Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/optimizers/adagrad.py

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2023-06-19 00:49:18 +02:00
# Copyright 2021 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."""
import tensorflow.compat.v2 as tf
from keras import initializers
from keras.optimizers import optimizer
from keras.saving.object_registration import register_keras_serializable
# isort: off
from tensorflow.python.util.tf_export import keras_export
@register_keras_serializable()
@keras_export(
"keras.optimizers.experimental.Adagrad", "keras.optimizers.Adagrad", v1=[]
)
class Adagrad(optimizer.Optimizer):
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.
{{base_optimizer_keyword_args}}
Reference:
- [Duchi et al., 2011](
http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
"""
def __init__(
self,
learning_rate=0.001,
initial_accumulator_value=0.1,
epsilon=1e-7,
weight_decay=None,
clipnorm=None,
clipvalue=None,
global_clipnorm=None,
use_ema=False,
ema_momentum=0.99,
ema_overwrite_frequency=None,
jit_compile=True,
name="Adagrad",
**kwargs
):
super().__init__(
weight_decay=weight_decay,
clipnorm=clipnorm,
clipvalue=clipvalue,
global_clipnorm=global_clipnorm,
use_ema=use_ema,
ema_momentum=ema_momentum,
ema_overwrite_frequency=ema_overwrite_frequency,
jit_compile=jit_compile,
name=name,
**kwargs
)
self._learning_rate = self._build_learning_rate(learning_rate)
self.initial_accumulator_value = initial_accumulator_value
self.epsilon = epsilon
def build(self, var_list):
super().build(var_list)
if hasattr(self, "_built") and self._built:
return
self._built = True
self._accumulators = []
initializer = initializers.Constant(self.initial_accumulator_value)
for var in var_list:
self._accumulators.append(
self.add_variable_from_reference(
var,
"accumulator",
initial_value=initializer(shape=var.shape, dtype=var.dtype),
)
)
def update_step(self, grad, variable):
"""Update step given gradient and the associated model variable."""
lr = tf.cast(self.learning_rate, variable.dtype)
var_key = self._var_key(variable)
accumulator = self._accumulators[self._index_dict[var_key]]
if isinstance(grad, tf.IndexedSlices):
# Sparse gradients.
accumulator.scatter_add(
tf.IndexedSlices(grad.values * grad.values, grad.indices)
)
sparse_accumulator = tf.gather(accumulator, indices=grad.indices)
sparse_denominator = tf.sqrt(sparse_accumulator + self.epsilon)
variable.scatter_add(
tf.IndexedSlices(
-lr * grad.values / sparse_denominator, grad.indices
)
)
else:
# Dense gradients.
accumulator.assign_add(grad * grad)
variable.assign_sub(lr * grad / tf.sqrt(accumulator + self.epsilon))
def get_config(self):
config = super().get_config()
config.update(
{
"learning_rate": self._serialize_hyperparameter(
self._learning_rate
),
"initial_accumulator_value": self.initial_accumulator_value,
"epsilon": self.epsilon,
}
)
return config
Adagrad.__doc__ = Adagrad.__doc__.replace(
"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
)