Pytorch之parameters的使用
1.预构建网络
classNet(nn.Module): def__init__(self): super(Net,self).__init__() #1inputimagechannel,6outputchannels,5*5squareconvolution #kernel self.conv1=nn.Conv2d(1,6,5) self.conv2=nn.Conv2d(6,16,5) #anaffineoperation:y=Wx+b self.fc1=nn.Linear(16*5*5,120) self.fc2=nn.Linear(120,84) self.fc3=nn.Linear(84,10) defforward(self,x): #maxpoolingovera(2,2)window x=F.max_pool2d(F.relu(self.conv1(x)),(2,2)) #Ifsizeisasquareyoucanonlyspecifyasinglenumber x=F.max_pool2d(F.relu(self.conv2(x)),2) x=x.view(-1,self.num_flat_features(x)) x=F.relu(self.fc1(x)) x=F.relu(self.fc2(x)) x=self.fc3(x) returnx defnum_flat_features(self,x): size=x.size()[1:]#alldimensionsexceptthebatchdimension num_features=1 forsinsize: num_features*=s returnnum_features net=Net()
网络结构
Net( (conv1):Conv2d(1,6,kernel_size=(5,5),stride=(1,1)) (conv2):Conv2d(6,16,kernel_size=(5,5),stride=(1,1)) (fc1):Linear(in_features=400,out_features=120,bias=True) (fc2):Linear(in_features=120,out_features=84,bias=True) (fc3):Linear(in_features=84,out_features=10,bias=True) )
2.net.parameters()
构建好神经网络后，网络的参数都保存在parameters()函数当中
print(net.parameters())
输出
para=list(net.parameters()) print(para) #len返回列表项个数 print(len(para))
输出
[Parametercontaining: tensor([[[[-0.0596,0.1908,0.1831,0.0542,-0.0283], [-0.0542,-0.1680,0.1566,0.1036,-0.1756], [-0.1437,0.0083,0.0871,0.1549,0.1556], [0.1360,0.0171,0.1034,-0.1548,-0.1343], [-0.0978,-0.1803,-0.0701,-0.0377,0.0290]]], [[[-0.1020,0.0862,-0.1227,-0.1742,0.1510], [0.0728,0.1725,0.0352,0.1579,0.0367], [0.0862,-0.0995,0.1276,-0.1895,-0.1346], [0.1938,0.1387,-0.1983,-0.1015,-0.0740], [-0.0248,-0.0546,0.0849,0.1510,-0.0066]]], [[[0.1333,0.0300,0.0969,-0.0295,0.0879], [0.1216,-0.0864,0.0259,0.0157,-0.1330], [-0.1873,0.1309,0.1947,0.1886,0.1944], [-0.0647,0.0957,0.1592,0.1894,0.1862], [0.0896,0.1287,-0.0650,0.0684,0.1182]]], [[[-0.0816,0.0968,0.1259,-0.1124,-0.0864], [-0.0450,0.0737,0.0483,0.1180,-0.0933], [-0.0925,-0.0549,0.1191,0.0165,0.1369], [-0.1771,-0.1937,0.1542,0.1105,0.1572], [0.1163,-0.1577,0.1426,0.0431,-0.0362]]], [[[-0.0675,-0.1039,0.0762,-0.1798,0.0071], [-0.1794,0.1942,0.0540,0.1887,0.1413], [0.1366,0.0682,0.1230,0.0184,-0.0980], [-0.1613,0.1225,-0.0734,0.1938,0.1919], [0.1745,-0.1550,0.0663,0.0044,-0.0538]]], [[[-0.0926,0.1146,0.1008,0.1644,0.1046], [-0.1230,0.0080,0.0198,-0.1216,-0.1942], [0.0327,0.0205,0.0862,-0.1714,0.0955], [0.0358,-0.1350,0.1387,-0.1365,-0.1600], [0.0368,-0.1323,-0.0127,0.0917,-0.1892]]]], requires_grad=True),Parametercontaining: tensor([-0.0229,-0.1387,-0.1571,-0.0381,-0.1559,0.0946],requires_grad=True),Parametercontaining: