Struct Adam 

pub struct Adam {
    pub lr: f32,
    pub beta1: f32,
    pub beta2: f32,
    pub eps: f32,
    pub weight_decay: f32,
    /* private fields */
}

Bias-corrected first/second moment optimizer.

Per-tensor state: two f32 buffers (m, v) of the same shape as the parameter.

Fields

lr: f32

Learning rate. Typical: 1e-3 for from-scratch CNNs, 1e-4 for transformer fine-tuning.

beta1: f32

First-moment EMA decay β₁ ∈ [0, 1). Default 0.9.

beta2: f32

Second-moment EMA decay β₂ ∈ [0, 1). Default 0.999.

eps: f32

Stability constant in the denominator. Default 1e-8.

weight_decay: f32

L2 weight decay coefficient. Folded into the gradient (the “classic Adam” rule); use crate::AdamW for decoupled decay. Default 0.0.

Implementations

impl Adam

pub fn new(lr: f32) -> Self

Construct with the given learning rate and the standard (β₁, β₂, ε) = (0.9, 0.999, 1e-8) defaults.

pub fn with_betas(self, b1: f32, b2: f32) -> Self

Override (β₁, β₂).

pub fn with_eps(self, eps: f32) -> Self

Override the denominator stability constant ε.

pub fn with_weight_decay(self, wd: f32) -> Self

Override the L2 weight-decay coefficient.

pub fn current_step(&self) -> u64

1-based iteration counter. Starts at 1 (so the first call to step() sees t=1), advances on Optimizer::end_iteration.

Trait Implementations

impl Clone for Adam

fn clone(&self) -> Adam

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Adam

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

Formats the value using the given formatter.

impl Optimizer for Adam

fn step( &mut self, name: &str, _shape: &[usize], param: &mut [f32], grad: &[f32], )

fn end_iteration(&mut self)

Advance the global step counter. Most algorithms increment per call to [step], so most implementations leave this a no-op.

fn lr_scale(&self, _name: &str) -> f32

Per-tensor multiplier on the effective learning rate. Default is 1.0 for every name. Override when wrapping this crate to support per-name LR schedules (e.g. embedding-vs-attention splits, or the Gaussian-splat attribute-typed LR setup). The CPU impls in this crate currently honor this only when the caller passes a pre-scaled lr for the relevant call — backends are encouraged to consult it inside their fused kernel.