macOS Sierra (10.12.4)系统上Caffe借助现有的模型训练自己的数据集 – 默默的点滴
2017年十一月
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Caffe代码中自带一些模型的例子，这些例子在源代码的
models目录下，这些都是其他项目中用来训练的配置文件，学习的时候,我们没有必要完全自己从头到尾搭建自己的网络模型，而是直接使用例子中的模型，后期在这些模型上简单调整一下，一般可以满足大多数的需求。
下面我们以
models/bvlc_alexnet目录下的模型配置文件为例子，训练我们自己的神经网络。
这个目录下有四个文件，如下图：
简单介绍一下这个几个文件：
train_val.prototxt用来定义训练神经网络时候的模型信息，主要定义训练时候的神经网络应该如何构造，比如分多少层，每层的行为是什么。
solver.prototxt用来定义神经网络运行的参数，比如使用
GPU，神经网络的配置文件（
train_val.prototxt）的位置，以及文件名。
deploy.prototxt用来定义使用神经网络识别图片的时候使用的网络定义文件，一般是
train_val.prototxt简单修改后得到的。
介绍完成神经网络模型，我们接下来需要提供我们的训练图片，用来让神经网络根据我们指定的图片进行学习。
iPad上的植物大战僵尸的资源图片为例子,让
Caffe帮我们识别哪些是僵尸，哪些是植物。完整的例子可以从。
简单讲一下图片目录，其中的
train目录下的图片为训练的图片，而
detect目录为用来测试识别的图片。
本次，我们在新建的
examples/PlantsVsZombies这个目录下进行操作。操作之前请参照保证可以成功编译
注意，由于我们使用现有的神经网络模型来训练数据，因此，我们只需要提供训练图片集合就可以了，不需要提供验证图片集合，这个集合是用来调教神经网络配置文件的，我们已经有了配置好的文件，就没必要再重新调教神经网络配置了。
1.生成训练图片的索引文件
train.txt，里面的内容如下：
peashooter.png 0
squash.png 0
sunflower.png 0
wallnut.png 0
caiwen.png 0
Gargantuan.png 1
Truckman.png 1
Yeti.png 1
MetalPail.png 1
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peashooter.png 0squash.png 0sunflower.png 0wallnut.png 0caiwen.png 0Gargantuan.png 1Truckman.png 1Yeti.png 1MetalPail.png 1
注意后面的索引编号对应我们下面的标签文件的索引序号。
2.生成标签文件
labels.txt，用来描述图片的内容：
labels.txt
0 Plant1 Zombie
3.整合图片以及描述信息为
lmdb格式，方便
Caffe进行高性能的
examples/PlantsVsZombies目录下创建
create_images.sh脚本，脚本内容如下：
create_images.sh
#!/usr/bin/env sh
# Create the imagenet lmdb inputs
# N.B. set the path to the imagenet train + val data dirs
EXAMPLE=examples/PlantsVsZombies
TOOLS=build/tools
TRAIN_DATA_ROOT=examples/PlantsVsZombies/images/train/ #训练样本的存放路径
# Set RESIZE=true to resize the images to 256x256. Leave as false if images have
# already been resized using another tool.
RESIZE=true
if $RESIZE; then
RESIZE_HEIGHT=256 #改变图片的大小为256*256
RESIZE_WIDTH=256
RESIZE_HEIGHT=0
RESIZE_WIDTH=0
# 判断路径是否正确的提示信息
if [ ! -d "$TRAIN_DATA_ROOT" ]; then
echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT"
echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \
"where the images training data is stored."
rm -rf $EXAMPLE/train_lmdb
echo "Creating train lmdb..."
##调用convert_imageset文件转换文件格式，后面为输入参数
GLOG_logtostderr=1 $TOOLS/convert_imageset \
--resize_height=$RESIZE_HEIGHT \
--resize_width=$RESIZE_WIDTH \
$TRAIN_DATA_ROOT \
$EXAMPLE/train.txt \
$EXAMPLE/train_lmdb
echo "Done."
