122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
from keras_core import operations as 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.Adadelta"])
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class Adadelta(optimizer.Optimizer):
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"""Optimizer that implements the Adadelta algorithm.
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Adadelta optimization is a stochastic gradient descent method that is based
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on adaptive learning rate per dimension to address two drawbacks:
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- The continual decay of learning rates throughout training.
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- The need for a manually selected global learning rate.
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Adadelta is a more robust extension of Adagrad that adapts learning rates
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based on a moving window of gradient updates, instead of accumulating all
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past gradients. This way, Adadelta continues learning even when many updates
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have been done. Compared to Adagrad, in the original version of Adadelta you
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don't have to set an initial learning rate. In this version, the initial
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learning rate can be set, as in most other Keras optimizers.
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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. Note that `Adadelta`
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tends to benefit from higher initial learning rate values compared
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to other optimizers. To match the exact form in the original paper,
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use 1.0.
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rho: A floating point value. The decay rate. Defaults to 0.95.
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epsilon: Small floating point value for maintaining numerical stability.
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{{base_optimizer_keyword_args}}
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Reference:
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- [Zeiler, 2012](http://arxiv.org/abs/1212.5701)
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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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rho=0.95,
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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="adadelta",
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):
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super().__init__(
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learning_rate=learning_rate,
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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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name=name,
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)
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self.rho = rho
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self.epsilon = epsilon
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def build(self, var_list):
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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._accumulated_grads = []
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self._accumulated_delta_vars = []
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for var in var_list:
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self._accumulated_grads.append(
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self.add_variable_from_reference(var, "accumulated_grad")
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)
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self._accumulated_delta_vars.append(
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self.add_variable_from_reference(var, "accumulated_delta_var")
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)
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def update_step(self, grad, 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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grad = ops.cast(grad, variable.dtype)
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rho = self.rho
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accumulated_grad = self._accumulated_grads[
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self._get_variable_index(variable)
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]
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accumulated_delta_var = self._accumulated_delta_vars[
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self._get_variable_index(variable)
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]
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def rms(x):
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return ops.sqrt(x + self.epsilon)
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accumulated_grad.assign(
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rho * accumulated_grad + (1 - rho) * grad * grad
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)
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delta_var = -rms(accumulated_delta_var) * grad / rms(accumulated_grad)
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accumulated_delta_var.assign(
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rho * accumulated_delta_var + (1 - rho) * delta_var * delta_var
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)
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variable.assign(variable + lr * delta_var)
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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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"rho": self.rho,
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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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Adadelta.__doc__ = Adadelta.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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