Packages

package root

Definition Classes: root

package lamp

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 autograd

Implements reverse mode automatic differentiaton

The main types in this package are lamp.autograd.Variable and lamp.autograd.Op. The computational graph built by this package consists of vertices representing values (as lamp.autograd.Variable) and vertices representing operations (as lamp.autograd.Op).

Variables contain the value of a Rⁿ => R^m function. Variables may also contain the partial derivative of their argument with respect to a single scalar. A Variable whose value is a scalar (m=1) can trigger the computation of partial derivatives of all the intermediate upstream Variables. Computing partial derivatives with respect to non-scalar variables is not supported.

A constant Variable may be created with the const or param factory method in this package. const may be used for constants which do not need their partial derivatives to be computed. param on the other hand create Variables which will fill in their partial derivatives. Further variables may be created by the methods in this class, eventually expressing more complex Rⁿ => R^m functions.

Example

lamp.Scope.root{ implicit scope =>
  // x is constant (depends on no other variables) and won't compute a partial derivative
  val x = lamp.autograd.const(STen.eye(3, STenOptions.d))
  // y is constant but will compute a partial derivative
  val y = lamp.autograd.param(STen.ones(List(3,3), STenOptions.d))

  // z is a Variable with x and y dependencies
  val z = x+y

  // w is a Variable with z as a direct and x, y as transient dependencies
  val w = z.sum
  // w is a scalar (number of elements is 1), thus we can call backprop() on it.
  // calling backprop will fill out the partial derivatives of the upstream variables
  w.backprop()

  // partialDerivative is empty since we created `x` with `const`
  assert(x.partialDerivative.isEmpty)

  // `y`'s partial derivatie is defined and is computed
  // it holds `y`'s partial derivative with respect to `w`, the scalar which we called backprop() on
  assert(y.partialDerivative.isDefined)

}

This package may be used to compute the derivative of any function, provided the function can be composed out of the provided methods. A particular use case is gradient based optimization.

Packages

Overview

How to get data into lamp

How to define a custom neural network layer

How to compose neural network layers

How to train models

Example

MaxPool2D

case class MaxPool2D(scope: Scope, input: Variable, kernelSize: Long, stride: Long, padding: Long, dilation: Long) extends Op with Product with Serializable

Instance Constructors

Value Members

Deprecated Value Members

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Op

Inherited from AnyRef

Inherited from Any

Ungrouped

MaxPool2D