我觉得步骤应该是：
但是目的就是 保证参数更新的时候（严格讲是计算梯度前防止累加） 梯度不要累加，就等于参数更新前得清零， 等同于 .zero_gard_() 在计算梯度backward（）前都行。 你可以 调换 l = loss(net(X) ,y)和
trainer.zero_grad()的位置， 都不影响。 简单通俗来说 早上出门前先吃饭先刷牙都不重要，只要刷牙吃饭都在出门前完成就好
看你最后一行打印loss的函数，你这样得到的是l.sum()也就是训练集所有样本的损失之和当然很大！！老师代码第一次打印的是平均损失，所以你只要把这个结果 /1000 是不是发现很合理了
我也是这么觉得的，每一个epoch最后打印的那个loss是一个单独的计算图，就算不用清空计算图应该问题也不大。
#!/usr/bin/env python3
import torch
from torch import nn
class Linear:
def __init__(self, n, eta=0.01):
# self.theta = torch.normal(0.0, 0.01, size=(n+1, 1), requires_grad=True)
self.theta = torch.tensor([[0.0], [0.0], [0.00]], requires_grad=True)
self.eta = eta
def cal(self, x):
return torch.concat((x, torch.ones(x.shape[0], 1)), dim=1) @ self.theta
def loss(self, x, y):
# return (y - self.cal(x)) ** 2
return nn.MSELoss()(self.cal(x), y)
def batch(self, x, y):
loss = self.loss(x, y)
loss.backward()
with torch.no_grad():
self.theta -= self.eta * self.theta.grad / x.shape[0]
print(f'loss={loss};theta={self.theta.reshape(3)}')
# not sure if this is necessary
# self.theta.grad.zero_()
def synthetic_data(w, b, num_examples):
"""生成y=Xw+b+噪声"""
X = torch.normal(0.0, 1.0, (num_examples, len(w)))
y = torch.matmul(X, w) + b
y += torch.normal(0, 0.01, y.shape)
return X, y.reshape((-1, 1))
true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = synthetic_data(true_w, true_b, 10)
mod = Linear(2)
net = nn.Sequential(nn.Linear(2, 1))
net[0].weight.data.fill_(0.0)
net[0].bias.data.fill_(0.00)
loss = nn.MSELoss()
trainer = torch.optim.SGD(net.parameters(), lr=0.01)
for i in range(100):
print(f'epoch {i}')
x, y = synthetic_data(true_w, true_b, 10)
for i, j in zip(x, y):
l = loss(net(i) ,j)
trainer.zero_grad()
l.backward()
trainer.step()
l = loss(net(features), labels)
theta = torch.concat((net[0].weight.data[0], net[0].bias.data), dim=0)
print(f'loss={l};theta={theta} +vs+ ', end='')
mod.batch(x, y)
前一节贴了自己根据理解写的，现在把torch的训练结果写在一起对比，相同的lr(0.01)，初始值也相同，但是结果差的很远
epoch 0
loss=40.199493408203125;theta=tensor([ 0.7239, -0.1254, 0.8538]) +vs+ loss=29.813140869140625;theta=tensor([ 0.0080, -0.0013, 0.0093], requires_grad=True)
epoch 1
loss=23.488977432250977;theta=tensor([ 1.1116, -0.7396, 1.7217]) +vs+ loss=43.919803619384766;theta=tensor([ 0.0221, -0.0100, 0.0307], requires_grad=True)
epoch 2
loss=15.439271926879883;theta=tensor([ 1.2333, -1.1667, 2.2884]) +vs+ loss=35.333126068115234;theta=tensor([ 0.0408, -0.0238, 0.0627], requires_grad=True)
epoch 3
loss=9.107278823852539;theta=tensor([ 1.3226, -1.6589, 2.8143]) +vs+ loss=41.886661529541016;theta=tensor([ 0.0616, -0.0472, 0.1062], requires_grad=True)
epoch 4
loss=4.869641304016113;theta=tensor([ 1.4891, -2.0748, 3.2445]) +vs+ loss=47.77497482299805;theta=tensor([ 0.0866, -0.0816, 0.1619], requires_grad=True)
