258 lines
9.5 KiB
Python
258 lines
9.5 KiB
Python
# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""FTRL optimizer implementation."""
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import tensorflow.compat.v2 as tf
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from keras.optimizers import optimizer
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from keras.saving.object_registration import register_keras_serializable
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@register_keras_serializable()
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@keras_export(
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"keras.optimizers.experimental.Ftrl", "keras.optimizers.Ftrl", v1=[]
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)
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class Ftrl(optimizer.Optimizer):
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r"""Optimizer that implements the FTRL algorithm.
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"Follow The Regularized Leader" (FTRL) is an optimization algorithm
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developed at Google for click-through rate prediction in the early 2010s. It
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is most suitable for shallow models with large and sparse feature spaces.
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The algorithm is described by
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[McMahan et al., 2013](https://research.google.com/pubs/archive/41159.pdf).
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The Keras version has support for both online L2 regularization
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(the L2 regularization described in the paper
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above) and shrinkage-type L2 regularization
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(which is the addition of an L2 penalty to the loss function).
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Initialization:
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```python
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n = 0
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sigma = 0
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z = 0
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```
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Update rule for one variable `w`:
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```python
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prev_n = n
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n = n + g ** 2
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sigma = (n ** -lr_power - prev_n ** -lr_power) / lr
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z = z + g - sigma * w
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if abs(z) < lambda_1:
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w = 0
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else:
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w = (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2)
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```
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Notation:
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- `lr` is the learning rate
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- `g` is the gradient for the variable
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- `lambda_1` is the L1 regularization strength
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- `lambda_2` is the L2 regularization strength
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- `lr_power` is the power to scale n.
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Check the documentation for the `l2_shrinkage_regularization_strength`
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parameter for more details when shrinkage is enabled, in which case gradient
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is replaced with a gradient with shrinkage.
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Args:
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learning_rate: A `Tensor`, floating point value, a schedule that is a
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`tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable that
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takes no arguments and returns the actual value to use. The learning
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rate. Defaults to 0.001.
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learning_rate_power: A float value, must be less or equal to zero.
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Controls how the learning rate decreases during training. Use zero for a
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fixed learning rate.
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initial_accumulator_value: The starting value for accumulators. Only zero
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or positive values are allowed.
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l1_regularization_strength: A float value, must be greater than or equal
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to zero. Defaults to 0.0.
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l2_regularization_strength: A float value, must be greater than or equal
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to zero. Defaults to 0.0.
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l2_shrinkage_regularization_strength: A float value, must be greater than
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or equal to zero. This differs from L2 above in that the L2 above is a
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stabilization penalty, whereas this L2 shrinkage is a magnitude penalty.
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When input is sparse shrinkage will only happen on the active weights.
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beta: A float value, representing the beta value from the paper. Defaults
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to 0.0.
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{{base_optimizer_keyword_args}}
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"""
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def __init__(
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self,
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learning_rate=0.001,
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learning_rate_power=-0.5,
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initial_accumulator_value=0.1,
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l1_regularization_strength=0.0,
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l2_regularization_strength=0.0,
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l2_shrinkage_regularization_strength=0.0,
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beta=0.0,
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weight_decay=None,
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clipnorm=None,
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clipvalue=None,
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global_clipnorm=None,
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use_ema=False,
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ema_momentum=0.99,
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ema_overwrite_frequency=None,
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jit_compile=True,
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name="Ftrl",
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**kwargs,
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):
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super().__init__(
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name=name,
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weight_decay=weight_decay,
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clipnorm=clipnorm,
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clipvalue=clipvalue,
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global_clipnorm=global_clipnorm,
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use_ema=use_ema,
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ema_momentum=ema_momentum,
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ema_overwrite_frequency=ema_overwrite_frequency,
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jit_compile=jit_compile,
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**kwargs,
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)
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if initial_accumulator_value < 0.0:
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raise ValueError(
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"`initial_accumulator_value` needs to be positive or zero. "
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"Received: initial_accumulator_value="
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f"{initial_accumulator_value}."
