Base Optimizer API These methods and attributes are common to all Keras optimizers. [source] Optimizer class keras.optimizers.Optimizer()

learning_rate: A float, a keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use.

learning_rate: A float, a keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use.

Optimizers SGD RMSprop Adam AdamW Adadelta Adagrad Adamax Adafactor Nadam Ftrl [source] apply_gradients method Optimizer.apply_gradients( grads_and_vars, name=None, …

learning_rate: A float, a keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use.

learning_rate: A float, keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use. The learning rate.

learning_rate: A float, a keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use.

learning_rate: A float, a keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use.

The learning rate schedule is also serializable and deserializable using keras.optimizers.schedules.serialize and keras.optimizers.schedules.deserialize. Arguments

The learning rate schedule base class. You can use a learning rate schedule to modulate how the learning rate of your optimizer changes over time. Several built-in learning rate schedules are …