Packages

•  package root

  Definition Classes

  root

•  package lamp

  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 MLP

  Factory for multilayer fully connected feed forward networks

  Factory for multilayer fully connected feed forward networks

  Returned network has the following repeated structure: [linear -> batchnorm -> nonlinearity -> dropout]*

  The last block does not include the nonlinearity and the dropout.

  Definition Classes

  nn

•  object NormType

  Definition Classes

  MLP

• BatchNorm
• LayerNorm
• NoNorm

lamp.nn.MLP.NormType

NoNorm