class RMSprop(optimizer.GradientMethod):
"""RMSprop optimizer.
See: T. Tieleman and G. Hinton (2012). Lecture 6.5 - rmsprop, COURSERA:
Neural Networks for Machine Learning.
Args:
lr (float): Learning rate.
alpha (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
"""
def __init__(self,
lr=_default_hyperparam.lr,
alpha=_default_hyperparam.alpha,
eps=_default_hyperparam.eps):
super(RMSprop, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.alpha = alpha
self.hyperparam.eps = eps
lr = optimizer.HyperparameterProxy('lr')
alpha = optimizer.HyperparameterProxy('alpha')
eps = optimizer.HyperparameterProxy('eps')
def create_update_rule(self):
return RMSpropRule(self.hyperparam)
class RMSpropGraves(optimizer.GradientMethod):
"""Alex Graves's RMSprop.
See: http://arxiv.org/abs/1308.0850
Args:
lr (float): Learning rate.
alpha (float): Exponential decay rate of the first and second order
moments of the raw gradient.
momentum (float): Exponential decay rate of the first order moment of
the adjusted gradient.
eps (float): Small value for the numerical stability.
"""
def __init__(self,
lr=_default_hyperparam.lr,
alpha=_default_hyperparam.alpha,
momentum=_default_hyperparam.momentum,
eps=_default_hyperparam.eps,
model=None):
super(RMSpropGraves, self).__init__(model)
self.hyperparam.lr = lr
self.hyperparam.alpha = alpha
self.hyperparam.momentum = momentum
self.hyperparam.eps = eps
lr = optimizer.HyperparameterProxy('lr')
alpha = optimizer.HyperparameterProxy('alpha')
momentum = optimizer.HyperparameterProxy('momentum')
eps = optimizer.HyperparameterProxy('eps')
def create_update_rule(self):
return RMSpropGravesRule(self.hyperparam)
class AdaDeltaWithLearningRate(optimizer.GradientMethod):
"""Zeiler's ADADELTA.
See: http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf
Args:
rho (float): Exponential decay rate of the first and second order
moments.
eps (float): Small value for the numerical stability.
"""
def __init__(self, lr=_default_hyperparam.lr,
rho=_default_hyperparam.rho, eps=_default_hyperparam.eps):
super(AdaDeltaWithLearningRate, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.rho = rho
self.hyperparam.eps = eps
lr = optimizer.HyperparameterProxy('lr')
rho = optimizer.HyperparameterProxy('rho')
eps = optimizer.HyperparameterProxy('eps')
def create_update_rule(self):
return AdaDeltaRuleWithLearningRate(self.hyperparam)
class RAdam(optimizer.GradientMethod):
"""Rectified Adam optimizer.
See: `On the Variance of the Adaptive Learning Rate and Beyond \
<https://arxiv.org/abs/1908.03265>`_
Args:
alpha (float): Coefficient of learning rate.
beta1 (float): Exponential decay rate of the first order moment.
beta2 (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
eta (float): Schedule multiplier, can be used for warm restarts.
weight_decay_rate (float): Weight decay rate.
"""
def __init__(self,
alpha=_default_hyperparam.alpha,
beta1=_default_hyperparam.beta1,
beta2=_default_hyperparam.beta2,
eps=_default_hyperparam.eps,
weight_decay_rate=_default_hyperparam.weight_decay_rate):
super(RAdam, self).__init__()
self.hyperparam.alpha = alpha
self.hyperparam.beta1 = beta1
self.hyperparam.beta2 = beta2
self.hyperparam.eps = eps
self.hyperparam.weight_decay_rate = weight_decay_rate
alpha = optimizer.HyperparameterProxy('alpha')
beta1 = optimizer.HyperparameterProxy('beta1')
beta2 = optimizer.HyperparameterProxy('beta2')
eps = optimizer.HyperparameterProxy('eps')
weight_decay_rate = optimizer.HyperparameterProxy('weight_decay_rate')
def create_update_rule(self):
return RAdamRule(self.hyperparam)
@property
def alpha_t(self):
return _learning_rate(self.hyperparam, self.t)
@property
def lr(self):
warnings.warn(
'RAdam.lr has been renamed to RAdamRule.alpha_t. '
'Use of Adam.lr is deprecated in Chainer v6.',
DeprecationWarning)
return self.alpha_t
class Hamiltonian(optimizer.GradientMethod):
def __init__(self,
epsilon=_default_hyperparam.epsilon,
delta=_default_hyperparam.delta):
super(Hamiltonian, self).__init__()
self.hyperparam.epsilon = epsilon
self.hyperparam.delta = delta
epsilon = optimizer.HyperparameterProxy('epsilon')
delta = optimizer.HyperparameterProxy('delta')
def create_update_rule(self):
return HamiltonianRule(self.hyperparam)
class CorrectedMomentumSGD(optimizer.GradientMethod):
"""Momentum SGD optimizer.
