# Basic Layers

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

Flux.ChainType
Chain(layers...)
Chain(name = layer, ...)

Collects multiple layers / functions to be called in sequence on a given input. Supports indexing and slicing, m or m[1:end-1], and if names are given, m[:name] == m etc.

Examples

julia> m = Chain(x -> x^2, x -> x+1);

julia> m(5) == 26
true

julia> m = Chain(Dense(10 => 5, tanh), Dense(5 => 2));

julia> x = rand(10, 32);

julia> m(x) == m(m(x))
true

julia> m2 = Chain(enc = Chain(Flux.flatten, Dense(10 => 5, tanh)),
dec = Dense(5 => 2));

julia> m2(x) == (m2[:dec] ∘ m2[:enc])(x)
true

For large models, there is a special type-unstable path which can reduce compilation times. This can be used by supplying a vector of layers Chain([layer1, layer2, ...]). This feature is somewhat experimental, beware!

source
Flux.DenseType
Dense(in => out, σ=identity; bias=true, init=glorot_uniform)
Dense(W::AbstractMatrix, [bias, σ])

Create a traditional fully connected 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]])
source

## Convolution and Pooling Layers

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

Flux.ConvType
Conv(filter, in => out, σ = identity;
stride = 1, pad = 0, dilation = 1, groups = 1, [bias, 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 specifies the number of elements added to the borders of the data array. It 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.
• bias - The initial bias vector is all zero by default. Trainable bias can be disabled entirely by setting this to false, or another vector can be provided such as bias = randn(Float32, out).

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)
source
Flux.ConvMethod
Conv(weight::AbstractArray, [bias, activation; stride, pad, dilation])

Constructs a convolutional layer with the given weight and bias. Accepts the same keywords and has the same defaults as Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ; ...).

julia> weight = rand(3, 4, 5);

julia> bias = zeros(5);

julia> layer = Conv(weight, bias, sigmoid)  # expects 1 spatial dimension
Conv((3,), 4 => 5, σ)  # 65 parameters

julia> layer(randn(100, 4, 64)) |> size
(98, 5, 64)

julia> Flux.params(layer) |> length
2
source
Flux.AdaptiveMaxPoolType
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).

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
source
Flux.MaxPoolType
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().

Examples

julia> xs = rand(Float32, 100, 100, 3, 50);  # batch of 50 RGB images

Chain(
Conv((5, 5), 3 => 7, pad=2),          # 532 parameters
)

julia> m(xs) |> size
(100, 100, 7, 50)

julia> m(xs) |> size
(20, 20, 7, 50)

julia> layer = MaxPool((5,), pad=2, stride=(3,))  # one-dimensional window

julia> layer(rand(Float32, 100, 7, 50)) |> size
(34, 7, 50)
source
Flux.GlobalMaxPoolType
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.

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)
source
Flux.AdaptiveMeanPoolType
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).

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
source
Flux.MeanPoolType
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().

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
)

julia> m(xs) |> size
(96, 96, 7, 50)

julia> m(xs) |> size
(20, 20, 7, 50)
source
Flux.GlobalMeanPoolType
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)
source
Flux.DepthwiseConvFunction
DepthwiseConv(filter, in => out, σ=identity; stride=1, pad=0, dilation=1, [bias, init])
DepthwiseConv(weight::AbstractArray, [bias, activation; stride, pad, dilation])

Return a depthwise convolutional layer, that is a Conv layer with number of groups equal to the number of input channels.

See Conv for a description of the arguments.

