"""Wrappers for training optimizers."""
import math
import torch
from tensorflow import keras
[docs]def get_optimizer(framework, config):
"""Get the optimizer specified in config for model training.
Arguments
---------
framework : str
Name of the deep learning framework used. Current options are
``['torch', 'keras']``.
config : dict
The config dict generated from the YAML config file.
Returns
-------
An optimizer object for the specified deep learning framework.
"""
if config['training']['optimizer'] is None:
raise ValueError('An optimizer must be specified in the config '
'file.')
if framework in ['torch', 'pytorch']:
return torch_optimizers.get(config['training']['optimizer'].lower())
elif framework == 'keras':
return keras_optimizers.get(config['training']['optimizer'].lower())
[docs]class TorchAdamW(torch.optim.Optimizer):
"""AdamW algorithm as implemented in `Torch_AdamW`_.
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay coefficient (default: 1e-2)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
.. _Torch_AdamW: https://github.com/pytorch/pytorch/pull/3740
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _Decoupled Weight Decay Regularization:
https://arxiv.org/abs/1711.05101
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=1e-2, amsgrad=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, amsgrad=amsgrad)
super(TorchAdamW, self).__init__(params, defaults)
def __setstate__(self, state):
super(TorchAdamW, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
[docs] def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
# Perform stepweight decay
p.data.mul_(1 - group['lr'] * group['weight_decay'])
# Perform optimization step
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse'
'gradients, please consider SparseAdam'
' instead')
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
return loss
torch_optimizers = {
'adadelta': torch.optim.Adadelta,
'adam': torch.optim.Adam,
'adamw': TorchAdamW,
'sparseadam': torch.optim.SparseAdam,
'adamax': torch.optim.Adamax,
'asgd': torch.optim.ASGD,
'rmsprop': torch.optim.RMSprop,
'sgd': torch.optim.SGD,
}
keras_optimizers = {
'adadelta': keras.optimizers.Adadelta,
'adagrad': keras.optimizers.Adagrad,
'adam': keras.optimizers.Adam,
'adamax': keras.optimizers.Adamax,
'nadam': keras.optimizers.Nadam,
'rmsprop': keras.optimizers.RMSprop,
'sgd': keras.optimizers.SGD
}