亚达克斯（Adamax）

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Adamax class

keras.optimizers.Adamax(
    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
)

实现Adamax算法的优化器.

Adamax是一种基于无穷范数的Adam变体,是一种一阶梯度优化方法.由于其能够根据数据特性调整学习率的能力,它适合学习时间变量过程,例如,在动态变化的噪声条件下处理语音数据.默认参数遵循论文中提供的参数（见下面的参考文献）.

初始化:

m = 0  # 初始化初始一阶矩向量
u = 0  # 初始化指数加权无穷范数
t = 0  # 初始化时间步

参数w与梯度g的更新规则在论文第7.1节的末尾描述（见参考文献部分）:

t += 1
m = beta1 * m + (1 - beta) * g
u = max(beta2 * u, abs(g))
current_lr = learning_rate / (1 - beta1 ** t)
w = w - current_lr * m / (u + epsilon)

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: 一个浮点值或一个常量浮点张量.一阶矩估计的指数衰减率. beta_2: 一个浮点值或一个常量浮点张量.指数加权无穷范数的指数衰减率. epsilon: 一个小的常数,用于数值稳定性. 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:

• Kingma et al., 2014