做防水保温怎么建网站,wordpress seo文件,上海品划做网站,人力资源外包平台反向传播算法 数学公式算法代码结果 算法中一些函数的区别 数学公式 算法代码 这里用反向传播算法#xff0c;计算 y w * x模型 import numpy as np
import matplotlib.pyplot as ply#反向传播算法#xff0c;需要使用pytorch框架#xff0c;
#这里导入pytorch框架#xf… 反向传播算法 数学公式算法代码结果 算法中一些函数的区别 数学公式 算法代码 这里用反向传播算法计算 y w * x模型 import numpy as np
import matplotlib.pyplot as ply#反向传播算法需要使用pytorch框架
#这里导入pytorch框架用torchimport torch#用反向传播算法计算 y w * x模型
x_data [1.0, 2.0, 3.0, 4.0]
y_data [2.0, 4.0, 6.0, 8.0]w torch.Tensor(1) # 设置初始值
w.requires_grad True #计算梯度默认是不计算的def forward(x):return w*xdef loss(x, y):y_pred forward(x)return (y_pred - y)**2print(Predict (befortraining),4,forward(4))#注意在pytorch中只有浮点类型的数才有梯度因此在定义张量时一定要将类型指定为float型
#100轮
for epoch in range(100):l loss(1, 2) # 为了在for循环之前定义l,以便之后的输出无实际意义for x, y in zip(x_data, y_data):l loss(x, y)l.backward() #计算梯度的函数作用单独查下print(\tgrad:, x, y, w.grad.item())w.data w.data - 0.01 * w.grad.data # w.grad是一个张量所以要取dataw.grad.data.zero_() #释放之前的梯度print(Epoch:, epoch, l.item())print(Predict(after training), 4, forward(4).item())结果 “C:\Program Files\Python38\python.exe” C:\Users\惊艳了时光\Desktop\code\机器学习\反向传播算法.py Predict (befortraining) 4 tensor([0.], grad_fn) grad: 1.0 2.0 -4.0 Epoch: 0 4.0 grad: 2.0 4.0 -15.680000305175781 Epoch: 0 15.366400718688965 grad: 3.0 6.0 -32.457603454589844 Epoch: 0 29.263774871826172 grad: 4.0 8.0 -47.31596755981445 Epoch: 0 34.98126220703125 grad: 1.0 2.0 -2.0109286308288574 Epoch: 1 1.0109584331512451 grad: 2.0 4.0 -7.882840156555176 Epoch: 1 3.883697986602783 grad: 3.0 6.0 -16.31747817993164 Epoch: 1 7.396113872528076 grad: 4.0 8.0 -23.78725814819336 Epoch: 1 8.841151237487793 grad: 1.0 2.0 -1.0109584331512451 Epoch: 2 0.25550922751426697 grad: 2.0 4.0 -3.9629573822021484 Epoch: 2 0.981564462184906 grad: 3.0 6.0 -8.203322410583496 Epoch: 2 1.8692916631698608 grad: 4.0 8.0 -11.9586181640625 Epoch: 2 2.234508514404297 grad: 1.0 2.0 -0.5082411766052246 Epoch: 3 0.06457727402448654 grad: 2.0 4.0 -1.9923057556152344 Epoch: 3 0.24808013439178467 grad: 3.0 6.0 -4.124073028564453 Epoch: 3 0.4724438488483429 grad: 4.0 8.0 -6.011981964111328 Epoch: 3 0.5647488832473755 grad: 1.0 2.0 -0.2555091381072998 Epoch: 4 0.016321230679750443 grad: 2.0 4.0 -1.0015954971313477 Epoch: 4 0.06269959360361099 grad: 3.0 6.0 -2.07330322265625 Epoch: 4 0.11940517276525497 grad: 4.0 8.0 -3.0224151611328125 Epoch: 4 0.14273427426815033 grad: 1.0 2.0 -0.12845253944396973 Epoch: 5 0.0041250139474868774 grad: 2.0 4.0 -0.5035343170166016 Epoch: 5 0.015846675261855125 grad: 3.0 6.0 -1.0423164367675781 Epoch: 5 0.030178431421518326 grad: 4.0 8.0 -1.5194625854492188 Epoch: 5 0.036074478179216385 grad: 1.0 2.0 -0.06457710266113281 Epoch: 6 0.001042550546117127 grad: 2.0 4.0 -0.2531423568725586 Epoch: 6 0.004005065653473139 grad: 3.0 6.0 -0.5240049362182617 Epoch: 6 0.007627254817634821 grad: 4.0 8.0 -0.7638816833496094 Epoch: 6 0.009117425419390202 grad: 1.0 2.0 -0.03246498107910156 Epoch: 7 0.0002634937409311533 grad: 2.0 4.0 -0.12726306915283203 Epoch: 7 0.0010122430976480246 grad: 3.0 6.0 -0.26343441009521484 Epoch: 7 0.0019277135143056512 grad: 4.0 8.0 -0.3840293884277344 Epoch: 7 0.0023043525870889425 grad: 1.0 2.0 -0.016321182250976562 Epoch: 8 6.659524660790339e-05 grad: 2.0 4.0 -0.0639791488647461 Epoch: 8 0.0002558332053013146 grad: 3.0 6.0 -0.13243675231933594 Epoch: 8 0.00048720816266722977 grad: 4.0 8.0 -0.19306182861328125 Epoch: 8 0.0005823886021971703 grad: 1.0 2.0 -0.008205175399780273 Epoch: 9 1.6831225366331637e-05 grad: 2.0 4.0 -0.032164573669433594 Epoch: 9 6.465998740168288e-05 grad: 3.0 6.0 -0.06658172607421875 Epoch: 9 0.00012314239575061947 grad: 4.0 8.0 -0.0970611572265625 Epoch: 9 0.00014720106264576316 grad: 1.0 2.0 -0.004125118255615234 Epoch: 10 4.2541500988591e-06 grad: 2.0 4.0 -0.016170501708984375 Epoch: 10 1.634281943552196e-05 grad: 3.0 6.0 -0.033473968505859375 Epoch: 10 3.112518243142404e-05 grad: 4.0 8.0 -0.048797607421875 Epoch: 10 3.7206351407803595e-05 grad: 1.0 2.0 -0.002074003219604492 Epoch: 11 