epoch 5
loss=2.0043132305145264;theta=tensor([ 1.6313, -2.5625, 3.6038]) +vs+ loss=62.35139083862305;theta=tensor([ 0.1175, -0.1330, 0.2318], requires_grad=True)
epoch 6
loss=1.372653841972351;theta=tensor([ 1.6587, -2.6748, 3.7628]) +vs+ loss=31.220203399658203;theta=tensor([ 0.1505, -0.1898, 0.3120], requires_grad=True)
epoch 7
loss=0.8469464182853699;theta=tensor([ 1.8353, -2.7874, 3.8238]) +vs+ loss=31.033191680908203;theta=tensor([ 0.1938, -0.2514, 0.3992], requires_grad=True)
epoch 8
loss=0.4125572144985199;theta=tensor([ 1.8380, -2.9837, 3.9591]) +vs+ loss=45.233741760253906;theta=tensor([ 0.2380, -0.3272, 0.4982], requires_grad=True)
epoch 9
loss=0.2835714817047119;theta=tensor([ 1.8280, -3.0981, 3.9877]) +vs+ loss=23.174217224121094;theta=tensor([ 0.2812, -0.4115, 0.6030], requires_grad=True)
epoch 10
loss=0.19699090719223022;theta=tensor([ 1.8194, -3.1531, 4.0481]) +vs+ loss=27.476261138916016;theta=tensor([ 0.3224, -0.5036, 0.7175], requires_grad=True)
epoch 11
loss=0.08127938956022263;theta=tensor([ 1.9149, -3.2377, 4.0813]) +vs+ loss=39.93544387817383;theta=tensor([ 0.3781, -0.6057, 0.8398], requires_grad=True)
epoch 12
loss=0.07076151669025421;theta=tensor([ 1.9121, -3.2523, 4.0923]) +vs+ loss=9.7304105758667;theta=tensor([ 0.4335, -0.7093, 0.9666], requires_grad=True)
epoch 13
loss=0.04454811289906502;theta=tensor([ 1.9538, -3.2907, 4.0909]) +vs+ loss=17.899154663085938;theta=tensor([ 0.4971, -0.8190, 1.0956], requires_grad=True)
epoch 14
loss=0.028745323419570923;theta=tensor([ 1.9640, -3.3082, 4.1150]) +vs+ loss=19.298988342285156;theta=tensor([ 0.5644, -0.9330, 1.2316], requires_grad=True)
epoch 15
loss=0.014224288985133171;theta=tensor([ 1.9800, -3.3323, 4.1395]) +vs+ loss=27.38421058654785;theta=tensor([ 0.6372, -1.0552, 1.3765], requires_grad=True)
epoch 16
loss=0.007395690772682428;theta=tensor([ 1.9806, -3.3572, 4.1549]) +vs+ loss=23.562267303466797;theta=tensor([ 0.7108, -1.1880, 1.5290], requires_grad=True)
epoch 17
loss=0.004921122919768095;theta=tensor([ 1.9848, -3.3638, 4.1641]) +vs+ loss=14.054956436157227;theta=tensor([ 0.7868, -1.3247, 1.6876], requires_grad=True)
epoch 18
loss=0.0037674028426408768;theta=tensor([ 1.9860, -3.3691, 4.1687]) +vs+ loss=8.764888763427734;theta=tensor([ 0.8635, -1.4649, 1.8499], requires_grad=True)
epoch 19
loss=0.00217236066237092;theta=tensor([ 1.9892, -3.3788, 4.1752]) +vs+ loss=14.227798461914062;theta=tensor([ 0.9433, -1.6116, 2.0175], requires_grad=True)
epoch 20
loss=0.0014804631937295198;theta=tensor([ 1.9888, -3.3842, 4.1805]) +vs+ loss=8.872236251831055;theta=tensor([ 1.0231, -1.7626, 2.1897], requires_grad=True)
epoch 21
