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caffe非常简单,训练时只需写prototxt文件即可,其大致的步骤为:
如下图所示,其训练的过程,关于卷积神经网络(CNN)可以参考:
下面对手写数字识别进行训练。
mnist是一个大型的手写数字库,其包含60000个训练集和10000个测试机,每张图片已经进行了尺度归一化等操作,因此可以直接拿过来使用。
可以在Caffe
源码框架的/data/mnist
下执行,如果没有安装Caffe,请参考:
cd data/mnist./get_mnist.sh
下载后可以看到其文件:
yqtao@yqtao:~/caffe/data/mnist$ tree.├── get_mnist.sh├── t10k-images-idx3-ubyte├── t10k-labels-idx1-ubyte├── train-images-idx3-ubyte└── train-labels-idx1-ubyte
注意:下载后的文件需要转换存储格式为LEVELDB或LMDB,这要做有两个原因:
- 转换成统一的格式可以简化数据读取层的实现
- 提高磁盘I/O的利用率
// 执行命令yqtao@yqtao:~/caffe$ ./examples/mnist/create_mnist.sh
这要会在example/mnist
产生 mnist_test_lmdb
和mnist_train_lmdb
两个目录分别存放测试集和训练集。
这是非常重要的一步,但是其完全是模板话的定义,如下图所示为LeNet-5模型所定义的CNN:
这张图非常的重要,有了它,编写后面的网络结构就好非常的清晰了。
关于上图的结构是写到.prototxt
文件中的,其文件描述在 /example/mnist/lenet_train_val.prototxt
中。 数据层的图示:
1 name: "LeNet" //Net的名称 2 layer { 3 name: "mnist" 4 type: "Data" //表明为数据层 5 top: "data" //top,表示输出 6 top: "label" 7 include { //只在训练时有效 8 phase: TRAIN 9 } 10 transform_param { 11 scale: 0.00390625 //数据变化缩放因子 12 } 13 data_param { //数据层的参数 14 source: "examples/mnist/mnist_train_lmdb" //来源 15 batch_size: 64 //一次读取64张图片 16 backend: LMDB //数据格式 17 } 18 }
卷积层的图示:
如下卷积层的定义:
36 layer { 37 name: "conv1" 38 type: "Convolution" 39 bottom: "data" //上一层的输出,这一层的输入 40 top: "conv1" //这一层的输出 41 param { //学习率 42 lr_mult: 1 43 } 44 param { 45 lr_mult: 2 46 } 47 convolution_param { 48 num_output: 20 //也就是depth 49 kernel_size: 5 //核的大小5*5 50 stride: 1 //步长1 51 weight_filler { //权值初始方式 52 type: "xavier" 53 } 54 bias_filler { 55 type: "constant" 56 } 57 } 58 }
注意:在top,和bottom中一定不要写错了!
池化层图示:
其定义如下:
59 layer { 60 name: "pool1" 61 type: "Pooling" 62 bottom: "conv1" 63 top: "pool1" 64 pooling_param { 65 pool: MAX //下采样的方法 66 kernel_size: 2 //窗口 67 stride: 2 //步长 68 } 69 }
其定义如下:
104 layer {105 name: "ip1"106 type: "InnerProduct"107 bottom: "pool2"108 top: "ip1"109 param {110 lr_mult: 1111 }112 param {113 lr_mult: 2114 }115 inner_product_param {116 num_output: 500117 weight_filler {118 type: "xavier"119 }120 bias_filler {121 type: "constant"122 }123 }124 }
其图示如下:
定义如下:
125 layer {126 name: "relu1"127 type: "ReLU"128 bottom: "ip1"129 top: "ip1"130 }
定义如下:
162 layer { 163 name: "loss" 164 type: "SoftmaxWithLoss"165 bottom: "ip2"166 bottom: "label"167 top: "loss"168 }
注意:计算损失的时候的输入为label
为数据层的一个输出,和全连接层的输出ip2
,这一层的输出为loss
。
有了上面的网络结构的文件后还需要一个solver.prototxt
的文件,其指定了训练的超参数。
其文件目录在example/mnist/lenet_solver.prototxt
,每一项都有详细的解析。
1 # The train/test net protocol buffer definition 2 net: "examples/mnist/lenet_train_test.prototxt" 3 # test_iter specifies how many forward passes the test should carry out. 4 # In the case of MNIST, we have test batch size 100 and 100 test iterations, 5 # covering the full 10,000 testing images. 6 test_iter: 100 7 # Carry out testing every 500 training iterations. 8 test_interval: 500 9 # The base learning rate, momentum and the weight decay of the network. 10 base_lr: 0.01 11 momentum: 0.9 12 weight_decay: 0.0005 13 # The learning rate policy 14 lr_policy: "inv" 15 gamma: 0.0001 16 power: 0.75 17 # Display every 100 iterations 18 display: 100 19 # The maximum number of iterations 20 max_iter: 10000 21 # snapshot intermediate results 22 snapshot: 5000 23 snapshot_prefix: "examples/mnist/lenet" 24 # solver mode: CPU or GPU 25 solver_mode: CPU
首先了解build/tools/caffe.bin
的用法,如下所示:
