-
Notifications
You must be signed in to change notification settings - Fork 0
/
HardwareTracker.txt
228 lines (217 loc) · 8.18 KB
/
HardwareTracker.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
BiTeI
Testing on IBM Quito, Trivial = 0, Topological = 1:
Date and time = 09/05/2023 11:00:40 -- 1110
Target: 1
Network output: tensor([[-0.0047, 1.6062]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 11:02:44 -- 0001
Target: 0
Network output: tensor([[ 2.0096, -0.0076]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 11:03:18 -- 0001
Target: 0
Network output: tensor([[ 2.2957, -0.0097]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 11:03:53 -- 0001
Target: 0
Network output: tensor([[ 2.1867, -0.0088]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 11:04:37 -- 1110
Target: 1
Network output: tensor([[-0.0101, 2.1046]], device='cuda:0')
Network predicted correct
Testing on IBM Quito, Trivial = 0, Topological = 1:
Date and time = 09/05/2023 18:52:49 -- 0011 0111
Target: 1
Network output: tensor([[-0.0040, 2.1985]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 18:54:34 -- 1000 1100
Target: 0
Network output: tensor([[ 2.1548, -0.0068]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 18:55:18 -- 0011 0111
Target: 1
Network output: tensor([[-1.5885e-03, 1.8911e+00]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 18:56:13 -- 1000 1100
Target: 0
Network output: tensor([[ 2.1283, -0.0072]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 18:57:48 -- 0011 0111
Target: 1
Network output: tensor([[0.0616, 1.6352]], device='cuda:0')
Network predicted correct
Testing on IBM Quito, Trivial = 0, Topological = 1:
Date and time = 09/05/2023 19:51:47 -- 0001
Target: 0
Network output: tensor([[ 1.6590e+00, -2.4564e-04]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 19:52:42 -- 0001
Target: 0
Network output: tensor([[ 1.5606e+00, -1.1622e-03]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 19:58:46 -- 0001, lots of noise
Target: 0
Network output: tensor([[1.0515, 0.2484]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 19:59:51 -- 1010? lots of noise
Target: 1
Network output: tensor([[0.0231, 0.8550]], device='cuda:0')
Network predicted correct
Date and time = 09/05/2023 20:03:16 -- 0001
Target: 0
Network output: tensor([[ 1.3610, -0.0027]], device='cuda:0')
Network predicted correct
Running on ibm_quito number of test samples are 5, number of correct: 5
Testing on IBM Quito, Trivial = 0, Topological = 1:
Date and time = 10/05/2023 00:08:48 -- 1111 & 1110 significantly higher
Target: 0
Network output: tensor([[ 1.4026, -0.0037]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 00:37:55 -- 1110 high, 1111 higher
Target: 0
Network output: tensor([[1.2008, 0.1523]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 01:02:02 -- 1110 high, 1111 higher
Target: 0
Network output: tensor([[ 1.2567, -0.0038]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 01:08:17 -- 1000 1001 highest
Target: 1
Network output: tensor([[-0.0063, 1.7736]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 01:31:52 -- 1110 & 1111 higher
Target: 0
Network output: tensor([[ 1.7757, -0.0023]], device='cuda:0')
Network predicted correct
Running on ibm_quito number of test samples are 5, number of correct: 5
Testing on IBM Quito, Trivial = 0, Topological = 1:
Date and time = 10/05/2023 02:37:16 -- 0100 higher
Target: 0
Network output: tensor([[-0.0012, -0.0023]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 02:40:03 -- 0011 high, but lots of noise
Target: 0
Network output: tensor([[-0.0012, -0.0073]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 03:15:11 -- 0010 high, high noise
Target: 0
Network output: tensor([[-0.0013, -0.0016]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 03:38:57 -- 1000 & 1010 highe
Target: 1
Network output: tensor([[-6.9698e-04, 1.2266e+00]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 03:45:32 - 1000 hiest
Target: 1
