1.数据准备

本实验使用的数据集是CIFAR-10，一共有60000张32*32的彩色图像，其中50000张是训练集，另外10000张是测试集。数据集共有10个类别，分别如下所示

在./data/cifar10目录下运行get_cifar10.sh脚本，得到解压缩的数据集，其脚本内容如下：

#!/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."

主要是从官网下载http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz二进制的原始文件（163M），速度可能较慢，然后再对二进制压缩文件进行解压，解压后的结果如下：

主要是生成了原始训练数据以及其5个训练分块和1个测试分块，均为二进制文件。

2.数据格式转换

caffe只能识别lmdb和leveldb格式的输入文件，所以需要对原始数据进行格式转换，转换的文件位于./examples/cifar10目录下。在caffe根目录下运行

./examples/cifar10/create_cifar10.sh

create_cifar10.sh脚本文件的内容如下：

#!/usr/bin/env sh # This script converts the cifar data into leveldb format. EXAMPLE=examples/cifar10 DATA=data/cifar10 echo "Creating leveldb..." rm -rf $EXAMPLE/cifar10_train_leveldb $EXAMPLE/cifar10_test_leveldb ./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE echo "Computing image mean..." ./build/tools/compute_image_mean -backend = leveldb $EXAMPLE/cifar10_train_leveldb \ $EXAMPLE/mean.binaryproto echo "Done."

运行后将会在examples中出现数据集./cifar10_xxx_lmdb和数据集图像均值./mean.binaryproto。

3.训练

cifar-10训练的方法主要有全部训练和快速训练，两者的区别主要在于迭代的次数。快速训练迭代次数只有5000次，训练时间较少，相对应的精度有所下降，本次实验采用快速训练主要是出于时间效率方面的考虑。在caffe根目录下运行：

./examples/cifar10/train_quick.sh

train_quick.sh文件的内容如下：

#!/usr/bin/env sh TOOLS=./build/tools $TOOLS/caffe train \ --solver=examples/cifar10/cifar10_quick_solver.prototxt # reduce learning rate by factor of 10 after 8 epochs $TOOLS/caffe train \ --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \ --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate

可已发现，网络训练过程中有两个网络描述文件依次被使用，分别是cifar10_quick_solver.prototxt和cifar10_quick_solver_lr1.prototxt。两者的内容分别如下：

1.cifar10_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: "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: 0.001 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: 4000 # snapshot intermediate results snapshot: 4000 snapshot_prefix: "examples/cifar10/cifar10_quick" # solver mode: CPU or GPU solver_mode: CPU

2.cifar10_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: "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_prefix: "examples/cifar10/cifar10_quick" # solver mode: CPU or GPU solver_mode: CPU

4.实验结果

本次试验从上午10:20:27开始，上午11:27:50结束，历时1小时7分钟。

前4000次迭代结束时间为11:13:16，测试结果的准确度为accuracy=0.7152,loss=0.858176

到了5000次迭代结束的时候，测试结果的准确度为accuracy=0.7513,loss=0.743963

实验过程部分截图如下：