Examples

julia> xs = rand(Float32, 100, 100, 3, 50);  # a batch of 50 RGB images

julia> layer = DepthwiseConv((5,5), 3 => 6, relu; bias=false)
Conv((5, 5), 3 => 6, relu, groups=3, bias=false)  # 150 parameters

julia> layer(xs) |> size
(96, 96, 6, 50)

julia> DepthwiseConv((5, 5), 3 => 9, stride=2, pad=2)(xs) |> size
(50, 50, 9, 50)
source
Flux.ConvTransposeType
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> layer = ConvTranspose((5,5), 3 => 7, relu)
ConvTranspose((5, 5), 3 => 7, relu)  # 532 parameters

julia> layer(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)
source
Flux.ConvTransposeMethod
ConvTranspose(weight::AbstractArray, [bias, activation; stride, pad, dilation, groups])

Constructs a ConvTranspose layer with the given weight and bias. Accepts the same keywords and has the same defaults as ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ; ...).

Examples

julia> weight = rand(3, 4, 5);

julia> bias = zeros(4);

julia> layer = ConvTranspose(weight, bias, sigmoid)
ConvTranspose((3,), 5 => 4, σ)  # 64 parameters

julia> layer(randn(100, 5, 64)) |> size  # transposed convolution will increase the dimension size (upsampling)
(102, 4, 64)

julia> Flux.params(layer) |> length
2
source
Flux.CrossCorType
CrossCor(filter, in => out, σ=identity; stride=1, pad=0, dilation=1, [bias, init])

Standard cross correlation 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> layer = CrossCor((5,5), 3 => 6, relu; bias=false)
CrossCor((5, 5), 3 => 6, relu, bias=false)  # 450 parameters

julia> layer(xs) |> size
(96, 96, 6, 50)

julia> CrossCor((5,5), 3 => 7, stride=3, pad=(2,0))(xs) |> size
(34, 32, 7, 50)
source
Flux.CrossCorMethod
CrossCor(weight::AbstractArray, [bias, activation; stride, pad, dilation])

Constructs a CrossCor layer with the given weight and bias. Accepts the same keywords and has the same defaults as CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ; ...).

Examples

julia> weight = rand(3, 4, 5);

julia> bias = zeros(5);

julia> layer = CrossCor(weight, bias, relu)
CrossCor((3,), 4 => 5, relu)  # 65 parameters

julia> layer(randn(100, 4, 64)) |> size
(98, 5, 64)
source
Flux.SamePadType
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. When stride≠1, the output size equals ceil(input_size/stride).

See also Conv, MaxPool.

Examples

julia> xs = rand(Float32, 100, 100, 3, 50);  # a batch of images

julia> layer = Conv((2,2), 3 => 7, pad=SamePad())
Conv((2, 2), 3 => 7, pad=(1, 0, 1, 0))  # 91 parameters

julia> layer(xs) |> size  # notice how the dimensions stay the same with this padding
(100, 100, 7, 50)

julia> layer2 = Conv((2,2), 3 => 7)
Conv((2, 2), 3 => 7)  # 91 parameters

julia> layer2(xs) |> size  # the output dimension changes as the padding was not "same"
(99, 99, 7, 50)

julia> layer3 = Conv((5, 5), 3 => 7, stride=2, pad=SamePad())
Conv((5, 5), 3 => 7, pad=2, stride=2)  # 532 parameters

julia> layer3(xs) |> size  # output size = ceil(input_size/stride) = 50
(50, 50, 7, 50)
source
Flux.flattenFunction
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 |> size
(10, 10, 4, 7)

julia> xs |> m |> size
(33, 7)
source

## Upsampling Layers

Flux.UpsampleType
Upsample(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 modes and corresponding NNlib's methods are:

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)
source
Flux.PixelShuffleType
PixelShuffle(r::Int)

Pixel shuffling layer with upscale factor r. Usually used for generating higher resolution images while upscaling them.

