先从一个具体的例子来开始Caffe，以MNIST手写数据为例。
1.下载数据
下载mnist到caffe-masterdatamnist文件夹。
THE MNIST DATABASE：Yann LeCun et al.
train-images-idx3-ubyte.gz: training set images (9912422 bytes)
train-labels-idx1-ubyte.gz: training set labels (28881 bytes)
t10k-images-idx3-ubyte.gz: test set images (1648877 bytes)
t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)
2.生成lmdb文件
使用convert_mnist_data project转换数据。
打开Caffe.sln，设置convert_mnist_data为启动项目，修改convert_mnist_data.cpp中代码。
在main函数中设置了转换数据的路径，具体的代码如下：
//get mnist train and test lmdb data By Xiaopan Lyu=====================
argc = 4;
argv[0] = "lmdb";
//convert train mnist data=============================================
argv[1] = "../../data/mnist/train-images.idx3-ubyte";
argv[2] = "../../data/mnist/train-labels.idx1-ubyte";
argv[3] = "../../data/mnist/mnist_train_lmdb";
//convert test mnist data=============================================
argv[1] = "../../data/mnist/t10k-images.idx3-ubyte";
argv[2] = "../../data/mnist/t10k-labels.idx1-ubyte";
argv[3] = "../../data/mnist/mnist_test_lmdb";
//======================================================================
这段代码在main函数中的位置如下：
int main(int argc, char** argv) {
#ifndef GFLAGS_GFLAGS_H_
namespace gflags = google;
#endif
FLAGS_alsologtostderr = 1;
//get mnist train and test lmdb data By Xiaopan Lyu=====================
argc = 4;
argv[0] = "lmdb";
//convert train mnist data=============================================
argv[1] = "../../data/mnist/train-images.idx3-ubyte";
argv[2] = "../../data/mnist/train-labels.idx1-ubyte";
argv[3] = "../../data/mnist/mnist_train_lmdb";
//convert test mnist data=============================================
argv[1] = "../../data/mnist/t10k-images.idx3-ubyte";
argv[2] = "../../data/mnist/t10k-labels.idx1-ubyte";
argv[3] = "../../data/mnist/mnist_test_lmdb";
//======================================================================
gflags::SetUsageMessage("This script converts the MNIST dataset ton"
"the lmdb/leveldb format used by Caffe to load data.n"
"Usage:n"
" convert_mnist_data [FLAGS] input_image_file input_label_file "
"output_db_filen"
"The MNIST dataset could be downloaded atn"
" http://yann.lecun.com/exdb/mnist/n"
"You should gunzip them after downloading,"
"or directly use data/mnist/get_mnist.shn");
gflags::ParseCommandLineFlags(&argc, &argv, true);
const string& db_backend = FLAGS_backend;
if (argc != 4) {
gflags::ShowUsageWithFlagsRestrict(argv[0],
"examples/mnist/convert_mnist_data");
}
else {
google::InitGoogleLogging(argv[0]);
convert_dataset(argv[1], argv[2], argv[3], db_backend);
}
system("pause");
return 0;
}
两次运行代码，分别得到train和test data。
get mnist_train_lmdb
get mnist_test_lmdb
Notes.
1）argv[0]、argv[1]、argv[2]、argv[3]分别表示的含义：[FLAGS] input_image_file input_label_file output_db_file.
2）output_db_file设置中最后的一级文件夹不要事先自己建立好，代码中不支持覆盖，如果存在文件夹会报错。
3）这些路径的设置是在debug模式下，文件的层级是以当前的.cpp文件为基础的，与实际EXE文件有所不同。
3.配置网络为TRAIN模式
1）配置lenet_train_test.prototxt
caffe在mnist自带的是使用lenet的网络结。lenet网络的定义在examplesmnistlenet_train_test.prototxt文件中。
注意配置好改网络定义中的数据路径。如下所示，注意line 14&31，如果目录比较混淆，可以直接写成绝对路径。
1: name: "LeNet"
2: layer {
3: name: "mnist"
4: type: "Data"
5: top: "data"
6: top: "label"
7: include {
8: phase: TRAIN
9: }
10: transform_param {
11: scale: 0.00390625
12: }
13: data_param {
14: source: "E:/MyCode/DL/caffe-master/examples/mnist/mnist_train_lmdb"
15: batch_size: 64
16: backend: LMDB
17: }
18: }
19: layer {
20: name: "mnist"
21: type: "Data"
22: top: "data"
23: top: "label"
24: include {
25: phase: TEST
26: }
27: transform_param {
28: scale: 0.00390625
29: }
30: data_param {
31: source: "E:/MyCode/DL/caffe-master/examples/mnist/mnist_test_lmdb"
32: batch_size: 100
33: backend: LMDB
34: }
35: }
2）配置lenet_solver.prototxt
lenet_solver.prototxt中实际上是定义了一种解决方案。
注意line 2,23&25,这三行的数据需要修改，这里也是用了绝对路径。只使用CPU训练。
1: # The train/test net protocol buffer definition
2: net: "E:/MyCode/DL/caffe-master/examples/mnist/lenet_train_test.prototxt"
3: # test_iter specifies how many forward passes the test should carry out.
