Adadelta

[source]

Adadelta class

tf_keras.optimizers.Adadelta(
    learning_rate=0.001,
    rho=0.95,
    epsilon=1e-07,
    weight_decay=None,
    clipnorm=None,
    clipvalue=None,
    global_clipnorm=None,
    use_ema=False,
    ema_momentum=0.99,
    ema_overwrite_frequency=None,
    jit_compile=True,
    name="Adadelta",
    **kwargs
)

Optimizer that implements the Adadelta algorithm.

Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks:

• The continual decay of learning rates throughout training.
• The need for a manually selected global learning rate.

Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This way, Adadelta continues learning even when many updates have been done. Compared to Adagrad, in the original version of Adadelta you don't have to set an initial learning rate. In this version, the initial learning rate can be set, as in most other TF-Keras optimizers.

Arguments

• learning_rate: Initial value for the learning rate: either a floating point value, or a tf.keras.optimizers.schedules.LearningRateSchedule instance. Defaults to 0.001. Note that Adadelta tends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0. rho: A Tensor or a floating point value. The decay rate. Defaults to 0.95. epsilon: Small floating point value used to maintain numerical stability. Defaults to 1e-7.
• name: String. The name to use for momentum accumulator weights created by the optimizer.
• weight_decay: Float, defaults to None. 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.
• jit_compile: Boolean, defaults to True. If True, the optimizer will use XLA compilation. If no GPU device is found, this flag will be ignored.
• mesh: optional tf.experimental.dtensor.Mesh instance. When provided, the optimizer will be run in DTensor mode, e.g. state tracking variable will be a DVariable, and aggregation/reduction will happen in the global DTensor context.
• **kwargs: keyword arguments only used for backward compatibility.

Reference

• Zeiler, 2012