【Python机器学习】实验13 基于神经网络的回归-分类实验,dopod s1

文件名：【Python机器学习】实验13 基于神经网络的回归-分类实验,dopod s1 【Python机器学习】实验13 基于神经网络的回归-分类实验

文章目录 神经网络例1 基于神经网络的回归(简单例子)1.1 导入包1.2 构造数据集（随机构造的）1.3 构造训练集和测试集1.4 构建神经网络模型1.5 采用训练数据来训练神经网络模型 实验：基于神经网络的分类(鸢尾花数据集)1. 导入包2. 构造数据集3. 构造训练集和测试集4. 构建神经网络模型5. 采用训练数据来训练神经网络模型

神经网络 例1 基于神经网络的回归(简单例子) 1.1 导入包 import torch import numpy as npfrom torch import nnfrom sklearn.model_selection import train_test_split 1.2 构造数据集（随机构造的） from torch.autograd import Variablebatch_n=100hidden_layer=100input_data=1000output_data=10 x=Variable(torch.randn(batch_n,input_data),requires_grad=True)y=Variable(torch.randn(batch_n,output_data),requires_grad=True) 1.3 构造训练集和测试集 x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0) x_train.shape,x_test.shape,y_train.shape,y_test.shape (torch.Size([80, 1000]),torch.Size([20, 1000]),torch.Size([80, 10]),torch.Size([20, 10])) torch.Tensor(np.array([1,2])) tensor([1., 2.]) y_test tensor([[-0.1810, 0.2906, 0.4490, 1.3190, -1.1832, -0.0035, 0.5440, -0.8954,0.7686, 1.3758],[ 1.1767, -0.6170, -0.7946, -1.2191, 0.5998, -0.8591, -2.7796, -0.7918,-0.1282, 0.2730],[ 1.8079, 0.9862, -1.7850, -0.4031, 1.5472, 0.1663, -0.5043, 1.2402,-2.2270, 1.9437],[-0.0478, 0.1177, -0.4014, 0.6531, -2.0040, 1.5664, 2.0697, -0.5635,-0.4687, 1.5910],[ 1.5076, 1.0444, -1.7943, 0.7268, 1.1636, 0.1772, -1.0183, -1.0916,0.5012, 2.0798],[ 0.7027, -0.0999, -0.0670, -0.1838, 0.6959, 1.5484, 0.1950, -0.5757,1.4192, -0.6865],[ 1.7699, -1.9956, 0.1742, -0.6788, -2.0619, 0.8384, 2.1277, -1.2390,-1.0382, 0.5834],[ 0.8416, 1.6485, -0.0215, 0.0048, -1.7932, 0.1007, -2.4015, 0.3087,-0.7603, 0.9714],[-0.6723, -1.3535, -0.8598, -0.4294, -1.6416, 0.3986, -0.3160, 0.9952,0.6939, -1.2953],[ 0.1403, 0.2171, -1.0277, -0.6372, 0.2468, 1.6663, 0.3363, 0.5068,-0.0259, -0.8080],[ 0.9330, 0.8476, -0.3819, 0.8394, 1.1713, -0.6932, -0.0453, -1.3850,0.6089, -0.7219],[-0.1061, -2.8115, -1.7533, -0.3561, 0.5066, 0.5846, 0.2225, 0.7907,0.6693, 0.1164],[ 1.4511, -0.7063, -0.2785, 1.1644, -0.4726, -0.9858, 0.1105, 2.6274,0.8037, 0.1488],[ 0.9054, -0.1386, 0.6521, -2.7186, -1.1272, -0.7584, -1.1367, -0.0416,-0.0663, 0.6517],[-0.9568, -0.0174, -0.8611, 0.5748, -0.9300, 1.1043, -1.6796, 0.9629,-1.1011, 0.6005],[ 0.9963, 0.5226, 0.5209, 1.0107, 0.6931, 1.6149, -0.3450, 0.5082,1.2774, -0.1767],[ 0.3884, -1.8515, -0.6365, -0.1225, 1.2765, -0.1700, 0.4384, 0.0291,0.4540, 0.7085],[ 0.9688, 1.4026, 1.1516, -0.1575, 0.6101, -0.5406, 1.9612, 0.1654,-0.8425, -0.0459],[-1.5699, 0.0486, -1.7415, 1.5327, 0.0225, -1.1386, -0.6188, 0.3958,0.5564, -1.1593],[ 0.5734, 0.8675, 0.0328, -0.2371, -0.5879, 0.7541, 0.5935, 0.9097,0.9884, 0.6365]], grad_fn=<IndexBackward0>) 1.4 构建神经网络模型 class Nerual_Network(nn.Module):def __init__(self):super().__init__()self.hidden1=nn.Linear(input_data,hidden_layer)self.output=nn.Linear(hidden_layer,output_data)self.relu=nn.ReLU()self.softmax=nn.Softmax(dim=1)def forward(self,x):x=self.hidden1(x)x=self.relu(x)x=self.output(x)x=self.softmax(x)return x import torch.optim as optimmodel=Nerual_Network()model Nerual_Network((hidden1): Linear(in_features=1000, out_features=100, bias=True)(output): Linear(in_features=100, out_features=10, bias=True)(relu): ReLU()(softmax): Softmax(dim=1)) 1.5 采用训练数据来训练神经网络模型 epochs=1000learnng_rate=0.003critier=nn.MSELoss()optimizer=optim.Adam(model.parameters(),lr=learnng_rate) for i in range(epochs):outputs=model(x_train)loss=critier(outputs,y_train)print("Epoch:{},Loss:{:4f}".format(i,loss))optimizer.zero_grad()loss.backward(retain_graph=True)optimizer.step() Epoch:0,Loss:0.948208Epoch:1,Loss:0.896322Epoch:2,Loss:0.855293Epoch:3,Loss:0.819206Epoch:4,Loss:0.790216Epoch:5,Loss:0.769548Epoch:6,Loss:0.755935Epoch:7,Loss:0.747829Epoch:8,Loss:0.743429Epoch:9,Loss:0.741071Epoch:10,Loss:0.739489Epoch:11,Loss:0.738407Epoch:12,Loss:0.737566Epoch:13,Loss:0.736756Epoch:14,Loss:0.736009Epoch:15,Loss:0.735342Epoch:16,Loss:0.734747Epoch:17,Loss:0.734446Epoch:18,Loss:0.734121Epoch:19,Loss:0.733825Epoch:20,Loss:0.733538Epoch:21,Loss:0.733174Epoch:22,Loss:0.732976Epoch:23,Loss:0.732888Epoch:24,Loss:0.732744Epoch:25,Loss:0.732587Epoch:26,Loss:0.732487Epoch:27,Loss:0.732393Epoch:28,Loss:0.732277Epoch:29,Loss:0.732168Epoch:30,Loss:0.732101Epoch:31,Loss:0.732098Epoch:32,Loss:0.731946Epoch:33,Loss:0.731655Epoch:34,Loss:0.731511Epoch:35,Loss:0.731603Epoch:36,Loss:0.731634Epoch:37,Loss:0.731516Epoch:38,Loss:0.731375Epoch:39,Loss:0.731263Epoch:40,Loss:0.731153Epoch:41,Loss:0.731199Epoch:42,Loss:0.731237Epoch:43,Loss:0.731082Epoch:44,Loss:0.730953Epoch:45,Loss:0.730905Epoch:46,Loss:0.730879Epoch:47,Loss:0.730842Epoch:48,Loss:0.730784Epoch:49,Loss:0.730665Epoch:50,Loss:0.730640Epoch:51,Loss:0.730709Epoch:52,Loss:0.730659Epoch:53,Loss:0.730601Epoch:54,Loss:0.730571Epoch:55,Loss:0.730595Epoch:56,Loss:0.730605Epoch:57,Loss:0.730550Epoch:58,Loss:0.730524Epoch:59,Loss:0.730512Epoch:60,Loss:0.730482Epoch:61,Loss:0.730442Epoch:62,Loss:0.730421Epoch:63,Loss:0.730365Epoch:64,Loss:0.730232Epoch:65,Loss:0.730102Epoch:66,Loss:0.730107Epoch:67,Loss:0.730175Epoch:68,Loss:0.730177Epoch:69,Loss:0.730097Epoch:70,Loss:0.730023Epoch:71,Loss:0.730047Epoch:72,Loss:0.730051Epoch:73,Loss:0.729966Epoch:74,Loss:0.729911Epoch:75,Loss:0.729961Epoch:76,Loss:0.729982Epoch:77,Loss:0.729963Epoch:78,Loss:0.729940Epoch:79,Loss:0.729932Epoch:80,Loss:0.729937Epoch:81,Loss:0.729935Epoch:82,Loss:0.729909Epoch:83,Loss:0.729893Epoch:84,Loss:0.729907Epoch:85,Loss:0.729910Epoch:86,Loss:0.729892Epoch:87,Loss:0.729884Epoch:88,Loss:0.729888Epoch:89,Loss:0.729883Epoch:90,Loss:0.729874Epoch:91,Loss:0.729868Epoch:92,Loss:0.729864Epoch:93,Loss:0.729858Epoch:94,Loss:0.729847Epoch:95,Loss:0.729843Epoch:96,Loss:0.729848Epoch:97,Loss:0.729852Epoch:98,Loss:0.729849Epoch:99,Loss:0.729840Epoch:100,Loss:0.729836Epoch:101,Loss:0.729834Epoch:102,Loss:0.729832Epoch:103,Loss:0.729832Epoch:104,Loss:0.729834Epoch:105,Loss:0.729833Epoch:106,Loss:0.729828Epoch:107,Loss:0.729825Epoch:108,Loss:0.729824Epoch:109,Loss:0.729821Epoch:110,Loss:0.729816Epoch:111,Loss:0.729813Epoch:112,Loss:0.729810Epoch:113,Loss:0.729806Epoch:114,Loss:0.729799Epoch:115,Loss:0.729792Epoch:116,Loss:0.729782Epoch:117,Loss:0.729771Epoch:118,Loss:0.729763Epoch:119,Loss:0.729760Epoch:120,Loss:0.729763Epoch:121,Loss:0.729765Epoch:122,Loss:0.729761Epoch:123,Loss:0.729753Epoch:124,Loss:0.729747Epoch:125,Loss:0.729744Epoch:126,Loss:0.729743Epoch:127,Loss:0.729739Epoch:128