Expand description
§Concision
Concision aims to be a complete machine learning library written in pure Rust.
Modules§
- Data
- Functional
- Graph Neural Networks (GNN)
- Kolmogorov-Arnold Networks (KAN)
- Linear Models
- Operations
- Parameters
Macros§
Structs§
Enums§
Constants§
Traits§
- Backward describes an object capable of backward propagation.
- Decrement generally describes an object capable of decrementing itself;
- Forward describes an object capable of forward propagation.
- ForwardIter describes any iterators whose elements implement Forward. This trait is typically used in deep neural networks who need to forward propagate across a number of layers.
- A
Moduledefines any object that may be used as a layer in a neural network. Config is a type that defines the configuration of the module; including any and all hyperparameters. Params is a type that defines the parameters of the module; typically references a Linear set of parameters { weights, bias } - Constructors for n-dimensional arrays with random elements.
Functions§
- Creates an n-dimensional array from an iterator of n dimensional arrays.
- creates a matrix from the given shape filled with numerical elements [0, n) spaced evenly by 1
- Round the given value to the given number of decimal places.
- Creates a larger array from an iterator of smaller arrays.
- Returns the lower triangular portion of a matrix.
- Returns the upper triangular portion of a matrix.