caffe利用lenet_5模型跑MNIST数据

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一：MNIST数据集的获取

1.获取MNIST数据集

cd data/mnist/./get_mnist.shtree

2.此时可以查看一下caffe下get_mnist.sh脚本的内容

#!/usr/bin/env sh# This scripts downloads the mnist data and unzips it.DIR="$( cd "$(dirname "$0")" ; pwd -P )"cd "$DIR"echo "Downloading..."for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubytedo    if [ ! -e $fname ]; then        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz        gunzip ${fname}.gz    fidone

3.目前下载下来的是压缩形式的bin二进制文件，需要转换为LEVELDB或者LMDB形式才能被caffe所识别

执行：

./examples/mnist/create_mnist.sh

查看所生成的目录文件：

ls -l examples/mnist/mnist_train_lmdb/ls -l examples/mnist/mnist_test_lmdb/

当然具体大家可以使用vi或者gedit去查看该文件的具体内容以及将MNIST数据集转换为llmdb格式的源文件：

examples/mnist/convert_mnist_data.cpp

二：跑lenet-5模型

1.查看lenet-5模型描述文件

gedit examples/mnist/lenet_train_val.prototxt

2.执行训练模型的脚本

gedit examples/mnist/lenet_solver.prototxt

修改为CPU，我自己使用CPU跑

执行：

examples/mnist/train_lenet.sh

开始盖楼：

模型描述

name: "LeNet"state {  phase: TEST}layer {  name: "mnist"  type: "Data"  top: "data"  top: "label"  include {    phase: TEST  }  transform_param {    scale: 0.00390625  }  data_param {    source: "examples/mnist/mnist_test_lmdb"    batch_size: 100    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 20    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "conv2"  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"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool2"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "ip1"  top: "ip1"}layer {  name: "ip2"  type: "InnerProduct"  bottom: "ip1"  top: "ip2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 10    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}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"}

开始训练

最终训练模型结果是保存在examples/mnist/lenet_iter_10000.caffemodel，训练状态保存在
examples/mnist/lenet_iter_10000.solverstate中，均是protobuffer二进制格式文件。

3.测试模型

./build/tools/caffe.bin test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -iterations 100

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