text: Title: The problem of accuracy of Dnn_regression model using tflearn
  Body: Question 1.
  I want to check the accuracy of the regression result window, not binary_acc. Because of this, only the binary_acc is displayed in the tensorboard graph, and it is displayed as 0 only.
  
  Question 2.
  I would like to apply the loss function to mean absolute percentage error instead of mse. I would like to ask for advice on the code that should be added to objectives.py.
  
  Question 3.
  If there is a theoretical error of deep running in my code, I would appreciate your advice.
  
  Here is my code and the results:
  
  
  # -*- coding: utf-8 -*-
  from xlrd import open_workbook
  from sklearn.model_selection import train_test_split
  import tflearn
  import numpy as np
  import random
  import tensorflow as tf
  
  
  
  class EarlyStoppingCallback(tflearn.callbacks.Callback):
      def __init__(self, loss_threshold):
          self.loss_threshold = loss_threshold
  
      def on_epoch_end(self, training_state):
          if training_state.val_loss is None: return
          if training_state.val_loss < self.loss_threshold and training_state.val_loss!=0.:
              raise StopIteration
  
  # Loss function
  def mean_absolute_percentage_error(y_test, estimation_result):
      y_test, estimation_result = np.array(y_test), np.array(estimation_result)
      return np.mean(np.abs((y_test - estimation_result) / y_test)) * 100
  
  # control variables
  needed_col_numbers_for_data = [2,3,4,5,6,7,8,9,10,11]
  num_feat = 9
  needed_col_numbers_for_label = [11]
  starting_row_index = 4  # this variable can be modified according to the data format in given excel file
  
  # read data from excel file
  filename = 'concrete_mix_data_6.xlsx'
  workbook = open_workbook(filename)
  worksheet = workbook.sheet_by_index(0)
  nrows = worksheet.nrows
  ncols = worksheet.ncols
  
  # data preparation --> data & label
  ending_row_index = nrows
  data_and_label = []
  for row_num in range(starting_row_index-1, ending_row_index):
      data_and_label.append([worksheet.row_values(row_num)[i] for i in needed_col_numbers_for_data])
  random.shuffle(data_and_label)
  data_and_label = np.array(data_and_label)
  X = data_and_label[:,:-1]
  Y = np.array(data_and_label[:,-1])
  Y = np.reshape(Y, (len(Y),1))
  
  # split data into training and testing group
  x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)
  
  
  # init
  tflearn.init_graph(num_cores=4, gpu_memory_fraction=0.5)
  
  print('======================= DNN regression model  ==================')
  
  # Build neural network
  net = tflearn.input_data(shape=[None, num_feat])
  net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer1')
  net = tflearn.dropout(net, 0.5, name='Dropout1')
  net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer2')
  net = tflearn.dropout(net, 0.5, name='Dropout2')
  net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer3')
  net = tflearn.dropout(net, 0.5, name='Dropout3')
  net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer4')
  # net = tflearn.dropout(net, 0.5, name='Dropout4')
  net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer5')
  # net = tflearn.dropout(net, 0.5, name='Dropout5')
  
  net = tflearn.fully_connected(net, 1, activation='linear')
  
  net = tflearn.regression(net, optimizer='Adam', loss='mean_square', metric='accuracy', learning_rate=0.001)
  
  # Define model
  model = tflearn.DNN(net, tensorboard_verbose=3, tensorboard_dir="/tmp/tflearn_logs/test")
  
  # Start training (apply gradient descent algorithm)
  # print X
  # print Y
  earlyStoppingCallback = EarlyStoppingCallback(loss_threshold = 60)
  model.fit(X, Y, n_epoch=10000, batch_size=60, show_metric=True,  snapshot_epoch=True,  validation_set=0.25, callbacks=earlyStoppingCallback,  run_id="tensorboard_test")
  
  
  # Predictive Data('best')
  estimation_result = model.predict(x_test)
  
  
  # Results
  result = np.concatenate((estimation_result,y_test),axis=1)
  print('======================= DNN regression Results  ==================')
  print('Estimation Result / Target ')
  print(result)
  print('==================================================================')
  mape = mean_absolute_percentage_error(y_test, estimation_result)
  print('Mean absolute percentage error :',mape)
  # mse = mean_squared_error(y_test, estimation_result)
  # print(mse)
  
  # # Save a model
  model.save('tensorboard_test2.tflearn')
  
  
  Training Step: 7377  | total loss: 133.95444 | time: 0.787s
  | Adam | epoch: 1054 | loss: 133.95444 - binary_acc: 0.0000 -- iter: 360/390
  Training Step: 7378  | total loss: 132.92976 | time: 1.935s
  | Adam | epoch: 1054 | loss: 132.92976 - binary_acc: 0.0000 | val_loss: 76.89469 - val_acc: 0.0000 -- iter: 390/390