DeepQuantom-CNN/random_qccnn_metrics.csv at cb15dfb430bb8f8085fbcaca3c004fe6de69588a

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

2.2 KiB

Raw Blame History

1		epoch	train_acc	valid_acc	train_loss	valid_loss
2	0	1	0.59325	0.6854838709677419	1.1784187927246095	0.8956879992638865
3	1	2	0.713	0.7127016129032258	0.788702152967453	0.7735376723351017
4	2	3	0.755	0.7389112903225806	0.7006268813610077	0.7271041427889178
5	3	4	0.763625	0.7273185483870968	0.668900458574295	0.7277685692233424
6	4	5	0.76275	0.748991935483871	0.6469288661479949	0.672559670863613
7	5	6	0.773625	0.7434475806451613	0.6203210880756378	0.6748099394382969
8	6	7	0.771625	0.7494959677419355	0.6234635796546936	0.6763338706185741
9	7	8	0.784125	0.7580645161290323	0.5965014040470124	0.6313219945276937
10	8	9	0.781	0.7419354838709677	0.5893729448318481	0.6552943125847848
11	9	10	0.785375	0.7600806451612904	0.582163923740387	0.6227685501498561
12	10	11	0.790625	0.7605846774193549	0.5674120993614197	0.6145914264263646
13	11	12	0.7995	0.765625	0.5617314722537995	0.6158498919779255
14	12	13	0.7965	0.7883064516129032	0.5522617139816284	0.581030644716755
15	13	14	0.803125	0.7817540322580645	0.5367116575241089	0.5911272004727395
16	14	15	0.80025	0.7752016129032258	0.5398472018241882	0.5930967292478008
17	15	16	0.808125	0.7620967741935484	0.5326456694602967	0.6306540206555398
18	16	17	0.8145	0.7772177419354839	0.5097175936698913	0.5843374094655437
19	17	18	0.817375	0.7918346774193549	0.5045695571899415	0.5643920071663395
20	18	19	0.81175	0.7681451612903226	0.5022978014945984	0.593349628871487
21	19	20	0.824375	0.7928427419354839	0.4974315061569214	0.5595071181174247
22	20	21	0.818	0.7762096774193549	0.49526858401298524	0.5555580258369446
23	21	22	0.815625	0.7862903225806451	0.4976941578388214	0.5627694274148634
24	22	23	0.825875	0.7908266129032258	0.4906779823303223	0.5514134312829664
25	23	24	0.823875	0.7953629032258065	0.4869117760658264	0.5559701804191836
26	24	25	0.823	0.7807459677419355	0.48527450203895567	0.5541293746040713
27	25	26	0.824625	0.795866935483871	0.4811685870885849	0.5384060884675672
28	26	27	0.827875	0.7938508064516129	0.48096660792827606	0.5493545234203339
29	27	28	0.826875	0.7998991935483871	0.477142098903656	0.5478304624557495
30	28	29	0.82725	0.7948588709677419	0.47373487854003904	0.547797841410483
31	29	30	0.825375	0.7847782258064516	0.47746446084976196	0.5636355328944421
32	30	31	0.828875	0.7757056451612904	0.4754366238117218	0.5671144060550197
33	31	32	0.8315	0.7908266129032258	0.4769072663784027	0.5523799340571126

2.2 KiB Raw Blame History

2.2 KiB

Raw Blame History