.(x), dims = dims))`

See also `softmax`

.

## Pooling

`Flux`

's `AdaptiveMaxPool`

, `AdaptiveMeanPool`

, `GlobalMaxPool`

, `GlobalMeanPool`

, `MaxPool`

, and `MeanPool`

use `NNlib.PoolDims`

, `NNlib.maxpool`

, and `NNlib.meanpool`

as their backend.

`NNlib.PoolDims`

— Type```
PoolDims(x_size::NTuple{M}, k::Union{NTuple{L, Int}, Int};
stride=k, padding=0, dilation=1) where {M, L}
```

Dimensions for a "pooling" operation that can have an arbitrary input size, kernel size, stride, dilation, and channel count. Used to dispatch onto efficient implementations at compile-time.

`NNlib.maxpool`

— Function`maxpool(x, k::NTuple; pad=0, stride=k)`

Perform max pool operation with window size `k`

on input tensor `x`

.

`NNlib.meanpool`

— Function`meanpool(x, k::NTuple; pad=0, stride=k)`

Perform mean pool operation with window size `k`

on input tensor `x`

.

## Padding

`NNlib.pad_reflect`

— Function```
pad_reflect(x, pad::Tuple; [dims])
pad_reflect(x, pad::Int; [dims])
```

Pad the array `x`

reflecting its values across the border.

`pad`

can a tuple of integers `(l1, r1, ..., ln, rn)`

of some length `2n`

that specifies the left and right padding size for each of the dimensions in `dims`

. If `dims`

is not given, it defaults to the first `n`

dimensions.

For integer `pad`

input instead, it is applied on both sides on every dimension in `dims`

. In this case, `dims`

defaults to the first `ndims(x)-2`

dimensions (i.e. excludes the channel and batch dimension).

See also `pad_repeat`

and `pad_constant`

.

```
julia> r = reshape(1:9, 3, 3)
3×3 reshape(::UnitRange{Int64}, 3, 3) with eltype Int64:
1 4 7
2 5 8
3 6 9
julia> pad_reflect(r, (1,2,1,2))
6×6 Matrix{Int64}:
5 2 5 8 5 2
4 1 4 7 4 1
5 2 5 8 5 2
6 3 6 9 6 3
5 2 5 8 5 2
4 1 4 7 4 1
```

`NNlib.pad_constant`

— Function```
pad_constant(x, pad::Tuple, val = 0; [dims = :])
pad_constant(x, pad::Int, val = 0; [dims = :])
```

Pad the array `x`

with the constant value `val`

.

`pad`

can be a tuple of integers. If it is of some length `2 * length(dims)`

that specifies the left and right padding size for each of the dimensions in `dims`

as `(l1, r1, ..., ln, rn)`

. If supplied with a tuple of length `length(dims)`

instead, it applies symmetric padding. If `dims`

is not given, it defaults to all dimensions.

For integer `pad`

input, it is applied on both sides on every dimension in `dims`

.

See also `pad_zeros`

, `pad_reflect`

and `pad_repeat`

.

