Struct RMSprop 

pub struct RMSprop {
    pub learning_rate: f32,
    pub alpha: f32,
    pub eps: f32,
    pub momentum: f32,
    pub centered: bool,
    pub weight_decay: f32,
    pub t: usize,
    /* private fields */
}

RMSProp optimizer for adaptive learning rate training.

Fields

learning_rate: f32

Step size multiplier

alpha: f32

Controls how quickly the cache forgets old gradients

eps: f32

Small constant to avoid division by zero

momentum: f32

Momentum

centered: bool

Centered

weight_decay: f32

Weight decay

t: usize

t

Implementations

impl RMSprop

pub fn update<'a>( &mut self, parameters: impl IntoIterator<Item = &'a mut Tensor>, gradients: impl IntoIterator<Item = Tensor>, )

update

Trait Implementations

impl Default for RMSprop

fn default() -> Self

Returns the “default value” for a type.

impl<'a> IntoIterator for &'a RMSprop

type Item = &'a Tensor

The type of the elements being iterated over.

type IntoIter = IntoIter<&'a Tensor>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl<'a> IntoIterator for &'a mut RMSprop

type Item = &'a mut Tensor

The type of the elements being iterated over.

type IntoIter = IntoIter<&'a mut Tensor>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl Module for RMSprop

fn iter<'a>(&'a self) -> impl Iterator<Item = &'a Tensor>

Iterate over all tensors immutably

fn iter_mut<'a>(&'a mut self) -> impl Iterator<Item = &'a mut Tensor>

Iterate over all tensors mutably

fn iter_tensors<'a>(&'a self) -> impl Iterator<Item = (String, &'a Tensor)>

Iterate over tensors without consuming the module

fn iter_tensors_mut<'a>( &'a mut self, ) -> impl Iterator<Item = (String, &'a mut Tensor)>

From tensors

fn set_params(&mut self, params: &mut HashMap<String, Tensor>)

Set parameters, removes them from params, skips parameters that are not found in params.

fn save(&self, path: impl AsRef<Path>) -> Result<(), ZyxError>

Save tensors or modules to a file determined by file extension. Currently only safetensors is supported format.

fn save_numpy(&self, path: impl AsRef<Path>) -> Result<(), ZyxError>

Save a single tensor to a .npy file (numpy array format). Mirrors load_numpy: little-endian, C order (Fortran order is never written). Header is padded so data starts at a 64-byte boundary, like numpy >= 1.9. Numpy files hold a single array, so saving a module with more than one tensor is an error.