resnet50
Find this model in the ResNetV2 model summary
resnet50 Model Summary Plots
resnet50 Model Selected Details
layer_type
N
M
Q
alpha
D
alpha-hat
log_SN
% Rand
num_traps
num_fingers
rank_loss
layer_id
1
CONV2D
64
3
21.33
2.13
0.10
3.05
1.43
88.37
90
0
0
7
CONV2D
64
64
1.00
2.52
0.21
1.51
0.60
90.51
4
7
0
9
CONV2D
64
64
1.00
1.88
0.06
1.54
0.82
82.27
22
0
4
11
CONV2D
256
64
4.00
3.09
0.11
3.34
1.08
80.94
1
0
0
15
CONV2D
256
64
4.00
3.68
0.09
5.35
1.45
80.62
7
0
0
18
CONV2D
256
64
4.00
3.60
0.14
2.04
0.57
86.08
0
0
1
20
CONV2D
64
64
1.00
1.89
0.08
0.96
0.51
85.48
21
0
9
22
CONV2D
256
64
4.00
4.42
0.12
2.21
0.50
90.36
1
0
0
26
CONV2D
256
64
4.00
3.84
0.14
2.00
0.52
85.82
0
0
0
28
CONV2D
64
64
1.00
1.97
0.11
0.58
0.29
85.32
17
5
4
30
CONV2D
256
64
4.00
6.00
0.07
2.73
0.45
90.86
0
0
0
35
CONV2D
256
128
2.00
1.90
0.15
1.43
0.75
86.41
0
1
0
37
CONV2D
128
128
1.00
3.10
0.08
0.23
0.07
90.81
16
0
5
39
CONV2D
512
128
4.00
10.76
0.15
5.53
0.51
89.65
0
1
0
43
CONV2D
512
256
2.00
2.20
0.06
2.12
0.97
72.98
2
0
0
46
CONV2D
512
128
4.00
2.48
0.11
1.21
0.49
81.13
0
0
0
48
CONV2D
128
128
1.00
1.94
0.05
0.92
0.48
76.15
26
0
6
50
CONV2D
512
128
4.00
5.95
0.08
4.19
0.70
85.21
0
0
0
54
CONV2D
512
128
4.00
3.39
0.09
1.63
0.48
85.28
0
0
0
56
CONV2D
128
128
1.00
2.03
0.07
0.53
0.26
82.88
18
0
3
58
CONV2D
512
128
4.00
4.43
0.09
2.44
0.55
86.67
0
0
0
62
CONV2D
512
128
4.00
2.44
0.14
1.46
0.60
85.43
0
3
0
64
CONV2D
128
128
1.00
2.52
0.09
0.54
0.22
85.33
16
0
2
66
CONV2D
512
128
4.00
3.50
0.08
2.21
0.63
86.21
0
0
0
71
CONV2D
512
256
2.00
2.90
0.11
2.76
0.95
82.46
0
1
0
73
CONV2D
256
256
1.00
4.60
0.08
-0.12
-0.03
95.56
34
0
9
75
CONV2D
1024
256
4.00
4.35
0.11
3.10
0.71
88.04
0
1
0
79
CONV2D
1024
512
2.00
2.72
0.06
2.20
0.81
78.49
4
0
0
82
CONV2D
1024
256
4.00
3.26
0.13
1.60
0.49
83.10
0
5
0
84
CONV2D
256
256
1.00
2.54
0.07
1.12
0.44
82.38
37
0
8
86
CONV2D
1024
256
4.00
3.60
0.04
2.94
0.82
84.86
2
0
0
90
CONV2D
1024
256
4.00
3.14
0.13
1.49
0.47
81.90
0
2
0
92
CONV2D
256
256
1.00
2.30
0.08
0.49
0.21
82.44
16
1
13
94
CONV2D
1024
256
4.00
3.45
0.10
2.17
0.63
83.93
0
1
0
98
CONV2D
1024
256
4.00
3.67
0.11
2.10
0.57
87.36
0
1
0
100
CONV2D
256
256
1.00
2.14
0.07
0.43
0.20
84.64
25
0
8
102
CONV2D
1024
256
4.00
4.11
0.07
2.83
0.69
84.56
1
0
0
106
CONV2D
1024
256
4.00
3.50
0.09
1.91
0.55
86.61
0
0
0
108
CONV2D
256
256
1.00
2.85
0.06
0.43
0.15
85.47
