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//! Provides the generics and interfaces for the specific Solvers. //! //! See [Solvers][solvers] //! [solvers]: ../solvers/index.html pub mod confusion_matrix; pub use self::confusion_matrix::ConfusionMatrix; use std::rc::Rc; use std::marker::PhantomData; use co::prelude::*; use layer::*; use layers::SequentialConfig; use solvers::*; use util::{ArcLock, LayerOps, SolverOps}; #[derive(Debug)] /// Solver that optimizes a [Layer][1] with a given objective. /// [1]: ../layer/index.html pub struct Solver<SolverB: IBackend + SolverOps<f32>, B: IBackend + LayerOps<f32>> { net: Layer<B>, objective: Layer<SolverB>, /// The implementation of the Solver pub worker: Box<ISolver<SolverB, B>>, config: SolverConfig, /// The current iteration / number of times weights have been updated iter: usize, solver_backend: PhantomData<SolverB>, } impl<SolverB: IBackend + SolverOps<f32> + 'static, B: IBackend + LayerOps<f32> + 'static> Solver<SolverB, B> { /// Create Solver from [SolverConfig][1] /// [1]: ./struct.SolverConfig.html /// /// This is the **preferred method** to create a Solver for training a neural network. pub fn from_config(net_backend: Rc<B>, obj_backend: Rc<SolverB>, config: &SolverConfig) -> Solver<SolverB, B> { let network = Layer::from_config(net_backend, &config.network); let mut worker = config.solver.with_config(obj_backend.clone(), &config); worker.init(&network); Solver { worker: worker, net: network, objective: Layer::from_config(obj_backend, &config.objective), iter: 0, config: config.clone(), solver_backend: PhantomData::<SolverB>, } } } impl<SolverB: IBackend + SolverOps<f32> + 'static, B: IBackend + LayerOps<f32> + 'static> Solver<SolverB, B>{ fn init(&mut self, backend: Rc<B>) { info!("Initializing solver from configuration"); let mut config = self.config.clone(); self.init_net(backend, &mut config); } /// Initialize the training net fn init_net(&mut self, backend: Rc<B>, param: &mut SolverConfig) { self.net = Layer::from_config(backend, ¶m.network); } /// Train the network with one minibatch pub fn train_minibatch(&mut self, mb_data: ArcLock<SharedTensor<f32>>, mb_target: ArcLock<SharedTensor<f32>>) -> ArcLock<SharedTensor<f32>> { // forward through network and classifier let network_out = self.net.forward(&[mb_data])[0].clone(); let _ = self.objective.forward(&[network_out.clone(), mb_target]); // forward through network and classifier let classifier_gradient = self.objective.backward(&[]); self.net.backward(&classifier_gradient[0 .. 1]); self.worker.compute_update(&self.config, &mut self.net, self.iter); self.net.update_weights(self.worker.backend()); self.iter += 1; network_out } /// Returns the network trained by the solver. /// /// This is the recommended method to get a usable trained network. pub fn network(&self) -> &Layer<B> { &self.net } /// Returns the network trained by the solver. /// /// This is the recommended method to get a trained network, /// if you want to alter the network. Keep in mind that altering the network /// might render the solver unusable and continuing training the network with it will yield /// unexpected results. pub fn mut_network(&mut self) -> &mut Layer<B> { &mut self.net } } /// Implementation of a specific Solver. /// /// See [Solvers][1] /// [1]: ../solvers/index.html pub trait ISolver<SolverB, B: IBackend + LayerOps<f32>> { /// Initialize the solver, setting up any network related data. fn init(&mut self, net: &Layer<B>) {} /// Update the weights of the net with part of the gradient. /// /// The [second phase of backpropagation learning][1]. /// Calculates the gradient update that should be applied to the network, /// and then applies that gradient to the network, changing its weights. /// /// [1]: https://en.wikipedia.org/wiki/Backpropagation#Phase_2:_Weight_update /// /// Used by [step][2] to optimize the network. /// /// [2]: ./struct.Solver.html#method.step fn compute_update(&mut self, param: &SolverConfig, network: &mut Layer<B>, iter: usize); /// Returns the backend used by the solver. fn backend(&self) -> &SolverB; } impl<SolverB, B: IBackend + LayerOps<f32>> ::std::fmt::Debug for ISolver<SolverB, B> { fn fmt(&self, f: &mut ::std::fmt::Formatter) -> ::std::fmt::Result { write!