Source code for pyro.optim.clipped_adam

from __future__ import absolute_import, division, print_function

import math

from torch.optim.optimizer import Optimizer

[docs]class ClippedAdam(Optimizer): """ :param params: iterable of parameters to optimize or dicts defining parameter groups :param lr: learning rate (default: 1e-3) :param Tuple betas: coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) :param eps: term added to the denominator to improve numerical stability (default: 1e-8) :param weight_decay: weight decay (L2 penalty) (default: 0) :param clip_norm: magnitude of norm to which gradients are clipped (default: 10.0) :param lrd: rate at which learning rate decays (default: 1.0) Small modification to the Adam algorithm implemented in torch.optim.Adam to include gradient clipping and learning rate decay. Reference `A Method for Stochastic Optimization`, Diederik P. Kingma, Jimmy Ba """ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, clip_norm=10.0, lrd=1.0): defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, clip_norm=clip_norm, lrd=lrd) super(ClippedAdam, self).__init__(params, defaults)
[docs] def step(self, closure=None): """ :param closure:: An optional closure that reevaluates the model and returns the loss. Performs a single optimization step. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: group['lr'] *= group['lrd'] for p in group['params']: if p.grad is None: continue grad = grad.clamp_(-group['clip_norm'], group['clip_norm']) state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = # Exponential moving average of squared gradient values state['exp_avg_sq'] = exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] beta1, beta2 = group['betas'] state['step'] += 1 if group['weight_decay'] != 0: grad = grad.add(group['weight_decay'], # 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) 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, exp_avg, denom) return loss