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)
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if learning_rate_power > 0.0:
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raise ValueError(
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"`learning_rate_power` needs to be negative or zero. Received: "
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f"learning_rate_power={learning_rate_power}."
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)
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if l1_regularization_strength < 0.0:
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raise ValueError(
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"`l1_regularization_strength` needs to be positive or zero. "
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"Received: l1_regularization_strength="
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f"{l1_regularization_strength}."
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)
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if l2_regularization_strength < 0.0:
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raise ValueError(
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"`l2_regularization_strength` needs to be positive or zero. "
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"Received: l2_regularization_strength="
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f"{l2_regularization_strength}."
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)
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if l2_shrinkage_regularization_strength < 0.0:
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raise ValueError(
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"`l2_shrinkage_regularization_strength` needs to be positive "
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"or zero. Received: l2_shrinkage_regularization_strength"
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f"={l2_shrinkage_regularization_strength}."
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)
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self._learning_rate = self._build_learning_rate(learning_rate)
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self.learning_rate_power = learning_rate_power
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self.initial_accumulator_value = initial_accumulator_value
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self.l1_regularization_strength = l1_regularization_strength
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self.l2_regularization_strength = l2_regularization_strength
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self.l2_shrinkage_regularization_strength = (
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l2_shrinkage_regularization_strength
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)
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self.beta = beta
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def build(self, var_list):
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"""Initialize optimizer variables.
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Args:
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var_list: list of model variables to build Ftrl variables on.
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"""
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super().build(var_list)
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if hasattr(self, "_built") and self._built:
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return
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self._accumulators = []
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self._linears = []
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for var in var_list:
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self._accumulators.append(
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self.add_variable_from_reference(
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model_variable=var,
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variable_name="accumulator",
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initial_value=tf.cast(
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tf.fill(
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dims=var.shape, value=self.initial_accumulator_value
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),
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dtype=var.dtype,
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),
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)
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)
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self._linears.append(
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self.add_variable_from_reference(
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model_variable=var, variable_name="linear"
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)
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)
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self._built = True
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def update_step(self, gradient, variable):
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"""Update step given gradient and the associated model variable."""
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lr = tf.cast(self.learning_rate, variable.dtype)
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var_key = self._var_key(variable)
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accum = self._accumulators[self._index_dict[var_key]]
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linear = self._linears[self._index_dict[var_key]]
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lr_power = self.learning_rate_power
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l2_reg = self.l2_regularization_strength
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l2_reg = l2_reg + self.beta / (2.0 * lr)
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# Ftrl optimizer has the same implementation for sparse and dense
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# gradients update.
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grad_to_use = (
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gradient + 2 * self.l2_shrinkage_regularization_strength * variable
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)
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new_accum = accum + tf.pow(gradient, 2)
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linear.assign_add(
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grad_to_use
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- (tf.pow(new_accum, -lr_power) - tf.pow(accum, -lr_power))
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/ lr
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* variable
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)
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quadratic = tf.pow(new_accum, (-lr_power)) / lr + 2 * l2_reg
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linear_clipped = tf.clip_by_value(
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linear,
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-self.l1_regularization_strength,
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self.l1_regularization_strength,
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)
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variable.assign((linear_clipped - linear) / quadratic)
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accum.assign(new_accum)
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def get_config(self):
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config = super().get_config()
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config.update(
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{
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"learning_rate": self._serialize_hyperparameter(
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self._learning_rate
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),
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"learning_rate_power": self.learning_rate_power,
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"initial_accumulator_value": self.initial_accumulator_value,
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"l1_regularization_strength": self.l1_regularization_strength,
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"l2_regularization_strength": self.l2_regularization_strength,
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"l2_shrinkage_regularization_strength": self.l2_shrinkage_regularization_strength, # noqa: E501
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"beta": self.beta,
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}
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)
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return config
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Ftrl.__doc__ = Ftrl.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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