diff --git a/latest/gpu.html b/latest/gpu.html index 4236dd9e..0de021b6 100644 --- a/latest/gpu.html +++ b/latest/gpu.html @@ -37,4 +37,14 @@ julia> m(x) Tracked 5-element CuArray{Float32,1}: -0.30535 ⋮ - -0.618002

The analogue cpu is also available for moving models and data back off of the GPU.

``` julia> x = rand(10) |> gpu 10-element CuArray{Float32,1}: 0.235164 ⋮ 0.192538

julia> x |> cpu 10-element Array{Float32,1}: 0.235164 ⋮ 0.192538 ```

+ -0.618002

The analogue cpu is also available for moving models and data back off of the GPU.

julia> x = rand(10) |> gpu
+10-element CuArray{Float32,1}:
+ 0.235164
+ ⋮
+ 0.192538
+
+julia> x |> cpu
+10-element Array{Float32,1}:
+ 0.235164
+ ⋮
+ 0.192538
diff --git a/latest/models/layers.html b/latest/models/layers.html index 44d9a7b7..9ddbfcd8 100644 --- a/latest/models/layers.html +++ b/latest/models/layers.html @@ -8,18 +8,18 @@ ga('create', 'UA-36890222-9', 'auto'); ga('send', 'pageview');
Model Reference

Basic Layers

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

Chain
 Dense
-Conv2D

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.

source
Flux.LSTM — Function.
LSTM(in::Integer, out::Integer, σ = tanh)

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.

source
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)
+Conv2D

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.

source
Flux.LSTM — Function.
LSTM(in::Integer, out::Integer, σ = tanh)

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.

source
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
source

Activation Functions

Non-linearities that go between layers of your model. Most of these functions are defined in NNlib but are available by default in Flux.

Note that, unless otherwise stated, activation functions operate on scalars. To apply them to an array you can call σ.(xs), relu.(xs) and so on.

NNlib.σ — Function.
σ(x) = 1 / (1 + exp(-x))

Classic sigmoid activation function.

source
NNlib.relu — Function.
relu(x) = max(0, x)

Rectified Linear Unit activation function.

source
NNlib.leakyrelu — Function.
leakyrelu(x) = max(0.01x, x)

Leaky Rectified Linear Unit activation function. You can also specify the coefficient explicitly, e.g. leakyrelu(x, 0.01).

source
NNlib.elu — Function.
elu(x, α = 1) =
+rnn.state # 60
source

Activation Functions

Non-linearities that go between layers of your model. Most of these functions are defined in NNlib but are available by default in Flux.

Note that, unless otherwise stated, activation functions operate on scalars. To apply them to an array you can call σ.(xs), relu.(xs) and so on.

NNlib.σ — Function.
σ(x) = 1 / (1 + exp(-x))

Classic sigmoid activation function.

source
NNlib.relu — Function.
relu(x) = max(0, x)

Rectified Linear Unit activation function.

source
NNlib.leakyrelu — Function.
leakyrelu(x) = max(0.01x, x)

Leaky Rectified Linear Unit activation function. You can also specify the coefficient explicitly, e.g. leakyrelu(x, 0.01).

source
NNlib.elu — Function.
elu(x, α = 1) =
   x > 0 ? x : α * (exp(x) - 1)

Exponential Linear Unit activation function. See Fast and Accurate Deep Network Learning by Exponential Linear Units. You can also specify the coefficient explicitly, e.g. elu(x, 1).

source
NNlib.swish — Function.
swish(x) = x * σ(x)

Self-gated actvation function. See Swish: a Self-Gated Activation Function.

source

Normalisation & Regularisation

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

Flux.testmode! — Function.
testmode!(m)
-testmode!(m, false)

Put layers like Dropout and BatchNorm into testing mode (or back to training mode with false).

source
Flux.BatchNorm — Type.
BatchNorm(dims...; λ = identity,
+testmode!(m, false)

Put layers like Dropout and BatchNorm into testing mode (or back to training mode with false).

source
Flux.BatchNorm — Type.
