Function plot_model

tensorflow/python/keras/utils/vis_utils.py:253–300

Converts a Keras model to dot format and save to a file. Arguments: model: A Keras model instance to_file: File name of the plot image. show_shapes: whether to display shape information. show_layer_names: whether to display layer names. rankdir: `rankdir` argument passed to Py

(model,
               to_file='model.png',
               show_shapes=False,
               show_layer_names=True,
               rankdir='TB',
               expand_nested=False,
               dpi=96)

@keras_export('keras.utils.plot_model')
def plot_model(model,
               to_file='model.png',
               show_shapes=False,
               show_layer_names=True,
               rankdir='TB',
               expand_nested=False,
               dpi=96):
  """Converts a Keras model to dot format and save to a file.

  Arguments:
    model: A Keras model instance
    to_file: File name of the plot image.
    show_shapes: whether to display shape information.
    show_layer_names: whether to display layer names.
    rankdir: `rankdir` argument passed to PyDot,
        a string specifying the format of the plot:
        'TB' creates a vertical plot;
        'LR' creates a horizontal plot.
    expand_nested: Whether to expand nested models into clusters.
    dpi: Dots per inch.

  Returns:
    A Jupyter notebook Image object if Jupyter is installed.
    This enables in-line display of the model plots in notebooks.
  """
  dot = model_to_dot(model,
                     show_shapes=show_shapes,
                     show_layer_names=show_layer_names,
                     rankdir=rankdir,
                     expand_nested=expand_nested,
                     dpi=dpi)
  if dot is None:
    return
  _, extension = os.path.splitext(to_file)
  if not extension:
    extension = 'png'
  else:
    extension = extension[1:]
  # Save image to disk.
  dot.write(to_file, format=extension)
  # Return the image as a Jupyter Image object, to be displayed in-line.
  # Note that we cannot easily detect whether the code is running in a
  # notebook, and thus we always return the Image if Jupyter is available.
  try:
    from IPython import display
    return display.Image(filename=to_file)
  except ImportError:
    pass