Caffe框架,了解三个文件

不知道从什么时候开始，Deep Learning成为了各个领域研究的热点，也不知道从什么时候开始，2015CVPR的文章出现了很多Deep Learning的文章，更不知道从什么时候开始，三维重建各个研究方向也要被Deep Learning攻破了。
从这个时候开始，我要开始学习Deep Learning了，因为我研究的方向已然被攻破！

以上是引言部分，下面开始介绍本文的内容。
我前段时间已经配置好Caffe这个框架，现在来摸索一下。本文分为两个部分，第一部分说明学习Caffe框架需要重点记住那些文件；第二部分使用Caffe框架对MNIST数据集进行训练学习。

一. Caffe框架文件
以‘$root’作为Caffe的主目录，以MNIST数据集训练学习作为例子，我觉得只要掌握三个文件就够了：
1. train_lenet.sh $root /examples/mnist/train_lenet.sh

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

使用caffe调用lenet_solver.prototxt进行train，’.prototxt’是一种文本文件，这里需要知道的是lenet_solver.prototxt是CNN网络学习的核心，下面我们将要学习它。
2. lenet_solver.prototxt $root /examples/mnist/lenet_solver.prototxt

# The train/test net protocol buffer definitionnet: "examples/mnist/lenet_train_test.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: GPU

net: “examples/mnist/lenet_train_test.prototxt”是网络结构设置，其他部分是参数设置，看注释就很明白了。
3. lenet_train_test.prototxt $root /examples/mnist/lenet_train_test.prototxt

name: "LeNet"layer {  name: "mnist"  type: "Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  transform_param {    scale: 0.00390625  }  data_param {    source: "examples/mnist/mnist_train_lmdb"    batch_size: 64    backend: LMDB  }}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"}

这是各层网络的设置，看内容就知道了。需要注意的是，include {phase: TEST}是指测试网络，未标明的是train和test都可以使用。
二. MNIST数据集进行训练学习

cd $root./data/mnist/get_mnist.sh./examples/mnist/create_mnist.sh./examples/mnist/train_lenet.sh

get_mnist.sh下载MNIST数据集
create_mnist.sh将MNIST数据转换为lmdb格式的数据
在网络中的数据存储和操作是以Blobs形式
train_lenet.sh训练

参考：http://caffe.berkeleyvision.org/gathered/examples/mnist.html