Struct NNGradient

pub struct NNGradient {
    pub derivatives: NeuralNet,
}

A Gradient representation

Fields

derivatives: NeuralNet

Implementations

impl NNGradient

pub fn from_nn_shape(neuralnet: NeuralNet) -> NNGradient

pub fn norm(&self) -> f64

pub fn set_length(&mut self, length: f64)

pub fn as_vec(&self) -> Matrix<f64>

pub fn from_vec(grad: Matrix<f64>, shape: Vec<usize>) -> NNGradient

Trait Implementations

impl AddAssign for NNGradient

fn add_assign(&mut self, rhs: Self)

Performs the += operation.

impl Clone for NNGradient

fn clone(&self) -> NNGradient

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for NNGradient

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl GradientUpdateSchedule<NNGradient> for AdaGrad

fn next_gradient(&mut self, gradient: &mut NNGradient)

impl GradientUpdateSchedule<NNGradient> for FixedLR

fn next_gradient(&mut self, gradient: &mut NNGradient)

Normalizes the nn gradient to have a fixed length

impl PartialEq for NNGradient

fn eq(&self, other: &Self) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl SubAssign<NNGradient> for NeuralNet

fn sub_assign(&mut self, rhs: NNGradient)

Performs the -= operation.