This implements momentum correction discussed in the third section of
`Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
<https://arxiv.org/abs/1706.02677>`_.
:class:`~chainer.optimizers.MomentumSGD` implements the equation (10) of
the paper. This optimizer implements the equation (9).
To get better understanding between the two methods,
we show the equivalence between the equation (9) and modification of
the equation (10) that takes momentum correction into account.
First, we set :math:`v_{t} = \\eta_{t} u_t`.
We substitute this relation to the equation (10).
.. math::
v_{t+1} &= m\\frac{\\eta_{t+1}}{\\eta_{t}}v_t + \\eta_{t+1}g_t \\\\
&= m\\frac{\\eta_{t+1}}{\\eta_{t}}\\eta_{t}u_t +
\\eta_{t+1}g_t \\\\
&= \\eta_{t+1}(m u_t + g_t) \\\\
From this result, we derive :math:`u_{t+1} = m u_t + g_t`, which is how
update tensors are calculated by
:class:`~chainer.optimizers.CorrectedMomentumSGD`. Thus, the equivalence
is shown.
Args:
lr (float): Learning rate.
momentum (float): Exponential decay rate of the first order moment.
"""
def __init__(self, lr=_default_hyperparam.lr,
momentum=_default_hyperparam.momentum):
super(CorrectedMomentumSGD, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.momentum = momentum
lr = optimizer.HyperparameterProxy('lr')
momentum = optimizer.HyperparameterProxy('momentum')
def create_update_rule(self):
return CorrectedMomentumSGDRule(self.hyperparam)
class LBFGS(optimizer.GradientMethod):
"""L-BFGS.
"""
def __init__(self,
lr=_default_hyperparam.lr,
stack_size=_default_hyperparam.stack_size,
min_ro=_default_hyperparam.min_ro):
super(LBFGS, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.stack_size = stack_size
self.hyperparam.min_ro = min_ro
lr = optimizer.HyperparameterProxy('lr')
stack_size = optimizer.HyperparameterProxy('stack_size')
min_ro = optimizer.HyperparameterProxy('min_ro')
def create_update_rule(self):
return LBFGSRule(self.hyperparam)
class MSVAG(optimizer.GradientMethod):
"""M-SVAG optimizer.
See: `Dissecting Adam: The Sign, Magnitude and Variance of Stochastic \
Gradients <https://arxiv.org/abs/1705.07774>`_
Modified for proper weight decay (also called AdamW).
AdamW introduces the additional parameters ``eta``
and ``weight_decay_rate``, which can be used to properly scale the
learning rate, and decouple the weight decay rate from ``alpha``,
as shown in the below paper.
See: `Fixing Weight Decay Regularization in Adam \
<https://openreview.net/forum?id=rk6qdGgCZ>`_
Args:
lr (float): Learning rate.
beta (float): Exponential decay rate of the first and second order
moment.
eta (float): Schedule multiplier, can be used for warm restarts.
weight_decay_rate (float): Weight decay rate.
"""
def __init__(self,
lr=_default_hyperparam.lr,
beta=_default_hyperparam.beta,
eta=_default_hyperparam.eta,
weight_decay_rate=_default_hyperparam.weight_decay_rate):
super(MSVAG, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.beta = beta
self.hyperparam.eta = eta
self.hyperparam.weight_decay_rate = weight_decay_rate
lr = optimizer.HyperparameterProxy('lr')
beta = optimizer.HyperparameterProxy('beta')
eta = optimizer.HyperparameterProxy('eta')
weight_decay_rate = optimizer.HyperparameterProxy('weight_decay_rate')
def create_update_rule(self):
return MSVAGRule(self.hyperparam)
class Yogi(optimizer.GradientMethod):
"""Yogi optimizer.
See: `Adaptive Methods for Nonconvex Optimization \
<https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>`_
See :class:`~chainer.optimizers.Adam` for weight decay and AMSGrad options.
Args:
alpha (float): Coefficient of learning rate.
beta1 (float): Exponential decay rate of the first order moment.
beta2 (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
eta (float): Schedule multiplier, can be used for warm restarts.
weight_decay_rate (float): Weight decay rate.
amsgrad (bool): Whether to use AMSGrad variant of Yogi.