```
julia> r = reshape(1:4, 2, 2)
2×2 reshape(::UnitRange{Int64}, 2, 2) with eltype Int64:
1 3
2 4
julia> pad_constant(r, (1, 2, 3, 4), 8)
5×9 Matrix{Int64}:
8 8 8 8 8 8 8 8 8
8 8 8 1 3 8 8 8 8
8 8 8 2 4 8 8 8 8
8 8 8 8 8 8 8 8 8
8 8 8 8 8 8 8 8 8
julia> pad_constant(r, 1, 8)
4×4 Matrix{Int64}:
8 8 8 8
8 1 3 8
8 2 4 8
8 8 8 8
julia> r = reshape(1:27, 3, 3, 3)
3×3×3 reshape(::UnitRange{Int64}, 3, 3, 3) with eltype Int64:
[:, :, 1] =
1 4 7
2 5 8
3 6 9
[:, :, 2] =
10 13 16
11 14 17
12 15 18
[:, :, 3] =
19 22 25
20 23 26
21 24 27
julia> pad_constant(r, (2,1), dims = 1) # assymetric padding
6×3×3 Array{Int64, 3}:
[:, :, 1] =
0 0 0
0 0 0
1 4 7
2 5 8
3 6 9
0 0 0
[:, :, 2] =
0 0 0
0 0 0
10 13 16
11 14 17
12 15 18
0 0 0
[:, :, 3] =
0 0 0
0 0 0
19 22 25
20 23 26
21 24 27
0 0 0
julia> pad_constant(r, (2,1, 3), dims = (1,2)) # padding must always be either the same length as dims, or double it
ERROR: ArgumentError: Could not parse padding (2, 1, 3) and dims (1, 2)
Stacktrace:
[...]
```

`NNlib.pad_repeat`

— Function```
pad_repeat(x, pad::Tuple; [dims])
pad_repeat(x, pad::Int; [dims])
```

Pad the array `x`

repeating the values on the border.

`pad`

can a tuple of integers `(l1, r1, ..., ln, rn)`

of some length `2n`

that specifies the left and right padding size for each of the dimensions in `dims`

. If `dims`

is not given, it defaults to the first `n`

dimensions.

For integer `pad`

input instead, it is applied on both sides on every dimension in `dims`

. In this case, `dims`

defaults to the first `ndims(x)-2`

dimensions (i.e. excludes the channel and batch dimension).

See also `pad_reflect`

and `pad_constant`

.

```
julia> r = reshape(1:9, 3, 3)
3×3 reshape(::UnitRange{Int64}, 3, 3) with eltype Int64:
1 4 7
2 5 8
3 6 9
julia> pad_repeat(r, (1,2,3,4))
6×10 Matrix{Int64}:
1 1 1 1 4 7 7 7 7 7
1 1 1 1 4 7 7 7 7 7
2 2 2 2 5 8 8 8 8 8
3 3 3 3 6 9 9 9 9 9
3 3 3 3 6 9 9 9 9 9
3 3 3 3 6 9 9 9 9 9
```

`NNlib.pad_zeros`

— Function```
pad_zeros(x, pad::Tuple; [dims])
pad_zeros(x, pad::Int; [dims])
```

Pad the array `x`

with zeros. Equivalent to `pad_constant`

with the constant equal to 0.

## Convolution

`Flux`

's `Conv`

and `CrossCor`

layers use `NNlib.DenseConvDims`

and `NNlib.conv`

internally.

`NNlib.conv`

— Function`conv(x, w; stride = 1, pad = 0, dilation = 1, flipped = false, groups = 1)`

Apply convolution filter `w`

to input `x`

. `x`

and `w`

are 3d/4d/5d tensors in 1d/2d/3d convolutions respectively.

`NNlib.ConvDims`

— Type`ConvDims`

Type system-level information about convolution dimensions. Critical for things like `im2col!()`

to generate efficient code, and helpful to reduce the number of kwargs getting passed around.

`NNlib.depthwiseconv`

— Function`depthwiseconv(x, w; stride=1, pad=0, dilation=1, flipped=false)`

Depthwise convolution operation with filter `w`

on input `x`

. `x`

and `w`

are 3d/4d/5d tensors in 1d/2d/3d convolutions respectively.

`NNlib.DepthwiseConvDims`

— Type`DepthwiseConvDims`

Concrete subclass of `ConvDims`

for a depthwise convolution. Differs primarily due to characterization by C*in, C*mult, rather than C*in, C*out. Useful to be separate from DenseConvDims primarily for channel calculation differences.

`NNlib.DenseConvDims`

— Type`DenseConvDims`

Concrete subclass of `ConvDims`

for a normal, dense, conv2d/conv3d.

## Upsampling

`Flux`

's `Upsample`

layer uses `NNlib.upsample_nearest`

, `NNlib.upsample_bilinear`

, and `NNlib.upsample_trilinear`

as its backend. Additionally, `Flux`

's `PixelShuffle`

layer uses `NNlib.pixel_shuffle`

as its backend.

`NNlib.upsample_nearest`

— Function```
upsample_nearest(x, scale::NTuple{S,Int})
upsample_nearest(x; size::NTuple{S,Int})
```