(f, "({})", "ILayer") } } #[derive(Debug, Clone)] /// Configuration for a Solver pub struct SolverConfig { /// Name of the solver. pub name: String, /// The [LayerConfig][1] that is used to initialize the network. /// [1]: ../layer/struct.LayerConfig.html pub network: LayerConfig, /// The [LayerConfig][1] that is used to initialize the objective. /// [1]: ../layer/struct.LayerConfig.html pub objective: LayerConfig, /// The [Solver implementation][1] to be used. /// [1]: ../solvers/index.html pub solver: SolverKind, /// Accumulate gradients over `minibatch_size` instances. /// /// Default: 1 pub minibatch_size: usize, /// The learning rate policy to be used. /// /// Default: Fixed pub lr_policy: LRPolicy, /// The base learning rate. /// /// Default: 0.01 pub base_lr: f32, /// gamma as used in the calculation of most learning rate policies. /// /// Default: 0.1 pub gamma: f32, /// The stepsize used in Step and Sigmoid learning policies. /// /// Default: 10 pub stepsize: usize, /// The threshold for clipping gradients. /// /// Gradient values will be scaled to their [L2 norm][1] of length `clip_gradients` /// if their L2 norm is larger than `clip_gradients`. /// If set to `None` gradients will not be clipped. /// /// [1]: https://en.wikipedia.org/wiki/Norm_(mathematics)#Euclidean_norm /// /// Default: None pub clip_gradients: Option<f32>, /// The global [weight decay][1] multiplier for [regularization][2]. /// [1]: http://www.alglib.net/dataanalysis/improvinggeneralization.php#header3 /// [2]: https://cs231n.github.io/neural-networks-2/#reg /// /// Regularization can prevent [overfitting][3]. /// /// If set to `None` no regularization will be performed. /// /// [3]: https://cs231n.github.io/neural-networks-2/#reg pub weight_decay: Option<f32>, /// The method of [regularization][1] to use. /// [1]: https://cs231n.github.io/neural-networks-2/#reg /// /// There are different methods for regularization. /// The two most common ones are [L1 regularization][1] and [L2 regularization][1]. /// /// See [RegularizationMethod][2] for all implemented methods. /// /// [2]: ./enum.RegularizationMethod.html /// /// Currently only L2 regularization is implemented. /// See [Issue #23](https://github.com/autumnai/leaf/issues/23). pub regularization_method: Option<RegularizationMethod>, /// The [momentum][1] multiplier for [SGD solvers][2]. /// [1]: https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Momentum /// [2]: ../solvers/sgd/index.html /// /// For more information see [SGD with momentum][3] /// [3]: ../solvers/sgd/momentum/index.html /// /// 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 pub momentum: f32, } impl Default for SolverConfig { fn default() -> SolverConfig { SolverConfig { name: "".to_owned(), network: LayerConfig::new("default", SequentialConfig::default()), objective: LayerConfig::new("default", SequentialConfig::default()), solver: SolverKind::SGD(SGDKind::Momentum), minibatch_size: 1, lr_policy: LRPolicy::Fixed, base_lr: 0.01f32, gamma: 0.1f32, stepsize: 10, clip_gradients: None, weight_decay: None, regularization_method: None, momentum: 0f32, } } } impl SolverConfig { /// Return the learning rate for a supplied iteration. /// /// The way the learning rate is calculated depends on the configured [LRPolicy][1]. /// /// [1]: ./enum.LRPolicy.html /// /// Used by the [Solver][2] 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][3] you are using. /// /// [2]: ./struct.Solver.html /// [3]: ../solvers/index.html pub fn get_learning_rate(&self, iter: usize) -> f32 { match self.lr_policy() { LRPolicy::Fixed => { self.base_lr() } LRPolicy::Step => { let current_step = self.step(iter); self.base_lr() * self.gamma().powf(current_step as f32) } // LRPolicy::Multistep => { // // TODO: the current step can be calculated on-demand // // if (this->current_step_ < this->param_.stepvalue_size() && // // this->iter_ >= this->param_.stepvalue(this->current_step_)) { // // this->current_step_++; // // LOG(INFO) << "MultiStep Status: Iteration " << // // this->iter_ << ", step = " << this->current_step_; // // } // // rate = this->param_.base_lr() * // // pow(this->param_.gamma(), this->current_step_); // unimplemented!