EVOLUTION-MANAGER

Edit File: ftrl.py

# 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.
# ==============================================================================
"""Ftrl-proximal optimizer implementation."""
# pylint: disable=g-classes-have-attributes
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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.ops import math_ops
from tensorflow.python.training import gen_training_ops
from tensorflow.python.util.tf_export import keras_export

@keras_export('keras.optimizers.Ftrl')
class Ftrl(optimizer_v2.OptimizerV2):
  r"""Optimizer that implements the FTRL algorithm.

See Algorithm 1 of this
  [paper](https://research.google.com/pubs/archive/41159.pdf).
  This version has support for both online L2 (the L2 penalty given in the paper
  above) and shrinkage-type L2 (which is the addition of an L2 penalty to the
  loss function).

Initialization:
  $$t = 0$$
  $$n_{0} = 0$$
  $$\sigma_{0} = 0$$
  $$z_{0} = 0$$

Update ($$i$$ is variable index, $$\alpha$$ is the learning rate):
  $$t = t + 1$$
  $$n_{t,i} = n_{t-1,i} + g_{t,i}^{2}$$
  $$\sigma_{t,i} = (\sqrt{n_{t,i}} - \sqrt{n_{t-1,i}}) / \alpha$$
  $$z_{t,i} = z_{t-1,i} + g_{t,i} - \sigma_{t,i} * w_{t,i}$$
  $$w_{t,i} = - ((\beta+\sqrt{n_{t,i}}) / \alpha + 2 * \lambda_{2})^{-1} *
              (z_{i} - sgn(z_{i}) * \lambda_{1}) if \abs{z_{i}} > \lambda_{i}
                                                 else 0$$

Check the documentation for the l2_shrinkage_regularization_strength
  parameter for more details when shrinkage is enabled, in which case gradient
  is replaced with gradient_with_shrinkage.

Args:
    learning_rate: A `Tensor`, floating point value, or a schedule that is a
      `tf.keras.optimizers.schedules.LearningRateSchedule`. The learning rate.
    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.
    l2_regularization_strength: A float value, must be greater than or
      equal to zero.
    name: Optional name prefix for the operations created when applying
      gradients.  Defaults to `"Ftrl"`.
    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.
    **kwargs: Keyword arguments. Allowed to be one of
      `"clipnorm"` or `"clipvalue"`.
      `"clipnorm"` (float) clips gradients by norm; `"clipvalue"` (float) clips
      gradients by value.

Reference:
    - [paper](
      https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf)
  """

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,
               name='Ftrl',
               l2_shrinkage_regularization_strength=0.0,
               beta=0.0,
               **kwargs):
    super(Ftrl, self).__init__(name, **kwargs)

if initial_accumulator_value < 0.0:
      raise ValueError(
          'initial_accumulator_value %f needs to be positive or zero' %
          initial_accumulator_value)
    if learning_rate_power > 0.0:
      raise ValueError('learning_rate_power %f needs to be negative or zero' %
                       learning_rate_power)
    if l1_regularization_strength < 0.0:
      raise ValueError(
          'l1_regularization_strength %f needs to be positive or zero' %
          l1_regularization_strength)
    if l2_regularization_strength < 0.0:
      raise ValueError(
          'l2_regularization_strength %f needs to be positive or zero' %
          l2_regularization_strength)
    if l2_shrinkage_regularization_strength < 0.0:
      raise ValueError(
          'l2_shrinkage_regularization_strength %f needs to be positive'
          ' or zero' % l2_shrinkage_regularization_strength)

self._set_hyper('learning_rate', learning_rate)
    self._set_hyper('decay', self._initial_decay)
    self._set_hyper('learning_rate_power', learning_rate_power)
    self._set_hyper('l1_regularization_strength', l1_regularization_strength)
    self._set_hyper('l2_regularization_strength', l2_regularization_strength)
    self._set_hyper('beta', beta)
    self._initial_accumulator_value = initial_accumulator_value
    self._l2_shrinkage_regularization_strength = (
        l2_shrinkage_regularization_strength)

