tf.keras.optimizers.Adadelta( learning_rate=0.001, rho=0.95, epsilon=1e-07, 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:
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 Keras optimizers.
tf.keras.optimizers.schedules.LearningRateScheduleinstance. Defaults to 0.001. Note that
Adadeltatends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0.
Tensoror a floating point value. The decay rate.
clipvalue(float) is set, the gradient of each weight is clipped to be no higher than this value. If
clipnorm(float) is set, the gradient of each weight is individually clipped so that its norm is no higher than this value. If
global_clipnorm(float) is set the gradient of all weights is clipped so that their global norm is no higher than this value.