进入…\caffe-windows\scripts，用文本编辑器如Notepad++打开build_win.cmd文件（我的目录F:\vs2015\caffe\caffe-windows\scripts\build_win.cmd），下面进行修改编译配置： 我的配置如下，如果你是Vs2013，把MSVC_VERSION=14改为12。WITH_NINJA=0，除非你想用ninja编译器来进行编译。Python版本设置为2或者3，取决于装的版本，这里编译只支持2.7或者3.5版本，其他不用怎么改变。
else ( :: Change the settings here to match your setup :: Change MSVC_VERSION to 12 to use VS 2013 if NOT DEFINED MSVC_VERSION set MSVC_VERSION=14 :: Change to 1 to use Ninja generator (builds much faster) if NOT DEFINED WITH_NINJA set WITH_NINJA=0 :: Change to 1 to build caffe without CUDA support if NOT DEFINED CPU_ONLY set CPU_ONLY=0 :: Change to Debug to build Debug. This is only relevant for the Ninja generator the Visual Studio generator will generate both Debug and Release configs if NOT DEFINED CMAKE_CONFIG set CMAKE_CONFIG=Release :: Set to 1 to use NCCL if NOT DEFINED USE_NCCL set USE_NCCL=0 :: Change to 1 to build a caffe.dll if NOT DEFINED CMAKE_BUILD_SHARED_LIBS set CMAKE_BUILD_SHARED_LIBS=0 :: Change to 3 if using python 3.5 (only 2.7 and 3.5 are supported) if NOT DEFINED PYTHON_VERSION set PYTHON_VERSION=2 :: Change these options for your needs. if NOT DEFINED BUILD_PYTHON set BUILD_PYTHON=1 if NOT DEFINED BUILD_PYTHON_LAYER set BUILD_PYTHON_LAYER=1 if NOT DEFINED BUILD_MATLAB set BUILD_MATLAB=0 :: If python is on your path leave this alone if NOT DEFINED PYTHON_EXE set PYTHON_EXE=python :: Run the tests if NOT DEFINED RUN_TESTS set RUN_TESTS=0 :: Run lint if NOT DEFINED RUN_LINT set RUN_LINT=0 :: Build the install target if NOT DEFINED RUN_INSTALL set RUN_INSTALL=0 )修改完build_win.cmd保存好后，打开cmd命令提示符，进入该文件目录，执行
F:\vs2015\caffe\caffe-windows\scripts>build_win.cmd等待编译，先下载依赖包，然后进行caffe编译，大约20-30分钟编译成功。最后结果大致：
CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-string-l1-1-0.dll' returning type 'system' -- possibly incorrect [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_pre requisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-heap-l1-1-0.dll' [F:\vs2015\caffe\caffe-windows\scripts\b uild\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-heap-l1-1-0.dll' returning type 's ystem' -- possibly incorrect [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prere quisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-stdio-l1-1-0.dll' [F:\vs2015\caffe\caffe-windows\scripts\ build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-stdio-l1-1-0.dll' returning type ' system' -- possibly incorrect [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prer equisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-convert-l1-1-0.dll' [F:\vs2015\caffe\caffe-windows\script s\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-convert-l1-1-0.dll' returning type 'system' -- possibly incorrect [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_pr erequisites.vcxproj] 13407 个警告 0 个错误 已用时间 00:19:12.74 编译完成后，会在…/caffe-windows/scripts目录下多出build文件夹，里面的Caffe.sln用Vs2015打开就可以了，如果想再编译，选择Release方式。在…/caffe-windows/scripts/build/tools/Release下有编译生成的Caffe.exe,使用它便可以测试数据集。设定prototxt文件，给出相应的路径：
lenet_train_test.prototxt，这个文件即之前说的lenet_train.prototxt，为了便于区分在后面加了一个_test。路径改变如下(修改部分截取，未修改部分没有贴上来)：
name: "LeNet" layer { name: "mnist" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { scale: 0.00390625 } data_param { source: "mnist_test/mnist-train-leveldb" ---------》修改部分 batch_size: 64 backend: LEVELDB ---------》修改部分 } } layer { name: "mnist" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { scale: 0.00390625 } data_param { source: "mnist_test/mnist-test-leveldb" ---------》修改部分 batch_size: 100 backend: LEVELDB ---------》修改部分 } } ...... .....lenet_solver.prototxt 设置相对路径：