16
0
9
110
CONV2D
1024
256
4.00
4.79
0.06
3.50
0.73
83.97
1
0
0
114
CONV2D
1024
256
4.00
4.59
0.09
2.97
0.65
89.60
0
1
0
116
CONV2D
256
256
1.00
3.15
0.04
0.70
0.22
88.43
29
0
8
118
CONV2D
1024
256
4.00
4.12
0.03
3.94
0.95
87.04
1
0
0
123
CONV2D
1024
512
2.00
2.89
0.06
3.19
1.10
84.46
0
0
0
125
CONV2D
512
512
1.00
5.80
0.04
2.13
0.37
96.71
49
0
10
127
CONV2D
2048
512
4.00
6.17
0.05
7.97
1.29
93.84
1
0
0
131
CONV2D
2048
1024
2.00
3.85
0.02
3.41
0.89
87.35
1
0
0
134
CONV2D
2048
512
4.00
6.29
0.05
5.96
0.95
92.36
1
0
0
136
CONV2D
512
512
1.00
3.57
0.02
3.00
0.84
88.31
28
0
13
138
CONV2D
2048
512
4.00
4.16
0.05
5.37
1.29
89.02
1
0
0
142
CONV2D
2048
512
4.00
4.65
0.07
6.32
1.36
89.96
1
0
0
144
CONV2D
512
512
1.00
3.27
0.03
2.66
0.82
86.26
55
0
12
146
CONV2D
2048
512
4.00
2.97
0.02
5.56
1.87
78.28
1
0
0
150
DENSE
2048
1000
2.05
4.41
0.03
6.78
1.54
94.41
20
0
0
resnet50 Layer Plots
Layer 1
Layer=1 | N=64 | M=3 | Q=21.33 | alpha=2.13 | D_ks=0.10 | alpha-hat=3.05 | num traps=90
Layer 7
Layer=7 | N=64 | M=64 | Q=1.00 | alpha=2.52 | D_ks=0.21 | alpha-hat=1.51 | num traps=4
Layer 9
Layer=9 | N=64 | M=64 | Q=1.00 | alpha=1.88 | D_ks=0.06 | alpha-hat=1.54 | num traps=22
Layer 11
Layer=11 | N=256 | M=64 | Q=4.00 | alpha=3.09 | D_ks=0.11 | alpha-hat=3.34 | num traps=1
Layer 15
Layer=15 | N=256 | M=64 | Q=4.00 | alpha=3.68 | D_ks=0.09 | alpha-hat=5.35 | num traps=7
Layer 18
Layer=18 | N=256 | M=64 | Q=4.00 | alpha=3.60 | D_ks=0.14 | alpha-hat=2.04 | num traps=0
Layer 20
Layer=20 | N=64 | M=64 | Q=1.00 | alpha=1.89 | D_ks=0.08 | alpha-hat=0.96 | num traps=21
Layer 22
Layer=22 | N=256 | M=64 | Q=4.00 | alpha=4.42 | D_ks=0.12 | alpha-hat=2.21 | num traps=1
Layer 26
Layer=26 | N=256 | M=64 | Q=4.00 | alpha=3.84 | D_ks=0.14 | alpha-hat=2.00 | num traps=0
Layer 28
Layer=28 | N=64 | M=64 | Q=1.00 | alpha=1.97 | D_ks=0.11 | alpha-hat=0.58 | num traps=17
Layer 30
Layer=30 | N=256 | M=64 | Q=4.00 | alpha=6.00 | D_ks=0.07 | alpha-hat=2.73 | num traps=0
Layer 35
Layer=35 | N=256 | M=128 | Q=2.00 | alpha=1.90 | D_ks=0.15 | alpha-hat=1.43 | num traps=0
Layer 37
Layer=37 | N=128 | M=128 | Q=1.00 | alpha=3.10 | D_ks=0.08 | alpha-hat=0.23 | num traps=16
Layer 39
Layer=39 | N=512 | M=128 | Q=4.00 | alpha=10.76 | D_ks=0.15 | alpha-hat=5.53 | num traps=0
Layer 43
Layer=43 | N=512 | M=256 | Q=2.00 | alpha=2.20 | D_ks=0.06 | alpha-hat=2.12 | num traps=2