Examples

julia> p = PixelShuffle(2);

julia> xs = [2row + col + channel/10 for row in 1:2, col in 1:2, channel in 1:4, n in 1:1]
2×2×4×1 Array{Float64, 4}:
[:, :, 1, 1] =
3.1  4.1
5.1  6.1

[:, :, 2, 1] =
3.2  4.2
5.2  6.2

[:, :, 3, 1] =
3.3  4.3
5.3  6.3

[:, :, 4, 1] =
3.4  4.4
5.4  6.4

julia> p(xs)
4×4×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
3.1  3.3  4.1  4.3
3.2  3.4  4.2  4.4
5.1  5.3  6.1  6.3
5.2  5.4  6.2  6.4

julia> xs = [3row + col + channel/10 for row in 1:2, col in 1:3, channel in 1:4, n in 1:1]
2×3×4×1 Array{Float64, 4}:
[:, :, 1, 1] =
4.1  5.1  6.1
7.1  8.1  9.1

[:, :, 2, 1] =
4.2  5.2  6.2
7.2  8.2  9.2

[:, :, 3, 1] =
4.3  5.3  6.3
7.3  8.3  9.3

[:, :, 4, 1] =
4.4  5.4  6.4
7.4  8.4  9.4

julia> p(xs)
4×6×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
4.1  4.3  5.1  5.3  6.1  6.3
4.2  4.4  5.2  5.4  6.2  6.4
7.1  7.3  8.1  8.3  9.1  9.3
7.2  7.4  8.2  8.4  9.2  9.4
source

## 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.RNNFunction
RNN(in => out, 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.

The arguments in and out describe the size of the feature vectors passed as input and as output. That is, it accepts a vector of length in or a batch of vectors represented as a in x B matrix and outputs a vector of length out or a batch of vectors of size out x B.

This constructor is syntactic sugar for Recur(RNNCell(a...)), and so RNNs are stateful. Note that the state shape can change depending on the inputs, and so it is good to reset! the model between inference calls if the batch size changes. See the examples below.

Examples

julia> r = RNN(3 => 5)
Recur(
RNNCell(3 => 5, tanh),                # 50 parameters
)         # Total: 4 trainable arrays, 50 parameters,
# plus 1 non-trainable, 5 parameters, summarysize 432 bytes.

julia> r(rand(Float32, 3)) |> size
(5,)

julia> Flux.reset!(r);

julia> r(rand(Float32, 3, 10)) |> size # batch size of 10
(5, 10)
Batch size changes

Failing to call reset! when the input batch size changes can lead to unexpected behavior. See the following example:

julia> r = RNN(3 => 5)
Recur(
RNNCell(3 => 5, tanh),                # 50 parameters
)         # Total: 4 trainable arrays, 50 parameters,
# plus 1 non-trainable, 5 parameters, summarysize 432 bytes.

julia> r.state |> size
(5, 1)

julia> r(rand(Float32, 3)) |> size
(5,)

julia> r.state |> size
(5, 1)

julia> r(rand(Float32, 3, 10)) |> size # batch size of 10
(5, 10)

julia> r.state |> size # state shape has changed
(5, 10)

julia> r(rand(Float32, 3)) |> size # erroneously outputs a length 5*10 = 50 vector.
(50,)

Note:

RNNCells can be constructed directly by specifying the non-linear function, the W_i and W_h internal matrices, a bias vector b, and a learnable initial state state0. The W_i and W_h matrices do not need to be the same type, but if W_h is dxd, then W_i should be of shape dxN.

julia julia> using LinearAlgebra

julia> r = Flux.Recur(Flux.RNNCell(tanh, rand(5, 4), Tridiagonal(rand(5, 5)), rand(5), rand(5, 1)))

julia> r(rand(4, 10)) |> size # batch size of 10 (5, 10) 

source
Flux.LSTMFunction
LSTM(in => out)

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

The arguments in and out describe the size of the feature vectors passed as input and as output. That is, it accepts a vector of length in or a batch of vectors represented as a in x B matrix and outputs a vector of length out or a batch of vectors of size out x B.

This constructor is syntactic sugar for Recur(LSTMCell(a...)), and so LSTMs are stateful. Note that the state shape can change depending on the inputs, and so it is good to reset! the model between inference calls if the batch size changes. See the examples below.

