476 lines
20 KiB
Python
476 lines
20 KiB
Python
![]() |
# Copyright 2020 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.
|
||
|
# ==============================================================================
|
||
|
"""Adam optimizer implementation."""
|
||
|
# pylint: disable=g-classes-have-attributes
|
||
|
|
||
|
from tensorflow.python.eager import def_function
|
||
|
from tensorflow.python.framework import indexed_slices
|
||
|
from tensorflow.python.framework import ops
|
||
|
from tensorflow.python.keras import backend_config
|
||
|
from tensorflow.python.keras.optimizer_v2 import optimizer_v2
|
||
|
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 gen_training_ops
|
||
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
||
|
|
||
|
@keras_export('keras.optimizers.Adam')
|
||
|
class Adam(optimizer_v2.OptimizerV2):
|
||
|
r"""Optimizer that implements the Adam algorithm.
|
||
|
|
||
|
Adam optimization is a stochastic gradient descent method that is based on
|
||
|
adaptive estimation of first-order and second-order moments.
|
||
|
|
||
|
According to
|
||
|
[Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
|
||
|
the method is "*computationally
|
||
|
efficient, has little memory requirement, invariant to diagonal rescaling of
|
||
|
gradients, and is well suited for problems that are large in terms of
|
||
|
data/parameters*".
|
||
|
|
||
|
Args:
|
||
|
learning_rate: A `Tensor`, floating point value, or a schedule that is a
|
||
|
`tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable
|
||
|
that takes no arguments and returns the actual value to use, The
|
||
|
learning rate. Defaults to 0.001.
|
||
|
beta_1: A float value or a constant float tensor, or a callable
|
||
|
that takes no arguments and returns the actual value to use. The
|
||
|
exponential decay rate for the 1st moment estimates. Defaults to 0.9.
|
||
|
beta_2: A float value or a constant float tensor, or a callable
|
||
|
that takes no arguments and returns the actual value to use, The
|
||
|
exponential decay rate for the 2nd moment estimates. Defaults to 0.999.
|
||
|
epsilon: 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. Defaults to
|
||
|
1e-7.
|
||
|
amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm from
|
||
|
the paper "On the Convergence of Adam and beyond". Defaults to `False`.
|
||
|
name: Optional name for the operations created when applying gradients.
|
||
|
Defaults to `"Adam"`.
|
||
|
**kwargs: Keyword arguments. Allowed to be one of
|
||
|
`"clipnorm"` or `"clipvalue"`.
|
||
|
`"clipnorm"` (float) clips gradients by norm; `"clipvalue"` (float) clips
|
||
|
gradients by value.
|
||
|
|
||
|
Usage:
|
||
|
|
||
|
>>> opt = tf.keras.optimizers.Adam(learning_rate=0.1)
|
||
|
>>> var1 = tf.Variable(10.0)
|
||
|
>>> loss = lambda: (var1 ** 2)/2.0 # d(loss)/d(var1) == var1
|
||
|
>>> step_count = opt.minimize(loss, [var1]).numpy()
|
||
|
>>> # The first step is `-learning_rate*sign(grad)`
|
||
|
>>> var1.numpy()
|
||
|
9.9
|
||
|
|
||
|
Reference:
|
||
|
- [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
|
||
|
- [Reddi et al., 2018](
|
||
|
https://openreview.net/pdf?id=ryQu7f-RZ) for `amsgrad`.
|
||
|
|
||
|
Notes:
|
||
|
|
||
|
The default value of 1e-7 for epsilon might not be a good default in
|
||
|
general. For example, when training an Inception network on ImageNet a
|
||
|
current good choice is 1.0 or 0.1. Note that since Adam uses the
|
||
|
formulation just before Section 2.1 of the Kingma and Ba paper rather than
|
||
|
the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon
|
||
|
hat" in the paper.
|
||
|
|
||
|
The sparse implementation of this algorithm (used when the gradient is an
|
||
|
IndexedSlices object, typically because of `tf.gather` or an embedding
|
||
|
lookup in the forward pass) does apply momentum to variable slices even if
|
||
|
they were not used in the forward pass (meaning they have a gradient equal
|
||
|
to zero). Momentum decay (beta1) is also applied to the entire momentum
|
||
|
accumulator. This means that the sparse behavior is equivalent to the dense
|
||
|
behavior (in contrast to some momentum implementations which ignore momentum
|
||
|
unless a variable slice was actually used).