Upsamples the array `x`

by integer multiples along the first `S`

dimensions. Subsequent dimensions of `x`

are not altered.

Either the `scale`

factors or the final output `size`

can be specified.

See also `upsample_bilinear`

, for two dimensions of an `N=4`

array.

**Example**

```
julia> upsample_nearest([1 2 3; 4 5 6], (2, 3))
4×9 Matrix{Int64}:
1 1 1 2 2 2 3 3 3
1 1 1 2 2 2 3 3 3
4 4 4 5 5 5 6 6 6
4 4 4 5 5 5 6 6 6
julia> ans == upsample_nearest([1 2 3; 4 5 6]; size=(4, 9)) # equivalent
true
julia> upsample_nearest([1 2 3; 4 5 6], (2,))
4×3 Matrix{Int64}:
1 2 3
1 2 3
4 5 6
4 5 6
julia> ans == upsample_nearest([1 2 3; 4 5 6], size=(4,))
true
```

Missing docstring for `∇upsample_nearest`

. Check Documenter's build log for details.

Missing docstring for `upsample_linear`

. Check Documenter's build log for details.

`NNlib.∇upsample_linear`

— Function`∇upsample_linear(Δ::AbstractArray{T,3}; size::Integer) where T`

**Arguments**

`Δ`

: Incoming gradient array, backpropagated from downstream layers`size`

: Size of the image upsampled in the first place

**Outputs**

`dx`

: Downsampled version of`Δ`

`NNlib.upsample_bilinear`

— Function```
upsample_bilinear(x::AbstractArray{T,4}, scale::NTuple{2,Real})
upsample_bilinear(x::AbstractArray{T,4}; size::NTuple{2,Integer})
```

Upsamples the first 2 dimensions of the array `x`

by the upsample factors stored in `scale`

, using bilinear interpolation. As an alternative to using `scale`

, the resulting image `size`

can be directly specified with a keyword argument.

The size of the output is equal to `(scale[1]*S1, scale[2]*S2, S3, S4)`

, where `S1, S2, S3, S4 = size(x)`

.

**Examples**

```
julia> x = reshape(Float32[1 2 3; 4 5 6], (2,3,1,1))
2×3×1×1 Array{Float32, 4}:
[:, :, 1, 1] =
1.0 2.0 3.0
4.0 5.0 6.0
julia> upsample_bilinear(x, (2, 3))
4×9×1×1 Array{Float32, 4}:
[:, :, 1, 1] =
1.0 1.25 1.5 1.75 2.0 2.25 2.5 2.75 3.0
2.0 2.25 2.5 2.75 3.0 3.25 3.5 3.75 4.0
3.0 3.25 3.5 3.75 4.0 4.25 4.5 4.75 5.0
4.0 4.25 4.5 4.75 5.0 5.25 5.5 5.75 6.0
julia> ans == upsample_bilinear(x; size=(4, 9)) # specify ouput size instead
true
julia> upsample_bilinear(x, (2.5, 3.5)) # non-integer scaling factors are allowed
5×10×1×1 Array{Float32, 4}:
[:, :, 1, 1] =
1.0 1.22222 1.44444 1.66667 1.88889 … 2.33333 2.55556 2.77778 3.0
1.75 1.97222 2.19444 2.41667 2.63889 3.08333 3.30556 3.52778 3.75
2.5 2.72222 2.94444 3.16667 3.38889 3.83333 4.05556 4.27778 4.5
3.25 3.47222 3.69444 3.91667 4.13889 4.58333 4.80556 5.02778 5.25
4.0 4.22222 4.44444 4.66667 4.88889 5.33333 5.55556 5.77778 6.0
```

`NNlib.∇upsample_bilinear`

— Function`∇upsample_bilinear(Δ::AbstractArray{T,4}; size::NTuple{2,Integer}) where T`

**Arguments**

`Δ`

: Incoming gradient array, backpropagated from downstream layers`size`

: Lateral (W,H) size of the image upsampled in the first place

**Outputs**

`dx`

: Downsampled version of`Δ`

`NNlib.upsample_trilinear`

— Function```
upsample_trilinear(x::AbstractArray{T,5}, scale::NTuple{3,Real})
upsample_trilinear(x::AbstractArray{T,5}; size::NTuple{3,Integer})
```

