DeepQuantom-CNN/qccnn_metrics.csv at 9266859f0ae2fd898dcacbc09a11eb2a190a36b2

fly6516 9266859f0a 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 15:07:08 +08:00

3.3 KiB

Raw Blame History

1		epoch	train_acc	valid_acc	train_loss	valid_loss
2	0	1	0.391375	0.5882056451612904	1.7793689422607422	1.3356874796652025
3	1	2	0.617125	0.6365927419354839	1.059556163787842	0.9690603344671188
4	2	3	0.663125	0.6577620967741935	0.9102170023918151	0.9078671547674364
5	3	4	0.69	0.6819556451612904	0.8537670917510987	0.8649420180628377
6	4	5	0.71125	0.7056451612903226	0.8021836678981781	0.8317571128568342
7	5	6	0.719375	0.6844758064516129	0.7773117737770081	0.8293971457789021
8	6	7	0.733	0.7247983870967742	0.7432277894020081	0.7570091389840649
9	7	8	0.727125	0.7051411290322581	0.7392586808204651	0.7708722833664187
10	8	9	0.740375	0.7091733870967742	0.716008551120758	0.7547545788749572
11	9	10	0.74775	0.7091733870967742	0.6988923935890198	0.7634256799374858
12	10	11	0.75025	0.7328629032258065	0.6836859595775604	0.7220065324537216
13	11	12	0.75525	0.7313508064516129	0.6790840411186219	0.7320531125991575
14	12	13	0.755875	0.734375	0.6686462206840516	0.7067360877990723
15	13	14	0.755875	0.7338709677419355	0.659578008890152	0.6924379564100697
16	14	15	0.758625	0.7217741935483871	0.6591809678077698	0.7092515037905786
17	15	16	0.770125	0.7474798387096774	0.6366513178348542	0.6689942498360911
18	16	17	0.76975	0.75	0.64143789935112	0.6781372427940369
19	17	18	0.766625	0.7469758064516129	0.6327295961380005	0.6749444469328849
20	18	19	0.772125	0.7298387096774194	0.6213552060127259	0.6950428524324971
21	19	20	0.771625	0.7560483870967742	0.619529750585556	0.6620656597998834
22	20	21	0.77425	0.7459677419354839	0.6170263500213623	0.6944894117693747
23	21	22	0.77375	0.7379032258064516	0.610548350572586	0.698592597438443
24	22	23	0.774125	0.7494959677419355	0.6073116481304168	0.6649176555295144
25	23	24	0.78	0.75	0.601892656326294	0.6502023070089279
26	24	25	0.777875	0.7671370967741935	0.5965238115787506	0.6416350141648324
27	25	26	0.7875	0.751008064516129	0.584319313287735	0.6557927843063108
28	26	27	0.784625	0.7651209677419355	0.5858220131397247	0.6265111680953733
29	27	28	0.77975	0.7610887096774194	0.5928270950317382	0.6353864400617538
30	28	29	0.780625	0.7520161290322581	0.5824392430782318	0.6569667564284417
31	29	30	0.78475	0.7686491935483871	0.5810435285568237	0.6306867743692091
32	30	31	0.789	0.7706653225806451	0.5672282783985138	0.6261125274242894
33	31	32	0.78525	0.7560483870967742	0.5757509377002716	0.6505500414679127
34	32	33	0.792	0.7681451612903226	0.5613697295188904	0.629849144527989
35	33	34	0.793875	0.7620967741935484	0.5625830183029175	0.6189906856706066
36	34	35	0.791875	0.7681451612903226	0.5602230775356293	0.6212261732547514
37	35	36	0.794625	0.7615927419354839	0.5545250961780548	0.619811047469416
38	36	37	0.794625	0.7701612903225806	0.5573954427242279	0.6205428954093687
39	37	38	0.79425	0.764616935483871	0.5514744529724122	0.628905875067557
40	38	39	0.792375	0.7842741935483871	0.5618353080749512	0.5954909113145643
41	39	40	0.796375	0.7711693548387096	0.5491654114723206	0.6137347759739045
42	40	41	0.799	0.7641129032258065	0.5372960684299469	0.6470560056547965
43	41	42	0.800375	0.7671370967741935	0.5395989503860473	0.6211921880322118
44	42	43	0.806	0.7671370967741935	0.5370515692234039	0.6075864828401997
45	43	44	0.801875	0.7605846774193549	0.5388010408878326	0.5891174308715328
46	44	45	0.800875	0.766633064516129	0.539761929512024	0.610026998865989
47	45	46	0.802375	0.780241935483871	0.5270701496601105	0.6000283591208919
48	46	47	0.799875	0.7772177419354839	0.5320828959941865	0.5864833074231302
49	47	48	0.807	0.7837701612903226	0.5309389193058014	0.5761975809451072

3.3 KiB Raw Blame History

3.3 KiB

Raw Blame History