Adadelta
classtf.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:
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.
Arguments
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.Tensor
or a floating point value. The decay rate. Defaults to
0.95.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
.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.Reference