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:

  • nn.GenericModule is an abstraction on parametric functions
  • nn.Optimizer is an abstraction of gradient based optimizers
  • nn.LossFunction is an abstraction of loss functions, see the companion object for the implemented losses
  • nn.SupervisedModel combines a module with a loss

  Optimizers:

  • nn.AdamW
  • nn.SGDW
  • nn.RAdam
  • nn.Yogi

  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:

  • nn.Linear implements W X + b with parameters W and b and input X
  • nn.BatchNorm, nn.LayerNorm implement batch and layer normalization
  • nn.MLP is a factory of a multilayer perceptron architecture

  Definition Classes

  lamp

•  object WeightNormLinear extends Serializable

  Definition Classes

  nn

• Bias
• WeightsG
• WeightsV

lamp.nn.WeightNormLinear

Bias