# Optimizers¶

The various optimizers that you can use to tune your parameters

struct SimpleSGDTrainer : public dynet::Trainer
#include <training.h>

This trainer performs stochastic gradient descent, the goto optimization procedure for neural networks. In the standard setting, the learning rate at epoch $$t$$ is $$\eta_t=\frac{\eta_0}{1+\eta_{\mathrm{decay}}t}$$

Reference : reference needed

Public Functions

SimpleSGDTrainer(ParameterCollection &m, real learning_rate = 0.1)

Constructor.

Parameters
• m: ParameterCollection to be trained
• learning_rate: Initial learning rate

struct CyclicalSGDTrainer : public dynet::Trainer
#include <training.h>

Cyclical learning rate SGD.

This trainer performs stochastic gradient descent with a cyclical learning rate as proposed in Smith, 2015.

This uses a triangular function with optional exponential decay.

More specifically, at each update, the learning rate $$\eta$$ is updated according to :

$$\begin{split} \text{cycle} &= \left\lfloor 1 + \frac{\texttt{it}}{2 \times\texttt{step_size}} \right\rfloor\\ x &= \left\vert \frac{\texttt{it}}{\texttt{step_size}} - 2 \times \text{cycle} + 1\right\vert\\ \eta &= \eta_{\text{min}} + (\eta_{\text{max}} - \eta_{\text{min}}) \times \max(0, 1 - x) \times \gamma^{\texttt{it}}\\ \end{split}$$

Public Functions

CyclicalSGDTrainer(ParameterCollection &m, float learning_rate_min = 0.01, float learning_rate_max = 0.1, float step_size = 2000, float gamma = 1.0, float edecay = 0.0)

Constructor.

Parameters
• m: ParameterCollection to be trained
• learning_rate_min: Lower learning rate
• learning_rate_max: Upper learning rate
• step_size: Period of the triangular function in number of iterations (not epochs). According to the original paper, this should be set around (2-8) x (training iterations in epoch)
• gamma: Learning rate upper bound decay parameter
• edecay: Learning rate decay parameter. Ideally you shouldn’t use this with cyclical learning rate since decay is already handled by $$\gamma$$

struct MomentumSGDTrainer : public dynet::Trainer
#include <training.h>

This is a modified version of the SGD algorithm with momentum to stablize the gradient trajectory. The modified gradient is $$\theta_{t+1}=\mu\theta_{t}+\nabla_{t+1}$$ where $$\mu$$ is the momentum.

Reference : reference needed

Public Functions

MomentumSGDTrainer(ParameterCollection &m, real learning_rate = 0.01, real mom = 0.9)

Constructor.

Parameters
• m: ParameterCollection to be trained
• learning_rate: Initial learning rate
• mom: Momentum

struct AdagradTrainer : public dynet::Trainer
#include <training.h>

The adagrad algorithm assigns a different learning rate to each parameter according to the following formula : $$\delta_\theta^{(t)}=-\frac{\eta_0}{\epsilon+\sum_{i=0}^{t-1}(\nabla_\theta^{(i)})^2}\nabla_\theta^{(t)}$$

Reference : Duchi et al., 2011

Public Functions

dynet::AdagradTrainer::AdagradTrainer(ParameterCollection & m, real learning_rate = 0.1, real eps = 1e-20)

Constructor.

Parameters
• m: ParameterCollection to be trained
• learning_rate: Initial learning rate
• eps: Bias parameter $$\epsilon$$ in the adagrad formula

struct AdadeltaTrainer : public dynet::Trainer
#include <training.h>

The AdaDelta optimizer is a variant of Adagrad where $$\frac{\eta_0}{\sqrt{\epsilon+\sum_{i=0}^{t-1}(\nabla_\theta^{(i)})^2}}$$ is replaced by $$\frac{\sqrt{\epsilon+\sum_{i=0}^{t-1}\rho^{t-i-1}(1-\rho)(\delta_\theta^{(i)})^2}}{\sqrt{\epsilon+\sum_{i=0}^{t-1}(\nabla_\theta^{(i)})^2}}$$, hence eliminating the need for an initial learning rate.

Public Functions

dynet::AdadeltaTrainer::AdadeltaTrainer(ParameterCollection & m, real eps = 1e-6, real rho = 0.95)

Constructor.

