Module keras.optimizer_v2.adadelta
Adadelta optimizer implementation.
Expand source code
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Adadelta optimizer implementation."""
import tensorflow.compat.v2 as tf
# pylint: disable=g-classes-have-attributes
import numpy as np
from keras import backend_config
from keras.optimizer_v2 import optimizer_v2
from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.optimizers.Adadelta')
class Adadelta(optimizer_v2.OptimizerV2):
r"""Optimizer that implements the Adadelta algorithm.
Adadelta optimization is a stochastic gradient descent method that is based on
adaptive learning rate per dimension to address two drawbacks:
- The continual decay of learning rates throughout training.
- The need for a manually selected global learning rate.
Adadelta is a more robust extension of Adagrad that adapts learning rates
based on a moving window of gradient updates, instead of accumulating all
past gradients. This way, Adadelta continues learning even when many updates
have been done. Compared to Adagrad, in the original version of Adadelta you
don't have to set an initial learning rate. In this version, the initial
learning rate can be set, as in most other Keras optimizers.
Args:
learning_rate: Initial value for the learning rate:
either a floating point value,
or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance.
Defaults to 0.001.
Note that `Adadelta` tends to benefit from higher initial learning rate
values compared to other optimizers.
To match the exact form in the original paper, use 1.0.
rho: A `Tensor` or a floating point value. The decay rate.
epsilon: Small floating point value used to maintain numerical stability.
name: Optional name prefix for the operations created when applying
gradients. Defaults to `"Adadelta"`.
**kwargs: Keyword arguments. Allowed to be one of
`"clipnorm"` or `"clipvalue"`.
`"clipnorm"` (float) clips gradients by norm and represents
the maximum norm of each parameter;
`"clipvalue"` (float) clips gradient by value and represents the
maximum absolute value of each parameter.
Reference:
- [Zeiler, 2012](http://arxiv.org/abs/1212.5701)
"""
_HAS_AGGREGATE_GRAD = True
def __init__(self,
learning_rate=0.001,
rho=0.95,
epsilon=1e-7,
name='Adadelta',
**kwargs):
super(Adadelta, self).__init__(name, **kwargs)
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
self._set_hyper('decay', self._initial_decay)
self._set_hyper('rho', rho)
self.epsilon = epsilon or backend_config.epsilon()
def _create_slots(self, var_list):
# Separate for-loops to respect the ordering of slot variables from v1.
for v in var_list:
self.add_slot(v, 'accum_grad')
for v in var_list:
self.add_slot(v, 'accum_var')
def _prepare_local(self, var_device, var_dtype, apply_state):
super(Adadelta, self)._prepare_local(var_device, var_dtype, apply_state)
apply_state[(var_device, var_dtype)].update(
dict(
epsilon=tf.convert_to_tensor(
self.epsilon, var_dtype),
rho=tf.identity(self._get_hyper('rho', var_dtype))))
def set_weights(self, weights):
params = self.weights
# Override set_weights for backward compatibility of Keras V1 optimizer
# since it does not include iteration at head of the weight list. Set
# iteration to 0.
if len(params) == len(weights) + 1:
weights = [np.array(0)] + weights
super(Adadelta, self).set_weights(weights)
def _resource_apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
accum_grad = self.get_slot(var, 'accum_grad')
accum_var = self.get_slot(var, 'accum_var')
return tf.raw_ops.ResourceApplyAdadelta(
var=var.handle,
accum=accum_grad.handle,
accum_update=accum_var.handle,
lr=coefficients['lr_t'],
rho=coefficients['rho'],
epsilon=coefficients['epsilon'],
grad=grad,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
accum_grad = self.get_slot(var, 'accum_grad')
accum_var = self.get_slot(var, 'accum_var')
return tf.raw_ops.ResourceSparseApplyAdadelta(
var=var.handle,
accum=accum_grad.handle,
accum_update=accum_var.handle,
lr=coefficients['lr_t'],
rho=coefficients['rho'],
epsilon=coefficients['epsilon'],
grad=grad,
indices=indices,
use_locking=self._use_locking)
def get_config(self):
config = super(Adadelta, self).get_config()
config.update({
'learning_rate': self._serialize_hyperparameter('learning_rate'),
'decay': self._initial_decay,
'rho': self._serialize_hyperparameter('rho'),
'epsilon': self.epsilon,
})
return config
Classes
class Adadelta (learning_rate=0.001, rho=0.95, epsilon=1e-07, name='Adadelta', **kwargs)
-
Optimizer that implements the Adadelta algorithm.
Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks:
- The continual decay of learning rates throughout training.
- The need for a manually selected global learning rate.
Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This way, Adadelta continues learning even when many updates have been done. Compared to Adagrad, in the original version of Adadelta you don't have to set an initial learning rate. In this version, the initial learning rate can be set, as in most other Keras optimizers.
Args
learning_rate
- Initial value for the learning rate:
either a floating point value,
or a
tf.keras.optimizers.schedules.LearningRateSchedule
instance. Defaults to 0.001. Note thatAdadelta
tends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0. rho
- A
Tensor
or a floating point value. The decay rate. epsilon
- Small floating point value used to maintain numerical stability.
name
- Optional name prefix for the operations created when applying
gradients.
Defaults to
"Adadelta"
. **kwargs
- Keyword arguments. Allowed to be one of
"clipnorm"
or"clipvalue"
."clipnorm"
(float) clips gradients by norm and represents the maximum norm of each parameter;"clipvalue"
(float) clips gradient by value and represents the maximum absolute value of each parameter.
Reference
Create a new Optimizer.