"""
def __init__(self,
alpha=_default_hyperparam.alpha,
beta1=_default_hyperparam.beta1,
beta2=_default_hyperparam.beta2,
eps=_default_hyperparam.eps,
eta=_default_hyperparam.eta,
weight_decay_rate=_default_hyperparam.weight_decay_rate,
amsgrad=_default_hyperparam.amsgrad):
super(Yogi, self).__init__()
self.hyperparam.alpha = alpha
self.hyperparam.beta1 = beta1
self.hyperparam.beta2 = beta2
self.hyperparam.eps = eps
self.hyperparam.eta = eta
self.hyperparam.weight_decay_rate = weight_decay_rate
self.hyperparam.amsgrad = amsgrad
alpha = optimizer.HyperparameterProxy('alpha')
beta1 = optimizer.HyperparameterProxy('beta1')
beta2 = optimizer.HyperparameterProxy('beta2')
eps = optimizer.HyperparameterProxy('eps')
eta = optimizer.HyperparameterProxy('eta')
weight_decay_rate = optimizer.HyperparameterProxy('weight_decay_rate')
amsgrad = optimizer.HyperparameterProxy('amsgrad')
def create_update_rule(self):
return YogiRule(self.hyperparam)
@property
def alpha_t(self):
return _learning_rate(self.hyperparam, self.t)
@property
def lr(self):
warnings.warn(
'Yogi.lr has been renamed to YogiRule.alpha_t. '
'Use of Yogi.lr is deprecated in Chainer v6.', DeprecationWarning)
return self.alpha_t
class MomentumSGD(optimizer.GradientMethod):
"""Momentum SGD optimizer.
Args:
lr (float): Learning rate.
momentum (float): Exponential decay rate of the first order moment.
"""
def __init__(self,
lr=_default_hyperparam.lr,
momentum=_default_hyperparam.momentum):
super(MomentumSGD, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.momentum = momentum
lr = optimizer.HyperparameterProxy('lr')
momentum = optimizer.HyperparameterProxy('momentum')
def create_update_rule(self):
return MomentumSGDRule(self.hyperparam)
class AdaGrad(optimizer.GradientMethod):
"""AdaGrad optimizer.
See: http://jmlr.org/papers/v12/duchi11a.html
Args:
lr (float): Learning rate.
eps (float): Small value for the numerical stability.
"""
def __init__(self, lr=_default_hyperparam.lr, eps=_default_hyperparam.eps):
super(AdaGrad, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.eps = eps
lr = optimizer.HyperparameterProxy('lr')
eps = optimizer.HyperparameterProxy('eps')
def create_update_rule(self):
return AdaGradRule(self.hyperparam)
class SMORMS3(optimizer.GradientMethod):
"""Simon Funk's SMORMS3.
See http://sifter.org/~simon/journal/20150420.html.
Args:
lr (float): Learning rate.
eps (float): Small value for the numerical stability.
"""
def __init__(self, lr=_default_hyperparam.lr, eps=_default_hyperparam.eps):
super(SMORMS3, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.eps = eps
lr = optimizer.HyperparameterProxy('lr')
eps = optimizer.HyperparameterProxy('eps')
def create_update_rule(self):
return SMORMS3Rule(self.hyperparam)
class RMSpropAsync(optimizer.GradientMethod):
"""RMSprop for asynchronous methods.
The only difference from chainer.optimizers.RMSprop in that the epsilon is
outside the square root.
"""
def __init__(self,
lr=_default_hyperparam.lr,
alpha=_default_hyperparam.alpha,
eps=_default_hyperparam.eps):
super(RMSpropAsync, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.alpha = alpha
self.hyperparam.eps = eps
lr = optimizer.HyperparameterProxy('lr')
alpha = optimizer.HyperparameterProxy('alpha')
eps = optimizer.HyperparameterProxy('eps')
def create_update_rule(self):
return RMSpropAsyncRule(self.hyperparam)
class NesterovAG(optimizer.GradientMethod):
"""Nesterov's Accelerated Gradient.
See: http://arxiv.org/abs/1212.0901
Args:
lr (float): Learning rate.
momentum (float): Exponential decay rate of the first order moment.
"""
def __init__(self,
lr=_default_hyperparam.lr,
momentum=_default_hyperparam.momentum):
super(NesterovAG, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.momentum = momentum
lr = optimizer.HyperparameterProxy('lr')
momentum = optimizer.HyperparameterProxy('momentum')
def create_update_rule(self):
return NesterovAGRule(self.hyperparam)
class Adam(optimizer.GradientMethod):
"""Adam optimizer.
See: `Adam: A Method for Stochastic Optimization \
<http://arxiv.org/abs/1412.6980v8>`_
Modified for proper weight decay (also called AdamW).
AdamW introduces the additional parameters ``eta``
and ``weight_decay_rate``, which can be used to properly scale the
learning rate, and decouple the weight decay rate from ``alpha``,
as shown in the below paper.
Note that with the default values ``eta = 1`` and
``weight_decay_rate = 0``, this implementation is identical to
the standard Adam method.
See: `Fixing Weight Decay Regularization in Adam \
<https://openreview.net/forum?id=rk6qdGgCZ>`_
Args:
alpha (float): Step size.
beta1 (float): Exponential decay rate of the first order moment.
beta2 (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
eta (float): Schedule multiplier, can be used for warm restarts.
weight_decay_rate (float): Weight decay rate.
