warp.optim.SGD#

class warp.optim.SGD(
params=None,
lr=0.001,
momentum=0.0,
dampening=0.0,
weight_decay=0.0,
nesterov=False,
)[source]#

Stochastic Gradient Descent (SGD) optimizer with optional momentum.

This optimizer implements gradient descent with support for momentum, Nesterov accelerated gradient, and weight decay (L2 regularization).

The interface is similar to PyTorch’s torch.optim.SGD.

Parameters:

  • params – List of warp.array objects to optimize. Can be None and set later via set_params().

  • lr – Learning rate (step size).

  • momentum – Momentum factor for accelerating SGD in relevant directions.

  • dampening – Dampening factor applied to the momentum.

  • weight_decay – Weight decay coefficient (L2 regularization).

  • nesterov – Whether to use Nesterov momentum. Requires momentum > 0 and dampening = 0.

__init__(
params=None,
lr=0.001,
momentum=0.0,
dampening=0.0,
weight_decay=0.0,
nesterov=False,
)[source]#

Methods

__init__([params, lr, momentum, dampening, ...])

reset_internal_state()

set_params(params)

step(grad)

step_detail(g, b, lr, momentum, dampening, ...)

set_params(params)[source]#
reset_internal_state()[source]#
step(grad)[source]#
static step_detail(
g,
b,
lr,
momentum,
dampening,
weight_decay,
nesterov,
t,
params,
)[source]#