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@Yano 2017-07-15T21:41:40.000000Z 字数 5823 阅读 2072

Caffe MNIST 简要分析

deeplearning

说明

要训练 MNIST，实际上只需要 3 个脚本文件即可完成：

  1. cd $CAFFE_ROOT
  2. ./data/mnist/get_mnist.sh
  3. ./examples/mnist/create_mnist.sh
  4. ./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。

数据分布

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.

./data/mnist/get_mnist.sh

  1. #!/usr/bin/env sh
  2. # This scripts downloads the mnist data and unzips it.
  4. DIR="$( cd "$(dirname "$0")" ; pwd -P )"
  5. cd "$DIR"
  7. echo "Downloading..."
  9. for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
  10. do
  11.     if [ ! -e $fname ]; then
  12.         wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
  13.         gunzip ${fname}.gz
  14.     fi
  15. 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）：

  1. train-images-idx3-ubyte 
  2. train-labels-idx1-ubyte 
  3. t10k-images-idx3-ubyte 
  4. t10k-labels-idx1-ubyte

./examples/mnist/create_mnist.sh

  1. #!/usr/bin/env sh
  2. # This script converts the mnist data into lmdb/leveldb format,
  3. # depending on the value assigned to $BACKEND.
  4. set -e
  6. EXAMPLE=examples/mnist
  7. DATA=data/mnist
  8. BUILD=build/examples/mnist
  10. BACKEND="lmdb"
  12. echo "Creating ${BACKEND}..."
  14. rm -rf $EXAMPLE/mnist_train_${BACKEND}
  15. rm -rf $EXAMPLE/mnist_test_${BACKEND}
  17. $BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \
  18.   $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} --backend=${BACKEND}
  19. $BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \
  20.   $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} --backend=${BACKEND}
  22. 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：

./examples/mnist/train_lenet.sh

  1. #!/usr/bin/env sh
  2. set -e
  4. ./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。

  1. # The train/test net protocol buffer definition
  2. net: "examples/mnist/lenet_train_test.prototxt"
  3. # test_iter specifies how many forward passes the test should carry out.
  4. # In the case of MNIST, we have test batch size 100 and 100 test iterations,
  5. # covering the full 10,000 testing images.
  6. test_iter: 100
  7. # Carry out testing every 500 training iterations.
  8. test_interval: 500
  9. # The base learning rate, momentum and the weight decay of the network.
  10. base_lr: 0.01
  11. momentum: 0.9
  12. weight_decay: 0.0005
  13. # The learning rate policy
  14. lr_policy: "inv"
  15. gamma: 0.0001
  16. power: 0.75
  17. # Display every 100 iterations
  18. display: 100
  19. # The maximum number of iterations
  20. max_iter: 10000
  21. # snapshot intermediate results
  22. snapshot: 5000
  23. snapshot_prefix: "examples/mnist/lenet"
  24. # solver mode: CPU or GPU
  25. 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：

  1. # The train/test net protocol buffer definition
  2. 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。

