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

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# Copyright 2022 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.
# ==============================================================================
"""Adamax optimizer implementation."""
import tensorflow.compat.v2 as tf
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.Adamax", "keras.optimizers.Adamax", v1=[]
)
class Adamax(optimizer.Optimizer):
"""Optimizer that implements the Adamax algorithm.
Adamax, a variant of Adam based on the infinity norm, is a first-order
gradient-based optimization method. Due to its capability of adjusting the
learning rate based on data characteristics, it is suited to learn
time-variant process, e.g., speech data with dynamically changed noise
conditions. Default parameters follow those provided in the paper (see
references below).
Initialization:
```python
m = 0 # Initialize initial 1st moment vector
u = 0 # Initialize the exponentially weighted infinity norm
t = 0 # Initialize timestep
```
The update rule for parameter `w` with gradient `g` is described at the end
of section 7.1 of the paper (see the referenece section):
```python
t += 1
m = beta1 * m + (1 - beta) * g
u = max(beta2 * u, abs(g))
current_lr = learning_rate / (1 - beta1 ** t)
w = w - current_lr * m / (u + epsilon)
```
Args:
learning_rate: A `tf.Tensor`, floating point value, a schedule that is a
`tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable
that takes no arguments and returns the actual value to use. The
learning rate. Defaults to 0.001.
beta_1: A float value or a constant float tensor. The exponential decay
rate for the 1st moment estimates.
beta_2: A float value or a constant float tensor. The exponential decay
rate for the exponentially weighted infinity norm.
epsilon: A small constant for numerical stability.
{{base_optimizer_keyword_args}}
Reference:
- [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
"""
def __init__(
self,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
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="Adamax",
**kwargs
):
super().__init__(
name=name,
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,
**kwargs
)
self._learning_rate = self._build_learning_rate(learning_rate)
self.beta_1 = beta_1
self.beta_2 = beta_2
self.epsilon = epsilon
def build(self, var_list):
"""Initialize optimizer variables.
Adamax optimizer has 2 types of variables: momentums (denoted as m),
exponentially weighted infinity norm (denoted as u).
Args:
var_list: list of model variables to build Adamax variables on.
"""
super().build(var_list)
if hasattr(self, "_built") and self._built:
return
self._built = True
self._m = []
self._u = []
for var in var_list:
self._m.append(
self.add_variable_from_reference(
model_variable=var, variable_name="m"
)
)
self._u.append(
self.add_variable_from_reference(
model_variable=var, variable_name="u"
)
)
def update_step(self, gradient, variable):
"""Update step given gradient and the associated model variable."""
lr = tf.cast(self.learning_rate, variable.dtype)
local_step = tf.cast(self.iterations + 1, variable.dtype)
beta_1_power = tf.pow(tf.cast(self.beta_1, variable.dtype), local_step)
var_key = self._var_key(variable)
m = self._m[self._index_dict[var_key]]
u = self._u[self._index_dict[var_key]]
if isinstance(gradient, tf.IndexedSlices):
# Sparse gradients.
indices = gradient.indices
m.assign_add(-m * (1 - self.beta_1))
m.scatter_add(
tf.IndexedSlices(gradient.values * (1 - self.beta_1), indices)
)
u.assign(u * self.beta_2)
u_slice = tf.gather(u, indices)
u_slice_incremental = (
tf.maximum(u_slice, tf.abs(gradient.values)) - u_slice
)
u.scatter_add(tf.IndexedSlices(u_slice_incremental, indices))
variable.assign_sub(
(lr * m) / ((1 - beta_1_power) * (u + self.epsilon))
)
else:
# Dense gradients.
m.assign_add((gradient - m) * (1 - self.beta_1))
u.assign(tf.maximum(self.beta_2 * u, tf.abs(gradient)))
variable.assign_sub(
(lr * m) / ((1 - beta_1_power) * (u + self.epsilon))
)
def get_config(self):
config = super().get_config()
config.update(
{
"learning_rate": self._serialize_hyperparameter(
self._learning_rate
),
"beta_1": self.beta_1,
"beta_2": self.beta_2,
"epsilon": self.epsilon,
}
)
return config
Adamax.__doc__ = Adamax.__doc__.replace(
"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
)