Parameters
• m: ParameterCollection to be trained
• eps: Bias parameter $$\epsilon$$ in the adagrad formula
• rho: Update parameter for the moving average of updates in the numerator

struct RMSPropTrainer : public dynet::Trainer
#include <training.h>

RMSProp optimizer.

The RMSProp optimizer is a variant of Adagrad where the squared sum of previous gradients is replaced with a moving average with parameter $$\rho$$.

Reference : reference needed

Public Functions

dynet::RMSPropTrainer::RMSPropTrainer(ParameterCollection & m, real learning_rate = 0.1, real eps = 1e-20, real rho = 0.95)

Constructor.

Parameters
• m: ParameterCollection to be trained
• learning_rate: Initial learning rate
• eps: Bias parameter $$\epsilon$$ in the adagrad formula
• rho: Update parameter for the moving average (rho = 0 is equivalent to using Adagrad)

struct AdamTrainer : public dynet::Trainer
#include <training.h>

The Adam optimizer is similar to RMSProp but uses unbiased estimates of the first and second moments of the gradient

Reference : Adam: A Method for Stochastic Optimization

Public Functions

dynet::AdamTrainer::AdamTrainer(ParameterCollection & m, float learning_rate = 0.001, float beta_1 = 0.9, float beta_2 = 0.999, float eps = 1e-8)

Constructor.

Parameters
• m: ParameterCollection to be trained
• learning_rate: Initial learning rate
• beta_1: Moving average parameter for the mean
• beta_2: Moving average parameter for the variance
• eps: Bias parameter $$\epsilon$$

struct AmsgradTrainer : public dynet::Trainer
#include <training.h>

The AMSGrad optimizer is similar to Adam which uses unbiased estimates of the first and second moments of the gradient, however AMSGrad keeps the maximum of all the second moments and uses that instead of the actual second moments

Reference : On the Convergence of Adam and Beyond

Public Functions

dynet::AmsgradTrainer::AmsgradTrainer(ParameterCollection & m, float learning_rate = 0.001, float beta_1 = 0.9, float beta_2 = 0.999, float eps = 1e-8)

Constructor.

Parameters
• m: ParameterCollection to be trained
• learning_rate: Initial learning rate
• beta_1: Moving average parameter for the mean
• beta_2: Moving average parameter for the variance
• eps: Bias parameter $$\epsilon$$

struct EGTrainer : public dynet::Trainer
#include <training.h>

Exponentiated gradient optimizer with momentum and cyclical learning rate.

FIXME

Reference : FIXME

struct Trainer
#include <training.h>

General trainer struct.

Public Functions

Trainer(ParameterCollection &m, real learning_rate)

General constructor for a Trainer.

Parameters
• m: ParameterCollection to be trained
• learning_rate: Initial learning rate

void update()

Update parameters.

Update the parameters according to the appropriate update rule

void update(const std::vector<unsigned> &updated_params, const std::vector<unsigned> &updated_lookup_params)

Update subset of parameters.

Update some but not all of the parameters included in the model. This is the update_subset() function in the Python bindings. The parameters to be updated are specified by index, which can be found for Parameter and LookupParameter objects through the “index” variable (or the get_index() function in the Python bindings).

Parameters
• updated_params: The parameter indices to be updated
• updated_lookup_params: The lookup parameter indices to be updated

virtual void restart() = 0

Restarts the optimizer.

Clears all momentum values and assimilate (if applicable). This method does not update the current hyperparameters . (for example the bias parameter of the AdadeltaTrainer is left unchanged).

void restart(real lr)

Restarts the optimizer with a new learning rate.

Clears all momentum values and assimilate (if applicable) and resets the learning rate. This method does not update the current hyperparameters . (for example the bias parameter of the AdadeltaTrainer is left unchanged).

Parameters
• learning_rate: New learning rate

void save(std::ostream &os)

Save the optimizer state.

Write all hyperparameters, momentum values and assimilate (if applicable) to stream.

Parameters
• os: Output stream

void populate(std::istream &is)

Read all hyperparameters, momentum values and assimilate (if applicable) from stream.

Parameters
• os: Input stream

void populate(std::istream &is, real lr)

Read all hyperparameters, momentum values and assimilate (if applicable) from stream.

Parameters
• os: Input stream
• lr: New learning rate

float clip_gradients()

bool sparse_updates_enabled