4: # In the case of MNIST, we have test batch size 100 and 100 test iterations,
5: # covering the full 10,000 testing images.
6: test_iter: 100
7: # Carry out testing every 500 training iterations.
8: test_interval: 500
9: # The base learning rate, momentum and the weight decay of the network.
10: base_lr: 0.01
11: momentum: 0.9
12: weight_decay: 0.0005
13: # The learning rate policy
14: lr_policy: "inv"
15: gamma: 0.0001
16: power: 0.75
17: # Display every 100 iterations
18: display: 100
19: # The maximum number of iterations
20: max_iter: 10000
21: # snapshot intermediate results
22: snapshot: 5000
23: snapshot_prefix: "E:/MyCode/DL/caffe-master/examples/mnist/lenet"
24: # solver mode: CPU or GPU
25: solver_mode: CPU
3）修改了source code为train模式
修改了caffe.cpp文件的相关内容。增加了line15到line21的代码，顺便说一句Google的gflags解析命令行参数甚是优雅。
1: int main(int argc, char** argv) {
2: // Print output to stderr (while still logging).
3: FLAGS_alsologtostderr = 1;
4: // Set version
5: gflags::SetVersionString(AS_STRING(CAFFE_VERSION));
6: // Usage message.
7: gflags::SetUsageMessage("command line brewn"
8: "usage: caffe <command> <args>nn"
9: "commands:n"
10: " train train or finetune a modeln"
11: " test score a modeln"
12: " device_query show GPU diagnostic informationn"
13: " time benchmark model execution time");
14: // Run tool or show usage.
15: //train lenet By XiaopanLyu====================================================
16: argc = 3;
17: argv[0] = "caffe";
18: argv[1] = "train";
19: argv[2] = "-solver=E:/MyCode/DL/caffe-master/examples/mnist/lenet_solver.prototxt";
20: //argv[1] = "solver=../examples/mnist/lenet_solver.prototxt";
21: //=============================================================================
22: caffe::GlobalInit(&argc, &argv);
23: if (argc == 2) {
24: #ifdef WITH_PYTHON_LAYER
25: try {
26: #endif
27: return GetBrewFunction(caffe::string(argv[1]))();
28: system("pause");
29: #ifdef WITH_PYTHON_LAYER
30: }
31: catch (bp::error_already_set) {
32: PyErr_Print();
33: return 1;
34: }
35: #endif
36: }
37: else {
38: gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
39: }
40: }
4.Training LeNet
运行caffe project，mnist demo就开始运行了，以下就是运行的过程和结果。
1）运行过程
2）运行结果
运行完成后，生成了4个文件。查看lenet_solver.prototxt可知，最大迭代次数为10000次，5000次保存一次快照结果。
5.配置网络为TEST模式
修改caffe.cpp文件，增加参数配置代码
1: //test lenet By XiaopanLyu====================================================
2: argc = 5;
3: argv[0] = "caffe";
4: argv[1] = "test";
5: argv[2] = "-model=E:/MyCode/DL/caffe-master/examples/mnist/lenet_train_test.prototxt";
6: argv[3] = "-weights=E:/MyCode/DL/caffe-master/examples/mnist/lenet_iter_10000.caffemodel";
7: argv[4] = "-iterations=100";
8: //=============================================================================
9: caffe::GlobalInit(&argc, &argv);
6.Testing LeNet
用LeNet的网络配置运行mnist测试数据集，几分钟的时间得到如下效果。
迭代100次，测试数据集的准确率为99.02%。
7.NOTES
在第3、5部分，配置网络的参数可以参考Caffe的官方辅导文档：http://caffe.berkeleyvision.org/tutorial/interfaces.html
本站文章如无特殊说明，均为本站原创，如若转载，请注明出处：Chapter 3 Start Caffe with MNIST Demo - Python技术站
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