Struct leaf::solver::SolverConfig [−] [src]

pub struct SolverConfig {
    pub name: String,
    pub network: LayerConfig,
    pub objective: LayerConfig,
    pub solver: SolverKind,
    pub minibatch_size: usize,
    pub lr_policy: LRPolicy,
    pub base_lr: f32,
    pub gamma: f32,
    pub stepsize: usize,
    pub clip_gradients: Option<f32>,
    pub weight_decay: Option<f32>,
    pub regularization_method: Option<RegularizationMethod>,
    pub momentum: f32,
}

Configuration for a Solver

Fields

name

Name of the solver.

network

The LayerConfig that is used to initialize the network.

objective

The LayerConfig that is used to initialize the objective.

solver

The Solver implementation to be used.

minibatch_size

Accumulate gradients over minibatch_size instances.

Default: 1

lr_policy

The learning rate policy to be used.

Default: Fixed

base_lr

The base learning rate.

Default: 0.01

gamma

gamma as used in the calculation of most learning rate policies.

Default: 0.1

stepsize

The stepsize used in Step and Sigmoid learning policies.

Default: 10

clip_gradients

The threshold for clipping gradients.

Gradient values will be scaled to their L2 norm of length clip_gradients if their L2 norm is larger than clip_gradients. If set to None gradients will not be clipped.

Default: None

weight_decay

The global weight decay multiplier for regularization.

Regularization can prevent overfitting.

If set to None no regularization will be performed.

regularization_method

The method of regularization to use.

There are different methods for regularization. The two most common ones are L1 regularization and L2 regularization.

See RegularizationMethod for all implemented methods.

Currently only L2 regularization is implemented. See Issue #23.

momentum

The momentum multiplier for SGD solvers.

For more information see SGD with momentum

The value should always be between 0 and 1 and dictates how much of the previous gradient update will be added to the current one.

Default: 0

Methods

impl SolverConfig

fn get_learning_rate(&self, iter: usize) -> f32

Return the learning rate for a supplied iteration.

The way the learning rate is calculated depends on the configured LRPolicy.

Used by the Solver to calculate the learning rate for the current iteration. The calculated learning rate has a different effect on training dependent on what type of Solver you are using.

Trait Implementations

impl Default for SolverConfig

fn default() -> SolverConfig

Derived Implementations

impl Clone for SolverConfig

fn clone(&self) -> SolverConfig

1.0.0fn clone_from(&mut self, source: &Self)

impl Debug for SolverConfig

fn fmt(&self, __arg_0: &mut Formatter) -> Result