AttentionLayer

This package implements the attention mechanism as a Lux layer. It can then be used for closure modeling.

Install

using Pkg
Pkg.add(url="git@github.com:DEEPDIP-project/AttentionLayer.jl.git")

Usage

You are probably interested in using the attentioncnn model, which is a built-in cnn that uses the attention mechanism. Here is an example of how to use it:

• first you have to define the parameters of the model

    T = Float32 # the type of the data
    N = 16 # size of the input
    D = 2 # number of channels
    rng = Xoshiro(123) # random number generator
    r = [2, 2] # radii of the attention mechanism
    c = [4, 2] # number of features of the intermediate layers
    σ = [tanh, identity] # activation functions
    b = [true, false] # use bias
    emb_sizes = [8, 8] # size of the embeddings
    patch_sizes = [8, 5] # size of the patches in which the attention mechanism is applied
    n_heads = [2, 2] # number of heads of the attention mechanism
    use_attention = [true, true] # use the attention at this layer
    sum_attention = [false, false] # use attention in sum mode instead of concat mode (BUG)

• then you can call the model

    closure, θ, st = attentioncnn(
        T = T,
        N = N,
        D = D,
        data_ch = D,
        radii = r,
        channels = c,
        activations = σ,
        use_bias = b,
        use_attention = use_attention,
        emb_sizes = emb_sizes,
        patch_sizes = patch_sizes,
        n_heads = n_heads,
        sum_attention = sum_attention,
        rng = rng,
        use_cuda = false,
    )

Look in test/ for more examples about how to use the package.

How to Cite

If you use AttentionLayer.jl in your work, please cite using the reference given in CITATION.cff.

Contributing

If you want to make contributions of any kind, please first that a look into our contributing guide directly on GitHub or the contributing page on the website

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Contributors

SCiarella 💻 ⚠️ 🚧