,Loss:0.729731Epoch:129,Loss:0.729718Epoch:130,Loss:0.729700Epoch:131,Loss:0.729674Epoch:132,Loss:0.729634Epoch:133,Loss:0.729571Epoch:134,Loss:0.729517Epoch:135,Loss:0.729545Epoch:136,Loss:0.729541Epoch:137,Loss:0.729501Epoch:138,Loss:0.729543Epoch:139,Loss:0.729531Epoch:140,Loss:0.729507Epoch:141,Loss:0.729527Epoch:142,Loss:0.729508Epoch:143,Loss:0.729499Epoch:144,Loss:0.729505Epoch:145,Loss:0.729486Epoch:146,Loss:0.729480Epoch:147,Loss:0.729476Epoch:148,Loss:0.729455Epoch:149,Loss:0.729445Epoch:150,Loss:0.729428Epoch:151,Loss:0.729400Epoch:152,Loss:0.729373Epoch:153,Loss:0.729345Epoch:154,Loss:0.729355Epoch:155,Loss:0.729364Epoch:156,Loss:0.729335Epoch:157,Loss:0.729335Epoch:158,Loss:0.729328Epoch:159,Loss:0.729310Epoch:160,Loss:0.729303Epoch:161,Loss:0.729285Epoch:162,Loss:0.729242Epoch:163,Loss:0.729181Epoch:164,Loss:0.729270Epoch:165,Loss:0.729187Epoch:166,Loss:0.729191Epoch:167,Loss:0.729215Epoch:168,Loss:0.729211Epoch:169,Loss:0.729182Epoch:170,Loss:0.729173Epoch:171,Loss:0.729202Epoch:172,Loss:0.729167Epoch:173,Loss:0.729181Epoch:174,Loss:0.729184Epoch:175,Loss:0.729166Epoch:176,Loss:0.729160Epoch:177,Loss:0.729178Epoch:178,Loss:0.729157Epoch:179,Loss:0.729164Epoch:180,Loss:0.729166Epoch:181,Loss:0.729156Epoch:182,Loss:0.729158Epoch:183,Loss:0.729161Epoch:184,Loss:0.729151Epoch:185,Loss:0.729155Epoch:186,Loss:0.729156Epoch:187,Loss:0.729150Epoch:188,Loss:0.729153Epoch:189,Loss:0.729153Epoch:190,Loss:0.729149Epoch:191,Loss:0.729151Epoch:192,Loss:0.729149Epoch:193,Loss:0.729147Epoch:194,Loss:0.729149Epoch:195,Loss:0.729147Epoch:196,Loss:0.729147Epoch:197,Loss:0.729147Epoch:198,Loss:0.729146Epoch:199,Loss:0.729145Epoch:200,Loss:0.729145Epoch:201,Loss:0.729144Epoch:202,Loss:0.729145Epoch:203,Loss:0.729144Epoch:204,Loss:0.729144Epoch:205,Loss:0.729143Epoch:206,Loss:0.729143Epoch:207,Loss:0.729142Epoch:208,Loss:0.729142Epoch:209,Loss:0.729142Epoch:210,Loss:0.729141Epoch:211,Loss:0.729141Epoch:212,Loss:0.729140Epoch:213,Loss:0.729140Epoch:214,Loss:0.729139Epoch:215,Loss:0.729139Epoch:216,Loss:0.729138Epoch:217,Loss:0.729138Epoch:218,Loss:0.729137Epoch:219,Loss:0.729137Epoch:220,Loss:0.729136Epoch:221,Loss:0.729135Epoch:222,Loss:0.729134Epoch:223,Loss:0.729134Epoch:224,Loss:0.729133Epoch:225,Loss:0.729132Epoch:226,Loss:0.729131Epoch:227,Loss:0.729131Epoch:228,Loss:0.729130Epoch:229,Loss:0.729129Epoch:230,Loss:0.729129Epoch:231,Loss:0.729128Epoch:232,Loss:0.729127Epoch:233,Loss:0.729126Epoch:234,Loss:0.729125Epoch:235,Loss:0.729124Epoch:236,Loss:0.729123Epoch:237,Loss:0.729121Epoch:238,Loss:0.729119Epoch:239,Loss:0.729116Epoch:240,Loss:0.729112Epoch:241,Loss:0.729106Epoch:242,Loss:0.729095Epoch:243,Loss:0.729075Epoch:244,Loss:0.729035Epoch:245,Loss:0.728994Epoch:246,Loss:0.729103Epoch:247,Loss:0.729000Epoch:248,Loss:0.729026Epoch:249,Loss:0.729047Epoch:250,Loss:0.729044Epoch:251,Loss:0.729022Epoch:252,Loss:0.728987Epoch:253,Loss:0.729031Epoch:254,Loss:0.728992Epoch:255,Loss:0.728995Epoch:256,Loss:0.729006Epoch:257,Loss:0.728994Epoch:258,Loss:0.728984Epoch:259,Loss:0.728999Epoch:260,Loss:0.728980Epoch:261,Loss:0.728992Epoch:262,Loss:0.728987Epoch:263,Loss:0.728978Epoch:264,Loss:0.728986Epoch:265,Loss:0.728979Epoch:266,Loss:0.728976Epoch:267,Loss:0.728983Epoch:268,Loss:0.728976Epoch:269,Loss:0.728975Epoch:270,Loss:0.728978Epoch:271,Loss:0.728973Epoch:272,Loss:0.728976Epoch:273,Loss:0.728974Epoch:274,Loss:0.728972Epoch:275,Loss:0.728973Epoch:276,Loss:0.728970Epoch:277,Loss:0.728972Epoch:278,Loss:0.728970Epoch:279,Loss:0.728969Epoch:280,Loss:0.728970Epoch:281,Loss:0.728968Epoch:282,Loss:0.728967Epoch:283,Loss:0.728967Epoch:284,Loss:0.728965Epoch:285,Loss:0.728964Epoch:286,Loss:0.728962Epoch:287,Loss:0.728961Epoch:288,Loss:0.728958Epoch:289,Loss:0.728954Epoch:290,Loss:0.728950Epoch:291,Loss:0.728942Epoch:292,Loss:0.728928Epoch:293,Loss:0.728899Epoch:294,Loss:0.728823Epoch:295,Loss:0.728630Epoch:296,Loss:0.728751Epoch:297,Loss:0.728786Epoch:298,Loss:0.728595Epoch:299,Loss:0.728683Epoch:300,Loss:0.728725Epoch:301,Loss:0.728664Epoch:302,Loss:0.728570Epoch:303,Loss:0.728632Epoch:304,Loss:0.728585Epoch:305,Loss:0.728485Epoch:306,Loss:0.728588Epoch:307,Loss:0.728561Epoch:308,Loss:0.728491Epoch:309,Loss:0.728501Epoch:310,Loss:0.728533Epoch:311,Loss:0.728463Epoch:312,Loss:0.728422Epoch:313,Loss:0.728431Epoch:314,Loss:0.728469Epoch:315,Loss:0.728421Epoch:316,Loss:0.728418Epoch:317,Loss:0.728427Epoch:318,Loss:0.728412Epoch:319,Loss:0.728418Epoch:320,Loss:0.728410Epoch:321,Loss:0.728394Epoch:322,Loss:0.728381Epoch:323,Loss:0.728382Epoch:324,Loss:0.728369Epoch:325,Loss:0.728349Epoch:326,Loss:0.728347Epoch:327,Loss:0.728362Epoch:328,Loss:0.728340Epoch:329,Loss:0.728344Epoch:330,Loss:0.728345Epoch:331,Loss:0.728342Epoch:332,Loss:0.728344Epoch:333,Loss:0.728341Epoch:334,Loss:0.728331Epoch:335,Loss:0.728325Epoch:336,Loss:0.728333Epoch:337,Loss:0.728325Epoch:338,Loss:0.728315Epoch:339,Loss:0.728312Epoch:340,Loss:0.728302Epoch:341,Loss:0.728283Epoch:342,Loss:0.728251Epoch:343,Loss:0.728215Epoch:344,Loss:0.728237Epoch:345,Loss:0.728224Epoch:346,Loss:0.728241Epoch:347,Loss:0.728240Epoch:348,Loss:0.728236Epoch:349,Loss:0.728247Epoch:350,Loss:0.728234Epoch:351,Loss:0.728240Epoch:352,Loss:0.728227Epoch:353,Loss:0.728231Epoch:354,Loss:0.728216Epoch:355,Loss:0.728212Epoch:356,Loss:0.728203Epoch:357,Loss:0.728202Epoch:358,Loss:0.728209Epoch:359,Loss:0.728204Epoch:360,Loss:0.728200Epoch:361,Loss:0.728195Epoch:362,Loss:0.728183Epoch:363,Loss:0.728181Epoch:364,Loss:0.728175Epoch:365,Loss:0.728174Epoch:366,Loss:0.728175Epoch:367,Loss:0.728168Epoch:368,Loss:0.728171Epoch:369,Loss:0.728168Epoch:370,Loss:0.728167Epoch:371,Loss:0.728168Epoch:372,Loss:0.728167Epoch:373,Loss:0.728169Epoch:374,Loss:0.728166Epoch:375,Loss:0.728165Epoch:376,Loss:0.728165Epoch:377,Loss:0.728163Epoch:378,Loss:0.728163Epoch:379,Loss:0.728162Epoch:380,Loss:0.728160Epoch:381,Loss:0.728159Epoch:382,Loss:0.728158Epoch:383,Loss:0.728158Epoch:384,Loss:0.728158Epoch:385,Loss:0.728159Epoch:386,Loss:0.728159Epoch:387,Loss:0.728158Epoch:388,Loss:0.728157Epoch:389,Loss:0.728156Epoch:390,Loss:0.728156Epoch:391,Loss:0.728156Epoch:392,Loss:0.728156Epoch:393,Loss:0.728156Epoch:394,Loss:0.728156Epoch:395,Loss:0.728155Epoch:396,Loss:0.728155Epoch:397,Loss:0.728154Epoch:398,Loss:0.728154Epoch:399,Loss:0.728153Epoch:400,Loss:0.728153Epoch:401,Loss:0.728153Epoch:402,Loss:0.728153Epoch:403,Loss:0.728153Epoch:404,Loss:0.728153Epoch:405,Loss:0.