See this article for a good overview of the internals.

Examples

julia> l = LSTM(3 => 5)
Recur(
LSTMCell(3 => 5),                     # 190 parameters
)         # Total: 5 trainable arrays, 190 parameters,
# plus 2 non-trainable, 10 parameters, summarysize 1.062 KiB.

julia> l(rand(Float32, 3)) |> size
(5,)

julia> Flux.reset!(l);

julia> l(rand(Float32, 3, 10)) |> size # batch size of 10
(5, 10)
Batch size changes

Failing to call reset! when the input batch size changes can lead to unexpected behavior. See the example in RNN.

source
Flux.GRUFunction
GRU(in => out)

Gated Recurrent Unit layer. Behaves like an RNN but generally exhibits a longer memory span over sequences. This implements the variant proposed in v1 of the referenced paper.

The integer arguments in and out describe the size of the feature vectors passed as input and as output. That is, it accepts a vector of length in or a batch of vectors represented as a in x B matrix and outputs a vector of length out or a batch of vectors of size out x B.

This constructor is syntactic sugar for Recur(GRUCell(a...)), and so GRUs are stateful. Note that the state shape can change depending on the inputs, and so it is good to reset! the model between inference calls if the batch size changes. See the examples below.

See this article for a good overview of the internals.

Examples

julia> g = GRU(3 => 5)
Recur(
GRUCell(3 => 5),                      # 140 parameters
)         # Total: 4 trainable arrays, 140 parameters,
# plus 1 non-trainable, 5 parameters, summarysize 792 bytes.

julia> g(rand(Float32, 3)) |> size
(5,)

julia> Flux.reset!(g);

julia> g(rand(Float32, 3, 10)) |> size # batch size of 10
(5, 10)
Batch size changes

Failing to call reset! when the input batch size changes can lead to unexpected behavior. See the example in RNN.

source
Flux.GRUv3Function
GRUv3(in => out)

Gated Recurrent Unit layer. Behaves like an RNN but generally exhibits a longer memory span over sequences. This implements the variant proposed in v3 of the referenced paper.

The arguments in and out describe the size of the feature vectors passed as input and as output. That is, it accepts a vector of length in or a batch of vectors represented as a in x B matrix and outputs a vector of length out or a batch of vectors of size out x B.

This constructor is syntactic sugar for Recur(GRUv3Cell(a...)), and so GRUv3s are stateful. Note that the state shape can change depending on the inputs, and so it is good to reset! the model between inference calls if the batch size changes. See the examples below.

See this article for a good overview of the internals.

Examples

julia> g = GRUv3(3 => 5)
Recur(
GRUv3Cell(3 => 5),                    # 140 parameters
)         # Total: 5 trainable arrays, 140 parameters,
# plus 1 non-trainable, 5 parameters, summarysize 848 bytes.

julia> g(rand(Float32, 3)) |> size
(5,)

julia> Flux.reset!(g);

julia> g(rand(Float32, 3, 10)) |> size # batch size of 10
(5, 10)
Batch size changes

Failing to call reset! when the input batch size changes can lead to unexpected behavior. See the example in RNN.

source
Flux.RecurType
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:

Examples

julia> accum(h, x) = (h + x, x)
accum (generic function with 1 method)

julia> rnn = Flux.Recur(accum, 0)
Recur(accum)

julia> rnn(2)
2

julia> rnn(3)
3

julia> rnn.state
5

Folding over a 3d Array of dimensions (features, batch, time) is also supported:

julia> accum(h, x) = (h .+ x, x)
accum (generic function with 1 method)

julia> rnn = Flux.Recur(accum, zeros(Int, 1, 1))
Recur(accum)

julia> rnn()
1-element Vector{Int64}:
2

julia> rnn()
1-element Vector{Int64}:
3

julia> rnn.state
1×1 Matrix{Int64}:
5

julia> out = rnn(reshape(1:10, 1, 1, :));  # apply to a sequence of (features, batch, time)

julia> out |> size
(1, 1, 10)

julia> vec(out)
10-element Vector{Int64}:
1
2
3
4
5
6
7
8
9
10

julia> rnn.state
1×1 Matrix{Int64}:
60
source
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)