"""
def __init__(self,
alpha=_default_hyperparam.alpha,
beta1=_default_hyperparam.beta1,
beta2=_default_hyperparam.beta2,
eps=_default_hyperparam.eps,
eta=_default_hyperparam.eta,
weight_decay_rate=_default_hyperparam.weight_decay_rate):
super(Adam, self).__init__()
self.hyperparam.alpha = alpha
self.hyperparam.beta1 = beta1
self.hyperparam.beta2 = beta2
self.hyperparam.eps = eps
self.hyperparam.eta = eta
self.hyperparam.weight_decay_rate = weight_decay_rate
alpha = optimizer.HyperparameterProxy('alpha')
beta1 = optimizer.HyperparameterProxy('beta1')
beta2 = optimizer.HyperparameterProxy('beta2')
eps = optimizer.HyperparameterProxy('eps')
eta = optimizer.HyperparameterProxy('eta')
weight_decay_rate = optimizer.HyperparameterProxy('weight_decay_rate')
def create_update_rule(self):
return AdamRule(self.hyperparam)
@property
def lr(self):
fix1 = 1. - math.pow(self.hyperparam.beta1, self.t)
fix2 = 1. - math.pow(self.hyperparam.beta2, self.t)
return self.hyperparam.alpha * math.sqrt(fix2) / fix1
class RMSprop(optimizer.GradientMethod):
"""RMSprop optimizer.
See: T. Tieleman and G. Hinton (2012). Lecture 6.5 - rmsprop, COURSERA:
Neural Networks for Machine Learning.
Args:
lr (float): Learning rate.
alpha (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
eps_inside_sqrt (bool): When ``True``, gradient will be divided by
:math:`\\sqrt{ms + eps}` where ``ms`` is the mean square. When
``False`` (default), gradient will be divided by
:math:`\\sqrt{ms} + eps` instead.
This option may be convenient for users porting code from other
frameworks;
see `#4754 <https://github.com/chainer/chainer/issues/4754>`__ for
details.
"""
def __init__(self,
lr=_default_hyperparam.lr,
alpha=_default_hyperparam.alpha,
eps=_default_hyperparam.eps,
eps_inside_sqrt=_default_hyperparam.eps_inside_sqrt):
super(RMSprop, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.alpha = alpha
self.hyperparam.eps = eps
self.hyperparam.eps_inside_sqrt = eps_inside_sqrt
lr = optimizer.HyperparameterProxy('lr')
alpha = optimizer.HyperparameterProxy('alpha')
eps = optimizer.HyperparameterProxy('eps')
eps_inside_sqrt = optimizer.HyperparameterProxy('eps_inside_sqrt')
def create_update_rule(self):
return RMSpropRule(self.hyperparam)
class VaswaniAdam(chainer.optimizers.Adam):
def __init__(self, factor, warmup, model_size, inverse_square=False, **kwargs):
super(VaswaniAdam, self).__init__(**kwargs)
# Vaswani
self.hyperparam.factor = factor
self.hyperparam.warmup = warmup
self.hyperparam.model_size = model_size
self.inverse_square = inverse_square
def create_update_rule(self):
return VaswaniAdamRule(self.hyperparam, inverse_square=self.inverse_square)
# Vaswani
factor = optimizer.HyperparameterProxy('factor')
warmup = optimizer.HyperparameterProxy('warmup')
model_size = optimizer.HyperparameterProxy('model_size')
@property
def lr(self):
if self.inverse_square:
return _learning_rate_fairseq(self.hyperparam, self.t)
else:
return _learning_rate(self.hyperparam, self.t)
class Adam(optimizer.GradientMethod):
"""Adam optimizer.
See: http://arxiv.org/abs/1412.6980v8
Args:
alpha (float): Step size.
beta1 (float): Exponential decay rate of the first order moment.
beta2 (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
"""
def __init__(self,
alpha=_default_hyperparam.alpha,
beta1=_default_hyperparam.beta1,
beta2=_default_hyperparam.beta2,
eps=_default_hyperparam.eps,
model=None):
super(Adam, self).__init__(model)
self.hyperparam.alpha = alpha
self.hyperparam.beta1 = beta1
self.hyperparam.beta2 = beta2
self.hyperparam.eps = eps
alpha = optimizer.HyperparameterProxy('alpha')
beta1 = optimizer.HyperparameterProxy('beta1')
beta2 = optimizer.HyperparameterProxy('beta2')
eps = optimizer.HyperparameterProxy('eps')
def create_update_rule(self):
return AdamRule(self.hyperparam)
@property
def lr(self):
fix1 = 1. - math.pow(self.hyperparam.beta1, self.t)
fix2 = 1. - math.pow(self.hyperparam.beta2, self.t)
return self.hyperparam.alpha * math.sqrt(fix2) / fix1
class SGD(optimizer.GradientMethod):
"""Vanilla Stochastic Gradient Descent.