|
||
|
"""
|
||
|
|
||
|
_HAS_AGGREGATE_GRAD = True
|
||
|
|
||
|
def __init__(self,
|
||
|
learning_rate=0.001,
|
||
|
beta_1=0.9,
|
||
|
beta_2=0.999,
|
||
|
epsilon=1e-7,
|
||
|
amsgrad=False,
|
||
|
name='Adam',
|
||
|
**kwargs):
|
||
|
super(Adam, self).__init__(name, **kwargs)
|
||
|
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
|
||
|
self._set_hyper('decay', self._initial_decay)
|
||
|
self._set_hyper('beta_1', beta_1)
|
||
|
self._set_hyper('beta_2', beta_2)
|
||
|
self.epsilon = epsilon or backend_config.epsilon()
|
||
|
self.amsgrad = amsgrad
|
||
|
|
||
|
def _create_slots(self, var_list):
|
||
|
# Create slots for the first and second moments.
|
||
|
# Separate for-loops to respect the ordering of slot variables from v1.
|
||
|
for var in var_list:
|
||
|
self.add_slot(var, 'm')
|
||
|
for var in var_list:
|
||
|
self.add_slot(var, 'v')
|
||
|
if self.amsgrad:
|
||
|
for var in var_list:
|
||
|
self.add_slot(var, 'vhat')
|
||
|
|
||
|
def _prepare_local(self, var_device, var_dtype, apply_state):
|
||
|
super(Adam, self)._prepare_local(var_device, var_dtype, apply_state)
|
||
|
|
||
|
local_step = math_ops.cast(self.iterations + 1, var_dtype)
|
||
|
beta_1_t = array_ops.identity(self._get_hyper('beta_1', var_dtype))
|
||
|
beta_2_t = array_ops.identity(self._get_hyper('beta_2', var_dtype))
|
||
|
beta_1_power = math_ops.pow(beta_1_t, local_step)
|
||
|
beta_2_power = math_ops.pow(beta_2_t, local_step)
|
||
|
lr = (apply_state[(var_device, var_dtype)]['lr_t'] *
|
||
|
(math_ops.sqrt(1 - beta_2_power) / (1 - beta_1_power)))
|
||
|
apply_state[(var_device, var_dtype)].update(
|
||
|
dict(
|
||
|
lr=lr,
|
||
|
epsilon=ops.convert_to_tensor_v2_with_dispatch(
|
||
|
self.epsilon, var_dtype),
|
||
|
beta_1_t=beta_1_t,
|
||
|
beta_1_power=beta_1_power,
|
||
|
one_minus_beta_1_t=1 - beta_1_t,
|
||
|
beta_2_t=beta_2_t,
|
||
|
beta_2_power=beta_2_power,
|
||
|
one_minus_beta_2_t=1 - beta_2_t))
|
||
|
|
||
|
def set_weights(self, weights):