BatchNorm(dims...; λ = identity,
           initβ = zeros, initγ = ones, ϵ = 1e-8, momentum = .1)

Batch Normalization Layer for Dense layer.

See Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

In the example of MNIST, in order to normalize the input of other layer, put the BatchNorm layer before activation function.

m = Chain(
   Dense(28^2, 64),
   BatchNorm(64, λ = relu),
   Dense(64, 10),
   BatchNorm(10),
-  softmax)
source
Flux.Dropout — Type.
Dropout(p)

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

Does nothing to the input once in testmode!.

source
Flux.LayerNorm — Type.
LayerNorm(h::Integer)

A normalisation layer designed to be used with recurrent hidden states of size h. Normalises the mean/stddev of each input before applying a per-neuron gain/bias.

source
+ softmax)source
Flux.Dropout — Type.
Dropout(p)

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

Does nothing to the input once in testmode!.

source
Flux.LayerNorm — Type.
LayerNorm(h::Integer)

A normalisation layer designed to be used with recurrent hidden states of size h. Normalises the mean/stddev of each input before applying a per-neuron gain/bias.

source
diff --git a/latest/search_index.js b/latest/search_index.js index 6207f8c6..3cef3c6b 100644 --- a/latest/search_index.js +++ b/latest/search_index.js @@ -405,7 +405,7 @@ var documenterSearchIndex = {"docs": [ "page": "GPU Support", "title": "GPU Support", "category": "section", - "text": "Support for array operations on other hardware backends, like GPUs, is provided by external packages like CuArrays. Flux is agnostic to array types, so we simply need to move model weights and data to the GPU and Flux will handle it.For example, we can use CuArrays (with the cu converter) to run our basic example on an NVIDIA GPU.using CuArrays\n\nW = cu(rand(2, 5)) # a 2×5 CuArray\nb = cu(rand(2))\n\npredict(x) = W*x .+ b\nloss(x, y) = sum((predict(x) .- y).^2)\n\nx, y = cu(rand(5)), cu(rand(2)) # Dummy data\nloss(x, y) # ~ 3Note that we convert both the parameters (W, b) and the data set (x, y) to cuda arrays. Taking derivatives and training works exactly as before.If you define a structured model, like a Dense layer or Chain, you just need to convert the internal parameters. Flux provides mapleaves, which allows you to alter all parameters of a model at once.d = Dense(10, 5, σ)\nd = mapleaves(cu, d)\nd.W # Tracked CuArray\nd(cu(rand(10))) # CuArray output\n\nm = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)\nm = mapleaves(cu, m)\nd(cu(rand(10)))As a convenience, Flux provides the gpu function to convert models and data to the GPU if one is available. By default, it\'ll do nothing, but loading CuArrays will cause it to move data to the GPU instead.julia> using Flux, CuArrays\n\njulia> m = Dense(10,5) |> gpu\nDense(10, 5)\n\njulia> x = rand(10) |> gpu\n10-element CuArray{Float32,1}:\n 0.800225\n ⋮\n 0.511655\n\njulia> m(x)\nTracked 5-element CuArray{Float32,1}:\n -0.30535\n ⋮\n -0.618002The analogue cpu is also available for moving models and data back off of the GPU.``` julia> x = rand(10) |> gpu 10-element CuArray{Float32,1}: 0.235164 ⋮ 0.192538julia> x |> cpu 10-element Array{Float32,1}: 0.235164 ⋮ 0.192538 ```" + "text": "Support for array operations on other hardware backends, like GPUs, is provided by external packages like CuArrays. Flux is agnostic to array types, so we simply need to move model weights and data to the GPU and Flux will handle it.For example, we can use CuArrays (with the cu converter) to run our basic example on an NVIDIA GPU.using CuArrays\n\nW = cu(rand(2, 5)) # a 2×5 CuArray\nb = cu(rand(2))\n\npredict(x) = W*x .+ b\nloss(x, y) = sum((predict(x) .- y).