code: def get_data(path, activation):
      '''Get the dataset
      '''
      data = []
      image_names = []
      for filename in os.listdir(path):
          img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
          image_names.append(filename)
          if img is not None:
              data.append(img)
  
      data = np.asarray(data)
  
      if activation == 'sigmoid':
          data = data.astype(np.float32)/(255.0)
      elif activation == 'tanh':
          data = data.astype(np.float32)/(255.0/2) - 1.0
  
      data = data.reshape((data.shape[0], 1, data.shape[1], data.shape[2]))
  
      np.random.seed(1234)
      p = np.random.permutation(data.shape[0])
      X = data[p]
  
      return X, image_namef get_data(path, activation):
      '''Get the dataset
      '''
      data = []
      image_names = []
      for filename in os.listdir(path):
          img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
          image_names.append(filename)
          if img is not None:
              data.append(img)
  
      data = np.asarray(data)
  
      if activation == 'sigmoid':
          data = data.astype(np.float32)/(255.0)
      elif activation == 'tanh':
          data = data.astype(np.float32)/(255.0/2) - 1.0
  
      data = data.reshape((data.shape[0], 1, data.shape[1], data.shape[2]))
  
      np.random.seed(1234)
      p = np.random.permutation(data.shape[0])
      X = data[p]
  
      return X, image_names
docs: Get the dataset