Upsamples the first 3 dimensions of the array `x`

by the upsample factors stored in `scale`

, using trilinear interpolation. As an alternative to using `scale`

, the resulting image `size`

can be directly specified with a keyword argument.

The size of the output is equal to `(scale[1]*S1, scale[2]*S2, scale[3]*S3, S4, S5)`

, where `S1, S2, S3, S4, S5 = size(x)`

.

**Examples**

```
upsample_trilinear(x, (2, 3, 4))
upsample_trilinear(x; size=(4, 9, 11)) # specify ouput size instead
upsample_trilinear(x, (2.5, 3.5, pi)) # non-integer scaling factors are allowed
```

`NNlib.∇upsample_trilinear`

— Function`∇upsample_trilinear(Δ::AbstractArray{T,5}; size::NTuple{3,Integer}) where T`

**Arguments**

`Δ`

: Incoming gradient array, backpropagated from downstream layers`size`

: Lateral size & depth (W,H,D) of the image upsampled in the first place

**Outputs**

`dx`

: Downsampled version of`Δ`

`NNlib.pixel_shuffle`

— Function`pixel_shuffle(x, r::Integer)`

Pixel shuffling operation, upscaling by a factor `r`

.

For 4-arrays representing `N`

images, the operation converts input `size(x) == (W, H, r^2*C, N)`

to output of size `(r*W, r*H, C, N)`

. For `D`

-dimensional data, it expects `ndims(x) == D+2`

with channel and batch dimensions, and divides the number of channels by `r^D`

.

Used in super-resolution networks to upsample towards high resolution features. Reference: Shi et. al., "Real-Time Single Image and Video Super-Resolution ...", CVPR 2016, https://arxiv.org/abs/1609.05158

**Examples**

```
julia> x = [10i + j + channel/10 for i in 1:2, j in 1:3, channel in 1:4, batch in 1:1]
2×3×4×1 Array{Float64, 4}:
[:, :, 1, 1] =
11.1 12.1 13.1
21.1 22.1 23.1
[:, :, 2, 1] =
11.2 12.2 13.2
21.2 22.2 23.2
[:, :, 3, 1] =
11.3 12.3 13.3
21.3 22.3 23.3
[:, :, 4, 1] =
11.4 12.4 13.4
21.4 22.4 23.4
julia> pixel_shuffle(x, 2) # 4 channels used up as 2x upscaling of image dimensions
4×6×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
11.1 11.3 12.1 12.3 13.1 13.3
11.2 11.4 12.2 12.4 13.2 13.4
21.1 21.3 22.1 22.3 23.1 23.3
21.2 21.4 22.2 22.4 23.2 23.4
julia> y = [i + channel/10 for i in 1:3, channel in 1:6, batch in 1:1]
3×6×1 Array{Float64, 3}:
[:, :, 1] =
1.1 1.2 1.3 1.4 1.5 1.6
2.1 2.2 2.3 2.4 2.5 2.6
3.1 3.2 3.3 3.4 3.5 3.6
julia> pixel_shuffle(y, 2) # 1D image, with 6 channels reduced to 3
6×3×1 Array{Float64, 3}:
[:, :, 1] =
1.1 1.3 1.5
1.2 1.4 1.6
2.1 2.3 2.5
2.2 2.4 2.6
3.1 3.3 3.5
3.2 3.4 3.6
```

## Batched Operations

`Flux`

's `Bilinear`

layer uses `NNlib.batched_mul`

internally.

