src.model.deeplearn.optimizer.centralized_adamax

Classes

CentralizedAdamax([learning_rate, beta_1, ...])

ADAM optimizer using the infinity norm.

class src.model.deeplearn.optimizer.centralized_adamax.CentralizedAdamax(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, weight_decay=None, clipnorm=None, clipvalue=None, global_clipnorm=None, use_ema=False, ema_momentum=0.99, ema_overwrite_frequency=None, loss_scale_factor=None, gradient_accumulation_steps=None, name='adamax', **kwargs)

ADAM optimizer using the infinity norm. The optimizer behaves exactly like keras.optimizers.Adamax but its gradients are centered.

update_step(gradient, variable, learning_rate)

Modify the gradients of the backbone optimizer by centering them before applying them to fit the model’s parameters.

See CentralizedAdam.center_gradients().

Returns:

Nothing at all, but the parameters are updated with the centered gradients instead of the original ones.