Examples

julia> r = Flux.RNNCell(relu, ones(1,1), zeros(1,1), ones(1,1), zeros(1,1));  # users should use the RNN wrapper struct instead

julia> y = Flux.Recur(r, ones(1,1));

julia> y.state
1×1 Matrix{Float64}:
1.0

julia> y(ones(1,1))  # relu(1*1 + 1)
1×1 Matrix{Float64}:
2.0

julia> y.state
1×1 Matrix{Float64}:
2.0

julia> Flux.reset!(y)
1×1 Matrix{Float64}:
0.0

julia> y.state
1×1 Matrix{Float64}:
0.0
source

## 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.MaxoutType
Maxout(layers...)
Maxout(f, n_alts)

This contains a number of internal layers, each of which receives the same input. Its output is the elementwise maximum of the the internal layers' outputs.

Instead of defining layers individually, you can provide a zero-argument function which constructs them, and the number to construct.

Maxout over linear dense layers satisfies the univeral approximation theorem. See Goodfellow, Warde-Farley, Mirza, Courville & Bengio "Maxout Networks" https://arxiv.org/abs/1302.4389.

See also Parallel to reduce with other operators.

Examples

julia> m = Maxout(x -> abs2.(x), x -> x .* 3);

julia> m([-2 -1 0 1 2])
1×5 Matrix{Int64}:
4  1  0  3  6

julia> m3 = Maxout(() -> Dense(5 => 7, tanh), 3)
Maxout(
Dense(5 => 7, tanh),                  # 42 parameters
Dense(5 => 7, tanh),                  # 42 parameters
Dense(5 => 7, tanh),                  # 42 parameters
)                   # Total: 6 arrays, 126 parameters, 888 bytes.

julia> Flux.outputsize(m3, (5, 11))
(7, 11)
source
Flux.SkipConnectionType
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

See also Parallel, Maxout.

source
Flux.ParallelType
Parallel(connection, layers...)
Parallel(connection; name = layer, ...)

Create a layer which passes an input array to each path in layers, before reducing the output with connection.

Called with one input x, this is equivalent to connection([l(x) for l in layers]...). If called with multiple inputs, one is passed to each layer, thus Parallel(+, f, g)(x, y) = f(x) + g(y).

Like Chain, its sub-layers may be given names using the keyword constructor. These can be accessed by indexing: m == m[:name] is the first layer.

See also SkipConnection which is Parallel with one identity, and Maxout which reduces by broadcasting max.

Examples

julia> model = Chain(Dense(3 => 5),
Parallel(vcat, Dense(5 => 4), Chain(Dense(5 => 7), Dense(7 => 4))),
Dense(8 => 17));

julia> model(rand(3)) |> size
(17,)

julia> model2 = Parallel(+; α = Dense(10, 2, tanh), β = Dense(5, 2))
Parallel(
+,
α = Dense(10 => 2, tanh),             # 22 parameters
β = Dense(5 => 2),                    # 12 parameters
)                   # Total: 4 arrays, 34 parameters, 392 bytes.

julia> model2(rand(10), rand(5)) |> size
(2,)

julia> model2[:α](rand(10)) |> size
(2,)

julia> model2[:β] == model2
true
source
Flux.BilinearType
Bilinear((in1, in2) => out, σ=identity; bias=true, init=glorot_uniform)
Bilinear(W::AbstractArray, [bias, σ])

Creates a layer which is fully connected between two inputs and the output, and otherwise similar to Dense. 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 the Bilinear layer.

If the second input 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.

