# Basic Layers

These core layers form the foundation of almost all neural networks.

`Flux.Chain`

— Type`Chain(layers...)`

Chain multiple layers / functions together, so that they are called in sequence on a given input.

`Chain`

also supports indexing and slicing, e.g. `m[2]`

or `m[1:end-1]`

. `m[1:3](x)`

will calculate the output of the first three layers.

**Examples**

```
julia> m = Chain(x -> x^2, x -> x+1);
julia> m(5) == 26
true
julia> m = Chain(Dense(10, 5), Dense(5, 2));
julia> x = rand(10);
julia> m(x) == m[2](m[1](x))
true
```

`Flux.Dense`

— Type```
Dense(in, out, σ=identity; bias=true, init=glorot_uniform)
Dense(W::AbstractMatrix, [bias, σ])
```

Create a traditional `Dense`

layer, whose forward pass is given by:

`y = σ.(W * x .+ bias)`

The input `x`

should be a vector of length `in`

, or batch of vectors represented as an `in × N`

matrix, or any array with `size(x,1) == in`

. The out `y`

will be a vector of length `out`

, or a batch with `size(y) == (out, size(x)[2:end]...)`

Keyword `bias=false`

will switch off trainable bias for the layer. The initialisation of the weight matrix is `W = init(out, in)`

, calling the function given to keyword `init`

, with default `glorot_uniform`

. The weight matrix and/or the bias vector (of length `out`

) may also be provided explicitly.

**Examples**

```
julia> d = Dense(5, 2)
Dense(5, 2) # 12 parameters
julia> d(rand(Float32, 5, 64)) |> size
(2, 64)
julia> d(rand(Float32, 5, 1, 1, 64)) |> size # treated as three batch dimensions
(2, 1, 1, 64)
julia> d1 = Dense(ones(2, 5), false, tanh) # using provided weight matrix
Dense(5, 2, tanh; bias=false) # 10 parameters
julia> d1(ones(5))
2-element Vector{Float64}:
0.9999092042625951
0.9999092042625951
julia> Flux.params(d1) # no trainable bias
Params([[1.0 1.0 … 1.0 1.0; 1.0 1.0 … 1.0 1.0]])
```

## Convolution and Pooling Layers

These layers are used to build convolutional neural networks (CNNs).

`Flux.Conv`

— Type```
Conv(filter, in => out, σ = identity;
stride = 1, pad = 0, dilation = 1, groups = 1, [bias, weight, init])
```

Standard convolutional layer. `filter`

is a tuple of integers specifying the size of the convolutional kernel; `in`

and `out`

specify the number of input and output channels.

Image data should be stored in WHCN order (width, height, channels, batch). In other words, a 100×100 RGB image would be a `100×100×3×1`

array, and a batch of 50 would be a `100×100×3×50`

array. This has `N = 2`

spatial dimensions, and needs a kernel size like `(5,5)`

, a 2-tuple of integers.

To take convolutions along `N`

feature dimensions, this layer expects as input an array with `ndims(x) == N+2`

, where `size(x, N+1) == in`

is the number of input channels, and `size(x, ndims(x))`

is (as always) the number of observations in a batch. Then:

`filter`

should be a tuple of`N`

integers.- Keywords
`stride`

and`dilation`

should each be either single integer, or a tuple with`N`

integers. - Keyword
`pad`

can be:- a single integer for equal padding all around,
- a tuple of
`N`

integers, to apply the same padding at begin/end of each spatial dimension, - a tuple of
`2*N`

integers, for asymmetric padding, or - the singleton
`SamePad()`

, to calculate padding such that`size(output,d) == size(x,d) / stride`

(possibly rounded) for each spatial dimension.

- Keyword
`groups`

is expected to be an`Int`

. It specifies the number of groups to divide a convolution into.

Keywords to control initialization of the layer:

`init`

- Function used to generate initial weights. Defaults to`glorot_uniform`

.`weight`

- Initial weights of the layer. Typically an array, and can be used to override other configurations. By default, these are generated using`convfilter`

.`bias`

- Initial bias is zero by default, this can be disabled entirely by setting it to`Flux.Zeros()`

or equivalently`false`

, or another vector provided as`bias = randn(Float32, out)`

.

