ensemble-kalman-optimiser/mnist_cnn_model at main · tejas7777/ensemble-kalman-optimiser · GitHub

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
digraph {
	graph [size="12,12"]
	node [align=left fontname=monospace fontsize=10 height=0.2 ranksep=0.1 shape=box style=filled]
	4521125328 [label="
 (1, 10)" fillcolor=darkolivegreen1]
	4708193408 [label=LogSoftmaxBackward0]
	4708188608 -> 4708193408
	4708188608 [label=AddmmBackward0]
	4981729040 -> 4708188608
	4521116128 [label="fc2.bias
 (10)" fillcolor=lightblue]
	4521116128 -> 4981729040
	4981729040 [label=AccumulateGrad]
	4708201616 -> 4708188608
	4708201616 [label=ReluBackward0]
	5091932480 -> 4708201616
	5091932480 [label=AddmmBackward0]
	5092253296 -> 5091932480
	4521131968 [label="fc1.bias
 (128)" fillcolor=lightblue]
	4521131968 -> 5092253296
	5092253296 [label=AccumulateGrad]
	5092253488 -> 5091932480
	5092253488 [label=ViewBackward0]
	5092253776 -> 5092253488
	5092253776 [label=MaxPool2DWithIndicesBackward0]
	5092254256 -> 5092253776
	5092254256 [label=ReluBackward0]
	5092254352 -> 5092254256
	5092254352 [label=ConvolutionBackward0]
	5092254448 -> 5092254352
	5092254448 [label=MaxPool2DWithIndicesBackward0]
	5092254640 -> 5092254448
	5092254640 [label=ReluBackward0]
	5092254736 -> 5092254640
	5092254736 [label=ConvolutionBackward0]
	5092254784 -> 5092254736
	5092254784 [label=MaxPool2DWithIndicesBackward0]
	5092255072 -> 5092254784
	5092255072 [label=ReluBackward0]
	5092255120 -> 5092255072
	5092255120 [label=ConvolutionBackward0]
	5092255264 -> 5092255120
	4521117568 [label="conv1.weight
 (16, 1, 3, 3)" fillcolor=lightblue]
	4521117568 -> 5092255264
	5092255264 [label=AccumulateGrad]
	5092255216 -> 5092255120
	4521126528 [label="conv1.bias
 (16)" fillcolor=lightblue]
	4521126528 -> 5092255216
	5092255216 [label=AccumulateGrad]
	5092254544 -> 5092254736
	4521117408 [label="conv2.weight
 (32, 16, 3, 3)" fillcolor=lightblue]
	4521117408 -> 5092254544
	5092254544 [label=AccumulateGrad]
	5092254880 -> 5092254736
	4521116608 [label="conv2.bias
 (32)" fillcolor=lightblue]
	4521116608 -> 5092254880
	5092254880 [label=AccumulateGrad]
	5092254400 -> 5092254352
	4521116688 [label="conv3.weight
 (64, 32, 3, 3)" fillcolor=lightblue]
	4521116688 -> 5092254400
	5092254400 [label=AccumulateGrad]
	5092254160 -> 5092254352
	4521116448 [label="conv3.bias
 (64)" fillcolor=lightblue]
	4521116448 -> 5092254160
	5092254160 [label=AccumulateGrad]
	5092252816 -> 5091932480
	5092252816 [label=TBackward0]
	5092254304 -> 5092252816
	4521116368 [label="fc1.weight
 (128, 576)" fillcolor=lightblue]
	4521116368 -> 5092254304
	5092254304 [label=AccumulateGrad]
	5091933392 -> 4708188608
	5091933392 [label=TBackward0]
	5092254208 -> 5091933392
	4521116208 [label="fc2.weight
 (10, 128)" fillcolor=lightblue]
	4521116208 -> 5092254208
	5092254208 [label=AccumulateGrad]
	4708193408 -> 4521125328
}