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# Copyright 2017 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.
# ==============================================================================
"""Implementation of PowerSign."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
class PowerSignOptimizer(optimizer.Optimizer):
"""Optimizer that implements the PowerSign update.
See [Bello et al., ICML2017],
[Neural Optimizer Search with RL](https://arxiv.org/abs/1709.07417).
"""
def __init__(self,
learning_rate=0.1,
base=math.e,
beta=0.9,
sign_decay_fn=None,
use_locking=False,
name='PowerSignOptimizer'):
"""Constructs a new PowerSignOptimizer object.
Initialization:
```
m_0 <- 0 (Initialize initial 1st moment vector)
t <- 0 (Initialize timestep)
```
Update:
```
t <- t + 1
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
sign_decay <- sign_decay_fn(t)
update <- base ** (sign_decay * sign(g) * sign(m)) * g
variable <- variable - lr_t * update
```
Example usage for PowerSign-cd (PowerSign with cosine sign decay)
```
decay_steps = 1000
linear_decay_fn = sign_decays.get_cosine_decay_fn(decay_steps)
opt = PowerSignOptimizer(learning_rate=0.1, sign_decay_fn=linear_decay_fn)
```
Args:
learning_rate: learning_rate used when taking a step.
base: base used in optimizer.
beta: decay used for computing the moving average m.
sign_decay_fn: decay function applied to the sign(g) sign(m) quantity.
Takes global_step as an argument. See sign_decay.py for some examples.
use_locking: If True, use locks for update operations.
name: Optional name for the operations created iwhen applying gradients.
Defaults to "PowerSignOptimizer".
"""
super(PowerSignOptimizer, self).__init__(use_locking, name)
self._lr = learning_rate
self._beta = beta
self._logbase = math.log(base)
self._sign_decay_fn = sign_decay_fn
# Tensor versions of the constructor arguments, created in _prepare().
self._lr_t = None
self._beta_t = None
self._logbase_t = None
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
if self._sign_decay_fn is not None:
self._sign_decay_t = ops.convert_to_tensor(
self._sign_decay_fn(global_step), name='sign_decay')
return super(PowerSignOptimizer, self).apply_gradients(
grads_and_vars, global_step=global_step, name=name)
def _create_slots(self, var_list):
# Create slots for the first moment.
for v in var_list:
self._zeros_slot(v, 'm', self._name)
def _prepare(self):
self._lr_t = ops.convert_to_tensor(self._lr, name='learning_rate')
self._beta_t = ops.convert_to_tensor(self._beta, name='beta')
self._logbase_t = ops.convert_to_tensor(self._logbase, name='logbase')
if self._sign_decay_fn is None:
self._sign_decay_t = ops.convert_to_tensor(1.0, name='sign_decay')
def _apply_dense(self, grad, var):
m = self.get_slot(var, 'm')
return training_ops.apply_power_sign(
var,
m,
math_ops.cast(self._lr_t, var.dtype.base_dtype),
math_ops.cast(self._logbase_t, var.dtype.base_dtype),
math_ops.cast(self._sign_decay_t, var.dtype.base_dtype),
math_ops.cast(self._beta_t, var.dtype.base_dtype),
grad,
use_locking=self._use_locking).op
def _resource_apply_dense(self, grad, var):
m = self.get_slot(var, 'm')
return training_ops.resource_apply_power_sign(
var.handle,
m.handle,
math_ops.cast(self._lr_t, var.dtype.base_dtype),
math_ops.cast(self._logbase_t, var.dtype.base_dtype),
math_ops.cast(self._sign_decay_t, var.dtype.base_dtype),
math_ops.cast(self._beta_t, var.dtype.base_dtype),
grad,
use_locking=self._use_locking)
def _apply_sparse(self, grad, var):
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta_t = math_ops.cast(self._beta_t, var.dtype.base_dtype)
logbase_t = math_ops.cast(self._logbase_t, var.dtype.base_dtype)
e_t = math_ops.cast(math.e, var.dtype.base_dtype)
m = self.get_slot(var, 'm')
m_t = state_ops.assign(
m, (m * beta_t) + (grad * (1 - beta_t)), use_locking=self._use_locking)
sign_g = ops.IndexedSlices(
math_ops.sign(grad.values), grad.indices, dense_shape=grad.dense_shape)
sign_gm = ops.IndexedSlices(
array_ops.gather(math_ops.sign(m_t), sign_g.indices) * sign_g.values,
sign_g.indices,
dense_shape=sign_g.dense_shape)
sign_decayed = math_ops.cast(
self._sign_decay_t, var.dtype.base_dtype)
multiplier_values = math_ops.pow(
e_t, logbase_t * sign_decayed * sign_gm.values)
multiplier = ops.IndexedSlices(
multiplier_values, sign_gm.indices, dense_shape=sign_gm.dense_shape)
final_update = ops.IndexedSlices(
lr_t * multiplier.values * grad.values,
multiplier.indices,
dense_shape=multiplier.dense_shape)
var_update = state_ops.scatter_sub(
var,
final_update.indices,
final_update.values,
use_locking=self._use_locking)
return control_flow_ops.group(* [var_update, m_t])
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