728153Epoch:406,Loss:0.728152Epoch:407,Loss:0.728152Epoch:408,Loss:0.728152Epoch:409,Loss:0.728152Epoch:410,Loss:0.728153Epoch:411,Loss:0.728153Epoch:412,Loss:0.728152Epoch:413,Loss:0.728152Epoch:414,Loss:0.728152Epoch:415,Loss:0.728152Epoch:416,Loss:0.728152Epoch:417,Loss:0.728152Epoch:418,Loss:0.728152Epoch:419,Loss:0.728152Epoch:420,Loss:0.728152Epoch:421,Loss:0.728152Epoch:422,Loss:0.728152Epoch:423,Loss:0.728152Epoch:424,Loss:0.728151Epoch:425,Loss:0.728151Epoch:426,Loss:0.728151Epoch:427,Loss:0.728151Epoch:428,Loss:0.728151Epoch:429,Loss:0.728151Epoch:430,Loss:0.728151Epoch:431,Loss:0.728151Epoch:432,Loss:0.728151Epoch:433,Loss:0.728151Epoch:434,Loss:0.728151Epoch:435,Loss:0.728152Epoch:436,Loss:0.728152Epoch:437,Loss:0.728153Epoch:438,Loss:0.728154Epoch:439,Loss:0.728158Epoch:440,Loss:0.728161Epoch:441,Loss:0.728168Epoch:442,Loss:0.728167Epoch:443,Loss:0.728169Epoch:444,Loss:0.728161Epoch:445,Loss:0.728157Epoch:446,Loss:0.728152Epoch:447,Loss:0.728151Epoch:448,Loss:0.728151Epoch:449,Loss:0.728153Epoch:450,Loss:0.728155Epoch:451,Loss:0.728156Epoch:452,Loss:0.728158Epoch:453,Loss:0.728156Epoch:454,Loss:0.728155Epoch:455,Loss:0.728153Epoch:456,Loss:0.728152Epoch:457,Loss:0.728151Epoch:458,Loss:0.728151Epoch:459,Loss:0.728152Epoch:460,Loss:0.728152Epoch:461,Loss:0.728153Epoch:462,Loss:0.728153Epoch:463,Loss:0.728153Epoch:464,Loss:0.728152Epoch:465,Loss:0.728152Epoch:466,Loss:0.728151Epoch:467,Loss:0.728150Epoch:468,Loss:0.728150Epoch:469,Loss:0.728150Epoch:470,Loss:0.728150Epoch:471,Loss:0.728150Epoch:472,Loss:0.728150Epoch:473,Loss:0.728150Epoch:474,Loss:0.728150Epoch:475,Loss:0.728151Epoch:476,Loss:0.728151Epoch:477,Loss:0.728152Epoch:478,Loss:0.728153Epoch:479,Loss:0.728154Epoch:480,Loss:0.728155Epoch:481,Loss:0.728157Epoch:482,Loss:0.728157Epoch:483,Loss:0.728159Epoch:484,Loss:0.728158Epoch:485,Loss:0.728159Epoch:486,Loss:0.728157Epoch:487,Loss:0.728157Epoch:488,Loss:0.728155Epoch:489,Loss:0.728154Epoch:490,Loss:0.728152Epoch:491,Loss:0.728152Epoch:492,Loss:0.728152Epoch:493,Loss:0.728155Epoch:494,Loss:0.728160Epoch:495,Loss:0.728176Epoch:496,Loss:0.728173Epoch:497,Loss:0.728173Epoch:498,Loss:0.728159Epoch:499,Loss:0.728152Epoch:500,Loss:0.728150Epoch:501,Loss:0.728154Epoch:502,Loss:0.728158Epoch:503,Loss:0.728160Epoch:504,Loss:0.728159Epoch:505,Loss:0.728151Epoch:506,Loss:0.728142Epoch:507,Loss:0.728133Epoch:508,Loss:0.728125Epoch:509,Loss:0.728117Epoch:510,Loss:0.728114Epoch:511,Loss:0.728127Epoch:512,Loss:0.728130Epoch:513,Loss:0.728116Epoch:514,Loss:0.728111Epoch:515,Loss:0.728115Epoch:516,Loss:0.728118Epoch:517,Loss:0.728120Epoch:518,Loss:0.728119Epoch:519,Loss:0.728117Epoch:520,Loss:0.728114Epoch:521,Loss:0.728115Epoch:522,Loss:0.728118Epoch:523,Loss:0.728117Epoch:524,Loss:0.728114Epoch:525,Loss:0.728116Epoch:526,Loss:0.728119Epoch:527,Loss:0.728122Epoch:528,Loss:0.728121Epoch:529,Loss:0.728120Epoch:530,Loss:0.728118Epoch:531,Loss:0.728119Epoch:532,Loss:0.728117Epoch:533,Loss:0.728115Epoch:534,Loss:0.728112Epoch:535,Loss:0.728111Epoch:536,Loss:0.728110Epoch:537,Loss:0.728109Epoch:538,Loss:0.728108Epoch:539,Loss:0.728107Epoch:540,Loss:0.728106Epoch:541,Loss:0.728106Epoch:542,Loss:0.728106Epoch:543,Loss:0.728105Epoch:544,Loss:0.728104Epoch:545,Loss:0.728104Epoch:546,Loss:0.728103Epoch:547,Loss:0.728102Epoch:548,Loss:0.728101Epoch:549,Loss:0.728099Epoch:550,Loss:0.728097Epoch:551,Loss:0.728093Epoch:552,Loss:0.728085Epoch:553,Loss:0.728074Epoch:554,Loss:0.728062Epoch:555,Loss:0.728072Epoch:556,Loss:0.728080Epoch:557,Loss:0.728063Epoch:558,Loss:0.728067Epoch:559,Loss:0.728073Epoch:560,Loss:0.728076Epoch:561,Loss:0.728074Epoch:562,Loss:0.728070Epoch:563,Loss:0.728068Epoch:564,Loss:0.728076Epoch:565,Loss:0.728081Epoch:566,Loss:0.728080Epoch:567,Loss:0.728084Epoch:568,Loss:0.728094Epoch:569,Loss:0.728093Epoch:570,Loss:0.728093Epoch:571,Loss:0.728088Epoch:572,Loss:0.728092Epoch:573,Loss:0.728093Epoch:574,Loss:0.728096Epoch:575,Loss:0.728087Epoch:576,Loss:0.728085Epoch:577,Loss:0.728076Epoch:578,Loss:0.728071Epoch:579,Loss:0.728065Epoch:580,Loss:0.728064Epoch:581,Loss:0.728064Epoch:582,Loss:0.728064Epoch:583,Loss:0.728064Epoch:584,Loss:0.728065Epoch:585,Loss:0.728067Epoch:586,Loss:0.728067Epoch:587,Loss:0.728069Epoch:588,Loss:0.728070Epoch:589,Loss:0.728073Epoch:590,Loss:0.728073Epoch:591,Loss:0.728076Epoch:592,Loss:0.728075Epoch:593,Loss:0.728076Epoch:594,Loss:0.728074Epoch:595,Loss:0.728073Epoch:596,Loss:0.728070Epoch:597,Loss:0.728070Epoch:598,Loss:0.728068Epoch:599,Loss:0.728067Epoch:600,Loss:0.728067Epoch:601,Loss:0.728068Epoch:602,Loss:0.728069Epoch:603,Loss:0.728070Epoch:604,Loss:0.728070Epoch:605,Loss:0.728071Epoch:606,Loss:0.728071Epoch:607,Loss:0.728071Epoch:608,Loss:0.728070Epoch:609,Loss:0.728070Epoch:610,Loss:0.728068Epoch:611,Loss:0.728065Epoch:612,Loss:0.728059Epoch:613,Loss:0.728049Epoch:614,Loss:0.728026Epoch:615,Loss:0.727994Epoch:616,Loss:0.728072Epoch:617,Loss:0.728004Epoch:618,Loss:0.728045Epoch:619,Loss:0.728061Epoch:620,Loss:0.728064Epoch:621,Loss:0.728058Epoch:622,Loss:0.728051Epoch:623,Loss:0.728018Epoch:624,Loss:0.728035Epoch:625,Loss:0.728020Epoch:626,Loss:0.728013Epoch:627,Loss:0.728013Epoch:628,Loss:0.728008Epoch:629,Loss:0.727999Epoch:630,Loss:0.727998Epoch:631,Loss:0.727995Epoch:632,Loss:0.727986Epoch:633,Loss:0.727996Epoch:634,Loss:0.727997Epoch:635,Loss:0.727989Epoch:636,Loss:0.727994Epoch:637,Loss:0.727995Epoch:638,Loss:0.727990Epoch:639,Loss:0.727997Epoch:640,Loss:0.727997Epoch:641,Loss:0.727991Epoch:642,Loss:0.727994Epoch:643,Loss:0.727990Epoch:644,Loss:0.727988Epoch:645,Loss:0.727991Epoch:646,Loss:0.727987Epoch:647,Loss:0.727985Epoch:648,Loss:0.727986Epoch:649,Loss:0.727983Epoch:650,Loss:0.727982Epoch:651,Loss:0.727982Epoch:652,Loss:0.727981Epoch:653,Loss:0.727980Epoch:654,Loss:0.727980Epoch:655,Loss:0.727978Epoch:656,Loss:0.727979Epoch:657,Loss:0.727979Epoch:658,Loss:0.727979Epoch:659,Loss:0.727981Epoch:660,Loss:0.727983Epoch:661,Loss:0.727985Epoch:662,Loss:0.727988Epoch:663,Loss:0.727992Epoch:664,Loss:0.727995Epoch:665,Loss:0.728004Epoch:666,Loss:0.728005Epoch:667,Loss:0.728013Epoch:668,Loss:0.728009Epoch:669,Loss:0.728011Epoch:670,Loss:0.728003Epoch:671,Loss:0.728001Epoch:672,Loss:0.727998Epoch:673,Loss:0.727997Epoch:674,Loss:0.727998Epoch:675,Loss:0.728001Epoch:676,Loss:0.728009Epoch:677,Loss:0.728015Epoch:678,Loss:0.728027Epoch:679,Loss:0.728025Epoch:680,Loss:0.728023Epoch:681,Loss:0.728011Epoch:682,Loss:0.728002Epoch