class_docstr: NP Semantic Segmentation classifier model (based on tf.Keras framework).
  
  Args:
      num_epochs(int): number of epochs to train the model
      **callback_args (dict): callback args keyword arguments to init a Callback for the model
      loss: the model's cost function. Default is 'tf.keras.losses.binary_crossentropy' loss
      optimizer (:obj:`tf.keras.optimizers`): the model's optimizer. Default is 'adam'
docstr: Evaluate the model's test_set on error_rate, test_accuracy_rate and precision_recall_rate
  
  Args:
      test_set (:obj:`numpy.ndarray`): The test set
  
  Returns:
      tuple(float): loss, binary_accuracy, precision, recall and f1 measures
code: def eval(self, test_set):
      """
      Evaluate the model's test_set on error_rate, test_accuracy_rate and precision_recall_rate
  
      Args:
          test_set (:obj:`numpy.ndarray`): The test set
  
      Returns:
          tuple(float): loss, binary_accuracy, precision, recall and f1 measures
      """
      return self.model.evaluate(
          test_set["X"], test_set["y"], batch_size=128, verbose=2
      )
  
file_path: nlp-architect/nlp_architect/models/np_semantic_segmentation.py
imports: import tensorflow
label_desc: Use image node_id 6 for calling the NpSemanticSegClassifier obj's underlying member method code with example usage: obj.eval(test_set) and returns: self