Source code for lmflow.optim.adagrad
#!/usr/bin/env python
import torch
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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)
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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