当前位置：首页 > news >正文

【Python机器学习】实验13 基于神经网络的回归-分类实验

news 文章来源：https://blog.csdn.net/m0_68111267/article/details/132127413 2025/4/20 19:17:09

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 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>)

【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.…...

编程日记 2023/8/20 23:56:39

【数据结构】二叉树的链式结构的实现 -- 详解

一、前置说明在学习二叉树的基本操作前，需先要创建一棵二叉树，然后才能学习其相关的基本操作。为了降低大家学习成本，此处手动快速创建一棵简单的二叉树，快速进入二叉树操作学习。 typedef char BTDataType;typedef struct Binar…...

编程日记 2023/8/20 23:55:38

【C语言】什么是结构体内存对齐？结构体的大小怎么计算？

目录 1.结构体内存对齐对偏移量的理解： 2.结构体的大小计算 2.1结构体中只有普通的数据类型的大小计算 2.2 结构体中有嵌套的结构体的大小计算 3.修改默认对齐数 4.为什么存在内存对齐? 这篇文章主要介绍结构体内存对齐和如何计算大小。在学习结构体内存…...

编程日记 2023/8/20 23:54:37

【Redis】Redis中的布隆过滤器

【Redis】Redis中的布隆过滤器前言在实际开发中，会遇到很多要判断一个元素是否在某个集合中的业务场景，类似于垃圾邮件的识别，恶意IP地址的访问，缓存穿透等情况。类似于缓存穿透这种情况，有许多的解决方法&#xf…...

编程日记 2023/8/20 23:53:34

接口测试 —— Jmeter 参数加密实现

Jmeter有两种方法可以实现算法加密 1、使用__digest自带函数参数说明： Digest algorithm：算法摘要，可输入值：MD2、MD5、SHA-1、SHA-224、SHA-256、SHA-384、SHA-512 String to be hashed：要加密的数据 Salt to be…...

编程日记 2023/8/20 23:52:33

Linux c语言字节序

文章目录一、简介二、大小端判断2.1 联合体2.2 指针2.3 网络字节序一、简介字节序（Byte Order）指的是在存储和表示多字节数据类型（如整数和浮点数）时，字节的排列顺序。常见的字节序有大端字节序（Big En…...

编程日记 2023/8/20 23:51:30

批量将excel中第5列中内容将人名和电话号码进行分列

使用Python可以使用openpyxl库来实现批量将Excel中第5列的内容分列为人名和电话号码的操作。下面是示例代码： import openpyxl def split_names_and_phone_numbers(file_path, sheet_name): # 加载Excel文件 workbook openpyxl.load_workbook(file_path) …...

编程日记 2023/8/20 23:50:29

WPF DataGrid columns表头根据数据集动态动态生成Demo

思路是这样的，数组集合装表头的信息，遍历这个集合，遍历过程中处理一下数据，然后就把每表头信息添加到dataGrid2.Columns.Add(templateColumn); 1，页面Xaml代码： <DataGrid x:Name"dataGrid" …...

编程日记 2023/8/20 23:49:27

1339. 分裂二叉树的最大乘积

链接： 1339. 分裂二叉树的最大乘积题解： /*** Definition for a binary tree node.* struct TreeNode {* int val;* TreeNode *left;* TreeNode *right;* TreeNode() : val(0), left(nullptr), right(nullptr) {}* …...

编程日记 2023/8/20 23:48:25

【C++】Stack和Queue

欢迎来到Cefler的博客😁 🕌博客主页：那个传说中的man的主页 🏠个人专栏：题目解析 🌎推荐文章：题目大解析3 目录 👉🏻Stack Constructor👉🏻Stack …...

编程日记 2023/8/20 23:47:24

Maven之tomcat7-maven-plugin 版本低的问题

tomcat7-maven-plugin 版本『低』的问题相较于当前最新版的 tomcat 10 而言，tomcat7-maven-plugin 确实看起来很显老旧。但是，这个问题并不是问题，至少不是大问题。原因 1：tomcat7-maven-plugin 仅用于我们（程序员&…...

编程日记 2023/8/20 23:46:22

在项目中如何解除idea和Git的绑定

在项目中如何解除idea和Git的绑定 1、点击File--->Settings...(CtrlAltS)--->Version Control--->Directory Mappings--->点击取消Git的注册根路径： 2、回到idea界面就没有Git了： 3、给这个项目初始化这样就可以重新绑定远程仓库了&#x…...

编程日记 2023/8/20 23:45:20

We believe our research will eventually lead to artificial general intelligence, a system that can solve human-level problems. Building safe and beneficial AGI is our mission. ---- OpenAI 通用人工智能 AGI 作为 AI 的终极形态，是 AI 行业内追求的演…...

编程日记 2023/8/20 23:44:18

线性代数的学习和整理9（草稿-----未完成）

3.3 特征值和特征向量是什么？ 直接说现在：特征向量这个块往哪个方向进行了拉伸，各个方向拉伸了几倍。这也让人很容易理解为什么，行列式的值就是特征值的乘积。特征向量也代表了一些良好的性质，即这些线在线性变换后…...

编程日记 2023/8/20 23:43:17

React的useReducer与Reudx对比

useReducer 和 Redux 都是用于处理应用程序的状态管理的工具，但它们在概念和使用场景上存在一些区别。 useReducer： useReducer 是 React 提供的一个 Hook，用于管理局部状态。它接受一个 reducer 函数和初始状态，并返回一个包含当…...

编程日记 2023/8/20 23:42:16

深度学习环境搭建 cuda、模型量化bitsandbytes安装教程 windows、linux

cuda、cudann、conda安装教程输入以下命令，查看 GPU 支持的最高 CUDA 版本。 nvidia-smi cuda安装（cudatoolkit） 前往 Nvidia 的 CUDA 官网：CUDA Toolkit Archive | NVIDIA Developer CUDA Toolkit 11.8 Downloads | NVIDIA …...