Layer 46
Layer=46 | N=512 | M=128 | Q=4.00 | alpha=2.48 | D_ks=0.11 | alpha-hat=1.21 | num traps=0
Layer 48
Layer=48 | N=128 | M=128 | Q=1.00 | alpha=1.94 | D_ks=0.05 | alpha-hat=0.92 | num traps=26
Layer 50
Layer=50 | N=512 | M=128 | Q=4.00 | alpha=5.95 | D_ks=0.08 | alpha-hat=4.19 | num traps=0
Layer 54
Layer=54 | N=512 | M=128 | Q=4.00 | alpha=3.39 | D_ks=0.09 | alpha-hat=1.63 | num traps=0
Layer 56
Layer=56 | N=128 | M=128 | Q=1.00 | alpha=2.03 | D_ks=0.07 | alpha-hat=0.53 | num traps=18
Layer 58
Layer=58 | N=512 | M=128 | Q=4.00 | alpha=4.43 | D_ks=0.09 | alpha-hat=2.44 | num traps=0
Layer 62
Layer=62 | N=512 | M=128 | Q=4.00 | alpha=2.44 | D_ks=0.14 | alpha-hat=1.46 | num traps=0
Layer 64
Layer=64 | N=128 | M=128 | Q=1.00 | alpha=2.52 | D_ks=0.09 | alpha-hat=0.54 | num traps=16
Layer 66
Layer=66 | N=512 | M=128 | Q=4.00 | alpha=3.50 | D_ks=0.08 | alpha-hat=2.21 | num traps=0
Layer 71
Layer=71 | N=512 | M=256 | Q=2.00 | alpha=2.90 | D_ks=0.11 | alpha-hat=2.76 | num traps=0
Layer 73
Layer=73 | N=256 | M=256 | Q=1.00 | alpha=4.60 | D_ks=0.08 | alpha-hat=-0.12 | num traps=34
Layer 75
Layer=75 | N=1024 | M=256 | Q=4.00 | alpha=4.35 | D_ks=0.11 | alpha-hat=3.10 | num traps=0
Layer 79
Layer=79 | N=1024 | M=512 | Q=2.00 | alpha=2.72 | D_ks=0.06 | alpha-hat=2.20 | num traps=4
Layer 82
Layer=82 | N=1024 | M=256 | Q=4.00 | alpha=3.26 | D_ks=0.13 | alpha-hat=1.60 | num traps=0
Layer 84
Layer=84 | N=256 | M=256 | Q=1.00 | alpha=2.54 | D_ks=0.07 | alpha-hat=1.12 | num traps=37
Layer 86
Layer=86 | N=1024 | M=256 | Q=4.00 | alpha=3.60 | D_ks=0.04 | alpha-hat=2.94 | num traps=2
Layer 90
Layer=90 | N=1024 | M=256 | Q=4.00 | alpha=3.14 | D_ks=0.13 | alpha-hat=1.49 | num traps=0
Layer 92
Layer=92 | N=256 | M=256 | Q=1.00 | alpha=2.30 | D_ks=0.08 | alpha-hat=0.49 | num traps=16
Layer 94
Layer=94 | N=1024 | M=256 | Q=4.00 | alpha=3.45 | D_ks=0.10 | alpha-hat=2.17 | num traps=0
Layer 98
Layer=98 | N=1024 | M=256 | Q=4.00 | alpha=3.67 | D_ks=0.11 | alpha-hat=2.10 | num traps=0
Layer 100
Layer=100 | N=256 | M=256 | Q=1.00 | alpha=2.14 | D_ks=0.07 | alpha-hat=0.43 | num traps=25
Layer 102
Layer=102 | N=1024 | M=256 | Q=4.00 | alpha=4.11 | D_ks=0.07 | alpha-hat=2.83 | num traps=1
Layer 106
Layer=106 | N=1024 | M=256 | Q=4.00 | alpha=3.50 | D_ks=0.09 | alpha-hat=1.91 | num traps=0
Layer 108