:683,Loss:0.727991Epoch:684,Loss:0.727984Epoch:685,Loss:0.727980Epoch:686,Loss:0.727978Epoch:687,Loss:0.727978Epoch:688,Loss:0.727979Epoch:689,Loss:0.727981Epoch:690,Loss:0.727982Epoch:691,Loss:0.727984Epoch:692,Loss:0.727986Epoch:693,Loss:0.727987Epoch:694,Loss:0.727988Epoch:695,Loss:0.727989Epoch:696,Loss:0.727989Epoch:697,Loss:0.727989Epoch:698,Loss:0.727988Epoch:699,Loss:0.727987Epoch:700,Loss:0.727986Epoch:701,Loss:0.727985Epoch:702,Loss:0.727983Epoch:703,Loss:0.727982Epoch:704,Loss:0.727980Epoch:705,Loss:0.727979Epoch:706,Loss:0.727978Epoch:707,Loss:0.727977Epoch:708,Loss:0.727977Epoch:709,Loss:0.727977Epoch:710,Loss:0.727978Epoch:711,Loss:0.727979Epoch:712,Loss:0.727980Epoch:713,Loss:0.727982Epoch:714,Loss:0.727985Epoch:715,Loss:0.727986Epoch:716,Loss:0.727989Epoch:717,Loss:0.727990Epoch:718,Loss:0.727994Epoch:719,Loss:0.727993Epoch:720,Loss:0.727995Epoch:721,Loss:0.727992Epoch:722,Loss:0.727991Epoch:723,Loss:0.727987Epoch:724,Loss:0.727986Epoch:725,Loss:0.727984Epoch:726,Loss:0.727984Epoch:727,Loss:0.727984Epoch:728,Loss:0.727985Epoch:729,Loss:0.727987Epoch:730,Loss:0.727988Epoch:731,Loss:0.727991Epoch:732,Loss:0.727993Epoch:733,Loss:0.727995Epoch:734,Loss:0.727998Epoch:735,Loss:0.728000Epoch:736,Loss:0.728001Epoch:737,Loss:0.728001Epoch:738,Loss:0.728000Epoch:739,Loss:0.727997Epoch:740,Loss:0.727994Epoch:741,Loss:0.727990Epoch:742,Loss:0.727986Epoch:743,Loss:0.727983Epoch:744,Loss:0.727980Epoch:745,Loss:0.727978Epoch:746,Loss:0.727976Epoch:747,Loss:0.727975Epoch:748,Loss:0.727974Epoch:749,Loss:0.727973Epoch:750,Loss:0.727973Epoch:751,Loss:0.727974Epoch:752,Loss:0.727974Epoch:753,Loss:0.727975Epoch:754,Loss:0.727976Epoch:755,Loss:0.727977Epoch:756,Loss:0.727978Epoch:757,Loss:0.727979Epoch:758,Loss:0.727980Epoch:759,Loss:0.727982Epoch:760,Loss:0.727985Epoch:761,Loss:0.727988Epoch:762,Loss:0.727990Epoch:763,Loss:0.727994Epoch:764,Loss:0.727996Epoch:765,Loss:0.727999Epoch:766,Loss:0.728003Epoch:767,Loss:0.728006Epoch:768,Loss:0.728010Epoch:769,Loss:0.728012Epoch:770,Loss:0.728014Epoch:771,Loss:0.728012Epoch:772,Loss:0.728011Epoch:773,Loss:0.728006Epoch:774,Loss:0.728004Epoch:775,Loss:0.727998Epoch:776,Loss:0.727997Epoch:777,Loss:0.727992Epoch:778,Loss:0.727992Epoch:779,Loss:0.727990Epoch:780,Loss:0.727992Epoch:781,Loss:0.727992Epoch:782,Loss:0.727995Epoch:783,Loss:0.727996Epoch:784,Loss:0.727999Epoch:785,Loss:0.727998Epoch:786,Loss:0.727997Epoch:787,Loss:0.727994Epoch:788,Loss:0.727991Epoch:789,Loss:0.727986Epoch:790,Loss:0.727983Epoch:791,Loss:0.727979Epoch:792,Loss:0.727977Epoch:793,Loss:0.727975Epoch:794,Loss:0.727974Epoch:795,Loss:0.727974Epoch:796,Loss:0.727974Epoch:797,Loss:0.727974Epoch:798,Loss:0.727975Epoch:799,Loss:0.727976Epoch:800,Loss:0.727977Epoch:801,Loss:0.727978Epoch:802,Loss:0.727979Epoch:803,Loss:0.727980Epoch:804,Loss:0.727981Epoch:805,Loss:0.727982Epoch:806,Loss:0.727983Epoch:807,Loss:0.727985Epoch:808,Loss:0.727987Epoch:809,Loss:0.727988Epoch:810,Loss:0.727991Epoch:811,Loss:0.727993Epoch:812,Loss:0.727996Epoch:813,Loss:0.727998Epoch:814,Loss:0.728001Epoch:815,Loss:0.728002Epoch:816,Loss:0.728004Epoch:817,Loss:0.728002Epoch:818,Loss:0.728001Epoch:819,Loss:0.727997Epoch:820,Loss:0.727995Epoch:821,Loss:0.727991Epoch:822,Loss:0.727989Epoch:823,Loss:0.727987Epoch:824,Loss:0.727988Epoch:825,Loss:0.727988Epoch:826,Loss:0.727993Epoch:827,Loss:0.727994Epoch:828,Loss:0.728000Epoch:829,Loss:0.727999Epoch:830,Loss:0.728003Epoch:831,Loss:0.728001Epoch:832,Loss:0.728002Epoch:833,Loss:0.728000Epoch:834,Loss:0.728000Epoch:835,Loss:0.727996Epoch:836,Loss:0.727994Epoch:837,Loss:0.727988Epoch:838,Loss:0.727984Epoch:839,Loss:0.727979Epoch:840,Loss:0.727974Epoch:841,Loss:0.727969Epoch:842,Loss:0.727967Epoch:843,Loss:0.727967Epoch:844,Loss:0.727969Epoch:845,Loss:0.727971Epoch:846,Loss:0.727973Epoch:847,Loss:0.727973Epoch:848,Loss:0.727973Epoch:849,Loss:0.727974Epoch:850,Loss:0.727975Epoch:851,Loss:0.727975Epoch:852,Loss:0.727975Epoch:853,Loss:0.727975Epoch:854,Loss:0.727974Epoch:855,Loss:0.727973Epoch:856,Loss:0.727972Epoch:857,Loss:0.727972Epoch:858,Loss:0.727972Epoch:859,Loss:0.727973Epoch:860,Loss:0.727975Epoch:861,Loss:0.727977Epoch:862,Loss:0.727979Epoch:863,Loss:0.727981Epoch:864,Loss:0.727984Epoch:865,Loss:0.727985Epoch:866,Loss:0.727988Epoch:867,Loss:0.727989Epoch:868,Loss:0.727991Epoch:869,Loss:0.727994Epoch:870,Loss:0.727997Epoch:871,Loss:0.728004Epoch:872,Loss:0.728004Epoch:873,Loss:0.728011Epoch:874,Loss:0.728003Epoch:875,Loss:0.728000Epoch:876,Loss:0.727988Epoch:877,Loss:0.727983Epoch:878,Loss:0.727978Epoch:879,Loss:0.727980Epoch:880,Loss:0.727976Epoch:881,Loss:0.727973Epoch:882,Loss:0.727965Epoch:883,Loss:0.727958Epoch:884,Loss:0.727957Epoch:885,Loss:0.727967Epoch:886,Loss:0.727960Epoch:887,Loss:0.727967Epoch:888,Loss:0.727977Epoch:889,Loss:0.727987Epoch:890,Loss:0.727992Epoch:891,Loss:0.727996Epoch:892,Loss:0.727995Epoch:893,Loss:0.727995Epoch:894,Loss:0.727992Epoch:895,Loss:0.727986Epoch:896,Loss:0.727973Epoch:897,Loss:0.727966Epoch:898,Loss:0.727960Epoch:899,Loss:0.727956Epoch:900,Loss:0.727953Epoch:901,Loss:0.727951Epoch:902,Loss:0.727951Epoch:903,Loss:0.727954Epoch:904,Loss:0.727955Epoch:905,Loss:0.727955Epoch:906,Loss:0.727954Epoch:907,Loss:0.727952Epoch:908,Loss:0.727950Epoch:909,Loss:0.727948Epoch:910,Loss:0.727945Epoch:911,Loss:0.727944Epoch:912,Loss:0.727945Epoch:913,Loss:0.727945Epoch:914,Loss:0.727946Epoch:915,Loss:0.727947Epoch:916,Loss:0.727949Epoch:917,Loss:0.727949Epoch:918,Loss:0.727949Epoch:919,Loss:0.727949Epoch:920,Loss:0.727949Epoch:921,Loss:0.727949Epoch:922,Loss:0.727949Epoch:923,Loss:0.727950Epoch:924,Loss:0.727952Epoch:925,Loss:0.727952Epoch:926,Loss:0.727955Epoch:927,Loss:0.727956Epoch:928,Loss:0.727960Epoch:929,Loss:0.727961Epoch:930,Loss:0.727965Epoch:931,Loss:0.727963Epoch:932,Loss:0.727965Epoch:933,Loss:0.727962Epoch:934,Loss:0.727963Epoch:935,Loss:0.727961Epoch:936,Loss:0.727962Epoch:937,Loss:0.727962Epoch:938,Loss:0.727964Epoch:939,Loss:0.727966Epoch:940,Loss:0.727968Epoch:941,Loss:0.727969Epoch:942,Loss:0.727969Epoch:943,Loss:0.727967Epoch:944,Loss:0.727965Epoch:945,Loss:0.727962Epoch:946,Loss:0.727960Epoch:947,Loss:0.727959Epoch:948,Loss:0.727960Epoch:949,Loss:0.727962Epoch:950,Loss:0.727965Epoch:951,Loss:0.727969Epoch:952,Loss:0.727972Epoch:953,Loss:0.727974Epoch:954,Loss:0.727974Epoch:955,Loss:0.727973Epoch:956,Loss:0.727969Epoch:957,Loss:0.727964Epoch:958,Loss:0.727958Epoch:959,Loss:0.727952Epoch:960,Loss:0.727947Epoch:961,Loss:0.727945Epoch:962,Loss:0.727943Epoch:963,Loss:0.727943Epoch:964,Loss:0.727944Epoch:965,Loss:0.727946Epoch:966,Loss:0.727947Epoch:967,Loss:0.727950Epoch:968,Loss:0.727952Epoch:969,Loss:0.727954Epoch:970,Loss:0.727954Epoch:971,Loss:0.727956Epoch:972,Loss:0.727956Epoch:973,Loss:0.727957Epoch:974,Loss:0.727956Epoch:975,Loss:0.727958Epoch:976,Loss:0.727958Epoch:977,Loss:0.727962Epoch:978,Loss:0.727962Epoch:979,Loss:0.727968Epoch:980,Loss:0.727968Epoch:981,Loss:0.727973Epoch:982,Loss:0.727968Epoch:983,Loss:0.727967Epoch:984,Loss:0.727960Epoch:985,Loss:0.727957Epoch:986,Loss:0.727953Epoch:987,Loss:0.727951Epoch:988,Loss:0.727950Epoch:989,Loss:0.727950Epoch:990,Loss:0.727950Epoch:991,Loss:0.727951Epoch:992,Loss:0.727952Epoch:993,Loss:0.727953Epoch:994,Loss:0.727955Epoch:995,Loss:0.727956Epoch:996,Loss:0.727957Epoch:997,Loss:0.727957Epoch:998,Loss:0.727958Epoch:999,Loss:0.727956 