loss=0.0012339865788817406;theta=tensor([ 1.9911, -3.3848, 4.1822]) +vs+ loss=4.2144083976745605;theta=tensor([ 1.1060, -1.9144, 2.3639], requires_grad=True)
epoch 22
loss=0.0007785544148646295;theta=tensor([ 1.9924, -3.3900, 4.1860]) +vs+ loss=7.574267387390137;theta=tensor([ 1.1906, -2.0702, 2.5423], requires_grad=True)
epoch 23
loss=0.000381416582968086;theta=tensor([ 1.9960, -3.3950, 4.1908]) +vs+ loss=11.108306884765625;theta=tensor([ 1.2794, -2.2328, 2.7266], requires_grad=True)
epoch 24
loss=0.00030026386957615614;theta=tensor([ 1.9966, -3.3958, 4.1930]) +vs+ loss=3.9018654823303223;theta=tensor([ 1.3683, -2.3976, 2.9144], requires_grad=True)
epoch 25
loss=0.0002697670424822718;theta=tensor([ 1.9967, -3.3964, 4.1940]) +vs+ loss=1.767190933227539;theta=tensor([ 1.4576, -2.5630, 3.1042], requires_grad=True)
epoch 26
loss=0.00023465680715162307;theta=tensor([ 1.9968, -3.3982, 4.1951]) +vs+ loss=2.983660936355591;theta=tensor([ 1.5483, -2.7309, 3.2970], requires_grad=True)
epoch 27
loss=0.00020080586546100676;theta=tensor([ 1.9970, -3.3992, 4.1971]) +vs+ loss=1.412642240524292;theta=tensor([ 1.6387, -2.9000, 3.4920], requires_grad=True)
epoch 28
loss=0.00018914752581622452;theta=tensor([ 1.9976, -3.3985, 4.1978]) +vs+ loss=0.9602915048599243;theta=tensor([ 1.7311, -3.0703, 3.6880], requires_grad=True)
epoch 29
loss=0.0001670362544246018;theta=tensor([ 1.9993, -3.3990, 4.1994]) +vs+ loss=0.6071179509162903;theta=tensor([ 1.8252, -3.2410, 3.8851], requires_grad=True)
epoch 30
loss=0.0001656902168178931;theta=tensor([ 1.9989, -3.3986, 4.1997]) +vs+ loss=0.11326725780963898;theta=tensor([ 1.9192, -3.4121, 4.0829], requires_grad=True)
epoch 31
loss=0.00017027936701197177;theta=tensor([ 1.9986, -3.3979, 4.1991]) +vs+ loss=0.017175797373056412;theta=tensor([ 2.0132, -3.5832, 4.2809], requires_grad=True)
epoch 32
loss=0.00017005577683448792;theta=tensor([ 1.9984, -3.3992, 4.1995]) +vs+ loss=0.07364974915981293;theta=tensor([ 2.1074, -3.7535, 4.4786], requires_grad=True)
epoch 33
loss=0.0001676440006121993;theta=tensor([ 1.9991, -3.3987, 4.1992]) +vs+ loss=0.25866103172302246;theta=tensor([ 2.2012, -3.9231, 4.6757], requires_grad=True)
epoch 34
loss=0.00015877402620390058;theta=tensor([ 2.0002, -3.3987, 4.2006]) +vs+ loss=0.5912841558456421;theta=tensor([ 2.2952, -4.0914, 4.8714], requires_grad=True)
epoch 35
loss=0.00016528442210983485;theta=tensor([ 2.0008, -3.3997, 4.2003]) +vs+ loss=1.139038324356079;theta=tensor([ 2.3881, -4.2578, 5.0664], requires_grad=True)
epoch 36
loss=0.0001614181383047253;theta=tensor([ 2.0006, -3.3992, 4.2010]) +vs+ loss=1.3117530345916748;theta=tensor([ 2.4807, -4.4225, 5.2600], requires_grad=True)
epoch 37
