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 np from torch import nn from sklearn.model_selection import train_test_split
1.2 构造数据集（随机构造的）
from torch.autograd import Variable batch_n=100 hidden_layer=100 input_data=1000 output_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 optim model=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=1000 learnng_rate=0.003 critier=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:0.948208 Epoch:1,Loss:0.896322 Epoch:2,Loss:0.855293 Epoch:3,Loss:0.819206 Epoch:4,Loss:0.790216 Epoch:5,Loss:0.769548 Epoch:6,Loss:0.755935 Epoch:7,Loss:0.747829 Epoch:8,Loss:0.743429 Epoch:9,Loss:0.741071 Epoch:10,Loss:0.739489 Epoch:11,Loss:0.738407 Epoch:12,Loss:0.737566 Epoch:13,Loss:0.736756 Epoch:14,Loss:0.736009 Epoch:15,Loss:0.735342 Epoch:16,Loss:0.734747 Epoch:17,Loss:0.734446 Epoch:18,Loss:0.734121 Epoch:19,Loss:0.733825 Epoch:20,Loss:0.733538 Epoch:21,Loss:0.733174 Epoch:22,Loss:0.732976 Epoch:23,Loss:0.732888 Epoch:24,Loss:0.732744 Epoch:25,Loss:0.732587 Epoch:26,Loss:0.732487 Epoch:27,Loss:0.732393 Epoch:28,Loss:0.732277 Epoch:29,Loss:0.732168 Epoch:30,Loss:0.732101 Epoch:31,Loss:0.732098 Epoch:32,Loss:0.731946 Epoch:33,Loss:0.731655 Epoch:34,Loss:0.731511 Epoch:35,Loss:0.731603 Epoch:36,Loss:0.731634 Epoch:37,Loss:0.731516 Epoch:38,Loss:0.731375 Epoch:39,Loss:0.731263 Epoch:40,Loss:0.731153 Epoch:41,Loss:0.731199 Epoch:42,Loss:0.731237 Epoch:43,Loss:0.731082 Epoch:44,Loss:0.730953 Epoch:45,Loss:0.730905 Epoch:46,Loss:0.730879 Epoch:47,Loss:0.730842 Epoch:48,Loss:0.730784 Epoch:49,Loss:0.730665 Epoch:50,Loss:0.730640 Epoch:51,Loss:0.730709 Epoch:52,Loss:0.730659 Epoch:53,Loss:0.730601 Epoch:54,Loss:0.730571 Epoch:55,Loss:0.730595 Epoch:56,Loss:0.730605 Epoch:57,Loss:0.730550 Epoch:58,Loss:0.730524 Epoch:59,Loss:0.730512 Epoch:60,Loss:0.730482 Epoch:61,Loss:0.730442 Epoch:62,Loss:0.730421 Epoch:63,Loss:0.730365 Epoch:64,Loss:0.730232 Epoch:65,Loss:0.730102 Epoch:66,Loss:0.730107 Epoch:67,Loss:0.730175 Epoch:68,Loss:0.730177 Epoch:69,Loss:0.730097 Epoch:70,Loss:0.730023 Epoch:71,Loss:0.730047 Epoch:72,Loss:0.730051 Epoch:73,Loss:0.729966 Epoch:74,Loss:0.729911 Epoch:75,Loss:0.729961 Epoch:76,Loss:0.729982 Epoch:77,Loss:0.729963 Epoch:78,Loss:0.729940 Epoch:79,Loss:0.729932 Epoch:80,Loss:0.729937 Epoch:81,Loss:0.729935 Epoch:82,Loss:0.729909 Epoch:83,Loss:0.729893 Epoch:84,Loss:0.729907 Epoch:85,Loss:0.729910 Epoch:86,Loss:0.729892 Epoch:87,Loss:0.729884 Epoch:88,Loss:0.729888 Epoch:89,Loss:0.729883 Epoch:90,Loss:0.729874 Epoch:91,Loss:0.729868 Epoch:92,Loss:0.729864 Epoch:93,Loss:0.729858 Epoch:94,Loss:0.729847 Epoch:95,Loss:0.729843 Epoch:96,Loss:0.729848 Epoch:97,Loss:0.729852 Epoch:98,Loss:0.729849 Epoch:99,Loss:0.729840 Epoch:100,Loss:0.729836 Epoch:101,Loss:0.729834 Epoch:102,Loss:0.729832 Epoch:103,Loss:0.729832 Epoch:104,Loss:0.729834 Epoch:105,Loss:0.729833 Epoch:106,Loss:0.729828 Epoch:107,Loss:0.729825 Epoch:108,Loss:0.729824 Epoch:109,Loss:0.729821 Epoch:110,Loss:0.729816 Epoch:111,Loss:0.729813 Epoch:112,Loss:0.729810 Epoch:113,Loss:0.729806 Epoch:114,Loss:0.729799 Epoch:115,Loss:0.729792 Epoch:116,Loss:0.729782 Epoch:117,Loss:0.729771 Epoch:118,Loss:0.729763 Epoch:119,Loss:0.729760 Epoch:120,Loss:0.729763 Epoch:121,Loss:0.729765 Epoch:122,Loss:0.729761 Epoch:123,Loss:0.729753 Epoch:124,Loss:0.729747 Epoch:125,Loss:0.729744 Epoch:126,Loss:0.729743 Epoch:127,Loss:0.729739 Epoch:128,Loss:0.729731 Epoch:129,Loss:0.729718 Epoch:130,Loss:0.729700 Epoch:131,Loss:0.729674 Epoch:132,Loss:0.729634 Epoch:133,Loss:0.729571 Epoch:134,Loss:0.729517 Epoch:135,Loss:0.729545 Epoch:136,Loss:0.729541 Epoch:137,Loss:0.729501 Epoch:138,Loss:0.729543 Epoch:139,Loss:0.729531 Epoch:140,Loss:0.729507 Epoch:141,Loss:0.729527 Epoch:142,Loss:0.729508 Epoch:143,Loss:0.729499 Epoch:144,Loss:0.729505 Epoch:145,Loss:0.729486 Epoch:146,Loss:0.729480 Epoch:147,Loss:0.729476 Epoch:148,Loss:0.729455 Epoch:149,Loss:0.729445 Epoch:150,Loss:0.729428 Epoch:151,Loss:0.729400 Epoch:152,Loss:0.729373 Epoch:153,Loss:0.729345 Epoch:154,Loss:0.729355 Epoch:155,Loss:0.729364 Epoch:156,Loss:0.729335 Epoch:157,Loss:0.729335 Epoch:158,Loss:0.729328 Epoch:159,Loss:0.729310 Epoch:160,Loss:0.729303 Epoch:161,Loss:0.729285 Epoch:162,Loss:0.729242 Epoch:163,Loss:0.729181 Epoch:164,Loss:0.729270 Epoch:165,Loss:0.729187 Epoch:166,Loss:0.729191 Epoch:167,Loss:0.729215 Epoch:168,Loss:0.729211 Epoch:169,Loss:0.729182 Epoch:170,Loss:0.729173 Epoch:171,Loss:0.729202 Epoch:172,Loss:0.729167 Epoch:173,Loss:0.729181 Epoch:174,Loss:0.729184 Epoch:175,Loss:0.729166 Epoch:176,Loss:0.729160 Epoch:177,Loss:0.729178 Epoch:178,Loss:0.729157 Epoch:179,Loss:0.729164 Epoch:180,Loss:0.729166 Epoch:181,Loss:0.729156 Epoch:182,Loss:0.729158 Epoch:183,Loss:0.729161 Epoch:184,Loss:0.729151 Epoch:185,Loss:0.729155 Epoch:186,Loss:0.729156 Epoch:187,Loss:0.729150 Epoch:188,Loss:0.729153 Epoch:189,Loss:0.729153 Epoch:190,Loss:0.729149 Epoch:191,Loss:0.729151 Epoch:192,Loss:0.729149 Epoch:193,Loss:0.729147 Epoch:194,Loss:0.729149 Epoch:195,Loss:0.729147 Epoch:196,Loss:0.729147 Epoch:197,Loss:0.729147 Epoch:198,Loss:0.729146 Epoch:199,Loss:0.729145 Epoch:200,Loss:0.729145 Epoch:201,Loss:0.729144 Epoch:202,Loss:0.729145 Epoch:203,Loss:0.729144 Epoch:204,Loss:0.729144 Epoch:205,Loss:0.729143 Epoch:206,Loss:0.729143 Epoch:207,Loss:0.729142 Epoch:208,Loss:0.729142 Epoch:209,Loss:0.729142 Epoch:210,Loss:0.729141 Epoch:211,Loss:0.729141 Epoch:212,Loss:0.729140 Epoch:213,Loss:0.729140 Epoch:214,Loss:0.729139 Epoch:215,Loss:0.729139 Epoch:216,Loss:0.729138 Epoch:217,Loss:0.729138 Epoch:218,Loss:0.729137 Epoch:219,Loss:0.729137 Epoch:220,Loss:0.729136 Epoch:221,Loss:0.729135 Epoch:222,Loss:0.729134 Epoch:223,Loss:0.729134 Epoch:224,Loss:0.729133 Epoch:225,Loss:0.729132 Epoch:226,Loss:0.729131 Epoch:227,Loss:0.729131 Epoch:228,Loss:0.729130 Epoch:229,Loss:0.729129 Epoch:230,Loss:0.729129 Epoch:231,Loss:0.729128 Epoch:232,Loss:0.729127 Epoch:233,Loss:0.729126 Epoch:234,Loss:0.729125 Epoch:235,Loss:0.729124 Epoch:236,Loss:0.729123 Epoch:237,Loss:0.729121 Epoch:238,Loss:0.729119 Epoch:239,Loss:0.729116 