1.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

caffe mnist实例 --lenet_train_test.prototxt 网络配置详解

原文地址：http://www.cnblogs.com/is-Tina/p/7747844.html