loss=0.00016502727521583438;theta=tensor([ 2.0006, -3.3996, 4.2002]) +vs+ loss=2.4010090827941895;theta=tensor([ 2.5720, -4.5848, 5.4522], requires_grad=True)
epoch 38
loss=0.0001618371024960652;theta=tensor([ 1.9999, -3.3992, 4.2007]) +vs+ loss=1.7867286205291748;theta=tensor([ 2.6609, -4.7470, 5.6425], requires_grad=True)
epoch 39
loss=0.00016230842447839677;theta=tensor([ 1.9996, -3.3982, 4.1996]) +vs+ loss=6.560575008392334;theta=tensor([ 2.7493, -4.9038, 5.8292], requires_grad=True)
epoch 40
loss=0.00016202664119191468;theta=tensor([ 1.9992, -3.3980, 4.1998]) +vs+ loss=3.9900474548339844;theta=tensor([ 2.8344, -5.0588, 6.0140], requires_grad=True)
epoch 41
loss=0.00016224353748839349;theta=tensor([ 1.9992, -3.3984, 4.2000]) +vs+ loss=3.3899340629577637;theta=tensor([ 2.9202, -5.2123, 6.1963], requires_grad=True)
epoch 42
loss=0.0001664403680479154;theta=tensor([ 1.9990, -3.3989, 4.1996]) +vs+ loss=3.928368330001831;theta=tensor([ 3.0061, -5.3636, 6.3765], requires_grad=True)
epoch 43
loss=0.00016333414532709867;theta=tensor([ 1.9999, -3.3990, 4.1998]) +vs+ loss=7.6767778396606445;theta=tensor([ 3.0890, -5.5130, 6.5528], requires_grad=True)
epoch 44
loss=0.00016104819951578975;theta=tensor([ 2.0003, -3.3989, 4.2003]) +vs+ loss=14.649149894714355;theta=tensor([ 3.1676, -5.6565, 6.7238], requires_grad=True)
epoch 45
loss=0.00015725786215625703;theta=tensor([ 2.0002, -3.3983, 4.2007]) +vs+ loss=7.038060188293457;theta=tensor([ 3.2458, -5.7980, 6.8913], requires_grad=True)
epoch 46
loss=0.00015804634313099086;theta=tensor([ 1.9999, -3.3987, 4.2012]) +vs+ loss=6.785189628601074;theta=tensor([ 3.3240, -5.9385, 7.0547], requires_grad=True)
epoch 47
loss=0.0001646848686505109;theta=tensor([ 2.0000, -3.3996, 4.2004]) +vs+ loss=11.080676078796387;theta=tensor([ 3.4008, -6.0762, 7.2133], requires_grad=True)
epoch 48
loss=0.00016850080282893032;theta=tensor([ 2.0003, -3.3996, 4.1991]) +vs+ loss=9.164220809936523;theta=tensor([ 3.4758, -6.2131, 7.3675], requires_grad=True)
epoch 49
loss=0.00017005126574076712;theta=tensor([ 1.9994, -3.3999, 4.1995]) +vs+ loss=37.14043045043945;theta=tensor([ 3.5417, -6.3406, 7.5109], requires_grad=True)
epoch 50
loss=0.00017227436183020473;theta=tensor([ 1.9995, -3.4002, 4.1992]) +vs+ loss=17.466419219970703;theta=tensor([ 3.6056, -6.4652, 7.6472], requires_grad=True)
epoch 51
loss=0.0001666880853008479;theta=tensor([ 1.9994, -3.3997, 4.2000]) +vs+ loss=22.53124237060547;theta=tensor([ 3.6692, -6.5842, 7.7755], requires_grad=True)
epoch 52
loss=0.0001748951181070879;theta=tensor([ 1.9999, -3.4009, 4.1996]) +vs+ loss=29.02981948852539;theta=tensor([ 3.7296, -6.6960, 7.8955], requires_grad=True)
epoch 53
loss=0.0001712619123281911;theta=tensor([ 2.0000, -3.4003, 4.1995]) +vs+ loss=22.801115036010742;theta=tensor([ 3.7873, -6.8039, 8.0079], requires_grad=True)
