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]( Flux.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(rand32(5, 64)) |> size
(2, 64)

julia> d(rand32(5, 6, 4, 64)) |> size  # treated as three batch dimensions
(2, 6, 4, 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]])