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Adadelta Adafactor Adagrad Adam Adamax AdamW ASGD AveragedModel ChainedScheduler ConstantLR CosineAnnealingLR CosineAnnealingWarmRestarts CyclicLR ExponentialLR LambdaLR LBFGS LinearLR LRScheduler MultiplicativeLR MultiStepLR Muon NAdam OneCycleLR Optimizer PolynomialLR RAdam ReduceLROnPlateau RMSprop Rprop SequentialLR SGD SparseAdam StepLR SWALR tensorplay.optim.optimizer.register_optimizer_step_post_hook tensorplay.optim.optimizer.register_optimizer_step_pre_hook tensorplay.optim.swa_utils.get_ema_avg_fn tensorplay.optim.swa_utils.get_ema_multi_avg_fn tensorplay.optim.swa_utils.get_swa_avg_fn tensorplay.optim.swa_utils.get_swa_multi_avg_fn tensorplay.optim.swa_utils.update_bn tensorplay.optim.swap_in_optimizer_params_and_state

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Optimizer

class tensorplay.optim.optimizer.Optimizer(params, defaults)[source]

Base class for optimizers.

Parameters:

params (iterable) – an iterable of Tensor s or dict s. Specifies what Tensors should be optimized.
defaults – (dict): a dict containing default values of optimization options (used when a parameter group doesn’t specify them).

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OneCycleLR

Sets the learning rate of each parameter group according to the 1cycle learning rate policy.

PolynomialLR

Decays the learning rate of each parameter group using a polynomial function in the given total_iters.