# The train/test net protocol buffer definition net: "mnist_test/lenet_train_test.prototxt" ---------》修改部分 # test_iter specifies how many forward passes the test should carry out. # In the case of MNIST, we have test batch size 100 and 100 test iterations, # covering the full 10,000 testing images. test_iter: 100 # Carry out testing every 500 training iterations. test_interval: 500 # The base learning rate, momentum and the weight decay of the network. base_lr: 0.01 momentum: 0.9 weight_decay: 0.0005 # The learning rate policy lr_policy: "inv" gamma: 0.0001 power: 0.75 # Display every 100 iterations display: 100 # The maximum number of iterations max_iter: 10000 # snapshot intermediate results snapshot: 5000 snapshot_prefix: "mnist_test/lenet" ---------》修改部分 # solver mode: CPU or GPU solver_mode: GPU ---------》使用GPU打开cmd，进入caffe.exe的目录：
C:\Users\Mr.sorrow>f: F:\>cd F:\vs2015\caffe\caffe-windows\scripts\build\tools\Release F:\vs2015\caffe\caffe-windows\scripts\build\tools\Release>运行：
caffe.exe train --solver=mnist_test/lenet_solver.prototxt结果：
....... ....... I0309 13:46:14.446481 3708 solver.cpp:219] Iteration 9600 (165.724 iter/s, 0.603412s/100 iters), loss = 0.0029485 I0309 13:46:14.446481 3708 solver.cpp:238] Train net output #0: loss = 0.0029485 (* 1 = 0.0029485 loss) I0309 13:46:14.447450 3708 sgd_solver.cpp:105] Iteration 9600, lr = 0.00603682 I0309 13:46:15.059074 3708 solver.cpp:219] Iteration 9700 (164.181 iter/s, 0.609083s/100 iters), loss = 0.00178553 I0309 13:46:15.059074 3708 solver.cpp:238] Train net output #0: loss = 0.00178552 (* 1 = 0.00178552 loss) I0309 13:46:15.060080 3708 sgd_solver.cpp:105] Iteration 9700, lr = 0.00601382 I0309 13:46:15.664719 3708 solver.cpp:219] Iteration 9800 (165.979 iter/s, 0.602486s/100 iters), loss = 0.0119374 I0309 13:46:15.665698 3708 solver.cpp:238] Train net output #0: loss = 0.0119374 (* 1 = 0.0119374 loss) I0309 13:46:15.665698 3708 sgd_solver.cpp:105] Iteration 9800, lr = 0.00599102 I0309 13:46:16.286525 3708 solver.cpp:219] Iteration 9900 (161.895 iter/s, 0.617683s/100 iters), loss = 0.00379853 I0309 13:46:16.286990 3708 solver.cpp:238] Train net output #0: loss = 0.00379854 (* 1 = 0.00379854 loss) I0309 13:46:16.287490 3708 sgd_solver.cpp:105] Iteration 9900, lr = 0.00596843 I0309 13:46:16.890439 3708 solver.cpp:448] Snapshotting to binary proto file mnist_test/mnist/lenet_iter_10000.caffemodel I0309 13:46:16.903506 3708 sgd_solver.cpp:273] Snapshotting solver state to binary proto file mnist_test/mnist/lenet_iter_10000.solverstate I0309 13:46:16.910498 3708 solver.cpp:311] Iteration 10000, loss = 0.00267322 I0309 13:46:16.910498 3708 solver.cpp:331] Iteration 10000, Testing net (#0) I0309 13:46:17.131078 7880 data_layer.cpp:73] Restarting data prefetching from start. I0309 13:46:17.140102 3708 solver.cpp:398] Test net output #0: accuracy = 0.99 I0309 13:46:17.140102 3708 solver.cpp:398] Test net output #1: loss = 0.0297552 (* 1 = 0.0297552 loss) I0309 13:46:17.143108 3708 solver.cpp:316] Optimization Done. I0309 13:46:17.143108 3708 caffe.cpp:260] Optimization Done.在….\caffe-windows\scripts\build\tools\Release\mnist_test目录下，即生成四个文件：
lenet_iter_5000.caffemodellenet_iter_5000.solverstatelenet_iter_10000.caffemodellenet_iter_10000.solverstate