|
||
|
params = self.weights
|
||
|
# If the weights are generated by Keras V1 optimizer, it includes vhats
|
||
|
# even without amsgrad, i.e, V1 optimizer has 3x + 1 variables, while V2
|
||
|
# optimizer has 2x + 1 variables. Filter vhats out for compatibility.
|
||
|
num_vars = int((len(params) - 1) / 2)
|
||
|
if len(weights) == 3 * num_vars + 1:
|
||
|
weights = weights[:len(params)]
|
||
|
super(Adam, self).set_weights(weights)
|
||
|
|
||
|
def _resource_apply_dense(self, grad, var, apply_state=None):
|
||
|
var_device, var_dtype = var.device, var.dtype.base_dtype
|
||
|
coefficients = ((apply_state or {}).get((var_device, var_dtype))
|
||
|
or self._fallback_apply_state(var_device, var_dtype))
|
||
|
|
||
|
m = self.get_slot(var, 'm')
|
||
|
v = self.get_slot(var, 'v')
|
||
|
|
||
|
if not self.amsgrad:
|
||
|
return gen_training_ops.ResourceApplyAdam(
|
||
|
var=var.handle,
|
||
|
m=m.handle,
|
||
|
v=v.handle,
|
||
|
beta1_power=coefficients['beta_1_power'],
|
||
|
beta2_power=coefficients['beta_2_power'],
|
||
|
lr=coefficients['lr_t'],
|
||
|
beta1=coefficients['beta_1_t'],
|
||
|
beta2=coefficients['beta_2_t'],
|
||
|
epsilon=coefficients['epsilon'],
|
||
|
grad=grad,
|
||
|
use_locking=self._use_locking)
|
||
|
else:
|
||
|
vhat = self.get_slot(var, 'vhat')
|
||
|
return gen_training_ops.ResourceApplyAdamWithAmsgrad(
|
||
|
var=var.handle,
|
||
|
m=m.handle,
|
||
|
v=v.handle,
|
||
|
vhat=vhat.handle,
|
||
|
beta1_power=coefficients['beta_1_power'],
|
||
|
beta2_power=coefficients['beta_2_power'],
|
||
|
lr=coefficients['lr_t'],
|
||
|
beta1=coefficients['beta_1_t'],
|
||
|
beta2=coefficients['beta_2_t'],
|
||
|
epsilon=coefficients['epsilon'],
|
||
|
grad=grad,
|
||
|
use_locking=self._use_locking)
|
||
|
|
||
|
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
|
||
|
var_device, var_dtype = var.device, var.dtype.base_dtype
|
||
|
coefficients = ((apply_state or {}).get((var_device, var_dtype))
|
||
|
or self._fallback_apply_state(var_device, var_dtype))
|
||
|
|
||
|
# m_t = beta1 * m + (1 - beta1) * g_t
|
||
|
m = self.get_slot(var, 'm')
|
||
|
m_scaled_g_values = grad * coefficients['one_minus_beta_1_t']
|
||
|
m_t = state_ops.assign(m, m * coefficients['beta_1_t'],
|
||
|
use_locking=self._use_locking)
|
||
|
with ops.control_dependencies([m_t]):
|
||
|
m_t = self._resource_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) * coefficients['one_minus_beta_2_t']
|
||
|
v_t = state_ops.assign(v, v * coefficients['beta_2_t'],
|
||
|
use_locking=self._use_locking)
|
||
|
with ops.control_dependencies([v_t]):
|
||
|
v_t = self._resource_scatter_add(v, indices, v_scaled_g_values)
|
||
|
|
||
|
if not self.amsgrad:
|
||
|
v_sqrt = math_ops.sqrt(v_t)
|
||
|
var_update = state_ops.assign_sub(
|
||
|
var, coefficients['lr'] * m_t / (v_sqrt + coefficients['epsilon']),
|
||
|
use_locking=self._use_locking)
|
||
|
return control_flow_ops.group(*[var_update, m_t, v_t])
|
||
|
else:
|
||
|
v_hat = self.get_slot(var, 'vhat')
|
||
|
v_hat_t = math_ops.maximum(v_hat, v_t)
|
||
|
with ops.control_dependencies([v_hat_t]):
|
||
|
v_hat_t = state_ops.assign(
|
||
|
v_hat, v_hat_t, use_locking=self._use_locking)
|
||
|
v_hat_sqrt = math_ops.sqrt(v_hat_t)
|
||
|
var_update = state_ops.assign_sub(
|
||
|
var,
|
||
|
coefficients['lr'] * m_t / (v_hat_sqrt + coefficients['epsilon']),
|
||
|
use_locking=self._use_locking)
|
||
|
return control_flow_ops.group(*[var_update, m_t, v_t, v_hat_t])
|
||
|
|
||
|
def get_config(self):
|
||
|
config = super(Adam, self).get_config()
|
||
|
config.update({
|
||
|
'learning_rate': self._serialize_hyperparameter('learning_rate'),
|
||
|
'decay': self._initial_decay,
|
||
|
'beta_1': self._serialize_hyperparameter('beta_1'),
|
||
|
'beta_2': self._serialize_hyperparameter('beta_2'),
|
||
|
'epsilon': self.epsilon,
|
||
|
'amsgrad': self.amsgrad,
|
||
|
})
|
||
|
return config
|
||
|
|
||
|
|
||
|
class NonFusedAdam(optimizer_v2.OptimizerV2):
|
||
|
r"""Optimizer that implements the Adam algorithm without fused kernels.