tensor([[[[-0.0497,-0.0356,-0.0272,-0.0519,0.0451], [-0.0247,0.0228,0.0705,-0.0341,-0.0454], [0.0129,-0.0385,0.0682,-0.0613,0.0497], [-0.0394,0.0218,-0.0056,0.0204,-0.0668], [0.0469,0.0649,-0.0470,0.0138,-0.0686]], [[0.0647,0.0554,-0.0220,-0.0295,-0.0145], [0.0500,-0.0026,0.0545,0.0415,0.0020], [-0.0802,0.0742,-0.0291,0.0679,-0.0657], [0.0309,0.0729,-0.0158,-0.0495,-0.0220], [-0.0433,0.0440,-0.0485,0.0478,0.0618]], [[0.0523,-0.0072,-0.0786,0.0569,0.0334], [-0.0254,-0.0043,-0.0113,0.0755,-0.0590], [0.0113,-0.0170,0.0318,-0.0764,-0.0210], [-0.0203,-0.0273,0.0634,0.0380,0.0014], [-0.0112,0.0555,-0.0129,-0.0395,0.0624]], [[0.0387,0.0189,-0.0007,-0.0604,0.0114], [0.0481,0.0551,0.0182,0.0474,0.0390], [0.0152,-0.0106,-0.0381,-0.0630,-0.0645], [0.0092,-0.0295,-0.0616,0.0571,0.0562], [0.0418,-0.0372,0.0269,0.0109,-0.0758]], [[-0.0751,-0.0610,0.0269,-0.0331,-0.0193], [0.0577,0.0430,-0.0201,-0.0017,-0.0408], [-0.0590,-0.0148,0.0790,0.0575,-0.0786], [0.0168,0.0335,0.0170,-0.0792,0.0344], [-0.0738,0.0193,-0.0732,-0.0666,-0.0734]], [[0.0154,0.0712,0.0540,-0.0429,0.0573], [-0.0423,0.0424,-0.0488,0.0317,0.0808], [0.0605,0.0324,-0.0020,-0.0538,0.0664], [0.0243,-0.0452,0.0070,-0.0287,-0.0476], [0.0087,0.0561,-0.0076,-0.0391,0.0795]]], [[[0.0773,0.0748,-0.0133,0.0651,0.0659], [0.0254,0.0222,0.0017,-0.0722,0.0667], [0.0357,-0.0677,0.0085,-0.0005,-0.0313], [0.0672,-0.0359,-0.0243,-0.0811,-0.0726], [0.0011,0.0226,0.0278,-0.0615,-0.0410]], [[0.0202,0.0519,0.0527,-0.0086,-0.0683], [0.0694,0.0434,0.0746,0.0754,0.0073], 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[[0.0514,0.0622,-0.0556,-0.0048,-0.0279], [0.0112,-0.0413,-0.0483,0.0166,0.0690], [-0.0433,0.0410,-0.0335,-0.0458,-0.0055], [0.0229,0.0289,0.0695,0.0574,0.0075], [0.0651,-0.0337,-0.0130,-0.0381,0.0272]]], ..., [[[-0.0538,0.0321,0.0302,0.0222,-0.0062], [0.0050,-0.0461,0.0084,-0.0448,-0.0604], [-0.0457,0.0455,-0.0773,-0.0437,0.0446], [0.0691,0.0390,-0.0040,0.0035,-0.0133], [0.0545,0.0517,-0.0067,0.0314,-0.0448]], [[0.0029,-0.0675,-0.0254,-0.0168,-0.0563], [0.0163,-0.0621,-0.0561,-0.0151,-0.0306], [-0.0021,0.0389,-0.0429,0.0778,0.0451], [-0.0578,0.0123,0.0049,-0.0728,-0.0408], [0.0722,0.0388,0.0177,0.0526,-0.0291]], [[0.0369,0.0502,0.0646,0.0388,-0.0091], [0.0066,0.0501,0.0114,0.0243,-0.0455], [0.0494,0.0495,-0.0257,0.0165,-0.0024], [-0.0476,-0.0552,0.0029,-0.0813,0.0698], [-0.0704,-0.0590,-0.0641,0.0284,0.0578]], [[0.0180,0.0794,-0.0090,-0.0081,0.0570], [-0.0529,0.0517,0.0045,-0.0580,-0.0192], [-0.0289,0.0261,0.0107,0.0180,-0.0062], [-0.0162,0.0607,0.0154,0.0450,0.0694], 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0.1046,0.0657,-0.0190,-0.0595,0.0981,0.1066,-0.0814,-0.0410, -0.0362,0.0359,-0.0863,-0.0438,0.0252,0.0351,0.1041,-0.0746, 0.0200,0.0790,-0.1074,0.0858,-0.0384,-0.0569,0.0093,0.1086, 0.0327,0.0278,-0.0765,-0.0221,0.0803,0.0598,-0.0706,0.0766, 0.0773,-0.0794,-0.0625,-0.0219]],requires_grad=True),Parametercontaining: tensor([-0.1061,-0.0962,0.0518,0.0545,-0.0188,-0.0293,-0.0951,0.0373, -0.0218,-0.0068],requires_grad=True)] 10
