我使用的模型没有加入均值文件进行训练，但是官方给的example中必须要用mean_file。因此进行修改。

classification.h
#ifndef CLC
#define CLC
#include <caffe/caffe.hpp>
#include <opencv2/core/core.hpp>

#include <string>
#include <utility>
#include <vector>


std::vector<std::string> split(std::string str, std::string pattern);
/* Pair (label, confidence) representing a prediction. */
typedef std::pair<std::string, float> Prediction;
class Classifier {
public:
    Classifier(const std::string& model_file,
        const std::string& trained_file,
        const std::string& label_file,
        const std::string& mean_file = "NULL");

    std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);

private:
    void SetMean(const std::string& mean_file);
     
    std::vector<float> Predict(const cv::Mat& img);

    void WrapInputLayer(std::vector<cv::Mat>* input_channels);

    void Preprocess(const cv::Mat& img,
        std::vector<cv::Mat>* input_channels);

private:
    boost::shared_ptr<caffe::Net<float> > net_;
    cv::Size input_geometry_;
    int num_channels_;
    cv::Mat mean_;
    std::vector<std::string> labels_;
};
#endif

classification.cpp
#include "classification.h"
#include <caffe/caffe.hpp>
#include "head.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include <algorithm>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include <iostream>

#include <iostream>
#include <sstream>
#include <fstream>
#include <vector>
using namespace cv;
using namespace std;
using namespace caffe;  // NOLINT(build/namespaces)
using std::string;

/* Pair (label, confidence) representing a prediction. */
typedef std::pair<string, float> Prediction;

Classifier::Classifier(const string& model_file,
                       const string& trained_file,
                       const string& label_file,
                       const string& mean_file ) {
#ifdef CPU_ONLY
  Caffe::set_mode(Caffe::CPU);
#else
  Caffe::set_mode(Caffe::GPU);
#endif

  /* Load the network. */
  net_.reset(new Net<float>(model_file, TEST));
  net_->CopyTrainedLayersFrom(trained_file);

  CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
  CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";

  Blob<float>* input_layer = net_->input_blobs()[0];
  num_channels_ = input_layer->channels();
  CHECK(num_channels_ == 3 || num_channels_ == 1)
    << "Input layer should have 1 or 3 channels.";
  input_geometry_ = cv::Size(input_layer->width(), input_layer->height());

  /* Load the binaryproto mean file. */
  SetMean(mean_file);

  /* Load labels. */
  std::ifstream labels(label_file.c_str());
  CHECK(labels) << "Unable to open labels file " << label_file;
  string line;
  while (std::getline(labels, line))
    labels_.push_back(string(line));

  Blob<float>* output_layer = net_->output_blobs()[0];
  CHECK_EQ(labels_.size(), output_layer->channels())
    << "Number of labels is different from the output layer dimension.";
}

static bool PairCompare(const std::pair<float, int>& lhs,
                        const std::pair<float, int>& rhs) {
  return lhs.first > rhs.first;
}

/* Return the indices of the top N values of vector v. */
static std::vector<int> Argmax(const std::vector<float>& v, int N) {
  std::vector<std::pair<float, int> > pairs;
  for (size_t i = 0; i < v.size(); ++i)
    pairs.push_back(std::make_pair(v[i], i));
  std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);

  std::vector<int> result;
  for (int i = 0; i < N; ++i)
    result.push_back(pairs[i].second);
  return result;
}

/* Return the top N predictions. */
std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
  std::vector<float> output = Predict(img);
  //int N = 1;
  N = std::min<int>(labels_.size(), N);
  std::vector<int> maxN = Argmax(output, N);
  std::vector<Prediction> predictions;
  for (int i = 0; i < N; ++i) {
    int idx = maxN[i];
    predictions.push_back(std::make_pair(labels_[idx], output[idx]));
  }

  return predictions;
}

/* Load the mean file in binaryproto format. */
void Classifier::SetMean(const string& mean_file) {
    if (mean_file == "NULL")
    {
        if (num_channels_ == 1)
        {
            Scalar ZERO(0);
            mean_ = cv::Mat(input_geometry_, CV_32FC1, ZERO);
        }
        else if (num_channels_ == 3)
        {
            Scalar ZERO(0,0,0);
            mean_ = cv::Mat(input_geometry_, CV_32FC3, ZERO);
        }
        else { cout << "Unsupport channels!" << endl; return; }
    }
    else
    {
        BlobProto blob_proto;
        ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);

