graphvite.optimizer

Optimizer module of GraphVite

class graphvite.optimizer.Optimizer(type=auto, *args, **kwargs)

Create an optimizer instance of any type.

Parameters

type (str or auto) – optimizer type, can be ‘SGD’, ‘Momentum’, ‘AdaGrad’, ‘RMSprop’ or ‘Adam’

class graphvite.optimizer.LRSchedule(*args, **kwargs)

Learning Rate Schedule.

This class has 2 constructors

LRSchedule(schedule='constant')

LRSchedule(schedule_function)

Parameters

• schedule (str, optional) – ‘constant’ or ‘linear’

• schedule_function (callable) – function that returns a multiplicative factor, given batch id and total number of batches

class graphvite.optimizer.SGD(lr=1e-4, weight_decay=0, schedule='linear')

Stochastic gradient descent optimizer.

Parameters

• lr (float, optional) – initial learning rate

• weight_decay (float, optional) – weight decay (L2 regularization)

• schedule (str or callable, optional) – learning rate schedule

class graphvite.optimizer.Momentum(lr=1e-4, weight_decay=0, momentum=0.999, schedule='linear')

Momentum optimizer.

Parameters

• lr (float, optional) – initial learning rate

• weight_decay (float, optional) – weight decay (L2 regularization)

• momentum (float, optional) – momentum coefficient

• schedule (str or callable, optional) – learning rate schedule

class graphvite.optimizer.AdaGrad(lr=1e-4, weight_decay=0, epsilon=1e-10, schedule='linear')

AdaGrad optimizer.

Parameters

• lr (float, optional) – initial learning rate

• weight_decay (float, optional) – weight decay (L2 regularization)

• epsilon (float, optional) – smooth term for numerical stability

• schedule (str or callable, optional) – learning rate schedule

class graphvite.optimizer.RMSprop(lr=1e-4, weight_decay=0, alpha=0.999, epsilon=1e-8, schedule='linear')

RMSprop optimizer.

Parameters

• lr (float, optional) – initial learning rate

• weight_decay (float, optional) – weight decay (L2 regularization)

• alpha (float, optional) – coefficient for moving average of squared gradient

• epsilon (float, optional) – smooth term for numerical stability

• schedule (str or callable, optional) – learning rate schedule

class graphvite.optimizer.Adam(lr=1e-4, weight_decay=0, beta1=0.999, beta2=0.99999, epsilon=1e-8, schedule='linear')

Adam optimizer.

Parameters

• lr (float, optional) – initial learning rate

• weight_decay (float, optional) – weight decay (L2 regularization)

• beta1 (float, optional) – coefficient for moving average of gradient

• beta2 (float, optional) – coefficient for moving average of squared gradient

• epsilon (float, optional) – smooth term for numerical stability

• schedule (str or callable, optional) – learning rate schedule