text: # -*- coding: utf-8 -*-
  TRAIN_SET_IMAGES = 'emnist-byclass/train-images-idx3-ubyte.gz'
  TRAIN_SET_LABELS = 'emnist-byclass/train-labels-idx1-ubyte.gz'
  TEST_SET_IMAGES = 'emnist-byclass/test-images-idx3-ubyte.gz'
  TEST_SET_LABELS = 'emnist-byclass/test-labels-idx1-ubyte.gz'
  
  IMAGE_SIZE = 28
  TRAIN_DATA = 697932
  # 697932
  TEST_DATA = 116323
  # 116323
  
  LABEL_ENCODING = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S','T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
  
  
  def decode(position):
      return LABEL_ENCODING[position]
  
  
  EPOCHS = 15
  BATCH_SIZE = 256
  CLASS_NUMBER = 62
  
  SIZE_FOR_ANALISIS = 1000
  ACCTIVATION_BOUNDARY = 2.0
  
  EXT_CSV = '.xlsm'
  LAYERS_FOR_ACT = ['FLATTEN', 'HIDDEN1']
  START_OF_EXCEL = 2
  
  
  
  