wehub-resource-sync
264 lines
11 KiB
Python
264 lines
11 KiB
Python
# Copyright 2018 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-proximal optimizer implementation."""
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# pylint: disable=g-classes-have-attributes
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from tensorflow.python.keras.optimizer_v2 import optimizer_v2
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_training_ops
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import math_ops
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class Ftrl(optimizer_v2.OptimizerV2):
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r"""Optimizer that implements the FTRL algorithm.
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"Follow The Regularized Leader" (FTRL) is an optimization algorithm developed
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at Google for click-through rate prediction in the early 2010s. It is most
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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 = (sqrt(n) - sqrt(prev_n)) / 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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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, or a schedule that is a
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`tf.keras.optimizers.schedules.LearningRateSchedule`. The learning rate.
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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
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a fixed learning rate.
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initial_accumulator_value: The starting value for accumulators.
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Only zero or positive values are allowed.
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l1_regularization_strength: A float value, must be greater than or
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equal to zero. Defaults to 0.0.
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l2_regularization_strength: A float value, must be greater than or
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equal to zero. Defaults to 0.0.
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name: Optional name prefix for the operations created when applying
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gradients. Defaults to `"Ftrl"`.
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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.
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Defaults to 0.0.
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**kwargs: Keyword arguments. Allowed to be one of
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`"clipnorm"` or `"clipvalue"`.
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`"clipnorm"` (float) clips gradients by norm; `"clipvalue"` (float) clips
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gradients by value.
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Reference:
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- [McMahan et al., 2013](
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https://research.google.com/pubs/archive/41159.pdf)
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"""
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def __init__(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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name='Ftrl',
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l2_shrinkage_regularization_strength=0.0,
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beta=0.0,
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**kwargs):
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super(Ftrl, self).__init__(name, **kwargs)
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if initial_accumulator_value < 0.0:
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raise ValueError(
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'initial_accumulator_value %f needs to be positive or zero' %
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initial_accumulator_value)
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if learning_rate_power > 0.0:
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raise ValueError('learning_rate_power %f needs to be negative or zero' %
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learning_rate_power)
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if l1_regularization_strength < 0.0:
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raise ValueError(
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'l1_regularization_strength %f needs to be positive or zero' %
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l1_regularization_strength)
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if l2_regularization_strength < 0.0:
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raise ValueError(
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'l2_regularization_strength %f needs to be positive or zero' %
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l2_regularization_strength)
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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 %f needs to be positive'
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' or zero' % l2_shrinkage_regularization_strength)
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self._set_hyper('learning_rate', learning_rate)
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self._set_hyper('decay', self._initial_decay)
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self._set_hyper('learning_rate_power', learning_rate_power)
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self._set_hyper('l1_regularization_strength', l1_regularization_strength)
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self._set_hyper('l2_regularization_strength', l2_regularization_strength)
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self._set_hyper('beta', beta)
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self._initial_accumulator_value = initial_accumulator_value
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self._l2_shrinkage_regularization_strength = (
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l2_shrinkage_regularization_strength)
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def _create_slots(self, var_list):
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# Create the "accum" and "linear" slots.
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for var in var_list:
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dtype = var.dtype.base_dtype
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init = init_ops.constant_initializer(
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self._initial_accumulator_value, dtype=dtype)
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self.add_slot(var, 'accumulator', init)
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self.add_slot(var, 'linear')
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def _prepare_local(self, var_device, var_dtype, apply_state):
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super(Ftrl, self)._prepare_local(var_device, var_dtype, apply_state)
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apply_state[(var_device, var_dtype)].update(
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dict(
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learning_rate_power=array_ops.identity(
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self._get_hyper('learning_rate_power', var_dtype)),
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l1_regularization_strength=array_ops.identity(
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self._get_hyper('l1_regularization_strength', var_dtype)),
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l2_regularization_strength=array_ops.identity(
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self._get_hyper('l2_regularization_strength', var_dtype)),
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beta=array_ops.identity(self._get_hyper('beta', var_dtype)),
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l2_shrinkage_regularization_strength=math_ops.cast(
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self._l2_shrinkage_regularization_strength, var_dtype)))
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def _resource_apply_dense(self, grad, var, apply_state=None):
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var_device, var_dtype = var.device, var.dtype.base_dtype
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coefficients = ((apply_state or {}).get((var_device, var_dtype))
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or self._fallback_apply_state(var_device, var_dtype))
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# Adjust L2 regularization strength to include beta to avoid the underlying
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# TensorFlow ops needing to include it.