逐列表项输出列表元素和index
利用enumerate函数实现
#逐列表项输出参数和当前参数位于列表的第几项
fornum,tempinenumerate(para):
print('number:',num)
print(temp)
输出
number:0 Parametercontaining: tensor([[[[-0.0596,0.1908,0.1831,0.0542,-0.0283], [-0.0542,-0.1680,0.1566,0.1036,-0.1756], [-0.1437,0.0083,0.0871,0.1549,0.1556], [0.1360,0.0171,0.1034,-0.1548,-0.1343], [-0.0978,-0.1803,-0.0701,-0.0377,0.0290]]], [[[-0.1020,0.0862,-0.1227,-0.1742,0.1510], [0.0728,0.1725,0.0352,0.1579,0.0367], [0.0862,-0.0995,0.1276,-0.1895,-0.1346], [0.1938,0.1387,-0.1983,-0.1015,-0.0740], [-0.0248,-0.0546,0.0849,0.1510,-0.0066]]], [[[0.1333,0.0300,0.0969,-0.0295,0.0879], [0.1216,-0.0864,0.0259,0.0157,-0.1330], [-0.1873,0.1309,0.1947,0.1886,0.1944], [-0.0647,0.0957,0.1592,0.1894,0.1862], [0.0896,0.1287,-0.0650,0.0684,0.1182]]], [[[-0.0816,0.0968,0.1259,-0.1124,-0.0864], [-0.0450,0.0737,0.0483,0.1180,-0.0933], [-0.0925,-0.0549,0.1191,0.0165,0.1369], [-0.1771,-0.1937,0.1542,0.1105,0.1572], [0.1163,-0.1577,0.1426,0.0431,-0.0362]]], [[[-0.0675,-0.1039,0.0762,-0.1798,0.0071], [-0.1794,0.1942,0.0540,0.1887,0.1413], [0.1366,0.0682,0.1230,0.0184,-0.0980], [-0.1613,0.1225,-0.0734,0.1938,0.1919], [0.1745,-0.1550,0.0663,0.0044,-0.0538]]], [[[-0.0926,0.1146,0.1008,0.1644,0.1046], [-0.1230,0.0080,0.0198,-0.1216,-0.1942], [0.0327,0.0205,0.0862,-0.1714,0.0955], [0.0358,-0.1350,0.1387,-0.1365,-0.1600], [0.0368,-0.1323,-0.0127,0.0917,-0.1892]]]], requires_grad=True) number:1 Parametercontaining: tensor([-0.0229,-0.1387,-0.1571,-0.0381,-0.1559,0.0946],requires_grad=True) number:2 Parametercontaining: tensor([[[[-0.0497,-0.0356,-0.0272,-0.0519,0.0451], [-0.0247,0.0228,0.0705,-0.0341,-0.0454], [0.0129,-0.0385,0.0682,-0.0613,0.0497], [-0.0394,0.0218,-0.0056,0.0204,-0.0668], [0.0469,0.0649,-0.0470,0.0138,-0.0686]], [[0.0647,0.0554,-0.0220,-0.0295,-0.0145], [0.0500,-0.0026,0.0545,0.0415,0.0020], [-0.0802,0.0742,-0.0291,0.0679,-0.0657], [0.0309,0.0729,-0.0158,-0.0495,-0.0220], [-0.0433,0.0440,-0.0485,0.0478,0.0618]], [[0.0523,-0.0072,-0.0786,0.0569,0.0334], [-0.0254,-0.0043,-0.0113,0.0755,-0.0590], [0.0113,-0.0170,0.0318,-0.0764,-0.0210], 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-0.0240,0.0733,0.0580,0.0733,0.0953,-0.0651,-0.0860,0.0124, 0.1046,0.0657,-0.0190,-0.0595,0.0981,0.1066,-0.0814,-0.0410, -0.0362,0.0359,-0.0863,-0.0438,0.0252,0.0351,0.1041,-0.0746, 0.0200,0.0790,-0.1074,0.0858,-0.0384,-0.0569,0.0093,0.1086, 0.0327,0.0278,-0.0765,-0.0221,0.0803,0.0598,-0.0706,0.0766, 0.0773,-0.0794,-0.0625,-0.0219]],requires_grad=True) number:9 Parametercontaining: tensor([-0.1061,-0.0962,0.0518,0.0545,-0.0188,-0.0293,-0.0951,0.0373, -0.0218,-0.0068],requires_grad=True)
以上这篇Pytorch之parameters的使用就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持毛票票。
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