mnist实例

1.数据下载

获得mnist的数据包，在caffe根目录下执行./data/mnist/get_mnist.sh脚本。
get_mnist.sh脚本先下载样本库并进行解压缩，得到四个文件。

2.生成LMDB

成功解压缩下载的样本库后，然后执行./examples/mnist/create_mnist.sh。
create_mnist.sh脚本先利用caffe-master/build/examples/mnist/目录下的convert_mnist_data.bin工具，将mnist data转化为caffe可用的lmdb格式文件，然后将生成的mnist-train-lmdb和mnist-test-lmdb两个文件放在caffe-master/example/mnist目录下面。

3.网络配置

LeNet网络定义在./examples/mnist/lenet_train_test.prototxt 文件中。

name: "LeNet"layer {  name: "mnist"    //输入层的名称mnist  type: "Data"     //输入层的类型为Data层  top: "data"      //本层下一场连接data层和label blob空间  top: "label"  include {    phase: TRAIN   //训练阶段  }  transform_param {    scale: 0.00390625  //输入图片像素归一到[0,1].1除以256为0.00390625  }  data_param {    source: "examples/mnist/mnist_train_lmdb"  //从mnist_train_lmdb中读入数据    batch_size: 64    //batch大小为64，一次训练64条数据    backend: LMDB  }}layer {  name: "mnist"    //输入层的名称mnist  type: "Data"     //输入层的类型为Data层  top: "data"      //本层下一场连接data层和label blob空间  top: "label"  include {    phase: TEST   //测试阶段  }  transform_param {    scale: 0.00390625  //输入图片像素归一到[0,1].1除以256为0.00390625  }  data_param {    source: "examples/mnist/mnist_test_lmdb"  //从mnist_test_lmdb中读入数据    batch_size: 100    //batch大小为100，一次训练100条数据    backend: LMDB  }}layer {  name: "conv1"    //卷积层名称conv1  type: "Convolution"    //层类型为卷积层  bottom: "data"    //本层使用上一层的data，生成下一层conv1的blob  top: "conv1"  param {    lr_mult: 1    //权重参数w的学习率倍数  }  param {    lr_mult: 2    //偏置参数b的学习率倍数  }  convolution_param {    num_output: 20    //输出单元数20    kernel_size: 5    //卷积核大小为5*5    stride: 1         //步长为1    weight_filler {   //允许用随机值初始化权重和偏置值      type: "xavier"  //使用xavier算法自动确定基于输入—输出神经元数量的初始规模    }    bias_filler {      type: "constant"    //偏置值初始化为常数，默认为0    }  }}layer {  name: "pool1"      //层名称为pool1  type: "Pooling"    //层类型为pooling  bottom: "conv1"    //本层的上一层是conv1，生成下一层pool1的blob  top: "pool1"  pooling_param {    //pooling层的参数    pool: MAX        //pooling的方式是MAX    kernel_size: 2   //pooling核是2*2    stride: 2        //pooling步长是2  }}layer {  name: "conv2"    //第二个卷积层，同第一个卷积层相同，只是卷积核为50  type: "Convolution"  bottom: "pool1"  top: "conv2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 50    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool2"     //第二个pooling层，与第一个pooling层相同  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {            //全连接层  name: "ip1"      //全连接层名称ip1  type: "InnerProduct"    //层类型为全连接层  bottom: "pool2"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {     //全连接层的参数    num_output: 500         //输出500个节点    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"       //ReLU层  type: "ReLU"        //层名称为relu1  bottom: "ip1"       //层类型为ReLU  top: "ip1"}layer {  name: "ip2"         //第二个全连接层  type: "InnerProduct"  bottom: "ip1"  top: "ip2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 10     //输出10个单元    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "accuracy"  type: "Accuracy"  bottom: "ip2"  bottom: "label"  top: "accuracy"  include {    phase: TEST  }}layer {        //loss层，softmax_loss层实现softmax和多项Logistic损失  name: "loss"  type: "SoftmaxWithLoss"  bottom: "ip2"  bottom: "label"  top: "loss"}

4.训练网络

运行./examples/mnist/train_lenet.sh。
执行此脚本是，实际运行的是lenet_solver.prototxt中的定义。

# The train/test net protocol buffer definitionnet: "examples/mnist/lenet_train_test.prototxt"    //网络具体定义# 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    //test迭代次数，若batch_size=100，则100张图一批，训练100次，可覆盖1000张图# Carry out testing every 500 training iterations.test_interval: 500    //训练迭代500次，测试一次# The base learning rate, momentum and the weight decay of the network.base_lr: 0.01    //网络参数：学习率，动量，权重的衰减momentum: 0.9weight_decay: 0.0005# The learning rate policy    //学习策略：有固定学习率和每步递减学习率lr_policy: "inv"    //当前使用递减学习率gamma: 0.0001power: 0.75# Display every 100 iterations    //每迭代100次显示一次display: 100# The maximum number of iterations   //最大迭代数max_iter: 10000# snapshot intermediate results    //每5000次迭代存储一次数据snapshot: 5000snapshot_prefix: "examples/mnist/lenet"# solver mode: CPU or GPUsolver_mode: CPU    //本例用CPU训练

数据训练结束后，会生成以下四个文件：

5.测试网络

运行./build/tools/caffe.bin test -model=examples/mnist/lenet_train_test.prototxt -weights=examples/mnist/lenet_iter_10000.caffemodel

test：表示对训练好的模型进行Testing，而不是training。其他参数包括train, time, device_query。

-model=XXX：指定模型prototxt文件，这是一个文本文件，详细描述了网络结构和数据集信息。

从上面的打印输出可看出，测试数据中的accruacy平均成功率为98%。

mnist手写测试

手写数字的图片必须满足以下条件：

• 必须是256位黑白色
• 必须是黑底白字
• 像素大小必须是28*28
• 数字在图片中间，上下左右没有过多的空白。

测试图片

手写数字识别脚本

import osimport sysimport numpy as npimport matplotlib.pyplot as pltcaffe_root = '/home/lynn/caffe/'sys.path.insert(0, caffe_root + 'python')import caffeMODEL_FILE = '/home/lynn/caffe/examples/mnist/lenet.prototxt'PRETRAINED = '/home/lynn/caffe/examples/mnist/lenet_iter_10000.caffemodel'IMAGE_FILE = '/home/lynn/test.bmp'input_image = caffe.io.load_image(IMAGE_FILE, color=False)#print input_imagenet = caffe.Classifier(MODEL_FILE, PRETRAINED)prediction = net.predict([input_image], oversample = False)caffe.set_mode_cpu()print 'predicted class: ', prediction[0].argmax()

测试结果