梯度衰减调大学习率管用吗？

不管用。因为学习率乘以梯度是步长，而梯度衰减返回的梯度是0所以调大学习率不管用。

from enum import auto

from scipy.io import loadmat

import numpy as np

import torch

import torch.utils.data as data_utils

from torch import nn

import torch.optim as optim

network=nn.Linear(1,1)

#network1=nn.BatchNorm1d(1)

w=nn.Sigmoid()

tr=torch.Tensor([[100000],[200000]])

#tr=torch.Tensor([[1],[2]])

test=torch.Tensor([[150000],[300000]])

optimizer = optim.Adam(network.parameters(), lr=4000000000000000)

#optimizer1 = optim.Adam(network1.parameters(), lr=0.04)

l1=0

while True:

    network.train()

    #network1.train()

    #network1.eval()#

    optimizer.zero_grad()

    #optimizer1.zero_grad()

    l=w(network(tr))

    #l=w(network1(network(tr)))

    #print(network1(network(tr)))

    l=(l[0]-0)**2+(l[1]-1)**2

    l.backward()

    for name, parms in network.named_parameters():  

        print('-->name:', name)

        print('-->para:', parms)

        print('-->grad_requirs:',parms.requires_grad)

        print('-->grad_value:',parms.grad)

        print("===")

输出是0