text: """--------------------
  Tries to load DNN. If one does not exist then will create a new one and fit it.
  Out:
      model - The neural net model that will be used to predict responses.
  --------------------"""
  
  
  import tensorflow as tf
  import tflearn as tfl
  import os
  
  
  def load_model(self):
      tf.compat.v1.reset_default_graph()
      net = tfl.input_data(shape=[None, len(self.training[0])])
      net = tfl.fully_connected(net, 8)
      net = tfl.fully_connected(net, 8)
      net = tfl.fully_connected(net, len(self.output[0]), activation="softmax")
      net = tfl.regression(net)
  
      self.model = tfl.DNN(net)
  
      try:
          self.model.load(os.path.join(os.path.dirname(__file__), "..", "..", "Data", "model.tfl"))  # Tries to load the model
      except Exception:  # If the model couldn't load, it trains a new one and saves it in a "Data" folder
          tf.compat.v1.reset_default_graph()
          net = tfl.input_data(shape=[None, len(self.training[0])])
          net = tfl.fully_connected(net, 8)
          net = tfl.fully_connected(net, 8)
          net = tfl.fully_connected(net, len(self.output[0]), activation="softmax")
          net = tfl.regression(net)
          model = tfl.DNN(net)
  
          model.fit(self.training, self.output, n_epoch=1000, batch_size=8, show_metric="true")  # Trains the model
          model.save(os.path.join(os.path.dirname(__file__), "..", "..", "Data", "model.tfl"))
  
          self.model = model
  