1.0753723245215951e-06 grad: 2.0 4.0 -0.008130073547363281 Epoch: 11 4.131130936002592e-06 grad: 3.0 6.0 -0.016828536987304688 Epoch: 11 7.866657142585609e-06 grad: 4.0 8.0 -0.024532318115234375 Epoch: 11 9.403665899299085e-06 grad: 1.0 2.0 -0.0010426044464111328 Epoch: 12 2.7175599370821146e-07 grad: 2.0 4.0 -0.0040874481201171875 Epoch: 12 1.0442020084155956e-06 grad: 3.0 6.0 -0.008460044860839844 Epoch: 12 1.988121084650629e-06 grad: 4.0 8.0 -0.012332916259765625 Epoch: 12 2.3765753667248646e-06 grad: 1.0 2.0 -0.0005240440368652344 Epoch: 13 6.865553814350278e-08 grad: 2.0 4.0 -0.0020542144775390625 Epoch: 13 2.637373199831927e-07 grad: 3.0 6.0 -0.0042514801025390625 Epoch: 13 5.020856406190433e-07 grad: 4.0 8.0 -0.006198883056640625 Epoch: 13 6.004086117172847e-07 grad: 1.0 2.0 -0.00026345252990722656 Epoch: 14 1.7351808878629527e-08 grad: 2.0 4.0 -0.0010328292846679688 Epoch: 14 6.667102070423425e-08 grad: 3.0 6.0 -0.0021371841430664062 Epoch: 14 1.2687655726040248e-07 grad: 4.0 8.0 -0.003116607666015625 Epoch: 14 1.5176942724792752e-07 grad: 1.0 2.0 -0.00013256072998046875 Epoch: 15 4.393086783238687e-09 grad: 2.0 4.0 -0.0005197525024414062 Epoch: 15 1.68839164871315e-08 grad: 3.0 6.0 -0.00107574462890625 Epoch: 15 3.2145180739462376e-08 grad: 4.0 8.0 -0.001567840576171875 Epoch: 15 3.84081886295462e-08 grad: 1.0 2.0 -6.651878356933594e-05 Epoch: 16 1.1061871418860392e-09 grad: 2.0 4.0 -0.00026035308837890625 Epoch: 16 4.2364831642771605e-09 grad: 3.0 6.0 -0.000537872314453125 Epoch: 16 8.036295184865594e-09 grad: 4.0 8.0 -0.00078582763671875 Epoch: 16 9.648829291108996e-09 grad: 1.0 2.0 -3.337860107421875e-05 Epoch: 17 2.7853275241795927e-10 grad: 2.0 4.0 -0.00013065338134765625 Epoch: 17 1.0668941285985056e-09 grad: 3.0 6.0 -0.0002689361572265625 Epoch: 17 2.0090737962163985e-09 grad: 4.0 8.0 -0.000392913818359375 Epoch: 17 2.412207322777249e-09 grad: 1.0 2.0 -1.6689300537109375e-05 Epoch: 18 6.963318810448982e-11 grad: 2.0 4.0 -6.580352783203125e-05 Epoch: 18 2.7063151719630696e-10 grad: 3.0 6.0 -0.00013446807861328125 Epoch: 18 5.022684490540996e-10 grad: 4.0 8.0 -0.0001983642578125 Epoch: 18 6.148184183984995e-10 grad: 1.0 2.0 -8.344650268554688e-06 Epoch: 19 1.7408297026122455e-11 grad: 2.0 4.0 -3.24249267578125e-05 Epoch: 19 6.571099220309407e-11 grad: 3.0 6.0 -6.580352783203125e-05 Epoch: 19 1.2028067430946976e-10 grad: 4.0 8.0 -9.5367431640625e-05 Epoch: 19 1.4210854715202004e-10 grad: 1.0 2.0 -4.0531158447265625e-06 Epoch: 20 4.106937012693379e-12 grad: 2.0 4.0 -1.621246337890625e-05 Epoch: 20 1.6427748050773516e-11 grad: 3.0 6.0 -3.4332275390625e-05 Epoch: 20 3.2741809263825417e-11 grad: 4.0 8.0 -4.9591064453125e-05 Epoch: 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-3.814697265625e-06 Epoch: 31 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 32 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 32 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 32 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 32 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 33 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 33 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 33 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 33 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 34 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 34 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 34 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 34 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 35 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 35 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-9.5367431640625e-07 Epoch: 46 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 46 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 46 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 47 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 47 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 47 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 