See also `ConvTranspose`

, `DepthwiseConv`

, `CrossCor`

.

**Examples**

```
julia> xs = rand(Float32, 100, 100, 3, 50); # a batch of images
julia> layer = Conv((5,5), 3 => 7, relu; bias = false)
Conv((5, 5), 3 => 7, relu, bias=false) # 525 parameters
julia> layer(xs) |> size
(96, 96, 7, 50)
julia> Conv((5,5), 3 => 7; stride = 2)(xs) |> size
(48, 48, 7, 50)
julia> Conv((5,5), 3 => 7; stride = 2, pad = SamePad())(xs) |> size
(50, 50, 7, 50)
julia> Conv((1,1), 3 => 7; pad = (20,10,0,0))(xs) |> size
(130, 100, 7, 50)
julia> Conv((5,5), 3 => 7; stride = 2, dilation = 4)(xs) |> size
(42, 42, 7, 50)
```

`Flux.AdaptiveMaxPool`

— Type`AdaptiveMaxPool(out::NTuple)`

Adaptive max pooling layer. Calculates the necessary window size such that its output has `size(y)[1:N] == out`

.

Expects as input an array with `ndims(x) == N+2`

, i.e. channel and batch dimensions, after the `N`

feature dimensions, where `N = length(out)`

.

See also `MaxPool`

, `AdaptiveMeanPool`

.

**Examples**

```
julia> xs = rand(Float32, 100, 100, 3, 50); # batch of 50 RGB images
julia> AdaptiveMaxPool((25, 25))(xs) |> size
(25, 25, 3, 50)
julia> MaxPool((4,4))(xs) ≈ AdaptiveMaxPool((25, 25))(xs)
true
```

`Flux.MaxPool`

— Type`MaxPool(window::NTuple; pad=0, stride=window)`

Max pooling layer, which replaces all pixels in a block of size `window`

with one.

Expects as input an array with `ndims(x) == N+2`

, i.e. channel and batch dimensions, after the `N`

feature dimensions, where `N = length(window)`

.

By default the window size is also the stride in each dimension. The keyword `pad`

accepts the same options as for the `Conv`

layer, including `SamePad()`

.

See also `Conv`

, `MeanPool`

, `AdaptiveMaxPool`

, `GlobalMaxPool`

.

**Examples**

```
julia> xs = rand(Float32, 100, 100, 3, 50); # batch of 50 RGB images
julia> m = Chain(Conv((5, 5), 3 => 7, pad=SamePad()), MaxPool((5, 5), pad=SamePad()))
Chain(
Conv((5, 5), 3 => 7, pad=2), # 532 parameters
MaxPool((5, 5), pad=2),
)
julia> m[1](xs) |> size
(100, 100, 7, 50)
julia> m(xs) |> size
(20, 20, 7, 50)
julia> lay = MaxPool((5,), pad=2, stride=(3,)) # one-dimensional window
MaxPool((5,), pad=2, stride=3)
julia> lay(rand(Float32, 100, 7, 50)) |> size
(34, 7, 50)
```

`Flux.GlobalMaxPool`

— Type`GlobalMaxPool()`

Global max pooling layer.

Transforms (w,h,c,b)-shaped input into (1,1,c,b)-shaped output, by performing max pooling on the complete (w,h)-shaped feature maps.

See also `MaxPool`

, `GlobalMeanPool`

.

```
julia> xs = rand(Float32, 100, 100, 3, 50);
julia> m = Chain(Conv((3,3), 3 => 7), GlobalMaxPool());
julia> m(xs) |> size
(1, 1, 7, 50)
julia> GlobalMaxPool()(rand(3,5,7)) |> size # preserves 2 dimensions
(1, 5, 7)
```

`Flux.AdaptiveMeanPool`

— Type`AdaptiveMeanPool(out::NTuple)`

Adaptive mean pooling layer. Calculates the necessary window size such that its output has `size(y)[1:N] == out`

.

