158 lines
5.3 KiB
Python
158 lines
5.3 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.RMSprop"])
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class RMSprop(optimizer.Optimizer):
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"""Optimizer that implements the RMSprop algorithm.
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The gist of RMSprop is to:
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- Maintain a moving (discounted) average of the square of gradients
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- Divide the gradient by the root of this average
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This implementation of RMSprop uses plain momentum, not Nesterov momentum.
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The centered version additionally maintains a moving average of the
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gradients, and uses that average to estimate the variance.
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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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rho: float, defaults to 0.9. Discounting factor for the old gradients.
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momentum: float, defaults to 0.0. If not 0.0., the optimizer tracks the
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momentum value, with a decay rate equals to `1 - momentum`.
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epsilon: A small constant for numerical stability. This epsilon is
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"epsilon hat" in the Kingma and Ba paper (in the formula just before
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Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults
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to 1e-7.
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centered: Boolean. If `True`, gradients are normalized by the estimated
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variance of the gradient; if False, by the uncentered second moment.
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Setting this to `True` may help with training, but is slightly more
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expensive in terms of computation and memory. Defaults to `False`.
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{{base_optimizer_keyword_args}}
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Usage:
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>>> opt = keras_core.optimizers.RMSprop(learning_rate=0.1)
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>>> var1 = keras_core.backend.Variable(10.0)
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>>> loss = lambda: (var1 ** 2) / 2.0 # d(loss) / d(var1) = var1
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>>> opt.minimize(loss, [var1])
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>>> var1
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9.683772
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Reference:
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- [Hinton, 2012](
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http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
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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=0.9,
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momentum=0.0,
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epsilon=1e-7,
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centered=False,
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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=100,
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name="rmsprop",
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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.momentum = momentum
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self.epsilon = epsilon
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self.centered = centered
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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._velocities = []
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for var in var_list:
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self._velocities.append(
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self.add_variable_from_reference(var, "velocity")
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)
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self._momentums = []
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if self.momentum > 0:
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for var in var_list:
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self._momentums.append(
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self.add_variable_from_reference(var, "momentum")
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)
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self._average_gradients = []
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if self.centered:
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for var in var_list:
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self._average_gradients.append(
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self.add_variable_from_reference(var, "average_gradient")
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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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velocity = self._velocities[self._get_variable_index(variable)]
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momentum = None
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if self.momentum > 0:
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momentum = self._momentums[self._get_variable_index(variable)]
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average_grad = None
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if self.centered:
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average_grad = self._average_gradients[
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self._get_variable_index(variable)
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]
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rho = self.rho
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velocity.assign(rho * velocity + (1 - rho) * ops.square(gradient))
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if self.centered:
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average_grad.assign(rho * average_grad + (1 - rho) * gradient)
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denominator = velocity - ops.square(average_grad) + self.epsilon
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else:
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denominator = velocity + self.epsilon
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increment = lr * gradient / ops.sqrt(denominator)
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if self.momentum > 0:
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momentum.assign(self.momentum * momentum + increment)
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variable.assign(variable - momentum)
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else:
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variable.assign(variable - increment)
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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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"momentum": self.momentum,
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"epsilon": self.epsilon,
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"centered": self.centered,
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}
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)
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return config
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RMSprop.__doc__ = RMSprop.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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