^2)\n\nx, y = cu(rand(5)), cu(rand(2)) # Dummy data\nloss(x, y) # ~ 3Note that we convert both the parameters (W, b) and the data set (x, y) to cuda arrays. Taking derivatives and training works exactly as before.If you define a structured model, like a Dense layer or Chain, you just need to convert the internal parameters. Flux provides mapleaves, which allows you to alter all parameters of a model at once.d = Dense(10, 5, σ)\nd = mapleaves(cu, d)\nd.W # Tracked CuArray\nd(cu(rand(10))) # CuArray output\n\nm = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)\nm = mapleaves(cu, m)\nd(cu(rand(10)))As a convenience, Flux provides the gpu function to convert models and data to the GPU if one is available. By default, it\'ll do nothing, but loading CuArrays will cause it to move data to the GPU instead.julia> using Flux, CuArrays\n\njulia> m = Dense(10,5) |> gpu\nDense(10, 5)\n\njulia> x = rand(10) |> gpu\n10-element CuArray{Float32,1}:\n 0.800225\n ⋮\n 0.511655\n\njulia> m(x)\nTracked 5-element CuArray{Float32,1}:\n -0.30535\n ⋮\n -0.618002The analogue cpu is also available for moving models and data back off of the GPU.julia> x = rand(10) |> gpu\n10-element CuArray{Float32,1}:\n 0.235164\n ⋮\n 0.192538\n\njulia> x |> cpu\n10-element Array{Float32,1}:\n 0.235164\n ⋮\n 0.192538" }, { diff --git a/latest/training/optimisers.html b/latest/training/optimisers.html index 1cefb5f7..09d439d4 100644 --- a/latest/training/optimisers.html +++ b/latest/training/optimisers.html @@ -24,4 +24,4 @@ end

If we call update, the parameters W Dense(10, 5, σ), Dense(5, 2), softmax)

Instead of having to write [m[1].W, m[1].b, ...], Flux provides a params function params(m) that returns a list of all parameters in the model for you.

For the update step, there's nothing whatsoever wrong with writing the loop above – it'll work just fine – but Flux provides various optimisers that make it more convenient.

opt = SGD([W, b], 0.1) # Gradient descent with learning rate 0.1
 
-opt() # Carry out the update, modifying `W` and `b`.

An optimiser takes a parameter list and returns a function that does the same thing as update above. We can pass either opt or update to our training loop, which will then run the optimiser after every mini-batch of data.

Optimiser Reference

All optimisers return a function that, when called, will update the parameters passed to it.

Flux.Optimise.SGD — Function.
SGD(params, η = 0.1; decay = 0)

Classic gradient descent optimiser with learning rate η. For each parameter p and its gradient δp, this runs p -= η*δp.

Supports inverse decaying learning rate if the decay argument is provided.

source
Flux.Optimise.Momentum — Function.
Momentum(params, η = 0.01; ρ = 0.9, decay = 0)

SGD with learning rate η, momentum ρ and optional learning rate inverse decay.

source
Flux.Optimise.Nesterov — Function.
Nesterov(params, η = 0.01; ρ = 0.9, decay = 0)

SGD with learning rate η, Nesterov momentum ρ and optional learning rate inverse decay.

source
Flux.Optimise.ADAM — Function.
ADAM(params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)

ADAM optimiser.

source
+opt() # Carry out the update, modifying `W` and `b`.

An optimiser takes a parameter list and returns a function that does the same thing as update above. We can pass either opt or update to our training loop, which will then run the optimiser after every mini-batch of data.

Optimiser Reference

All optimisers return a function that, when called, will update the parameters passed to it.

Flux.Optimise.SGD — Function.
SGD(params, η = 0.1; decay = 0)

Classic gradient descent optimiser with learning rate η. For each parameter p and its gradient δp, this runs p -= η*δp.

Supports inverse decaying learning rate if the decay argument is provided.

source
Flux.Optimise.Momentum — Function.
Momentum(params, η = 0.01; ρ = 0.9, decay = 0)

SGD with learning rate η, momentum ρ and optional learning rate inverse decay.

source
Flux.Optimise.Nesterov — Function.
Nesterov(params, η = 0.01; ρ = 0.9, decay = 0)

SGD with learning rate η, Nesterov momentum ρ and optional learning rate inverse decay.

source
Flux.Optimise.ADAM — Function.
ADAM(params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)

ADAM optimiser.

source