Epoch:240,Loss:0.729112 Epoch:241,Loss:0.729106 Epoch:242,Loss:0.729095 Epoch:243,Loss:0.729075 Epoch:244,Loss:0.729035 Epoch:245,Loss:0.728994 Epoch:246,Loss:0.729103 Epoch:247,Loss:0.729000 Epoch:248,Loss:0.729026 Epoch:249,Loss:0.729047 Epoch:250,Loss:0.729044 Epoch:251,Loss:0.729022 Epoch:252,Loss:0.728987 Epoch:253,Loss:0.729031 Epoch:254,Loss:0.728992 Epoch:255,Loss:0.728995 Epoch:256,Loss:0.729006 Epoch:257,Loss:0.728994 Epoch:258,Loss:0.728984 Epoch:259,Loss:0.728999 Epoch:260,Loss:0.728980 Epoch:261,Loss:0.728992 Epoch:262,Loss:0.728987 Epoch:263,Loss:0.728978 Epoch:264,Loss:0.728986 Epoch:265,Loss:0.728979 Epoch:266,Loss:0.728976 Epoch:267,Loss:0.728983 Epoch:268,Loss:0.728976 Epoch:269,Loss:0.728975 Epoch:270,Loss:0.728978 Epoch:271,Loss:0.728973 Epoch:272,Loss:0.728976 Epoch:273,Loss:0.728974 Epoch:274,Loss:0.728972 Epoch:275,Loss:0.728973 Epoch:276,Loss:0.728970 Epoch:277,Loss:0.728972 Epoch:278,Loss:0.728970 Epoch:279,Loss:0.728969 Epoch:280,Loss:0.728970 Epoch:281,Loss:0.728968 Epoch:282,Loss:0.728967 Epoch:283,Loss:0.728967 Epoch:284,Loss:0.728965 Epoch:285,Loss:0.728964 Epoch:286,Loss:0.728962 Epoch:287,Loss:0.728961 Epoch:288,Loss:0.728958 Epoch:289,Loss:0.728954 Epoch:290,Loss:0.728950 Epoch:291,Loss:0.728942 Epoch:292,Loss:0.728928 Epoch:293,Loss:0.728899 Epoch:294,Loss:0.728823 Epoch:295,Loss:0.728630 Epoch:296,Loss:0.728751 Epoch:297,Loss:0.728786 Epoch:298,Loss:0.728595 Epoch:299,Loss:0.728683 Epoch:300,Loss:0.728725 Epoch:301,Loss:0.728664 Epoch:302,Loss:0.728570 Epoch:303,Loss:0.728632 Epoch:304,Loss:0.728585 Epoch:305,Loss:0.728485 Epoch:306,Loss:0.728588 Epoch:307,Loss:0.728561 Epoch:308,Loss:0.728491 Epoch:309,Loss:0.728501 Epoch:310,Loss:0.728533 Epoch:311,Loss:0.728463 Epoch:312,Loss:0.728422 Epoch:313,Loss:0.728431 Epoch:314,Loss:0.728469 Epoch:315,Loss:0.728421 Epoch:316,Loss:0.728418 Epoch:317,Loss:0.728427 Epoch:318,Loss:0.728412 Epoch:319,Loss:0.728418 Epoch:320,Loss:0.728410 Epoch:321,Loss:0.728394 Epoch:322,Loss:0.728381 Epoch:323,Loss:0.728382 Epoch:324,Loss:0.728369 Epoch:325,Loss:0.728349 Epoch:326,Loss:0.728347 Epoch:327,Loss:0.728362 Epoch:328,Loss:0.728340 Epoch:329,Loss:0.728344 Epoch:330,Loss:0.728345 Epoch:331,Loss:0.728342 Epoch:332,Loss:0.728344 Epoch:333,Loss:0.728341 Epoch:334,Loss:0.728331 Epoch:335,Loss:0.728325 Epoch:336,Loss:0.728333 Epoch:337,Loss:0.728325 Epoch:338,Loss:0.728315 Epoch:339,Loss:0.728312 Epoch:340,Loss:0.728302 Epoch:341,Loss:0.728283 Epoch:342,Loss:0.728251 Epoch:343,Loss:0.728215 Epoch:344,Loss:0.728237 Epoch:345,Loss:0.728224 Epoch:346,Loss:0.728241 Epoch:347,Loss:0.728240 Epoch:348,Loss:0.728236 Epoch:349,Loss:0.728247 Epoch:350,Loss:0.728234 Epoch:351,Loss:0.728240 Epoch:352,Loss:0.728227 Epoch:353,Loss:0.728231 Epoch:354,Loss:0.728216 Epoch:355,Loss:0.728212 Epoch:356,Loss:0.728203 Epoch:357,Loss:0.728202 Epoch:358,Loss:0.728209 Epoch:359,Loss:0.728204 Epoch:360,Loss:0.728200 Epoch:361,Loss:0.728195 Epoch:362,Loss:0.728183 Epoch:363,Loss:0.728181 Epoch:364,Loss:0.728175 Epoch:365,Loss:0.728174 Epoch:366,Loss:0.728175 Epoch:367,Loss:0.728168 Epoch:368,Loss:0.728171 Epoch:369,Loss:0.728168 Epoch:370,Loss:0.728167 Epoch:371,Loss:0.728168 Epoch:372,Loss:0.728167 Epoch:373,Loss:0.728169 Epoch:374,Loss:0.728166 Epoch:375,Loss:0.728165 Epoch:376,Loss:0.728165 Epoch:377,Loss:0.728163 Epoch:378,Loss:0.728163 Epoch:379,Loss:0.728162 Epoch:380,Loss:0.728160 Epoch:381,Loss:0.728159 Epoch:382,Loss:0.728158 Epoch:383,Loss:0.728158 Epoch:384,Loss:0.728158 Epoch:385,Loss:0.728159 Epoch:386,Loss:0.728159 Epoch:387,Loss:0.728158 Epoch:388,Loss:0.728157 Epoch:389,Loss:0.728156 Epoch:390,Loss:0.728156 Epoch:391,Loss:0.728156 Epoch:392,Loss:0.728156 Epoch:393,Loss:0.728156 Epoch:394,Loss:0.728156 Epoch:395,Loss:0.728155 Epoch:396,Loss:0.728155 Epoch:397,Loss:0.728154 Epoch:398,Loss:0.728154 Epoch:399,Loss:0.728153 Epoch:400,Loss:0.728153 Epoch:401,Loss:0.728153 Epoch:402,Loss:0.728153 Epoch:403,Loss:0.728153 Epoch:404,Loss:0.728153 Epoch:405,Loss:0.728153 Epoch:406,Loss:0.728152 Epoch:407,Loss:0.728152 Epoch:408,Loss:0.728152 Epoch:409,Loss:0.728152 Epoch:410,Loss:0.728153 Epoch:411,Loss:0.728153 Epoch:412,Loss:0.728152 Epoch:413,Loss:0.728152 Epoch:414,Loss:0.728152 Epoch:415,Loss:0.728152 Epoch:416,Loss:0.728152 Epoch:417,Loss:0.728152 Epoch:418,Loss:0.728152 Epoch:419,Loss:0.728152 Epoch:420,Loss:0.728152 Epoch:421,Loss:0.728152 Epoch:422,Loss:0.728152 Epoch:423,Loss:0.728152 Epoch:424,Loss:0.728151 Epoch:425,Loss:0.728151 Epoch:426,Loss:0.728151 Epoch:427,Loss:0.728151 Epoch:428,Loss:0.728151 Epoch:429,Loss:0.728151 Epoch:430,Loss:0.728151 Epoch:431,Loss:0.728151 Epoch:432,Loss:0.728151 Epoch:433,Loss:0.728151 Epoch:434,Loss:0.728151 Epoch:435,Loss:0.728152 Epoch:436,Loss:0.728152 Epoch:437,Loss:0.728153 Epoch:438,Loss:0.728154 Epoch:439,Loss:0.728158 Epoch:440,Loss:0.728161 Epoch:441,Loss:0.728168 Epoch:442,Loss:0.728167 Epoch:443,Loss:0.728169 Epoch:444,Loss:0.728161 Epoch:445,Loss:0.728157 Epoch:446,Loss:0.728152 Epoch:447,Loss:0.728151 Epoch:448,Loss:0.728151 Epoch:449,Loss:0.728153 Epoch:450,Loss:0.728155 Epoch:451,Loss:0.728156 Epoch:452,Loss:0.728158 Epoch:453,Loss:0.728156 Epoch:454,Loss:0.728155 Epoch:455,Loss:0.728153 Epoch:456,Loss:0.728152 Epoch:457,Loss:0.728151 Epoch:458,Loss:0.728151 Epoch:459,Loss:0.728152 Epoch:460,Loss:0.728152 Epoch:461,Loss:0.728153 Epoch:462,Loss:0.728153 Epoch:463,Loss:0.728153 Epoch:464,Loss:0.728152 Epoch:465,Loss:0.728152 Epoch:466,Loss:0.728151 Epoch:467,Loss:0.728150 Epoch:468,Loss:0.728150 Epoch:469,Loss:0.728150 Epoch:470,Loss:0.728150 Epoch:471,Loss:0.728150 Epoch:472,Loss:0.728150 Epoch:473,Loss:0.728150 Epoch:474,Loss:0.728150 Epoch:475,Loss:0.728151 Epoch:476,Loss:0.728151 Epoch:477,Loss:0.728152 Epoch:478,Loss:0.728153 Epoch:479,Loss:0.728154 Epoch:480,Loss:0.728155 Epoch:481,Loss:0.728157 Epoch:482,Loss:0.728157 Epoch:483,Loss:0.728159 Epoch:484,Loss:0.728158 Epoch:485,Loss:0.728159 Epoch:486,Loss:0.728157 Epoch:487,Loss:0.728157 Epoch:488,Loss:0.728155 Epoch:489,Loss:0.728154 Epoch:490,Loss:0.728152 Epoch:491,Loss:0.728152 Epoch:492,Loss:0.728152 