`NNlib.batched_mul`

— Function```
batched_mul(A, B) -> C
A ⊠ B # \boxtimes
```

Batched matrix multiplication. Result has `C[:,:,k] == A[:,:,k] * B[:,:,k]`

for all `k`

. If `size(B,3) == 1`

then instead `C[:,:,k] == A[:,:,k] * B[:,:,1]`

, and similarly for `A`

.

To transpose each matrix, apply `batched_transpose`

to the array, or `batched_adjoint`

for conjugate-transpose:

```
julia> A, B = randn(2,5,17), randn(5,9,17);
julia> A ⊠ B |> size
(2, 9, 17)
julia> batched_adjoint(A) |> size
(5, 2, 17)
julia> batched_mul(A, batched_adjoint(randn(9,5,17))) |> size
(2, 9, 17)
julia> A ⊠ randn(5,9,1) |> size
(2, 9, 17)
julia> batched_transpose(A) == PermutedDimsArray(A, (2,1,3))
true
```

The equivalent `PermutedDimsArray`

may be used in place of `batched_transpose`

. Other permutations are also handled by BLAS, provided that the batch index `k`

is not the first dimension of the underlying array. Thus `PermutedDimsArray(::Array, (1,3,2))`

and `PermutedDimsArray(::Array, (3,1,2))`

are fine.

However, `A = PermutedDimsArray(::Array, (3,2,1))`

is not acceptable to BLAS, since the batch dimension is the contiguous one: `stride(A,3) == 1`

. This will be copied, as doing so is faster than `batched_mul_generic!`

.

Both this `copy`

and `batched_mul_generic!`

produce `@debug`

messages, and setting for instance `ENV["JULIA_DEBUG"] = NNlib`

will display them.

```
batched_mul(A::Array{T,3}, B::Matrix)
batched_mul(A::Matrix, B::Array{T,3})
A ⊠ B
```

This is always matrix-matrix multiplication, but either `A`

or `B`

may lack a batch index.

When

`B`

is a matrix, result has`C[:,:,k] == A[:,:,k] * B[:,:]`

for all`k`

.When

`A`

is a matrix, then`C[:,:,k] == A[:,:] * B[:,:,k]`

. This can also be done by reshaping and calling`*`

, for instance`A ⊡ B`

using TensorCore.jl, but is implemented here using`batched_gemm`

instead of`gemm`

.

```
julia> randn(16,8,32) ⊠ randn(8,4) |> size
(16, 4, 32)
julia> randn(16,8,32) ⊠ randn(8,4,1) |> size # equivalent
(16, 4, 32)
julia> randn(16,8) ⊠ randn(8,4,32) |> size
(16, 4, 32)
```

See also `batched_vec`

to regard `B`

as a batch of vectors, `A[:,:,k] * B[:,k]`

.

`NNlib.batched_mul!`

— Function```
batched_mul!(C, A, B) -> C
batched_mul!(C, A, B, α=1, β=0)
```

In-place batched matrix multiplication, equivalent to `mul!(C[:,:,k], A[:,:,k], B[:,:,k], α, β)`

for all `k`

. If `size(B,3) == 1`

then every batch uses `B[:,:,1]`

instead.

This will call `batched_gemm!`

whenever possible. For real arrays this means that, for `X ∈ [A,B,C]`

, either `strides(X,1)==1`

or `strides(X,2)==1`

, the latter may be caused by `batched_transpose`

or by for instance `PermutedDimsArray(::Array, (3,1,2))`

. Unlike `batched_mul`

this will never make a copy.