This must be called by the constructors of subclasses. Note that Optimizer instances should not bind to a single graph, and so shouldn't keep Tensors as member variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead.
This class is stateful and thread-compatible.
Example of custom gradient transformations:
def my_gradient_transformer(grads_and_vars): # Simple example, double the gradients. return [(2. * g, v) for g, v in grads_and_vars] optimizer = tf.keras.optimizers.SGD( 1e-3, gradient_transformers=[my_gradient_transformer])
Args
name
- String. The name to use for momentum accumulator weights created by the optimizer.
gradient_aggregator
- The function to use to aggregate gradients across
devices (when using
tf.distribute.Strategy
). IfNone
, defaults to summing the gradients across devices. The function should accept and return a list of(gradient, variable)
tuples. gradient_transformers
- Optional. List of functions to use to transform
gradients before applying updates to Variables. The functions are
applied after
gradient_aggregator
. The functions should accept and return a list of(gradient, variable)
tuples. **kwargs
- keyword arguments. Allowed arguments are
clipvalue
,clipnorm
,global_clipnorm
. Ifclipvalue
(float) is set, the gradient of each weight is clipped to be no higher than this value. Ifclipnorm
(float) is set, the gradient of each weight is individually clipped so that its norm is no higher than this value. Ifglobal_clipnorm
(float) is set the gradient of all weights is clipped so that their global norm is no higher than this value.
Raises
ValueError
- in case of any invalid argument.
Expand source code
class Adadelta(optimizer_v2.OptimizerV2): r"""Optimizer that implements the Adadelta algorithm. Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks: - The continual decay of learning rates throughout training. - The need for a manually selected global learning rate. Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This way, Adadelta continues learning even when many updates have been done. Compared to Adagrad, in the original version of Adadelta you don't have to set an initial learning rate. In this version, the initial learning rate can be set, as in most other Keras optimizers. Args: learning_rate: Initial value for the learning rate: either a floating point value, or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance. Defaults to 0.001. Note that `Adadelta` tends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0. rho: A `Tensor` or a floating point value. The decay rate. epsilon: Small floating point value used to maintain numerical stability. name: Optional name prefix for the operations created when applying gradients. Defaults to `"Adadelta"`. **kwargs: Keyword arguments. Allowed to be one of `"clipnorm"` or `"clipvalue"`. `"clipnorm"` (float) clips gradients by norm and represents the maximum norm of each parameter; `"clipvalue"` (float) clips gradient by value and represents the maximum absolute value of each parameter. Reference: - [Zeiler, 2012](http://arxiv.org/abs/1212.5701) """ _HAS_AGGREGATE_GRAD = True def __init__(self, learning_rate=0.001, rho=0.95, epsilon=1e-7, name='Adadelta', **kwargs): super(Adadelta, self).__init__(name, **kwargs) self._set_hyper('learning_rate', kwargs.get('lr', learning_rate)) self._set_hyper('decay', self._initial_decay) self._set_hyper('rho', rho) self.epsilon = epsilon or backend_config.epsilon() def _create_slots(self, var_list): # Separate for-loops to respect the ordering of slot variables from v1. for v in var_list: self.add_slot(v, 'accum_grad') for v in var_list: self.add_slot(v, 'accum_var') def _prepare_local(self, var_device, var_dtype, apply_state): super(Adadelta, self)._prepare_local(var_device, var_dtype, apply_state) apply_state[(var_device, var_dtype)].update( dict( epsilon=tf.convert_to_tensor( self.epsilon, var_dtype), rho=tf.identity(self._get_hyper('rho', var_dtype)))) def set_weights(self, weights): params = self.weights # Override set_weights for backward compatibility of Keras V1 optimizer # since it does not include iteration at head of the weight list. Set # iteration to 0. if len(params) == len(weights) + 1: weights = [np.array(0)] + weights super(Adadelta, self).set_weights(weights) def _resource_apply_dense(self, grad, var, apply_state=None): var_device, var_dtype = var.device, var.dtype.base_dtype coefficients = ((apply_state or {}).get((var_device, var_dtype)) or self._fallback_apply_state(var_device, var_dtype)) accum_grad = self.get_slot(var, 'accum_grad') accum_var = self.get_slot(var, 'accum_var') return tf.raw_ops.ResourceApplyAdadelta( var=var.handle, accum=accum_grad.handle, accum_update=accum_var.handle, lr=coefficients['lr_t'], rho=coefficients['rho'], epsilon=coefficients['epsilon'], grad=grad, use_locking=self._use_locking) def _resource_apply_sparse(self, grad, var, indices, apply_state=None): var_device, var_dtype = var.device, var.dtype.base_dtype coefficients = ((apply_state or {}).get((var_device, var_dtype)) or self._fallback_apply_state(var_device, var_dtype)) accum_grad = self.get_slot(var, 'accum_grad') accum_var = self.get_slot(var, 'accum_var') return tf.raw_ops.ResourceSparseApplyAdadelta( var=var.handle, accum=accum_grad.handle, accum_update=accum_var.handle, lr=coefficients['lr_t'], rho=coefficients['rho'], epsilon=coefficients['epsilon'], grad=grad, indices=indices, use_locking=self._use_locking) def get_config(self): config = super(Adadelta, self).get_config() config.update({ 'learning_rate': self._serialize_hyperparameter('learning_rate'), 'decay': self._initial_decay, 'rho': self._serialize_hyperparameter('rho'), 'epsilon': self.epsilon, }) return config
Ancestors
- OptimizerV2
- tensorflow.python.training.tracking.base.Trackable
Inherited members