Expects as input an array with `ndims(x) == N+2`

, i.e. channel and batch dimensions, after the `N`

feature dimensions, where `N = length(out)`

.

See also `MaxPool`

, `AdaptiveMaxPool`

.

**Examples**

```
julia> xs = rand(Float32, 100, 100, 3, 50); # batch of 50 RGB images
julia> AdaptiveMeanPool((25, 25))(xs) |> size
(25, 25, 3, 50)
julia> MeanPool((4,4))(xs) ≈ AdaptiveMeanPool((25, 25))(xs)
true
```

`Flux.MeanPool`

— Type`MeanPool(window::NTuple; pad=0, stride=window)`

Mean pooling layer, averaging all pixels in a block of size `window`

.

Expects as input an array with `ndims(x) == N+2`

, i.e. channel and batch dimensions, after the `N`

feature dimensions, where `N = length(window)`

.

By default the window size is also the stride in each dimension. The keyword `pad`

accepts the same options as for the `Conv`

layer, including `SamePad()`

.

See also `Conv`

, `MaxPool`

, `AdaptiveMeanPool`

.

**Examples**

```
julia> xs = rand(Float32, 100, 100, 3, 50);
julia> m = Chain(Conv((5,5), 3 => 7), MeanPool((5,5), pad=SamePad()))
Chain(
Conv((5, 5), 3 => 7), # 532 parameters
MeanPool((5, 5), pad=2),
)
julia> m[1](xs) |> size
(96, 96, 7, 50)
julia> m(xs) |> size
(20, 20, 7, 50)
```

`Flux.GlobalMeanPool`

— Type`GlobalMeanPool()`

Global mean pooling layer.

Transforms (w,h,c,b)-shaped input into (1,1,c,b)-shaped output, by performing mean pooling on the complete (w,h)-shaped feature maps.

```
julia> xs = rand(Float32, 100, 100, 3, 50);
julia> m = Chain(Conv((3,3), 3 => 7), GlobalMeanPool());
julia> m(xs) |> size
(1, 1, 7, 50)
```

`Flux.DepthwiseConv`

— Type`DepthwiseConv(filter, in => out, σ=identity; stride=1, pad=0, dilation=1, [bias, init])`

Depthwise convolutional layer. `filter`

is a tuple of integers specifying the size of the convolutional kernel, while `in`

and `out`

specify the number of input and output channels.

Note that `out`

must be an integer multiple of `in`

.

Parameters are controlled by additional keywords, with defaults `init=glorot_uniform`

and `bias=true`

.

See also `Conv`

for more detailed description of keywords.

**Examples**

```
julia> xs = rand(Float32, 100, 100, 3, 50); # a batch of 50 RGB images
julia> lay = DepthwiseConv((5,5), 3 => 6, relu; bias=false)
DepthwiseConv((5, 5), 3 => 6, relu, bias=false) # 150 parameters
julia> lay(xs) |> size
(96, 96, 6, 50)
julia> DepthwiseConv((5,5), 3 => 9, stride=2, pad=2)(xs) |> size
(50, 50, 9, 50)
```

`Flux.ConvTranspose`

— Type`ConvTranspose(filter, in => out, σ=identity; stride=1, pad=0, dilation=1, [bias, init])`

Standard convolutional transpose layer. `filter`

is a tuple of integers specifying the size of the convolutional kernel, while `in`

and `out`

specify the number of input and output channels.

Note that `pad=SamePad()`

here tries to ensure `size(output,d) == size(x,d) * stride`

.

Parameters are controlled by additional keywords, with defaults `init=glorot_uniform`

and `bias=true`

.

See also `Conv`

for more detailed description of keywords.

