170 lines
6.6 KiB
Python
170 lines
6.6 KiB
Python
![]() |
# 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.
|
||
|
# ==============================================================================
|
||
|
"""Adagrad optimizer implementation."""
|
||
|
# pylint: disable=g-classes-have-attributes
|
||
|
|
||
|
import numpy as np
|
||
|
|
||
|
from tensorflow.python.framework import dtypes
|
||
|
from tensorflow.python.framework import ops
|
||
|
from tensorflow.python.keras import backend_config
|
||
|
from tensorflow.python.keras.optimizer_v2 import optimizer_v2
|
||
|
from tensorflow.python.ops import array_ops
|
||
|
from tensorflow.python.ops import init_ops
|
||
|
from tensorflow.python.training import gen_training_ops
|
||
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
||
|
|
||
|
@keras_export('keras.optimizers.Adagrad')
|
||
|
class Adagrad(optimizer_v2.OptimizerV2):
|
||
|
r"""Optimizer that implements the Adagrad algorithm.
|
||
|
|
||
|
Adagrad is an optimizer with parameter-specific learning rates,
|
||
|
which are adapted relative to how frequently a parameter gets
|
||
|
updated during training. The more updates a parameter receives,
|
||
|
the smaller the updates.
|
||
|
|
||
|
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 `Adagrad` 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.
|
||
|
initial_accumulator_value: Floating point value.
|
||
|
Starting value for the accumulators (per-parameter momentum values).
|
||
|
Must be non-negative.
|
||
|
epsilon: Small floating point value used to maintain numerical stability.
|
||
|
name: Optional name prefix for the operations created when applying
|
||
|
gradients. Defaults to `"Adagrad"`.
|
||
|
**kwargs: Keyword arguments. Allowed to be one of
|
||
|
`"clipnorm"` or `"clipvalue"`.
|
||
|
`"clipnorm"` (float) clips gradients by norm and represents
|
||
|
the maximum L2 norm of each weight variable;
|
||
|
`"clipvalue"` (float) clips gradient by value and represents the
|
||
|
maximum absolute value of each weight variable.
|
||
|
|
||
|
Reference:
|
||
|
- [Duchi et al., 2011](
|
||
|
http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
|
||
|
"""
|
||
|
|
||
|
_HAS_AGGREGATE_GRAD = True
|
||
|
|
||
|
def __init__(self,
|
||
|
learning_rate=0.001,
|
||
|
initial_accumulator_value=0.1,
|
||
|
epsilon=1e-7,
|
||
|
name='Adagrad',
|
||
|
**kwargs):
|
||
|
if initial_accumulator_value < 0.0:
|
||
|
raise ValueError('initial_accumulator_value must be non-negative: %s' %
|
||
|
initial_accumulator_value)
|
||
|
if epsilon is None:
|
||
|
epsilon = backend_config.epsilon()
|
||
|
super(Adagrad, self).__init__(name, **kwargs)
|
||
|
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
|
||
|
self._set_hyper('decay', self._initial_decay)
|
||
|
self._initial_accumulator_value = initial_accumulator_value
|
||
|
self.epsilon = epsilon or backend_config.epsilon()
|
||
|
|
||
|
def _create_slots(self, var_list):
|
||
|
for var in var_list:
|
||
|
dtype = var.dtype.base_dtype
|
||
|
init = init_ops.constant_initializer(
|
||
|
self._initial_accumulator_value, dtype=dtype)
|
||
|
self.add_slot(var, 'accumulator', init)
|
||
|
|
||
|
def _prepare_local(self, var_device, var_dtype, apply_state):
|
||
|
super(Adagrad, self)._prepare_local(var_device, var_dtype, apply_state)
|
||
|
apply_state[(var_device, var_dtype)].update(
|
||
|
dict(
|
||
|
epsilon=ops.convert_to_tensor_v2_with_dispatch(
|
||
|
self.epsilon, var_dtype),
|
||
|
neg_lr_t=-apply_state[(var_device, var_dtype)]['lr_t'],
|
||
|
zero=array_ops.zeros((), dtype=dtypes.int64)))
|
||
|
|
||
|
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(Adagrad, self).set_weights(weights)
|
||
|
|
||
|
@classmethod
|
||
|
def from_config(cls, config, custom_objects=None):
|
||
|
"""Creates an optimizer from its config.
|
||
|
|
||
|
This method is the reverse of `get_config`,
|
||
|
capable of instantiating the same optimizer from the config
|
||
|
dictionary.
|
||
|
|
||
|
Args:
|
||
|
config: A Python dictionary, typically the output of get_config.
|
||
|
custom_objects: A Python dictionary mapping names to additional Python
|
||
|
objects used to create this optimizer, such as a function used for a
|
||
|
hyperparameter.
|
||
|
|
||
|
Returns:
|
||
|
An optimizer instance.
|
||
|
"""
|
||
|
if 'initial_accumulator_value' not in config:
|
||
|
config['initial_accumulator_value'] = 0.1
|
||
|
if 'lr' in config:
|
||
|
config['learning_rate'] = config.pop('lr')
|
||
|
return cls(**config)
|
||
|
|
||
|
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))
|
||
|
|
||
|
acc = self.get_slot(var, 'accumulator')
|
||
|
return gen_training_ops.ResourceApplyAdagradV2(
|
||
|
var=var.handle,
|
||
|
accum=acc.handle,
|
||
|
lr=coefficients['lr_t'],
|
||
|
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))
|
||
|
|
||
|
acc = self.get_slot(var, 'accumulator')
|
||
|
return gen_training_ops.ResourceSparseApplyAdagradV2(
|
||
|
var=var.handle,
|
||
|
accum=acc.handle,
|
||
|
lr=coefficients['lr_t'],
|
||
|
epsilon=coefficients['epsilon'],
|
||
|
grad=grad,
|
||
|
indices=indices,
|
||
|
use_locking=self._use_locking)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = super(Adagrad, self).get_config()
|
||
|
config.update({
|
||
|
'learning_rate': self._serialize_hyperparameter('learning_rate'),
|
||
|
'decay': self._initial_decay,
|
||
|
'initial_accumulator_value': self._initial_accumulator_value,
|
||
|
'epsilon': self.epsilon,
|
||
|
})
|
||
|
return config
|