Struct SGD 

pub struct SGD<T> {
    pub learning_rate: T,
}

Stochastic Gradient Descent optimizer

Updates weights: w = w - learning_rate * gradient

Fields

learning_rate: T

Implementations

impl<T: Clone> SGD<T>

pub fn new(learning_rate: T) -> Self

pub fn step(&self, weights: &mut Tensor<T>, gradients: &Tensor<T>)

where T: Mul<Output = T> + Sub<Output = T> + Clone,

Update parameters in place

weights = weights - learning_rate * gradients