Packages

  • package root
    Definition Classes
    root
  • package lamp

    Lamp provides utilities to build state of the art machine learning applications

    Lamp provides utilities to build state of the art machine learning applications

    Overview

    Notable types and packages:

    • lamp.STen is a memory managed wrapper around aten.ATen, an off the heap, native n-dimensionl array backed by libtorch.
    • lamp.autograd implements reverse mode automatic differentiation.
    • lamp.nn contains neural network building blocks, see e.g. lamp.nn.Linear.
    • lamp.data.IOLoops implements a training loop and other data related abstractions.
    • lamp.knn implements k-nearest neighbor search on the CPU and GPU
    • lamp.umap.Umap implements the UMAP dimension reduction algorithm
    • lamp.onnx implements serialization of computation graphs into ONNX format
    • lamp.io contains CSV and NPY readers
    How to get data into lamp

    Use one of the file readers in lamp.io or one of the factories in lamp.STen$.

    How to define a custom neural network layer

    See the documentation on lamp.nn.GenericModule

    How to compose neural network layers

    See the documentation on lamp.nn

    How to train models

    See the training loops in lamp.data.IOLoops

    Definition Classes
    root
  • package nn

    Provides building blocks for neural networks

    Provides building blocks for neural networks

    Notable types:

    Optimizers:

    Modules facilitating composing other modules:

    • nn.Sequential composes a homogenous list of modules (analogous to List)
    • nn.sequence composes a heterogeneous list of modules (analogous to tuples)
    • nn.EitherModule composes two modules in a scala.Either

    Examples of neural network building blocks, layers etc:

    Definition Classes
    lamp
  • package graph
    Definition Classes
    nn
  • GCN
  • Graph
  • GraphAttention
  • MPNN
  • VertexPooling

object GraphAttention extends Serializable

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  4. def apply[S](nodeDim: Int, edgeDim: Int, attentionKeyHiddenDimPerHead: Int, attentionNumHeads: Int, valueDimPerHead: Int, dropout: Double, tOpt: STenOptions, dotProductAttention: Boolean, nonLinearity: Boolean)(implicit arg0: Sc[S]): GraphAttention
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  12. implicit val load: Load[GraphAttention]
  13. def multiheadGraphAttention[S](nodeFeatures: Variable, edgeFeatures: Variable, edgeI: STen, edgeJ: STen, wNodeKey1: Variable, wNodeKey2: Variable, wEdgeKey: Variable, wNodeValue: Variable, wAttention: Option[Variable], numHeads: Int)(implicit arg0: Sc[S]): Variable

    Graph Attention Network https://arxiv.org/pdf/1710.10903.pdf Non-linearity in eq 4 and dropout is not applied to the final vertex activations

    Graph Attention Network https://arxiv.org/pdf/1710.10903.pdf Non-linearity in eq 4 and dropout is not applied to the final vertex activations

    Needs self edges to be already present in the graph

    returns

    next node representation (without relu, dropout) and a tensor with the original node and edge features ligned up like [N_i, N_j, E_ij]

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  19. implicit val tr: TrainingMode[GraphAttention]
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