Source code for lmflow.optim.adagrad

#!/usr/bin/env python

import torch


[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().__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