Epoch:493,Loss:0.728155 Epoch:494,Loss:0.728160 Epoch:495,Loss:0.728176 Epoch:496,Loss:0.728173 Epoch:497,Loss:0.728173 Epoch:498,Loss:0.728159 Epoch:499,Loss:0.728152 Epoch:500,Loss:0.728150 Epoch:501,Loss:0.728154 Epoch:502,Loss:0.728158 Epoch:503,Loss:0.728160 Epoch:504,Loss:0.728159 Epoch:505,Loss:0.728151 Epoch:506,Loss:0.728142 Epoch:507,Loss:0.728133 Epoch:508,Loss:0.728125 Epoch:509,Loss:0.728117 Epoch:510,Loss:0.728114 Epoch:511,Loss:0.728127 Epoch:512,Loss:0.728130 Epoch:513,Loss:0.728116 Epoch:514,Loss:0.728111 Epoch:515,Loss:0.728115 Epoch:516,Loss:0.728118 Epoch:517,Loss:0.728120 Epoch:518,Loss:0.728119 Epoch:519,Loss:0.728117 Epoch:520,Loss:0.728114 Epoch:521,Loss:0.728115 Epoch:522,Loss:0.728118 Epoch:523,Loss:0.728117 Epoch:524,Loss:0.728114 Epoch:525,Loss:0.728116 Epoch:526,Loss:0.728119 Epoch:527,Loss:0.728122 Epoch:528,Loss:0.728121 Epoch:529,Loss:0.728120 Epoch:530,Loss:0.728118 Epoch:531,Loss:0.728119 Epoch:532,Loss:0.728117 Epoch:533,Loss:0.728115 Epoch:534,Loss:0.728112 Epoch:535,Loss:0.728111 Epoch:536,Loss:0.728110 Epoch:537,Loss:0.728109 Epoch:538,Loss:0.728108 Epoch:539,Loss:0.728107 Epoch:540,Loss:0.728106 Epoch:541,Loss:0.728106 Epoch:542,Loss:0.728106 Epoch:543,Loss:0.728105 Epoch:544,Loss:0.728104 Epoch:545,Loss:0.728104 Epoch:546,Loss:0.728103 Epoch:547,Loss:0.728102 Epoch:548,Loss:0.728101 Epoch:549,Loss:0.728099 Epoch:550,Loss:0.728097 Epoch:551,Loss:0.728093 Epoch:552,Loss:0.728085 Epoch:553,Loss:0.728074 Epoch:554,Loss:0.728062 Epoch:555,Loss:0.728072 Epoch:556,Loss:0.728080 Epoch:557,Loss:0.728063 Epoch:558,Loss:0.728067 Epoch:559,Loss:0.728073 Epoch:560,Loss:0.728076 Epoch:561,Loss:0.728074 Epoch:562,Loss:0.728070 Epoch:563,Loss:0.728068 Epoch:564,Loss:0.728076 Epoch:565,Loss:0.728081 Epoch:566,Loss:0.728080 Epoch:567,Loss:0.728084 Epoch:568,Loss:0.728094 Epoch:569,Loss:0.728093 Epoch:570,Loss:0.728093 Epoch:571,Loss:0.728088 Epoch:572,Loss:0.728092 Epoch:573,Loss:0.728093 Epoch:574,Loss:0.728096 Epoch:575,Loss:0.728087 Epoch:576,Loss:0.728085 Epoch:577,Loss:0.728076 Epoch:578,Loss:0.728071 Epoch:579,Loss:0.728065 Epoch:580,Loss:0.728064 Epoch:581,Loss:0.728064 Epoch:582,Loss:0.728064 Epoch:583,Loss:0.728064 Epoch:584,Loss:0.728065 Epoch:585,Loss:0.728067 Epoch:586,Loss:0.728067 Epoch:587,Loss:0.728069 Epoch:588,Loss:0.728070 Epoch:589,Loss:0.728073 Epoch:590,Loss:0.728073 Epoch:591,Loss:0.728076 Epoch:592,Loss:0.728075 Epoch:593,Loss:0.728076 Epoch:594,Loss:0.728074 Epoch:595,Loss:0.728073 Epoch:596,Loss:0.728070 Epoch:597,Loss:0.728070 Epoch:598,Loss:0.728068 Epoch:599,Loss:0.728067 Epoch:600,Loss:0.728067 Epoch:601,Loss:0.728068 Epoch:602,Loss:0.728069 Epoch:603,Loss:0.728070 Epoch:604,Loss:0.728070 Epoch:605,Loss:0.728071 Epoch:606,Loss:0.728071 Epoch:607,Loss:0.728071 Epoch:608,Loss:0.728070 Epoch:609,Loss:0.728070 Epoch:610,Loss:0.728068 Epoch:611,Loss:0.728065 Epoch:612,Loss:0.728059 Epoch:613,Loss:0.728049 Epoch:614,Loss:0.728026 Epoch:615,Loss:0.727994 Epoch:616,Loss:0.728072 Epoch:617,Loss:0.728004 Epoch:618,Loss:0.728045 Epoch:619,Loss:0.728061 Epoch:620,Loss:0.728064 Epoch:621,Loss:0.728058 Epoch:622,Loss:0.728051 Epoch:623,Loss:0.728018 Epoch:624,Loss:0.728035 Epoch:625,Loss:0.728020 Epoch:626,Loss:0.728013 Epoch:627,Loss:0.728013 Epoch:628,Loss:0.728008 Epoch:629,Loss:0.727999 Epoch:630,Loss:0.727998 Epoch:631,Loss:0.727995 Epoch:632,Loss:0.727986 Epoch:633,Loss:0.727996 Epoch:634,Loss:0.727997 Epoch:635,Loss:0.727989 Epoch:636,Loss:0.727994 Epoch:637,Loss:0.727995 Epoch:638,Loss:0.727990 Epoch:639,Loss:0.727997 Epoch:640,Loss:0.727997 Epoch:641,Loss:0.727991 Epoch:642,Loss:0.727994 Epoch:643,Loss:0.727990 Epoch:644,Loss:0.727988 Epoch:645,Loss:0.727991 Epoch:646,Loss:0.727987 Epoch:647,Loss:0.727985 Epoch:648,Loss:0.727986 Epoch:649,Loss:0.727983 Epoch:650,Loss:0.727982 Epoch:651,Loss:0.727982 Epoch:652,Loss:0.727981 Epoch:653,Loss:0.727980 Epoch:654,Loss:0.727980 Epoch:655,Loss:0.727978 Epoch:656,Loss:0.727979 Epoch:657,Loss:0.727979 Epoch:658,Loss:0.727979 Epoch:659,Loss:0.727981 Epoch:660,Loss:0.727983 Epoch:661,Loss:0.727985 Epoch:662,Loss:0.727988 Epoch:663,Loss:0.727992 Epoch:664,Loss:0.727995 Epoch:665,Loss:0.728004 Epoch:666,Loss:0.728005 Epoch:667,Loss:0.728013 Epoch:668,Loss:0.728009 Epoch:669,Loss:0.728011 Epoch:670,Loss:0.728003 Epoch:671,Loss:0.728001 Epoch:672,Loss:0.727998 Epoch:673,Loss:0.727997 Epoch:674,Loss:0.727998 Epoch:675,Loss:0.728001 Epoch:676,Loss:0.728009 Epoch:677,Loss:0.728015 Epoch:678,Loss:0.728027 Epoch:679,Loss:0.728025 Epoch:680,Loss:0.728023 Epoch:681,Loss:0.728011 Epoch:682,Loss:0.728002 Epoch:683,Loss:0.727991 Epoch:684,Loss:0.727984 Epoch:685,Loss:0.727980 Epoch:686,Loss:0.727978 Epoch:687,Loss:0.727978 Epoch:688,Loss:0.727979 Epoch:689,Loss:0.727981 Epoch:690,Loss:0.727982 Epoch:691,Loss:0.727984 Epoch:692,Loss:0.727986 Epoch:693,Loss:0.727987 Epoch:694,Loss:0.727988 Epoch:695,Loss:0.727989 Epoch:696,Loss:0.727989 Epoch:697,Loss:0.727989 Epoch:698,Loss:0.727988 Epoch:699,Loss:0.727987 Epoch:700,Loss:0.727986 Epoch:701,Loss:0.727985 Epoch:702,Loss:0.727983 Epoch:703,Loss:0.727982 Epoch:704,Loss:0.727980 Epoch:705,Loss:0.727979 Epoch:706,Loss:0.727978 Epoch:707,Loss:0.727977 Epoch:708,Loss:0.727977 Epoch:709,Loss:0.727977 Epoch:710,Loss:0.727978 Epoch:711,Loss:0.727979 Epoch:712,Loss:0.727980 Epoch:713,Loss:0.727982 Epoch:714,Loss:0.727985 Epoch:715,Loss:0.727986 Epoch:716,Loss:0.727989 Epoch:717,Loss:0.727990 Epoch:718,Loss:0.727994 Epoch:719,Loss:0.727993 Epoch:720,Loss:0.727995 Epoch:721,Loss:0.727992 Epoch:722,Loss:0.727991 Epoch:723,Loss:0.727987 Epoch:724,Loss:0.727986 Epoch:725,Loss:0.727984 Epoch:726,Loss:0.727984 Epoch:727,Loss:0.727984 Epoch:728,Loss:0.727985 Epoch:729,Loss:0.727987 Epoch:730,Loss:0.727988 Epoch:731,Loss:0.727991 Epoch:732,Loss:0.727993 Epoch:733,Loss:0.727995 Epoch:734,Loss:0.727998 Epoch:735,Loss:0.728000 Epoch:736,Loss:0.728001 Epoch:737,Loss:0.728001 Epoch:738,Loss:0.728000 Epoch:739,Loss:0.727997 Epoch:740,Loss:0.727994 Epoch:741,Loss:0.727990 Epoch:742,Loss:0.727986 Epoch:743,Loss:0.727983 Epoch:744,Loss:0.727980 Epoch:745,Loss:0.727978 