|
||
|
|
||
|
Adam optimization is a stochastic gradient descent method that is based on
|
||
|
adaptive estimation of first-order and second-order moments.
|
||
|
According to the paper
|
||
|
[Adam: A Method for Stochastic Optimization. Kingma et al.,
|
||
|
2014](http://arxiv.org/abs/1412.6980), the method is "*computationally
|
||
|
efficient, has little memory requirement, invariant to diagonal rescaling of
|
||
|
gradients, and is well suited for problems that are large in terms of
|
||
|
data/parameters*".
|
||
|
|
||
|
For AMSGrad see [On The Convergence Of Adam And Beyond.
|
||
|
Reddi et al., 5-8](https://openreview.net/pdf?id=ryQu7f-RZ).
|
||
|
|
||
|
**If amsgrad = False**:
|
||
|
|
||
|
initialize $m_0$ as 1st moment vector
|
||
|
initialize $v_0$ as 2nd moment vector
|
||
|
|
||
|
The update rule for $\theta$ with gradient $g$ uses an optimization
|
||
|
described at the end of section 2 of the paper:
|
||
|
|
||
|
$$lr_t = \mathrm{learning\_rate} *
|
||
|
\sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$
|
||
|
$$m_t = \beta_1 * m_{t-1} + (1 - \beta_1) * g$$
|
||
|
$$v_t = \beta_2 * v_{t-1} + (1 - \beta_2) * g^2$$
|
||
|
$$\theta_t = \theta_{t-1} - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$
|
||
|
|
||
|
**If amsgrad = True**:
|
||
|
|
||
|
initialize $m_0$ as 1st moment vector
|
||
|
initialize $v_0$ as 2nd moment vector
|
||
|
initialize $\hat{v}_0$ as 2nd moment vector
|
||
|
|
||
|
The update rule for $\theta$ with gradient $g$ uses an optimization
|
||
|
described at the end of section 2 of the paper:
|
||
|
|
||
|
$$lr_t = \mathrm{learning\_rate} *
|
||
|
\sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$
|
||
|
|
||
|
$$m_t = \beta_1 * m_{t-1} + (1 - \beta_1) * g$$
|
||
|
$$v_t = \beta_2 * v_{t-1} + (1 - \beta_2) * g^2$$
|
||
|
$$\hat{v}_t = \max(\hat{v}_{t-1}, v_t)$$
|
||
|
$$\theta_t = \theta_{t-1} - lr_t * m_t / (\sqrt{\hat{v}_t} + \epsilon)$$
|
||
|
|
||
|
The default value of 1e-7 for epsilon might not be a good default in
|
||
|
general. For example, when training an Inception network on ImageNet a
|
||
|
current good choice is 1.0 or 0.1. Note that since Adam uses the
|
||
|
formulation just before Section 2.1 of the Kingma and Ba paper rather than
|
||
|
the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon
|
||
|
hat" in the paper.
|
||
|
|
||
|
The sparse implementation of this algorithm (used when the gradient is an
|
||
|
IndexedSlices object, typically because of `tf.gather` or an embedding
|
||
|
lookup in the forward pass) does apply momentum to variable slices even if
|
||
|
they were not used in the forward pass (meaning they have a gradient equal
|
||
|
to zero). Momentum decay (beta1) is also applied to the entire momentum
|
||
|
accumulator. This means that the sparse behavior is equivalent to the dense
|
||
|
behavior (in contrast to some momentum implementations which ignore momentum
|
||
|
unless a variable slice was actually used).
|
||
|
|
||
|
Usage:
|
||
|
|
||
|
>>> opt = tf.keras.optimizers.Adam(learning_rate=0.1)
|
||
|
>>> var1 = tf.Variable(10.0)
|
||
|
>>> loss = lambda: (var1 ** 2)/2.0 # d(loss)/d(var1) == var1
|
||
|
>>> step_count = opt.minimize(loss, [var1]).numpy()
|
||
|
>>> # The first step is `-learning_rate*sign(grad)`
|
||
|
>>> var1.numpy()
|
||
|
9.9
|
||
|
"""
|
||
|
|
||
|
_HAS_AGGREGATE_GRAD = True
|
||
|
|
||
|
def __init__(self,
|
||
|
learning_rate=0.001,
|
||
|
beta_1=0.9,
|
||
|
beta_2=0.999,
|
||
|
epsilon=1e-7,
|
||
|
amsgrad=False,
|
||
|
name='Adam',
|
||
|
**kwargs):
|
||
|
"""Construct a new Adam optimizer.
