src.model.deeplearn.optimizer.centralized_ftrl

Classes

CentralizedFTRL([learning_rate, ...])

Follow the regularized leader (FTRL) optimizer which behaves exactly like keras.optimizers.ftrl but with centered gradients.

class src.model.deeplearn.optimizer.centralized_ftrl.CentralizedFTRL(learning_rate=0.001, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, l2_shrinkage_regularization_strength=0.0, beta=0.0, 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='ftrl', **kwargs)

Follow the regularized leader (FTRL) optimizer which behaves exactly like keras.optimizers.ftrl but with centered gradients.

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.