def _create_slots(self, var_list):
    # Create the "accum" and "linear" slots.
    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)
      self.add_slot(var, 'linear')

def _prepare_local(self, var_device, var_dtype, apply_state):
    super(Ftrl, self)._prepare_local(var_device, var_dtype, apply_state)
    apply_state[(var_device, var_dtype)].update(
        dict(
            learning_rate_power=array_ops.identity(
                self._get_hyper('learning_rate_power', var_dtype)),
            l1_regularization_strength=array_ops.identity(
                self._get_hyper('l1_regularization_strength', var_dtype)),
            l2_regularization_strength=array_ops.identity(
                self._get_hyper('l2_regularization_strength', var_dtype)),
            beta=array_ops.identity(self._get_hyper('beta', var_dtype)),
            l2_shrinkage_regularization_strength=math_ops.cast(
                self._l2_shrinkage_regularization_strength, var_dtype)))

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))

# Adjust L2 regularization strength to include beta to avoid the underlying
    # TensorFlow ops needing to include it.
    adjusted_l2_regularization_strength = (
        coefficients['l2_regularization_strength'] + coefficients['beta'] /
        (2. * coefficients['lr_t']))

accum = self.get_slot(var, 'accumulator')
    linear = self.get_slot(var, 'linear')

if self._l2_shrinkage_regularization_strength <= 0.0:
      return gen_training_ops.ResourceApplyFtrl(
          var=var.handle,
          accum=accum.handle,
          linear=linear.handle,
          grad=grad,
          lr=coefficients['lr_t'],
          l1=coefficients['l1_regularization_strength'],
          l2=adjusted_l2_regularization_strength,
          lr_power=coefficients['learning_rate_power'],
          use_locking=self._use_locking)
    else:
      return gen_training_ops.ResourceApplyFtrlV2(
          var=var.handle,
          accum=accum.handle,
          linear=linear.handle,
          grad=grad,
          lr=coefficients['lr_t'],
          l1=coefficients['l1_regularization_strength'],
          l2=adjusted_l2_regularization_strength,
          l2_shrinkage=coefficients['l2_shrinkage_regularization_strength'],
          lr_power=coefficients['learning_rate_power'],
          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))

accum = self.get_slot(var, 'accumulator')
    linear = self.get_slot(var, 'linear')

if self._l2_shrinkage_regularization_strength <= 0.0:
      return gen_training_ops.ResourceSparseApplyFtrl(
          var=var.handle,
          accum=accum.handle,
          linear=linear.handle,
          grad=grad,
          indices=indices,
          lr=coefficients['lr_t'],
          l1=coefficients['l1_regularization_strength'],
          l2=adjusted_l2_regularization_strength,
          lr_power=coefficients['learning_rate_power'],
          use_locking=self._use_locking)
    else:
      return gen_training_ops.ResourceSparseApplyFtrlV2(
          var=var.handle,
          accum=accum.handle,
          linear=linear.handle,
          grad=grad,
          indices=indices,
          lr=coefficients['lr_t'],
          l1=coefficients['l1_regularization_strength'],
          l2=adjusted_l2_regularization_strength,
          l2_shrinkage=coefficients['l2_shrinkage_regularization_strength'],
          lr_power=coefficients['learning_rate_power'],
          use_locking=self._use_locking)

def get_config(self):
    config = super(Ftrl, self).get_config()
    config.update({
        'learning_rate':
            self._serialize_hyperparameter('learning_rate'),
        'decay':
            self._serialize_hyperparameter('decay'),
        'initial_accumulator_value':
            self._initial_accumulator_value,
        'learning_rate_power':
            self._serialize_hyperparameter('learning_rate_power'),
        'l1_regularization_strength':
            self._serialize_hyperparameter('l1_regularization_strength'),
        'l2_regularization_strength':
            self._serialize_hyperparameter('l2_regularization_strength'),
        'beta':
            self._serialize_hyperparameter('beta'),
        'l2_shrinkage_regularization_strength':
            self._l2_shrinkage_regularization_strength,
    })
    return config