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Module: tfa.optimizers

Additional optimizers that conform to Keras API.

Classes

class AdamW: Optimizer that implements the Adam algorithm with weight decay.

class AveragedOptimizerWrapper: Base class for Keras optimizers.

class ConditionalGradient: Optimizer that implements the Conditional Gradient optimization.

class CyclicalLearningRate: A LearningRateSchedule that uses cyclical schedule.

class DecoupledWeightDecayExtension: This class allows to extend optimizers with decoupled weight decay.

class ExponentialCyclicalLearningRate: A LearningRateSchedule that uses cyclical schedule.

class LAMB: Optimizer that implements the Layer-wise Adaptive Moments (LAMB).

class LazyAdam: Variant of the Adam optimizer that handles sparse updates more

class Lookahead: This class allows to extend optimizers with the lookahead mechanism.

class MovingAverage: Optimizer that computes a moving average of the variables.

class MultiOptimizer: Multi Optimizer Wrapper for Discriminative Layer Training.

class NovoGrad: Optimizer that implements NovoGrad.

class ProximalAdagrad: Optimizer that implements the Proximal Adagrad algorithm.

class RectifiedAdam: Variant of the Adam optimizer whose adaptive learning rate is rectified

class SGDW: Optimizer that implements the Momentum algorithm with weight_decay.

class SWA: This class extends optimizers with Stochastic Weight Averaging (SWA).

class Triangular2CyclicalLearningRate: A LearningRateSchedule that uses cyclical schedule.

class TriangularCyclicalLearningRate: A LearningRateSchedule that uses cyclical schedule.

class Yogi: Optimizer that implements the Yogi algorithm in Keras.

Functions

extend_with_decoupled_weight_decay(...): Factory function returning an optimizer class with decoupled weight