loss=critier(model(x_test),y_test)loss tensor(1.0953, grad_fn=<MseLossBackward0>) 实验：基于神经网络的分类(鸢尾花数据集)

1 数据用鸢尾花数据集（所有样本的四个特征，三个类别） 2 输出标签（one hot vector） 3 构建模型时输出端映射到0,1之间 4 修改损失函数为交叉熵函数

1. 导入包 import torchimport torch.nn as nnimport torch.optim as optimfrom torch.utils.data import DataLoaderfrom sklearn.datasets import load_irisfrom sklearn.preprocessing import OneHotEncoderfrom sklearn.model_selection import train_test_split 2. 构造数据集 iris=load_iris()X,y=iris.data,iris.target one_hot_vector=OneHotEncoder(sparse=False)y=one_hot_vector.fit_transform(y.reshape(-1,1)) 3. 构造训练集和测试集 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2) X_train = torch.Tensor(X_train)X_test = torch.Tensor(X_test)y_train = torch.Tensor(y_train)y_test = torch.Tensor(y_test) X_train.shape,X_test.shape,y_train.shape,y_test.shape (torch.Size([120, 4]),torch.Size([30, 4]),torch.Size([120, 3]),torch.Size([30, 3])) 4. 构建神经网络模型 class Nerual_Network(nn.Module):def __init__(self):super().__init__()self.output=nn.Linear(X_train.shape[1],y_train.shape[1])self.sigmoid=nn.Sigmoid()self.softmax=nn.Softmax(dim=1)def forward(self,x):x=self.output(x)x=self.softmax(x)x=self.sigmoid(x)return x model=Nerual_Network() model Nerual_Network((output): Linear(in_features=4, out_features=3, bias=True)(sigmoid): Sigmoid()(softmax): Softmax(dim=1)) 5. 采用训练数据来训练神经网络模型 epochs=1000learnng_rate=0.003critier=nn.BCELoss()optimizer=optim.Adam(model.parameters(),lr=learnng_rate) for i in range(epochs):outputs=model(X_train)loss=critier(outputs,y_train)print("Epoch:{},Loss:{:4f}".format(i,loss))optimizer.zero_grad()loss.backward(retain_graph=True)optimizer.step() Epoch:0,Loss:0.788641Epoch:1,Loss:0.787205Epoch:2,Loss:0.785736Epoch:3,Loss:0.784244Epoch:4,Loss:0.782739Epoch:5,Loss:0.781233Epoch:6,Loss:0.779737Epoch:7,Loss:0.778262Epoch:8,Loss:0.776822Epoch:9,Loss:0.775428Epoch:10,Loss:0.774091Epoch:11,Loss:0.772821Epoch:12,Loss:0.771625Epoch:13,Loss:0.770507Epoch:14,Loss:0.769467Epoch:15,Loss:0.768504Epoch:16,Loss:0.767612Epoch:17,Loss:0.766784Epoch:18,Loss:0.766013Epoch:19,Loss:0.765293Epoch:20,Loss:0.764617Epoch:21,Loss:0.763979Epoch:22,Loss:0.763374Epoch:23,Loss:0.762798Epoch:24,Loss:0.762245Epoch:25,Loss:0.761712Epoch:26,Loss:0.761195Epoch:27,Loss:0.760692Epoch:28,Loss:0.760198Epoch:29,Loss:0.759711Epoch:30,Loss:0.759228Epoch:31,Loss:0.758748Epoch:32,Loss:0.758268Epoch:33,Loss:0.757787Epoch:34,Loss:0.757302Epoch:35,Loss:0.756813Epoch:36,Loss:0.756318Epoch:37,Loss:0.755816Epoch:38,Loss:0.755305Epoch:39,Loss:0.754786Epoch:40,Loss:0.754257Epoch:41,Loss:0.753716Epoch:42,Loss:0.753165Epoch:43,Loss:0.752602Epoch:44,Loss:0.752026Epoch:45,Loss:0.751437Epoch:46,Loss:0.750836Epoch:47,Loss:0.750221Epoch:48,Loss:0.749594Epoch:49,Loss:0.748953Epoch:50,Loss:0.748300Epoch:51,Loss:0.747635Epoch:52,Loss:0.746958Epoch:53,Loss:0.746270Epoch:54,Loss:0.745572Epoch:55,Loss:0.744864Epoch:56,Loss:0.744148Epoch:57,Loss:0.743425Epoch:58,Loss:0.742694Epoch:59,Loss:0.741958Epoch:60,Loss:0.741217Epoch:61,Loss:0.740473Epoch:62,Loss:0.739725Epoch:63,Loss:0.738975Epoch:64,Loss:0.738224Epoch:65,Loss:0.737471Epoch:66,Loss:0.736718Epoch:67,Loss:0.735964Epoch:68,Loss:0.735211Epoch:69,Loss:0.734458Epoch:70,Loss:0.733706Epoch:71,Loss:0.732954Epoch:72,Loss:0.732204Epoch:73,Loss:0.731456Epoch:74,Loss:0.730709Epoch:75,Loss:0.729964Epoch:76,Loss:0.729223Epoch:77,Loss:0.728484Epoch:78,Loss:0.727750Epoch:79,Loss:0.727019Epoch:80,Loss:0.726294Epoch:81,Loss:0.725574Epoch:82,Loss:0.724860Epoch:83,Loss:0.724152Epoch:84,Loss:0.723452Epoch:85,Loss:0.722758Epoch:86,Loss:0.722072Epoch:87,Loss:0.721393Epoch:88,Loss:0.720722Epoch:89,Loss:0.720058Epoch:90,Loss:0.719403Epoch:91,Loss:0.718755Epoch:92,Loss:0.718115Epoch:93,Loss:0.717483Epoch:94,Loss:0.716859Epoch:95,Loss:0.716242Epoch:96,Loss:0.715634Epoch:97,Loss:0.715033Epoch:98,Loss:0.714440Epoch:99,Loss:0.713856Epoch:100,Loss:0.713278Epoch:101,Loss:0.712709Epoch:102,Loss:0.712148Epoch:103,Loss:0.711594Epoch:104,Loss:0.711047Epoch:105,Loss:0.710508Epoch:106,Loss:0.709977Epoch:107,Loss:0.709452Epoch:108,Loss:0.708935Epoch:109,Loss:0.708425Epoch:110,Loss:0.707922Epoch:111,Loss:0.707425Epoch:112,Loss:0.706936Epoch:113,Loss:0.706452Epoch:114,Loss:0.705976Epoch:115,Loss:0.705505Epoch:116,Loss:0.705041Epoch:117,Loss:0.704583Epoch:118,Loss:0.704132Epoch:119,Loss:0.703686Epoch:120,Loss:0.703246Epoch:121,Loss:0.702812Epoch:122,Loss:0.702383Epoch:123,Loss:0.701960Epoch:124,Loss:0.701543Epoch:125,Loss:0.701130Epoch:126,Loss:0.700724Epoch:127,Loss:0.700322Epoch:128,Loss:0.699925Epoch:129,Loss:0.699534Epoch:130,Loss:0.699147Epoch:131,Loss:0.698766Epoch:132,Loss:0.698389Epoch:133,Loss:0.698016Epoch:134,Loss:0.697648Epoch:135,Loss:0.697285Epoch:136,Loss:0.696926Epoch:137,Loss:0.696571Epoch:138,Loss:0.696221Epoch:139,Loss:0.695875Epoch:140,Loss:0.695533Epoch:141,Loss:0.695194Epoch:142,Loss:0.694860Epoch:143,Loss:0.694529Epoch:144,Loss:0.694202Epoch:145,Loss:0.693879Epoch:146,Loss:0.693560Epoch:147,Loss:0.693243Epoch:148,Loss:0.692931Epoch:149,Loss:0.692621Epoch:150,Loss:0.692315Epoch:151,Loss:0.692012Epoch:152,Loss:0.691712Epoch:153,Loss:0.691416Epoch:154,Loss:0.691122Epoch:155,Loss:0.690832Epoch:156,Loss:0.690544Epoch:157,Loss:0.690259Epoch:158,Loss:0.689977Epoch:159,Loss:0.689698Epoch:160,Loss:0.689421Epoch:161,Loss:0.689147Epoch:162,Loss:0.688875Epoch:163,Loss:0.688606Epoch:164,Loss:0.688340Epoch:165,Loss:0.688076Epoch:166,Loss:0.687814Epoch:167,Loss:0.687554Epoch:168,Loss:0.687297Epoch:169,Loss:0.687042Epoch:170,Loss:0.686789Epoch:171,Loss:0.