Crate minidx_core

The core types and logic implementing the minidx crate.

Re-exports

pub use gradients::Gradients;

pub use shapes::*;

Modules

gradients

layers

Composable neural-network layers

loss

misc

optimizers

shapes

Low-level types describing data dimensionality

Structs

LoadSaveError

An error loading or saving parameters.

Traits

BackpropModule

Some sequential computation which can perform backprop on itself given trace state and the gradients of its outputs: computing parameter updates for itself.

Dtype

Represents a data type or element of an array that can have arithmatic operations applied to it. The main difference between Dtype and Unit is that bool is Unit, but not Dtype.

Float

Trait for floating-point numbers.

LoadableModule

A module who’s parameters can be loaded or saved.

Module

A unit of computation that consumes Input and produces Module::Output.

ResetParams

Something that can have its learnable parameters reset.

RevModule

A unit of computation which can do backpropagation without knowledge of any additional state.

TracedModule

Some sequential computation that consumes Input and produces Module::Output, but also produces artifacts describing the execution that can later be used during backprop.

Unit

Represents a unit type, but no arithmetic.

VisualizableUnit

Something which can have its parameters visualized.

Functions

train_batch

Does a training minibatch, updating a network based on averaged gradients from computing N input-output pairs.

train_batch_parallel

Parallel version of train_batch. More threads is not necessarily faster.

train_step

Does a training step, updating a network using a pair of inputs and outputs.

Type Aliases

Error