Epoch:746,Loss:0.727976 Epoch:747,Loss:0.727975 Epoch:748,Loss:0.727974 Epoch:749,Loss:0.727973 Epoch:750,Loss:0.727973 Epoch:751,Loss:0.727974 Epoch:752,Loss:0.727974 Epoch:753,Loss:0.727975 Epoch:754,Loss:0.727976 Epoch:755,Loss:0.727977 Epoch:756,Loss:0.727978 Epoch:757,Loss:0.727979 Epoch:758,Loss:0.727980 Epoch:759,Loss:0.727982 Epoch:760,Loss:0.727985 Epoch:761,Loss:0.727988 Epoch:762,Loss:0.727990 Epoch:763,Loss:0.727994 Epoch:764,Loss:0.727996 Epoch:765,Loss:0.727999 Epoch:766,Loss:0.728003 Epoch:767,Loss:0.728006 Epoch:768,Loss:0.728010 Epoch:769,Loss:0.728012 Epoch:770,Loss:0.728014 Epoch:771,Loss:0.728012 Epoch:772,Loss:0.728011 Epoch:773,Loss:0.728006 Epoch:774,Loss:0.728004 Epoch:775,Loss:0.727998 Epoch:776,Loss:0.727997 Epoch:777,Loss:0.727992 Epoch:778,Loss:0.727992 Epoch:779,Loss:0.727990 Epoch:780,Loss:0.727992 Epoch:781,Loss:0.727992 Epoch:782,Loss:0.727995 Epoch:783,Loss:0.727996 Epoch:784,Loss:0.727999 Epoch:785,Loss:0.727998 Epoch:786,Loss:0.727997 Epoch:787,Loss:0.727994 Epoch:788,Loss:0.727991 Epoch:789,Loss:0.727986 Epoch:790,Loss:0.727983 Epoch:791,Loss:0.727979 Epoch:792,Loss:0.727977 Epoch:793,Loss:0.727975 Epoch:794,Loss:0.727974 Epoch:795,Loss:0.727974 Epoch:796,Loss:0.727974 Epoch:797,Loss:0.727974 Epoch:798,Loss:0.727975 Epoch:799,Loss:0.727976 Epoch:800,Loss:0.727977 Epoch:801,Loss:0.727978 Epoch:802,Loss:0.727979 Epoch:803,Loss:0.727980 Epoch:804,Loss:0.727981 Epoch:805,Loss:0.727982 Epoch:806,Loss:0.727983 Epoch:807,Loss:0.727985 Epoch:808,Loss:0.727987 Epoch:809,Loss:0.727988 Epoch:810,Loss:0.727991 Epoch:811,Loss:0.727993 Epoch:812,Loss:0.727996 Epoch:813,Loss:0.727998 Epoch:814,Loss:0.728001 Epoch:815,Loss:0.728002 Epoch:816,Loss:0.728004 Epoch:817,Loss:0.728002 Epoch:818,Loss:0.728001 Epoch:819,Loss:0.727997 Epoch:820,Loss:0.727995 Epoch:821,Loss:0.727991 Epoch:822,Loss:0.727989 Epoch:823,Loss:0.727987 Epoch:824,Loss:0.727988 Epoch:825,Loss:0.727988 Epoch:826,Loss:0.727993 Epoch:827,Loss:0.727994 Epoch:828,Loss:0.728000 Epoch:829,Loss:0.727999 Epoch:830,Loss:0.728003 Epoch:831,Loss:0.728001 Epoch:832,Loss:0.728002 Epoch:833,Loss:0.728000 Epoch:834,Loss:0.728000 Epoch:835,Loss:0.727996 Epoch:836,Loss:0.727994 Epoch:837,Loss:0.727988 Epoch:838,Loss:0.727984 Epoch:839,Loss:0.727979 Epoch:840,Loss:0.727974 Epoch:841,Loss:0.727969 Epoch:842,Loss:0.727967 Epoch:843,Loss:0.727967 Epoch:844,Loss:0.727969 Epoch:845,Loss:0.727971 Epoch:846,Loss:0.727973 Epoch:847,Loss:0.727973 Epoch:848,Loss:0.727973 Epoch:849,Loss:0.727974 Epoch:850,Loss:0.727975 Epoch:851,Loss:0.727975 Epoch:852,Loss:0.727975 Epoch:853,Loss:0.727975 Epoch:854,Loss:0.727974 Epoch:855,Loss:0.727973 Epoch:856,Loss:0.727972 Epoch:857,Loss:0.727972 Epoch:858,Loss:0.727972 Epoch:859,Loss:0.727973 Epoch:860,Loss:0.727975 Epoch:861,Loss:0.727977 Epoch:862,Loss:0.727979 Epoch:863,Loss:0.727981 Epoch:864,Loss:0.727984 Epoch:865,Loss:0.727985 Epoch:866,Loss:0.727988 Epoch:867,Loss:0.727989 Epoch:868,Loss:0.727991 Epoch:869,Loss:0.727994 Epoch:870,Loss:0.727997 Epoch:871,Loss:0.728004 Epoch:872,Loss:0.728004 Epoch:873,Loss:0.728011 Epoch:874,Loss:0.728003 Epoch:875,Loss:0.728000 Epoch:876,Loss:0.727988 Epoch:877,Loss:0.727983 Epoch:878,Loss:0.727978 Epoch:879,Loss:0.727980 Epoch:880,Loss:0.727976 Epoch:881,Loss:0.727973 Epoch:882,Loss:0.727965 Epoch:883,Loss:0.727958 Epoch:884,Loss:0.727957 Epoch:885,Loss:0.727967 Epoch:886,Loss:0.727960 Epoch:887,Loss:0.727967 Epoch:888,Loss:0.727977 Epoch:889,Loss:0.727987 Epoch:890,Loss:0.727992 Epoch:891,Loss:0.727996 Epoch:892,Loss:0.727995 Epoch:893,Loss:0.727995 Epoch:894,Loss:0.727992 Epoch:895,Loss:0.727986 Epoch:896,Loss:0.727973 Epoch:897,Loss:0.727966 Epoch:898,Loss:0.727960 Epoch:899,Loss:0.727956 Epoch:900,Loss:0.727953 Epoch:901,Loss:0.727951 Epoch:902,Loss:0.727951 Epoch:903,Loss:0.727954 Epoch:904,Loss:0.727955 Epoch:905,Loss:0.727955 Epoch:906,Loss:0.727954 Epoch:907,Loss:0.727952 Epoch:908,Loss:0.727950 Epoch:909,Loss:0.727948 Epoch:910,Loss:0.727945 Epoch:911,Loss:0.727944 Epoch:912,Loss:0.727945 Epoch:913,Loss:0.727945 Epoch:914,Loss:0.727946 Epoch:915,Loss:0.727947 Epoch:916,Loss:0.727949 Epoch:917,Loss:0.727949 Epoch:918,Loss:0.727949 Epoch:919,Loss:0.727949 Epoch:920,Loss:0.727949 Epoch:921,Loss:0.727949 Epoch:922,Loss:0.727949 Epoch:923,Loss:0.727950 Epoch:924,Loss:0.727952 Epoch:925,Loss:0.727952 Epoch:926,Loss:0.727955 Epoch:927,Loss:0.727956 Epoch:928,Loss:0.727960 Epoch:929,Loss:0.727961 Epoch:930,Loss:0.727965 Epoch:931,Loss:0.727963 Epoch:932,Loss:0.727965 Epoch:933,Loss:0.727962 Epoch:934,Loss:0.727963 Epoch:935,Loss:0.727961 Epoch:936,Loss:0.727962 Epoch:937,Loss:0.727962 Epoch:938,Loss:0.727964 Epoch:939,Loss:0.727966 Epoch:940,Loss:0.727968 Epoch:941,Loss:0.727969 Epoch:942,Loss:0.727969 Epoch:943,Loss:0.727967 Epoch:944,Loss:0.727965 Epoch:945,Loss:0.727962 Epoch:946,Loss:0.727960 Epoch:947,Loss:0.727959 Epoch:948,Loss:0.727960 Epoch:949,Loss:0.727962 Epoch:950,Loss:0.727965 Epoch:951,Loss:0.727969 Epoch:952,Loss:0.727972 Epoch:953,Loss:0.727974 Epoch:954,Loss:0.727974 Epoch:955,Loss:0.727973 Epoch:956,Loss:0.727969 Epoch:957,Loss:0.727964 Epoch:958,Loss:0.727958 Epoch:959,Loss:0.727952 Epoch:960,Loss:0.727947 Epoch:961,Loss:0.727945 Epoch:962,Loss:0.727943 Epoch:963,Loss:0.727943 Epoch:964,Loss:0.727944 Epoch:965,Loss:0.727946 Epoch:966,Loss:0.727947 Epoch:967,Loss:0.727950 Epoch:968,Loss:0.727952 Epoch:969,Loss:0.727954 Epoch:970,Loss:0.727954 Epoch:971,Loss:0.727956 Epoch:972,Loss:0.727956 Epoch:973,Loss:0.727957 Epoch:974,Loss:0.727956 Epoch:975,Loss:0.727958 Epoch:976,Loss:0.727958 Epoch:977,Loss:0.727962 Epoch:978,Loss:0.727962 Epoch:979,Loss:0.727968 Epoch:980,Loss:0.727968 Epoch:981,Loss:0.727973 Epoch:982,Loss:0.727968 Epoch:983,Loss:0.727967 Epoch:984,Loss:0.727960 Epoch:985,Loss:0.727957 Epoch:986,Loss:0.727953 Epoch:987,Loss:0.727951 Epoch:988,Loss:0.727950 Epoch:989,Loss:0.727950 Epoch:990,Loss:0.727950 Epoch:991,Loss:0.727951 Epoch:992,Loss:0.727952 Epoch:993,Loss:0.727953 Epoch:994,Loss:0.727955 Epoch:995,Loss:0.727956 Epoch:996,Loss:0.727957 Epoch:997,Loss:0.727957 Epoch:998,Loss:0.727958 