caffe 从mnsit示例到自己创建数据集再到识别手写图片

记录下使用caffe mnist数据集，训练+识别图片过程中遇到的问题。
很不详细。

1. 按部就班，获取mnist数据集、创建lmdb、使用example文件训练，得到caffemodel，找了图片预测，错误率很高
   原因： mnist原始图片是黑底白字，找的图片是白底黑字。。。自己来重做lmdb吧。—注意灰度图的格式
   2.网络上找来mnist手写图片，白底黑字，使用脚本生成lmdb

cp create_imagenet.sh create_imagenet_my_mnist.shcat create_imagenet_my_mnist.sh#!/usr/bin/env sh# Create the imagenet lmdb inputs# N.B. set the path to the imagenet train + val data dirsEXAMPLE=examples/mnistDATA=data/mnist/mnistTOOLS=build/toolsTRAIN_DATA_ROOT=/root/caffe-0.16.3/data/mnist/mnist/train/VAL_DATA_ROOT=/root/caffe-0.16.3/data/mnist/mnist/test/# Set RESIZE=true to resize the images to 256x256. Leave as false if images have# already been resized using another tool.RESIZE=trueif $RESIZE; then  RESIZE_HEIGHT=28  RESIZE_WIDTH=28else  RESIZE_HEIGHT=0  RESIZE_WIDTH=0fiif [ ! -d "$TRAIN_DATA_ROOT" ]; then  echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT"  echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \       "where the ImageNet training data is stored."  exit 1fiif [ ! -d "$VAL_DATA_ROOT" ]; then  echo "Error: VAL_DATA_ROOT is not a path to a directory: $VAL_DATA_ROOT"  echo "Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path" \       "where the ImageNet validation data is stored."  exit 1fiecho "Creating train lmdb..."GLOG_logtostderr=1 $TOOLS/convert_imageset \    --resize_height=$RESIZE_HEIGHT \    --resize_width=$RESIZE_WIDTH \    --shuffle \    --gray \    $TRAIN_DATA_ROOT \     $DATA/train.txt \  ##注意如果图片就在$DATA下面，train.txt和val.txt里的路径$DATA下的路径就可以    $EXAMPLE/my_mnist_train_lmdbecho "Creating val lmdb..."GLOG_logtostderr=1 $TOOLS/convert_imageset \    --resize_height=$RESIZE_HEIGHT \    --resize_width=$RESIZE_WIDTH \    --shuffle \    --gray \  ###默认是RGB，需要加个强制灰度，否则预测依然不准    $VAL_DATA_ROOT \    $DATA/val.txt \    $EXAMPLE/my_mnist_val_lmdbecho "Done."###开始识别name: "LeNet"layer {  name: "data"  type: "Input"  top: "data"  input_param { shape: { dim: 1 dim: 1 dim: 28 dim: 28 } }}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: "prob"  type: "Softmax"  bottom: "ip2"  top: "prob"}[root@7ed2726e0c5a caffe-0.16.3]# ./build/examples/cpp_classification/classification.bin examples/mnist/lenet_deploy.prototxt examples/mnist/lenet_iter_10000.caffemodel examples/mnist/mean.binaryproto data/mnist/mnist/test/labels.txt /root/8.png---------- Prediction for /root/8.png ----------1.0000 - "8"0.0000 - "5"0.0000 - "3"0.0000 - "1"0.0000 - "4"##导出镜像，有空再继续

遇到的几个小坑：
1. 可能是第一次弄不太熟悉，需要保证训练集-模型-测试集，图片格式的一致
2. 注意均值文件，训练用了，预测的时候不能忘用
3. 创建lmdb时 灰度图不是默认选项，需要加参数