C++ little errors , Big problem

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Q1. compile caffe .cpp file ,   come out an error :

d302@d302-MS-7816-04:~/wangxiao/spl-caffe-master$ make -j8
NVCC src/caffe/layers/euclidean_loss_layer.cu
src/caffe/layers/euclidean_loss_layer.cu(43): error: a value of type "const float *" cannot be used to initialize an entity of type "float *"
          detected during instantiation of "void caffe::EuclideanLossLayer<Dtype>::Backward_gpu(const std::vector<caffe::Blob<Dtype> *, std::allocator<caffe::Blob<Dtype> *>> &, const std::vector<__nv_bool, std::allocator<__nv_bool>> &, const std::vector<caffe::Blob<Dtype> *, std::allocator<caffe::Blob<Dtype> *>> &) [with Dtype=float]"
(105): here

the original code is :

             Dtype* diff_cpu_data = bottom[i]->mutable_cpu_diff();
       const Dtype* label_data = bottom[]->cpu_data();    // label data: 0 or 1
       const Dtype* predict_data = bottom[]->cpu_data();  // predict data

       int spl_num = ;
       int al_num = ;

       for(int id = ; id < bottom[i]->count(); ++id) {    // 35*12=420 

         // Self Paced Learning
         if (label_data[id]==){
           // negative samples ... do nothing
         }
         else{
           if(predict_data[id]>0.7 && label_data[id]== ) {
               spl_num ++ ;
             // if the condition is met,  transmit the gradient
             // else  make the gradient equal to zero...
           }
           else {
             diff_cpu_data[id] = ;
             // bottom[i]->mutable_cpu_diff()[id] = 0;
           }
         }

         // Active Learning
         if (0.4 < predict_data[id] && predict_data[id] < 0.5){

           if (label_data[id] == ){

             predict_data[id] =  ;
           }else
           if (label_data[id] == ){
             predict_data[id] =  ;
           }

           al_num++;

         }

Solution 1: No solution, because the char* can not give to const char*, and the value of const char* can not be changed .  and in my problem, we don't need change the predict score at all.

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Q2. when trained a AlexNet caffe model, and use the Matlab Interface to extract Features or predicted Scores , However it tell me errors like the following :

d302@d302-MS-7816-04:~$ matlab
libprotobuf ERROR google/protobuf/text_format.cc:172] Error parsing text-format caffe.NetParameter: 339:2: Expected identifier.
WARNING: Logging before InitGoogleLogging() is written to STDERR
F0116 15:34:34.112346 25564 upgrade_proto.cpp:928] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: ../../models/bvlc_alexnet/alex_hat_deploy.prototxt
*** Check failure stack trace: ***
Killed

Solution 2: layer 6 was repaired when I train my model , i.e.

layer {
  name: "fc6_wx"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6_wx"
  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
    }
  }
}

change the name: "fc6_wx"  into name: "fc6", and it will be OK .

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