For complex arrays, the wrapper made by `batched_adjoint`

must be outermost to be seen. In this case the strided accepted by BLAS are more restricted, if `stride(C,1)==1`

then only `stride(AorB::BatchedAdjoint,2) == 1`

is accepted.

`NNlib.batched_adjoint`

— Function```
batched_transpose(A::AbstractArray{T,3})
batched_adjoint(A)
```

Equivalent to applying `transpose`

or `adjoint`

to each matrix `A[:,:,k]`

.

These exist to control how `batched_mul`

behaves, as it operates on such matrix slices of an array with `ndims(A)==3`

.

`PermutedDimsArray(A, (2,1,3))`

is equivalent to `batched_transpose(A)`

, and is also understood by `batched_mul`

(and more widely supported elsewhere).

```
BatchedTranspose{T, S} <: AbstractBatchedMatrix{T, 3}
BatchedAdjoint{T, S}
```

Lazy wrappers analogous to `Transpose`

and `Adjoint`

, returned by `batched_transpose`

etc.

`NNlib.batched_transpose`

— Function```
batched_transpose(A::AbstractArray{T,3})
batched_adjoint(A)
```

Equivalent to applying `transpose`

or `adjoint`

to each matrix `A[:,:,k]`

.

These exist to control how `batched_mul`

behaves, as it operates on such matrix slices of an array with `ndims(A)==3`

.

`PermutedDimsArray(A, (2,1,3))`

is equivalent to `batched_transpose(A)`

, and is also understood by `batched_mul`

(and more widely supported elsewhere).

```
BatchedTranspose{T, S} <: AbstractBatchedMatrix{T, 3}
BatchedAdjoint{T, S}
```

Lazy wrappers analogous to `Transpose`

and `Adjoint`

, returned by `batched_transpose`

etc.

`NNlib.batched_vec`

— Function```
batched_vec(A::Array{T,3}, B::Matrix)
batched_vec(A::Array{T,3}, b::Vector)
```

Batched matrix-vector multiplication: the result has `C[:,:,k] == A[:,:,k] * B[:,k]`

for all `k`

, or else `C[:,:,k] == A[:,:,k] * b`

for `b::Vector`

.

With the same argument types, `batched_mul(A, B)`

would regard `B`

as a fixed matrix, not a batch of vectors. Both reshape and then call `batched_mul(::Array{T,3}, ::Array{T,3})`

.

```
julia> A, B, b = randn(16,8,32), randn(8,32), randn(8);
julia> batched_vec(A,B) |> size
(16, 32)
julia> batched_vec(A,b) |> size
(16, 32)
```

## Gather and Scatter

`Flux`

's `Embedding`

layer uses `NNlib.gather`

as its backend.

`NNlib.gather`

— Function`NNlib.gather(src, idx) -> dst`

Reverse operation of `scatter`

. Gathers data from source `src`

and writes it in a destination `dst`

according to the index array `idx`

. For each `k`

in `CartesianIndices(idx)`

, assign values to `dst`

according to

`dst[:, ... , k] .= src[:, ... , idx[k]...]`

Notice that if `idx`

is a vector containing integers and `src`

is a matrix, previous expression simplifies to

`dst[:, k] .= src[:, idx[k]]`

and `k`

will run over `1:length(idx)`

.

The elements of `idx`

can be integers or integer tuples and may be repeated. A single `src`

column can end up being copied into zero, one, or multiple `dst`

columns.

See `gather!`

for an in-place version.

**Examples**