epoch 54
loss=0.0001726361660985276;theta=tensor([ 2.0001, -3.4008, 4.2001]) +vs+ loss=34.576438903808594;theta=tensor([ 3.8427, -6.9041, 8.1101], requires_grad=True)
epoch 55
loss=0.00017347534594591707;theta=tensor([ 1.9999, -3.4007, 4.1994]) +vs+ loss=21.741161346435547;theta=tensor([ 3.8985, -6.9985, 8.2061], requires_grad=True)
epoch 56
loss=0.00017639700672589242;theta=tensor([ 1.9993, -3.4010, 4.1995]) +vs+ loss=17.54810905456543;theta=tensor([ 3.9536, -7.0890, 8.2973], requires_grad=True)
epoch 57
loss=0.00017972828936763108;theta=tensor([ 1.9998, -3.4012, 4.1987]) +vs+ loss=38.845252990722656;theta=tensor([ 4.0028, -7.1705, 8.3804], requires_grad=True)
epoch 58
loss=0.0001698939158814028;theta=tensor([ 1.9989, -3.3998, 4.1997]) +vs+ loss=24.035633087158203;theta=tensor([ 4.0506, -7.2466, 8.4576], requires_grad=True)
epoch 59
loss=0.00016960882931016386;theta=tensor([ 1.9987, -3.3993, 4.1995]) +vs+ loss=74.61981201171875;theta=tensor([ 4.0879, -7.3052, 8.5205], requires_grad=True)
epoch 60
loss=0.00017015331832226366;theta=tensor([ 1.9987, -3.3988, 4.1992]) +vs+ loss=55.80016326904297;theta=tensor([ 4.1123, -7.3517, 8.5749], requires_grad=True)
epoch 61
loss=0.00017030151502694935;theta=tensor([ 1.9983, -3.3988, 4.1994]) +vs+ loss=21.357677459716797;theta=tensor([ 4.1337, -7.3972, 8.6219], requires_grad=True)
epoch 62
loss=0.00016912302817218006;theta=tensor([ 1.9990, -3.4003, 4.2007]) +vs+ loss=62.74077606201172;theta=tensor([ 4.1522, -7.4262, 8.6567], requires_grad=True)
epoch 63
loss=0.00017075186769943684;theta=tensor([ 1.9997, -3.4006, 4.2007]) +vs+ loss=23.32436752319336;theta=tensor([ 4.1711, -7.4515, 8.6842], requires_grad=True)
epoch 64
loss=0.00017476364155299962;theta=tensor([ 2.0004, -3.4010, 4.2003]) +vs+ loss=59.611053466796875;theta=tensor([ 4.1798, -7.4662, 8.6996], requires_grad=True)
epoch 65
loss=0.0001723233435768634;theta=tensor([ 2.0004, -3.4007, 4.1999]) +vs+ loss=51.41381072998047;theta=tensor([ 4.1790, -7.4733, 8.7038], requires_grad=True)
epoch 66
loss=0.00017222696624230593;theta=tensor([ 2.0008, -3.4005, 4.1994]) +vs+ loss=19.92046546936035;theta=tensor([ 4.1777, -7.4790, 8.7005], requires_grad=True)
epoch 67
loss=0.00017040420789271593;theta=tensor([ 2.0016, -3.3999, 4.1990]) +vs+ loss=36.07746124267578;theta=tensor([ 4.1770, -7.4757, 8.6890], requires_grad=True)
epoch 68
loss=0.0001699236163403839;theta=tensor([ 2.0014, -3.3997, 4.1988]) +vs+ loss=60.047691345214844;theta=tensor([ 4.1709, -7.4596, 8.6651], requires_grad=True)
epoch 69
loss=0.00017378793563693762;theta=tensor([ 2.0015, -3.4006, 4.1993]) +vs+ loss=38.65812301635742;theta=tensor([ 4.1598, -7.4350, 8.6340], requires_grad=True)
