[caffe学习笔记][03][生成配置文件]

说明：

caffe通过配置文件prototxt来描述网路结构，通过Python接口来生成网路配置文件比较简单。这里生成train.prototxt和test.prototxt，分别用于训练阶段和验证阶段。

步骤：

1.生成配置文件

touch create_train_val_prototxt.py

spyder create_train_val_prototxt.py

 1 # -*- coding: utf-8 -*-
 2 """
 3 yuandanfei Editor
 4 
 5 This is a temporary script file.
 6 """
 7 
 8 from caffe import layers as L, params as P, to_proto
 9 path = '/home/yuandanfei/work/caffe/mnist/' #root path
10 train_lmdb = path + 'train_lmdb'           #train_lmdb path
11 test_lmdb = path + 'test_lmdb'             #test_lmdb path
12 mean_file = path + 'mean.binaryproto'      #mean.binaryproto path
13 train_proto = path + 'train.prototxt'      #train.prototxt path
14 test_proto = path + 'test.prototxt'        #test.prototxt path
15 
16 def create_net(lmdb, batch_size, include_acc=False):
17     #input layer
18     data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2, 
19                          transform_param=dict(crop_size=28, mean_file=mean_file, mirror=True))
20     #conv1 layer   n*c*w*h;c1=num_output;w1/h1=(w0/h0+2*pad-kernel_size)/stride+1;if stride=1 and pad=(kernel_size-1)/2, then w1/h1=w0/h0;
21     conv1 = L.Convolution(data, kernel_size=5, stride=1, pad=2, num_output=16, weight_filler=dict(type='xavier'))
22     #reul1 layer
23     relu1 = L.ReLU(conv1, in_place=True)
24     #pool1 layer   n*c*w*h;c1=c0;w1/h1=(w0/h0+2*pad-kernel_size)/stride+1;
25     pool1 = L.Pooling(relu1, pool=P.Pooling.MAX, kernel_size=3, stride=2)
26     #conv2 layer
27     conv2 = L.Convolution(pool1, kernel_size=3, stride=1, pad=1, num_output=32, weight_filler=dict(type='xavier'))
28     #relu2 layer
29     relu2 = L.ReLU(conv2, in_place=True)
30     #pool2 layer
31     pool2 = L.Pooling(relu2, pool=P.Pooling.MAX, kernel_size=3, stride=2)
32     #fc3 layer
33     fc3 = L.InnerProduct(pool2, num_output=1024, weight_filler=dict(type='xavier'))
34     #relu3 layer
35     relu3 = L.ReLU(fc3, in_place=True)
36     #drop3 layer
37     drop3 = L.Dropout(relu3, in_place=True)
38     #fc4 layer
39     fc4 = L.InnerProduct(drop3, num_output=10, weight_filler=dict(type='xavier'))
40     #softmax-loss layer
41     loss = L.SoftmaxWithLoss(fc4, label)
42     #accuracy layer
43     if include_acc: #test
44         acc = L.Accuracy(fc4, label)
45         return to_proto(loss, acc)
46     else:           #train
47         return to_proto(loss)
48 
49     
50 def write_net():
51     #write train prototxt
52     with open(train_proto, 'w') as f:
53         f.write(str(create_net(train_lmdb, batch_size=64)))
54     
55     #write test prototxt
56     with open(test_proto, 'w') as f:
57         f.write(str(create_net(test_lmdb, batch_size=32, include_acc=True)))
58 
59 
60 if __name__ == '__main__':
61     write_net()
62

2.绘制网络模型

touch draw_net.sh

vim draw_net.sh

1 #!/usr/bin/bash
2 
3 DATA=train
4 BULID=/home/yuandanfei/caffe/python/draw_net.py
5 
6 python $BULID ../out/$DATA.prototxt ../out/$DATA.png --rankdir=BT

参考资料：

https://www.cnblogs.com/denny402/p/5679037.html

https://www.cnblogs.com/denny402/p/5106764.html