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adjusted_l2_regularization_strength = (
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coefficients['l2_regularization_strength'] + coefficients['beta'] /
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(2. * coefficients['lr_t']))
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accum = self.get_slot(var, 'accumulator')
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linear = self.get_slot(var, 'linear')
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if self._l2_shrinkage_regularization_strength <= 0.0:
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return gen_training_ops.ResourceApplyFtrl(
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var=var.handle,
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accum=accum.handle,
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linear=linear.handle,
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grad=grad,
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lr=coefficients['lr_t'],
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l1=coefficients['l1_regularization_strength'],
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l2=adjusted_l2_regularization_strength,
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lr_power=coefficients['learning_rate_power'],
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use_locking=self._use_locking)
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else:
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return gen_training_ops.ResourceApplyFtrlV2(
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var=var.handle,
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accum=accum.handle,
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linear=linear.handle,
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grad=grad,
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lr=coefficients['lr_t'],
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l1=coefficients['l1_regularization_strength'],
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l2=adjusted_l2_regularization_strength,
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l2_shrinkage=coefficients['l2_shrinkage_regularization_strength'],
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lr_power=coefficients['learning_rate_power'],
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use_locking=self._use_locking)
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def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
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var_device, var_dtype = var.device, var.dtype.base_dtype
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coefficients = ((apply_state or {}).get((var_device, var_dtype))
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or self._fallback_apply_state(var_device, var_dtype))
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# Adjust L2 regularization strength to include beta to avoid the underlying
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# TensorFlow ops needing to include it.
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adjusted_l2_regularization_strength = (
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coefficients['l2_regularization_strength'] + coefficients['beta'] /
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(2. * coefficients['lr_t']))
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accum = self.get_slot(var, 'accumulator')
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linear = self.get_slot(var, 'linear')
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if self._l2_shrinkage_regularization_strength <= 0.0:
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return gen_training_ops.ResourceSparseApplyFtrl(
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var=var.handle,
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accum=accum.handle,
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linear=linear.handle,
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grad=grad,
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indices=indices,
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lr=coefficients['lr_t'],
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l1=coefficients['l1_regularization_strength'],
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l2=adjusted_l2_regularization_strength,
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lr_power=coefficients['learning_rate_power'],
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use_locking=self._use_locking)
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else:
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return gen_training_ops.ResourceSparseApplyFtrlV2(
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var=var.handle,
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accum=accum.handle,
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linear=linear.handle,
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grad=grad,
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indices=indices,
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lr=coefficients['lr_t'],
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l1=coefficients['l1_regularization_strength'],
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l2=adjusted_l2_regularization_strength,
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l2_shrinkage=coefficients['l2_shrinkage_regularization_strength'],
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lr_power=coefficients['learning_rate_power'],
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use_locking=self._use_locking)
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def get_config(self):
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config = super(Ftrl, self).get_config()
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config.update({
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'learning_rate':
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self._serialize_hyperparameter('learning_rate'),
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'decay':
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self._initial_decay,
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'initial_accumulator_value':
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self._initial_accumulator_value,
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'learning_rate_power':
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self._serialize_hyperparameter('learning_rate_power'),
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'l1_regularization_strength':
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self._serialize_hyperparameter('l1_regularization_strength'),
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'l2_regularization_strength':
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self._serialize_hyperparameter('l2_regularization_strength'),
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'beta':
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self._serialize_hyperparameter('beta'),
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'l2_shrinkage_regularization_strength':
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self._l2_shrinkage_regularization_strength,
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})
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return config
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