修改lenet网络进行训练（二）

参考文档为caffe官网指导文档 “training lenet on mnist with caffe"

准备数据集

定义MNIST网络

定义MNIST Solver

训练测试模型

（一）准备数据集

cd  /home/ypp/caffe-master   #cd 到caffe-master安装的根目录

sudo ./data/mnist/get_mnist.sh

sudo ./examples/mnist/create_mnist.sh

会在examples/mnist目录下生成测试和训练数据集

（二）定义MNIST网络

name: "LeNet"#writing the data layerlayer {  name: "mnist"  type: "Data"  data_param {    source: "mnist_train_lmdb"    backend: LMDB    batch_size: 64    scale: 0.00390625  }  top: "data"  top: "label"}#writing the convolution layerlayer {  name: "conv1"  type: "Convolution"  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"    }  }}#writing the pooling layerlayer {  name: "pool1"  type: "Pooling"  pooling_param {    kernel_size: 2    stride: 2    pool: MAX  }  bottom: "conv1"  top: "pool1"}layer {   name: "conv2"  type: "Convolution"  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"    }  }  bottom: "pool1"  top: "conv2"}layer {   name: "pool2"  type: "Pooling"  pooling_param {    kernel_size: 2    stride: 2    pool: MAX  }  bottom: "conv2"  top: "pool2"}#writing the fully connected layerlayer {  name: "ip1"  type: "InnerProduct"  param { lr_mult: 1 }  param { lr_mult: 2 }  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }  bottom: "pool2"  top: "ip1"}#writing the ReLU layerlayer {  name: "relu1"  type: "ReLU"  bottom: "ip1"  top: "ip1"}layer {  name: "ip2"  type: "InnerProduct"  param { lr_mult: 1 }  param { lr_mult: 2 }  inner_product_param {    num_output: 10    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }  bottom: "ip1"  top: "ip2"}#writing the loss layerlayer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "ip2"  bottom: "label"}

定义的网络为：输入->卷积层 ->降采样层->卷积层->降采样层 ->全连接层->ReLU层->全连接层->损失函数

也可以根据相应的网络自行修改

（三）定义MNIST Solver

<pre name="code" class="plain">#The train/test net protocol buffer definitionnet: "examples/mnist/define_myself_mnist.prototxt"# 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# Carry out testing every 500 training iterations.test_interval: 500# The base learning rate, momentum and the weight decay of the network.base_lr: 0.01momentum: 0.9weight_decay: 0.0005# The learning rate policylr_policy: "inv"gamma: 0.0001power: 0.75# Display every 100 iterationsdisplay: 100# The maximum number of iterationsmax_iter: 10000# snapshot intermediate resultssnapshot: 5000snapshot_prefix: "examples/mnist/lenet"# solver mode: CPU or GPUsolver_mode: CPU

(四)训练测试模型

新建脚本 train-lenet.sh

#!/usr/bin/env sh./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt

cd 到caffe-master根目录下 执行

sudo ./examples/mnist/train-lenet.sh