Args:
lr (float): Learning rate.
"""
def __init__(self, lr=_default_hyperparam.lr):
super(SGD, self).__init__()
self.hyperparam.lr = lr
lr = optimizer.HyperparameterProxy('lr')
def create_update_rule(self):
return SGDRule(self.hyperparam)
class RMSpropWarmup(optimizer.GradientMethod):
"""RMSprop optimizer.
See: T. Tieleman and G. Hinton (2012). Lecture 6.5 - rmsprop, COURSERA:
Neural Networks for Machine Learning.
Args:
lr (float): Learning rate.
alpha_sgd (float): Learning rate.
mu1 (float): Exponential decay rate of the first order moment.
alpha_rmsprop (float): Learning rate.
mu2 (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
"""
def __init__(self,
lr=_default_hyperparam.lr,
alpha_sgd=_default_hyperparam.alpha_sgd,
mu1=_default_hyperparam.mu1,
alpha_rmsprop=_default_hyperparam.alpha_rmsprop,
mu2=_default_hyperparam.mu2,
eps=_default_hyperparam.eps,
lars=False):
super(RMSpropWarmup, self).__init__()
self.hyperparam.lr = lr
self.hyperparam.alpha_sgd = alpha_sgd
self.hyperparam.mu1 = mu1
self.hyperparam.alpha_rmsprop = alpha_rmsprop
self.hyperparam.mu2 = mu2
self.hyperparam.eps = eps
self._lars = lars
lr = optimizer.HyperparameterProxy('lr')
alpha_sgd = optimizer.HyperparameterProxy('alpha_sgd')
mu1 = optimizer.HyperparameterProxy('mu1')
alpha_rmsprop = optimizer.HyperparameterProxy('alpha_rmsprop')
mu2 = optimizer.HyperparameterProxy('mu2')
eps = optimizer.HyperparameterProxy('eps')
def create_update_rule(self):
return RMSpropWarmupRule(self.hyperparam, self._lars)
class Adam(optimizer.GradientMethod):
"""Adam optimizer.
See: `Adam: A Method for Stochastic Optimization \
<https://arxiv.org/abs/1412.6980v8>`_
Modified for proper weight decay (also called AdamW).
AdamW introduces the additional parameters ``eta``
and ``weight_decay_rate``, which can be used to properly scale the
learning rate, and decouple the weight decay rate from ``alpha``,
as shown in the below paper.
Note that with the default values ``eta = 1`` and
``weight_decay_rate = 0``, this implementation is identical to
the standard Adam method.
See: `Fixing Weight Decay Regularization in Adam \
<https://openreview.net/forum?id=rk6qdGgCZ>`_
A flag ``amsgrad`` to use the AMSGrad variant of Adam from
the paper: `On the Convergence of Adam and Beyond \
<https://openreview.net/forum?id=ryQu7f-RZ>`_
A flag ``adastand`` to use the Adastand variant of Adam from
the paper: `Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks \
<https://www.ijcai.org/proceedings/2017/0267.pdf>`_
Args:
alpha (float): Step size.
beta1 (float): Exponential decay rate of the first order moment.
beta2 (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
eta (float): Schedule multiplier, can be used for warm restarts.
weight_decay_rate (float): Weight decay rate.
amsgrad (bool): Whether to use AMSGrad variant of Adam.
adastand (bool): Whether to use Adastand variant of Adam.
"""
def __init__(self,
alpha=_default_hyperparam.alpha,
beta1=_default_hyperparam.beta1,
beta2=_default_hyperparam.beta2,
eps=_default_hyperparam.eps,
eta=_default_hyperparam.eta,
weight_decay_rate=_default_hyperparam.weight_decay_rate,
amsgrad=_default_hyperparam.amsgrad,
adastand=_default_hyperparam.adastand):
super(Adam, self).__init__()
self.hyperparam.alpha = alpha
self.hyperparam.beta1 = beta1
self.hyperparam.beta2 = beta2
self.hyperparam.eps = eps
self.hyperparam.eta = eta
self.hyperparam.weight_decay_rate = weight_decay_rate
self.hyperparam.amsgrad = amsgrad
self.hyperparam.adastand = adastand
alpha = optimizer.HyperparameterProxy('alpha')
beta1 = optimizer.HyperparameterProxy('beta1')
beta2 = optimizer.HyperparameterProxy('beta2')
eps = optimizer.HyperparameterProxy('eps')
eta = optimizer.HyperparameterProxy('eta')
weight_decay_rate = optimizer.HyperparameterProxy('weight_decay_rate')
amsgrad = optimizer.HyperparameterProxy('amsgrad')
adastand = optimizer.HyperparameterProxy('adastand')
def create_update_rule(self):
return AdamRule(self.hyperparam)
@property
def lr(self):
return _learning_rate(self.hyperparam, self.t)
class Eve(optimizer.GradientMethod):
"""Eve optimizer.
