1、相关准备
1.1 手写数字数据集
这篇博客上有.jpg格式的图片下载，附带标签信息，有需要的自行下载，博客附带百度云盘下载地址(手写数字.jpg 格式)：http://blog.csdn.net/eddy_zheng/article/details/50496194
1.2深度学习框架
本实战基于caffe深度学习框架，需自行参考相关博客搭建环境，这里不再对如何搭建环境作介绍。
2、数据准备
2.1 准备训练与验证图像
准备好你想训练识别的图像数据之后，将其划分为训练集与验证集，并准备好对应的图像名称以及对应的标签信息。这里的验证集和测试集并是不同的，如下图所示，你可以这样简单的划分：
*这里要注意的是，图片名与对应的类别标签一定不能有错，不然你的训练就全乱套了。对了，图片名与标签之间对应一个 space 就可以了。
2.2 转换数据格式
以上工作准备完毕之后，还需将其转换为 caffe 训练的 lmdb 格式。找到你编译的图像转换 convert_imageset.exe 位置。如下我的 caffe bin目录：
转换训练数据：创建如下文件，写批处理命令：
内部代码如下所示，略作解释，1：是你转换图像 convert_imageset.exe 所在位置,2：转换图像数据所在的文件夹位置，3：接着是图像名称对应标签 .txt 文件,4：最后是生成的 lmdb 的位置及文件夹名字：
SET GLOG_logtostderr=1
C:\Users\Administrator\Desktop\caffe-windows-master\bin\convert_imageset.exe C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\train\ C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\train.txt C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\mtrainldb
pause
转换验证数据：操作同上，写批处理命令：
文件名：convert_imageldb_valset.bat
SET GLOG_logtostderr=1
C:\Users\Administrator\Desktop\caffe-windows-master\bin\convert_imageset.exe C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\val\ C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\val.txt C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\mvalldb
pause
3. 网络层参数
文件：train_val.prorotxt，参照 lenet-5 ; 注意将地址对应自己的转换数据的位置，代码如下：
name: "LeNet"
layer {
name: "mnist"
transform_param {
scale: 0.00390625 }
type: "Data"
top: "data"
top: "label"
data_param {
source: "C:/Users/Administrator/Desktop/caffe-windows-master/data/Digits/mtrainldb"
backend: LMDB
batch_size: 80
} include: { phase: TRAIN }
}
layer {
name: "mnist"
transform_param {
scale: 0.00390625
}
type: "Data"
top: "data"
top: "label"
data_param {
source: "C:/Users/Administrator/Desktop/caffe-windows-master/data/Digits/mvalldb"
backend: LMDB
batch_size: 4
} include: { phase: TEST }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
#decay_mult: 1
}
param {
lr_mult: 2
#decay_mult: 0
}
convolution_param {
num_output: 120
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv1"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
param {
lr_mult: 1
#decay_mult: 1
}
param {
lr_mult: 2
#decay_mult: 0
}
convolution_param {
num_output: 180
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm3"
type: "LRN"
bottom: "pool3"
top: "norm3"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv4"
type: "Convolution"
bottom: "norm3"
top: "conv4"
param {
lr_mult: 1
#decay_mult: 1
}
param {
lr_mult: 2
#decay_mult: 0
}
convolution_param {
num_output: 210
kernel_size: 3
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv4"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
}
}
layer {
name: "norm5"
type: "LRN"
bottom: "pool5"
top: "norm5"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "norm5"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu7"
type: "Insanity"
bottom: "ip1"
top: "ip1"
}
layer {
name: "drop1"
type: "Dropout"
bottom: "ip1"
top: "ip1"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 512
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu8"
type: "Insanity"
bottom: "ip2"
top: "ip2"
}
layer {
name: "drop2"
type: "Dropout"
bottom: "ip2"
top: "ip2"
dropout_param {
dropout_ratio: 0.5
}
} layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
4. 开始训练
Digist 文件夹下创建, caffe.bat,内容如下：
LOG=log/train-`date +%Y-%m-%d-%H-%M-%S`.log
C:\Users\Administrator\Desktop\caffe-windows-master\bin\caffe.exe train --solver C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\solver.prototxt
pause
准备完成之后，双击 caffe.bat；