#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2018-02-11 00:49:47
# @Author : Luo Chuwei
# @Link : http://github.com/luochuwei
# @Usage : AMSGrad
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.training import optimizer
class AMSGrad(optimizer.Optimizer):
"""Optimizer that implements the AMSGrad algorithm.
See (https://openreview.net/pdf?id=ryQu7f-RZ)
"""
def __init__(self, learning_rate=0.01, beta1=0.9, beta2=0.99, epsilon=1e-8, use_locking=False, name="AMSGrad"):
"""Construct a new AMSGrad optimizer.
Args:
learning_rate: A Tensor or a floating point value. The
learning rate.
beta1: A float value or a constant float tensor. The
exponential decay rate for the 1st moment estimates.
beta2: A float value or a constant float tensor. The
exponential decay rate for the 2nd moment estimates.
epsilon: A small constant for numerical stability.
name: Optional name for the operations created when applying gradients.
Defaults to "AMSGrad".
"""
super(AMSGrad, self).__init__(use_locking, name)
self._lr = learning_rate
self._beta1 = beta1
self._beta2 = beta2
self._epsilon = epsilon
self._lr_t = None
self._beta1_t = None
self._beta2_t = None
self._epsilon_t = None
self._beta1_power = None
self._beta2_power = None
def _create_slots(self, var_list):
first_var = min(var_list, key=lambda x: x.name)
create_new = self._beta1_power is None
if not create_new and context.in_graph_mode():
create_new = (self._beta1_power.graph is not first_var.graph)
if create_new:
with ops.colocate_with(first_var):
self._beta1_power = variable_scope.variable(self._beta1, name="beta1_power", trainable=False)
self._beta2_power = variable_scope.variable(self._beta2, name="beta2_power", trainable=False)
# Create slots for the first and second moments.
for v in var_list :
self._zeros_slot(v, "m", self._name)
self._zeros_slot(v, "v", self._name)
self._zeros_slot(v, "vhat", self._name)
def _prepare(self):
self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate")
self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1")
self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2")
self._epsilon_t = ops.convert_to_tensor(self._epsilon, name="epsilon")
def _apply_dense(self, grad, var):
beta1_power = math_ops.cast(self._beta1_power, var.dtype.base_dtype)
beta2_power = math_ops.cast(self._beta2_power, var.dtype.base_dtype)
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power))
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, "m")
m_scaled_g_values = grad * (1 - beta1_t)
m_t = state_ops.assign(m, beta1_t * m + m_scaled_g_values, use_locking=self._use_locking)
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
v = self.get_slot(var, "v")
v_scaled_g_values = (grad * grad) * (1 - beta2_t)
v_t = state_ops.assign(v, beta2_t * v + v_scaled_g_values, use_locking=self._use_locking)
# amsgrad
vhat = self.get_slot(var, "vhat")
vhat_t = state_ops.assign(vhat, math_ops.maximum(v_t, vhat))
v_sqrt = math_ops.sqrt(vhat_t)
var_update = state_ops.assign_sub(var, lr * m_t / (v_sqrt + epsilon_t), use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, m_t, v_t, vhat_t])
def _apply_sparse(self, grad, var):
return raise NotImplementedError("Sparse gradient updates are not supported yet.")