Layer=108 | N=256 | M=256 | Q=1.00 | alpha=2.85 | D_ks=0.06 | alpha-hat=0.43 | num traps=16
Layer 110
Layer=110 | N=1024 | M=256 | Q=4.00 | alpha=4.79 | D_ks=0.06 | alpha-hat=3.50 | num traps=1
Layer 114
Layer=114 | N=1024 | M=256 | Q=4.00 | alpha=4.59 | D_ks=0.09 | alpha-hat=2.97 | num traps=0
Layer 116
Layer=116 | N=256 | M=256 | Q=1.00 | alpha=3.15 | D_ks=0.04 | alpha-hat=0.70 | num traps=29
Layer 118
Layer=118 | N=1024 | M=256 | Q=4.00 | alpha=4.12 | D_ks=0.03 | alpha-hat=3.94 | num traps=1
Layer 123
Layer=123 | N=1024 | M=512 | Q=2.00 | alpha=2.89 | D_ks=0.06 | alpha-hat=3.19 | num traps=0
Layer 125
Layer=125 | N=512 | M=512 | Q=1.00 | alpha=5.80 | D_ks=0.04 | alpha-hat=2.13 | num traps=49
Layer 127
Layer=127 | N=2048 | M=512 | Q=4.00 | alpha=6.17 | D_ks=0.05 | alpha-hat=7.97 | num traps=1
Layer 131
Layer=131 | N=2048 | M=1024 | Q=2.00 | alpha=3.85 | D_ks=0.02 | alpha-hat=3.41 | num traps=1
Layer 134
Layer=134 | N=2048 | M=512 | Q=4.00 | alpha=6.29 | D_ks=0.05 | alpha-hat=5.96 | num traps=1
Layer 136
Layer=136 | N=512 | M=512 | Q=1.00 | alpha=3.57 | D_ks=0.02 | alpha-hat=3.00 | num traps=28
Layer 138
Layer=138 | N=2048 | M=512 | Q=4.00 | alpha=4.16 | D_ks=0.05 | alpha-hat=5.37 | num traps=1
Layer 142
Layer=142 | N=2048 | M=512 | Q=4.00 | alpha=4.65 | D_ks=0.07 | alpha-hat=6.32 | num traps=1
Layer 144
Layer=144 | N=512 | M=512 | Q=1.00 | alpha=3.27 | D_ks=0.03 | alpha-hat=2.66 | num traps=55
Layer 146
Layer=146 | N=2048 | M=512 | Q=4.00 | alpha=2.97 | D_ks=0.02 | alpha-hat=5.56 | num traps=1
Layer 150
Layer=150 | N=2048 | M=1000 | Q=2.05 | alpha=4.41 | D_ks=0.03 | alpha-hat=6.78 | num traps=20
resnet50 Model Summary Plots
resnet50 Model Selected Details
| layer_type | N | M | Q | alpha | D | alpha-hat | log_SN | % Rand | num_traps | num_fingers | rank_loss | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| layer_id | ||||||||||||
| 1 | CONV2D | 64 | 3 | 21.33 | 2.13 | 0.10 | 3.05 | 1.43 | 88.37 | 90 | 0 | 0 |
| 7 | CONV2D | 64 | 64 | 1.00 | 2.52 | 0.21 | 1.51 | 0.60 | 90.51 | 4 | 7 | 0 |
| 9 | CONV2D | 64 | 64 | 1.00 | 1.88 | 0.06 | 1.54 | 0.82 | 82.27 | 22 | 0 | 4 |
| 11 | CONV2D | 256 | 64 | 4.00 | 3.09 | 0.11 | 3.34 | 1.08 | 80.94 | 1 | 0 | 0 |
| 15 | CONV2D | 256 | 64 | 4.00 | 3.68 | 0.09 | 5.35 | 1.45 | 80.62 | 7 | 0 | 0 |
| 18 | CONV2D | 256 | 64 | 4.00 | 3.60 | 0.14 | 2.04 | 0.57 | 86.08 | 0 | 0 | 1 |
| 20 | CONV2D | 64 | 64 | 1.00 | 1.89 | 0.08 | 0.96 | 0.51 | 85.48 | 21 | 0 | 9 |
