1、数据集的获取
首先需要安装Git和Wget，方法请参考
上一篇博客
执行根目录data/cifar10目录下的get_cifar.sh，cifar内容如下：
#!/usr/bin/env sh
# This scripts downloads the CIFAR10 (binary version) data and unzips it.
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"
echo "Downloading..."
wget --no-check-certificate http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
echo "Unzipping..."
tar -xf cifar-10-binary.tar.gz && rm -f cifar-10-binary.tar.gz
mv cifar-10-batches-bin/* . && rm -rf cifar-10-batches-bin
# Creation is split out because leveldb sometimes causes segfault
# and needs to be re-created.
echo "Done."
数据下载完成，在/data/cifar文件夹下多了一些文件。这些文件无法在caffe框架下直接运行，需要转换格式
2、数据格式转换
执行/examples/cifar10目录下的create_cifar10.sh，这里需要做一些修改，我已经标记为黄色底纹
#!/usr/bin/env sh
# This script converts the cifar data into leveldb format.
set -e
LOG_FILE=./LOG.txt
EXAMPLE=./
DATA=../../data/cifar10
DBTYPE=lmdb
echo "Creating $DBTYPE..."
rm -rf $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/cifar10_test_$DBTYPE
exec 2>>$LOG_FILE
../../Build/x64/Release/convert_cifar_data.exe $DATA $EXAMPLE $DBTYPE
echo "Computing image mean..."
../../Build/x64/Release/compute_image_mean.exe -backend=$DBTYPE \
$EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/mean.binaryproto
echo "Done."
其中LOG_FILE=./LOG.TXT和exec 2>>$LOG_FILE是为了打印错误的语句到日志文件，方便我们检查错误
执行后的结果为：
3.神经网络的训练
修改exampe/cifar10文件夹下train_quick.sh，修改后的内容如下
#!/usr/bin/env sh
set -e
CAFFE_ROOT=D:/Caffe/Caffe_BVLC
TOOLS=$CAFFE_ROOT/Build/x64/Release
exec 2>>log.txt
$TOOLS/caffe train \
--solver=$CAFFE_ROOT/examples/cifar10/cifar10_quick_solver.prototxt $@
# reduce learning rate by factor of 10 after 8 epochs
$TOOLS/caffe train \
--solver=$CAFFE_ROOT/examples/cifar10/cifar10_quick_solver_lr1.prototxt \
--snapshot=$CAFFE_ROOT/examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 $@
修改 cifar10_quick_solver.prototxt、cifar10_quick_solver_lr1.prototxt，GPU or CPU根据自己的情况修改
①cifar10_quick_solver.prototxt
# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
# The train/test net protocol buffer definition
net: "
D:/Caffe/Caffe_BVLC/examples/cifar10/cifar10_quick_train_test.prototxt"
# 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: 0.0001
momentum: 0.9
weight_decay: 0.004
# The learning rate policy
lr_policy: "fixed"
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 5000
# snapshot intermediate results
snapshot: 5000
snapshot_format: HDF5
snapshot_prefix: "
D:/Caffe/Caffe_BVLC/examples/cifar10/cifar10_quick"
# solver mode: CPU or GPU
solver_mode:
CPU
②cifar10_quick_solver_lr1.prototxt
# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10 # The train/test net protocol buffer definition net: "
D:/Caffe/Caffe_BVLC/examples/cifar10/cifar10_quick_train_test.prototxt"
# 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: 0.0001
momentum: 0.9
weight_decay: 0.004
# The learning rate policy
lr_policy: "fixed"
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 5000
# snapshot intermediate results
snapshot: 5000
snapshot_format: HDF5
snapshot_prefix: "
D:/Caffe/Caffe_BVLC/examples/cifar10/cifar10_quick"
# solver mode: CPU or GPU
solver_mode:
CPU
调整好后，点击train_quick.sh进行训练，由于使用git bash没有中间结果在屏幕上显示，我将文档写到了log.txt (exec 2>>log.txt)，整个模型训练下来大约20多分钟
训练后的结果如下：精度约达到74.83%
4、模型测试
因为cifar模型中没有给我们提供测试模板，需要自己创建一个，在example/cifar10目录下新建一个文本文件，重命名为test_cifar10_quick.bat
内容如下：
..\..\Build\x64\Release\caffe.exe test -model=.\cifar10_quick_train_test.prototxt -weights=.\cifar10_quick_iter_5000.caffemodel.h5 -iterations=100
pause这里需要注意修改cifar10_quick_train_test.prototxt的路径
执行完成的结果如下：
至此，cifar demo的训练和测试完成
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