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#!/usr/bin/env sh# Create the imagenet lmdb inputs# N.B. set the path to the imagenet train + val data dirs&EXAMPLE=examples/PlantsVsZombiesTOOLS=build/tools&TRAIN_DATA_ROOT=examples/PlantsVsZombies/images/train/ #训练样本的存放路径&# Set RESIZE=true to resize the images to 256x256. Leave as false if images have# already been resized using another tool.RESIZE=trueif $RESIZE; then&&RESIZE_HEIGHT=256 #改变图片的大小为256*256&&RESIZE_WIDTH=256else&&RESIZE_HEIGHT=0&&RESIZE_WIDTH=0fi# 判断路径是否正确的提示信息if [ ! -d "$TRAIN_DATA_ROOT" ]; then&&echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT"&&echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \&&&&&& "where the images training data is stored."&&exit 1fi&rm -rf $EXAMPLE/train_lmdb&echo "Creating train lmdb..."&##调用convert_imageset文件转换文件格式，后面为输入参数GLOG_logtostderr=1 $TOOLS/convert_imageset \&&&&--resize_height=$RESIZE_HEIGHT \&&&&--resize_width=$RESIZE_WIDTH \&&&&$TRAIN_DATA_ROOT \&&&&$EXAMPLE/train.txt \&&&&$EXAMPLE/train_lmdb&echo "Done."
在代码的根目录下执行如下脚本：
$ sh examples/PlantsVsZombies/create_images.sh
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$ sh examples/PlantsVsZombies/create_images.sh
4.生成图片均值文件，提高训练效率
examples/PlantsVsZombies目录下创建
make_mean.sh脚本，脚本内容如下：
make_mean.sh
#!/usr/bin/env sh
EXAMPLE=examples/PlantsVsZombies
DATA=examples/PlantsVsZombies
TOOLS=build/tools
$TOOLS/compute_image_mean $EXAMPLE/train_lmdb $DATA/mean.binaryproto
echo "Done."
#!/usr/bin/env sh&EXAMPLE=examples/PlantsVsZombiesDATA=examples/PlantsVsZombies TOOLS=build/tools&$TOOLS/compute_image_mean $EXAMPLE/train_lmdb $DATA/mean.binaryproto&echo "Done."
在代码的根目录下执行如下脚本：
$ sh examples/PlantsVsZombies/make_mean.sh
$ sh examples/PlantsVsZombies/make_mean.sh
5.拷贝代码中的
models/bvlc_alexnet中的文件到我们自己的工程目录中，并进行修改
$ mkdir examples/PlantsVsZombies/model/
$ cp -r models/bvlc_alexnet/* examples/PlantsVsZombies/model/
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$ mkdir examples/PlantsVsZombies/model/$ cp -r models/bvlc_alexnet/* examples/PlantsVsZombies/model/
修改配置信息
$ vim examples/PlantsVsZombies/model/solver.prototxt
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$ vim examples/PlantsVsZombies/model/solver.prototxt
原始内容为：
solver.prototxt
net: "models/bvlc_alexnet/train_val.prototxt"
test_iter: 1000
test_interval: 1000
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 20
max_iter: 450000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "models/bvlc_alexnet/caffe_alexnet_train"
solver_mode: GPU
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net: "models/bvlc_alexnet/train_val.prototxt"test_iter: 1000test_interval: 1000base_lr: 0.01lr_policy: "step"gamma: 0.1stepsize: 100000display: 20max_iter: 450000momentum: 0.9weight_decay: 0.0005snapshot: 10000snapshot_prefix: "models/bvlc_alexnet/caffe_alexnet_train"solver_mode: GPU
solver.prototxt
# 训练的prototxt在哪，路径
net: "examples/PlantsVsZombies/model/train_val.prototxt"
# 测试要迭代多少个Batch
test_iter * batchsize(测试集的)＝ 测试集的大小
#这个参数决定了整个的训练时间长度，如果设置为2，则在2分钟就可以，如果设置为1000，则需要28分钟以上,一般这个数字设置成分类的数量即可了,这个数字对后续的识别效果影响根据数据集的不同而有差别，如果识别率偏低，可以试试改大这个数字
test_iter: 2
# 每500次迭代，就在用测试集进行测试
test_interval: 500
# 设置初始化的学习率为0.01
base_lr: 0.01
# 权重衰减策略
lr_policy: "step"
# 初始的学习率为0.01，并且每100000次迭代中进行学习率下降
gamma: 0.1
stepsize: 100000
# 每20次epoch就显示出一些数据信息
display: 20
# 迭代次数,我们数据集非常小，太高没意义，这个参数决定了整个的执行时间 5已经比较大了,后续图片增多后再调整大这个数字
max_iter: 5
# 一直都是0.9，固定不变；迭代的数据更快，步伐更快
momentum: 0.9
# 权重衰减因子为0.0005
weight_decay: 0.0005
# 每10000次迭代中，就生成当前状态的快照
snapshot: 10000
# 模型快照保存
snapshot_prefix: "examples/PlantsVsZombies/model/caffe_alexnet_train"
# 可以设定GPU还是CPU
solver_mode: CPU