| 22 | CONV2D | 256 | 64 | 4.00 | 4.42 | 0.12 | 2.21 | 0.50 | 90.36 | 1 | 0 | 0 |
| 26 | CONV2D | 256 | 64 | 4.00 | 3.84 | 0.14 | 2.00 | 0.52 | 85.82 | 0 | 0 | 0 |
| 28 | CONV2D | 64 | 64 | 1.00 | 1.97 | 0.11 | 0.58 | 0.29 | 85.32 | 17 | 5 | 4 |
| 30 | CONV2D | 256 | 64 | 4.00 | 6.00 | 0.07 | 2.73 | 0.45 | 90.86 | 0 | 0 | 0 |
| 35 | CONV2D | 256 | 128 | 2.00 | 1.90 | 0.15 | 1.43 | 0.75 | 86.41 | 0 | 1 | 0 |
| 37 | CONV2D | 128 | 128 | 1.00 | 3.10 | 0.08 | 0.23 | 0.07 | 90.81 | 16 | 0 | 5 |
| 39 | CONV2D | 512 | 128 | 4.00 | 10.76 | 0.15 | 5.53 | 0.51 | 89.65 | 0 | 1 | 0 |
| 43 | CONV2D | 512 | 256 | 2.00 | 2.20 | 0.06 | 2.12 | 0.97 | 72.98 | 2 | 0 | 0 |
| 46 | CONV2D | 512 | 128 | 4.00 | 2.48 | 0.11 | 1.21 | 0.49 | 81.13 | 0 | 0 | 0 |
| 48 | CONV2D | 128 | 128 | 1.00 | 1.94 | 0.05 | 0.92 | 0.48 | 76.15 | 26 | 0 | 6 |
| 50 | CONV2D | 512 | 128 | 4.00 | 5.95 | 0.08 | 4.19 | 0.70 | 85.21 | 0 | 0 | 0 |
| 54 | CONV2D | 512 | 128 | 4.00 | 3.39 | 0.09 | 1.63 | 0.48 | 85.28 | 0 | 0 | 0 |
| 56 | CONV2D | 128 | 128 | 1.00 | 2.03 | 0.07 | 0.53 | 0.26 | 82.88 | 18 | 0 | 3 |
| 58 | CONV2D | 512 | 128 | 4.00 | 4.43 | 0.09 | 2.44 | 0.55 | 86.67 | 0 | 0 | 0 |
| 62 | CONV2D | 512 | 128 | 4.00 | 2.44 | 0.14 | 1.46 | 0.60 | 85.43 | 0 | 3 | 0 |
| 64 | CONV2D | 128 | 128 | 1.00 | 2.52 | 0.09 | 0.54 | 0.22 | 85.33 | 16 | 0 | 2 |
| 66 | CONV2D | 512 | 128 | 4.00 | 3.50 | 0.08 | 2.21 | 0.63 | 86.21 | 0 | 0 | 0 |
| 71 | CONV2D | 512 | 256 | 2.00 | 2.90 | 0.11 | 2.76 | 0.95 | 82.46 | 0 | 1 | 0 |
| 73 | CONV2D | 256 | 256 | 1.00 | 4.60 | 0.08 | -0.12 | -0.03 | 95.56 | 34 | 0 | 9 |
| 75 | CONV2D | 1024 | 256 | 4.00 | 4.35 | 0.11 | 3.10 | 0.71 | 88.04 | 0 | 1 | 0 |
| 79 | CONV2D | 1024 | 512 | 2.00 | 2.72 | 0.06 | 2.20 | 0.81 | 78.49 | 4 | 0 | 0 |
| 82 | CONV2D | 1024 | 256 | 4.00 | 3.26 | 0.13 | 1.60 | 0.49 | 83.10 | 0 | 5 | 0 |
| 84 | CONV2D | 256 | 256 | 1.00 | 2.54 | 0.07 | 1.12 | 0.44 | 82.38 | 37 | 0 | 8 |
| 86 | CONV2D | 1024 | 256 | 4.00 | 3.60 | 0.04 | 2.94 | 0.82 | 84.86 | 2 | 0 | 0 |
| 90 | CONV2D | 1024 | 256 | 4.00 | 3.14 | 0.13 | 1.49 | 0.47 | 81.90 | 0 | 2 | 0 |
| 92 | CONV2D | 256 | 256 | 1.00 | 2.30 | 0.08 | 0.49 | 0.21 | 82.44 | 16 | 1 | 13 |
| 94 | CONV2D | 1024 | 256 | 4.00 | 3.45 | 0.10 | 2.17 | 0.63 | 83.93 | 0 | 1 | 0 |
| 98 | CONV2D | 1024 | 256 | 4.00 | 3.67 | 0.11 | 2.10 | 0.57 | 87.36 | 0 | 1 | 0 |
