caffe网络配置文件字段意义详解

解决方案：lenet_solver.prototxt

# The train/test net protocol buffer definition
net: "examples/mnist/lenet_train_test.prototxt"
//网络协议具体定义
# 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 如果batch_size =100,则100张图一批，训练100次，则可以覆盖10000张图的需求
# 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
momentum: 0.9
weight_decay: 0.0005
//网络参数：学习率，动量，权重的衰减
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
//学习策略：有固定学习率和每步递减学习率
# Display every 100 iterations
display: 100
//每迭代100次显示一次
# The maximum number of iterations
max_iter: 10000
//最大迭代次数
# snapshot intermediate results
snapshot: 5000
//每5000次迭代存储一次数据，路径前缀是examples/mnist/lenet
snapshot_prefix: "examples/mnist/lenet"
# solver mode: CPU or GPU
solver_mode: GPU
//是否使用GPU还是CPU
网络构造：lenet_train_test.prototxt

name: "LeNet" 网络名
layer {
name: "mnist" 本层名称
type: "Data" 层类型
top: "data" 下一层接口
top: "label" 下一层接口
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625 #1/256，预处理如减均值，尺寸变换，随机剪，镜像等
}
data_param {
source: "examples/mnist/mnist_train_lmdb" 训练数据位置
batch_size: 64 一次训练的样本数
backend: LMDB 读入的训练数据格式，默认leveldb
}
}

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 一次测试使用100个数据
backend: LMDB
}
}

layer {
name: "conv1"
type: "Convolution" 卷积层
bottom: "data" 上一层名“data”
top: "conv1" 下一层接口“conv1”
param {
lr_mult: 1 （weights的学习率与全局相同）
}
param {
lr_mult: 2 （biases的学习率是全局的2倍）
}
convolution_param {
num_output: 20 卷积核20个
kernel_size: 5 卷积核尺寸5×5
stride: 1 步长1
weight_filler {
type: "xavier" （随机的初始化权重和偏差）
}
bias_filler {
type: "constant" bias用0初始化
}
}
}

layer {
name: "pool1"
type: "Pooling" 池化层
bottom: "conv1" 上层“conv1”
top: "pool1" 下层接口“pool1”
pooling_param {
pool: MAX 池化函数用MAX
kernel_size: 2 池化核函数大小2×2
stride: 2 步长2
}
}

layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50 卷积核50个
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" 全连接层
bottom: "pool2" 上层连接“pool2”
top: "ip1" “下层输出接口ip1”
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500 输出数量500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}

layer {
name: "relu1"
type: "ReLU" 激活函数
bottom: "ip1"
top: "ip1" （这个地方还是ip1，底层与顶层相同减少开支，下一层全连接层的输入也还是ip1）
}

layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10 输出结果10个
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}

layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2" 上层连接ip2全连接层
bottom: "label" 上层连接label层
top: "accuracy" 输出接口为accuracy
include {
phase: TEST
}
}

layer {
name: "loss"
type: "SoftmaxWithLoss" 损失函数
bottom: "ip2"
bottom: "label"
top: "loss"
}
训练网络模型：

# -*- coding: utf-8 -*-

import caffe
#caffe.set_device(0)
#caffe.set_mode_gpu()
caffe.set_mode_cpu()
solver = caffe.SGDSolver('/root/AI/lenet/solver.prototxt')
solver.solve()