Struct XavierUniform

pub struct XavierUniform<T>
where
    T: SampleUniform,
{ /* private fields */ }

Uniform Xavier initializers use a uniform distribution to initialize the weights of a neural network within a given range.

Implementations

impl<T> XavierUniform<T>

where T: SampleUniform,

pub fn new(inputs: usize, outputs: usize) -> Result<Self, InitError>

where T: Float + FromPrimitive,

Trait Implementations

impl<T> Clone for XavierUniform<T>

where T: Clone + SampleUniform, <T as SampleUniform>::Sampler: Clone,

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<T> Distribution<T> for XavierUniform<T>

where T: Float + SampleUniform,

fn sample<R>(&self, rng: &mut R) -> T

where R: RngCore + ?Sized,

Generate a random value of T, using rng as the source of randomness.

fn sample_iter<R>(self, rng: R) -> Iter<Self, R, T>

where R: Rng, Self: Sized,

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> Map<Self, F, T, S>

where F: Fn(T) -> S, Self: Sized,

Map sampled values to type S

impl<T> PartialEq for XavierUniform<T>

where T: PartialEq + SampleUniform, <T as SampleUniform>::Sampler: PartialEq,

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

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

const 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<T> Copy for XavierUniform<T>

where T: Copy + SampleUniform, <T as SampleUniform>::Sampler: Copy,

impl<T> Eq for XavierUniform<T>

where T: Eq + SampleUniform, <T as SampleUniform>::Sampler: Eq,