```
julia> NNlib.gather([1,20,300,4000], [2,4,2])
3-element Vector{Int64}:
20
4000
20
julia> NNlib.gather([1 2 3; 4 5 6], [1,3,1,3,1])
2×5 Matrix{Int64}:
1 3 1 3 1
4 6 4 6 4
```

`NNlib.gather!`

— Function`NNlib.gather!(dst, src, idx)`

Reverse operation of `scatter!`

. Gathers data from source `src`

and writes it in destination `dst`

according to the index array `idx`

. For each `k`

in `CartesianIndices(idx)`

, assign values to `dst`

according to

`dst[:, ... , k] .= src[:, ... , idx[k]...]`

Notice that if `idx`

is a vector containing integers, and both `dst`

and `src`

are matrices, previous expression simplifies to

`dst[:, k] .= src[:, idx[k]]`

and `k`

will run over `1:length(idx)`

.

The elements of `idx`

can be integers or integer tuples and may be repeated. A single `src`

column can end up being copied into zero, one, or multiple `dst`

columns.

See `gather`

for an allocating version.

`NNlib.scatter`

— Function`NNlib.scatter(op, src, idx; [init, dstsize])`

Scatter operation allocating a destination array `dst`

and calling `scatter!(op, dst, src, idx)`

on it.

If keyword

`init`

is provided, it is used to initialize the content of`dst`

. Otherwise, the init values is inferred from the reduction operator`op`

for some common operators (e.g.`init = 0`

for`op = +`

).If

`dstsize`

is provided, it will be used to define the size of destination array, otherwise it will be inferred by`src`

and`idx`

.

See `scatter!`

for full details on how `idx`

works.

**Examples**

```
julia> NNlib.scatter(+, [10,100,1000], [3,1,2])
3-element Vector{Int64}:
100
1000
10
julia> NNlib.scatter(+, [1 2 3 4; 5 6 7 8], [2,1,1,5])
2×5 Matrix{Int64}:
5 1 0 0 4
13 5 0 0 8
julia> NNlib.scatter(*, [10,200,3000], [1,4,2]; init = 10, dstsize = 6)
6-element Vector{Int64}:
100
30000
10
2000
10
10
```

`NNlib.scatter!`

— Function`NNlib.scatter!(op, dst, src, idx)`

Scatter operation, which writes data in `src`

into `dst`

at locations `idx`

. A binary reduction operator `op`

is applied during the scatter. For each index `k`

in `idx`

, accumulates values in `dst`

according to

`dst[:, ..., idx[k]...] = (op).(dst[:, ..., idx[k]...], src[:, ..., k...])`

**Arguments**

`op`

: Operations to be applied on`dst`

and`src`

, e.g.`+`

,`-`

,`*`

,`/`

,`max`

,`min`

and`mean`

.`dst`

: The destination for`src`

to aggregate to. This argument will be mutated.`src`

: The source data for aggregating.`idx`

: The mapping for aggregation from source (index) to destination (value). The`idx`

array can contain either integers or tuples.

**Examples**

```
julia> NNlib.scatter!(+, ones(3), [10,100], [1,3])
3-element Vector{Float64}:
11.0
1.0
101.0
julia> NNlib.scatter!(*, fill(0.5, 2, 4), [1 10; 100 1000], [3,2])
2×4 Matrix{Float64}:
0.5 5.0 0.5 0.5
0.5 500.0 50.0 0.5
```

## Sampling

`NNlib.grid_sample`

— Function`grid_sample(input::AbstractArray{T, 4}, grid::AbstractArray{T, 4}; padding_mode = :zeros)`

Given `input`

, compute output by sampling `input`

values at pixel locations from `grid`

. Uses bilinear interpolation to calculate output values.

This implementation assumes the extrema (`-1`

and `1`

) are considered as referring to the center points of the input’s corner pixels (i.e. align corners is `true`

).

**Arguments**

`input`

: Input array in`(W_in, H_in, C, N)`

shape.`grid`

: Input grid in`(2, W_out, H_out, N)`

shape. Where for each`(W_out, H_out, N)`

grid contains`(x, y)`

coordinates that specify sampling locations normalized by the`input`

shape.Therefore,

`x`

and`y`

should have values in`[-1, 1]`

range. For example,`(x = -1, y = -1)`

is the left-top pixel of`input`

, and`(x = 1, y = 1)`

is the right-bottom pixel of`input`

.Out-of-bound values are handled according to the

`padding_mode`

.`padding_mode`

: Out-of-bound padding.`:zeros`

to use`0`

for out-of-bound grid locations.`:border`

to use border values for out-of-bound grid locations. Default is`:zeros`

.

**Returns**

`(W_out, H_out, C, N)`

sampled grid from `input`

.

**Examples**

In the example below, grid contains two out-of-bound sampling locations, which are handled differently, depending on the `padding_mode`

.