Network output: tensor([[-9.1970e-04, 1.4382e+00]], device='cuda:0')
Network predicted correct
Running on ibm_quito number of test samples are 5, number of correct: 5
Testing on IBM Quito, Trivial = 0, Topological = 1:
Date and time = 10/05/2023 04:41:43 -- 0100
Target: 0
Network output: tensor([[ 2.0568e+00, -1.5910e-03]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 04:50:28 -- 0010
Target: 0
Network output: tensor([[ 1.5549e+00, -4.4614e-04]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 05:16:37 -- 0010
Target: 0
Network output: tensor([[ 1.2795e+00, -1.1872e-04]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 05:17:04 -- 0100
Target: 0
Network output: tensor([[ 1.6026e+00, -1.2821e-04]], device='cuda:0')
Network predicted correct
Date and time = 10/05/2023 05:17:49 -- 1111
Target: 1
Network output: tensor([[-0.0019, 1.6722]], device='cuda:0')
Network predicted correct
Running on ibm_quito number of test samples are 5, number of correct: 5
MNIST
Testing on IBM Quito, 0 = 0, 1 = 1
Loss of trained network: 0.060248474547206755
Date and time = 11/05/2023 00:51:22 -- 1011 and 1111
Target: 1
Network output: tensor([[0.4015, 2.0161]], device='cuda:0')
Network predicted correct
Date and time = 11/05/2023 01:16:28 -- 0001 and 0101
Target: 0
Network output: tensor([[1.7158, 0.3096]], device='cuda:0')
Network predicted correct
Date and time = 11/05/2023 03:10:53 -- 0001 0101
Target: 0
Network output: tensor([[1.7574, 0.2914]], device='cuda:0')
Network predicted correct
Date and time = 11/05/2023 03:31:08 -- 1011 1111
Target: 1
Network output: tensor([[0.5330, 2.1236]], device='cuda:0')
Network predicted correct
Date and time = 11/05/2023 03:32:32 -- 0001 0101
Target: 0
Network output: tensor([[1.6746, 0.1637]], device='cuda:0')
Network predicted correct
Running on ibm_quito number of test samples are 5, number of correct: 5
Testing on IBM Quito, 0 = 0, 1 = 1
Loss of trained network: 0.036093437698001815
Date and time = 12/05/2023 01:17:41 -- 1011
Target: 0
Network output: tensor([[1.8196, 0.2146]])
Network predicted correct
Date and time = 12/05/2023 01:51:47 -- 0101
Target: 1
Network output: tensor([[0.3003, 1.8973]])
Network predicted correct
Date and time = 12/05/2023 02:23:20 -- 0101
Target: 1
Network output: tensor([[0.0498, 1.9525]])
Network predicted correct
Date and time = 12/05/2023 02:52:56 -- 0101
Target: 1
Network output: tensor([[0.0030, 2.0860]])
Network predicted correct
Date and time = 12/05/2023 02:53:10 - 1011
Target: 0
Network output: tensor([[2.0360, 0.0434]])
Network predicted correct
Running on ibm_quito number of test samples are 5, number of correct: 5
Running the selected data to test top triv and close transition
Testing on IBM Quito, 0 = Trivial, 1 = Topological.
Loss of trained network: 0.05090104751102626
Date and time = 13/05/2023 16:33:45
Alpha value: 0.0
Target: 0.0
Network output: tensor([ 1.2990e+00, -1.2117e-03], device='cuda:0')
Date and time = 13/05/2023 16:33:59
Alpha value: 0.0
Target: 0.0
Network output: tensor([ 1.7580, -0.0019], device='cuda:0')
Date and time = 13/05/2023 16:34:13
Alpha value: 0.0
Target: 0.0
Network output: tensor([ 1.7898, -0.0022], device='cuda:0')
Date and time = 13/05/2023 16:40:07
Alpha value: 0.77
Target: 1.0
Network output: tensor([-1.1113e-03, 1.7916e+00], device='cuda:0')
Date and time = 13/05/2023 16:42:22
Alpha value: 0.77
Target: 1.0
Network output: tensor([-0.0020, 1.5058], device='cuda:0')
Date and time = 13/05/2023 16:42:36
Alpha value: 0.77
Target: 1.0
Network output: tensor([-8.8386e-04, 1.8303e+00], device='cuda:0')
Date and time = 13/05/2023 16:42:50
Alpha value: 0.885
Target: 1.0
Network output: tensor([-0.0040, 2.0244], device='cuda:0')
Date and time = 13/05/2023 16:43:04
Alpha value: 0.885
Target: 1.0
Network output: tensor([-0.0035, 2.1090], device='cuda:0')
Date and time = 13/05/2023 16:43:18
Alpha value: 0.885
Target: 1.0
Network output: tensor([-0.0029, 1.7698], device='cuda:0')