Defined in tensorflow/contrib/opt/__init__.py.
A module containing optimization routines.
Classes
class AGNCustomGetter: Custom_getter class is used to do:
class AGNOptimizer: Wrapper that implements the Accumulated GradientNormalization algorithm.
class AdaMaxOptimizer: Optimizer that implements the AdaMax algorithm.
class AdamGSOptimizer: Optimizer that implements the Adam algorithm.
class AdamWOptimizer: Optimizer that implements the Adam algorithm with weight decay.
class AddSignOptimizer: Optimizer that implements the AddSign update.
class DecoupledWeightDecayExtension: This class allows to extend optimizers with decoupled weight decay.
class DropStaleGradientOptimizer: Wrapper optimizer that checks and drops stale gradient.
class ElasticAverageCustomGetter: Custom_getter class is used to do:
class ElasticAverageOptimizer: Wrapper optimizer that implements the Elastic Average SGD algorithm.
class ExternalOptimizerInterface: Base class for interfaces with external optimization algorithms.
class GGTOptimizer: Optimizer that implements the GGT algorithm.
class LARSOptimizer: Layer-wise Adaptive Rate Scaling for large batch training.
class LazyAdamGSOptimizer: Variant of the Adam optimizer that handles sparse updates more efficiently.
class LazyAdamOptimizer: Variant of the Adam optimizer that handles sparse updates more efficiently.
class ModelAverageCustomGetter: Custom_getter class is used to do.
class ModelAverageOptimizer: Wrapper optimizer that implements the Model Average algorithm.
class MomentumWOptimizer: Optimizer that implements the Momentum algorithm with weight_decay.
class MovingAverageOptimizer: Optimizer that computes a moving average of the variables.
class MultitaskOptimizerWrapper: Optimizer wrapper making all-zero gradients harmless.
class NadamOptimizer: Optimizer that implements the Nadam algorithm.
class PowerSignOptimizer: Optimizer that implements the PowerSign update.
class RegAdagradOptimizer: RegAdagrad: Adagrad with updates that optionally skip updating the slots.
class ScipyOptimizerInterface: Wrapper allowing scipy.optimize.minimize to operate a tf.Session.
class ShampooOptimizer: The Shampoo Optimizer
class VariableClippingOptimizer: Wrapper optimizer that clips the norm of specified variables after update.
Functions
clip_gradients_by_global_norm(...): Clips gradients of a multitask loss by their global norm.
extend_with_decoupled_weight_decay(...): Factory function returning an optimizer class with decoupled weight decay.