See: https://arxiv.org/abs/1611.01505v3
Args:
alpha (float): Coefficient of learning rate.
beta1 (float): Exponential decay rate of the first order moment.
beta2 (float): Exponential decay rate of the second order moment.
beta3 (float): Exponential decay rate of the objective-dependent
coefficient of learning rate.
c (float): Constant used to clip the objective-dependent coefficient.
eps (float): Small value for the numerical stability.
eta (float): Schedule multiplier, can be used for warm restarts.
f_star (float): Minimum value that the loss function can take.
weight_decay_rate (float): Weight decay rate.
amsgrad (bool): Whether to use AMSGrad variant of Eve.
"""
def __init__(self,
alpha=_default_hyperparam.alpha,
beta1=_default_hyperparam.beta1,
beta2=_default_hyperparam.beta2,
beta3=_default_hyperparam.beta3,
c=_default_hyperparam.c,
eps=_default_hyperparam.eps,
eta=_default_hyperparam.eta,
f_star=_default_hyperparam.f_star,
weight_decay_rate=_default_hyperparam.weight_decay_rate,
amsgrad=_default_hyperparam.amsgrad):
super(Eve, self).__init__()
self.hyperparam.alpha = alpha
self.hyperparam.beta1 = beta1
self.hyperparam.beta2 = beta2
self.hyperparam.beta3 = beta3
self.hyperparam.c = c
self.hyperparam.eps = eps
self.hyperparam.eta = eta
self.hyperparam.f_star = f_star
self.hyperparam.weight_decay_rate = weight_decay_rate
self.hyperparam.amsgrad = amsgrad
alpha = optimizer.HyperparameterProxy('alpha')
beta1 = optimizer.HyperparameterProxy('beta1')
beta2 = optimizer.HyperparameterProxy('beta2')
beta3 = optimizer.HyperparameterProxy('beta3')
c = optimizer.HyperparameterProxy('c')
eps = optimizer.HyperparameterProxy('eps')
eta = optimizer.HyperparameterProxy('eta')
f_star = optimizer.HyperparameterProxy('f_star')
weight_decay_rate = optimizer.HyperparameterProxy('weight_decay_rate')
amsgrad = optimizer.HyperparameterProxy('amsgrad')
def setup(self, link):
"""Sets a target link and initializes the optimizer states.
Given link is set to the :attr:`target` attribute. It also prepares the
optimizer state dictionaries corresponding to all parameters in the
link hierarchy. The existing states are discarded.
Args:
link (~chainer.Link): Target link object.
Returns:
The optimizer instance.
.. note::
As of v4.0.0, this function returns the optimizer instance itself
so that you can instantiate and setup the optimizer in one line,
e.g., ``optimizer = SomeOptimizer().setup(link)``.
"""
super(Eve, self).setup(link)
self.d_tilde = numpy.nan
self.f = numpy.nan
return self
def create_update_rule(self):
return EveRule(self.hyperparam)
@property
def lr(self):
return _learning_rate(self.hyperparam, self.t, self.d_tilde)
def update(self, lossfun, *args, **kwds):
"""Updates parameters based on a loss function or computed gradients.
Because Eve uses loss values, `lossfun` is required unlike in the
case of other optimizers.
Args:
lossfun (callable): Callable that returns a ~chainer.Variable to be
minimized.
*args, **kwds: Arguments passed to `lossfun`.
"""
assert lossfun is not None, 'Eve requires lossfun to be specified'
use_cleargrads = getattr(self, '_use_cleargrads', True)
loss = lossfun(*args, **kwds)
if use_cleargrads:
self.target.cleargrads()
else:
self.target.zerograds()
loss.backward(loss_scale=self._loss_scale)
loss_value = float(loss.array)
del loss
self.reallocate_cleared_grads()
self.call_hooks('pre')
self.t += 1
self._update_d_tilde_and_f(loss_value)
for param in self.target.params():
param.update_rule.d_tilde = self.d_tilde
param.update()
self.reallocate_cleared_grads()
self.call_hooks('post')
def serialize(self, serializer):
"""Serializes or deserializes the optimizer.
It only saves or loads the following things:
- Optimizer states
- Global states (:attr:`t`, :attr:`epoch`, :attr:`d_tilde`, and
:attr:`f`)
**It does not saves nor loads the parameters of the target link.** They
should be separately saved or loaded.
Args:
serializer (~chainer.AbstractSerializer): Serializer or
deserializer object.
