Source code for lmflow.optim.adagrad

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import torch
from torch.optim.optimizer import Optimizer

[docs] class AdaGrad(torch.optim.Optimizer): def __init__(self, params, lr=0.001, eps=1e-8, weight_decay=0): defaults = dict(lr=lr, eps=eps, weight_decay=weight_decay) super(AdaGrad, self).__init__(params, defaults)
[docs] def step(self, closure=None): 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 grad = p.grad.data if group['weight_decay'] != 0: grad = grad.add(group['weight_decay'], p.data) state = self.state[p] if len(state) == 0: state['sum'] = torch.zeros_like(p.data) sum = state['sum'] sum.addcmul_(1, grad, grad) std = sum.sqrt().add_(group['eps']) p.data.addcdiv_(-group['lr'], grad, std) return loss