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# 训练的prototxt在哪，路径net: "examples/PlantsVsZombies/model/train_val.prototxt"# 测试要迭代多少个Batch&&test_iter * batchsize(测试集的)＝ 测试集的大小#这个参数决定了整个的训练时间长度，如果设置为2，则在2分钟就可以，如果设置为1000，则需要28分钟以上,一般这个数字设置成分类的数量即可了,这个数字对后续的识别效果影响根据数据集的不同而有差别，如果识别率偏低，可以试试改大这个数字test_iter: 2# 每500次迭代，就在用测试集进行测试test_interval: 500# 设置初始化的学习率为0.01base_lr: 0.01# 权重衰减策略lr_policy: "step"# 初始的学习率为0.01，并且每100000次迭代中进行学习率下降gamma: 0.1stepsize: 100000# 每20次epoch就显示出一些数据信息display: 20# 迭代次数,我们数据集非常小，太高没意义，这个参数决定了整个的执行时间 5已经比较大了,后续图片增多后再调整大这个数字max_iter: 5# 一直都是0.9，固定不变；迭代的数据更快，步伐更快momentum: 0.9# 权重衰减因子为0.0005weight_decay: 0.0005# 每10000次迭代中，就生成当前状态的快照snapshot: 10000# 模型快照保存snapshot_prefix: "examples/PlantsVsZombies/model/caffe_alexnet_train"# 可以设定GPU还是CPUsolver_mode: CPU
$ vim examples/PlantsVsZombies/model/train_val.prototxt
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$ vim examples/PlantsVsZombies/model/train_val.prototxt
原始内容为：
train_val.prototxt
name: "AlexNet"
name: "data"
type: "Data"
top: "data"
top: "label"
phase: TRAIN
transform_param {
mirror: true
crop_size: 227
mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
data_param {
source: "examples/imagenet/ilsvrc12_train_lmdb"
batch_size: 256
backend: LMDB
name: "data"
type: "Data"
top: "data"
top: "label"
phase: TEST
transform_param {
mirror: false
crop_size: 227
mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
data_param {
source: "examples/imagenet/ilsvrc12_val_lmdb"
batch_size: 50
backend: LMDB
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 96
kernel_size: 11
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
kernel_size: 3
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 256
kernel_size: 5
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
value: 0.1
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
kernel_size: 3
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 384
kernel_size: 3
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 384
kernel_size: 3
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
value: 0.1
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 256
kernel_size: 3
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
value: 0.1
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
kernel_size: 3
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
bias_filler {
type: "constant"
value: 0.1
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
bias_filler {
type: "constant"
value: 0.1
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
inner_product_param {
num_output: 1000
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
phase: TEST
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
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class="crayon-num crayon-striped-num" data-line="crayon-5a067d6<div class="crayon-num" data-line="crayon-5a067d7<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d8<div class="crayon-num" data-line="crayon-5a067d9<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d0<div class="crayon-num" data-line="crayon-5a067d1<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d2<div class="crayon-num" data-line="crayon-5a067d3<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d4<div class="crayon-num" data-line="crayon-5a067d5<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d6<div class="crayon-num" data-line="crayon-5a067d7<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d8<div class="crayon-num" data-line="crayon-5a067d9<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d0<div class="crayon-num" data-line="crayon-5a067d1<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d2<div class="crayon-num" data-line="crayon-5a067d3<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d4<div class="crayon-num" data-line="crayon-5a067d5<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d6<div class="crayon-num" data-line="crayon-5a067d7<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d8<div class="crayon-num" data-line="crayon-5a067d9<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d0<div class="crayon-num" data-line="crayon-5a067d1<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d2<div class="crayon-num" data-line="crayon-5a067d3<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d4<div class="crayon-num" data-line="crayon-5a067d5<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d6<div