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rmsprop.py
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rmsprop.py
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# Copyright 2021, The TensorFlow Federated Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""RMSprop optimizer."""
import collections
from typing import Any, TypeVar
import tensorflow as tf
from tensorflow_federated.python.common_libs import structure
from tensorflow_federated.python.learning.optimizers import optimizer
_DECAY_KEY = 'decay'
_PRECONDITIONER_KEY = 'preconditioner'
_EPSILON_KEY = 'epsilon'
_HPARAMS_KEYS = [optimizer.LEARNING_RATE_KEY, _DECAY_KEY, _EPSILON_KEY]
State = TypeVar('State', bound=collections.OrderedDict[str, Any])
Hparams = TypeVar('Hparams', bound=collections.OrderedDict[str, float])
class _RmsProp(optimizer.Optimizer[State, optimizer.Weights, Hparams]):
"""RMSprop optimizer, see `build_rmsprop` for details."""
def __init__(
self,
learning_rate: optimizer.Float,
decay: optimizer.Float = 0.9,
epsilon: optimizer.Float = 1e-7,
):
"""Initializes RMSprop optimizer."""
if learning_rate < 0.0:
raise ValueError(
f'RMSProp `learning_rate` must be nonnegative, found {learning_rate}.'
)
if decay < 0.0 or decay > 1.0:
raise ValueError(
f'RMSProp `decay` must be in the interval [0.0, 1.0], found {decay}.'
)
if epsilon < 0.0:
raise ValueError(
f'RMSProp `epsilon` must be nonnegative, found {epsilon}.'
)
self._lr = learning_rate
self._decay = decay
self._epsilon = epsilon
def initialize(self, specs: Any) -> State:
initial_preconditioner = tf.nest.map_structure(
lambda s: tf.zeros(s.shape, s.dtype), specs
)
state = collections.OrderedDict([
(optimizer.LEARNING_RATE_KEY, self._lr),
(_DECAY_KEY, self._decay),
(_EPSILON_KEY, self._epsilon),
(_PRECONDITIONER_KEY, initial_preconditioner),
])
return state
def next(
self, state: State, weights: optimizer.Weights, gradients: Any
) -> tuple[State, optimizer.Weights]:
gradients = optimizer.handle_indexed_slices_gradients(gradients)
optimizer.check_weights_gradients_match(weights, gradients)
lr = state[optimizer.LEARNING_RATE_KEY]
decay = state[_DECAY_KEY]
epsilon = state[_EPSILON_KEY]
preconditioner = state[_PRECONDITIONER_KEY]
optimizer.check_weights_state_match(
weights, preconditioner, 'preconditioner'
)
updated_preconditioner = tf.nest.map_structure(
lambda p, g: p + (tf.math.square(g) - p) * (1 - decay),
preconditioner,
gradients,
)
updated_weights = tf.nest.map_structure(
lambda w, g, p: w - lr * g / (tf.math.sqrt(p) + epsilon),
weights,
gradients,
updated_preconditioner,
)
updated_state = collections.OrderedDict([
(optimizer.LEARNING_RATE_KEY, lr),
(_DECAY_KEY, decay),
(_EPSILON_KEY, epsilon),
(_PRECONDITIONER_KEY, updated_preconditioner),
])
return updated_state, updated_weights
def get_hparams(self, state: State) -> Hparams:
return collections.OrderedDict([(k, state[k]) for k in _HPARAMS_KEYS])
def set_hparams(self, state: State, hparams: Hparams) -> State:
# TODO: b/245962555 - Find an alternative to `update_struct` if it
# interferes with typing guarantees.
# We use `tff.structure.update_struct` (rather than something like
# `copy.deepcopy`) to ensure that this can be called within a
# `tff.Computation`.
return structure.update_struct(state, **hparams)
def build_rmsprop(
learning_rate: optimizer.Float,
decay: optimizer.Float = 0.9,
epsilon: optimizer.Float = 1e-7,
) -> optimizer.Optimizer:
"""Returns a `tff.learning.optimizers.Optimizer` for RMSprop.
The RMSprop optimizer is based on [Tieleman and Hinton, 2012](
http://www.cs.toronto.edu/~hinton/coursera/lecture6/lec6.pdf).
The update rule given learning rate `lr`, epsilon `eps`, decay `d`,
preconditioner `s`, weights `w` and gradients `g` is:
```
s = d * s + (1 - d) * g**2
w = w - lr * g / (sqrt(s) + eps)
```
Args:
learning_rate: A positive float for learning rate, default to 0.01.
decay: A float between 0.0 and 1.0 for the decay used to track the magnitude
of previous gradients.
epsilon: A small non-negative float, used to maintain numerical stability.
"""
return _RmsProp(learning_rate, decay, epsilon)