aiRtrafficNN/Code/python/notebooks/hyperparameter_scan_results.csv

1	Model	Hidden Size	RNN Size	Step	Final Loss
2	RNN	16	32	1	261.2352640931035
3	RNN	16	64	1	154.6564223061145
4	RNN	16	128	1	196.3427066534338
5	RNN	32	32	1	384.9678237270301
6	RNN	32	64	1	410.67893423161036
7	RNN	32	128	1	291.4480094372387
8	RNN	64	32	1	405.7067755040988
9	RNN	64	64	1	2116.495914942782
10	RNN	64	128	1	2116.4957490303145
11	LSTM	16	32	1	30.830584109669
12	LSTM	16	64	1	24.41159848763909
13	LSTM	16	128	1
14	LSTM	32	32	1	18.46859591443774
15	LSTM	32	64	1	20.514015533554723
16	LSTM	32	128	1	16.6853289604187
17	LSTM	64	32	1	64.03490912746376
18	LSTM	64	64	1	15.621828354580302
19	LSTM	64	128	1	23.594708127035222
20	GRU	16	32	1
21	GRU	16	64	1
22	GRU	16	128	1	35.8418803282187
23	GRU	32	32	1
24	GRU	32	64	1	19.816746349066076
25	GRU	32	128	1
26	GRU	64	32	1
27	GRU	64	64	1	99.63193988800049
28	GRU	64	128	1	62.531928075871
29	RNN	16	32	5	215.5662602169413
30	RNN	16	64	5	458.9189094221088
31	RNN	16	128	5	568.8256333042199
32	RNN	32	32	5	367.05963134765625
33	RNN	32	64	5	391.08609277429713
34	RNN	32	128	5	86.18239271137077
35	RNN	64	32	5	379.8394951618893
36	RNN	64	64	5	217.72880833585498
37	RNN	64	128	5	193.88096043761348
38	LSTM	16	32	5	17.732036187615194
39	LSTM	16	64	5	12.483971592406151
40	LSTM	16	128	5	18.235054378778162
41	LSTM	32	32	5	16.88911421869842
42	LSTM	32	64	5	13.982023883873309
43	LSTM	32	128	5	13.012413780454178
44	LSTM	64	32	5	18.227851189358134
45	LSTM	64	64	5	18.786675762122787
46	LSTM	64	128	5	25.950177515056772
47	GRU	16	32	5	19.275062708787516
48	GRU	16	64	5	29.5315320995492
49	GRU	16	128	5	44.03425704257589
50	GRU	32	32	5	25.158055748738033
51	GRU	32	64	5	17.85035125302597
52	GRU	32	128	5	92.48385832343303
53	GRU	64	32	5	92.20544084360901
54	GRU	64	64	5	108.83066752259161
55	GRU	64	128	5	168.5157569562885
56	RNN	16	32	10	331.5480924794372
57	RNN	16	64	10	328.6068296835456
58	RNN	16	128	10	134.54841076488225
59	RNN	32	32	10	299.88682910758007
60	RNN	32	64	10	306.9416514651876
61	RNN	32	128	10	162.43678724262077
62	RNN	64	32	10	329.1637651685258
63	RNN	64	64	10	334.56139105138647
64	RNN	64	128	10	141.24655634920362
65	LSTM	16	32	10	92.42624144486977
66	LSTM	16	64	10	14.689397603693143
67	LSTM	16	128	10	11.110472313115295
68	LSTM	32	32	10	20.380187215939372
69	LSTM	32	64	10	15.061512013556252
70	LSTM	32	128	10	22.62677042249223
71	LSTM	64	32	10	26.77031899170137
72	LSTM	64	64	10	24.62572886910237
73	LSTM	64	128	10	26.26676934873554
74	GRU	16	32	10	21.257314131293498
75	GRU	16	64	10	19.206207382846888
76	GRU	16	128	10	43.89531944167446
77	GRU	32	32	10	20.500510410523752
78	GRU	32	64	10	21.27962108397148
79	GRU	32	128	10	26.415901694499272
80	GRU	64	32	10	102.7421861836608
81	GRU	64	64	10	108.29447157953827
82	GRU	64	128	10	144.37117619581625
83	RNN	16	32	30	207.72070333991252
84	RNN	16	64	30	259.24992929378027
85	RNN	16	128	30	106.49237761698978
86	RNN	32	32	30	108.70668032471563
87	RNN	32	64	30	292.42346556757536
88	RNN	32	128	30	2260.9625183965118
89	RNN	64	32	30	255.18154520384022
90	RNN	64	64	30	275.74408292098786
91	RNN	64	128	30	303.8554754122882
92	LSTM	16	32	30	120.18003799545933
93	LSTM	16	64	30	32.07153643352885
94	LSTM	16	128	30	29.8134809682067
95	LSTM	32	32	30	30.940696246187454
96	LSTM	32	64	30	27.958127700107198
97	LSTM	32	128	30	112.83767892273379
98	LSTM	64	32	30	130.2808960928044
99	LSTM	64	64	30	117.1968483589065
100	LSTM	64	128	30	109.4370427198813
101	GRU	16	32	30	56.353233820955516
102	GRU	16	64	30	33.99593487591811
103	GRU	16	128	30	59.830814415300395
104	GRU	32	32	30	33.456441832260346
105	GRU	32	64	30	34.80982407046036
106	GRU	32	128	30	136.90971965521155
107	GRU	64	32	30	139.1069736480713
108	GRU	64	64	30	62.417733837181416
109	GRU	64	128	30	64.40956481073944