► Keras 3 API 文档 / 优化器 / RMSprop

RMSprop

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RMSprop class

keras.optimizers.RMSprop(
    learning_rate=0.001,
    rho=0.9,
    momentum=0.0,
    epsilon=1e-07,
    centered=False,
    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="rmsprop",
    **kwargs
)

实现RMSprop算法的优化器.

RMSprop的核心思想是:

  • 维护梯度平方的移动（折扣）平均值
  • 将梯度除以该平均值的平方根

此实现使用普通动量,而不是Nesterov动量.

中心化版本还维护梯度的移动平均值,并使用该平均值来估计方差.

参数: learning_rate: 一个浮点数,一个 keras.optimizers.schedules.LearningRateSchedule实例,或 一个不带参数并返回实际使用值的调用.学习率.默认为0.001. rho: 浮点数,默认为0.9.旧梯度的折扣因子. momentum: 浮点数,默认为0.0.如果不是0.0,优化器跟踪动量值,衰减速率等于1 - momentum. epsilon: 用于数值稳定的小常数.这是Kingma和Ba论文中（在公式2.1之前）的"epsilon hat”,不是论文中算法1的epsilon.默认为1e-7. centered: 布尔值.如果为True,梯度通过估计的梯度方差归一化;如果为False,通过未中心化的二阶矩归一化.设置为True可能有助于训练,但在计算和内存方面稍微昂贵.默认为False. 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).

示例:

>>> opt = keras.optimizers.RMSprop(learning_rate=0.1)
>>> var1 = keras.backend.Variable(10.0)
>>> loss = lambda: (var1 ** 2) / 2.0  # d(loss) / d(var1) = var1
>>> opt.minimize(loss, [var1])
>>> var1
9.683772

参考:

RMSprop
RMSprop class