epoch 70
loss=0.00017749964899849147;theta=tensor([ 2.0021, -3.4009, 4.1992]) +vs+ loss=40.08657455444336;theta=tensor([ 4.1406, -7.4041, 8.5945], requires_grad=True)
epoch 71
loss=0.00017457007197663188;theta=tensor([ 2.0013, -3.4006, 4.1989]) +vs+ loss=46.079322814941406;theta=tensor([ 4.1117, -7.3663, 8.5450], requires_grad=True)
epoch 72
loss=0.00017598320846445858;theta=tensor([ 2.0006, -3.4009, 4.1991]) +vs+ loss=38.65458297729492;theta=tensor([ 4.0776, -7.3232, 8.4852], requires_grad=True)
epoch 73
loss=0.00018053247185889632;theta=tensor([ 2.0008, -3.4013, 4.1985]) +vs+ loss=70.78762817382812;theta=tensor([ 4.0319, -7.2583, 8.4178], requires_grad=True)
epoch 74
loss=0.00018454341625329107;theta=tensor([ 2.0007, -3.4012, 4.1975]) +vs+ loss=20.685842514038086;theta=tensor([ 3.9843, -7.1897, 8.3450], requires_grad=True)
epoch 75
loss=0.0001805703795980662;theta=tensor([ 2.0007, -3.4011, 4.1981]) +vs+ loss=21.911151885986328;theta=tensor([ 3.9389, -7.1172, 8.2640], requires_grad=True)
epoch 76
loss=0.00017642680904828012;theta=tensor([ 2.0001, -3.4013, 4.2001]) +vs+ loss=30.90773582458496;theta=tensor([ 3.8961, -7.0373, 8.1734], requires_grad=True)
epoch 77
loss=0.00017017331265378743;theta=tensor([ 2.0000, -3.4005, 4.2005]) +vs+ loss=23.25639533996582;theta=tensor([ 3.8498, -6.9554, 8.0746], requires_grad=True)
epoch 78
loss=0.00016636324289720505;theta=tensor([ 1.9996, -3.4000, 4.2009]) +vs+ loss=13.450610160827637;theta=tensor([ 3.8072, -6.8692, 7.9711], requires_grad=True)
epoch 79
loss=0.00016418930317740887;theta=tensor([ 1.9996, -3.3997, 4.2013]) +vs+ loss=14.077974319458008;theta=tensor([ 3.7614, -6.7814, 7.8630], requires_grad=True)
epoch 80
loss=0.0001676852989476174;theta=tensor([ 1.9991, -3.4002, 4.2013]) +vs+ loss=29.78688621520996;theta=tensor([ 3.7098, -6.6875, 7.7471], requires_grad=True)
epoch 81
loss=0.00016680985572747886;theta=tensor([ 2.0005, -3.4000, 4.2017]) +vs+ loss=35.38242721557617;theta=tensor([ 3.6517, -6.5870, 7.6205], requires_grad=True)
epoch 82
loss=0.00016362463065888733;theta=tensor([ 1.9993, -3.3996, 4.2015]) +vs+ loss=29.195911407470703;theta=tensor([ 3.5847, -6.4811, 7.4862], requires_grad=True)
epoch 83
loss=0.00016521224461030215;theta=tensor([ 2.0000, -3.3998, 4.2019]) +vs+ loss=22.931034088134766;theta=tensor([ 3.5167, -6.3684, 7.3447], requires_grad=True)
epoch 84
loss=0.0001610375620657578;theta=tensor([ 2.0000, -3.3993, 4.2013]) +vs+ loss=23.00523567199707;theta=tensor([ 3.4449, -6.2458, 7.1998], requires_grad=True)
epoch 85
loss=0.000163917793543078;theta=tensor([ 2.0000, -3.3996, 4.2008]) +vs+ loss=19.570383071899414;theta=tensor([ 3.3679, -6.1186, 7.0488], requires_grad=True)
