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From GitBox <...@apache.org>
Subject [GitHub] theSparta opened a new issue #8126: Not able to train a neural network [XOR added]
Date Fri, 01 Feb 2019 03:08:53 GMT
theSparta opened a new issue #8126: Not able to train a neural network [XOR added]
URL: https://github.com/apache/incubator-mxnet/issues/8126
 
 
   ## Environment info
   Operating System: Ubuntu 16.04 
   Compiler: g++
   Package used (Python/R/Scala/Julia): C++
   
   MXNet version: 0.11
   
   Or if installed from source:
   MXNet commit hash (`git rev-parse HEAD`):  branch 0.11.0
   
   ## Error Message:
   **There is no error but the training accuracy always remains ZERO.** Is there a problem
with the code for constructing the neural network (the `siamese` symbol) or/and is the training
procedure correct, i.e. the gradient updates are correctly specified?
   
   ## Minimum reproducible example
   
   1. I defined a siamese net like architecture with `LogisticRegressionOutput` as final layer
using the code provided below:
   
   ```C++
   #include <iostream>
   #include <map>
   #include <string>
   #include "mxnet-cpp/MxNetCpp.h"
   #include "../include/mxnet-cpp/op.h"
   
   using namespace std;
   using namespace mxnet::cpp;
   
   Symbol mlp(const vector<int> &layers, const vector<Symbol> & weights,
               const std::vector<Symbol> & biases, const string & inp_name )
   {
     auto x = Symbol::Variable(inp_name);
     vector<Symbol> outputs(layers.size());
   
     for (size_t i = 0; i < layers.size(); ++i)
     {
       string istr = to_string(i);
       Symbol fc = FullyConnected(
         i == 0? x : outputs[i-1],  // data
         weights[i],
         biases[i],
         layers[i]);
       outputs[i] = i == layers.size()-1 ? fc : LeakyReLU(string("act") + istr, fc, 
       LeakyReLUActType::kLeaky);
     }
     return outputs.back();
   }
   
   int main(int argc, char** argv)
   {
       const int feature_size = 2;
       const vector<int> layers{32, 16, 1};
       const int batch_size = 100;
       const int max_epoch = 10;
       const float learning_rate = 0.01;
       const float weight_decay = 1e-2;
   
       auto ctx = Context::gpu(); // Use GPU for training
       auto ctx_cpu = Context::cpu();
   
       vector<Symbol> weights(layers.size());
       vector<Symbol> biases(layers.size());
   
       for (size_t i = 0; i < layers.size(); ++i)
       {
           string istr = to_string(i);
           weights[i] = Symbol::Variable("w" + istr);
           biases[i] = Symbol::Variable("b" + istr);
       }
       
       // CONSTRUCT the network
       /**********************************************************************/
       auto Net = mlp(layers, weights, biases, "X");
       auto Net2 = mlp(layers, weights, biases, "X1");
   
       auto sym_label = Symbol::Variable("label");
       auto siamese = LogisticRegressionOutput(string("sigmoid"), Net - Net2, sym_label);
   
       map<string, NDArray> args_map;
       map<string, NDArray> aux_map;
   
       /*we should tell mxnet the shape of data and label*/
       args_map["X"] = NDArray(Shape(batch_size, feature_size) , ctx);
       args_map["X1"] = NDArray(Shape(batch_size, feature_size) , ctx);
       args_map["label"] = NDArray(Shape(batch_size, 1), ctx);
   
       /*with data and label, executor can be generated automatically*/
       auto *exec = siamese.SimpleBind(ctx, args_map);
       siamese.InferArgsMap(ctx, &args_map, args_map);
       auto arg_names = siamese.ListArguments();
       /**********************************************************************/
   
       Xavier xavier = Xavier(Xavier::gaussian, Xavier::in, 2.34);
       for (auto &arg : args_map)
       {
           if (arg.first == "X" || arg.first == "X1" || arg.first == "label") continue;
           xavier(arg.first, &arg.second);
       }
   
       Optimizer* opt = OptimizerRegistry::Find("adam");
       // opt->SetParam("rescale_grad", 1.0 / batch_size)