"""
super(Eve, self).serialize(serializer)
self.d_tilde = serializer('d_tilde', self.d_tilde)
self.f = serializer('f', self.f)
def _update_d_tilde_and_f(self, loss):
if self.t > 1:
d = abs(loss - self.f) / (min(loss, self.f) - self.f_star)
d_hat = numpy.clip(d, 1 / self.c, self.c)
self.d_tilde = self.beta3 * self.d_tilde + (1 - self.beta3) * d_hat
else:
self.d_tilde = 1
self.f = loss
class KFAC(chainer.optimizer.GradientMethod):
"""K-FAC optimizer.
See: `Optimizing Neural Networks with \
Kronecker-factored Approximate Curvature \
<https://arxiv.org/abs/1503.05671>`_
Args:
lr (float): Learning rate.
momentum (float): Exponential decay rate of the first order moment.
cov_ema_decay (float): Decay factor used when calculating the
covariance estimate Exponential Moving Average.
inv_freq (int): Frequency to calculate the inverse of covariance
estimate EMA for each layer.
inv_alg (str): Algorithm used when calculating the inverse.
damping (float): Damping factor used to stabilize training
due to errors in the local approximation with the
Fisher information matrix.
Attributes:
fisher_blocks (list): Keep data to compute Fisher block.
"""
def __init__(
self,
communicator=None,
lr=_default_hyperparam.lr,
momentum=_default_hyperparam.momentum,
cov_ema_decay=_default_hyperparam.cov_ema_decay,
inv_freq=_default_hyperparam.inv_freq,
inv_alg=None,
damping=_default_hyperparam.damping,
):
super(KFAC, self).__init__()
self.communicator = communicator
self.hyperparam.lr = lr
self.hyperparam.momentum = momentum
self.hyperparam.cov_ema_decay = cov_ema_decay
self.hyperparam.inv_freq = inv_freq
self.hyperparam.damping = damping
self.fisher_blocks = []
self.inv_alg = inv_alg
lr = optimizer.HyperparameterProxy('lr')
momentum = optimizer.HyperparameterProxy('momentum')
cov_ema_decay = optimizer.HyperparameterProxy('cov_ema_decay')
inv_freq = optimizer.HyperparameterProxy('inv_freq')
damping = optimizer.HyperparameterProxy('damping')
def setup(self, link):
super(KFAC, self).setup(link)
for linkname, sub_link in link.namedlinks():
if isinstance(sub_link, _linear_link):
fb = fisher_block.FisherBlockLinear(sub_link, linkname)
elif isinstance(sub_link, _convolution_2d_link):
fb = fisher_block.FisherBlockConv2D(sub_link, linkname)
elif isinstance(sub_link, _batch_norm_link):
fb = fisher_block.FisherBlockBatchNorm(sub_link, linkname)
else:
continue
self.fisher_blocks.append(fb)
return self
def create_update_rule(self):
return KFACUpdateRule(self.hyperparam)
def update(self, lossfun=None, *args, **kwds):
if lossfun is not None:
use_cleargrads = getattr(self, '_use_cleargrads', True)
loss = lossfun(*args, **kwds)
if use_cleargrads:
self.target.cleargrads()
else:
self.target.zerograds()
# We removed ``loss.backward()`` from here.
# Do backprop, and obtain ``grads`` which contains the dependency
# graph inside.
backward_main = getattr(loss, '_backward_main')
self.kfac_backward(self.target, backward_main)
del loss # No more backward computation, free memory
# Update param.kfgrad for each layer
self.kfgrad_update()
self.reallocate_cleared_grads()
self.call_hooks('pre')
self.t += 1
for param in self.target.params():
param.update()
self.reallocate_cleared_grads()
self.call_hooks('post')
self.cov_ema_update()
def kfac_backward(self, link, backward_main):
"""Backward function for KFAC optimizer.
This function is invoked from ``KFAC.update()`` to:
1. calculate backprop
2. obtain the following data for each layer (`~chainer.link.Link`)
- acts (inputs = activations after previous layer)
- grads (gradients of outputs)
- rank (`~chainer.FunctionNode.rank`)
- conv2d_args (arguments of `~chainer.links.connection.\
convolution_2d.Convolution2D`)
"""
with chainer.using_config('enable_backprop', False):
# To obtain grads, we need to edit a file ``variable.py``
grads = backward_main(retain_grad=True, loss_scale=None)
namedparams = list(link.namedparams())
def get_linkname(param):
# Get a linkname from a parameter.
for _name, _param in namedparams:
if param is _param:
# Only return linkname NOT paramname.
return _name[:_name.rfind('/')]
return None
def get_fisher_block(linkname):
for fb in self.fisher_blocks:
if fb.linkname == linkname:
return fb
return None
for node, out_grads_var in grads.items():
creator_node = node.creator_node # parent function node
if creator_node is not None: # ignore leaf node
if not any(
[isinstance(creator_node, t) for t in _target_functions]):
continue
(in_acts_var, param) = creator_node.get_retained_inputs()
linkname = get_linkname(param)
fb = get_fisher_block(linkname)
fb.load_data(in_acts_var.data, out_grads_var.data)
fb.load_conv2d_args(creator_node, param)
def kfgrad_update(self):
"""Update param.kfgrad which used for K-FAC updates for each laeyer.