class="crayon-num" data-line="crayon-5a067d7<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d8<div class="crayon-num" data-line="crayon-5a067d9<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d0<div class="crayon-num" data-line="crayon-5a067d1<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d2<div class="crayon-num" data-line="crayon-5a067d3<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d4<div class="crayon-num" data-line="crayon-5a067d5<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d6<div class="crayon-num" data-line="crayon-5a067d7<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d8<div class="crayon-num" data-line="crayon-5a067d9<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d0<div class="crayon-num" data-line="crayon-5a067d1<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d2<div class="crayon-num" data-line="crayon-5a067d3<div class="crayon-num crayon-striped-num" data-line="crayon-5a067d4<div class="crayon-num" data-line="crayon-5a067d5<div class="crayon-num 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name: "AlexNet"layer {&&name: "data"&&type: "Data"&&top: "data"&&top: "label"&&include {&&&&phase: TRAIN&&}&&transform_param {&&&&mirror: true&&&&crop_size: 227&&&&mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"&&}&&data_param {&&&&source: "examples/imagenet/ilsvrc12_train_lmdb"&&&&batch_size: 256&&&&backend: LMDB&&}}layer {&&name: "data"&&type: "Data"&&top: "data"&&top: "label"&&include {&&&&phase: TEST&&}&&transform_param {&&&&mirror: false&&&&crop_size: 227&&&&mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"&&}&&data_param {&&&&source: "examples/imagenet/ilsvrc12_val_lmdb"&&&&batch_size: 50&&&&backend: LMDB&&}}layer {&&name: "conv1"&&type: "Convolution"&&bottom: "data"&&top: "conv1"&&param {&&&&lr_mult: 1&&&&decay_mult: 1&&}&&param {&&&&lr_mult: 2&&&&decay_mult: 0&&}&&convolution_param {&&&&num_output: 96&&&&kernel_size: 11&&&&stride: 4&&&&weight_filler {&&&&&&type: "gaussian"&&&&&&std: 0.01&&&&}&&&&bias_filler {&&&&&&type: "constant"&&&&&&value: 0&&&&}&&}}layer {&&name: "relu1"&&type: "ReLU"&&bottom: "conv1"&&top: "conv1"}layer {&&name: "norm1"&&type: "LRN"&&bottom: "conv1"&&top: "norm1"&&lrn_param {&&&&local_size: 5&&&&alpha: 0.0001&&&&beta: 0.75&&}}layer {&&name: "pool1"&&type: "Pooling"&&bottom: "norm1"&&top: "pool1"&&pooling_param {&&&&pool: MAX&&&&kernel_size: 3&&&&stride: 2&&}}layer {&&name: "conv2"&&type: "Convolution"&&bottom: "pool1"&&top: "conv2"&&param {&&&&lr_mult: 1&&&&decay_mult: 1&&}&&param {&&&&lr_mult: 2&&&&decay_mult: 0&&}&&convolution_param {&&&&num_output: 256&&&&pad: 2&&&&kernel_size: 5&&&&group: 2&&&&weight_filler {&&&&&&type: "gaussian"&&&&&&std: 0.01&&&&}&&&&bias_filler {&&&&&&type: "constant"&&&&&&value: 0.1&&&&}&&}}layer {&&name: "relu2"&&type: "ReLU"&&bottom: "conv2"&&top: "conv2"}layer {&&name: "norm2"&&type: "LRN"&&bottom: "conv2"&&top: "norm2"&&lrn_param {&&&&local_size: 5&&&&alpha: 0.0001&&&&beta: 0.75&&}}layer {&&name: "pool2"&&type: "Pooling"&&bottom: "norm2"&&top: "pool2"&&pooling_param {&&&&pool: MAX&&&&kernel_size: 3&&&&stride: 2&&}}layer {&&name: "conv3"&&type: "Convolution"&&bottom: "pool2"&&top: "conv3"&&param {&&&&lr_mult: 1&&&&decay_mult: 1&&}&&param {&&&&lr_mult: 2&&&&decay_mult: 0&&}&&convolution_param {&&&&num_output: 384&&&&pad: 1&&&&kernel_size: 3&&&&weight_filler {&&&&&&type: "gaussian"&&&&&&std: 0.01&&&&}&&&&bias_filler {&&&&&&type: "constant"&&&&&&value: 0&&&&}&&}}layer {&&name: "relu3"&&type: "ReLU"&&bottom: "conv3"&&top: "conv3"}layer {&&name: "conv4"&&type: "Convolution"&&bottom: "conv3"&&top: "conv4"&&param {&&&&lr_mult: 1&&&&decay_mult: 1&&}&&param {&&&&lr_mult: 2&&&&decay_mult: 0&&}&&convolution_param {&&&&num_output: 384&&&&pad: 1&&&&kernel_size: 3&&&&group: 2&&&&weight_filler {&&&&&&type: "gaussian"&&&&&&std: 0.01&&&&}&&&&bias_filler {&&&&&&type: "constant"&&&&&&value: 0.1&&&&}&&}}layer {&&name: "relu4"&&type: "ReLU"&&bottom: "conv4"&&top: "conv4"}layer {&&name: "conv5"&&type: "Convolution"&&bottom: "conv4"&&top: "conv5"&&param {&&&&lr_mult: 1&&&&decay_mult: 1&&}&&param {&&&&lr_mult: 2&&&&decay_mult: 0&&}&&convolution_param {&&&&num_output: 256&&&&pad: 1&&&&kernel_size: 3&&&&group: 2&&&&weight_filler {&&&&&&type: "gaussian"&&&&&&std: 0.01&&&&}&&&&bias_filler {&&&&&&type: "constant"&&&&&&value: 0.1&&&&}&&}}layer {&&name: "relu5"&&type: "ReLU"&&bottom: "conv5"&&top: "conv5"}layer {&&name: "pool5"&&type: "Pooling"&&bottom: "conv5"&&top: "pool5"&&pooling_param {&&&&pool: MAX&&&&kernel_size: 3&&&&stride: 2&&}}layer {&&name: "fc6"&&type: "InnerProduct"&&bottom: "pool5"&&top: "fc6"&&param {&&&&lr_mult: 1&&&&decay_mult: 1&&}&&param {&&&&lr_mult: 2&&&&decay_mult: 0&&}&&inner_product_param {&&&&num_output: 4096&&&&weight_filler {&&&&&&type: "gaussian"&&&&&&std: 0.005&&&&}&&&&bias_filler {&&&&&&type: "constant"&&&&&&value: 0.1&&&&}&&}}layer {&&name: "relu6"&&type: "ReLU"&&bottom: "fc6"&&top: "fc6"}layer {&&name: "drop6"&&type: "Dropout"&&bottom: "fc6"&&top: "fc6"&&dropout_param {&&&&dropout_ratio: 0.5&&}}layer {&&name: "fc7"&&type: "InnerProduct"&&bottom: "fc6"&&top: "fc7"&&param {&&&&lr_mult: 1&&&&decay_mult: 1&&}&&param {&&&&lr_mult: 2&&&&decay_mult: 0&&}&&inner_product_param {&&&&num_output: 4096&&&&weight_filler {&&&&&&type: "gaussian"&&&&&&std: 0.005&&&&}&&&&bias_filler {&&&&&&type: "constant"&&&&&&value: 0.1&&&&}&&}}layer {&&name: "relu7"&&type: "ReLU"&&bottom: "fc7"&&top: "fc7"}layer {&&name: "drop7"&&type: "Dropout"&&bottom: "fc7"&&top: "fc7"&&dropout_param {&&&&dropout_ratio: 0.5&&}}layer {&&name: "fc8"&&type: "InnerProduct"&&bottom: "fc7"&&top: "fc8"&&param {&&&&lr_mult: 1&&&&decay_mult: 1&&}&&param {&&&&lr_mult: 2&&&&decay_mult: 0&&}&&inner_product_param {&&&&num_output: 1000&&&&weight_filler {&&&&&&type: "gaussian"&&&&&&std: 0.01&&&&}&&&&bias_filler {&&&&&&type: "constant"&&&&&&value: 0&&&&}&&}}layer {&&name: "accuracy"&&type: "Accuracy"&&bottom: "fc8"&&bottom: "label"&&top: "accuracy"&&include {&&&&phase: TEST&&}}layer {&&name: "loss"&&type: "SoftmaxWithLoss"&&bottom: "fc8"&&bottom: "label"&&top: "loss"}
修改后为：
train_val.prototxt
name: "AlexNet"
name: "data"
type: "Data"
top: "data"
top: "label"
phase: TRAIN
transform_param {
mirror: true
crop_size: 227
mean_file: "examples/PlantsVsZombies/mean.binaryproto"
data_param {
source: "examples/PlantsVsZombies/train_lmdb"
batch_size: 256
backend: LMDB
name: "data"
type: "Data"
top: "data"
top: "label"
phase: TEST
transform_param {
mirror: false
crop_size: 227
mean_file: "examples/PlantsVsZombies/mean.binaryproto"
data_param {
source: "examples/PlantsVsZombies/train_lmdb"
batch_size: 50
backend: LMDB
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 96
kernel_size: 11
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
kernel_size: 3
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 256
kernel_size: 5
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
value: 0.1
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
kernel_size: 3
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 384
kernel_size: 3
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 384
kernel_size: 3
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
value: 0.1
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
convolution_param {
num_output: 256
kernel_size: 3
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
value: 0.1
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
kernel_size: 3
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
bias_filler {
type: "constant"
value: 0.1
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
bias_filler {
type: "constant"
value: 0.1
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
lr_mult: 1
decay_mult: 1
lr_mult: 2
decay_mult: 0
inner_product_param {
num_output: 2
weight_filler {
type: "gaussian"
bias_filler {
type: "constant"
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
phase: TEST
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
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