Add train function in NeuralNetwork Class.

Add train member function of NeuralNetwork Class.
- NeuralNetwork Class has DataBuffer instance to handle datas.
- New Keywords are introduced for specify data set
  . TrainData, ValidData, TestData, LabelData
- DataBuffer instance is initialized with parameters from Neural
Network instance.
- In train function,
   . Data Buffer instance is running ( collecting or generating data )
   . get the data from Data Buffer instance.
   . Backwarding
   . Display progress ( Data Buffer member funciton )
   . Validate if it is enabled.
- Add Classification Example with train member function

**Self evaluation:**
1. Build test:	 [X]Passed [ ]Failed [ ]Skipped
2. Run test:	 [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

Applications/Classification/res/Classification.ini

@@ -18,6 +18,9 @@ minibatch = 32		# mini batch size
 beta1 = 0.9 		# beta 1 for adam
 beta2 = 0.9999	# beta 2 for adam
 epsilon = 1e-7	# epsilon for adam
+TrainData="trainingSet.dat"
+ValidData="trainingSet.dat"
+LabelData="label.dat"
 
 # Layer Section : Name
 [inputlayer]

nntrainer/include/databuffer.h

@@ -76,6 +76,8 @@ class DataBuffer {
       val_thread(), test_thread() {
     for (int i = 0; i < NBUFTYPE; ++i)
       validation[i] = true;
+    input_size = 0;
+    class_num = 0;
   };
 
   /**
@@ -110,6 +112,11 @@ class DataBuffer {
             unsigned int max_val, unsigned int max_test, unsigned int in_size,
             unsigned int c_num);
 
+  /**
+   * @brief     Initialize Buffer with data buffer private variables
+   * @retval #ML_ERROR_NONE Successful.
+   * @retval #ML_ERROR_INVALID_PARAMETER invalid parameter.
+   */
   int init();
 
   /**
@@ -128,6 +135,13 @@ class DataBuffer {
    */
   void run(BufferType type, std::ifstream &file);
 
+  /**
+   * @brief     function for thread ( training, validation, test )
+   * @param[in] BufferType training, validation, test
+   * @retval    void
+   */
+  void run(BufferType type);
+
   /**
    * @brief     clear thread ( training, validation, test )
    * @param[in] BufferType training, validation, test
@@ -136,6 +150,13 @@ class DataBuffer {
    */
   void clear(BufferType type, std::ifstream &file);
 
+  /**
+   * @brief     clear thread ( training, validation, test )
+   * @param[in] BufferType training, validation, test
+   * @retval    void
+   */
+  void clear(BufferType type);
+
   /**
    * @brief     get Status of Buffer. if number of rest data
    *            is samller than minibatch, the return false
@@ -160,6 +181,18 @@ class DataBuffer {
     std::vector<std::vector<std::vector<float>>> &out_label, unsigned int batch,
     unsigned int width, unsigned int height, unsigned int c_num);
 
+  /**
+   * @brief     get Data from Data Buffer using databuffer param
+   * @param[in] BufferType training, validation, test
+   * @param[in] outVec feature data ( minibatch size )
+   * @param[in] outLabel label data ( minibatch size )
+   * @retval    true/false
+   */
+  bool
+  getDataFromBuffer(BufferType type,
+                    std::vector<std::vector<std::vector<float>>> &out_vec,
+                    std::vector<std::vector<std::vector<float>>> &out_label);
+
   /**
    * @brief     set train data file name
    * @param[in] path file path
@@ -201,6 +234,38 @@ class DataBuffer {
    */
   int setFeatureSize(unsigned int n);
 
+  /**
+   * @brief     set feature size
+   * @retval max_train
+   */
+  unsigned int getMaxTrain() { return max_train; }
+
+  /**
+   * @brief     set feature size
+   * @retval max_val
+   */
+  unsigned int getMaxVal() { return max_val; }
+
+  /**
+   * @brief     set feature size
+   * @retval max_test
+   */
+  unsigned int getMaxTest() { return max_test; }
+
+  /**
+   * @brief     Display Progress
+   * @param[in] count calculated set ( mini_batch size )
+   * @param[in] type buffer type ( BUF_TRAIN, BUF_VAL, BUF_TEST )
+   * @retval void
+   */
+  void displayProgress(const int count, BufferType type, float loss);
+
+  /**
+   * @brief     return validation of data set
+   * @retval validation
+   */
+  bool *getValidation() { return validation; }
+
 private:
   std::vector<std::vector<float>> train_data;
   std::vector<std::vector<float>> train_data_label;
@@ -244,9 +309,10 @@ class DataBuffer {
   std::thread val_thread;
   std::thread test_thread;
 
-  std::string train_file;
-  std::string val_file;
-  std::string test_file;
+  std::ifstream train_stream;
+  std::ifstream val_stream;
+  std::ifstream test_stream;
+
   std::vector<std::string> labels;
   bool validation[NBUFTYPE];
 };

nntrainer/include/neuralnet.h

@@ -177,6 +177,8 @@ class NeuralNetwork {
    */
   void finalize();
 
+  int train();
+
 private:
   /**
    * @brief     batch size