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Lion class

keras.optimizers.Lion(
    learning_rate=0.001,
    beta_1=0.9,
    beta_2=0.99,
    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="lion",
    **kwargs
)

优化器实现了Lion算法.

Lion优化器是一种使用符号运算符来控制更新幅度的随机梯度下降方法,与其他依赖二阶矩的自适应优化器（如Adam）不同.这使得Lion更加节省内存,因为它只跟踪动量.根据作者（见参考文献）的说法,其性能优势在Adam之上随着批量大小的增加而增长.由于Lion的更新是通过符号运算产生的,导致范数较大,因此Lion的合适学习率通常比AdamW小3-10倍.Lion的权重衰减应反过来比AdamW大3-10倍,以保持相似的强度（lr * wd）.

参数: learning_rate: 一个浮点数,一个 keras.optimizers.schedules.LearningRateSchedule实例,或 一个不接受参数并返回实际值的调用.学习率.默认为0.001. beta_1: 一个浮点数值或一个常量浮点张量,或一个 不接受参数并返回实际值的调用.组合当前梯度和一阶矩估计的比率. 默认为0.9. beta_2: 一个浮点数值或一个常量浮点张量,或一个 不接受参数并返回实际值的调用.一阶矩估计的指数衰减率.默认为 0.99. name: String. The name to use for momentum accumulator weights created by the optimizer. weight_decay: Float. If set, weight decay is applied. clipnorm: Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value. clipvalue: Float. If set, the gradient of each weight is clipped to be no higher than this value. global_clipnorm: Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value. use_ema: Boolean, defaults to False. If True, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average. ema_momentum: Float, defaults to 0.99. Only used if 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_frequency: Int or None, defaults to None. Only used if use_ema=True. Every ema_overwrite_frequency steps 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. loss_scale_factor: Float or 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.LossScaleOptimizer will automatically set a loss scale factor. gradient_accumulation_steps: Int or None. If an int, model & optimizer variables will not be updated at every step; instead they will be updated every gradient_accumulation_steps steps, using the average value of the gradients since the last update. This is known as "gradient accumulation". This can be useful when your batch size is very small, in order to reduce gradient noise at each update step. EMA frequency will look at "accumulated" iterations value (optimizer steps // gradient_accumulation_steps). Learning rate schedules will look at "real" iterations value (optimizer steps).

参考文献:

• Chen et al., 2023
• 作者的实现