DeepQuantom-CNN/random_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

2.3 KiB

Raw Blame History

1		epoch	train_acc	valid_acc	train_loss	valid_loss
2	0	1	0.561125	0.6476814516129032	1.271985122203827	0.9911825291572078
3	1	2	0.676375	0.6668346774193549	0.9134431419372558	0.8937955517922679
4	2	3	0.699125	0.6824596774193549	0.8243759956359863	0.8520721235582905
5	3	4	0.716625	0.6995967741935484	0.7818560593128204	0.8005917687569896
6	4	5	0.725125	0.7101814516129032	0.745261854171753	0.8037946531849522
7	5	6	0.733375	0.7061491935483871	0.7344184167385102	0.7732808570707997
8	6	7	0.737875	0.7253024193548387	0.7150477197170257	0.74149090051651
9	7	8	0.74325	0.7253024193548387	0.7022454526424408	0.7516527022084882
10	8	9	0.742625	0.7298387096774194	0.7041031284332275	0.7247120468847214
11	9	10	0.747875	0.7116935483870968	0.6772661633491516	0.7338270487323884
12	10	11	0.757875	0.7212701612903226	0.6562292041778565	0.7387931039256435
13	11	12	0.761625	0.7328629032258065	0.6542983632087708	0.725050816612859
14	12	13	0.75925	0.7318548387096774	0.6493379819393158	0.7001086867624714
15	13	14	0.767375	0.7379032258064516	0.6460576868057251	0.6988139988914612
16	14	15	0.765875	0.7288306451612904	0.6339491362571716	0.7128441218406923
17	15	16	0.763125	0.7368951612903226	0.6262373595237732	0.714297366719092
18	16	17	0.76925	0.7227822580645161	0.6279029569625855	0.7181522269402781
19	17	18	0.77025	0.7449596774193549	0.6159816448688507	0.6757595616002237
20	18	19	0.7745	0.7283266129032258	0.6136245548725128	0.6998546681096477
21	19	20	0.775375	0.7469758064516129	0.6000997524261474	0.6749713065162781
22	20	21	0.7805	0.748991935483871	0.5928600332736969	0.6656959902855658
23	21	22	0.77525	0.7444556451612904	0.599046837568283	0.6857193170055267
24	22	23	0.785	0.7384072580645161	0.5875316572189331	0.6785462142959717
25	23	24	0.778375	0.765625	0.588378502368927	0.627939272311426
26	24	25	0.78575	0.7459677419354839	0.5700427904129028	0.6502643679418871
27	25	26	0.7805	0.75	0.5785817315578461	0.6712307555060233
28	26	27	0.78675	0.7474798387096774	0.5676946561336518	0.6555941143343526
29	27	28	0.787875	0.7505040322580645	0.575938116312027	0.6507430134281036
30	28	29	0.789	0.7605846774193549	0.5655779435634612	0.6520830373610219
31	29	30	0.790625	0.7434475806451613	0.5578647639751434	0.6789213486256138
32	30	31	0.787375	0.7565524193548387	0.5687701859474182	0.649224087115257
33	31	32	0.79575	0.7560483870967742	0.5432633152008056	0.6388471222692921
34	32	33	0.7895	0.7595766129032258	0.5557173223495483	0.6391933618053314

2.3 KiB Raw Blame History

2.3 KiB

Raw Blame History