keras.optimizers.Nadam( 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, name="nadam", **kwargs )
Optimizer that implements the Nadam algorithm.
Much like Adam is essentially RMSprop with momentum, Nadam is Adam with Nesterov momentum.
keras.optimizers.schedules.LearningRateScheduleinstance, or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to
use_ema=True. This is the momentum to use when computing the EMA of the model's weights:
new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value.
ema_overwrite_frequencysteps of iterations, we overwrite the model variable by its moving average. If None, the optimizer does not overwrite model variables in the middle of training, and you need to explicitly overwrite the variables at the end of training by calling
optimizer.finalize_variable_values()(which updates the model variables in-place). When using the built-in
fit()training loop, this happens automatically after the last epoch, and you don't need to do anything.
None. If a float, the scale factor will be multiplied the loss before computing gradients, and the inverse of the scale factor will be multiplied by the gradients before updating variables. Useful for preventing underflow during mixed precision training. Alternately,
keras.optimizers.LossScaleOptimizerwill automatically set a loss scale factor.