**Examples**

```
julia> xs = rand(Float32, 100, 100, 3, 50); # a batch of 50 RGB images
julia> lay = ConvTranspose((5,5), 3 => 7, relu)
ConvTranspose((5, 5), 3 => 7, relu) # 532 parameters
julia> lay(xs) |> size
(104, 104, 7, 50)
julia> ConvTranspose((5,5), 3 => 7, stride=2)(xs) |> size
(203, 203, 7, 50)
julia> ConvTranspose((5,5), 3 => 7, stride=3, pad=SamePad())(xs) |> size
(300, 300, 7, 50)
```

`Flux.CrossCor`

— Type`CrossCor(filter, in => out, σ=identity; stride=1, pad=0, dilation=1, [bias, init])`

Standard cross convolutional layer. `filter`

is a tuple of integers specifying the size of the convolutional kernel; `in`

and `out`

specify the number of input and output channels.

Parameters are controlled by additional keywords, with defaults `init=glorot_uniform`

and `bias=true`

.

See also `Conv`

for more detailed description of keywords.

**Examples**

```
julia> xs = rand(Float32, 100, 100, 3, 50); # a batch of 50 RGB images
julia> lay = CrossCor((5,5), 3 => 6, relu; bias=false)
CrossCor((5, 5), 3 => 6, relu, bias=false) # 450 parameters
julia> lay(xs) |> size
(96, 96, 6, 50)
julia> CrossCor((5,5), 3 => 7, stride=3, pad=(2,0))(xs) |> size
(34, 32, 7, 50)
```

`Flux.SamePad`

— Type`SamePad()`

Passed as an option to convolutional layers (and friends), this causes the padding to be chosen such that the input and output sizes agree (on the first `N`

dimensions, the kernel or window) when `stride==1`

.

`Flux.flatten`

— Function`flatten(x::AbstractArray)`

Reshape arbitrarly-shaped input into a matrix-shaped output, preserving the size of the last dimension.

See also `unsqueeze`

.

**Examples**

```
julia> rand(3,4,5) |> Flux.flatten |> size
(12, 5)
julia> xs = rand(Float32, 10,10,3,7);
julia> m = Chain(Conv((3,3), 3=>4, pad=1), Flux.flatten, Dense(400,33));
julia> xs |> m[1] |> size
(10, 10, 4, 7)
julia> xs |> m |> size
(33, 7)
```

`Flux.convfilter`

— Function`convfilter(filter::Tuple, in => out[; init = glorot_uniform])`

Constructs a standard convolutional weight matrix with given `filter`

and channels from `in`

to `out`

.

Accepts the keyword `init`

(default: `glorot_uniform`

) to control the sampling distribution.

See also: `depthwiseconvfilter`

`Flux.depthwiseconvfilter`

— Function`depthwiseconvfilter(filter::Tuple, in => out)`

Constructs a depthwise convolutional weight array defined by `filter`

and channels from `in`

to `out`

.

Accepts the keyword `init`

(default: `glorot_uniform`

) to control the sampling distribution.

See also: `convfilter`

## Upsampling Layers

`Flux.Upsample`

— TypeUpsample(mode = :nearest; [scale, size]) Upsample(scale, mode = :nearest)

An upsampling layer. One of two keywords must be given:

If `scale`

is a number, this applies to all but the last two dimensions (channel and batch) of the input. It may also be a tuple, to control dimensions individually. Alternatively, keyword `size`

accepts a tuple, to directly specify the leading dimensions of the output.

Currently supported upsampling `mode`

s and corresponding NNlib's methods are:

`:nearest`

->`NNlib.upsample_nearest`

`:bilinear`

->`NNlib.upsample_bilinear`

**Examples**

```
julia> m = Upsample(scale = (2, 3))
Upsample(:nearest, scale = (2, 3))
julia> m(ones(2, 2, 1, 1)) |> size
(4, 6, 1, 1)
julia> m = Upsample(:bilinear, size = (4, 5))
Upsample(:bilinear, size = (4, 5))
julia> m(ones(2, 2, 1, 1)) |> size
(4, 5, 1, 1)
```

`Flux.PixelShuffle`

— Type## Recurrent Layers

Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).

`Flux.RNN`

— Function`RNN(in::Integer, out::Integer, σ = tanh)`

The most basic recurrent layer; essentially acts as a `Dense`

layer, but with the output fed back into the input each time step.