| 100 | CONV2D | 256 | 256 | 1.00 | 2.14 | 0.07 | 0.43 | 0.20 | 84.64 | 25 | 0 | 8 |
| 102 | CONV2D | 1024 | 256 | 4.00 | 4.11 | 0.07 | 2.83 | 0.69 | 84.56 | 1 | 0 | 0 |
| 106 | CONV2D | 1024 | 256 | 4.00 | 3.50 | 0.09 | 1.91 | 0.55 | 86.61 | 0 | 0 | 0 |
| 108 | CONV2D | 256 | 256 | 1.00 | 2.85 | 0.06 | 0.43 | 0.15 | 85.47 | 16 | 0 | 9 |
| 110 | CONV2D | 1024 | 256 | 4.00 | 4.79 | 0.06 | 3.50 | 0.73 | 83.97 | 1 | 0 | 0 |
| 114 | CONV2D | 1024 | 256 | 4.00 | 4.59 | 0.09 | 2.97 | 0.65 | 89.60 | 0 | 1 | 0 |
| 116 | CONV2D | 256 | 256 | 1.00 | 3.15 | 0.04 | 0.70 | 0.22 | 88.43 | 29 | 0 | 8 |
| 118 | CONV2D | 1024 | 256 | 4.00 | 4.12 | 0.03 | 3.94 | 0.95 | 87.04 | 1 | 0 | 0 |
| 123 | CONV2D | 1024 | 512 | 2.00 | 2.89 | 0.06 | 3.19 | 1.10 | 84.46 | 0 | 0 | 0 |
| 125 | CONV2D | 512 | 512 | 1.00 | 5.80 | 0.04 | 2.13 | 0.37 | 96.71 | 49 | 0 | 10 |
| 127 | CONV2D | 2048 | 512 | 4.00 | 6.17 | 0.05 | 7.97 | 1.29 | 93.84 | 1 | 0 | 0 |
| 131 | CONV2D | 2048 | 1024 | 2.00 | 3.85 | 0.02 | 3.41 | 0.89 | 87.35 | 1 | 0 | 0 |
| 134 | CONV2D | 2048 | 512 | 4.00 | 6.29 | 0.05 | 5.96 | 0.95 | 92.36 | 1 | 0 | 0 |
| 136 | CONV2D | 512 | 512 | 1.00 | 3.57 | 0.02 | 3.00 | 0.84 | 88.31 | 28 | 0 | 13 |
| 138 | CONV2D | 2048 | 512 | 4.00 | 4.16 | 0.05 | 5.37 | 1.29 | 89.02 | 1 | 0 | 0 |
| 142 | CONV2D | 2048 | 512 | 4.00 | 4.65 | 0.07 | 6.32 | 1.36 | 89.96 | 1 | 0 | 0 |
| 144 | CONV2D | 512 | 512 | 1.00 | 3.27 | 0.03 | 2.66 | 0.82 | 86.26 | 55 | 0 | 12 |
| 146 | CONV2D | 2048 | 512 | 4.00 | 2.97 | 0.02 | 5.56 | 1.87 | 78.28 | 1 | 0 | 0 |
| 150 | DENSE | 2048 | 1000 | 2.05 | 4.41 | 0.03 | 6.78 | 1.54 | 94.41 | 20 | 0 | 0 |
resnet50 Layer Plots
Layer 1
Layer=1 | N=64 | M=3 | Q=21.33 | alpha=2.13 | D_ks=0.10 | alpha-hat=3.05 | num traps=90
Layer 7
Layer=7 | N=64 | M=64 | Q=1.00 | alpha=2.52 | D_ks=0.21 | alpha-hat=1.51 | num traps=4
Layer 9
Layer=9 | N=64 | M=64 | Q=1.00 | alpha=1.88 | D_ks=0.06 | alpha-hat=1.54 | num traps=22
Layer 11
Layer=11 | N=256 | M=64 | Q=4.00 | alpha=3.09 | D_ks=0.11 | alpha-hat=3.34 | num traps=1
Layer 15
Layer=15 | N=256 | M=64 | Q=4.00 | alpha=3.68 | D_ks=0.09 | alpha-hat=5.35 | num traps=7
Layer 18
Layer=18 | N=256 | M=64 | Q=4.00 | alpha=3.60 | D_ks=0.14 | alpha-hat=2.04 | num traps=0
Layer 20
Layer=20 | N=64 | M=64 | Q=1.00 | alpha=1.89 | D_ks=0.08 | alpha-hat=0.96 | num traps=21
Layer 22
