DeepQuantom-CNN/data/notebook2/vgg_metrics.csv

,epoch,train_acc,valid_acc,train_loss,valid_loss
0,1,0.575125,0.7011088709677419,2.031563010215759,1.8535375249001287
1,2,0.723875,0.7515120967741935,1.7430778923034669,1.713461822079074
2,3,0.76425,0.7525201612903226,1.6991203842163085,1.7051894049490652
3,4,0.779875,0.7716733870967742,1.6807003259658813,1.690080665772961
4,5,0.79525,0.7696572580645161,1.6654855661392212,1.6926762519344207
5,6,0.80175,0.7888104838709677,1.6598792200088501,1.6711357831954956
6,7,0.80875,0.7721774193548387,1.6529147624969482,1.6847506146277151
7,8,0.812375,0.8004032258064516,1.649049828529358,1.6597587523921844
8,9,0.815375,0.7893145161290323,1.6458229179382324,1.6707303101016628
9,10,0.825,0.811491935483871,1.6370085287094116,1.6518691432091497
10,11,0.832375,0.795866935483871,1.630222158432007,1.6616583139665666
11,12,0.83225,0.8145161290322581,1.6293829317092896,1.6454557026586225
12,13,0.834875,0.8125,1.6268642024993896,1.6500283018235238
13,14,0.840875,0.8104838709677419,1.6199358901977539,1.652194688397069
14,15,0.83975,0.8069556451612904,1.6210605192184449,1.6520756982987927
15,16,0.84175,0.8004032258064516,1.6195698108673096,1.6582853217278757
16,17,0.838625,0.8049395161290323,1.6228710346221924,1.656198097813514
17,18,0.83775,0.8140120967741935,1.6226149969100951,1.6462942131104008
18,19,0.845375,0.8251008064516129,1.6150365982055663,1.6358921681680987
19,20,0.85125,0.8296370967741935,1.6097205333709717,1.6333312757553593
20,21,0.851875,0.8210685483870968,1.609424132347107,1.6388044895664338
21,22,0.850375,0.8210685483870968,1.6101643466949462,1.6369191561975787
22,23,0.858125,0.8185483870967742,1.6031983375549317,1.6421872877305554
23,24,0.857125,0.8361895161290323,1.6038059520721435,1.62519553015309
24,25,0.854,0.8301411290322581,1.606280044555664,1.6290803609355804
25,26,0.858375,0.8119959677419355,1.6011180095672608,1.648349738890125
26,27,0.861375,0.8371975806451613,1.6000273780822754,1.6237776010267195
27,28,0.8705,0.8351814516129032,1.5907322645187378,1.6240986316434798
28,29,0.87175,0.828125,1.5896439476013184,1.6316479982868317
29,30,0.867375,0.8266129032258065,1.5936143741607667,1.632056209348863
30,31,0.86575,0.8377016129032258,1.5949567575454713,1.6240303862479426
31,32,0.86975,0.8336693548387096,1.5907692136764526,1.6251203167823054
32,33,0.86975,0.8397177419354839,1.5919580411911012,1.6214879328204739
33,34,0.872375,0.8422379032258065,1.5886561880111694,1.6194973222671016
34,35,0.876,0.8331653225806451,1.585767653465271,1.6247131862948019
35,36,0.871875,0.8392137096774194,1.5895709590911866,1.6215527211466143
36,37,0.871125,0.8245967741935484,1.590462643623352,1.6370407227546937
37,38,0.87325,0.8301411290322581,1.5876847248077393,1.6306157150576193
38,39,0.8705,0.8240927419354839,1.590844289779663,1.634965923524672
39,40,0.88125,0.8402217741935484,1.5803495874404907,1.6220719968118975
40,41,0.881875,0.8462701612903226,1.5788077726364136,1.614263488400367
41,42,0.87625,0.8487903225806451,1.5843929862976074,1.6131737078389814
42,43,0.88175,0.842741935483871,1.5789586782455445,1.6184405088424683
43,44,0.880375,0.8417338709677419,1.5802030544281005,1.6189708786626016