686539Epoch:172,Loss:0.686290Epoch:173,Loss:0.686044Epoch:174,Loss:0.685799Epoch:175,Loss:0.685557Epoch:176,Loss:0.685316Epoch:177,Loss:0.685077Epoch:178,Loss:0.684840Epoch:179,Loss:0.684605Epoch:180,Loss:0.684372Epoch:181,Loss:0.684141Epoch:182,Loss:0.683911Epoch:183,Loss:0.683683Epoch:184,Loss:0.683456Epoch:185,Loss:0.683231Epoch:186,Loss:0.683008Epoch:187,Loss:0.682786Epoch:188,Loss:0.682566Epoch:189,Loss:0.682347Epoch:190,Loss:0.682130Epoch:191,Loss:0.681914Epoch:192,Loss:0.681700Epoch:193,Loss:0.681486Epoch:194,Loss:0.681275Epoch:195,Loss:0.681064Epoch:196,Loss:0.680855Epoch:197,Loss:0.680647Epoch:198,Loss:0.680441Epoch:199,Loss:0.680236Epoch:200,Loss:0.680032Epoch:201,Loss:0.679829Epoch:202,Loss:0.679627Epoch:203,Loss:0.679426Epoch:204,Loss:0.679227Epoch:205,Loss:0.679029Epoch:206,Loss:0.678831Epoch:207,Loss:0.678635Epoch:208,Loss:0.678440Epoch:209,Loss:0.678246Epoch:210,Loss:0.678053Epoch:211,Loss:0.677861Epoch:212,Loss:0.677670Epoch:213,Loss:0.677480Epoch:214,Loss:0.677291Epoch:215,Loss:0.677102Epoch:216,Loss:0.676915Epoch:217,Loss:0.676729Epoch:218,Loss:0.676543Epoch:219,Loss:0.676359Epoch:220,Loss:0.676175Epoch:221,Loss:0.675992Epoch:222,Loss:0.675810Epoch:223,Loss:0.675628Epoch:224,Loss:0.675448Epoch:225,Loss:0.675268Epoch:226,Loss:0.675089Epoch:227,Loss:0.674911Epoch:228,Loss:0.674734Epoch:229,Loss:0.674557Epoch:230,Loss:0.674381Epoch:231,Loss:0.674206Epoch:232,Loss:0.674032Epoch:233,Loss:0.673858Epoch:234,Loss:0.673685Epoch:235,Loss:0.673513Epoch:236,Loss:0.673341Epoch:237,Loss:0.673170Epoch:238,Loss:0.673000Epoch:239,Loss:0.672830Epoch:240,Loss:0.672661Epoch:241,Loss:0.672493Epoch:242,Loss:0.672325Epoch:243,Loss:0.672158Epoch:244,Loss:0.671991Epoch:245,Loss:0.671825Epoch:246,Loss:0.671660Epoch:247,Loss:0.671495Epoch:248,Loss:0.671331Epoch:249,Loss:0.671167Epoch:250,Loss:0.671004Epoch:251,Loss:0.670842Epoch:252,Loss:0.670680Epoch:253,Loss:0.670518Epoch:254,Loss:0.670357Epoch:255,Loss:0.670197Epoch:256,Loss:0.670037Epoch:257,Loss:0.669878Epoch:258,Loss:0.669719Epoch:259,Loss:0.669561Epoch:260,Loss:0.669403Epoch:261,Loss:0.669246Epoch:262,Loss:0.669089Epoch:263,Loss:0.668932Epoch:264,Loss:0.668777Epoch:265,Loss:0.668621Epoch:266,Loss:0.668466Epoch:267,Loss:0.668312Epoch:268,Loss:0.668158Epoch:269,Loss:0.668004Epoch:270,Loss:0.667851Epoch:271,Loss:0.667699Epoch:272,Loss:0.667547Epoch:273,Loss:0.667395Epoch:274,Loss:0.667244Epoch:275,Loss:0.667093Epoch:276,Loss:0.666942Epoch:277,Loss:0.666792Epoch:278,Loss:0.666643Epoch:279,Loss:0.666493Epoch:280,Loss:0.666345Epoch:281,Loss:0.666196Epoch:282,Loss:0.666048Epoch:283,Loss:0.665901Epoch:284,Loss:0.665754Epoch:285,Loss:0.665607Epoch:286,Loss:0.665460Epoch:287,Loss:0.665314Epoch:288,Loss:0.665169Epoch:289,Loss:0.665023Epoch:290,Loss:0.664879Epoch:291,Loss:0.664734Epoch:292,Loss:0.664590Epoch:293,Loss:0.664446Epoch:294,Loss:0.664303Epoch:295,Loss:0.664160Epoch:296,Loss:0.664017Epoch:297,Loss:0.663875Epoch:298,Loss:0.663733Epoch:299,Loss:0.663591Epoch:300,Loss:0.663450Epoch:301,Loss:0.663309Epoch:302,Loss:0.663169Epoch:303,Loss:0.663028Epoch:304,Loss:0.662889Epoch:305,Loss:0.662749Epoch:306,Loss:0.662610Epoch:307,Loss:0.662471Epoch:308,Loss:0.662332Epoch:309,Loss:0.662194Epoch:310,Loss:0.662056Epoch:311,Loss:0.661919Epoch:312,Loss:0.661781Epoch:313,Loss:0.661644Epoch:314,Loss:0.661508Epoch:315,Loss:0.661372Epoch:316,Loss:0.661236Epoch:317,Loss:0.661100Epoch:318,Loss:0.660964Epoch:319,Loss:0.660829Epoch:320,Loss:0.660695Epoch:321,Loss:0.660560Epoch:322,Loss:0.660426Epoch:323,Loss:0.660292Epoch:324,Loss:0.660159Epoch:325,Loss:0.660026Epoch:326,Loss:0.659893Epoch:327,Loss:0.659760Epoch:328,Loss:0.659628Epoch:329,Loss:0.659496Epoch:330,Loss:0.659364Epoch:331,Loss:0.659232Epoch:332,Loss:0.659101Epoch:333,Loss:0.658970Epoch:334,Loss:0.658840Epoch:335,Loss:0.658709Epoch:336,Loss:0.658579Epoch:337,Loss:0.658450Epoch:338,Loss:0.658320Epoch:339,Loss:0.658191Epoch:340,Loss:0.658062Epoch:341,Loss:0.657933Epoch:342,Loss:0.657805Epoch:343,Loss:0.657677Epoch:344,Loss:0.657549Epoch:345,Loss:0.657421Epoch:346,Loss:0.657294Epoch:347,Loss:0.657167Epoch:348,Loss:0.657040Epoch:349,Loss:0.656914Epoch:350,Loss:0.656788Epoch:351,Loss:0.656662Epoch:352,Loss:0.656536Epoch:353,Loss:0.656411Epoch:354,Loss:0.656285Epoch:355,Loss:0.656161Epoch:356,Loss:0.656036Epoch:357,Loss:0.655911Epoch:358,Loss:0.655787Epoch:359,Loss:0.655663Epoch:360,Loss:0.655540Epoch:361,Loss:0.655416Epoch:362,Loss:0.655293Epoch:363,Loss:0.655171Epoch:364,Loss:0.655048Epoch:365,Loss:0.654925Epoch:366,Loss:0.654803Epoch:367,Loss:0.654682Epoch:368,Loss:0.654560Epoch:369,Loss:0.654438Epoch:370,Loss:0.654317Epoch:371,Loss:0.654196Epoch:372,Loss:0.654076Epoch:373,Loss:0.653955Epoch:374,Loss:0.653835Epoch:375,Loss:0.653715Epoch:376,Loss:0.653596Epoch:377,Loss:0.653476Epoch:378,Loss:0.653357Epoch:379,Loss:0.653238Epoch:380,Loss:0.653119Epoch:381,Loss:0.653001Epoch:382,Loss:0.652883Epoch:383,Loss:0.652765Epoch:384,Loss:0.652647Epoch:385,Loss:0.652529Epoch:386,Loss:0.652412Epoch:387,Loss:0.652295Epoch:388,Loss:0.652178Epoch:389,Loss:0.652062Epoch:390,Loss:0.651945Epoch:391,Loss:0.651829Epoch:392,Loss:0.651713Epoch:393,Loss:0.651597Epoch:394,Loss:0.651482Epoch:395,Loss:0.651367Epoch:396,Loss:0.651252Epoch:397,Loss:0.651137Epoch:398,Loss:0.651022Epoch:399,Loss:0.650908Epoch:400,Loss:0.650794Epoch:401,Loss:0.650680Epoch:402,Loss:0.650566Epoch:403,Loss:0.650453Epoch:404,Loss:0.650340Epoch:405,Loss:0.650227Epoch:406,Loss:0.650114Epoch:407,Loss:0.650001Epoch:408,Loss:0.649889Epoch:409,Loss:0.649777Epoch:410,Loss:0.649665Epoch:411,Loss:0.649553Epoch:412,Loss:0.649442Epoch:413,Loss:0.649331Epoch:414,Loss:0.649220Epoch:415,Loss:0.649109Epoch:416,Loss:0.648998Epoch:417,Loss:0.648888Epoch:418,Loss:0.648778Epoch:419,Loss:0.648668Epoch:420,Loss:0.648558Epoch:421,Loss:0.648448Epoch:422,Loss:0.648339Epoch:423,Loss:0.648230Epoch:424,Loss:0.648121Epoch:425,Loss:0.648013Epoch:426,Loss:0.647904Epoch:427,Loss:0.647796Epoch:428,Loss:0.647688Epoch:429,Loss:0.647580Epoch:430,Loss:0.647472Epoch:431,Loss:0.647365Epoch:432,Loss:0.647258Epoch:433,Loss:0.647151Epoch:434,Loss:0.647044Epoch:435,Loss:0.646937Epoch:436,Loss:0.646831Epoch:437,Loss:0.646725Epoch:438,Loss:0.646619Epoch:439,Loss:0.646513Epoch:440,Loss:0.646407Epoch:441,Loss:0.646302Epoch:442,Loss:0.646197Epoch:443,Loss:0.646092Epoch:444,Loss:0.645987Epoch:445,Loss:0.645882Epoch:446,Loss:0.645778Epoch:447,Loss:0.645674Epoch:448,Loss:0.645570Epoch