Expand description
§Concision
Concision aims to be a complete machine learning library written in pure Rust.
Re-exports§
pub use concision_data as data;pub use concision_gnn as gnn;pub use concision_linear as linear;
Modules§
- Functional
- 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.
- A trait for specifying the backend of a model.
- 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 }
Functions§
- Utilitary function that returns a new n-dimensional array of dimension
shapewith the same datatype and memory order as the inputarr. - A function helper for testing that some result is ok
- Creates an n-dimensional array from an iterator of n dimensional arrays.
- Generate a random array of complex numbers with real and imaginary parts in the range [0, 1)
- 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 random array from a uniform distribution using a given key
- Creates a larger array from an iterator of smaller arrays.
- Given a shape, generate a random array using the StandardNormal distribution
- Returns the lower triangular portion of a matrix.
- Returns the upper triangular portion of a matrix.