44,45,0.880375,0.8492943548387096,1.5803771505355835,1.610948174230514
45,46,0.877625,0.8523185483870968,1.5828620948791503,1.6089561793111986
46,47,0.882125,0.8442540322580645,1.578727219581604,1.6160847948443504
47,48,0.872875,0.8392137096774194,1.5874832077026366,1.6228746021947553
48,49,0.884125,0.8371975806451613,1.5771635084152222,1.6237398655183855
49,50,0.88975,0.8341733870967742,1.5712118272781372,1.6256768280459988
50,51,0.876,0.8432459677419355,1.585065812110901,1.6188401445265739
51,52,0.883,0.844758064516129,1.5785351629257203,1.6169025359615203
52,53,0.889125,0.8548387096774194,1.5723771095275878,1.607426397262081
53,54,0.88625,0.8518145161290323,1.5749757404327394,1.6076742141477522
54,55,0.891125,0.8452620967741935,1.5695797872543336,1.6156867473356185
55,56,0.891625,0.8497983870967742,1.5691107511520386,1.6118658050414054
56,57,0.883375,0.8508064516129032,1.577266471862793,1.6087883749315817
57,58,0.891625,0.8422379032258065,1.5702907581329346,1.6168161553721274
58,59,0.89375,0.8553427419354839,1.5679762859344482,1.6071604336461713
59,60,0.885875,0.8482862903225806,1.5749410953521727,1.6108381056016492
60,61,0.891875,0.8417338709677419,1.5696327953338622,1.6187968907817718
61,62,0.894,0.8392137096774194,1.5671770153045654,1.6208721822307957
62,63,0.891125,0.8392137096774194,1.569726734161377,1.6224797733368412
63,64,0.89325,0.8518145161290323,1.568245210647583,1.606977516605008
64,65,0.895125,0.8462701612903226,1.5658181638717652,1.6140611633177726
65,66,0.89175,0.8568548387096774,1.5686321334838866,1.6044510756769488
66,67,0.896375,0.8412298387096774,1.5650024271011354,1.6208048520549652
67,68,0.8895,0.8477822580645161,1.571241024017334,1.6136699338113107
68,69,0.901625,0.8472782258064516,1.5600450325012207,1.612250724146443
69,70,0.89025,0.8306451612903226,1.5705311574935914,1.630635638390818
70,71,0.89675,0.8538306451612904,1.5637341051101685,1.6056317244806597
71,72,0.897,0.8412298387096774,1.5633089447021484,1.6192954970944313
72,73,0.900375,0.8503024193548387,1.560647201538086,1.6097132775091356
73,74,0.895625,0.8533266129032258,1.565330421447754,1.6075265484471475
74,75,0.895125,0.8543346774193549,1.565690894126892,1.6060891266792052
75,76,0.900625,0.8608870967741935,1.560530616760254,1.6011101891917567
76,77,0.897125,0.8563508064516129,1.563725378036499,1.6037284789546844
77,78,0.89175,0.8482862903225806,1.5693257989883422,1.6122698245509979
78,79,0.90225,0.8487903225806451,1.5589981718063355,1.6111104180735927
79,80,0.893375,0.8442540322580645,1.5669814805984497,1.6161983090062295
80,81,0.897125,0.8573588709677419,1.56400607585907,1.6040064134905416
81,82,0.898,0.8568548387096774,1.5621892538070679,1.6044225615839804
82,83,0.89075,0.8503024193548387,1.5697740039825439,1.6114465228972896
83,84,0.900625,0.8598790322580645,1.5606124105453492,1.6002429416102748
84,85,0.89825,0.8553427419354839,1.5628600664138794,1.603792409743032
refactor(Modify.py): 重构代码并改进模型训练流程 - 重构了代码结构，优化了导入顺序和格式 - 改进了模型训练流程，添加了早停机制和学习率调度器- 增加了模型测试和可视化部分的代码 -优化了量子卷积层和模型的实现 - 调整了训练参数和数据预处理方法 2025-06-25 07:07:08 +00:00			`,epoch,train_acc,valid_acc,train_loss,valid_loss`
			`0,1,0.575125,0.7011088709677419,2.031563010215759,1.8535375249001287`