:449,Loss:0.645466Epoch:450,Loss:0.645362Epoch:451,Loss:0.645259Epoch:452,Loss:0.645156Epoch:453,Loss:0.645053Epoch:454,Loss:0.644950Epoch:455,Loss:0.644848Epoch:456,Loss:0.644745Epoch:457,Loss:0.644643Epoch:458,Loss:0.644541Epoch:459,Loss:0.644439Epoch:460,Loss:0.644338Epoch:461,Loss:0.644236Epoch:462,Loss:0.644135Epoch:463,Loss:0.644034Epoch:464,Loss:0.643933Epoch:465,Loss:0.643833Epoch:466,Loss:0.643732Epoch:467,Loss:0.643632Epoch:468,Loss:0.643532Epoch:469,Loss:0.643432Epoch:470,Loss:0.643332Epoch:471,Loss:0.643233Epoch:472,Loss:0.643133Epoch:473,Loss:0.643034Epoch:474,Loss:0.642935Epoch:475,Loss:0.642837Epoch:476,Loss:0.642738Epoch:477,Loss:0.642640Epoch:478,Loss:0.642542Epoch:479,Loss:0.642444Epoch:480,Loss:0.642346Epoch:481,Loss:0.642248Epoch:482,Loss:0.642151Epoch:483,Loss:0.642054Epoch:484,Loss:0.641956Epoch:485,Loss:0.641860Epoch:486,Loss:0.641763Epoch:487,Loss:0.641666Epoch:488,Loss:0.641570Epoch:489,Loss:0.641474Epoch:490,Loss:0.641378Epoch:491,Loss:0.641282Epoch:492,Loss:0.641187Epoch:493,Loss:0.641091Epoch:494,Loss:0.640996Epoch:495,Loss:0.640901Epoch:496,Loss:0.640806Epoch:497,Loss:0.640712Epoch:498,Loss:0.640617Epoch:499,Loss:0.640523Epoch:500,Loss:0.640429Epoch:501,Loss:0.640335Epoch:502,Loss:0.640241Epoch:503,Loss:0.640147Epoch:504,Loss:0.640054Epoch:505,Loss:0.639961Epoch:506,Loss:0.639867Epoch:507,Loss:0.639775Epoch:508,Loss:0.639682Epoch:509,Loss:0.639589Epoch:510,Loss:0.639497Epoch:511,Loss:0.639405Epoch:512,Loss:0.639313Epoch:513,Loss:0.639221Epoch:514,Loss:0.639129Epoch:515,Loss:0.639038Epoch:516,Loss:0.638947Epoch:517,Loss:0.638855Epoch:518,Loss:0.638764Epoch:519,Loss:0.638674Epoch:520,Loss:0.638583Epoch:521,Loss:0.638493Epoch:522,Loss:0.638402Epoch:523,Loss:0.638312Epoch:524,Loss:0.638222Epoch:525,Loss:0.638133Epoch:526,Loss:0.638043Epoch:527,Loss:0.637954Epoch:528,Loss:0.637864Epoch:529,Loss:0.637775Epoch:530,Loss:0.637686Epoch:531,Loss:0.637598Epoch:532,Loss:0.637509Epoch:533,Loss:0.637421Epoch:534,Loss:0.637332Epoch:535,Loss:0.637244Epoch:536,Loss:0.637156Epoch:537,Loss:0.637069Epoch:538,Loss:0.636981Epoch:539,Loss:0.636894Epoch:540,Loss:0.636806Epoch:541,Loss:0.636719Epoch:542,Loss:0.636632Epoch:543,Loss:0.636546Epoch:544,Loss:0.636459Epoch:545,Loss:0.636373Epoch:546,Loss:0.636286Epoch:547,Loss:0.636200Epoch:548,Loss:0.636114Epoch:549,Loss:0.636029Epoch:550,Loss:0.635943Epoch:551,Loss:0.635858Epoch:552,Loss:0.635772Epoch:553,Loss:0.635687Epoch:554,Loss:0.635602Epoch:555,Loss:0.635517Epoch:556,Loss:0.635433Epoch:557,Loss:0.635348Epoch:558,Loss:0.635264Epoch:559,Loss:0.635180Epoch:560,Loss:0.635096Epoch:561,Loss:0.635012Epoch:562,Loss:0.634928Epoch:563,Loss:0.634845Epoch:564,Loss:0.634761Epoch:565,Loss:0.634678Epoch:566,Loss:0.634595Epoch:567,Loss:0.634512Epoch:568,Loss:0.634430Epoch:569,Loss:0.634347Epoch:570,Loss:0.634265Epoch:571,Loss:0.634182Epoch:572,Loss:0.634100Epoch:573,Loss:0.634018Epoch:574,Loss:0.633937Epoch:575,Loss:0.633855Epoch:576,Loss:0.633773Epoch:577,Loss:0.633692Epoch:578,Loss:0.633611Epoch:579,Loss:0.633530Epoch:580,Loss:0.633449Epoch:581,Loss:0.633368Epoch:582,Loss:0.633288Epoch:583,Loss:0.633207Epoch:584,Loss:0.633127Epoch:585,Loss:0.633047Epoch:586,Loss:0.632967Epoch:587,Loss:0.632887Epoch:588,Loss:0.632807Epoch:589,Loss:0.632728Epoch:590,Loss:0.632648Epoch:591,Loss:0.632569Epoch:592,Loss:0.632490Epoch:593,Loss:0.632411Epoch:594,Loss:0.632332Epoch:595,Loss:0.632254Epoch:596,Loss:0.632175Epoch:597,Loss:0.632097Epoch:598,Loss:0.632019Epoch:599,Loss:0.631941Epoch:600,Loss:0.631863Epoch:601,Loss:0.631785Epoch:602,Loss:0.631708Epoch:603,Loss:0.631630Epoch:604,Loss:0.631553Epoch:605,Loss:0.631476Epoch:606,Loss:0.631399Epoch:607,Loss:0.631322Epoch:608,Loss:0.631245Epoch:609,Loss:0.631169Epoch:610,Loss:0.631092Epoch:611,Loss:0.631016Epoch:612,Loss:0.630940Epoch:613,Loss:0.630864Epoch:614,Loss:0.630788Epoch:615,Loss:0.630712Epoch:616,Loss:0.630637Epoch:617,Loss:0.630561Epoch:618,Loss:0.630486Epoch:619,Loss:0.630411Epoch:620,Loss:0.630336Epoch:621,Loss:0.630261Epoch:622,Loss:0.630186Epoch:623,Loss:0.630111Epoch:624,Loss:0.630037Epoch:625,Loss:0.629963Epoch:626,Loss:0.629888Epoch:627,Loss:0.629814Epoch:628,Loss:0.629741Epoch:629,Loss:0.629667Epoch:630,Loss:0.629593Epoch:631,Loss:0.629520Epoch:632,Loss:0.629446Epoch:633,Loss:0.629373Epoch:634,Loss:0.629300Epoch:635,Loss:0.629227Epoch:636,Loss:0.629154Epoch:637,Loss:0.629082Epoch:638,Loss:0.629009Epoch:639,Loss:0.628937Epoch:640,Loss:0.628864Epoch:641,Loss:0.628792Epoch:642,Loss:0.628720Epoch:643,Loss:0.628649Epoch:644,Loss:0.628577Epoch:645,Loss:0.628505Epoch:646,Loss:0.628434Epoch:647,Loss:0.628362Epoch:648,Loss:0.628291Epoch:649,Loss:0.628220Epoch:650,Loss:0.628149Epoch:651,Loss:0.628078Epoch:652,Loss:0.628008Epoch:653,Loss:0.627937Epoch:654,Loss:0.627867Epoch:655,Loss:0.627797Epoch:656,Loss:0.627726Epoch:657,Loss:0.627656Epoch:658,Loss:0.627587Epoch:659,Loss:0.627517Epoch:660,Loss:0.627447Epoch:661,Loss:0.627378Epoch:662,Loss:0.627308Epoch:663,Loss:0.627239Epoch:664,Loss:0.627170Epoch:665,Loss:0.627101Epoch:666,Loss:0.627032Epoch:667,Loss:0.626963Epoch:668,Loss:0.626895Epoch:669,Loss:0.626826Epoch:670,Loss:0.626758Epoch:671,Loss:0.626690Epoch:672,Loss:0.626622Epoch:673,Loss:0.626554Epoch:674,Loss:0.626486Epoch:675,Loss:0.626418Epoch:676,Loss:0.626351Epoch:677,Loss:0.626283Epoch:678,Loss:0.626216Epoch:679,Loss:0.626149Epoch:680,Loss:0.626082Epoch:681,Loss:0.626015Epoch:682,Loss:0.625948Epoch:683,Loss:0.625881Epoch:684,Loss:0.625814Epoch:685,Loss:0.625748Epoch:686,Loss:0.625682Epoch:687,Loss:0.625615Epoch:688,Loss:0.625549Epoch:689,Loss:0.625483Epoch:690,Loss:0.625417Epoch:691,Loss:0.625352Epoch:692,Loss:0.625286Epoch:693,Loss:0.625221Epoch:694,Loss:0.625155Epoch:695,Loss:0.625090Epoch:696,Loss:0.625025Epoch:697,Loss:0.624960Epoch:698,Loss:0.624895Epoch:699,Loss:0.624830Epoch:700,Loss:0.624765Epoch:701,Loss:0.624701Epoch:702,Loss:0.624637Epoch:703,Loss:0.624572Epoch:704,Loss:0.624508Epoch:705,Loss:0.624444Epoch:706,Loss:0.624380Epoch:707,Loss:0.624316Epoch:708,Loss:0.624252Epoch:709,Loss:0.624189Epoch:710,Loss:0.624125Epoch:711,Loss:0.624062Epoch:712,Loss:0.623999Epoch:713,Loss:0.623936Epoch:714,Loss:0.623873Epoch:715,Loss:0.623810Epoch:716,Loss:0.623747Epoch:717,Loss:0.623684Epoch:718,Loss:0.623622Epoch:719,Loss:0.623559Epoch:720,Loss:0.623497Epoch:721,Loss:0.623435Epoch:722,Loss:0.623372Epoch:723,Loss:0.623310Epoch:724,Loss:0.623249Epoch:725,Loss:0.623187Epoch:726,Loss