If the two input sizes are the same, in1 == in2, then you may write Bilinear(in => out, σ).

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)
Bilinear(5 => 7)    # 182 parameters

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)  # 512 parameters
source
Flux.ScaleType
Scale(size::Integer..., σ=identity; bias=true, init=ones32)
Scale(scale::AbstractArray, [bias, σ])

Create an element-wise layer, whose forward pass is given by:

y = σ.(scale .* x .+ bias)

This uses .* instead of matrix multiplication * of Dense.

The learnable scale & bias are initialised init(size...) and zeros32(size...), with init=ones32 by default. You may specify the function init, turn off trainable bias with bias=false, or provide the array(s) explicitly.

Used by LayerNorm with affine=true.

Examples

julia> a = Flux.Scale(2)
Scale(2)            # 4 parameters

julia> Flux.params(a)
Params([Float32[1.0, 1.0], Float32[0.0, 0.0]])

julia> a([1 2 3])
2×3 Matrix{Float32}:
1.0  2.0  3.0
1.0  2.0  3.0

julia> b = Flux.Scale([1 2 3 4], false, abs2)
Scale(1, 4, abs2; bias=false)  # 4 parameters

julia> b([1, 10])
2×4 Matrix{Int64}:
1    4    9    16
100  400  900  1600

julia> Flux.params(b)
Params([[1 2 3 4]])
source
Flux.EmbeddingType
Embedding(in => out; init=randn)

A lookup table that stores embeddings of dimension out for a vocabulary of size in.

This layer 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> vocab_size, embed_size = 1000, 4;

julia> model = Flux.Embedding(vocab_size => embed_size)
Embedding(1000 => 4)  # 4_000 parameters

julia> vocab_idxs = [1, 722, 53, 220, 3];

julia> x = Flux.onehotbatch(vocab_idxs, 1:vocab_size); summary(x)
"1000×5 OneHotMatrix(::Vector{UInt32}) with eltype Bool"

julia> model(x) |> summary
"4×5 Matrix{Float32}"

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

## Normalisation & Regularisation

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

Flux.normaliseFunction
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.

Examples

julia> using Statistics

julia> x = [9, 10, 20, 60];

julia> y = Flux.normalise(x);

julia> isapprox(std(y), 1, atol=0.2) && std(y) != std(x)
true

julia> x = rand(1:100, 10, 2);

julia> y = Flux.normalise(x, dims=1);

julia> isapprox(std(y, dims=1), ones(1, 2), atol=0.2) && std(y, dims=1) != std(x, dims=1)
true
source
Flux.BatchNormType
BatchNorm(channels::Integer, λ=identity;
initβ=zeros32, initγ=ones32,
affine = true, track_stats = true,
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-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
source
Flux.DropoutType
Dropout(p; dims=:, rng = default_rng_value())

Dropout layer.

While training, for each input, this layer either sets that input to 0 (with probability p) or scales it by 1 / (1 - p). 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). This is used as a regularisation, i.e. it reduces overfitting during training.

In the forward pass, this layer applies the Flux.dropout function. See that for more details.

Specify rng to use a custom RNG instead of the default. Custom RNGs are only supported on the CPU.

Does nothing to the input once Flux.testmode! is true.

Examples

julia> m = Chain(Dense(1 => 1), Dropout(1));

julia> Flux.trainmode!(m);

julia> y = m();

julia> y == 
true

julia> m = Chain(Dense(1000 => 1000), Dropout(0.5));

julia> Flux.trainmode!(m);

julia> y = m(ones(1000));

julia> isapprox(count(==(0), y) / length(y), 0.5, atol=0.1)
true
source
Flux.dropoutFunction
dropout([rng = rng_from_array(x)], 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. If active is false, it just returns the input x.

Specify rng for custom RNGs instead of the default RNG. Note that custom RNGs are only supported on the CPU.

