160 lines
5.3 KiB
Python
160 lines
5.3 KiB
Python
from keras_core import backend
|
|
from keras_core import operations as ops
|
|
from keras_core.api_export import keras_core_export
|
|
from keras_core.optimizers import optimizer
|
|
|
|
|
|
@keras_core_export(["keras_core.optimizers.Nadam"])
|
|
class Nadam(optimizer.Optimizer):
|
|
"""Optimizer that implements the Nadam algorithm.
|
|
|
|
Much like Adam is essentially RMSprop with momentum, Nadam is Adam with
|
|
Nesterov momentum.
|
|
|
|
Args:
|
|
learning_rate: A float, a
|
|
`keras_core.optimizers.schedules.LearningRateSchedule` instance, 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`.
|
|
{{base_optimizer_keyword_args}}
|
|
|
|
Reference:
|
|
|
|
- [Dozat, 2015](http://cs229.stanford.edu/proj2015/054_report.pdf).
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
learning_rate=0.001,
|
|
beta_1=0.9,
|
|
beta_2=0.999,
|
|
epsilon=1e-7,
|
|
weight_decay=None,
|
|
clipnorm=None,
|
|
clipvalue=None,
|
|
global_clipnorm=None,
|
|
use_ema=False,
|
|
ema_momentum=0.99,
|
|
ema_overwrite_frequency=None,
|
|
name="nadam",
|
|
):
|
|
super().__init__(
|
|
learning_rate=learning_rate,
|
|
name=name,
|
|
weight_decay=weight_decay,
|
|
clipnorm=clipnorm,
|
|
clipvalue=clipvalue,
|
|
global_clipnorm=global_clipnorm,
|
|
use_ema=use_ema,
|
|
ema_momentum=ema_momentum,
|
|
ema_overwrite_frequency=ema_overwrite_frequency,
|
|
)
|
|
self.beta_1 = beta_1
|
|
self.beta_2 = beta_2
|
|
self.epsilon = epsilon
|
|
|
|
def build(self, var_list):
|
|
"""Initialize optimizer variables.
|
|
|
|
Nadam optimizer has 2 types of variables: momentums and velocities.
|
|
|
|
Args:
|
|
var_list: list of model variables to build Nadam variables on.
|
|
"""
|
|
if self.built:
|
|
return
|
|
super().build(var_list)
|
|
self._momentums = []
|
|
self._velocities = []
|
|
self._u_product = backend.Variable(1.0, dtype=var_list[0].dtype)
|
|
# Keep a counter on how many times of _u_product has been computed to
|
|
# avoid duplicated computations.
|
|
self._u_product_counter = 1
|
|
|
|
for var in var_list:
|
|
self._momentums.append(
|
|
self.add_variable_from_reference(
|
|
reference_variable=var, name="m"
|
|
)
|
|
)
|
|
self._velocities.append(
|
|
self.add_variable_from_reference(
|
|
reference_variable=var, name="v"
|
|
)
|
|
)
|
|
|
|
def update_step(self, gradient, variable, learning_rate):
|
|
"""Update step given gradient and the associated model variable."""
|
|
var_dtype = variable.dtype
|
|
lr = ops.cast(learning_rate, var_dtype)
|
|
gradient = ops.cast(gradient, var_dtype)
|
|
|
|
local_step = ops.cast(self.iterations + 1, var_dtype)
|
|
next_step = ops.cast(self.iterations + 2, var_dtype)
|
|
decay = ops.cast(0.96, var_dtype)
|
|
beta_1 = ops.cast(self.beta_1, var_dtype)
|
|
beta_2 = ops.cast(self.beta_2, var_dtype)
|
|
u_t = beta_1 * (1.0 - 0.5 * (ops.power(decay, local_step)))
|
|
u_t_1 = beta_1 * (1.0 - 0.5 * (ops.power(decay, next_step)))
|
|
|
|
def get_cached_u_product():
|
|
return self._u_product
|
|
|
|
def compute_new_u_product():
|
|
u_product_t = self._u_product * u_t
|
|
self._u_product.assign(u_product_t)
|
|
self._u_product_counter += 1
|
|
return u_product_t
|
|
|
|
if self._u_product_counter == (self.iterations + 2):
|
|
u_product_t = get_cached_u_product()
|
|
else:
|
|
u_product_t = compute_new_u_product()
|
|
|
|
u_product_t_1 = u_product_t * u_t_1
|
|
beta_2_power = ops.power(beta_2, local_step)
|
|
|
|
m = self._momentums[self._get_variable_index(variable)]
|
|
v = self._velocities[self._get_variable_index(variable)]
|
|
|
|
m.assign(m + (gradient - m) * (1 - beta_1))
|
|
v.assign(v + (ops.square(gradient) - v) * (1 - beta_2))
|
|
m_hat = u_t_1 * m / (1 - u_product_t_1) + (1 - u_t) * gradient / (
|
|
1 - u_product_t
|
|
)
|
|
v_hat = v / (1 - beta_2_power)
|
|
|
|
variable.assign(
|
|
variable - (m_hat * lr) / (ops.sqrt(v_hat) + self.epsilon)
|
|
)
|
|
|
|
def get_config(self):
|
|
config = super().get_config()
|
|
|
|
config.update(
|
|
{
|
|
"beta_1": self.beta_1,
|
|
"beta_2": self.beta_2,
|
|
"epsilon": self.epsilon,
|
|
}
|
|
)
|
|
return config
|
|
|
|
|
|
Nadam.__doc__ = Nadam.__doc__.replace(
|
|
"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
|
|
)
|