#! /usr/bin/python
# -*- coding: utf-8 -*-
"""AMSGrad Implementation based on the paper: "On the Convergence of Adam and Beyond" (ICLR 2018)
Article Link: https://openreview.net/pdf?id=ryQu7f-RZ
Original Implementation by: https://github.com/taki0112/AMSGrad-Tensorflow
"""
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tensorflow.python.ops import (control_flow_ops, math_ops, resource_variable_ops, state_ops, variable_scope)
from tensorflow.python.training import optimizer
[docs]class AMSGrad(optimizer.Optimizer):
"""Implementation of the AMSGrad optimization algorithm.
See: `On the Convergence of Adam and Beyond - [Reddi et al., 2018] <https://openreview.net/pdf?id=ryQu7f-RZ>`__.
Parameters
----------
learning_rate: float
A Tensor or a floating point value. The learning rate.
beta1: float
A float value or a constant float tensor.
The exponential decay rate for the 1st moment estimates.
beta2: float
A float value or a constant float tensor.
The exponential decay rate for the 2nd moment estimates.
epsilon: float
A small constant for numerical stability.
This epsilon is "epsilon hat" in the Kingma and Ba paper
(in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper.
use_locking: bool
If True use locks for update operations.
name: str
Optional name for the operations created when applying gradients.
Defaults to "AMSGrad".
"""
def __init__(self, learning_rate=0.01, beta1=0.9, beta2=0.99, epsilon=1e-8, use_locking=False, name="AMSGrad"):
"""Construct a new Adam optimizer."""
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)
self._beta1_t = ops.convert_to_tensor(self._beta1)
self._beta2_t = ops.convert_to_tensor(self._beta2)
self._epsilon_t = ops.convert_to_tensor(self._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 _resource_apply_dense(self, grad, var):
var = var.handle
beta1_power = math_ops.cast(self._beta1_power, grad.dtype.base_dtype)
beta2_power = math_ops.cast(self._beta2_power, grad.dtype.base_dtype)
lr_t = math_ops.cast(self._lr_t, grad.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, grad.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, grad.dtype.base_dtype)
epsilon_t = math_ops.cast(self._epsilon_t, grad.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").handle
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").handle
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").handle
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_shared(self, grad, var, indices, scatter_add):
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, m * beta1_t, use_locking=self._use_locking)
with ops.control_dependencies([m_t]):
m_t = scatter_add(m, indices, m_scaled_g_values)
# 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, v * beta2_t, use_locking=self._use_locking)
with ops.control_dependencies([v_t]):
v_t = scatter_add(v, indices, v_scaled_g_values)
# 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 self._apply_sparse_shared(
grad.values,
var,
grad.indices,
lambda x, i, v: state_ops.
scatter_add( # pylint: disable=g-long-lambda
x, i, v, use_locking=self._use_locking
)
)
def _resource_scatter_add(self, x, i, v):
with ops.control_dependencies([resource_variable_ops.resource_scatter_add(x.handle, i, v)]):
return x.value()
def _resource_apply_sparse(self, grad, var, indices):
return self._apply_sparse_shared(grad, var, indices, self._resource_scatter_add)
def _finish(self, update_ops, name_scope):
# Update the power accumulators.
with ops.control_dependencies(update_ops):
with ops.colocate_with(self._beta1_power):
update_beta1 = self._beta1_power.assign(
self._beta1_power * self._beta1_t, use_locking=self._use_locking
)
update_beta2 = self._beta2_power.assign(
self._beta2_power * self._beta2_t, use_locking=self._use_locking
)
return control_flow_ops.group(*update_ops + [update_beta1, update_beta2], name=name_scope)