145 lines
4.6 KiB
Python
145 lines
4.6 KiB
Python
from keras_core import ops
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from keras_core.api_export import keras_core_export
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from keras_core.optimizers import optimizer
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@keras_core_export(["keras_core.optimizers.Adamax"])
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class Adamax(optimizer.Optimizer):
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"""Optimizer that implements the Adamax algorithm.
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Adamax, a variant of Adam based on the infinity norm, is a first-order
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gradient-based optimization method. Due to its capability of adjusting the
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learning rate based on data characteristics, it is suited to learn
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time-variant process, e.g., speech data with dynamically changed noise
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conditions. Default parameters follow those provided in the paper (see
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references below).
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Initialization:
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```python
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m = 0 # Initialize initial 1st moment vector
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u = 0 # Initialize the exponentially weighted infinity norm
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t = 0 # Initialize timestep
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```
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The update rule for parameter `w` with gradient `g` is described at the end
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of section 7.1 of the paper (see the referenece section):
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```python
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t += 1
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m = beta1 * m + (1 - beta) * g
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u = max(beta2 * u, abs(g))
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current_lr = learning_rate / (1 - beta1 ** t)
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w = w - current_lr * m / (u + epsilon)
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```
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Args:
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learning_rate: A float, a
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`keras_core.optimizers.schedules.LearningRateSchedule` instance, or
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a callable that takes no arguments and returns the actual value to
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use. The learning rate. Defaults to 0.001.
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beta_1: A float value or a constant float tensor. The exponential decay
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rate for the 1st moment estimates.
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beta_2: A float value or a constant float tensor. The exponential decay
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rate for the exponentially weighted infinity norm.
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epsilon: A small constant for numerical stability.
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{{base_optimizer_keyword_args}}
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Reference:
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- [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
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"""
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def __init__(
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self,
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learning_rate=0.001,
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beta_1=0.9,
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beta_2=0.999,
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epsilon=1e-7,
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weight_decay=None,
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clipnorm=None,
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clipvalue=None,
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global_clipnorm=None,
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use_ema=False,
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ema_momentum=0.99,
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ema_overwrite_frequency=None,
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name="adamax",
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**kwargs,
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):
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super().__init__(
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learning_rate=learning_rate,
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name=name,
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weight_decay=weight_decay,
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clipnorm=clipnorm,
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clipvalue=clipvalue,
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global_clipnorm=global_clipnorm,
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use_ema=use_ema,
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ema_momentum=ema_momentum,
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ema_overwrite_frequency=ema_overwrite_frequency,
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**kwargs,
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)
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self.beta_1 = beta_1
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self.beta_2 = beta_2
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self.epsilon = epsilon
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def build(self, var_list):
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"""Initialize optimizer variables.
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Adamax optimizer has 2 types of variables: momentums (denoted as m),
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exponentially weighted infinity norm (denoted as u).
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Args:
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var_list: list of model variables to build Adamax variables on.
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"""
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if self.built:
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return
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super().build(var_list)
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self._m = []
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self._u = []
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for var in var_list:
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self._m.append(
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self.add_variable_from_reference(
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reference_variable=var, name="momentum"
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)
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)
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self._u.append(
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self.add_variable_from_reference(
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reference_variable=var, name="norm"
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)
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)
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def update_step(self, gradient, variable, learning_rate):
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"""Update step given gradient and the associated model variable."""
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lr = ops.cast(learning_rate, variable.dtype)
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gradient = ops.cast(gradient, variable.dtype)
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local_step = ops.cast(self.iterations + 1, variable.dtype)
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beta_1_power = ops.power(
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ops.cast(self.beta_1, variable.dtype), local_step
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)
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m = self._m[self._get_variable_index(variable)]
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u = self._u[self._get_variable_index(variable)]
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m.assign(m + (gradient - m) * (1 - self.beta_1))
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u.assign(ops.maximum(self.beta_2 * u, ops.abs(gradient)))
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variable.assign(
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variable - (lr * m) / ((1 - beta_1_power) * (u + self.epsilon))
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)
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def get_config(self):
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config = super().get_config()
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config.update(
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{
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"beta_1": self.beta_1,
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"beta_2": self.beta_2,
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"epsilon": self.epsilon,
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}
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)
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return config
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Adamax.__doc__ = Adamax.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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