			`1,2,0.723875,0.7515120967741935,1.7430778923034669,1.713461822079074`
			`2,3,0.76425,0.7525201612903226,1.6991203842163085,1.7051894049490652`
			`3,4,0.779875,0.7716733870967742,1.6807003259658813,1.690080665772961`
			`4,5,0.79525,0.7696572580645161,1.6654855661392212,1.6926762519344207`
			`5,6,0.80175,0.7888104838709677,1.6598792200088501,1.6711357831954956`
			`6,7,0.80875,0.7721774193548387,1.6529147624969482,1.6847506146277151`
			`7,8,0.812375,0.8004032258064516,1.649049828529358,1.6597587523921844`
			`8,9,0.815375,0.7893145161290323,1.6458229179382324,1.6707303101016628`
			`9,10,0.825,0.811491935483871,1.6370085287094116,1.6518691432091497`
			`10,11,0.832375,0.795866935483871,1.630222158432007,1.6616583139665666`
			`11,12,0.83225,0.8145161290322581,1.6293829317092896,1.6454557026586225`
			`12,13,0.834875,0.8125,1.6268642024993896,1.6500283018235238`
			`13,14,0.840875,0.8104838709677419,1.6199358901977539,1.652194688397069`
			`14,15,0.83975,0.8069556451612904,1.6210605192184449,1.6520756982987927`
			`15,16,0.84175,0.8004032258064516,1.6195698108673096,1.6582853217278757`
			`16,17,0.838625,0.8049395161290323,1.6228710346221924,1.656198097813514`
			`17,18,0.83775,0.8140120967741935,1.6226149969100951,1.6462942131104008`
			`18,19,0.845375,0.8251008064516129,1.6150365982055663,1.6358921681680987`
			`19,20,0.85125,0.8296370967741935,1.6097205333709717,1.6333312757553593`
			`20,21,0.851875,0.8210685483870968,1.609424132347107,1.6388044895664338`
			`21,22,0.850375,0.8210685483870968,1.6101643466949462,1.6369191561975787`
			`22,23,0.858125,0.8185483870967742,1.6031983375549317,1.6421872877305554`
			`23,24,0.857125,0.8361895161290323,1.6038059520721435,1.62519553015309`
			`24,25,0.854,0.8301411290322581,1.606280044555664,1.6290803609355804`
			`25,26,0.858375,0.8119959677419355,1.6011180095672608,1.648349738890125`
			`26,27,0.861375,0.8371975806451613,1.6000273780822754,1.6237776010267195`
			`27,28,0.8705,0.8351814516129032,1.5907322645187378,1.6240986316434798`
			`28,29,0.87175,0.828125,1.5896439476013184,1.6316479982868317`
			`29,30,0.867375,0.8266129032258065,1.5936143741607667,1.632056209348863`
			`30,31,0.86575,0.8377016129032258,1.5949567575454713,1.6240303862479426`
			`31,32,0.86975,0.8336693548387096,1.5907692136764526,1.6251203167823054`
			`32,33,0.86975,0.8397177419354839,1.5919580411911012,1.6214879328204739`
			`33,34,0.872375,0.8422379032258065,1.5886561880111694,1.6194973222671016`
			`34,35,0.876,0.8331653225806451,1.585767653465271,1.6247131862948019`
			`35,36,0.871875,0.8392137096774194,1.5895709590911866,1.6215527211466143`
			`36,37,0.871125,0.8245967741935484,1.590462643623352,1.6370407227546937`
			`37,38,0.87325,0.8301411290322581,1.5876847248077393,1.6306157150576193`
			`38,39,0.8705,0.8240927419354839,1.590844289779663,1.634965923524672`
			`39,40,0.88125,0.8402217741935484,1.5803495874404907,1.6220719968118975`
			`40,41,0.881875,0.8462701612903226,1.5788077726364136,1.614263488400367`
			`41,42,0.87625,0.8487903225806451,1.5843929862976074,1.6131737078389814`
			`42,43,0.88175,0.842741935483871,1.5789586782455445,1.6184405088424683`
