Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/optimizers/ftrl.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.
# ==============================================================================
"""FTRL 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.Ftrl", "keras.optimizers.Ftrl", v1=[]
)
class Ftrl(optimizer.Optimizer):
r"""Optimizer that implements the FTRL algorithm.
"Follow The Regularized Leader" (FTRL) is an optimization algorithm
developed at Google for click-through rate prediction in the early 2010s. It
is most suitable for shallow models with large and sparse feature spaces.
The algorithm is described by
[McMahan et al., 2013](https://research.google.com/pubs/archive/41159.pdf).
The Keras version has support for both online L2 regularization
(the L2 regularization described in the paper
above) and shrinkage-type L2 regularization
(which is the addition of an L2 penalty to the loss function).
Initialization:
```python
n = 0
sigma = 0
z = 0
```
Update rule for one variable `w`:
```python
prev_n = n
n = n + g ** 2
sigma = (n ** -lr_power - prev_n ** -lr_power) / lr
z = z + g - sigma * w
if abs(z) < lambda_1:
w = 0
else:
w = (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2)
```
Notation:
- `lr` is the learning rate
- `g` is the gradient for the variable
- `lambda_1` is the L1 regularization strength
- `lambda_2` is the L2 regularization strength
- `lr_power` is the power to scale n.
Check the documentation for the `l2_shrinkage_regularization_strength`
parameter for more details when shrinkage is enabled, in which case gradient
is replaced with a gradient with shrinkage.
Args:
learning_rate: A `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.
learning_rate_power: A float value, must be less or equal to zero.
Controls how the learning rate decreases during training. Use zero for a
fixed learning rate.
initial_accumulator_value: The starting value for accumulators. Only zero
or positive values are allowed.
l1_regularization_strength: A float value, must be greater than or equal
to zero. Defaults to 0.0.
l2_regularization_strength: A float value, must be greater than or equal
to zero. Defaults to 0.0.
l2_shrinkage_regularization_strength: A float value, must be greater than
or equal to zero. This differs from L2 above in that the L2 above is a
stabilization penalty, whereas this L2 shrinkage is a magnitude penalty.
When input is sparse shrinkage will only happen on the active weights.
beta: A float value, representing the beta value from the paper. Defaults
to 0.0.
{{base_optimizer_keyword_args}}
"""
def __init__(
self,
learning_rate=0.001,
learning_rate_power=-0.5,
initial_accumulator_value=0.1,
l1_regularization_strength=0.0,
l2_regularization_strength=0.0,
l2_shrinkage_regularization_strength=0.0,
beta=0.0,
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="Ftrl",
**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,
)
if initial_accumulator_value < 0.0:
raise ValueError(
"`initial_accumulator_value` needs to be positive or zero. "
"Received: initial_accumulator_value="
f"{initial_accumulator_value}."
)
if learning_rate_power > 0.0:
raise ValueError(
"`learning_rate_power` needs to be negative or zero. Received: "
f"learning_rate_power={learning_rate_power}."
)
if l1_regularization_strength < 0.0:
raise ValueError(
"`l1_regularization_strength` needs to be positive or zero. "
"Received: l1_regularization_strength="
f"{l1_regularization_strength}."
)
if l2_regularization_strength < 0.0:
raise ValueError(
"`l2_regularization_strength` needs to be positive or zero. "
"Received: l2_regularization_strength="
f"{l2_regularization_strength}."
)
if l2_shrinkage_regularization_strength < 0.0:
raise ValueError(
"`l2_shrinkage_regularization_strength` needs to be positive "
"or zero. Received: l2_shrinkage_regularization_strength"
f"={l2_shrinkage_regularization_strength}."
)
self._learning_rate = self._build_learning_rate(learning_rate)
self.learning_rate_power = learning_rate_power
self.initial_accumulator_value = initial_accumulator_value
self.l1_regularization_strength = l1_regularization_strength
self.l2_regularization_strength = l2_regularization_strength
self.l2_shrinkage_regularization_strength = (
l2_shrinkage_regularization_strength
)
self.beta = beta
def build(self, var_list):
"""Initialize optimizer variables.
Args:
var_list: list of model variables to build Ftrl variables on.
"""
super().build(var_list)
if hasattr(self, "_built") and self._built:
return
self._accumulators = []
self._linears = []
for var in var_list:
self._accumulators.append(
self.add_variable_from_reference(
model_variable=var,
variable_name="accumulator",
initial_value=tf.cast(
tf.fill(
dims=var.shape, value=self.initial_accumulator_value
),
dtype=var.dtype,
),
)
)
self._linears.append(
self.add_variable_from_reference(
model_variable=var, variable_name="linear"
)
)
self._built = True
def update_step(self, gradient, variable):
"""Update step given gradient and the associated model variable."""
lr = tf.cast(self.learning_rate, variable.dtype)
var_key = self._var_key(variable)
accum = self._accumulators[self._index_dict[var_key]]
linear = self._linears[self._index_dict[var_key]]
lr_power = self.learning_rate_power
l2_reg = self.l2_regularization_strength
l2_reg = l2_reg + self.beta / (2.0 * lr)
# Ftrl optimizer has the same implementation for sparse and dense
# gradients update.
grad_to_use = (
gradient + 2 * self.l2_shrinkage_regularization_strength * variable
)
new_accum = accum + tf.pow(gradient, 2)
linear.assign_add(
grad_to_use
- (tf.pow(new_accum, -lr_power) - tf.pow(accum, -lr_power))
/ lr
* variable
)
quadratic = tf.pow(new_accum, (-lr_power)) / lr + 2 * l2_reg
linear_clipped = tf.clip_by_value(
linear,
-self.l1_regularization_strength,
self.l1_regularization_strength,
)
variable.assign((linear_clipped - linear) / quadratic)
accum.assign(new_accum)
def get_config(self):
config = super().get_config()
config.update(
{
"learning_rate": self._serialize_hyperparameter(
self._learning_rate
),
"learning_rate_power": self.learning_rate_power,
"initial_accumulator_value": self.initial_accumulator_value,
"l1_regularization_strength": self.l1_regularization_strength,
"l2_regularization_strength": self.l2_regularization_strength,
"l2_shrinkage_regularization_strength": self.l2_shrinkage_regularization_strength, # noqa: E501
"beta": self.beta,
}
)
return config
Ftrl.__doc__ = Ftrl.__doc__.replace(
"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
)