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Additional optimizers that conform to Keras API.
Classes
class AdaBelief: Variant of the Adam optimizer.
class AdamW: Optimizer that implements the Adam algorithm with weight decay.
class AveragedOptimizerWrapper: Base class for legacy Keras optimizers.
class COCOB: Optimizer that implements COCOB Backprop Algorithm
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 efficiently.
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 so as to have a consistent variance.
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 decay.