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.

source
Flux.AlphaDropoutType
AlphaDropout(p; rng = default_rng_value())

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.

Examples

julia> using Statistics

julia> x = randn(1000,1);

julia> m = Chain(Dense(1000 => 1000, selu), AlphaDropout(0.2));

julia> Flux.trainmode!(m);

julia> y = m(x);

julia> isapprox(std(x), std(y), atol=0.2)
true
source
Flux.LayerNormType
LayerNorm(size..., λ=identity; affine=true, ϵ=1fe-5)

A normalisation layer designed to be used with recurrent hidden states. The argument size should be an integer or a tuple of integers. In the forward pass, the layer normalises the mean and standard deviation of the input, then applies the elementwise activation λ. The input is normalised along the first length(size) dimensions for tuple size, and along the first dimension for integer size. The input is expected to have first dimensions' size equal to size.

If affine=true, it also applies a learnable shift and rescaling using the Scale layer.

Examples

julia> using Statistics

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

julia> m = LayerNorm(3);

julia> y = m(xs);

julia> isapprox(std(y, dims=1:3), ones(1, 1, 1, 2), atol=0.1) && std(y, dims=1:3) != std(xs, dims=1:3)
true
source
Flux.InstanceNormType
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-1th 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).

Examples

julia> using Statistics

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

julia> m = InstanceNorm(3);

julia> y = m(xs);

julia> isapprox(std(y, dims=1:2), ones(1, 1, 3, 2), atol=0.2) && std(y, dims=1:2) != std(xs, dims=1:2)
true
source
Flux.GroupNormType
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-1th 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-1th 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.

Examples

julia> using Statistics

julia> xs = rand(3, 3, 4, 2);  # a batch of 2 images, each having 4 channels

julia> m = GroupNorm(4, 2);

julia> y = m(xs);

julia> isapprox(std(y[:, :, 1:2, 1]), 1, atol=0.1) && std(xs[:, :, 1:2, 1]) != std(y[:, :, 1:2, 1])
true

julia> isapprox(std(y[:, :, 3:4, 2]), 1, atol=0.1) && std(xs[:, :, 3:4, 2]) != std(y[:, :, 3:4, 2])
true
source

### 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
source
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
source

## Listing All Layers

The modules command uses Functors to extract a flat list of all layers:

Flux.modulesFunction
modules(m)

Return an iterator over non-leaf objects that can be reached by recursing m over the children given by functor.

Useful for applying a function (e.g. a regularizer) over specific modules or subsets of the parameters (e.g. the weights but not the biases).

Examples

julia> m1 = Chain(Dense(28^2, 64), BatchNorm(64, relu));

julia> m2 = Chain(m1, Dense(64, 10))
Chain(
Chain(
Dense(784 => 64),                   # 50_240 parameters
BatchNorm(64, relu),                # 128 parameters, plus 128
),
Dense(64 => 10),                      # 650 parameters
)         # Total: 6 trainable arrays, 51_018 parameters,
# plus 2 non-trainable, 128 parameters, summarysize 200.312 KiB.

julia> Flux.modules(m2)
7-element Vector{Any}:
Chain(Chain(Dense(784 => 64), BatchNorm(64, relu)), Dense(64 => 10))  # 51_018 parameters, plus 128 non-trainable
(Chain(Dense(784 => 64), BatchNorm(64, relu)), Dense(64 => 10))
Chain(Dense(784 => 64), BatchNorm(64, relu))  # 50_368 parameters, plus 128 non-trainable
(Dense(784 => 64), BatchNorm(64, relu))
Dense(784 => 64)    # 50_240 parameters
BatchNorm(64, relu)  # 128 parameters, plus 128 non-trainable
Dense(64 => 10)     # 650 parameters

julia> L2(m) = sum(sum(abs2, l.weight) for l in Flux.modules(m) if l isa Dense)
L2 (generic function with 1 method)

julia> L2(m2) isa Float32
true`
source