                       coverage_ratio, config)
          self.config = config
          # Transform and Warp args
          self.input_size = input_size
          self.output_size = output_size
          # Warp args
          self.coverage_ratio = coverage_ratio  # Coverage ratio of full image. Eg: 256 * 0.625 = 160
          self.scale = 5  # Normal random variable scale
          logger.debug("Initialized %s", self.__class__.__name__)
  
 
</s> remove         logger.debug("Initializing %s: (input_size: %s, output_size: %s, coverage_ratio: %s, "
                     "config: %s)", self.__class__.__name__, input_size, output_size,
 </s> add         logger.debug("Initializing %s: (input_size: %s, output_shapes: %s, coverage_ratio: %s, "
                     "config: %s)", self.__class__.__name__, input_size, output_shapes, </s> remove             output_size: Size of the face that comes out of the modell
 </s> add             output_shapes: Shapes that come out of the model </s> remove     def __init__(self, input_size, output_size, coverage_ratio, config):
 </s> add     def __init__(self, input_size, output_shapes, coverage_ratio, config): </s> remove                                             model_output_size,
 </s> add                                             model_output_shapes, </s> remove             "loss": re.compile(r"([a-zA-Z_]+):.*?(\d+\.\d+)"),
 </s> add             "loss": re.compile(r"[\W]+(\d+)?[\W]+([a-zA-Z\s]*)[\W]+?(\d+\.\d+)"),
        self.output_sizes = [shape[1] for shape in output_shapes if shape[2] == 3]
        logger.debug("Output sizes: %s", self.output_sizes)
                       coverage_ratio, config)
          self.config = config
          # Transform and Warp args
          self.input_size = input_size
        self.output_sizes = [shape[1] for shape in output_shapes if shape[2] == 3]
        logger.debug("Output sizes: %s", self.output_sizes)
          # Warp args
          self.coverage_ratio = coverage_ratio  # Coverage ratio of full image. Eg: 256 * 0.625 = 160
          self.scale = 5  # Normal random variable scale
          logger.debug("Initialized %s", self.__class__.__name__)
  
keep keep keep keep replace keep keep keep keep keep
 Training: Add Multi-Output Support

- Name output nodes of models
- Add support for multiple outputs in models and in training
- Update loss output format for cli and gui
- Sort graphs tabs on training
- Fix analysis graph for new loss names
- Multi output support in convert 
lib/training_data.py