Layer=22 | N=256 | M=64 | Q=4.00 | alpha=4.42 | D_ks=0.12 | alpha-hat=2.21 | num traps=1
Layer 26
Layer=26 | N=256 | M=64 | Q=4.00 | alpha=3.84 | D_ks=0.14 | alpha-hat=2.00 | num traps=0
Layer 28
Layer=28 | N=64 | M=64 | Q=1.00 | alpha=1.97 | D_ks=0.11 | alpha-hat=0.58 | num traps=17
Layer 30
Layer=30 | N=256 | M=64 | Q=4.00 | alpha=6.00 | D_ks=0.07 | alpha-hat=2.73 | num traps=0
Layer 35
Layer=35 | N=256 | M=128 | Q=2.00 | alpha=1.90 | D_ks=0.15 | alpha-hat=1.43 | num traps=0
Layer 37
Layer=37 | N=128 | M=128 | Q=1.00 | alpha=3.10 | D_ks=0.08 | alpha-hat=0.23 | num traps=16
Layer 39
Layer=39 | N=512 | M=128 | Q=4.00 | alpha=10.76 | D_ks=0.15 | alpha-hat=5.53 | num traps=0
Layer 43
Layer=43 | N=512 | M=256 | Q=2.00 | alpha=2.20 | D_ks=0.06 | alpha-hat=2.12 | num traps=2
Layer 46
Layer=46 | N=512 | M=128 | Q=4.00 | alpha=2.48 | D_ks=0.11 | alpha-hat=1.21 | num traps=0
Layer 48
Layer=48 | N=128 | M=128 | Q=1.00 | alpha=1.94 | D_ks=0.05 | alpha-hat=0.92 | num traps=26
Layer 50
Layer=50 | N=512 | M=128 | Q=4.00 | alpha=5.95 | D_ks=0.08 | alpha-hat=4.19 | num traps=0
Layer 54
Layer=54 | N=512 | M=128 | Q=4.00 | alpha=3.39 | D_ks=0.09 | alpha-hat=1.63 | num traps=0
Layer 56
Layer=56 | N=128 | M=128 | Q=1.00 | alpha=2.03 | D_ks=0.07 | alpha-hat=0.53 | num traps=18
Layer 58
Layer=58 | N=512 | M=128 | Q=4.00 | alpha=4.43 | D_ks=0.09 | alpha-hat=2.44 | num traps=0
Layer 62
Layer=62 | N=512 | M=128 | Q=4.00 | alpha=2.44 | D_ks=0.14 | alpha-hat=1.46 | num traps=0
Layer 64
Layer=64 | N=128 | M=128 | Q=1.00 | alpha=2.52 | D_ks=0.09 | alpha-hat=0.54 | num traps=16
Layer 66
Layer=66 | N=512 | M=128 | Q=4.00 | alpha=3.50 | D_ks=0.08 | alpha-hat=2.21 | num traps=0
Layer 71
Layer=71 | N=512 | M=256 | Q=2.00 | alpha=2.90 | D_ks=0.11 | alpha-hat=2.76 | num traps=0
Layer 73
Layer=73 | N=256 | M=256 | Q=1.00 | alpha=4.60 | D_ks=0.08 | alpha-hat=-0.12 | num traps=34
Layer 75
Layer=75 | N=1024 | M=256 | Q=4.00 | alpha=4.35 | D_ks=0.11 | alpha-hat=3.10 | num traps=0
Layer 79
Layer=79 | N=1024 | M=512 | Q=2.00 | alpha=2.72 | D_ks=0.06 | alpha-hat=2.20 | num traps=4
Layer 82
Layer=82 | N=1024 | M=256 | Q=4.00 | alpha=3.26 | D_ks=0.13 | alpha-hat=1.60 | num traps=0
Layer 84
Layer=84 | N=256 | M=256 | Q=1.00 | alpha=2.54 | D_ks=0.07 | alpha-hat=1.12 | num traps=37
Layer 86
Layer=86 | N=1024 | M=256 | Q=4.00 | alpha=3.60 | D_ks=0.04 | alpha-hat=2.94 | num traps=2
