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# Copyright 2015 Google Inc. 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 for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import constant_op
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
class FtrlOptimizer(optimizer.Optimizer):
"""Optimizer that implements the FTRL algorithm.
See this [paper](
https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
@@__init__
"""
def __init__(self, learning_rate,
learning_rate_power=-0.5,
initial_accumulator_value=0.1,
l1_regularization_strength=0.0,
l2_regularization_strength=0.0,
use_locking=False, name="Ftrl"):
"""Construct a new FTRL optimizer.
Args:
learning_rate: A float value or a constant float `Tensor`.
learning_rate_power: A float value, must be less or equal to zero.
initial_accumulator_value: The starting value for accumulators.
Only 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.
use_locking: If `True` use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Ftrl".
Raises:
ValueError: If one of the arguments is invalid.
"""
super(FtrlOptimizer, self).__init__(use_locking, name)
if initial_accumulator_value <= 0.0:
raise ValueError("initial_accumulator_value %f needs to be positive" %
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)
self._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._learning_rate_tensor = None
self._learning_rate_power_tensor = None
self._l1_regularization_strength_tensor = None
self._l2_regularization_strength_tensor = None
def _create_slots(self, var_list):
# Create the "accum" and "linear" slots.
for v in var_list:
val = constant_op.constant(self._initial_accumulator_value,
dtype=v.dtype, shape=v.get_shape())
self._get_or_make_slot(v, val, "accum", self._name)
self._zeros_slot(v, "linear", self._name)
def _prepare(self):
self._learning_rate_tensor = ops.convert_to_tensor(
self._learning_rate,
name="learning_rate")
self._l1_regularization_strength_tensor = ops.convert_to_tensor(
self._l1_regularization_strength,
name="l1_regularization_strength")
self._l2_regularization_strength_tensor = ops.convert_to_tensor(
self._l2_regularization_strength,
name="l2_regularization_strength")
self._learning_rate_power_tensor = ops.convert_to_tensor(
self._learning_rate_power,
name="learning_rate_power")
def _apply_dense(self, grad, var):
accum = self.get_slot(var, "accum")
linear = self.get_slot(var, "linear")
return training_ops.apply_ftrl(
var, accum, linear, grad, self._learning_rate_tensor,
self._l1_regularization_strength_tensor,
self._l2_regularization_strength_tensor,
self._learning_rate_power_tensor,
use_locking=self._use_locking)
def _apply_sparse(self, grad, var):
accum = self.get_slot(var, "accum")
linear = self.get_slot(var, "linear")
return training_ops.sparse_apply_ftrl(
var, accum, linear, grad.values, grad.indices,
self._learning_rate_tensor,
self._l1_regularization_strength_tensor,
self._l2_regularization_strength_tensor,
self._learning_rate_power_tensor,
use_locking=self._use_locking)