|
||
|
|
||
|
Args:
|
||
|
learning_rate: A `Tensor`, floating point value, or a schedule that is a
|
||
|
`tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable that
|
||
|
takes no arguments and returns the actual value to use, The learning
|
||
|
rate. Defaults to 0.001.
|
||
|
beta_1: A float value or a constant float tensor, or a callable that takes
|
||
|
no arguments and returns the actual value to use. The exponential decay
|
||
|
rate for the 1st moment estimates. Defaults to 0.9.
|
||
|
beta_2: A float value or a constant float tensor, or a callable that takes
|
||
|
no arguments and returns the actual value to use, The exponential decay
|
||
|
rate for the 2nd moment estimates. Defaults to 0.999.
|
||
|
epsilon: 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. Defaults to
|
||
|
1e-7.
|
||
|
amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm from
|
||
|
the paper "On the Convergence of Adam and beyond". Defaults to `False`.
|
||
|
name: Optional name for the operations created when applying gradients.
|
||
|
Defaults to "Adam".
|
||
|
**kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`,
|
||
|
`decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip
|
||
|
gradients by value, `decay` is included for backward compatibility to
|
||
|
allow time inverse decay of learning rate. `lr` is included for backward
|
||
|
compatibility, recommended to use `learning_rate` instead.
|
||
|
"""
|
||
|
|
||
|
super(NonFusedAdam, self).__init__(name, **kwargs)
|
||
|
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
|
||
|
self._set_hyper('decay', self._initial_decay)
|
||
|
self._set_hyper('beta_1', beta_1)
|
||
|
self._set_hyper('beta_2', beta_2)
|
||
|
self.epsilon = epsilon or backend_config.epsilon()
|
||
|
self.amsgrad = amsgrad
|
||
|
|
||
|
def _create_slots(self, var_list):
|
||
|
# Create slots for the first and second moments.
|
||
|
# Separate for-loops to respect the ordering of slot variables from v1.
|
||
|
for var in var_list:
|
||
|
self.add_slot(var, 'm')
|
||
|
for var in var_list:
|
||
|
self.add_slot(var, 'v')
|
||
|
if self.amsgrad:
|
||
|
for var in var_list:
|
||
|
self.add_slot(var, 'vhat')
|
||
|
|
||
|
def _prepare_local(self, var_device, var_dtype, apply_state):
|
||
|
super(NonFusedAdam, self)._prepare_local(var_device, var_dtype, apply_state)
|
||
|
|
||
|
local_step = math_ops.cast(self.iterations + 1, var_dtype)
|
||
|
beta_1_t = array_ops.identity(self._get_hyper('beta_1', var_dtype))
|
||
|
beta_2_t = array_ops.identity(self._get_hyper('beta_2', var_dtype))
|
||
|
beta_1_power = math_ops.pow(beta_1_t, local_step)
|
||
|
beta_2_power = math_ops.pow(beta_2_t, local_step)
|
||
|
lr = (
|
||
|
apply_state[(var_device, var_dtype)]['lr_t'] *
|
||
|
(math_ops.sqrt(1 - beta_2_power) / (1 - beta_1_power)))
|
||
|
apply_state[(var_device, var_dtype)].update(
|
||
|
dict(
|
||
|
lr=lr,
|
||
|
epsilon=ops.convert_to_tensor_v2_with_dispatch(
|
||
|
self.epsilon, var_dtype),
|
||
|
beta_1_t=beta_1_t,
|
||
|
beta_1_power=beta_1_power,
|
||
|
one_minus_beta_1_t=1 - beta_1_t,
|
||
|
beta_2_t=beta_2_t,
|
||
|
beta_2_power=beta_2_power,
|
||
|
one_minus_beta_2_t=1 - beta_2_t))
|
||
|
|
||
|
def set_weights(self, weights):
