217 lines
7.6 KiB
Python
217 lines
7.6 KiB
Python
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""RMSprop optimizer implementation."""
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import tensorflow.compat.v2 as tf
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from keras.optimizers import optimizer
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from keras.saving.object_registration import register_keras_serializable
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@register_keras_serializable()
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@keras_export(
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"keras.optimizers.experimental.RMSprop", "keras.optimizers.RMSprop", v1=[]
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)
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class RMSprop(optimizer.Optimizer):
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r"""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: Initial value for the learning rate:
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either a floating point value,
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or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance.
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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 to
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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 = tf.keras.optimizers.experimental.RMSprop(learning_rate=0.1)
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>>> var1 = tf.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.numpy()
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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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jit_compile=True,
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name="RMSprop",
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**kwargs
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):
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super().__init__(
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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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jit_compile=jit_compile,
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name=name,
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**kwargs
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)
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self._learning_rate = self._build_learning_rate(learning_rate)
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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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super().build(var_list)
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if hasattr(self, "_built") and self._built:
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return
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self._built = True
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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):
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"""Update step given gradient and the associated model variable."""
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lr = tf.cast(self.learning_rate, variable.dtype)
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var_key = self._var_key(variable)
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velocity = self._velocities[self._index_dict[var_key]]
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momentum = None
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if self.momentum > 0:
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momentum = self._momentums[self._index_dict[var_key]]
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average_grad = None
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if self.centered:
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average_grad = self._average_gradients[self._index_dict[var_key]]
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rho = self.rho
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if isinstance(gradient, tf.IndexedSlices):
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# Sparse gradients.
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velocity.assign(rho * velocity)
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velocity.scatter_add(
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tf.IndexedSlices(
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tf.square(gradient.values) * (1 - rho), gradient.indices
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)
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)
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if self.centered:
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average_grad.assign(rho * average_grad)
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average_grad.scatter_add(
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tf.IndexedSlices(
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gradient.values * (1 - rho), gradient.indices
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)
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)
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denominator = velocity - tf.square(average_grad) + self.epsilon
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else:
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denominator = velocity + self.epsilon
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denominator_slices = tf.gather(denominator, gradient.indices)
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increment = tf.IndexedSlices(
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lr * gradient.values * tf.math.rsqrt(denominator_slices),
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gradient.indices,
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)
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if self.momentum > 0:
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momentum.assign(self.momentum * momentum)
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momentum.scatter_add(increment)
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variable.assign_add(-momentum)
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else:
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variable.scatter_add(-increment)
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else:
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# Dense gradients.
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velocity.assign(rho * velocity + (1 - rho) * tf.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 - tf.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 * tf.math.rsqrt(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_add(-momentum)
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else:
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variable.assign_add(-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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"learning_rate": self._serialize_hyperparameter(
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self._learning_rate
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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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