aiRtrafficNN/Code/python/notebooks/hyperparameter_scan_results.csv

1	Model	Hidden Size	RNN Size	Step	Final Loss
2	RNN	16	32	1	372.1873533829399
3	RNN	16	64	1	371.67606353759766
4	RNN	16	128	1	328.19922903309697
5	RNN	32	32	1	376.71770842179006
6	RNN	32	64	1	369.6340640524159
7	RNN	32	128	1	281.8589802617612
8	RNN	64	32	1	378.75125188412875
9	RNN	64	64	1	405.08673095703125
10	RNN	64	128	1	1088.4633191979449
11	LSTM	16	32	1	152.01849580847698
12	LSTM	16	64	1	152.46532937754756
13	LSTM	16	128	1	39.81503051260243
14	LSTM	32	32	1	45.500295265861176
15	LSTM	32	64	1	51.218478575996734
16	LSTM	32	128	1	32.29804623645285
17	LSTM	64	32	1	105.44863045733908
18	LSTM	64	64	1	62.87264318051545
19	LSTM	64	128	1	170.77305080579674
20	GRU	16	32	1	23.525951261105746
21	GRU	16	64	1	20.953380501788594
22	GRU	16	128	1	50.10249718375828
23	GRU	32	32	1	149.08109225397524
24	GRU	32	64	1	40.437404922817066
25	GRU	32	128	1	34.31250389762547
26	GRU	64	32	1	154.5310813240383
27	GRU	64	64	1	47.95653426128885
28	GRU	64	128	1	263.4846937760063
29	RNN	16	32	5	326.2015085634978
30	RNN	16	64	5	384.77328723409903
31	RNN	16	128	5	1099.3475819463315
32	RNN	32	32	5	246.47076250159222
33	RNN	32	64	5	329.85308439835256
34	RNN	32	128	5	1098.7943765391474
35	RNN	64	32	5	477.369012583857
36	RNN	64	64	5	365.6013999607252
37	RNN	64	128	5	413.95318603515625
38	LSTM	16	32	5	24.514795303344727
39	LSTM	16	64	5	24.9299375285273
40	LSTM	16	128	5	34.382882035296895
41	LSTM	32	32	5	171.14435179337212
42	LSTM	32	64	5	185.21222504325536
43	LSTM	32	128	5	24.800554503565248
44	LSTM	64	32	5	183.13448831309444
45	LSTM	64	64	5	37.47129328354545
46	LSTM	64	128	5	51.097612007804535
47	GRU	16	32	5	30.631973432457965
48	GRU	16	64	5	30.393669625987176
49	GRU	16	128	5	55.05557930987814
50	GRU	32	32	5	54.14869615306025
51	GRU	32	64	5	167.3384770932405
52	GRU	32	128	5	50.49319474593453
53	GRU	64	32	5	170.90516430398694
54	GRU	64	64	5	153.55044091266134
55	GRU	64	128	5	53.40392129317574
56	RNN	16	32	10	285.0945102857507
57	RNN	16	64	10	361.18107339610225
58	RNN	16	128	10	320.00166884712553
59	RNN	32	32	10	359.23063095756197
60	RNN	32	64	10	351.7510011092476
61	RNN	32	128	10	126.81333442356275
62	RNN	64	32	10	336.1739501953125
63	RNN	64	64	10	342.61622918170434
64	RNN	64	128	10	208.79256007982337
65	LSTM	16	32	10	47.18654777692712
66	LSTM	16	64	10	35.0142301061879
67	LSTM	16	128	10	31.15265079166578
68	LSTM	32	32	10	57.50707398290219
69	LSTM	32	64	10	21.263865159905475
70	LSTM	32	128	10	39.93263937079388
71	LSTM	64	32	10	57.33804578366487
72	LSTM	64	64	10	52.7249521587206
73	LSTM	64	128	10	59.768143446549125
74	GRU	16	32	10	28.489941555520762
75	GRU	16	64	10	25.493946241295856
76	GRU	16	128	10	35.874996682871945
77	GRU	32	32	10	39.71779649154
78	GRU	32	64	10	33.58222509467083
79	GRU	32	128	10	36.87489372750987
80	GRU	64	32	10	55.22760449285092
81	GRU	64	64	10	168.17254008417544
82	GRU	64	128	10	76.96743749535602
83	RNN	16	32	30	245.38945504893428
84	RNN	16	64	30	303.7835593845533
85	RNN	16	128	30	1168.2417987325916
86	RNN	32	32	30	221.63304668924084
87	RNN	32	64	30	274.024205746858
88	RNN	32	128	30	1167.9884391452956
89	RNN	64	32	30	316.6191160782524
90	RNN	64	64	30	252.00204235574475
91	RNN	64	128	30	1168.0314676036005
92	LSTM	16	32	30	46.3279622119406
93	LSTM	16	64	30	32.77271196116572
94	LSTM	16	128	30	1168.1180778171706
95	LSTM	32	32	30	192.53192719169286
96	LSTM	32	64	30	40.278250404026195
97	LSTM	32	128	30	35.424202234848686
98	LSTM	64	32	30	51.08382113083549
99	LSTM	64	64	30	186.7356419770614
100	LSTM	64	128	30	177.50895442133364
101	GRU	16	32	30	35.31677892933721
102	GRU	16	64	30	34.573822394661285
103	GRU	16	128	30	55.778304846390434
104	GRU	32	32	30	65.31020172782566
105	GRU	32	64	30	37.21405522719674
106	GRU	32	128	30	38.621485295503035
107	GRU	64	32	30	120.50829563970152
108	GRU	64	64	30	75.70721891651984
109	GRU	64	128	30	189.14709671683934