Nadam
[source]
Nadam
class
tf.keras.optimizers.Nadam(
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,
jit_compile=True,
name="Nadam",
**kwargs
)
Optimizer that implements the Nadam algorithm.
Much like Adam is essentially RMSprop with momentum, Nadam is Adam with
Nesterov momentum.
Arguments
- learning_rate: A
tf.Tensor
, floating point value, a schedule that is a
tf.keras.optimizers.schedules.LearningRateSchedule
, or a callable
that takes no arguments and returns the actual value to use. The
learning rate. Defaults to 0.001.
- beta_1: A float value or a constant float tensor, or a callable
that takes no arguments and returns the actual value to use. The
exponential decay rate for the 1st moment estimates. Defaults to 0.9.
- beta_2: A float value or a constant float tensor, or a callable
that takes no arguments and returns the actual value to use. The
exponential decay rate for the 2nd moment estimates. Defaults to 0.999.
- epsilon: A small constant for numerical stability. This epsilon is
"epsilon hat" in the Kingma and Ba paper (in the formula just before
Section 2.1), not the epsilon in Algorithm 1 of the paper. 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.
- **kwargs: keyword arguments only used for backward compatibility.
Reference