coremltools.models.neural_network.update_optimizer_utils¶

Neural Network optimizer utilities.

Classes

class coremltools.models.neural_network.update_optimizer_utils.AdamParams(lr=0.01, batch=10, beta1=0.9, beta2=0.999, eps=1e-08)¶

Adam - A Method for Stochastic Optimization.

Attributes:

lr: float The learning rate that controls learning step size. Adjustable in progress, default: 0.01. batch: int The mini-batch size, number of examples used to compute single gradient step, default: 10. beta1: float Controls the exponential decay rate for the first moment estimates, default: 0.9. beta2: float Controls the exponential decay rate for the second moment estimates, default: 0.999. eps: float The epsilon, a very small number to prevent any division by zero in the implementation, default: 1e-8.

Methods

set_lr(value, min, max)	Set value for learning rate.
set_batch(value, allow_set)	Set value for batch size.
set_beta1(value, min, max)	Set value for beta1.
set_beta2(value, min, max)	Set value for beta2.
set_eps(value, min, max)	Set value for epsilon.

__init__(self, lr=0.01, batch=10, beta1=0.9, beta2=0.999, eps=1e-08)¶

x.__init__(…) initializes x; see help(type(x)) for signature

class coremltools.models.neural_network.update_optimizer_utils.SgdParams(lr=0.01, batch=10, momentum=0)¶

SGD - Stochastic Gradient Descent optimizer.

Attributes:	lr: float The learning rate that controls learning step size. Adjustable in progress, default: 0.01. batch: int The mini-batch size, number of examples used to compute single gradient step, default: 10. momentum: float The momentum factor that helps accelerate gradients vectors in the right direction, default 0.

Methods

set_lr(value, min, max)	Set value for learning rate.
set_batch(value, allow_set)	Set value for batch size.
set_momentum(value, min, max)	Set value for momentum.

__init__(self, lr=0.01, batch=10, momentum=0)¶

x.__init__(…) initializes x; see help(type(x)) for signature