        /* Convert from BlobProto to Blob<float> */
        Blob<float> mean_blob;
        mean_blob.FromProto(blob_proto);
        CHECK_EQ(mean_blob.channels(), num_channels_)
            << "Number of channels of mean file doesn't match input layer.";

        /* The format of the mean file is planar 32-bit float BGR or grayscale. */
        std::vector<cv::Mat> channels;
        float* data = mean_blob.mutable_cpu_data();
        for (int i = 0; i < num_channels_; ++i) {
            /* Extract an individual channel. */
            cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
            channels.push_back(channel);
            data += mean_blob.height() * mean_blob.width();
        }

        /* Merge the separate channels into a single image. */
        cv::Mat mean;
        cv::merge(channels, mean);

        /* Compute the global mean pixel value and create a mean image
        * filled with this value. */
        cv::Scalar channel_mean = cv::mean(mean);
        mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
    }
}

std::vector<float> Classifier::Predict(const cv::Mat& img) {
  Blob<float>* input_layer = net_->input_blobs()[0];
  input_layer->Reshape(1, num_channels_,
                       input_geometry_.height, input_geometry_.width);
  /* Forward dimension change to all layers. */
  net_->Reshape();

  std::vector<cv::Mat> input_channels;
  WrapInputLayer(&input_channels);

  Preprocess(img, &input_channels);

  net_->Forward();

  /* Copy the output layer to a std::vector */
  Blob<float>* output_layer = net_->output_blobs()[0];
  const float* begin = output_layer->cpu_data();
  const float* end = begin + output_layer->channels();
  return std::vector<float>(begin, end);
}

/* Wrap the input layer of the network in separate cv::Mat objects
 * (one per channel). This way we save one memcpy operation and we
 * don't need to rely on cudaMemcpy2D. The last preprocessing
 * operation will write the separate channels directly to the input
 * layer. */
void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {
  Blob<float>* input_layer = net_->input_blobs()[0];

  int width = input_layer->width();
  int height = input_layer->height();
  float* input_data = input_layer->mutable_cpu_data();
  for (int i = 0; i < input_layer->channels(); ++i) {
    cv::Mat channel(height, width, CV_32FC1, input_data);
    input_channels->push_back(channel);
    input_data += width * height;
  }
}

void Classifier::Preprocess(const cv::Mat& img,
                            std::vector<cv::Mat>* input_channels) {
  /* Convert the input image to the input image format of the network. */
  cv::Mat sample;
  if (img.channels() == 3 && num_channels_ == 1)
    cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);
  else if (img.channels() == 4 && num_channels_ == 1)
    cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);
  else if (img.channels() == 4 && num_channels_ == 3)
    cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);
  else if (img.channels() == 1 && num_channels_ == 3)
    cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);
  else
    sample = img;

  cv::Mat sample_resized;
  if (sample.size() != input_geometry_)
    cv::resize(sample, sample_resized, input_geometry_);
  else
    sample_resized = sample;

  cv::Mat sample_float;
  if (num_channels_ == 3)
      sample_resized.convertTo(sample_float, CV_32FC3, 1.0 / 255);
  else
      sample_resized.convertTo(sample_float, CV_32FC1, 1.0 / 255);

  //cv::Mat sample_normalized;
  //cv::subtract(sample_float, mean_, sample_normalized);
  cv::Mat sample_normalized = sample_float;
  /* This operation will write the separate BGR planes directly to the
  * input layer of the network because it is wrapped by the cv::Mat
  * objects in input_channels. */
  cv::split(sample_normalized, *input_channels);

  CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
      == net_->input_blobs()[0]->cpu_data())
      << "Input channels are not wrapping the input layer of the network.";
}

在保留mean_file前提下进行修改，但是出现置信度一直很大，始终为1或0.99。
原因是源代码中

sample_resized.convertTo(sample_float, CV_32FC3);

改成

sample_resized.convertTo(sample_float, CV_32FC3, 1.0 / 255);

正常。