`Flux.LSTM`

— Function`LSTM(in::Integer, out::Integer)`

Long Short Term Memory recurrent layer. Behaves like an RNN but generally exhibits a longer memory span over sequences.

See this article for a good overview of the internals.

`Flux.GRU`

— Function`GRU(in::Integer, out::Integer)`

Gated Recurrent Unit layer. Behaves like an RNN but generally exhibits a longer memory span over sequences.

See this article for a good overview of the internals.

`Flux.Recur`

— Type`Recur(cell)`

`Recur`

takes a recurrent cell and makes it stateful, managing the hidden state in the background. `cell`

should be a model of the form:

`h, y = cell(h, x...)`

For example, here's a recurrent network that keeps a running total of its inputs:

```
accum(h, x) = (h + x, x)
rnn = Flux.Recur(accum, 0)
rnn(2) # 2
rnn(3) # 3
rnn.state # 5
rnn.(1:10) # apply to a sequence
rnn.state # 60
```

`Flux.reset!`

— Function`reset!(rnn)`

Reset the hidden state of a recurrent layer back to its original value.

Assuming you have a `Recur`

layer `rnn`

, this is roughly equivalent to:

`rnn.state = hidden(rnn.cell)`

## Other General Purpose Layers

These are marginally more obscure than the Basic Layers. But in contrast to the layers described in the other sections are not readily grouped around a particular purpose (e.g. CNNs or RNNs).

`Flux.Maxout`

— Type`Maxout(over)`

The Maxout layer has a number of internal layers which all receive the same input. It returns the elementwise maximum of the internal layers' outputs.

Maxout over linear dense layers satisfies the univeral approximation theorem.

`Flux.SkipConnection`

— Type`SkipConnection(layer, connection)`

Create a skip connection which consists of a layer or `Chain`

of consecutive layers and a shortcut connection linking the block's input to the output through a user-supplied 2-argument callable. The first argument to the callable will be propagated through the given `layer`

while the second is the unchanged, "skipped" input.

The simplest "ResNet"-type connection is just `SkipConnection(layer, +)`

. Here is a more complicated example:

```
julia> m = Conv((3,3), 4 => 7, pad=(1,1));
julia> x = ones(Float32, 5, 5, 4, 10);
julia> size(m(x)) == (5, 5, 7, 10)
true
julia> sm = SkipConnection(m, (mx, x) -> cat(mx, x, dims=3));
julia> size(sm(x)) == (5, 5, 11, 10)
true
```

`Flux.Parallel`

— Type`Parallel(connection, layers...)`

Create a 'Parallel' layer that passes an input array to each path in `layers`

, reducing the output with `connection`

.

Called with one input `x`

, this is equivalent to `reduce(connection, [l(x) for l in layers])`

. If called with multiple inputs, they are `zip`

ped with the layers, thus `Parallel(+, f, g)(x, y) = f(x) + g(y)`

.

**Examples**

```
julia> model = Chain(Dense(3, 5),
Parallel(vcat, Dense(5, 4), Chain(Dense(5, 7), Dense(7, 4))),
Dense(8, 17));
julia> size(model(rand(3)))
(17,)
julia> model = Parallel(+, Dense(10, 2), Dense(5, 2))
Parallel(
+,
Dense(10, 2), # 22 parameters
Dense(5, 2), # 12 parameters
) # Total: 4 arrays, 34 parameters, 392 bytes.
julia> size(model(rand(10), rand(5)))
(2,)
```

`Flux.Bilinear`

— Type```
Bilinear(in1, in2, out, σ=identity; bias=true, init=glorot_uniform)
Bilinear(W::AbstractArray, [bias, σ])
```

Creates a Bilinear layer, which operates on two inputs at the same time. Its output, given vectors `x`

& `y`

, is another vector `z`

with, for all `i ∈ 1:out`

:

`z[i] = σ(x' * W[i,:,:] * y + bias[i])`

If `x`

and `y`

are matrices, then each column of the output `z = B(x, y)`

is of this form, with `B`

a Bilinear layer.

If `y`

is not given, it is taken to be equal to `x`

, i.e. `B(x) == B(x, x)`

The two inputs may also be provided as a tuple, `B((x, y)) == B(x, y)`

, which is accepted as the input to a `Chain`

.

The initialisation works as for `Dense`

layer, with `W = init(out, in1, in2)`

. By default the bias vector is `zeros(Float32, out)`

, option `bias=false`

will switch off trainable bias. Either of these may be provided explicitly.

**Examples**