Epoch: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 torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from sklearn.datasets import load_iris from sklearn.preprocessing import OneHotEncoder from 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=1000 learnng_rate=0.003 critier=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:0.788641 Epoch:1,Loss:0.787205 Epoch:2,Loss:0.785736 Epoch:3,Loss:0.784244 Epoch:4,Loss:0.782739 Epoch:5,Loss:0.781233 Epoch:6,Loss:0.779737 Epoch:7,Loss:0.778262 Epoch:8,Loss:0.776822 Epoch:9,Loss:0.775428 Epoch:10,Loss:0.774091 Epoch:11,Loss:0.772821 Epoch:12,Loss:0.771625 Epoch:13,Loss:0.770507 Epoch:14,Loss:0.769467 Epoch:15,Loss:0.768504 Epoch:16,Loss:0.767612 Epoch:17,Loss:0.766784 Epoch:18,Loss:0.766013 Epoch:19,Loss:0.765293 Epoch:20,Loss:0.764617 Epoch:21,Loss:0.763979 Epoch:22,Loss:0.763374 Epoch:23,Loss:0.762798 Epoch:24,Loss:0.762245 Epoch:25,Loss:0.761712 Epoch:26,Loss:0.761195 Epoch:27,Loss:0.760692 Epoch:28,Loss:0.760198 Epoch:29,Loss:0.759711 Epoch:30,Loss:0.759228 Epoch:31,Loss:0.758748 Epoch:32,Loss:0.758268 Epoch:33,Loss:0.757787 Epoch:34,Loss:0.757302 Epoch:35,Loss:0.756813 Epoch:36,Loss:0.756318 Epoch:37,Loss:0.755816 Epoch:38,Loss:0.755305 Epoch:39,Loss:0.754786 Epoch:40,Loss:0.754257 Epoch:41,Loss:0.753716 Epoch:42,Loss:0.753165 Epoch:43,Loss:0.752602 Epoch:44,Loss:0.752026 Epoch:45,Loss:0.751437 Epoch:46,Loss:0.750836 Epoch:47,Loss:0.750221 Epoch:48,Loss:0.749594 Epoch:49,Loss:0.748953 Epoch:50,Loss:0.748300 Epoch:51,Loss:0.747635 Epoch:52,Loss:0.746958 Epoch:53,Loss:0.746270 Epoch:54,Loss:0.745572 Epoch:55,Loss:0.744864 Epoch:56,Loss:0.744148 Epoch:57,Loss:0.743425 Epoch:58,Loss:0.742694 Epoch:59,Loss:0.741958 Epoch:60,Loss:0.741217 Epoch:61,Loss:0.740473 Epoch:62,Loss:0.739725 Epoch:63,Loss:0.738975 Epoch:64,Loss:0.738224 Epoch:65,Loss:0.737471 Epoch:66,Loss:0.736718 Epoch:67,Loss:0.735964 Epoch:68,Loss:0.735211 Epoch:69,Loss:0.734458 Epoch:70,Loss:0.733706 Epoch:71,Loss:0.732954 Epoch:72,Loss:0.732204 Epoch:73,Loss:0.731456 Epoch:74,Loss:0.730709 Epoch:75,Loss:0.729964 Epoch:76,Loss:0.729223 Epoch:77,Loss:0.728484 Epoch:78,Loss:0.727750 Epoch:79,Loss:0.727019 Epoch:80,Loss:0.726294 Epoch:81,Loss:0.725574 Epoch:82,Loss:0.724860 Epoch:83,Loss:0.724152 Epoch:84,Loss:0.723452 Epoch:85,Loss:0.722758 Epoch:86,Loss:0.722072 Epoch:87,Loss:0.721393 Epoch:88,Loss:0.720722 Epoch:89,Loss:0.720058 Epoch:90,Loss:0.719403 Epoch:91,Loss:0.718755 Epoch:92,Loss:0.718115 Epoch:93,Loss:0.717483 Epoch:94,Loss:0.716859 Epoch:95,Loss:0.716242 Epoch:96,Loss:0.715634 Epoch:97,Loss:0.715033 Epoch:98,Loss:0.714440 Epoch:99,Loss:0.713856 Epoch:100,Loss:0.713278 Epoch:101,Loss:0.712709 Epoch:102,Loss:0.712148 Epoch:103,Loss:0.711594 Epoch:104,Loss:0.711047 Epoch:105,Loss:0.710508 Epoch:106,Loss:0.709977 Epoch:107,Loss:0.709452 Epoch:108,Loss:0.708935 Epoch:109,Loss:0.708425 Epoch:110,Loss:0.707922 Epoch:111,Loss:0.707425 Epoch:112,Loss:0.706936 Epoch:113,Loss:0.706452 Epoch:114,Loss:0.705976 Epoch:115,Loss:0.705505 Epoch:116,Loss:0.705041 Epoch:117,Loss:0.704583 Epoch:118,Loss:0.704132 Epoch:119,Loss:0.703686 Epoch:120,Loss:0.703246 Epoch:121,Loss:0.702812 Epoch:122,Loss:0.702383 Epoch:123,Loss:0.701960 Epoch:124,Loss:0.701543 Epoch:125,Loss:0.701130 Epoch:126,Loss:0.700724 Epoch:127,Loss:0.700322 Epoch:128,Loss:0.699925 Epoch:129,Loss:0.699534 Epoch:130,Loss:0.699147 Epoch:131,Loss:0.698766 Epoch:132,Loss:0.698389 Epoch:133,Loss:0.698016 Epoch:134,Loss:0.697648 Epoch:135,Loss:0.697285 Epoch:136,Loss:0.696926 Epoch:137,Loss:0.696571 Epoch:138,Loss:0.696221 Epoch:139,Loss:0.695875 Epoch:140,Loss:0.695533 Epoch:141,Loss:0.695194 Epoch:142,Loss:0.694860 Epoch:143,Loss:0.694529 Epoch:144,Loss:0.694202 Epoch:145,Loss:0.693879 Epoch:146,Loss:0.693560 Epoch:147,Loss:0.693243 Epoch:148,Loss:0.692931 Epoch:149,Loss:0.692621 Epoch:150,Loss:0.692315 Epoch:151,Loss:0.692012 Epoch:152,Loss:0.691712 Epoch:153,Loss:0.691416 Epoch:154,Loss:0.691122 Epoch:155,Loss:0.690832 Epoch:156,Loss:0.690544 Epoch:157,Loss:0.690259 Epoch:158,Loss:0.689977 Epoch:159,Loss:0.689698 Epoch:160,Loss:0.689421 Epoch:161,Loss:0.689147 Epoch:162,Loss:0.688875 Epoch:163,Loss:0.688606 Epoch:164,Loss:0.688340 Epoch:165,Loss:0.688076 Epoch:166,Loss:0.687814 Epoch:167,Loss:0.687554 Epoch:168,Loss:0.687297 Epoch:169,Loss:0.687042 Epoch:170,Loss:0.686789 Epoch:171,Loss:0.686539 Epoch:172,Loss:0.686290 Epoch:173,Loss:0.686044 Epoch:174,Loss:0.685799 Epoch:175,Loss:0.685557 Epoch:176,Loss:0.685316 Epoch:177,Loss:0.685077 Epoch:178,Loss:0.684840 Epoch:179,Loss:0.684605 Epoch:180,Loss:0.684372 Epoch:181,Loss:0.684141 Epoch:182,Loss:0.683911 Epoch:183,Loss:0.683683 Epoch:184,Loss:0.683456 Epoch:185,Loss:0.683231 Epoch:186,Loss:0.683008 Epoch:187,Loss:0.682786 Epoch:188,Loss:0.682566 Epoch:189,Loss:0.682347 Epoch:190,Loss:0.682130 Epoch:191,Loss:0.681914 Epoch:192,Loss:0.681700 Epoch:193,Loss:0.681486 Epoch:194,Loss:0.681275 Epoch:195,Loss:0.681064 Epoch:196,Loss:0.680855 Epoch:197,Loss:0.680647 Epoch:198,Loss:0.680441 Epoch:199,Loss:0.680236 Epoch:200,Loss:0.680032 Epoch:201,Loss:0.679829 Epoch:202,Loss:0.679627 Epoch:203,Loss:0.679426 Epoch:204,Loss:0.679227 Epoch:205,Loss:0.679029 Epoch:206,Loss:0.678831 Epoch:207,Loss:0.678635 Epoch:208,Loss:0.678440 Epoch:209,Loss:0.678246 Epoch:210,Loss:0.678053 Epoch:211,Loss:0.677861 Epoch:212,Loss:0.677670 Epoch:213,Loss:0.677480 Epoch:214,Loss:0.677291 Epoch:215,Loss:0.677102 Epoch:216,Loss:0.676915 Epoch:217,Loss:0.676729 Epoch:218,Loss:0.676543 Epoch:219,Loss:0.676359 Epoch:220,Loss:0.676175 Epoch:221,Loss:0.675992 Epoch:222,Loss:0.675810 Epoch:223,Loss:0.675628 Epoch:224,Loss:0.675448 Epoch:225,Loss:0.675268 Epoch:226,Loss:0.675089 Epoch:227,Loss:0.674911 Epoch:228,Loss:0.674734 Epoch:229,Loss:0.674557 Epoch:230,Loss:0.674381 Epoch:231,Loss:0.674206 Epoch:232,Loss:0.674032 Epoch:233,Loss:0.673858 Epoch:234,Loss:0.673685 Epoch:235,Loss:0.673513 Epoch:236,Loss:0.673341 Epoch:237,Loss:0.673170 Epoch:238,Loss:0.673000 Epoch:239,Loss:0.672830 Epoch:240,Loss:0.672661 Epoch:241,Loss:0.672493 Epoch:242,Loss:0.672325 Epoch:243,Loss:0.672158 