epoch 86
loss=0.00016396815772168338;theta=tensor([ 1.9997, -3.3995, 4.2005]) +vs+ loss=17.819217681884766;theta=tensor([ 3.2883, -5.9845, 6.8930], requires_grad=True)
epoch 87
loss=0.0001636362576391548;theta=tensor([ 1.9990, -3.3995, 4.2010]) +vs+ loss=4.124261856079102;theta=tensor([ 3.2094, -5.8497, 6.7346], requires_grad=True)
epoch 88
loss=0.0001627235469641164;theta=tensor([ 1.9995, -3.3995, 4.2015]) +vs+ loss=16.016010284423828;theta=tensor([ 3.1250, -5.7108, 6.5700], requires_grad=True)
epoch 89
loss=0.00016649524332024157;theta=tensor([ 1.9998, -3.3992, 4.1994]) +vs+ loss=8.818704605102539;theta=tensor([ 3.0429, -5.5676, 6.4012], requires_grad=True)
epoch 90
loss=0.00017015125195030123;theta=tensor([ 2.0000, -3.4003, 4.1997]) +vs+ loss=18.328296661376953;theta=tensor([ 2.9567, -5.4145, 6.2274], requires_grad=True)
epoch 91
loss=0.00017368022236041725;theta=tensor([ 2.0007, -3.4006, 4.1992]) +vs+ loss=6.945518493652344;theta=tensor([ 2.8665, -5.2590, 6.0510], requires_grad=True)
epoch 92
loss=0.00017715533613227308;theta=tensor([ 2.0012, -3.4012, 4.2005]) +vs+ loss=7.404855251312256;theta=tensor([ 2.7714, -5.1014, 5.8710], requires_grad=True)
epoch 93
loss=0.00017647151253186166;theta=tensor([ 2.0004, -3.4012, 4.2007]) +vs+ loss=7.432589054107666;theta=tensor([ 2.6745, -4.9397, 5.6871], requires_grad=True)
epoch 94
loss=0.00016964862879831344;theta=tensor([ 2.0006, -3.4004, 4.2007]) +vs+ loss=2.2681782245635986;theta=tensor([ 2.5772, -4.7772, 5.5012], requires_grad=True)
epoch 95
loss=0.00016688762116245925;theta=tensor([ 2.0002, -3.4001, 4.2013]) +vs+ loss=1.6974331140518188;theta=tensor([ 2.4791, -4.6135, 5.3141], requires_grad=True)
epoch 96
loss=0.00016662708367221057;theta=tensor([ 2.0001, -3.4000, 4.2016]) +vs+ loss=4.312170028686523;theta=tensor([ 2.3805, -4.4447, 5.1253], requires_grad=True)
epoch 97
loss=0.00016773059905972332;theta=tensor([ 2.0002, -3.4002, 4.2010]) +vs+ loss=2.584296703338623;theta=tensor([ 2.2811, -4.2734, 4.9339], requires_grad=True)
epoch 98
loss=0.0001651171623962;theta=tensor([ 1.9996, -3.3997, 4.2005]) +vs+ loss=2.0876243114471436;theta=tensor([ 2.1805, -4.0996, 4.7404], requires_grad=True)
epoch 99
loss=0.00016961248184088618;theta=tensor([ 2.0002, -3.4004, 4.2003]) +vs+ loss=0.5221318006515503;theta=tensor([ 2.0794, -3.9249, 4.5461], requires_grad=True)
首先，一定要有 self.theta.grad.zero_()梯度归零操作，这样才能保证每次参数更新是依据当前batch的数据反向传播梯度信息，否则很难收敛；其次，经测试，在同一学习率下，从零开始的线性回归（无论是你编写的模型还是上一节书上提供的模型）收敛速度就是要比依赖API的模型的收敛速度慢。调大学习率可以让前者加速，原因我也不懂 
在简洁实现中，为什么不用对l先sum()然后再反向传播求梯度，因为在loss函数中，已经对l累加过了，这就是封装的好处
练习3
for paprameter in net.parameters():
print(paprameter.grad)
每算一次损失是一个单独的计算图，不会累计的。累计的话需要再次使用loss吧。
用计时器把训练的代码围起来，发现简洁实现还稍慢一点（0.05s/0.07s）