"""
for fb in self.fisher_blocks:
fb.update_kfgrads()
def cov_ema_update(self):
"""Update EMA of covariance for each laeyer.
"""
for fb in self.fisher_blocks:
fb.update_cov_emas(alpha=self.hyperparam.cov_ema_decay)
def inv_update(self):
"""Update inverse of EMA of covariance for each laeyer.
"""
comm = self.communicator
if comm is not None:
indices
local_indices = indices[comm.rank]
fisher_blocks = [self.fisher_blocks[i] for i in local_indices]
else:
fisher_blocks = self.fisher_blocks
for fb in fisher_blocks:
fb.update_invs(damping=self.hyperparam.damping)
class Adam(optimizer.GradientMethod):
"""Adam optimizer.
See: `Adam: A Method for Stochastic Optimization
<https://arxiv.org/abs/1412.6980v8>`_
Modified for proper weight decay (also called
:class:`~chainer.optimizers.AdamW`).
AdamW introduces the additional parameters ``eta``
and ``weight_decay_rate``, which can be used to properly scale the
learning rate, and decouple the weight decay rate from ``alpha``,
as shown in the below paper.
Note that with the default values ``eta = 1`` and
``weight_decay_rate = 0``, this implementation is identical to
the standard Adam method.
See: `Fixing Weight Decay Regularization in Adam
<https://openreview.net/forum?id=rk6qdGgCZ>`_
A flag ``amsgrad`` to use the :class:`~chainer.optimizers.AMSGrad`
variant of Adam from the paper:
`On the Convergence of Adam and Beyond
<https://openreview.net/forum?id=ryQu7f-RZ>`_
A flag ``adabound`` to use the :class:`~chainer.optimizers.AdaBound`
variant of Adam from the paper:
`Adaptive Gradient Methods with Dynamic Bound of Learning Rate
<https://openreview.net/forum?id=Bkg3g2R9FX>`_
If both ``amsgrad`` and ``adabound`` are ``True``, the optimizer is
equivalent to :class:`~chainer.optimizers.AMSBound` proposed in the
AdaBound paper.
Args:
alpha (float): Coefficient of learning rate.
beta1 (float): Exponential decay rate of the first order moment.
beta2 (float): Exponential decay rate of the second order moment.
eps (float): Small value for the numerical stability.
eta (float): Schedule multiplier, can be used for warm restarts.
weight_decay_rate (float): Weight decay rate.
amsgrad (bool): Whether to use AMSGrad variant of Adam.
adabound (bool): Whether to use the AdaBound variant of Adam.
final_lr (float): Final (SGD) learning rate in AdaBound.
gamma (float): Convergence speed of the bound functions in AdaBound.
"""
def __init__(self,
alpha=_default_hyperparam.alpha,
beta1=_default_hyperparam.beta1,
beta2=_default_hyperparam.beta2,
eps=_default_hyperparam.eps,
eta=_default_hyperparam.eta,
weight_decay_rate=_default_hyperparam.weight_decay_rate,
amsgrad=_default_hyperparam.amsgrad,
adabound=_default_hyperparam.adabound,
final_lr=_default_hyperparam.final_lr,
gamma=_default_hyperparam.gamma):
super(Adam, self).__init__()
self.hyperparam.alpha = alpha
self.hyperparam.beta1 = beta1
self.hyperparam.beta2 = beta2
self.hyperparam.eps = eps
self.hyperparam.eta = eta
self.hyperparam.weight_decay_rate = weight_decay_rate
self.hyperparam.amsgrad = amsgrad
self.hyperparam.adabound = adabound
self.hyperparam.final_lr = final_lr
self.hyperparam.gamma = gamma
alpha = optimizer.HyperparameterProxy('alpha')
beta1 = optimizer.HyperparameterProxy('beta1')
beta2 = optimizer.HyperparameterProxy('beta2')
eps = optimizer.HyperparameterProxy('eps')
eta = optimizer.HyperparameterProxy('eta')
weight_decay_rate = optimizer.HyperparameterProxy('weight_decay_rate')
amsgrad = optimizer.HyperparameterProxy('amsgrad')
adabound = optimizer.HyperparameterProxy('adabound')
final_lr = optimizer.HyperparameterProxy('final_lr')
gamma = optimizer.HyperparameterProxy('gamma')
def create_update_rule(self):
return AdamRule(self.hyperparam)
@property
def alpha_t(self):
return _learning_rate(self.hyperparam, self.t)
@property
def lr(self):
warnings.warn(
'Adam.lr has been renamed to AdamRule.alpha_t. '
'Use of Adam.lr is deprecated in Chainer v6.', DeprecationWarning)
return self.alpha_t