编程日记 2023/8/20 23:41:14

pythond assert 0 ＜= colx ＜ X12_MAX_COLS AssertionError

python使用xlrd读取excel时，报错： assert 0 < colx < X12_MAX_COLS AssertionError 大意是excel列太多了。主要是xlrd库的问题。最好的方法是不用它，但是我用的其他人提供的工具用到它，没法改。尝试手动删除excel的列&am…...

编程日记 2023/8/20 23:40:13

js简介以及在html中的2种使用方式（hello world）

简介 javascript ：是一个跨平台的脚本语言；是一种轻量级的编程语言。 JavaScript 是 Web 的编程语言。所有现代的 HTML 页面都使用 JavaScript。 HTML： 结构 css： 表现 JS： 行为 HTMLCSS 只能称之为静态网页&#xff0…...

编程日记 2023/8/20 23:39:11

vsCode使用cuda

一、vsCode使用cuda 前情提要：配置好mingw： 1.安装cuda 参考： **CUDA Toolkit安装教程（Windows）：**https://blog.csdn.net/qq_42951560/article/details/116131410 2.在vscode中添加includePath c_cp…...

编程日记 2023/8/20 23:38:10

ubuntu无法使用apt命令时怎么安装库

如题因为某些原因，不能直接联网使用apt命令安装库。只能手动去ubuntu镜像源里找对应的包的deb安装文件镜像源地址（适用于AMD64架构,就是常见的PC的X86-64啦） 镜像源地址（适用于ARM64,armhf,ppc64el,riscv64,s390x架构&#xff…...

编程日记 2023/8/20 23:37:08

防火墙firewall

一、什么是防火墙二、iptables 1、iptables介绍 2、实验 138的已经被拒绝，1可以三、firewalld 1、firewalld简介关闭iptables，开启firewalld，curl不能使用，远程连接ssh可以使用添加80端口这样写也可以：添加http…...

编程日记 2023/8/20 23:36:06

拿来即用，自己封装的 axios

文章目录一、需求二、分析1. 安装axios2. 新建一个 ts 文件，封装 axios3. store 存放 token 信息4. 使用5. 文件 type.js 一、需求在日常开发中，我们会经常用到 axios ，那么如何在自己的项目中自己封装 axios 二、分析 1. 安装axios np…...

编程日记 2023/8/20 23:35:05

Hadoop小结（下）

HDFS 集群 HDFS 集群是建立在 Hadoop 集群之上的，由于 HDFS 是 Hadoop 最主要的守护进程，所以 HDFS 集群的配置过程是 Hadoop 集群配置过程的代表。使用 Docker 可以更加方便地、高效地构建出一个集群环境。每台计算机中的配置 Hadoop 如何配置集群…...

编程日记 2023/8/20 23:34:04

使用老北鼻AI免费GPT对话解决gun make安装和解析iso9660的问题

在学习解析ISO9660镜像文件时，使用了GPT来了解相关的库和gun make编译器的相关知识。这个过程可真是一言难尽，每个问题的回答都模棱两可都需要去证实，不能直接复制粘贴，也不能说GPT的回答一点用也没有，至少GPT给出了一…...

编程日记 2023/8/20 23:33:03

shell脚本语句

一、语句一、条件语句一、以用户为例演示一、显示当前登录系统的用户信息 w命令二、显示有多少个用户 w | wc -l 显示有7个用户前两个是固定标题，从第三个开始才是登录用户，所以要统计数量需要命令：echo $[$(w | wc -l) -2] 显示…...

编程日记 2023/8/20 23:32:02

【LeetCode】2235.两整数相加

题目给你两个整数 num1 和 num2，返回这两个整数的和。示例 1： 输入：num1 12, num2 5 输出：17 解释：num1 是 12，num2 是 5 ，它们的和是 12 5 17 ，因此返回 17 。示例 2&…...

编程日记 2023/8/20 23:31:01

springboot sl4j2 写入日志到mysql

问题描述 springboot初始化的时候，会先初始化日志然后再加载数据源如果用配置文件进行初始化，那么会出现数据源没有加载成功，导致空指针异常报错排查如下： 搜索报错信息，OBjects.invoke is Null打断点发现。dataso…...

编程日记 2023/8/20 23:29:59

用 PyTorch 编写分布式应用程序

用 PyTorch 编写分布式应用程序在这个简短的教程中，我们将介绍 PyTorch 的分布式软件包。我们将了解如何设置分布式设置，使用不同的交流策略以及如何仔细查看软件包的内部结构。设定 PyTorch 中包含的分布式软件包(即torch.distributed&#xff09…...

编程日记 2023/8/20 23:28:57

空间分析专属 Python 学习资料

空间数据分析能够帮助我们更好地理解地理空间中的模式和关系，从而为决策提供支持。例如，城市规划者可以使用空间数据分析来确定城市发展的最佳方向，环境科学家可以使用空间数据分析来评估污染的影响，而商业分析师可以使用空间数据…...

编程日记 2023/8/20 23:27:55

2. Linux Server 20.04 Qt5.14.2配置Jetson Orin Nano Developer Kit 交叉编译环境

最近公司给了我一块Jetson Orin Nano的板子，先刷了系统（1.Jetson Orin Nano Developer Kit系统刷机）又让我搭建交叉编译环境，所以有了下面的文章一 :Qt5.14.2交叉编译环境安装 1.准备 1.1设备环境 1.1.1 Server: Ubuntu20.0…...

编程日记 2023/8/20 23:26:54