claudson/ai/lib/python3.12/site-packages/keras/src/optimizers/adamax.py

from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer


@keras_export(["keras.optimizers.Adamax"])
class Adamax(optimizer.Optimizer):
    """Optimizer that implements the Adamax algorithm.

    Adamax, a variant of Adam based on the infinity norm, is a first-order
    gradient-based optimization method. Due to its capability of adjusting the
    learning rate based on data characteristics, it is suited to learn
    time-variant process, e.g., speech data with dynamically changed noise
    conditions. Default parameters follow those provided in the paper (see
    references below).

    Initialization:

    ```python
    m = 0  # Initialize initial 1st moment vector
    u = 0  # Initialize the exponentially weighted infinity norm
    t = 0  # Initialize timestep
    ```

    The update rule for parameter `w` with gradient `g` is described at the end
    of section 7.1 of the paper (see the reference section):

    ```python
    t += 1
    m = beta1 * m + (1 - beta) * g
    u = max(beta2 * u, abs(g))
    current_lr = learning_rate / (1 - beta1 ** t)
    w = w - current_lr * m / (u + epsilon)
    ```

    Args:
        learning_rate: A float, a
            `keras.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. The exponential decay
            rate for the 1st moment estimates.
        beta_2: A float value or a constant float tensor. The exponential decay
            rate for the exponentially weighted infinity norm.
        epsilon: A small constant for numerical stability.
            {{base_optimizer_keyword_args}}

    Reference:

    - [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
    """

    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,
        loss_scale_factor=None,
        gradient_accumulation_steps=None,
        name="adamax",
        **kwargs,
    ):
        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,
            loss_scale_factor=loss_scale_factor,
            gradient_accumulation_steps=gradient_accumulation_steps,
            **kwargs,
        )
        self.beta_1 = beta_1
        self.beta_2 = beta_2
        self.epsilon = epsilon

    def build(self, var_list):
        """Initialize optimizer variables.

        Adamax optimizer has 2 types of variables: momentums (denoted as m),
        exponentially weighted infinity norm (denoted as u).

        Args:
            var_list: list of model variables to build Adamax variables on.
        """
        if self.built:
            return
        super().build(var_list)
        self._m, self._u = self.add_optimizer_variables(
            var_list, ["momentum", "norm"]
        )

    def update_step(self, gradient, variable, learning_rate):
        """Update step given gradient and the associated model variable."""
        lr = ops.cast(learning_rate, variable.dtype)
        gradient = ops.cast(gradient, variable.dtype)
        local_step = ops.cast(self.iterations + 1, variable.dtype)
        beta_1_power = ops.power(
            ops.cast(self.beta_1, variable.dtype), local_step
        )

        m = self._m[self._get_variable_index(variable)]
        u = self._u[self._get_variable_index(variable)]

        self.assign_add(
            m, ops.multiply(ops.subtract(gradient, m), (1 - self.beta_1))
        )
        self.assign(
            u, ops.maximum(ops.multiply(self.beta_2, u), ops.abs(gradient))
        )
        self.assign_sub(
            variable,
            ops.divide(
                ops.multiply(lr, m),
                ops.multiply((1 - beta_1_power), ops.add(u, 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


Adamax.__doc__ = Adamax.__doc__.replace(
    "{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
)