```
julia> x, y = randn(Float32, 5, 32), randn(Float32, 5, 32);
julia> B = Flux.Bilinear(5, 5, 7);
julia> B(x) |> size # interactions based on one input
(7, 32)
julia> B(x,y) == B((x,y)) # two inputs, may be given as a tuple
true
julia> sc = SkipConnection(
Chain(Dense(5, 20, tanh), Dense(20, 9, tanh)),
Flux.Bilinear(9, 5, 3, bias=false),
); # used as the recombinator, with skip as the second input
julia> sc(x) |> size
(3, 32)
julia> Flux.Bilinear(rand(4,8,16), false, tanh) # first dim of weight is the output
Bilinear(8, 16, 4, tanh, bias=false)
```

`Flux.Diagonal`

— Type```
Diagonal(α, β)
Diagonal(size::Integer...)
```

Create an element-wise linear layer, which performs

`y = α .* x .+ β`

The learnable arrays are initialised `α = ones(Float32, size)`

and `β = zeros(Float32, size)`

.

Used by `LayerNorm`

.

`Flux.Embedding`

— Type`Embedding(in, out; init=randn)`

A lookup table that stores embeddings of dimension `out`

for a vocabulary of size `in`

.

This layers is often used to store word embeddings and retrieve them using indices. The input to the layer can be either a vector of indexes or the corresponding onehot encoding.

**Examples**

```
julia> using Flux: Embedding
julia> vocab_size, embed_size = 1000, 4;
julia> model = Embedding(vocab_size, embed_size)
Embedding(1000, 4)
julia> vocab_idxs = [1, 722, 53, 220, 3]
julia> x = OneHotMatrix(vocab_idxs, vocab_size);
julia> model(x)
4×5 Matrix{Float32}:
0.91139 0.670462 0.463217 0.670462 0.110932
0.247225 -0.0823874 0.698694 -0.0823874 0.945958
-0.393626 -0.590136 -0.545422 -0.590136 0.77743
-0.497621 0.87595 -0.870251 0.87595 -0.772696
```

julia> model(vocab_idxs) == model(x) true

## Normalisation & Regularisation

These layers don't affect the structure of the network but may improve training times or reduce overfitting.

`Flux.normalise`

— Function`normalise(x; dims=ndims(x), ϵ=1e-5)`

Normalise `x`

to mean 0 and standard deviation 1 across the dimension(s) given by `dims`

. Per default, `dims`

is the last dimension. `ϵ`

is a small additive factor added to the denominator for numerical stability.

`Flux.BatchNorm`

— Type```
BatchNorm(channels::Integer, λ=identity;
initβ=zeros32, initγ=ones32,
ϵ=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-1`

th 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**

```
m = Chain(
Dense(28^2, 64),
BatchNorm(64, relu),
Dense(64, 10),
BatchNorm(10),
softmax)
```

`Flux.dropout`

— Function`dropout(x, p; dims=:, active=true)`

The dropout function. If `active`

is `true`

, for each input, either sets that input to `0`

(with probability `p`

) or scales it by `1 / (1 - p)`

. `dims`

specifies the unbroadcasted dimensions, e.g. `dims=1`

applies dropout along columns and `dims=2`

along rows. This is used as a regularisation, i.e. it reduces overfitting during training.

If `active`

is `false`

, it just returns the input `x`

.

Warning: when using this function, you have to manually manage the activation state. Usually in fact, dropout is used while training but is deactivated in the inference phase. This can be automatically managed using the `Dropout`

layer instead of the `dropout`

function.

The `Dropout`

layer is what you should use in most scenarios.

