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Graph Convolutional Networks in TorchSharp.Fun

TorchSharp.Fun is thin functional wrapper in F# over TorchSharp (a .Net binding of PyTorch).

TorchSharp.Fun Example

Below is a simple sequential model. It is a composition over standard TorchSharp 'modules'. The composition is performed with the '->>' operator.

let model = 
    Linear(10L,5L) 
    ->> Dropout(0.5)
    ->> Linear(5L,1L) 
    ->> RelU()

GCN Model

The Graph Convolutional Network (GCN) model presented in this repo is based on the work of Thomas Kipf, Graph Convolutional Networks (2016).

It is a port of the Pytorch GCN model.

For more context see this LinkedIn article

TorchSharp.Fun

The code for TorchSharp.Fun is included in the repo. At this stage it is expected to undergo considerable churn and therefore is not released as an independent package.

Training the model

The data for the model included is however two changes to source are required to train the model. Both are in Program.fs file. These are:

  • Path to libtorch native library - download link
  • Path to the data folder

It is recommend to use Visual Studio code with F# / Ionide plug-in - just start the project after making the above changes.

Why TorchSharp.Fun?

A function-compositional approach to deep learning models arose when I could not easily create a deep ResNet model with 'standard' TorchSharp.

An alternative F# library was also tried. The library supports an elegant API; it was easy to create a deep ResNet model. Unfortunately at its current stage of development, the training performance for deep models is not on par with that of basic TorchSharp.

TorchSharp.Fun is a very thin wrapper over TorchSharp does not suffer any noticable performance hits when compared with TorchSharp (or PyTorch for that matter).

Below is an example of a 30 layer ResNet regression model:

module Resnet =
    let FTR_DIM = 310L
    let RESNET_DIM = 10L
    let RESNET_DEPTH = 30
    let act() = SELU() //SiLU()// LeakyReLU(0.05) // GELU() // GELU()
    //residual block
    let resnetCell (input: Model) =
        let cell =  
            act()
            ->> Linear(RESNET_DIM, RESNET_DIM) //weight layer 1  
            ->> Dropout(0.1)
            ->> act()
            ->> Linear(RESNET_DIM, RESNET_DIM)                
        //skip connection
        let join =
            F [input; cell] (fun ``input tensor`` -> 
                    use t1 = input.forward ``input tensor``
                    use t2 = cell.forward t1
                    t1 + t2)
        join ->> act()
    //model
    let model =
        let emb = Linear(FTR_DIM, RESNET_DIM, hasBias=false) |> M
        let rsLayers =
            (emb, [ 1 .. RESNET_DEPTH ])
            ||> List.fold (fun emb _ -> resnetCell emb) //stack blocks
        rsLayers
        ->> Linear(RESNET_DIM,10L) 
        ->> Linear(10L, 1L)