yqtao@yqtao:~/caffe$ ./build/tools/caffe.bincaffe.bin: command line brewusage: caffecommands: train train or finetune a model test score a model device_query show GPU diagnostic information time benchmark model execution time Flags from tools/caffe.cpp: -gpu (Optional; run in GPU mode on given device IDs separated by ','.Use '-gpu all' to run on all available GPUs. The effective training batch size is multiplied by the number of devices.) type: string default: "" -iterations (The number of iterations to run.) type: int32 default: 50 -level (Optional; network level.) type: int32 default: 0 -model (The model definition protocol buffer text file.) type: string default: "" -phase (Optional; network phase (TRAIN or TEST). Only used for 'time'.) type: string default: "" -sighup_effect (Optional; action to take when a SIGHUP signal is received: snapshot, stop or none.) type: string default: "snapshot" -sigint_effect (Optional; action to take when a SIGINT signal is received: snapshot, stop or none.) type: string default: "stop" -snapshot (Optional; the snapshot solver state to resume training.) type: string default: "" -solver (The solver definition protocol buffer text file.) type: string default: "" -stage (Optional; network stages (not to be confused with phase), separated by ','.) type: string default: "" -weights (Optional; the pretrained weights to initialize finetuning, separated by ','. Cannot be set simultaneously with snapshot.) type: string default: ""
则进行训练的命令为:
//执行命令./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt
其中solver=examples/mnist/lenet_solver.prototxt
为指定的超参数文件。
运行部分结果如下:
I0311 17:43:26.273123 16205 sgd_solver.cpp:106] Iteration 9900, lr = 0.00596843I0311 17:43:34.746616 16205 solver.cpp:454] Snapshotting to binary proto file examples/mnist/lenet_iter_10000.caffemodelI0311 17:43:34.758142 16205 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_10000.solverstateI0311 17:43:34.799706 16205 solver.cpp:317] Iteration 10000, loss = 0.00373883I0311 17:43:34.799777 16205 solver.cpp:337] Iteration 10000, Testing net (#0)I0311 17:43:40.162556 16205 solver.cpp:404] Test net output #0: accuracy = 0.9914I0311 17:43:40.162638 16205 solver.cpp:404] Test net output #1: loss = 0.0260208 (* 1 = 0.0260208 loss)I0311 17:43:40.162645 16205 solver.cpp:322] Optimization Done.I0311 17:43:40.162649 16205 caffe.cpp:254] Optimization Done.
可以看到,最终的训练模型的权值保存在
examples/mnist/lenet_iter_10000.caffemodel
训练的状态保存在 examples/mnist/lenet_iter_10000.solverstate
执行下面的命令,指定命令test
,参数网络定义的位置和权值的位置即可。
yqtao@yqtao:~/caffe$ ./build/tools/caffe.bin test \> -model examples/mnist/lenet_train_test.prototxt \> -weights examples/mnist/lenet_iter_10000.caffemodel \> -iterations 100
运行结果如下:
0311 17:49:28.120023 16423 caffe.cpp:308] Batch 96, accuracy = 0.97I0311 17:49:28.120096 16423 caffe.cpp:308] Batch 96, loss = 0.0561079I0311 17:49:28.174964 16423 caffe.cpp:308] Batch 97, accuracy = 0.98I0311 17:49:28.175036 16423 caffe.cpp:308] Batch 97, loss = 0.0847761I0311 17:49:28.229038 16423 caffe.cpp:308] Batch 98, accuracy = 1I0311 17:49:28.229110 16423 caffe.cpp:308] Batch 98, loss = 0.00344597I0311 17:49:28.286336 16423 caffe.cpp:308] Batch 99, accuracy = 1I0311 17:49:28.286495 16423 caffe.cpp:308] Batch 99, loss = 0.00835868I0311 17:49:28.286504 16423 caffe.cpp:313] Loss: 0.0260208I0311 17:49:28.286516 16423 caffe.cpp:325] accuracy = 0.9914I0311 17:49:28.286526 16423 caffe.cpp:325] loss = 0.0260208 (* 1 = 0.0260208 loss)
最终的精确度为accuracy = 0.9914
.
转换存储格式(LMDB/LevelDB
)
定义网络结构(编辑prototxt
)
定义solver
(编辑另一个prototxt
)
学习使用caffe.bin
命令的使用