           opt->SetParam("lr", learning_rate)
           ->SetParam("wd", weight_decay);
   
        /** DATA SETUP **/
       /**********************************************************************/
       int data_count = batch_size * 100;
       mx_float* aptr_x = new mx_float[data_count * 2];
       mx_float* aptr_x1 = new mx_float[data_count * 2];
       mx_float* aptr_y = new mx_float[data_count];
   
       // we make the data by hand, in 10 classes, with some pattern
       for (int i = 0; i < data_count; i++)
       {
           for (int j = 0; j < 2; j++)
           {
             aptr_x[i * 2 + j] = (i%100 + j) *1.0f;
             aptr_x1[i * 2 + j] = -(i%100 + j) *1.0f;
           }
           aptr_y[i] = 1;
       }
   
       NDArray data_array = NDArray(Shape(data_count, 2), ctx_cpu, false);  
       NDArray data1_array = NDArray(Shape(data_count, 2), ctx_cpu, false);  
       NDArray label_array = NDArray(Shape(data_count), ctx_cpu, false);
       data_array.SyncCopyFromCPU(aptr_x, data_count * 2);
       data1_array.SyncCopyFromCPU(aptr_x1, data_count*2);
       label_array.SyncCopyFromCPU(aptr_y, data_count);
       data_array.WaitToRead();
       data1_array.WaitToRead();
       label_array.WaitToRead();
   
       int val_fold = 1;
       size_t train_num = data_count * (1 - val_fold / 10.0);
       NDArray train_data, train1_data, val_data, val1_data;
       NDArray train_label, val_label;
       train_data = data_array.Slice(0, train_num);
       train1_data = data1_array.Slice(0, train_num);
       train_label = label_array.Slice(0, train_num);
       /**********************************************************************/
   
       // TRAINING the network
       Accuracy acu_train;
       for (int ITER = 0; ITER < max_epoch*100; ++ITER)
       {
           size_t start_index = 0;
           /*reset the metric every epoch*/
           acu_train.Reset();
           while (start_index < train_num)
           {
               if (start_index + batch_size > train_num)
               {
                 start_index = train_num - batch_size;
               }
               args_map["X"] =
                   train_data.Slice(start_index, start_index + batch_size).Copy(ctx);
               args_map["X1"] =
                   train1_data.Slice(start_index, start_index + batch_size).Copy(ctx);
               args_map["label"] =
                   train_label.Slice(start_index, start_index + batch_size).Copy(ctx);
               start_index += batch_size;
               NDArray::WaitAll();
   
               exec->Forward(true);
               exec->Backward();
               // Update parameters
               for (size_t i = 0; i < arg_names.size(); ++i)
               {
                   if (arg_names[i] == "X" || arg_names[i] == "X1" || arg_names[i] == "label")
continue;
                   opt->Update(i, exec->arg_arrays[i], exec->grad_arrays[i]);
               }
               NDArray::WaitAll();
               acu_train.Update(args_map["label"], exec->outputs[0]);
           }
           LG << "ITER: " << ITER << " Train Accuracy: " << acu_train.Get();
       }
   
       delete exec;
       delete [] aptr_x;
       delete [] aptr_x1;
       delete [] aptr_y;
       MXNotifyShutdown();
       return 0;
    }
   ```
   
   The output of the code is always something like:
   ```
   [18:43:06] neural_net_eval.cpp:172: ITER: 0 Train Accuracy: 0
   [18:43:06] neural_net_eval.cpp:172: ITER: 1 Train Accuracy: 0
   [18:43:06] neural_net_eval.cpp:172: ITER: 2 Train Accuracy: 0
   [18:43:06] neural_net_eval.cpp:172: ITER: 3 Train Accuracy: 0
   [18:43:06] neural_net_eval.cpp:172: ITER: 4 Train Accuracy: 0
   [18:43:06] neural_net_eval.cpp:172: ITER: 5 Train Accuracy: 0
   [18:43:06] neural_net_eval.cpp:172: ITER: 6 Train Accuracy: 0
   ```
   
   ## What have you tried to solve it?
   
   1. Tried different variations on training data but the network is always predicting ZERO
as its output.
   

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