【深度學習:caffe】Caffemodel解析
阿新 • • 發佈:2019-02-15
1 message NetParameter {
2
3 optional string name = 1; // 網路名稱
4
5 repeated string input = 3; // 網路輸入input blobs
6
7 repeated BlobShape input_shape = 8; // The shape of the input blobs
8
9 // 輸入維度blobs,4維(num, channels, height and width)
10
11 repeated int32 input_dim = 4 ;
12
13 // 網路是否強制每層進行反饋操作開關
14
15 // 如果設定為False,則會根據網路結構和學習率自動確定是否進行反饋操作
16
17 optional bool force_backward = 5 [default = false];
18
19 // 網路的state,部分網路層依賴,部分不依賴,需要看具體網路
20
21 optional NetState state = 6;
22
23 // 是否列印debug log
24
25 optional bool debug_info = 7 [default = false ];
26
27 // 網路層引數,Field Number 為100,所以網路層引數在最後
28
29 repeated LayerParameter layer = 100;
30
31 // 棄用: 用 'layer' 代替
32
33 repeated V1LayerParameter layers = 2;
34
35 }
36
37 // Specifies the shape (dimensions) of a Blob.
38
39 message BlobShape {
40
41 repeated int64 dim = 1 [packed = true ];
42
43 }
44
45 message BlobProto {
46
47 optional BlobShape shape = 7;
48
49 repeated float data = 5 [packed = true];
50
51 repeated float diff = 6 [packed = true];
52
53 optional int32 num = 1 [default = 0];
54
55 optional int32 channels = 2 [default = 0];
56
57 optional int32 height = 3 [default = 0];
58
59 optional int32 width = 4 [default = 0];
60
61 }
62
63
64
65 // The BlobProtoVector is simply a way to pass multiple blobproto instances
66
67 around.
68
69 message BlobProtoVector {
70
71 repeated BlobProto blobs = 1;
72
73 }
74
75 message NetState {
76
77 optional Phase phase = 1 [default = TEST];
78
79 optional int32 level = 2 [default = 0];
80
81 repeated string stage = 3;
82
83 }
84
85 message LayerParameter {
86
87 optional string name = 1; // the layer name
88
89 optional string type = 2; // the layer type
90
91 repeated string bottom = 3; // the name of each bottom blob
92
93 repeated string top = 4; // the name of each top blob
94
95 // The train/test phase for computation.
96
97 optional Phase phase = 10;
98
99 // Loss weight值:float
100
101 // 每一層為每一個top blob都分配了一個預設值,通常是0或1
102
103 repeated float loss_weight = 5;
104
105 // 指定的學習引數
106
107 repeated ParamSpec param = 6;
108
109 // The blobs containing the numeric parameters of the layer.
110
111 repeated BlobProto blobs = 7;
112
113 // included/excluded.
114
115 repeated NetStateRule include = 8;
116
117 repeated NetStateRule exclude = 9;
118
119 // Parameters for data pre-processing.
120
121 optional TransformationParameter transform_param = 100;
122
123 // Parameters shared by loss layers.
124
125 optional LossParameter loss_param = 101;
126
127 // 各種型別層引數
128
129 optional AccuracyParameter accuracy_param = 102;
130
131 optional ArgMaxParameter argmax_param = 103;
132
133 optional ConcatParameter concat_param = 104;
134
135 optional ContrastiveLossParameter contrastive_loss_param = 105;
136
137 optional ConvolutionParameter convolution_param = 106;
138
139 optional DataParameter data_param = 107;
140
141 optional DropoutParameter dropout_param = 108;
142
143 optional DummyDataParameter dummy_data_param = 109;
144
145 optional EltwiseParameter eltwise_param = 110;
146
147 optional ExpParameter exp_param = 111;
148
149 optional HDF5DataParameter hdf5_data_param = 112;
150
151 optional HDF5OutputParameter hdf5_output_param = 113;
152
153 optional HingeLossParameter hinge_loss_param = 114;
154
155 optional ImageDataParameter image_data_param = 115;
156
157 optional InfogainLossParameter infogain_loss_param = 116;
158
159 optional InnerProductParameter inner_product_param = 117;
160
161 optional LRNParameter lrn_param = 118;
162
163 optional MemoryDataParameter memory_data_param = 119;
164
165 optional MVNParameter mvn_param = 120;
166
167 optional PoolingParameter pooling_param = 121;
168
169 optional PowerParameter power_param = 122;
170
171 optional PythonParameter python_param = 130;
172
173 optional ReLUParameter relu_param = 123;
174
175 optional SigmoidParameter sigmoid_param = 124;
176
177 optional SoftmaxParameter softmax_param = 125;
178
179 optional SliceParameter slice_param = 126;
180
181 optional TanHParameter tanh_param = 127;
182
183 optional ThresholdParameter threshold_param = 128;
184
185 optional WindowDataParameter window_data_param = 129;
186
187 }