Epoch:244,Loss:0.671991 Epoch:245,Loss:0.671825 Epoch:246,Loss:0.671660 Epoch:247,Loss:0.671495 Epoch:248,Loss:0.671331 Epoch:249,Loss:0.671167 Epoch:250,Loss:0.671004 Epoch:251,Loss:0.670842 Epoch:252,Loss:0.670680 Epoch:253,Loss:0.670518 Epoch:254,Loss:0.670357 Epoch:255,Loss:0.670197 Epoch:256,Loss:0.670037 Epoch:257,Loss:0.669878 Epoch:258,Loss:0.669719 Epoch:259,Loss:0.669561 Epoch:260,Loss:0.669403 Epoch:261,Loss:0.669246 Epoch:262,Loss:0.669089 Epoch:263,Loss:0.668932 Epoch:264,Loss:0.668777 Epoch:265,Loss:0.668621 Epoch:266,Loss:0.668466 Epoch:267,Loss:0.668312 Epoch:268,Loss:0.668158 Epoch:269,Loss:0.668004 Epoch:270,Loss:0.667851 Epoch:271,Loss:0.667699 Epoch:272,Loss:0.667547 Epoch:273,Loss:0.667395 Epoch:274,Loss:0.667244 Epoch:275,Loss:0.667093 Epoch:276,Loss:0.666942 Epoch:277,Loss:0.666792 Epoch:278,Loss:0.666643 Epoch:279,Loss:0.666493 Epoch:280,Loss:0.666345 Epoch:281,Loss:0.666196 Epoch:282,Loss:0.666048 Epoch:283,Loss:0.665901 Epoch:284,Loss:0.665754 Epoch:285,Loss:0.665607 Epoch:286,Loss:0.665460 Epoch:287,Loss:0.665314 Epoch:288,Loss:0.665169 Epoch:289,Loss:0.665023 Epoch:290,Loss:0.664879 Epoch:291,Loss:0.664734 Epoch:292,Loss:0.664590 Epoch:293,Loss:0.664446 Epoch:294,Loss:0.664303 Epoch:295,Loss:0.664160 Epoch:296,Loss:0.664017 Epoch:297,Loss:0.663875 Epoch:298,Loss:0.663733 Epoch:299,Loss:0.663591 Epoch:300,Loss:0.663450 Epoch:301,Loss:0.663309 Epoch:302,Loss:0.663169 Epoch:303,Loss:0.663028 Epoch:304,Loss:0.662889 Epoch:305,Loss:0.662749 Epoch:306,Loss:0.662610 Epoch:307,Loss:0.662471 Epoch:308,Loss:0.662332 Epoch:309,Loss:0.662194 Epoch:310,Loss:0.662056 Epoch:311,Loss:0.661919 Epoch:312,Loss:0.661781 Epoch:313,Loss:0.661644 Epoch:314,Loss:0.661508 Epoch:315,Loss:0.661372 Epoch:316,Loss:0.661236 Epoch:317,Loss:0.661100 Epoch:318,Loss:0.660964 Epoch:319,Loss:0.660829 Epoch:320,Loss:0.660695 Epoch:321,Loss:0.660560 Epoch:322,Loss:0.660426 Epoch:323,Loss:0.660292 Epoch:324,Loss:0.660159 Epoch:325,Loss:0.660026 Epoch:326,Loss:0.659893 Epoch:327,Loss:0.659760 Epoch:328,Loss:0.659628 Epoch:329,Loss:0.659496 Epoch:330,Loss:0.659364 Epoch:331,Loss:0.659232 Epoch:332,Loss:0.659101 Epoch:333,Loss:0.658970 Epoch:334,Loss:0.658840 Epoch:335,Loss:0.658709 Epoch:336,Loss:0.658579 Epoch:337,Loss:0.658450 Epoch:338,Loss:0.658320 Epoch:339,Loss:0.658191 Epoch:340,Loss:0.658062 Epoch:341,Loss:0.657933 Epoch:342,Loss:0.657805 Epoch:343,Loss:0.657677 Epoch:344,Loss:0.657549 Epoch:345,Loss:0.657421 Epoch:346,Loss:0.657294 Epoch:347,Loss:0.657167 Epoch:348,Loss:0.657040 Epoch:349,Loss:0.656914 Epoch:350,Loss:0.656788 Epoch:351,Loss:0.656662 Epoch:352,Loss:0.656536 Epoch:353,Loss:0.656411 Epoch:354,Loss:0.656285 Epoch:355,Loss:0.656161 Epoch:356,Loss:0.656036 Epoch:357,Loss:0.655911 Epoch:358,Loss:0.655787 Epoch:359,Loss:0.655663 Epoch:360,Loss:0.655540 Epoch:361,Loss:0.655416 Epoch:362,Loss:0.655293 Epoch:363,Loss:0.655171 Epoch:364,Loss:0.655048 Epoch:365,Loss:0.654925 Epoch:366,Loss:0.654803 Epoch:367,Loss:0.654682 Epoch:368,Loss:0.654560 Epoch:369,Loss:0.654438 Epoch:370,Loss:0.654317 Epoch:371,Loss:0.654196 Epoch:372,Loss:0.654076 Epoch:373,Loss:0.653955 Epoch:374,Loss:0.653835 Epoch:375,Loss:0.653715 Epoch:376,Loss:0.653596 Epoch:377,Loss:0.653476 Epoch:378,Loss:0.653357 Epoch:379,Loss:0.653238 Epoch:380,Loss:0.653119 Epoch:381,Loss:0.653001 Epoch:382,Loss:0.652883 Epoch:383,Loss:0.652765 Epoch:384,Loss:0.652647 Epoch:385,Loss:0.652529 Epoch:386,Loss:0.652412 Epoch:387,Loss:0.652295 Epoch:388,Loss:0.652178 Epoch:389,Loss:0.652062 Epoch:390,Loss:0.651945 Epoch:391,Loss:0.651829 Epoch:392,Loss:0.651713 Epoch:393,Loss:0.651597 Epoch:394,Loss:0.651482 Epoch:395,Loss:0.651367 Epoch:396,Loss:0.651252 Epoch:397,Loss:0.651137 Epoch:398,Loss:0.651022 Epoch:399,Loss:0.650908 Epoch:400,Loss:0.650794 Epoch:401,Loss:0.650680 Epoch:402,Loss:0.650566 Epoch:403,Loss:0.650453 Epoch:404,Loss:0.650340 Epoch:405,Loss:0.650227 Epoch:406,Loss:0.650114 Epoch:407,Loss:0.650001 Epoch:408,Loss:0.649889 Epoch:409,Loss:0.649777 Epoch:410,Loss:0.649665 Epoch:411,Loss:0.649553 Epoch:412,Loss:0.649442 Epoch:413,Loss:0.649331 Epoch:414,Loss:0.649220 Epoch:415,Loss:0.649109 Epoch:416,Loss:0.648998 Epoch:417,Loss:0.648888 Epoch:418,Loss:0.648778 Epoch:419,Loss:0.648668 Epoch:420,Loss:0.648558 Epoch:421,Loss:0.648448 Epoch:422,Loss:0.648339 Epoch:423,Loss:0.648230 Epoch:424,Loss:0.648121 Epoch:425,Loss:0.648013 Epoch:426,Loss:0.647904 Epoch:427,Loss:0.647796 Epoch:428,Loss:0.647688 Epoch:429,Loss:0.647580 Epoch:430,Loss:0.647472 Epoch:431,Loss:0.647365 Epoch:432,Loss:0.647258 Epoch:433,Loss:0.647151 Epoch:434,Loss:0.647044 Epoch:435,Loss:0.646937 Epoch:436,Loss:0.646831 Epoch:437,Loss:0.646725 Epoch:438,Loss:0.646619 Epoch:439,Loss:0.646513 Epoch:440,Loss:0.646407 Epoch:441,Loss:0.646302 Epoch:442,Loss:0.646197 Epoch:443,Loss:0.646092 Epoch:444,Loss:0.645987 Epoch:445,Loss:0.645882 Epoch:446,Loss:0.645778 Epoch:447,Loss:0.645674 Epoch:448,Loss:0.645570 Epoch:449,Loss:0.645466 Epoch:450,Loss:0.645362 Epoch:451,Loss:0.645259 Epoch:452,Loss:0.645156 Epoch:453,Loss:0.645053 Epoch:454,Loss:0.644950 Epoch:455,Loss:0.644848 Epoch:456,Loss:0.644745 Epoch:457,Loss:0.644643 Epoch:458,Loss:0.644541 Epoch:459,Loss:0.644439 Epoch:460,Loss:0.644338 Epoch:461,Loss:0.644236 Epoch:462,Loss:0.644135 Epoch:463,Loss:0.644034 Epoch:464,Loss:0.643933 Epoch:465,Loss:0.643833 Epoch:466,Loss:0.643732 Epoch:467,Loss:0.643632 Epoch:468,Loss:0.643532 Epoch:469,Loss:0.643432 Epoch:470,Loss:0.643332 Epoch:471,Loss:0.643233 Epoch:472,Loss:0.643133 Epoch:473,Loss:0.643034 Epoch:474,Loss:0.642935 Epoch:475,Loss:0.642837 Epoch:476,Loss:0.642738 Epoch:477,Loss:0.642640 Epoch:478,Loss:0.642542 Epoch:479,Loss:0.642444 Epoch:480,Loss:0.642346 Epoch:481,Loss:0.642248 Epoch:482,Loss:0.642151 Epoch:483,Loss:0.642054 Epoch:484,Loss:0.641956 Epoch:485,Loss:0.641860 Epoch:486,Loss:0.641763 Epoch:487,Loss:0.641666 Epoch:488,Loss:0.641570 Epoch:489,Loss:0.641474 Epoch:490,Loss:0.641378 Epoch:491,Loss:0.641282 Epoch:492,Loss:0.641187 Epoch:493,Loss:0.641091 Epoch:494,Loss:0.640996 Epoch:495,Loss:0.640901 Epoch:496,Loss:0.640806 Epoch:497,Loss:0.640712 