			`43,44,0.880375,0.8417338709677419,1.5802030544281005,1.6189708786626016`
			`44,45,0.880375,0.8492943548387096,1.5803771505355835,1.610948174230514`
			`45,46,0.877625,0.8523185483870968,1.5828620948791503,1.6089561793111986`
			`46,47,0.882125,0.8442540322580645,1.578727219581604,1.6160847948443504`
			`47,48,0.872875,0.8392137096774194,1.5874832077026366,1.6228746021947553`
			`48,49,0.884125,0.8371975806451613,1.5771635084152222,1.6237398655183855`
			`49,50,0.88975,0.8341733870967742,1.5712118272781372,1.6256768280459988`
			`50,51,0.876,0.8432459677419355,1.585065812110901,1.6188401445265739`
			`51,52,0.883,0.844758064516129,1.5785351629257203,1.6169025359615203`
			`52,53,0.889125,0.8548387096774194,1.5723771095275878,1.607426397262081`
			`53,54,0.88625,0.8518145161290323,1.5749757404327394,1.6076742141477522`
			`54,55,0.891125,0.8452620967741935,1.5695797872543336,1.6156867473356185`
			`55,56,0.891625,0.8497983870967742,1.5691107511520386,1.6118658050414054`
			`56,57,0.883375,0.8508064516129032,1.577266471862793,1.6087883749315817`
			`57,58,0.891625,0.8422379032258065,1.5702907581329346,1.6168161553721274`
			`58,59,0.89375,0.8553427419354839,1.5679762859344482,1.6071604336461713`
			`59,60,0.885875,0.8482862903225806,1.5749410953521727,1.6108381056016492`
			`60,61,0.891875,0.8417338709677419,1.5696327953338622,1.6187968907817718`
			`61,62,0.894,0.8392137096774194,1.5671770153045654,1.6208721822307957`
			`62,63,0.891125,0.8392137096774194,1.569726734161377,1.6224797733368412`
			`63,64,0.89325,0.8518145161290323,1.568245210647583,1.606977516605008`
			`64,65,0.895125,0.8462701612903226,1.5658181638717652,1.6140611633177726`
			`65,66,0.89175,0.8568548387096774,1.5686321334838866,1.6044510756769488`
			`66,67,0.896375,0.8412298387096774,1.5650024271011354,1.6208048520549652`
			`67,68,0.8895,0.8477822580645161,1.571241024017334,1.6136699338113107`
			`68,69,0.901625,0.8472782258064516,1.5600450325012207,1.612250724146443`
			`69,70,0.89025,0.8306451612903226,1.5705311574935914,1.630635638390818`
			`70,71,0.89675,0.8538306451612904,1.5637341051101685,1.6056317244806597`
			`71,72,0.897,0.8412298387096774,1.5633089447021484,1.6192954970944313`
			`72,73,0.900375,0.8503024193548387,1.560647201538086,1.6097132775091356`
			`73,74,0.895625,0.8533266129032258,1.565330421447754,1.6075265484471475`
			`74,75,0.895125,0.8543346774193549,1.565690894126892,1.6060891266792052`
			`75,76,0.900625,0.8608870967741935,1.560530616760254,1.6011101891917567`
			`76,77,0.897125,0.8563508064516129,1.563725378036499,1.6037284789546844`
			`77,78,0.89175,0.8482862903225806,1.5693257989883422,1.6122698245509979`
			`78,79,0.90225,0.8487903225806451,1.5589981718063355,1.6111104180735927`
			`79,80,0.893375,0.8442540322580645,1.5669814805984497,1.6161983090062295`
			`80,81,0.897125,0.8573588709677419,1.56400607585907,1.6040064134905416`
			`81,82,0.898,0.8568548387096774,1.5621892538070679,1.6044225615839804`
			`82,83,0.89075,0.8503024193548387,1.5697740039825439,1.6114465228972896`
			`83,84,0.900625,0.8598790322580645,1.5606124105453492,1.6002429416102748`
			`84,85,0.89825,0.8553427419354839,1.5628600664138794,1.603792409743032`