Struct TensorFlowAdam 

pub struct TensorFlowAdam { /* private fields */ }

TensorFlow-compatible Adam optimizer

Implementations

impl TensorFlowAdam

pub fn new( learning_rate: f64, beta_1: f64, beta_2: f64, epsilon: f64, weight_decay: Option<f64>, clipnorm: Option<f64>, clipvalue: Option<f64>, global_clipnorm: Option<f64>, use_ema: bool, ema_momentum: f64, jit_compile: bool, name: Option<String>, ) -> Result<Self>

Create new TensorFlow-compatible Adam optimizer

pub fn with_defaults() -> Result<Self>

Create with default parameters

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

Create TensorFlow Adam optimizer from configuration

pub fn with_schedule( schedule: Box<dyn TensorFlowLearningRateSchedule>, beta_1: f64, beta_2: f64, epsilon: f64, weight_decay: Option<f64>, clipnorm: Option<f64>, clipvalue: Option<f64>, global_clipnorm: Option<f64>, use_ema: bool, ema_momentum: f64, jit_compile: bool, name: Option<String>, ) -> Result<Self>

Create with learning rate schedule

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

Add variable to optimizer

Trait Implementations

impl TensorFlowOptimizer for TensorFlowAdam

fn apply_gradients( &mut self, grads_and_vars: &[(Tensor, String)], global_step: Option<i64>, ) -> Result<()>

Apply gradients to variables

fn minimize( &mut self, loss_fn: Box<dyn Fn() -> Result<Tensor>>, var_list: &[String], global_step: Option<i64>, ) -> Result<Tensor>

Minimize loss function

fn get_config(&self) -> TensorFlowOptimizerConfig

Get optimizer configuration

fn variables(&self) -> Vec<String>

Get optimizer variables (state)

fn get_weights(&self) -> Vec<Tensor>

Get optimizer weights

fn set_weights(&mut self, weights: Vec<Tensor>) -> Result<()>

Set optimizer weights

fn get_learning_rate(&self) -> f64

Get learning rate

fn set_learning_rate(&mut self, lr: f64) -> Result<()>

Set learning rate

fn get_name(&self) -> &str

Get optimizer name