Epoch:498,Loss:0.640617 Epoch:499,Loss:0.640523 Epoch:500,Loss:0.640429 Epoch:501,Loss:0.640335 Epoch:502,Loss:0.640241 Epoch:503,Loss:0.640147 Epoch:504,Loss:0.640054 Epoch:505,Loss:0.639961 Epoch:506,Loss:0.639867 Epoch:507,Loss:0.639775 Epoch:508,Loss:0.639682 Epoch:509,Loss:0.639589 Epoch:510,Loss:0.639497 Epoch:511,Loss:0.639405 Epoch:512,Loss:0.639313 Epoch:513,Loss:0.639221 Epoch:514,Loss:0.639129 Epoch:515,Loss:0.639038 Epoch:516,Loss:0.638947 Epoch:517,Loss:0.638855 Epoch:518,Loss:0.638764 Epoch:519,Loss:0.638674 Epoch:520,Loss:0.638583 Epoch:521,Loss:0.638493 Epoch:522,Loss:0.638402 Epoch:523,Loss:0.638312 Epoch:524,Loss:0.638222 Epoch:525,Loss:0.638133 Epoch:526,Loss:0.638043 Epoch:527,Loss:0.637954 Epoch:528,Loss:0.637864 Epoch:529,Loss:0.637775 Epoch:530,Loss:0.637686 Epoch:531,Loss:0.637598 Epoch:532,Loss:0.637509 Epoch:533,Loss:0.637421 Epoch:534,Loss:0.637332 Epoch:535,Loss:0.637244 Epoch:536,Loss:0.637156 Epoch:537,Loss:0.637069 Epoch:538,Loss:0.636981 Epoch:539,Loss:0.636894 Epoch:540,Loss:0.636806 Epoch:541,Loss:0.636719 Epoch:542,Loss:0.636632 Epoch:543,Loss:0.636546 Epoch:544,Loss:0.636459 Epoch:545,Loss:0.636373 Epoch:546,Loss:0.636286 Epoch:547,Loss:0.636200 Epoch:548,Loss:0.636114 Epoch:549,Loss:0.636029 Epoch:550,Loss:0.635943 Epoch:551,Loss:0.635858 Epoch:552,Loss:0.635772 Epoch:553,Loss:0.635687 Epoch:554,Loss:0.635602 Epoch:555,Loss:0.635517 Epoch:556,Loss:0.635433 Epoch:557,Loss:0.635348 Epoch:558,Loss:0.635264 Epoch:559,Loss:0.635180 Epoch:560,Loss:0.635096 Epoch:561,Loss:0.635012 Epoch:562,Loss:0.634928 Epoch:563,Loss:0.634845 Epoch:564,Loss:0.634761 Epoch:565,Loss:0.634678 Epoch:566,Loss:0.634595 Epoch:567,Loss:0.634512 Epoch:568,Loss:0.634430 Epoch:569,Loss:0.634347 Epoch:570,Loss:0.634265 Epoch:571,Loss:0.634182 Epoch:572,Loss:0.634100 Epoch:573,Loss:0.634018 Epoch:574,Loss:0.633937 Epoch:575,Loss:0.633855 Epoch:576,Loss:0.633773 Epoch:577,Loss:0.633692 Epoch:578,Loss:0.633611 Epoch:579,Loss:0.633530 Epoch:580,Loss:0.633449 Epoch:581,Loss:0.633368 Epoch:582,Loss:0.633288 Epoch:583,Loss:0.633207 Epoch:584,Loss:0.633127 Epoch:585,Loss:0.633047 Epoch:586,Loss:0.632967 Epoch:587,Loss:0.632887 Epoch:588,Loss:0.632807 Epoch:589,Loss:0.632728 Epoch:590,Loss:0.632648 Epoch:591,Loss:0.632569 Epoch:592,Loss:0.632490 Epoch:593,Loss:0.632411 Epoch:594,Loss:0.632332 Epoch:595,Loss:0.632254 Epoch:596,Loss:0.632175 Epoch:597,Loss:0.632097 Epoch:598,Loss:0.632019 Epoch:599,Loss:0.631941 Epoch:600,Loss:0.631863 Epoch:601,Loss:0.631785 Epoch:602,Loss:0.631708 Epoch:603,Loss:0.631630 Epoch:604,Loss:0.631553 Epoch:605,Loss:0.631476 Epoch:606,Loss:0.631399 Epoch:607,Loss:0.631322 Epoch:608,Loss:0.631245 Epoch:609,Loss:0.631169 Epoch:610,Loss:0.631092 Epoch:611,Loss:0.631016 Epoch:612,Loss:0.630940 Epoch:613,Loss:0.630864 Epoch:614,Loss:0.630788 Epoch:615,Loss:0.630712 Epoch:616,Loss:0.630637 Epoch:617,Loss:0.630561 Epoch:618,Loss:0.630486 Epoch:619,Loss:0.630411 Epoch:620,Loss:0.630336 Epoch:621,Loss:0.630261 Epoch:622,Loss:0.630186 Epoch:623,Loss:0.630111 Epoch:624,Loss:0.630037 Epoch:625,Loss:0.629963 Epoch:626,Loss:0.629888 Epoch:627,Loss:0.629814 Epoch:628,Loss:0.629741 Epoch:629,Loss:0.629667 Epoch:630,Loss:0.629593 Epoch:631,Loss:0.629520 Epoch:632,Loss:0.629446 Epoch:633,Loss:0.629373 Epoch:634,Loss:0.629300 Epoch:635,Loss:0.629227 Epoch:636,Loss:0.629154 Epoch:637,Loss:0.629082 Epoch:638,Loss:0.629009 Epoch:639,Loss:0.628937 Epoch:640,Loss:0.628864 Epoch:641,Loss:0.628792 Epoch:642,Loss:0.628720 Epoch:643,Loss:0.628649 Epoch:644,Loss:0.628577 Epoch:645,Loss:0.628505 Epoch:646,Loss:0.628434 Epoch:647,Loss:0.628362 Epoch:648,Loss:0.628291 Epoch:649,Loss:0.628220 Epoch:650,Loss:0.628149 Epoch:651,Loss:0.628078 Epoch:652,Loss:0.628008 Epoch:653,Loss:0.627937 Epoch:654,Loss:0.627867 Epoch:655,Loss:0.627797 Epoch:656,Loss:0.627726 Epoch:657,Loss:0.627656 Epoch:658,Loss:0.627587 Epoch:659,Loss:0.627517 Epoch:660,Loss:0.627447 Epoch:661,Loss:0.627378 Epoch:662,Loss:0.627308 Epoch:663,Loss:0.627239 Epoch:664,Loss:0.627170 Epoch:665,Loss:0.627101 Epoch:666,Loss:0.627032 Epoch:667,Loss:0.626963 Epoch:668,Loss:0.626895 Epoch:669,Loss:0.626826 Epoch:670,Loss:0.626758 Epoch:671,Loss:0.626690 Epoch:672,Loss:0.626622 Epoch:673,Loss:0.626554 Epoch:674,Loss:0.626486 Epoch:675,Loss:0.626418 Epoch:676,Loss:0.626351 Epoch:677,Loss:0.626283 Epoch:678,Loss:0.626216 Epoch:679,Loss:0.626149 Epoch:680,Loss:0.626082 Epoch:681,Loss:0.626015 Epoch:682,Loss:0.625948 Epoch:683,Loss:0.625881 Epoch:684,Loss:0.625814 Epoch:685,Loss:0.625748 Epoch:686,Loss:0.625682 Epoch:687,Loss:0.625615 Epoch:688,Loss:0.625549 Epoch:689,Loss:0.625483 Epoch:690,Loss:0.625417 Epoch:691,Loss:0.625352 Epoch:692,Loss:0.625286 Epoch:693,Loss:0.625221 Epoch:694,Loss:0.625155 Epoch:695,Loss:0.625090 Epoch:696,Loss:0.625025 Epoch:697,Loss:0.624960 Epoch:698,Loss:0.624895 Epoch:699,Loss:0.624830 Epoch:700,Loss:0.624765 Epoch:701,Loss:0.624701 Epoch:702,Loss:0.624637 Epoch:703,Loss:0.624572 Epoch:704,Loss:0.624508 Epoch:705,Loss:0.624444 Epoch:706,Loss:0.624380 Epoch:707,Loss:0.624316 Epoch:708,Loss:0.624252 Epoch:709,Loss:0.624189 Epoch:710,Loss:0.624125 Epoch:711,Loss:0.624062 Epoch:712,Loss:0.623999 Epoch:713,Loss:0.623936 Epoch:714,Loss:0.623873 Epoch:715,Loss:0.623810 Epoch:716,Loss:0.623747 Epoch:717,Loss:0.623684 Epoch:718,Loss:0.623622 Epoch:719,Loss:0.623559 Epoch:720,Loss:0.623497 Epoch:721,Loss:0.623435 Epoch:722,Loss:0.623372 Epoch:723,Loss:0.623310 Epoch:724,Loss:0.623249 Epoch:725,Loss:0.623187 Epoch:726,Loss:0.623125 Epoch:727,Loss:0.623064 Epoch:728,Loss:0.623002 Epoch:729,Loss:0.622941 Epoch:730,Loss:0.622880 Epoch:731,Loss:0.622819 Epoch:732,Loss:0.622757 Epoch:733,Loss:0.622697 Epoch:734,Loss:0.622636 Epoch:735,Loss:0.622575 Epoch:736,Loss:0.622515 Epoch:737,Loss:0.622454 Epoch:738,Loss:0.622394 Epoch:739,Loss:0.622334 Epoch:740,Loss:0.622274 Epoch:741,Loss:0.622214 Epoch:742,Loss:0.622154 Epoch:743,Loss:0.622094 Epoch:744,Loss:0.622034 Epoch:745,Loss:0.621975 Epoch:746,Loss:0.621915 Epoch:747,Loss:0.621856 Epoch:748,Loss:0.621796 Epoch:749,Loss:0.621737 Epoch:750,Loss:0.621678 