(); // } LRPolicy::Exp => { self.base_lr() * self.gamma().powf(iter as f32) } // LRPolicy::Inv => { // // rate = this->param_.base_lr() * // // pow(Dtype(1) + this->param_.gamma() * this->iter_, // // - this->param_.power()); // unimplemented!(); // } // LRPolicy::Poly => { // // rate = this->param_.base_lr() * pow(Dtype(1.) - // // (Dtype(this->iter_) / Dtype(this->param_.max_iter())), // // this->param_.power()); // unimplemented!(); // } // LRPolicy::Sigmoid => { // // rate = this->param_.base_lr() * (Dtype(1.) / // // (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) - // // Dtype(this->param_.stepsize()))))); // unimplemented!(); // } } } /// Return current step at iteration `iter`. /// /// Small helper for learning rate calculation. fn step(&self, iter: usize) -> usize { iter / self.stepsize() } /// Return learning rate policy. fn lr_policy(&self) -> LRPolicy { self.lr_policy } /// Return the base learning rate. fn base_lr(&self) -> f32 { self.base_lr } /// Return the gamma for learning rate calculations. fn gamma(&self) -> f32 { self.gamma } /// Return the stepsize for learning rate calculations. fn stepsize(&self) -> usize { self.stepsize } } #[derive(Debug, Copy, Clone)] /// All available types of solvers. pub enum SolverKind { /// Stochastic Gradient Descent. /// See [SGDKind][1] for all available SGD solvers. /// [1]: ./enum.SGDKind.html SGD(SGDKind), } impl SolverKind { /// Create a Solver of the specified kind with the supplied SolverConfig. pub fn with_config<B: IBackend + SolverOps<f32> + 'static, NetB: IBackend + LayerOps<f32> + 'static>(&self, backend: Rc<B>, config: &SolverConfig) -> Box<ISolver<B, NetB>> { match *self { SolverKind::SGD(sgd) => { sgd.with_config(backend, config) } } } } #[derive(Debug, Copy, Clone)] /// All available types of Stochastic Gradient Descent solvers. pub enum SGDKind { /// Stochastic Gradient Descent with Momentum. See [implementation][1] /// [1] ../solvers/ Momentum, } impl SGDKind { /// Create a Solver of the specified kind with the supplied SolverConfig. pub fn with_config<B: IBackend + SolverOps<f32> + 'static, NetB: IBackend + LayerOps<f32> + 'static>(&self, backend: Rc<B>, config: &SolverConfig) -> Box<ISolver<B, NetB>> { match *self { SGDKind::Momentum => { Box::new(Momentum::<B>::new(backend)) } } } } #[derive(Debug, Copy, Clone)] /// Learning Rate Policy for a [Solver][1] /// [1]: ./struct.Solver.html /// /// The variables mentioned below are defined in the [SolverConfig][2] apart from /// iter, which is the current iteration of the solver, that is supplied as a parameter /// for the learning rate calculation. /// /// [2]: ./struct.SolverConfig.html pub enum LRPolicy { /// always return base_lr Fixed, /// learning rate decays every `step` iterations. /// return base_lr * gamma ^ (floor(iter / step)) Step, // /// similar to step but it allows non uniform steps defined by // /// stepvalue // Multistep, /// return base_lr * gamma ^ iter Exp, // /// return base_lr * (1 + gamma * iter) ^ (- power) // Inv, // /// the effective learning rate follows a polynomial decay, to be // /// zero by the max_iter. // /// return base_lr (1 - iter/max_iter) ^ (power) // Poly, // /// the effective learning rate follows a sigmod decay // /// return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize)))) // Sigmoid, } #[derive(Debug, Copy, Clone)] /// [Regularization][1] method for a [Solver][2]. /// [1]: https://cs231n.github.io/neural-networks-2/#reg /// [2]: ./struct.Solver.html pub enum RegularizationMethod { /// L2 regularization L2, }