47 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 48 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 48 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 48 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 48 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 49 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 49 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 49 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 49 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 50 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 50 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 50 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 50 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 51 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 51 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 51 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 51 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 52 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 52 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 52 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 52 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 53 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 53 5.684341886080802e-14 grad: 3.0 6.0 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-3.814697265625e-06 Epoch: 89 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 90 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 90 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 90 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 90 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 91 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 91 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 91 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 91 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 92 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 92 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 92 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 92 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 93 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 93 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 93 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 93 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 94 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 94 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 94 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 94 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 95 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 95 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 95 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 95 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 96 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 96 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 96 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 96 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 97 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 97 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 97 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 97 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 98 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 98 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 98 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 98 2.2737367544323206e-13 grad: 1.0 2.0 -2.384185791015625e-07 Epoch: 99 1.4210854715202004e-14 grad: 2.0 4.0 -9.5367431640625e-07 Epoch: 99 5.684341886080802e-14 grad: 3.0 6.0 -2.86102294921875e-06 Epoch: 99 2.2737367544323206e-13 grad: 4.0 8.0 -3.814697265625e-06 Epoch: 99 2.2737367544323206e-13 Predict(after training) 4 7.999999523162842 算法中一些函数的区别 1.w.data 表示张量w的值其本身也是张量,输出格式tensor[数]。 2.w.grad 表示张量w的梯度其本身w.grad是张量 用时(标量计算时)需要取w.grad.data表示张量w.grad的值,输出格式tensor[数](梯度输出时)需要取w.grad.item()表示返回的是一个具体的数值,输出格式 数 3.w.grad.item() l.item() 表示返回的是一个具体的数值,输出格式 数