```
julia> x = reshape(collect(1.0:4.0), (2, 2, 1, 1))
2×2×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
1.0 3.0
2.0 4.0
julia> grid = Array{Float64}(undef, 2, 3, 2, 1);
julia> grid[:, 1, 1, 1] .= (-3, -1);
julia> grid[:, 2, 1, 1] .= (0, -1);
julia> grid[:, 3, 1, 1] .= (1, -1);
julia> grid[:, 1, 2, 1] .= (-1, 1);
julia> grid[:, 2, 2, 1] .= (0, 1);
julia> grid[:, 3, 2, 1] .= (3, 1);
julia> grid_sample(x, grid; padding_mode=:zeros)
3×2×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
0.0 3.0
1.5 3.5
2.0 0.0
julia> grid_sample(x, grid; padding_mode=:border)
3×2×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
1.0 3.0
1.5 3.5
2.0 4.0
```

`NNlib.∇grid_sample`

— Function`∇grid_sample(Δ::AbstractArray{T, 4}, input::AbstractArray{T, 4}, grid::AbstractArray{T, 4}; padding_mode = :zeros) where T`

**Arguments**

`Δ`

: Input gradient in`(W_out, H_out, C, N)`

shape (same as output of the primal computation).`input`

: Input from primal computation in`(W_in, H_in, C, N)`

shape.`grid`

: Grid from primal computation in`(2, W_out, H_out, N)`

shape.`padding_mode`

: Out-of-bound padding.`:zeros`

to use`0`

for out-of-bound grid locations.`:border`

to use border values for out-of-bound grid locations. Should be the same as in primal computation. Default is`:zeros`

.

**Returns**

`dinput`

(same shape as `input`

) and `dgrid`

(same shape as `grid`

) gradients.

## Losses

`NNlib.ctc_loss`

— Functionctc*loss(ŷ, y) Computes the connectionist temporal classification loss between ŷ and y. ŷ must be a classes-by-time matrices, i.e., each row represents a class and each column represents a time step. Additionally, the logsoftmax function will be applied to ŷ, so ŷ must be the raw activation values from the neural network and not, for example, the activations after being passed through a softmax activation function. y must be a 1D array of the labels associated with ŷ. The blank label is assumed to be the last label category in ŷ, so it is equivalent to size(ŷ, 1). Used for sequence-to-sequence classification problems such as speech recognition and handwriting recognition where the exact time-alignment of the output (e.g., letters) is not needed to solve the problem. See [Graves et al. (2006)](https://www.cs.toronto.edu/~graves/icml*2006.pdf) or Graves (2012) for mathematical details.

## Miscellaneous

`NNlib.logsumexp`

— Function`logsumexp(x; dims = :)`

Computes `log.(sum(exp.(x); dims))`

in a numerically stable way. Without `dims`

keyword this returns a scalar.

See also `logsoftmax`

.

`NNlib.glu`

— Function`glu(x, dim = 1)`

The gated linear unit from the "Language Modeling with Gated Convolutional Networks" paper.

Calculates `a .* sigmoid(b)`

, where `x`

is split in half along given dimension `dim`

to form `a`

and `b`

.