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.
m = Chain(x -> x^2, x -> x+1)
m(5) == 26
m = Chain(Dense(10, 5), Dense(5, 2))
x = rand(10)
m(x) == m[2](m[1](x))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.
Flux.Dense — Type.Dense(in::Integer, out::Integer, σ = identity)Creates a traditional Dense layer with parameters W and b.
y = σ.(W * x .+ b)The input x must be a vector of length in, or a batch of vectors represented as an in × N matrix. The out y will be a vector or batch of length out.
julia> d = Dense(5, 2)
Dense(5, 2)
julia> d(rand(5))
Tracked 2-element Array{Float64,1}:
0.00257447
-0.00449443Recurrent Cells
Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).
RNN
LSTM
RecurActivation 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.
NNlib.relu — Function.relu(x) = max(0, x)Rectified Linear Unit activation function.
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).
NNlib.elu — Function.elu(x; α = 1) = x > 0 ? x : α * (exp(x) - one(x)Exponential Linear Unit activation function. See Fast and Accurate Deep Network Learning by Exponential Linear Units
NNlib.swish — Function.