:0.623125Epoch:727,Loss:0.623064Epoch:728,Loss:0.623002Epoch:729,Loss:0.622941Epoch:730,Loss:0.622880Epoch:731,Loss:0.622819Epoch:732,Loss:0.622757Epoch:733,Loss:0.622697Epoch:734,Loss:0.622636Epoch:735,Loss:0.622575Epoch:736,Loss:0.622515Epoch:737,Loss:0.622454Epoch:738,Loss:0.622394Epoch:739,Loss:0.622334Epoch:740,Loss:0.622274Epoch:741,Loss:0.622214Epoch:742,Loss:0.622154Epoch:743,Loss:0.622094Epoch:744,Loss:0.622034Epoch:745,Loss:0.621975Epoch:746,Loss:0.621915Epoch:747,Loss:0.621856Epoch:748,Loss:0.621796Epoch:749,Loss:0.621737Epoch:750,Loss:0.621678Epoch:751,Loss:0.621619Epoch:752,Loss:0.621561Epoch:753,Loss:0.621502Epoch:754,Loss:0.621443Epoch:755,Loss:0.621385Epoch:756,Loss:0.621326Epoch:757,Loss:0.621268Epoch:758,Loss:0.621210Epoch:759,Loss:0.621152Epoch:760,Loss:0.621094Epoch:761,Loss:0.621036Epoch:762,Loss:0.620978Epoch:763,Loss:0.620920Epoch:764,Loss:0.620863Epoch:765,Loss:0.620805Epoch:766,Loss:0.620748Epoch:767,Loss:0.620691Epoch:768,Loss:0.620634Epoch:769,Loss:0.620577Epoch:770,Loss:0.620520Epoch:771,Loss:0.620463Epoch:772,Loss:0.620406Epoch:773,Loss:0.620349Epoch:774,Loss:0.620293Epoch:775,Loss:0.620236Epoch:776,Loss:0.620180Epoch:777,Loss:0.620124Epoch:778,Loss:0.620068Epoch:779,Loss:0.620012Epoch:780,Loss:0.619956Epoch:781,Loss:0.619900Epoch:782,Loss:0.619844Epoch:783,Loss:0.619788Epoch:784,Loss:0.619733Epoch:785,Loss:0.619677Epoch:786,Loss:0.619622Epoch:787,Loss:0.619567Epoch:788,Loss:0.619512Epoch:789,Loss:0.619457Epoch:790,Loss:0.619402Epoch:791,Loss:0.619347Epoch:792,Loss:0.619292Epoch:793,Loss:0.619237Epoch:794,Loss:0.619183Epoch:795,Loss:0.619128Epoch:796,Loss:0.619074Epoch:797,Loss:0.619020Epoch:798,Loss:0.618965Epoch:799,Loss:0.618911Epoch:800,Loss:0.618857Epoch:801,Loss:0.618803Epoch:802,Loss:0.618750Epoch:803,Loss:0.618696Epoch:804,Loss:0.618642Epoch:805,Loss:0.618589Epoch:806,Loss:0.618535Epoch:807,Loss:0.618482Epoch:808,Loss:0.618429Epoch:809,Loss:0.618376Epoch:810,Loss:0.618322Epoch:811,Loss:0.618270Epoch:812,Loss:0.618217Epoch:813,Loss:0.618164Epoch:814,Loss:0.618111Epoch:815,Loss:0.618059Epoch:816,Loss:0.618006Epoch:817,Loss:0.617954Epoch:818,Loss:0.617901Epoch:819,Loss:0.617849Epoch:820,Loss:0.617797Epoch:821,Loss:0.617745Epoch:822,Loss:0.617693Epoch:823,Loss:0.617641Epoch:824,Loss:0.617589Epoch:825,Loss:0.617538Epoch:826,Loss:0.617486Epoch:827,Loss:0.617435Epoch:828,Loss:0.617383Epoch:829,Loss:0.617332Epoch:830,Loss:0.617281Epoch:831,Loss:0.617230Epoch:832,Loss:0.617179Epoch:833,Loss:0.617128Epoch:834,Loss:0.617077Epoch:835,Loss:0.617026Epoch:836,Loss:0.616975Epoch:837,Loss:0.616925Epoch:838,Loss:0.616874Epoch:839,Loss:0.616824Epoch:840,Loss:0.616773Epoch:841,Loss:0.616723Epoch:842,Loss:0.616673Epoch:843,Loss:0.616623Epoch:844,Loss:0.616573Epoch:845,Loss:0.616523Epoch:846,Loss:0.616473Epoch:847,Loss:0.616423Epoch:848,Loss:0.616374Epoch:849,Loss:0.616324Epoch:850,Loss:0.616275Epoch:851,Loss:0.616225Epoch:852,Loss:0.616176Epoch:853,Loss:0.616127Epoch:854,Loss:0.616078Epoch:855,Loss:0.616029Epoch:856,Loss:0.615980Epoch:857,Loss:0.615931Epoch:858,Loss:0.615882Epoch:859,Loss:0.615833Epoch:860,Loss:0.615785Epoch:861,Loss:0.615736Epoch:862,Loss:0.615688Epoch:863,Loss:0.615639Epoch:864,Loss:0.615591Epoch:865,Loss:0.615543Epoch:866,Loss:0.615495Epoch:867,Loss:0.615447Epoch:868,Loss:0.615399Epoch:869,Loss:0.615351Epoch:870,Loss:0.615303Epoch:871,Loss:0.615255Epoch:872,Loss:0.615208Epoch:873,Loss:0.615160Epoch:874,Loss:0.615113Epoch:875,Loss:0.615065Epoch:876,Loss:0.615018Epoch:877,Loss:0.614971Epoch:878,Loss:0.614923Epoch:879,Loss:0.614876Epoch:880,Loss:0.614829Epoch:881,Loss:0.614783Epoch:882,Loss:0.614736Epoch:883,Loss:0.614689Epoch:884,Loss:0.614642Epoch:885,Loss:0.614596Epoch:886,Loss:0.614549Epoch:887,Loss:0.614503Epoch:888,Loss:0.614456Epoch:889,Loss:0.614410Epoch:890,Loss:0.614364Epoch:891,Loss:0.614318Epoch:892,Loss:0.614272Epoch:893,Loss:0.614226Epoch:894,Loss:0.614180Epoch:895,Loss:0.614134Epoch:896,Loss:0.614088Epoch:897,Loss:0.614043Epoch:898,Loss:0.613997Epoch:899,Loss:0.613952Epoch:900,Loss:0.613906Epoch:901,Loss:0.613861Epoch:902,Loss:0.613816Epoch:903,Loss:0.613770Epoch:904,Loss:0.613725Epoch:905,Loss:0.613680Epoch:906,Loss:0.613635Epoch:907,Loss:0.613590Epoch:908,Loss:0.613545Epoch:909,Loss:0.613501Epoch:910,Loss:0.613456Epoch:911,Loss:0.613411Epoch:912,Loss:0.613367Epoch:913,Loss:0.613323Epoch:914,Loss:0.613278Epoch:915,Loss:0.613234Epoch:916,Loss:0.613190Epoch:917,Loss:0.613145Epoch:918,Loss:0.613101Epoch:919,Loss:0.613057Epoch:920,Loss:0.613014Epoch:921,Loss:0.612970Epoch:922,Loss:0.612926Epoch:923,Loss:0.612882Epoch:924,Loss:0.612839Epoch:925,Loss:0.612795Epoch:926,Loss:0.612752Epoch:927,Loss:0.612708Epoch:928,Loss:0.612665Epoch:929,Loss:0.612621Epoch:930,Loss:0.612578Epoch:931,Loss:0.612535Epoch:932,Loss:0.612492Epoch:933,Loss:0.612449Epoch:934,Loss:0.612406Epoch:935,Loss:0.612363Epoch:936,Loss:0.612321Epoch:937,Loss:0.612278Epoch:938,Loss:0.612235Epoch:939,Loss:0.612193Epoch:940,Loss:0.612150Epoch:941,Loss:0.612108Epoch:942,Loss:0.612066Epoch:943,Loss:0.612023Epoch:944,Loss:0.611981Epoch:945,Loss:0.611939Epoch:946,Loss:0.611897Epoch:947,Loss:0.611855Epoch:948,Loss:0.611813Epoch:949,Loss:0.611771Epoch:950,Loss:0.611729Epoch:951,Loss:0.611688Epoch:952,Loss:0.611646Epoch:953,Loss:0.611604Epoch:954,Loss:0.611563Epoch:955,Loss:0.611521Epoch:956,Loss:0.611480Epoch:957,Loss:0.611439Epoch:958,Loss:0.611398Epoch:959,Loss:0.611356Epoch:960,Loss:0.611315Epoch:961,Loss:0.611274Epoch:962,Loss:0.611233Epoch:963,Loss:0.611192Epoch:964,Loss:0.611151Epoch:965,Loss:0.611111Epoch:966,Loss:0.611070Epoch:967,Loss:0.611029Epoch:968,Loss:0.610989Epoch:969,Loss:0.610948Epoch:970,Loss:0.610908Epoch:971,Loss:0.610868Epoch:972,Loss:0.610827Epoch:973,Loss:0.610787Epoch:974,Loss:0.610747Epoch:975,Loss:0.610707Epoch:976,Loss:0.610667Epoch:977,Loss:0.610627Epoch:978,Loss:0.610587Epoch:979,Loss:0.610547Epoch:980,Loss:0.610507Epoch:981,Loss:0.610467Epoch:982,Loss:0.610428Epoch:983,Loss:0.610388Epoch:984,Loss:0.610349Epoch:985,Loss:0.610309Epoch:986,Loss:0.610270Epoch:987,Loss:0.610231Epoch:988,Loss:0.610191Epoch:989,Loss:0.610152Epoch:990,Loss:0.610113Epoch:991,Loss:0.610074Epoch:992,Loss:0.610035Epoch:993,Loss:0.609996Epoch:994,Loss:0.609957Epoch:995,Loss:0.609918Epoch:996,Loss:0.609879Epoch:997,Loss:0.609841Epoch:998,Loss:0.609802Epoch:999,Loss:0.609763 loss=critier(model(X_test),y_test)loss tensor(0.6142, grad_fn=<BinaryCrossEntropyBackward0>)