Layer 90
Layer=90 | N=1024 | M=256 | Q=4.00 | alpha=3.14 | D_ks=0.13 | alpha-hat=1.49 | num traps=0
Layer 92
Layer=92 | N=256 | M=256 | Q=1.00 | alpha=2.30 | D_ks=0.08 | alpha-hat=0.49 | num traps=16
Layer 94
Layer=94 | N=1024 | M=256 | Q=4.00 | alpha=3.45 | D_ks=0.10 | alpha-hat=2.17 | num traps=0
Layer 98
Layer=98 | N=1024 | M=256 | Q=4.00 | alpha=3.67 | D_ks=0.11 | alpha-hat=2.10 | num traps=0
Layer 100
Layer=100 | N=256 | M=256 | Q=1.00 | alpha=2.14 | D_ks=0.07 | alpha-hat=0.43 | num traps=25
Layer 102
Layer=102 | N=1024 | M=256 | Q=4.00 | alpha=4.11 | D_ks=0.07 | alpha-hat=2.83 | num traps=1
Layer 106
Layer=106 | N=1024 | M=256 | Q=4.00 | alpha=3.50 | D_ks=0.09 | alpha-hat=1.91 | num traps=0
Layer 108
Layer=108 | N=256 | M=256 | Q=1.00 | alpha=2.85 | D_ks=0.06 | alpha-hat=0.43 | num traps=16
Layer 110
Layer=110 | N=1024 | M=256 | Q=4.00 | alpha=4.79 | D_ks=0.06 | alpha-hat=3.50 | num traps=1
Layer 114
Layer=114 | N=1024 | M=256 | Q=4.00 | alpha=4.59 | D_ks=0.09 | alpha-hat=2.97 | num traps=0
Layer 116
Layer=116 | N=256 | M=256 | Q=1.00 | alpha=3.15 | D_ks=0.04 | alpha-hat=0.70 | num traps=29
Layer 118
Layer=118 | N=1024 | M=256 | Q=4.00 | alpha=4.12 | D_ks=0.03 | alpha-hat=3.94 | num traps=1
Layer 123
Layer=123 | N=1024 | M=512 | Q=2.00 | alpha=2.89 | D_ks=0.06 | alpha-hat=3.19 | num traps=0
Layer 125
Layer=125 | N=512 | M=512 | Q=1.00 | alpha=5.80 | D_ks=0.04 | alpha-hat=2.13 | num traps=49
Layer 127
Layer=127 | N=2048 | M=512 | Q=4.00 | alpha=6.17 | D_ks=0.05 | alpha-hat=7.97 | num traps=1
Layer 131
Layer=131 | N=2048 | M=1024 | Q=2.00 | alpha=3.85 | D_ks=0.02 | alpha-hat=3.41 | num traps=1
Layer 134
Layer=134 | N=2048 | M=512 | Q=4.00 | alpha=6.29 | D_ks=0.05 | alpha-hat=5.96 | num traps=1
Layer 136
Layer=136 | N=512 | M=512 | Q=1.00 | alpha=3.57 | D_ks=0.02 | alpha-hat=3.00 | num traps=28
Layer 138
Layer=138 | N=2048 | M=512 | Q=4.00 | alpha=4.16 | D_ks=0.05 | alpha-hat=5.37 | num traps=1
Layer 142
Layer=142 | N=2048 | M=512 | Q=4.00 | alpha=4.65 | D_ks=0.07 | alpha-hat=6.32 | num traps=1
Layer 144
Layer=144 | N=512 | M=512 | Q=1.00 | alpha=3.27 | D_ks=0.03 | alpha-hat=2.66 | num traps=55
Layer 146
Layer=146 | N=2048 | M=512 | Q=4.00 | alpha=2.97 | D_ks=0.02 | alpha-hat=5.56 | num traps=1
Layer 150
Layer=150 | N=2048 | M=1000 | Q=2.05 | alpha=4.41 | D_ks=0.03 | alpha-hat=6.78 | num traps=20