@Yano
2017-07-15T21:41:40.000000Z
字数 5823
阅读 2067
deeplearning
要训练 MNIST,实际上只需要 3 个脚本文件即可完成:
cd $CAFFE_ROOT
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
./examples/mnist/train_lenet.sh
MNIST database,一个手写数字的图片数据库,每一张图片都是0到9中的单个数字。每一张都是抗锯齿(Anti-aliasing)的灰度图,图片大小28*28像素,数字部分被归一化为20*20大小,位于图片的中间位置,保持了原来形状的比例.
官方链接是没有提供jpg图片格式的,如果我们想对数据有个直观的了解,可以通过此链接下载 jpg 图片。
MNIST数据库的来源是两个数据库的混合,一个来自Census Bureau employees(SD-3),一个来自high-school students(SD-1);有训练样本60000个,测试样本10000个.训练样本和测试样本中,employee和student写的都是各占一半.60000个训练样本一共大概250个人写的.训练样本和测试样本的来源人群没有交集.
MNIST数据库也保留了手写数字与身份的对应关系.
#!/usr/bin/env sh
# This scripts downloads the mnist data and unzips it.
DIR="$( 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-ubyte
do
if [ ! -e $fname ]; then
wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
gunzip ${fname}.gz
fi
done
这个脚本仅仅是把官网上的 mnist 的文件下载下来,其中 4 个文件分别是(测试数据和训练数据):
train-images-idx3-ubyte
train-labels-idx1-ubyte
t10k-images-idx3-ubyte
t10k-labels-idx1-ubyte
#!/usr/bin/env sh
# This script converts the mnist data into lmdb/leveldb format,
# depending on the value assigned to $BACKEND.
set -e
EXAMPLE=examples/mnist
DATA=data/mnist
BUILD=build/examples/mnist
BACKEND="lmdb"
echo "Creating ${BACKEND}..."
rm -rf $EXAMPLE/mnist_train_${BACKEND}
rm -rf $EXAMPLE/mnist_test_${BACKEND}
$BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \
$DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} --backend=${BACKEND}
$BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \
$DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} --backend=${BACKEND}
echo "Done."
步骤:
- 删除 examples/mnist 目录下的 mnist_train_lmdb
和 mnist_test_lmdb
,其中BACKEND="lmdb"
指定了数据格式,数据格式除了lmdb
,还有leveldb
。
- 将第一步下载好的文件,转换成lmdb
格式,保存在examples/mnist
目录下。
最终会出现下面的两个文件:
#!/usr/bin/env sh
set -e
./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt $@
使用编译好的caffe
,用lenet_solver.prototxt
这个proto文件的规则来训练,如果使用CPU的话,需要将最后一行的solver_mode
改为CPU
。
# The train/test net protocol buffer definition
net: "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: 0.01
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
# solver mode: CPU or GPU
solver_mode: CPU
这里面是详细的参数,并且附有详细的注释~~~w(゚Д゚)w 其中最重要的应该是下面这句话:
# The train/test net protocol buffer definition
net: "examples/mnist/lenet_train_test.prototxt"
把lenet_train_test.prototxt
全部贴出来,不做深入分析,对于 proto 语法,可以参考:Google Protocol Buffers 数据交换协议。
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"
}
最终的训练结果,准确率竟然高达 98.98% w(゚Д゚)w
在训练好模型之后,如何才能应用到实际当中呢?对于MNIST,就是要把符合格式的图片输入给神经网络,然后看预测是否符合标准。参考caffe笔记:测试自己的手写数字图片,用 caffe 测试自己的图片。在这里具体过程就不详细介绍,仅展示下最终结果(前面的链接写得非常全):
./build/examples/cpp_classification/classification.bin \
> examples/mnist/classificat_net.prototxt \
> examples/mnist/lenet_iter_10000.caffemodel \
> examples/mnist/mean.binaryproto \
> examples/mnist/label.txt \
> examples/mnist/0.png
注意一下,classificat_net.prototxt
是 examples/mnist/lenet_train_test.prototxt
的副本,但是要删掉
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
}
}
以及
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"
}
并在最后增加一层:
layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}
最终的预测结果如下,可以看到模型100%确定数字是0,预测正确:
---------- Prediction for examples/mnist/0.png ----------
1.0000 - "0"
0.0000 - "1"
0.0000 - "3"
0.0000 - "4"
0.0000 - "2"
对于模型及原理,网上已经有很多好的文章,这里不作介绍。今天在 centos 上安装 caffe,实在太费劲了…… 我在编译的时候,把 leveldb 和 lmdb 全部都取消了,这就导致 MNIST 的数据格式不能识别!解决方法:更改配置,重新编译。
参考了那么多的 caffe 安装教程,这篇文章最好:caffe 安装(centos 7)