1 name: "LeNet"
2 layers {
3 layer {
4 name: "mnist"
5 type: "data"
6 source: "data/mnist-train-leveldb"
7 batchsize: 64
8 scale: 0.00390625
9 }
10 top: "data"
11 top: "label"
12 }
13 layers {
14 layer {
15 name: "conv1"
16 type: "conv"
17 num_output: 20
18 kernelsize: 5
19 stride: 1
20 weight_filler {
21 type: "xavier"
22 }
23 bias_filler {
24 type: "constant"
25 }
26 blobs_lr: 1.
27 blobs_lr: 2.
28 }
29 bottom: "data"
30 top: "conv1"
31 }
32 layers {
33 layer {
34 name: "pool1"
35 type: "pool"
36 kernelsize: 2
37 stride: 2
38 pool: MAX
39 }
40 bottom: "conv1"
41 top: "pool1"
42 }
43 layers {
44 layer {
45 name: "conv2"
46 type: "conv"
47 num_output: 50
48 kernelsize: 5
49 stride: 1
50 weight_filler {
51 type: "xavier"
52 }
53 bias_filler {
54 type: "constant"
55 }
56 blobs_lr: 1.
57 blobs_lr: 2.
58 }
59 bottom: "pool1"
60 top: "conv2"
61 }
62 layers {
63 layer {
64 name: "pool2"
65 type: "pool"
66 kernelsize: 2
67 stride: 2
68 pool: MAX
69 }
70 bottom: "conv2"
71 top: "pool2"
72 }
73 layers {
74 layer {
75 name: "ip1"
76 type: "innerproduct"
77 num_output: 500
78 weight_filler {
79 type: "xavier"
80 }
81 bias_filler {
82 type: "constant"
83 }
84 blobs_lr: 1.
85 blobs_lr: 2.
86 }
87 bottom: "pool2"
88 top: "ip1"
89 }
90 layers {
91 layer {
92 name: "relu1"
93 type: "relu"
94 }
95 bottom: "ip1"
96 top: "ip1"
97 }
98 layers {
99 layer {
100 name: "ip2"
101 type: "innerproduct"
102 num_output: 10
103 weight_filler {
104 type: "xavier"
105 }
106 bias_filler {
107 type: "constant"
108 }
109 blobs_lr: 1.
110 blobs_lr: 2.
111 }
112 bottom: "ip1"
113 top: "ip2"
114 }
115 layers {
116 layer {
117 name: "prob"
118 type: "softmax_loss"
119 }
120 bottom: "ip2"
121 bottom: "label"
122 }