  1. name: "LeNet"
  2. layer {
  3.   name: "mnist"
  4.   type: "Data"
  5.   top: "data"
  6.   top: "label"
  7.   include {
  8.     phase: TRAIN
  9.   }
  10.   transform_param {
  11.     scale: 0.00390625
  12.   }
  13.   data_param {
  14.     source: "examples/mnist/mnist_train_lmdb"
  15.     batch_size: 64
  16.     backend: LMDB
  17.   }
  18. }
  19. layer {
  20.   name: "mnist"
  21.   type: "Data"
  22.   top: "data"
  23.   top: "label"
  24.   include {
  25.     phase: TEST
  26.   }
  27.   transform_param {
  28.     scale: 0.00390625
  29.   }
  30.   data_param {
  31.     source: "examples/mnist/mnist_test_lmdb"
  32.     batch_size: 100
  33.     backend: LMDB
  34.   }
  35. }
  36. layer {
  37.   name: "conv1"
  38.   type: "Convolution"
  39.   bottom: "data"
  40.   top: "conv1"
  41.   param {
  42.     lr_mult: 1
  43.   }
  44.   param {
  45.     lr_mult: 2
  46.   }
  47.   convolution_param {
  48.     num_output: 20
  49.     kernel_size: 5
  50.     stride: 1
  51.     weight_filler {
  52.       type: "xavier"
  53.     }
  54.     bias_filler {
  55.       type: "constant"
  56.     }
  57.   }
  58. }
  59. layer {
  60.   name: "pool1"
  61.   type: "Pooling"
  62.   bottom: "conv1"
  63.   top: "pool1"
  64.   pooling_param {
  65.     pool: MAX
  66.     kernel_size: 2
  67.     stride: 2
  68.   }
  69. }
  70. layer {
  71.   name: "conv2"
  72.   type: "Convolution"
  73.   bottom: "pool1"
  74.   top: "conv2"
  75.   param {
  76.     lr_mult: 1
  77.   }
  78.   param {
  79.     lr_mult: 2
  80.   }
  81.   convolution_param {
  82.     num_output: 50
  83.     kernel_size: 5
  84.     stride: 1
  85.     weight_filler {
  86.       type: "xavier"
  87.     }
  88.     bias_filler {
  89.       type: "constant"
  90.     }
  91.   }
  92. }
  93. layer {
  94.   name: "pool2"
  95.   type: "Pooling"
  96.   bottom: "conv2"
  97.   top: "pool2"
  98.   pooling_param {
  99.     pool: MAX
  100.     kernel_size: 2
  101.     stride: 2
  102.   }
  103. }
  104. layer {
  105.   name: "ip1"
  106.   type: "InnerProduct"
  107.   bottom: "pool2"
  108.   top: "ip1"
  109.   param {
  110.     lr_mult: 1
  111.   }
  112.   param {
  113.     lr_mult: 2
  114.   }
  115.   inner_product_param {
  116.     num_output: 500
  117.     weight_filler {
  118.       type: "xavier"
  119.     }
  120.     bias_filler {
  121.       type: "constant"
  122.     }
  123.   }
  124. }
  125. layer {
  126.   name: "relu1"
  127.   type: "ReLU"
  128.   bottom: "ip1"
  129.   top: "ip1"
  130. }
  131. layer {
  132.   name: "ip2"
  133.   type: "InnerProduct"
  134.   bottom: "ip1"
  135.   top: "ip2"
  136.   param {
  137.     lr_mult: 1
  138.   }
  139.   param {
  140.     lr_mult: 2
  141.   }
  142.   inner_product_param {
  143.     num_output: 10
  144.     weight_filler {
  145.       type: "xavier"
  146.     }
  147.     bias_filler {
  148.       type: "constant"
  149.     }
  150.   }
  151. }
  152. layer {
  153.   name: "accuracy"
  154.   type: "Accuracy"
  155.   bottom: "ip2"
  156.   bottom: "label"
  157.   top: "accuracy"
  158.   include {
  159.     phase: TEST
  160.   }
  161. }
  162. layer {
  163.   name: "loss"
  164.   type: "SoftmaxWithLoss"
  165.   bottom: "ip2"
  166.   bottom: "label"
  167.   top: "loss"
  168. }

训练结果

最终的训练结果，准确率竟然高达 98.98% w(ﾟДﾟ)w

测试自己的手写数字图片

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）：

  1. ./build/examples/cpp_classification/classification.bin \
  2. > examples/mnist/classificat_net.prototxt \
  3. > examples/mnist/lenet_iter_10000.caffemodel \
  4. > examples/mnist/mean.binaryproto \
  5. > examples/mnist/label.txt \
  6. > 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

  1. layer {
  2.   name: "mnist"
  3.   type: "Data"
  4.   top: "data"
  5.   top: "label"
  6.   include {
  7.     phase: TRAIN
  8.   }
  9.   transform_param {
  10.     scale: 0.00390625
  11.   }
  12.   data_param {
  13.     source: "examples/mnist/mnist_train_lmdb"
  14.     batch_size: 64
  15.     backend: LMDB
  16.   }
  17. }
  18. layer {
  19.   name: "mnist"
  20.   type: "Data"
  21.   top: "data"
  22.   top: "label"
  23.   include {
  24.     phase: TEST
  25.   }
  26.   transform_param {
  27.     scale: 0.00390625
  28.   }
  29.   data_param {
  30.     source: "examples/mnist/mnist_test_lmdb"
  31.     batch_size: 100
  32.     backend: LMDB
  33.   }
  34. }

以及

  1. layer {
  2.   name: "accuracy"
  3.   type: "Accuracy"
  4.   bottom: "ip2"
  5.   bottom: "label"
  6.   top: "accuracy"
  7.   include {
  8.     phase: TEST
  9.   }
  10. }
  11. layer {
  12.   name: "loss"
  13.   type: "SoftmaxWithLoss"
  14.   bottom: "ip2"
  15.   bottom: "label"
  16.   top: "loss"
  17. }

并在最后增加一层：

  1. layer {
  2.   name: "prob"
  3.   type: "Softmax"
  4.   bottom: "ip2"
  5.   top: "prob"
  6. }

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：

  1. ---------- Prediction for examples/mnist/0.png ----------
  2. 1.0000 - "0"
  3. 0.0000 - "1"
  4. 0.0000 - "3"
  5. 0.0000 - "4"
  6. 0.0000 - "2"

总结

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）

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