|
||
|
params = self.weights
|
||
|
# If the weights are generated by Keras V1 optimizer, it includes vhats
|
||
|
# even without amsgrad, i.e, V1 optimizer has 3x + 1 variables, while V2
|
||
|
# optimizer has 2x + 1 variables. Filter vhats out for compatibility.
|
||
|
num_vars = int((len(params) - 1) / 2)
|
||
|
if len(weights) == 3 * num_vars + 1:
|
||
|
weights = weights[:len(params)]
|
||
|
super(NonFusedAdam, self).set_weights(weights)
|
||
|
|
||
|
@def_function.function(jit_compile=True)
|
||
|
def _resource_apply_dense(self, grad, var, apply_state=None):
|
||
|
var_device, var_dtype = var.device, var.dtype.base_dtype
|
||
|
coefficients = ((apply_state or {}).get((var_device, var_dtype)) or
|
||
|
self._fallback_apply_state(var_device, var_dtype))
|
||
|
|
||
|
m = self.get_slot(var, 'm')
|
||
|
v = self.get_slot(var, 'v')
|
||
|
|
||
|
alpha = (
|
||
|
coefficients['lr_t'] * math_ops.sqrt(1 - coefficients['beta_2_power']) /
|
||
|
(1 - coefficients['beta_1_power']))
|
||
|
m.assign_add((grad - m) * (1 - coefficients['beta_1_t']))
|
||
|
v.assign_add((math_ops.square(grad) - v) * (1 - coefficients['beta_2_t']))
|
||
|
if self.amsgrad:
|
||
|
vhat = self.get_slot(var, 'vhat')
|
||
|
vhat.assign(math_ops.maximum(vhat, v))
|
||
|
v = vhat
|
||
|
var.assign_sub(
|
||
|
(m * alpha) / (math_ops.sqrt(v) - coefficients['epsilon']))
|
||
|
|
||
|
@def_function.function(jit_compile=True)
|
||
|
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
|
||
|
var_device, var_dtype = var.device, var.dtype.base_dtype
|
||
|
coefficients = ((apply_state or {}).get((var_device, var_dtype)) or
|
||
|
self._fallback_apply_state(var_device, var_dtype))
|
||
|
|
||
|
# m_t = beta1 * m + (1 - beta1) * g_t
|
||
|
m = self.get_slot(var, 'm')
|
||
|
m_scaled_g_values = grad * coefficients['one_minus_beta_1_t']
|
||
|
m.assign(m * coefficients['beta_1_t'])
|
||
|
m.scatter_add(indexed_slices.IndexedSlices(m_scaled_g_values, indices))
|
||
|
|
||
|
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
|
||
|
v = self.get_slot(var, 'v')
|
||
|
v_scaled_g_values = (grad * grad) * coefficients['one_minus_beta_2_t']
|
||
|
v.assign(v * coefficients['beta_2_t'])
|
||
|
v.scatter_add(indexed_slices.IndexedSlices(v_scaled_g_values, indices))
|
||
|
|
||
|
if not self.amsgrad:
|
||
|
var.assign_sub(coefficients['lr'] * m /
|
||
|
(math_ops.sqrt(v) + coefficients['epsilon']))
|
||
|
else:
|
||
|
v_hat = self.get_slot(var, 'vhat')
|
||
|
v_hat.assign(math_ops.maximum(v_hat, v))
|
||
|
var.assign_sub(coefficients['lr'] * m /
|
||
|
(math_ops.sqrt(v_hat) + coefficients['epsilon']))
|
||
|
|
||
|
def get_config(self):
|
||
|
config = super(NonFusedAdam, self).get_config()
|
||
|
config.update({
|
||
|
'learning_rate': self._serialize_hyperparameter('learning_rate'),
|
||
|
'decay': self._initial_decay,
|
||
|
'beta_1': self._serialize_hyperparameter('beta_1'),
|
||
|
'beta_2': self._serialize_hyperparameter('beta_2'),
|
||
|
'epsilon': self.epsilon,
|
||
|
'amsgrad': self.amsgrad,
|
||
|
})
|
||
|
return config
|