Struct PyTorchAdam 

pub struct PyTorchAdam { /* private fields */ }

PyTorch-compatible Adam optimizer

Implementations

impl PyTorchAdam

pub fn new( params: Vec<PyTorchParamGroup>, lr: f64, betas: (f64, f64), eps: f64, weight_decay: f64, _amsgrad: bool, ) -> Result<Self>

Create new PyTorch-compatible Adam optimizer

pub fn from_params( params: impl IntoIterator<Item = (String, Tensor)>, ) -> Result<Self>

Create with default parameters

pub fn from_config(config: PyTorchOptimizerConfig) -> Result<Self>

Create PyTorch Adam optimizer from configuration

pub fn from_cross_framework_config( config: PyTorchOptimizerConfig, ) -> Result<Self>

Create PyTorch Adam optimizer from cross-framework configuration

pub fn register_param(&mut self, name: String, param: Tensor) -> Result<()>

Register parameter

pub fn set_grad(&mut self, name: String, grad: Tensor) -> Result<()>

Set gradient for parameter

Trait Implementations

impl Debug for PyTorchAdam

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

Formats the value using the given formatter.

impl PyTorchOptimizer for PyTorchAdam

fn param_groups(&self) -> &[PyTorchParamGroup]

Get parameter groups

fn param_groups_mut(&mut self) -> &mut [PyTorchParamGroup]

Get mutable parameter groups

fn state_dict(&self) -> PyTorchOptimizerState

Get optimizer state

fn load_state_dict(&mut self, state: PyTorchOptimizerState) -> Result<()>

Load optimizer state

fn step(&mut self, closure: Option<Box<dyn Fn() -> f64>>) -> Result<Option<f64>>

Perform optimization step

fn zero_grad(&mut self, _set_to_none: bool) -> Result<()>

Zero gradients

fn add_param_group(&mut self, param_group: PyTorchParamGroup) -> Result<()>

Add parameter group

fn defaults(&self) -> PyTorchParamGroup

Get defaults