Epoch:751,Loss:0.621619 Epoch:752,Loss:0.621561 Epoch:753,Loss:0.621502 Epoch:754,Loss:0.621443 Epoch:755,Loss:0.621385 Epoch:756,Loss:0.621326 Epoch:757,Loss:0.621268 Epoch:758,Loss:0.621210 Epoch:759,Loss:0.621152 Epoch:760,Loss:0.621094 Epoch:761,Loss:0.621036 Epoch:762,Loss:0.620978 Epoch:763,Loss:0.620920 Epoch:764,Loss:0.620863 Epoch:765,Loss:0.620805 Epoch:766,Loss:0.620748 Epoch:767,Loss:0.620691 Epoch:768,Loss:0.620634 Epoch:769,Loss:0.620577 Epoch:770,Loss:0.620520 Epoch:771,Loss:0.620463 Epoch:772,Loss:0.620406 Epoch:773,Loss:0.620349 Epoch:774,Loss:0.620293 Epoch:775,Loss:0.620236 Epoch:776,Loss:0.620180 Epoch:777,Loss:0.620124 Epoch:778,Loss:0.620068 Epoch:779,Loss:0.620012 Epoch:780,Loss:0.619956 Epoch:781,Loss:0.619900 Epoch:782,Loss:0.619844 Epoch:783,Loss:0.619788 Epoch:784,Loss:0.619733 Epoch:785,Loss:0.619677 Epoch:786,Loss:0.619622 Epoch:787,Loss:0.619567 Epoch:788,Loss:0.619512 Epoch:789,Loss:0.619457 Epoch:790,Loss:0.619402 Epoch:791,Loss:0.619347 Epoch:792,Loss:0.619292 Epoch:793,Loss:0.619237 Epoch:794,Loss:0.619183 Epoch:795,Loss:0.619128 Epoch:796,Loss:0.619074 Epoch:797,Loss:0.619020 Epoch:798,Loss:0.618965 Epoch:799,Loss:0.618911 Epoch:800,Loss:0.618857 Epoch:801,Loss:0.618803 Epoch:802,Loss:0.618750 Epoch:803,Loss:0.618696 Epoch:804,Loss:0.618642 Epoch:805,Loss:0.618589 Epoch:806,Loss:0.618535 Epoch:807,Loss:0.618482 Epoch:808,Loss:0.618429 Epoch:809,Loss:0.618376 Epoch:810,Loss:0.618322 Epoch:811,Loss:0.618270 Epoch:812,Loss:0.618217 Epoch:813,Loss:0.618164 Epoch:814,Loss:0.618111 Epoch:815,Loss:0.618059 Epoch:816,Loss:0.618006 Epoch:817,Loss:0.617954 Epoch:818,Loss:0.617901 Epoch:819,Loss:0.617849 Epoch:820,Loss:0.617797 Epoch:821,Loss:0.617745 Epoch:822,Loss:0.617693 Epoch:823,Loss:0.617641 Epoch:824,Loss:0.617589 Epoch:825,Loss:0.617538 Epoch:826,Loss:0.617486 Epoch:827,Loss:0.617435 Epoch:828,Loss:0.617383 Epoch:829,Loss:0.617332 Epoch:830,Loss:0.617281 Epoch:831,Loss:0.617230 Epoch:832,Loss:0.617179 Epoch:833,Loss:0.617128 Epoch:834,Loss:0.617077 Epoch:835,Loss:0.617026 Epoch:836,Loss:0.616975 Epoch:837,Loss:0.616925 Epoch:838,Loss:0.616874 Epoch:839,Loss:0.616824 Epoch:840,Loss:0.616773 Epoch:841,Loss:0.616723 Epoch:842,Loss:0.616673 Epoch:843,Loss:0.616623 Epoch:844,Loss:0.616573 Epoch:845,Loss:0.616523 Epoch:846,Loss:0.616473 Epoch:847,Loss:0.616423 Epoch:848,Loss:0.616374 Epoch:849,Loss:0.616324 Epoch:850,Loss:0.616275 Epoch:851,Loss:0.616225 Epoch:852,Loss:0.616176 Epoch:853,Loss:0.616127 Epoch:854,Loss:0.616078 Epoch:855,Loss:0.616029 Epoch:856,Loss:0.615980 Epoch:857,Loss:0.615931 Epoch:858,Loss:0.615882 Epoch:859,Loss:0.615833 Epoch:860,Loss:0.615785 Epoch:861,Loss:0.615736 Epoch:862,Loss:0.615688 Epoch:863,Loss:0.615639 Epoch:864,Loss:0.615591 Epoch:865,Loss:0.615543 Epoch:866,Loss:0.615495 Epoch:867,Loss:0.615447 Epoch:868,Loss:0.615399 Epoch:869,Loss:0.615351 Epoch:870,Loss:0.615303 Epoch:871,Loss:0.615255 Epoch:872,Loss:0.615208 Epoch:873,Loss:0.615160 Epoch:874,Loss:0.615113 Epoch:875,Loss:0.615065 Epoch:876,Loss:0.615018 Epoch:877,Loss:0.614971 Epoch:878,Loss:0.614923 Epoch:879,Loss:0.614876 Epoch:880,Loss:0.614829 Epoch:881,Loss:0.614783 Epoch:882,Loss:0.614736 Epoch:883,Loss:0.614689 Epoch:884,Loss:0.614642 Epoch:885,Loss:0.614596 Epoch:886,Loss:0.614549 Epoch:887,Loss:0.614503 Epoch:888,Loss:0.614456 Epoch:889,Loss:0.614410 Epoch:890,Loss:0.614364 Epoch:891,Loss:0.614318 Epoch:892,Loss:0.614272 Epoch:893,Loss:0.614226 Epoch:894,Loss:0.614180 Epoch:895,Loss:0.614134 Epoch:896,Loss:0.614088 Epoch:897,Loss:0.614043 Epoch:898,Loss:0.613997 Epoch:899,Loss:0.613952 Epoch:900,Loss:0.613906 Epoch:901,Loss:0.613861 Epoch:902,Loss:0.613816 Epoch:903,Loss:0.613770 Epoch:904,Loss:0.613725 Epoch:905,Loss:0.613680 Epoch:906,Loss:0.613635 Epoch:907,Loss:0.613590 Epoch:908,Loss:0.613545 Epoch:909,Loss:0.613501 Epoch:910,Loss:0.613456 Epoch:911,Loss:0.613411 Epoch:912,Loss:0.613367 Epoch:913,Loss:0.613323 Epoch:914,Loss:0.613278 Epoch:915,Loss:0.613234 Epoch:916,Loss:0.613190 Epoch:917,Loss:0.613145 Epoch:918,Loss:0.613101 Epoch:919,Loss:0.613057 Epoch:920,Loss:0.613014 Epoch:921,Loss:0.612970 Epoch:922,Loss:0.612926 Epoch:923,Loss:0.612882 Epoch:924,Loss:0.612839 Epoch:925,Loss:0.612795 Epoch:926,Loss:0.612752 Epoch:927,Loss:0.612708 Epoch:928,Loss:0.612665 Epoch:929,Loss:0.612621 Epoch:930,Loss:0.612578 Epoch:931,Loss:0.612535 Epoch:932,Loss:0.612492 Epoch:933,Loss:0.612449 Epoch:934,Loss:0.612406 Epoch:935,Loss:0.612363 Epoch:936,Loss:0.612321 Epoch:937,Loss:0.612278 Epoch:938,Loss:0.612235 Epoch:939,Loss:0.612193 Epoch:940,Loss:0.612150 Epoch:941,Loss:0.612108 Epoch:942,Loss:0.612066 Epoch:943,Loss:0.612023 Epoch:944,Loss:0.611981 Epoch:945,Loss:0.611939 Epoch:946,Loss:0.611897 Epoch:947,Loss:0.611855 Epoch:948,Loss:0.611813 Epoch:949,Loss:0.611771 Epoch:950,Loss:0.611729 Epoch:951,Loss:0.611688 Epoch:952,Loss:0.611646 Epoch:953,Loss:0.611604 Epoch:954,Loss:0.611563 Epoch:955,Loss:0.611521 Epoch:956,Loss:0.611480 Epoch:957,Loss:0.611439 Epoch:958,Loss:0.611398 Epoch:959,Loss:0.611356 Epoch:960,Loss:0.611315 Epoch:961,Loss:0.611274 Epoch:962,Loss:0.611233 Epoch:963,Loss:0.611192 Epoch:964,Loss:0.611151 Epoch:965,Loss:0.611111 Epoch:966,Loss:0.611070 Epoch:967,Loss:0.611029 Epoch:968,Loss:0.610989 Epoch:969,Loss:0.610948 Epoch:970,Loss:0.610908 Epoch:971,Loss:0.610868 Epoch:972,Loss:0.610827 Epoch:973,Loss:0.610787 Epoch:974,Loss:0.610747 Epoch:975,Loss:0.610707 Epoch:976,Loss:0.610667 Epoch:977,Loss:0.610627 Epoch:978,Loss:0.610587 Epoch:979,Loss:0.610547 Epoch:980,Loss:0.610507 Epoch:981,Loss:0.610467 Epoch:982,Loss:0.610428 Epoch:983,Loss:0.610388 Epoch:984,Loss:0.610349 Epoch:985,Loss:0.610309 Epoch:986,Loss:0.610270 Epoch:987,Loss:0.610231 Epoch:988,Loss:0.610191 Epoch:989,Loss:0.610152 Epoch:990,Loss:0.610113 Epoch:991,Loss:0.610074 Epoch:992,Loss:0.610035 Epoch:993,Loss:0.609996 Epoch:994,Loss:0.609957 Epoch:995,Loss:0.609918 Epoch:996,Loss:0.609879 Epoch:997,Loss:0.609841 Epoch:998,Loss:0.609802 Epoch:999,Loss:0.609763
loss=critier(model(X_test),y_test) loss
tensor(0.6142, grad_fn=<BinaryCrossEntropyBackward0>)
