Caffe安装成功测试(CPU环境下mnist测试)

转自：(1)https://blog.csdn.net/u013295579/article/details/78398678

(2)https://blog.csdn.net/renyhui/article/details/60871888

测试数据和训练数据集的获取：https://pan.baidu.com/s/1hry1f4g 将下载下来并解压得到的测试和训练数据mnist-test-leveldb和mnist-train-leveldb复制到.\caffe-master\examples\mnist\目录下

将./caffe-master\windows\目录下的CommonSettings.props做如下改动并保存：

true
false
7.5 true
true（为了调用Python接口，将默认的false改为true）
C:\ProgramData\Anaconda3\（红色部分为Python.exe根目录，注意最后一道斜杠）

（CommonSettings.props文件修改完成）

修改.\caffe-master\examples\mnist\下的lenet_train_test.prototxt 做如下修改：
第13行修改为：

data_param {
    source: "C:/ProgramData/Caffe/caffe-master/examples/mnist/mnist-train-leveldb"
    batch_size: 64
    backend: LEVELDB
  }

第30行修改为：

data_param {
    source: "C:/ProgramData/Caffe/caffe-master/examples/mnist/mnist-train-leveldb"
    batch_size: 64
    backend: LEVELDB
  }

注意：source属性值的数据路径的斜杠是’/’而不是windows下的’\’
4. GPU和CPU的切换在lenet_solver.prototxt修改，最后一行把GPU改成CPU即可

5.编写windows下脚本文件run.bat

.\Build\x64\Release\caffe.exe train --solver=examples/mnist/lenet_solver.prototxt
pause

将run.bat文件放在./caffe-master/文件下,双击run.bat文件可以看到训练的结果如下：

...
...
I1030 22:57:11.207583 11204 sgd_solver.cpp:106] Iteration 9600, lr = 0.00603682
I1030 22:57:17.158367 11204 solver.cpp:228] Iteration 9700, loss = 0.00264511
I1030 22:57:17.158869 11204 solver.cpp:244]     Train net output #0: loss = 0.00264498 (* 1 = 0.00264498 loss)
I1030 22:57:17.159369 11204 sgd_solver.cpp:106] Iteration 9700, lr = 0.00601382
I1030 22:57:23.735081 11204 solver.cpp:228] Iteration 9800, loss = 0.0104211
I1030 22:57:23.735081 11204 solver.cpp:244]     Train net output #0: loss = 0.010421 (* 1 = 0.010421 loss)
I1030 22:57:23.735581 11204 sgd_solver.cpp:106] Iteration 9800, lr = 0.00599102
I1030 22:57:29.758888 11204 solver.cpp:228] Iteration 9900, loss = 0.00677528
I1030 22:57:29.759388 11204 solver.cpp:244]     Train net output #0: loss = 0.00677515 (* 1 = 0.00677515 loss)
I1030 22:57:29.759891 11204 sgd_solver.cpp:106] Iteration 9900, lr = 0.00596843
I1030 22:57:35.597347 11204 solver.cpp:454] Snapshotting to binary proto file examples/mnist/lenet_iter_10000.caffemodel
I1030 22:57:35.615355 11204 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_10000.solverstate
I1030 22:57:35.664417 11204 solver.cpp:317] Iteration 10000, loss = 0.00254389
I1030 22:57:35.664916 11204 solver.cpp:337] Iteration 10000, Testing net (#0)
I1030 22:57:39.652560 11204 solver.cpp:404]     Test net output #0: accuracy = 0.9912
I1030 22:57:39.653061 11204 solver.cpp:404]     Test net output #1: loss = 0.0287646 (* 1 = 0.0287646 loss)
I1030 22:57:39.653559 11204 solver.cpp:322] Optimization Done.
I1030 22:57:39.654062 11204 caffe.cpp:255] Optimization Done.

C:\ProgramData\Caffe\caffe-master>pause
请按任意键继续. . .

可以看到预测的准确率达到了0.9912 ,测试成功。

测试模型

在根目录下创建test_mnist.bat，内容如下：
```
.\Build\x64\Release\caffe.exe test -model=examples\mnist\lenet_train_test.prototxt -weights=examples\mnist\lenet_iter_10000.caffemodel -iterations=100
pause
```
注意：这里一定不要因为一行过长，使用回车，不然会报错，Check failure stack trace:

Caffe安装成功测试(CPU环境下mnist测试)

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