`Flux.Dropout`

— Type`Dropout(p; dims=:)`

Dropout layer. In the forward pass, apply the `Flux.dropout`

function on the input.

To apply dropout along certain dimension(s), specify the `dims`

keyword. e.g. `Dropout(p; dims = 3)`

will randomly zero out entire channels on WHCN input (also called 2D dropout).

Does nothing to the input once `Flux.testmode!`

is `true`

.

`Flux.AlphaDropout`

— Type`AlphaDropout(p)`

A dropout layer. Used in Self-Normalizing Neural Networks. The AlphaDropout layer ensures that mean and variance of activations remain the same as before.

Does nothing to the input once `testmode!`

is true.

`Flux.LayerNorm`

— Type`LayerNorm(sz, λ=identity; affine=true, ϵ=1fe-5)`

A normalisation layer designed to be used with recurrent hidden states. The argument `sz`

should be an integer or a tuple of integers. In the forward pass, the layer normalises the mean and standard deviation of the input, the applied the elementwise activation `λ`

. The input is normalised along the first `length(sz)`

dimensions for tuple `sz`

, along the first dimension for integer `sz`

. The input is expected to have first dimensions' size equal to `sz`

.

If `affine=true`

also applies a learnable shift and rescaling as in the `Diagonal`

layer.

Se also `BatchNorm`

, `InstanceNorm`

, `GroupNorm`

, and `normalise`

.

`Flux.InstanceNorm`

— Type```
InstanceNorm(channels::Integer, λ=identity;
initβ=zeros32, initγ=ones32,
affine=false, track_stats=false,
ϵ=1f-5, momentum=0.1f0)
```

Instance Normalization layer. `channels`

should be the size of the channel dimension in your data (see below).

Given an array with `N > 2`

dimensions, call the `N-1`

th the channel dimension. For `WHCN`

images it's the usual channel dimension.

`InstanceNorm`

computes the mean and variance for each `D_1×...×D_{N-2}×1×1`

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.

If `track_stats=true`

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

**Warning**: the defaults for `affine`

and `track_stats`

used to be `true`

in previous Flux versions (< v0.12).

`Flux.GroupNorm`

— Type```
GroupNorm(channels::Integer, G::Integer, λ=identity;
initβ=zeros32, initγ=ones32,
affine=true, track_stats=false,
ϵ=1f-5, momentum=0.1f0)
```

Group Normalization layer.

`chs`

is the number of channels, the channel dimension of your input. For an array of N dimensions, the `N-1`

th index is the channel dimension.

`G`

is the number of groups along which the statistics are computed. The number of channels must be an integer multiple of the number of groups.

`channels`

should be the size of the channel dimension in your data (see below).

Given an array with `N > 2`

dimensions, call the `N-1`

th the channel dimension. For `WHCN`

images it's the usual channel dimension.

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.

If `track_stats=true`

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

### Testmode

Many normalisation layers behave differently under training and inference (testing). By default, Flux will automatically determine when a layer evaluation is part of training or inference. Still, depending on your use case, it may be helpful to manually specify when these layers should be treated as being trained or not. For this, Flux provides `Flux.testmode!`

. When called on a model (e.g. a layer or chain of layers), this function will place the model into the mode specified.

`Flux.testmode!`

— Function`testmode!(m, mode = true)`

Set a layer or model's test mode (see below). Using `:auto`

mode will treat any gradient computation as training.

*Note*: if you manually set a model into test mode, you need to manually place it back into train mode during training phase.

Possible values include:

`false`

for training`true`

for testing`:auto`

or`nothing`

for Flux to detect the mode automatically

`Flux.trainmode!`

— Function`trainmode!(m, mode = true)`

Set a layer of model's train mode (see below). Symmetric to `testmode!`

(i.e. `trainmode!(m, mode) == testmode!(m, !mode)`

).

*Note*: if you manually set a model into train mode, you need to manually place it into test mode during testing phase.

Possible values include:

`true`

for training`false`

for testing`:auto`

or`nothing`

for Flux to detect the mode automatically